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Embodied Cognition

Embodied Cognition is a wide-ranging research program drawing from and inspiring work in psychology, neuroscience, ethology, philosophy, linguistics, robotics, and artificial intelligence. Whereas traditional cognitive science also encompasses these disciplines, it finds common purpose in a conception of mind wedded to computationalism: mental processes are computational processes; the brain, qua computer, is the seat of cognition. In contrast, embodied cognition variously rejects or reformulates the computational commitments of cognitive science, emphasizing the significance of an agent’s physical body in cognitive abilities. Unifying investigators of embodied cognition is the idea that the body or the body’s interactions with the environment constitute or contribute to cognition in ways that require a new framework for its investigation. Mental processes are not, or not only, computational processes. The brain is not a computer, or not the seat of cognition.

Once a fringe movement, embodied cognition now enjoys a fair amount of prominence. Unlike, say, ecological psychology, which has faced an uphill battle for mainstream acceptance, embodied cognition has gained a substantial following. The appointment of researchers who take an embodied perspective to cognition would, today, raise few eyebrows. Embodied cognition has been the subject of numerous articles in popular outlets. Moreover, there is not an area of cognitive science—perception, language, learning, memory, categorization, problem solving, emotion, social cognition—that has not received an embodied “make-over.”

None of this is to say, of course, that embodied cognition does not face hard questions, or has escaped harsh criticism. The numerous and sometimes incompatible claims it makes about the body’s role in cognition and the myriad methods it employs for understanding this role make it ripe for philosophical reflection. Critics charge embodied cognition with embracing a depleted conception of cognition, or with not offering a genuine replacement to computational cognitive science, or with claiming that bodies play a constitutive role in cognition when in fact their role is merely causal. Proponents have responded to all of these objections. A welcome byproduct of these debates is a new perspective on some old philosophical questions concerning what minds are, what concepts are, and how to understand the nature and significance of representation.

1.1 Ecological Psychology

1.2 connectionism.

• 1.3 Phenomenology

2.1 Three Themes of Embodied Cognition

2.2 embedded cognition, 2.3 extended cognition, 2.4 enactive cognition, 3.1 metaphor and basic concepts, 3.2 embodied concepts, 4.1 robotics, 4.2 dynamical systems approaches to cognition, 5.1 constitution through coupling, 5.2 constitution through parity and wide computationalism, 6.1 social cognition, 6.2 moral cognition, 7. conclusion, other internet resources, related entries, 1. the foils and inspirations for embodied cognition.

The ontological and methodological commitments of traditional computational cognitive science, which have been in play since at least the mid-Twentieth Century, are by now well understood. Early or influential applications of computationalism to cognition include theories of language acquisition (Chomsky 1959), attention (Broadbent 1958), problem solving (Newell, Shaw, and Simon 1958), attention, memory (Sternberg 1969), and perception (Marr 1982). Common to all computationally-oriented research is the idea that cognition involves a step-wise series of events, beginning with the transduction of stimulus energy into a symbolic expression, followed by transformations of this expression according to various rules, the result of which is a particular output—a grammatical linguistic utterance, isolation of one stream of words from another, a solution to a logic problem, the identification of a stimulus as being among a set of memorized stimuli, or a 3-D perception of the world.

The symbolic expressions over which cognitive processes operate, as well as the rules according to which these operations proceed, appear as representational states internal to the cognizing agent. They are individuated in terms of what they are about (phonemes, light intensity, edges, shapes, etc.). All of this cognitive activity takes place in the agent’s nervous system. It is in virtue of the activation of the nervous system that stimuli become encoded into a “mentalese” language of thought, akin to the programming languages found in ordinary computers; similarly, the rules dictating the manipulation of symbols in the language of thought are like the instructions that a C.P.U. executes in the course of carrying out a task. Rather than running spread sheets or displaying Tetris pieces, the computational brain produces language, or perceives the world, or retrieves items from memory. The methods of computational cognitive science reflect these ontological commitments. Experiments are designed to reveal the content of representational states or to uncover the steps by which mental algorithms transform input into output.

So pervasive has this computational conception of cognition been over the past decades that many cognitive scientists would be happy to identify cognition with computation, giving little thought to the possibility of alternatives. Certainly the great strides toward understanding cognition that the advent of computationalism has made possible invites the idea that computational cognitive science, if not the only game in town, is likely the best. However, ecological psychology, which J.J. Gibson (1966; 1979) began to develop around the time that computationalism came to dominate psychological practice, rejected nearly every plank of the information processing model of cognition that computational cognitive science epitomizes. More recently, connectionist cognitive science has challenged the symbolist commitments of computationalism even while conceding a role for computational processes. Both ecological psychology and connectionist psychology have played significant roles in the rise of embodied cognition and so a brief discussion of their points of influence is necessary to understand the “embodied turn.” Likewise, some embodied cognition researchers draw on a very different source for inspiration—the phenomenological tradition with special attention to Merleau-Ponty’s contributions. The next three subsections examine these various strands of influence.

A primary disagreement between computational and ecological psychologists concerns the nature of the stimuli to which organisms are exposed. Computationalists largely regard these stimuli as, in Chomsky’s terminology, impoverished (Chomsky 1980). The linguistic utterances to which an infant comes in contact do not, on their own, contain sufficient information to indicate the grammar of a language. Similarly, the visual information present in the light that stimulates an organism’s retina does not, on its own, specify the layout of surfaces in the organism’s environment. Visual perception faces an “inverse optics” problem. For any pattern of light on a retina, there exists an infinite number of possible distal surfaces capable of producing that pattern. The visual system thus seems to confront an impossible task—while it is possible to calculate the pattern of light a reflecting surface will produce on the retina, the inverse of this problem appears to be unsolvable, and yet visual systems solve it all the time and, phenomenologically speaking, immediately.

Computationalists regard the inescapable poverty of stimuli as placing on cognitive systems a need to draw inferences. Just as background knowledge allows you to infer from the footprints in the snow that a deer has passed by, cognitive systems, according to computationalists, rely on sub-conscious background knowledge to infer what the world is like given the partial clues the stimuli offer. The perception of an object’s size, for instance, would, according to the computationalist, be inferred on the basis of the size of the retinal image of the object together with knowledge of the object’s distance from the viewer. Perception of an object’s shape, similarly, is inferred from the shape of the retinal image along with knowledge of the object’s orientation relative to the viewer.

Ecological psychologists, on the other hand, deny that organisms encounter impoverished stimuli (Michaels and Palatinus 2014). Such a view, they believe, falsely identifies whole sensory systems with their parts—with eyes, or with retinal images, or with brain activity. Visual perceptual processes, for instance, are not exclusive to the eye or even the brain, but involve the whole organism as it moves about its environment. The motions of an organism create an ever-changing pattern of stimulation in which invariant features surface. The detection of these invariants, according to the ecological psychologist, provides all the information necessary for perception. Perception of an object’s shape, for instance, becomes apparent as a result of detecting the kinds of transformations in the stimulus pattern that occur when approaching or moving around the object. The edges of a square, for instance, will create patterns of light quite different from those that a diamond would reflect as one moves toward or around a square, thus eliminating the need for rule-guided inferences, drawing upon background knowledge, to distinguish the square from a diamond. Insights like these have encouraged embodied cognition proponents to seek explanations of cognition that minimize or disavow entirely the role of inference and, hence, the need for computation. Just as perception, according to the ecological psychologist, is an extended process involving whole organisms in motion through their environments, the same may well be true for many other cognitive achievements.

Connectionist systems offer a means of computation that, in many cases, eschews the symbolist commitments of computational cognitive science. In contrast to the computer that operates on symbols on the basis of internally stored rules, a connectionist system consists in networks of nodes that excite or inhibit each other’s activity according to weighted connections between them. Different stimuli will affect input nodes differently, causing distinct patterns of activation in deeper layers of nodes depending on the values of activation that the input nodes send upstream. The result of this activity will reveal itself in the activation values of a final layer of nodes—the output nodes.

Connectionist networks thus compute—they transform input activation values into output activation values—but the imputation of symbolic structures within this computational process, as well as explicit rules by which a C.P.U. executes operations upon these symbols, appears unfounded. As Hatfield (1991) describes connectionist networks, they are non-cognitive , in the sense that their operation involves none of the trappings of cognition upon which computationalists insist, and yet still computational, insofar as stimulation of their input nodes creates patterns of activation that lead to particular activation values in output nodes. For more on connectionism generally, see the entry on connectionism .

Many embodied cognition researchers saw in connectionist networks a new way to conceptualize cognition and, accordingly, to explain cognitive processes. Non-symbolic explanations of cognition, despite the “only game in town” mantra of computationalists (Fodor 1987), might be possible after all. Adding momentum to the connectionist challenge was the realization that the mathematics of dynamical systems theory could often illuminate the unfolding patterns of activity in connectionist networks and could as well be extended to include within its explanatory scope the body-environment interactions between which connectionist networks reside. Consequently, some embodied cognition researchers have argued that dynamical systems theory offers the best framework from which to understand cognition.

1.3. Phenomenology

Another source of inspiration for embodied cognition is the phenomenological tradition. Phenomenology investigates the nature and structure of our conscious, lived experiences. Although the subject of phenomenological analyses can vary widely from perception to imagination, emotion, willing, and intentional physical movements, all phenomenological analyses aim to elucidate the intentional structure of consciousness. They do so by analyzing our conscious experiences in terms of temporal, spatial, attentional, kinesthetic, social, and self awareness. In contrast to computational accounts of the mind that model consciousness in terms of input, processing, and output, phenomenological accounts ground consciousness in a host of rich and varied attentional experiences, which with practice can be described and analyzed. For more on phenomenology, see see the entry on phenomenology .

Some variations of embodied cognition are inspired by the works of phenomenologists like Martin Heidegger (1975), Edmund Husserl (1929), and Maurice Merleau-Ponty (1962) who emphasize the physical embodiment of our conscious cognitive experiences. These thinkers analyze the various ways in which our bodies shape our thoughts and how we experience our conscious activities. Some even argue that consciousness is constituted by embodiment. Merleau-Ponty, for example, argues that consciousness itself is embodied:

Insofar as, when I reflect on the essence of subjectivity, I find it bound up with that of the body and that of the world, this is because my existence as subjectivity [= consciousness] is merely one with my existence as a body and with the existence of the world, and because the subject that I am, when taken concretely, is inseparable from this body and this world (Merleau-Ponty 1962, p. 408).

This phenomenological influence can be seen clearly in embodied cognition analyses of the relation between mind and body. These analyses reject the idea that mentality is fundamentally different and separate from physicality and the corollary idea that others’ mentality is somehow hidden from view. Inspired by Husserl and other phenomenologists, embodied cognition proponents argue that Cartesian-style analyses of the mind and the body fundamentally misconstrue cognition (Gallagher and Zahavi, 2008). Cognition is not purely or even typically an intellectual, solipsistic introspection in the way Descartes’ Meditations suggest. Rather, cognition is physically interactive, embedded in physical contexts, and manifested in physical bodies. Even contemporary philosophers and cognitive scientists who reject mind-body dualism may fall into the trap of intuitively regarding mental and physical as distinct and thereby accept the idea that we must infer the existence and nature of other minds from indirect cues. From the perspective that phenomenologists favor, however, all cognition is embodied and interactive and embedded in dynamically changing environments. Attention to the way in which our own conscious experiences are structured by our bodies and environments reveals that there is no substantial distinction between mind and body. The embodiment of cognition makes our own and others’ minds just as observable as any other feature of the world. In other words, phenomenological analysis of our conscious experiences reveals the Mind-Body Problem and Problem of Other Minds to be merely illusory problems. This phenomenological analysis of the relation between mind and body and our relation to other minds deeply influenced proponents of embodied cognition such as Shaun Gallagher (2005), Dan Zahavi (2005), and Evan Thompson (2010).

2. Embodied Cognition: Themes and Close Relations

Unlike computational cognitive science, the commitments of which can be readily identified, embodied cognition is better characterized as a research program with no clear defining features other than the tenet that computational cognitive science has failed to appreciate the body’s significance in cognitive processing and to do so requires a dramatic re-conceptualization of the nature of cognition and how it must be investigated. Different researchers view the body’s significance for cognition as entailing different consequences for the subject matter and practice of cognitive science. Nevertheless, through this very broad diversity of views it is possible to extract three major themes around which discussion of embodied cognition can be organized (see Shapiro 2012; 2019a).

The three themes of embodiment around which most of the following discussion will be organized are as follows.

Conceptualization: The properties of an organism’s body limit or constrain the concepts an organism can acquire. That is, the concepts by which an organism understands its environment depend on the nature of its body in such a way that differently embodied organisms would understand their environments differently.

Replacement: The array of computationally-inspired concepts, including symbol , representation , and inference , on which traditional cognitive science has drawn must be abandoned in favor of others that are better-suited to the investigation of bodily-informed cognitive systems.

Constitution: The body (and, perhaps, parts of the world) does more than merely contribute causally to cognitive processes: it plays a constitutive role in cognition, literally as a part of a cognitive system. Thus, cognitive systems consist in more than just the nervous system and sensory organs.

The theses above are not intended to be individually exclusive—embodied cognition research might show tendencies toward more than one at a time. Similarly, descriptions of embodied cognition might be organized around a larger number of narrower themes (M. Wilson 2002); however, efforts to broaden the themes, thereby reducing their number, risks generalizing the description of embodied cognition to the extent that its purported novelty is jeopardized.

Before examining how these themes receive expression, it is worth pausing to compare embodied cognition to some closely related research areas. Sometimes embodied cognition is distinguished from embedded cognition, as well as extended cognition and enactive cognition. However, despite the distinctions between the four “Es”—embodied, embedded, enactive, and extended—it is not uncommon to use the label “embodied” to include any or all of these “Es”. The E-fields share the view, after all, that the brain-centrism of traditional cognitive science, as well as its dependence on the computer for inspiration, stands in the way of a correct understanding of cognition.

Embedded cognition assumes that cognitive tasks—dividing a number into fractions, navigating a large ship, retrieving the correct book from a shelf—require some quantity of cognitive effort. The cognitive “load” that a task requires can be reduced when the agent embeds herself within an appropriately designed physical or social environment. For instance, Martin and Schwartz (2005) found that children are more successful at calculating ¼ of 8 when allowed to manipulate pie pieces than if only viewing the pieces. The cognitive load required to navigate a large Navy vessel exceeds the capacity of any single individual, but can be distributed across a number of specialists, each with his or her own particular task (Hutchins 1996). Arranging books on a shelf alphabetically makes searching for a particular title much easier than it would be if the books were simply set randomly upon the shelf. In all of these cases, the cognitive capacities of an individual are enhanced when provided with the opportunity to interact with features of a suitably organized physical or social environment.

Close kin to embedded cognition, extended cognition moves from the claim that cognition is embedded to claim additionally that the environmental and social resources that enhance the cognitive capacities of an agent are in fact constituents of a larger cognitive system, rather than merely useful tools for a cognitive system that retains its traditional location wholly within an agent’s nervous system (Clark and Chalmers 1998; Menary 2008). Some interpret the thesis of extended cognition to mean that cognition actually takes place outside the nervous system—within the extra-cranial resources involved in the cognitive task (Adams and Aizawa 2001; 2008; 2009; 2010). Others interpret the thesis more modestly, as claiming that parts of an agent’s environment or body should be construed as parts of a cognitive system, even if cognition does not take place within these parts, thus extending cognitive systems beyond the agent’s nervous system (Clark and Chalmers 1998; Wilson 1994; Wilson and Clark 2001).

Enactivism is the view that cognition emerges from or is constituted by sensorimotor activity. Currently, there are three distinct strands of enactivism (Ward, Silverman, and Villalobos 2017). Autopoietic Enactivism conceives of cognition in terms of the biodynamics of living systems (Varela, Thompson, and Rosch 2017; Di Paolo 2005). Just as a bacterium is created and maintained by processes that span the organism and environment, so too is cognition generated and specified through operation of sensorimotor processes that crisscross the brain, body, and world. On this version of enactivism, there is no bright line between mental processes and non-mental biological processes. The former simply are an enriched version of the latter. Sensorimotor Enactivism is another strand of enactivism that focuses on explaining the intentionality and phenomenology of perceptual experiences in particular (O’Regan and Noë 2001; Noë 2004). This view holds that perception consists in active exploration of the environment, which establishes patterns of dependence between our movements, sensory states, and the world. Perceivers need not build and manipulate internal models of the external world. Instead, they need only skillfully exploit sensorimotor dependences that their exploratory activities reveal. Finally, Radical Enactivism aims to replace all representational explanations of cognition with embodied, interactive explanations (Hutto and Myin 2013; Chemero 2011). The primary tactic guiding Radical Enactivism is to deconstruct and eliminate the notion of mental content in cognitive science. This tactic manifests in critiques of attempts to naturalize intentionality, redescribing cognitive processes studied in mainstream cognitive science, and challenging concepts employed even by closely related views, such as Autopoietic Enactivism’s notion of sensemaking (Chemero 2016). These three strands of enactivism vary in their target explanations and methodology. However, they share the commitment to the idea that cognition emerges from sensorimotor activity.

3. Conceptualization

Returning now to the three themes around which this discussion of embodied cognition is organized, the first is Conceptualization. According to Conceptualization, the concepts by which organisms recognize and categorize objects in the world, reason and draw inferences, and communicate with each other, are heavily body-dependent. The morphological properties of an agent’s body will constrain and inform the meaning of its concepts. The claim that concepts are embodied in this way has been defended via quite distinct routes.

Lakoff and Johnson (1980; 1999) offered an early and influential defense of Conceptualization. Their argument begins with the plausible premise that human beings rely extensively upon metaphorical reasoning when learning or developing an understanding of unfamiliar concepts. Imagine, for instance, trying to explain to a child the meaning of election . Drawing a connection between election and a concept the child already understands, like foot race , makes the job easier. The “elections are races” metaphor provides a kind of scaffolding for introducing and explaining the content of the election concept. Candidates are like runners hoping to win the race . They will adopt various strategies . They must be careful not to start too fast or they might burn out before reaching the finish line . It’s about endurance through the long stretch —more a marathon than a sprint . Some will play dirty , trying to trip others up, knocking them off their stride . There will be sore losers but also graceful winners . Appeal to the content of a familiar concept— foot race —provides the child with a framework or stance for learning the unfamiliar concept— election.

The next step toward the embodiment of concepts proceeds with the observation that, through pain of regress, not all concepts can be acquired through metaphorical scaffolding. There must be a class of basic concepts that (if not innate) we learn some other way. Lakoff and Johnson argue that these basic concepts derive from the kinds of “direct physical experience” (1980: 57) that come from moving a human body through the environment. The concept up , for instance, is basic, emerging from possession of a body that stands erect, so that “[a]lmost every movement we make involves a motor program that either changes our up-down orientation, maintains it, presupposes it, or takes it into account in some way” (1980, 56). Lakoff and Johnson offer a similar account for how human beings come to possess concepts like front , back , pushing , pulling , and so on.

Basic concepts reflect the idiosyncrasies of particular kinds of bodies. Insofar as less-basic concepts depend upon metaphorical extensions of these most basic concepts, they will in turn reflect the idiosyncrasies of particular kinds of bodies. All concepts, Lakoff and Johnson appear to believe, are “stamped” with the body’s imprint as the characteristics of the body “trickle up” into more abstract concepts. They thus arrive at Conceptualization: “the peculiar nature of our bodies shapes our very possibilities for conceptualization and categorization” (1999, 19). Insofar as this is true, one should expect that differently-bodied organisms, equipped with a different class of basic concepts, would conceptualize and categorize their worlds in nonhuman ways.

Although Lakoff and Johnson see Conceptualization as incompatible with computational cognitive science, their grounds for doing so are tenuous. Metaphorical reasoning consists in applying aspects of one concept’s content to that of another. Because such reasoning is explicitly about content, and because computationalism is a theory about how to process mental states in virtue of their content, Lakoff and Johnson’s antagonism toward computationalism seems unwarranted. Additionally, their case for Conceptualization remains largely a priori . They claim that organisms morphologically distinct from human beings—spherical in shape, say—would be unable to develop some human concepts (1980, 57), but with no such beings available to test, this assertion is entirely speculative.

A far more developed and empirically grounded case for Conceptualization comes from psychological and neurological studies that show a connection between a subject’s use of a concept and activity in the subject’s sensorimotor systems. Arising from these studies is a view of concepts as containing within their content facts about the sensorimotor particularities of their possessors. Because these particularities reflect the properties of an organism’s body—how, for instance, it moves its limbs when interacting with the world—the content of its concepts too will be constrained and informed by the nature of its body.

Central to the idea that concepts are embodied is the description of such concepts as modal . This label is intended to make stark the anti-computationalism that proponents of embodied concepts endorse. Symbols in a computer—strings of 1s and 0s—are amodal , in the sense that their relationship to their contents is arbitrary. Words too are amodal symbols. The symbol ‘lake’ means lake , but not in virtue of any resemblance or nomological connection it bears towards lakes. There is no reason that ‘lake’, rather than some other symbol, should mean lake —as is obvious when thinking about words that mean lake in non-English languages. All mental symbols, from the perspective of computational cognitive science, are amodal in this sense.

Modal symbols, on the other hand, retain information about the sources of their origin. They are not just symbols, but, in Barsalou’s (1999; Barsalou et al. 2003) terminology, perceptual symbol s. Thoughts about a lake, for instance, consist in activation of the sensorimotor areas of the brain that had been activated during previous encounters with actual lakes. A lake thought re-activates areas of visual cortex that respond to visual information corresponding to lakes; areas of auditory cortex that respond to auditory information corresponding to lakes; areas of motor cortex that correspond to actions typically associated with lakes (although this activation is suppressed so that it does not lead to actual motion), and so on. The result is a lake concept that reflects the kinds of sensory and motor activities that are unique to human bodies and sensory systems. Lake means something like “thing that looks like this , sounds like this , smells like this , allows me to swim within it like this ”. Moreover, because how things look and sound depend on the properties of sensory systems, and because the interactions something affords depends on the properties of motor systems, concepts will be body-specific.

Much of the evidence for the modality of concepts arises from demonstrations of an orientation-dependent spatial compatibility effect (OSC) (Symes, Ellis, and Tucker 2007). Tucker and Ellis (1998), for instance, asked subjects to judge whether a given object, e.g., a pan, was right-side-up or upside down. The object was oriented either rightwards or leftwards. So, for instance, the pan’s handle extended toward the right or left. Subjects would indicate whether the object was right-side-up or upside down by pressing a button to their right with their right index finger or to their left with their left index finger. Subjects’ reaction times were shorter when using a right finger to indicate a response when the object was oriented to the right than when oriented toward the left, and, mutatis mutandis , for left-finger responses when the object was oriented to the left. Despite the fact that subjects were not asked to consider horizontal orientation of the stimulus object, this orientation influenced response times (for related work on the OSC, see Tucker and Ellis 2001; 2004).

Relatedly, Glenberg and Kaschak (2002) showed an action-sentence compatibility effect (ASC). Subjects were asked to judge the sensibility of sentences like “open the drawer” or “close the drawer.” Sentences of the first kind suggested actions that would require a motion of the hand toward the body and sentences of the second kind suggested actions with motions away from the body. Subjects would indicate the sensibility of the sentence by pressing a button that required a hand motion either away from the body or toward the body. Glenberg and Kaschak found that reaction times were shorter when the response motion was compatible with the motion suggested by the action sentences.

Both the OSC and ASC effects have been taken to show that concepts are modal. Thoughts about pans, for instance, activate areas in motor cortex that would be activated when actually manipulating a pan. Subjects are slower to respond to a leftward oriented pan with their right finger, because seeing the pan’s orientation activates motor areas in the brain associated with grasping the pan with the left hand, priming a left finger response while inhibiting a right finger response. Similarly, the meaning of words like “open” and “close” include in their content the kinds of motor activity that would be involved in opening or closing motions. The meaning of object concepts thus contain information about how objects might be manipulated by bodies like ours ; action concepts consist, in part, of information about how bodies like ours move.

Further evidence for the claim that concepts are packed with sensorimotor information comes from Edmiston and Lupyan (2017), who asked subjects questions that required for their answers either “encyclopedic” knowledge—“Does a swan lay eggs?”—or visual knowledge—“Does a swan have a beak?”. Interestingly, they found that visual interference during the task would diminish performance on questions requiring visual knowledge but not encyclopedic knowledge. They took this as evidence for the embodiment of concepts insofar as the effect of visual interference would be expected if concepts were modal—if, in this case, they involved the activation of vision centers in the brain—but not if concepts were amodal symbols, divorced from their sensorimotor origins.

A final source of evidence for embodied concepts comes from neurological investigations that reveal activation in the sensorimotor areas of the brain associated with particular actions. Reading a word like ‘kick’ or ‘punch’ causes activity in motor areas of the brain associated with kicking and punching (Pulvermüller 2005). Stimulation of these areas by transcranial magnetic stimulation can affect comprehension of such words (Pulvermüller 2005; Buccino et al. 2005). Again, results like these are precisely what an embodied theory of concepts would predict but would be unexpected on standard computational amodal theories of concepts. If the concept kick includes in its content motions distinctive of a human leg, as determined by activity in the motor cortex, then, as Conceptualization entails, it shows the imprint of a specific sort of embodiment.

Critics of embodied concepts have issued a number of challenges. Most basically, one might question whether empirical studies like those just mentioned are targeting concepts at all. Why think, for instance, that the meaning of the concept pan includes information about how pans must be grasped; and that the meaning of open includes information about how an arm should move? Claims like these seem inattentive to a distinction between a concept and a conception (Rey 1983; 1985; Shapiro 2019a). The meaning of the concept bachelor , for instance, is unmarried male . But apart from this concept is a conception of a bachelor, where a conception involves something like typical or representative features. A bachelor conception might include things like being a lothario, or being young, or participating in bro-culture. These concepts are associated with the concept bachelor , but not actually part of the meaning of the concept. Similarly, that pans might be grasped so , or opening involves moving an arm like so , might not be part of the meaning of the concepts pan and open , but instead features of one’s conception of pans and one’s conception of how to open things.

Just as defenders of embodied concepts might not be investigating concepts after all, but instead only conceptions—only features associated with concepts—it may be that the motor activity that accompanies thoughts about concepts does not contribute to the meaning of a concept but is instead only associated with the concept. The psychologists Mahon and Caramazza (2008) argue for this way of interpreting the neurological studies taken to support of embodied concepts. The finding that exposure to the word ‘kick’ causes activity in the motor areas of the brain responsible for kicking does not show that kick is a modal concept. Mahon and Caramazza suggest that linguistic processing of a word might create a cascade of activity that flows to areas of the brain that are associated with the meaning of the word. A thought about kick is associated with thoughts about moving one’s leg, which in turn causes activity in the motor system, but there is no motivation for regarding this activity as part of the kick concept—no motivation for seeing it as evidence for the modality of the concept (Mahon 2015). Thinking about a kick causes one to think about moving one’s leg, which causes activity in motor cortex, but the meaning of kick is independent of such activity.

Finally, even granting the modality of concepts like pan and kick , one might question whether all concepts are embodied, as some embodied cognition researchers suggest (Barsalou 1999). Of special concern are abstract concepts like democracy , justice , and morality (Dove 2009; 2016). Unlike pan , the meaning of which might involve information from sensory and motor systems, what sensory and motor activity might be included in the meaning of justice ? Barsalou (2008) and Barsalou and Wiemer-Hastings (2005) offer an account of how abstract concepts might be analyzed in modal terms, but debate over the issue is far from settled.

4. Replacement

Many who take an embodied perspective on cognition believe that the commitments of traditional cognitive science must be jettisoned and replaced with something else. No more computation, no more representation, no more manipulation of symbols. Researchers who promote the complete replacement of traditional cognitive science tend to show the influence of ecological psychology. Less radical are arguments for abandoning some elements of traditional cognitive science, for instance the idea that cognition is a product of rule-guided inference, while retaining others, e.g., the idea that cognition still involves representational states. This position has roots in the connectionist alternative to computationalism discussed in §1.2. Support for Replacement arrives from several directions.

Early ventures in robotics took on board the idea that cognition is computation over symbolic representations. The robot Shakey (1966–1972) for instance, created at the Artificial Intelligence Laboratory at what was then the Stanford Research Institute, was programmed to navigate through a room, avoiding or pushing blocks of various shapes and colors. Guiding Shakey’s behavior was a program, called STRIPS, which operated on symbolically encoded images of the blocks, combining them with stored descriptions of Shakey’s world. As the roboticist Brooks characterizes Shakey’s architecture, it cycles through iterations of sense-model-plan-act sequences (Brooks 1991a). A camera senses the environment, a computer builds a symbolic model of the environment from the camera images, the STRIPS program combines the model with stored symbolic descriptions of the environment, creating plans for a course of action. Shakey’s progress was slow—some tasks would take days to complete—and heavily dependent on an environment carefully structured to make images easier to process.

Brooks’s approach to robotics disavows the computational principles on which Shakey was designed, embracing instead a Gibsonian-inspired architecture. The result has been robots that exhibit far more versatility than Shakey ever displayed—robots that can roam cluttered environments, avoiding obstacles, setting goals for themselves, collecting soda cans for recycling, and more. Brooks’s “Creatures” run on what he calls a subsumption architecture. Rather than cycling through sense-model-plan-act sequences, Creatures contain arrays of sensors that are connected directly to behavior-generating mechanisms. For instance, the sensors on Brooks’s robot Allen were connected directly to three different kinds of behavior-generators: Avoid, Wander, and Explore. When sensors detected an object in Allen’s path, the Avoid mechanism would cause Allen to stop its forward motion, turn, and then proceed. The Wander generator would simply send Allen along a random heading, while the Explore generator would steer Allen toward a selected target. The three kinds of activity layers as Brooks called them, continually compete with each other. For instance, if Allen were Wandering and came across an obstacle, Avoid would step in and prevent Allen from a collision. Explore could inhibit Wander’s activation in order to keep Allen on course toward a target. From the competitive interactions of the three layers emerged unexpectedly flexible and seemingly goal-oriented behavior.

According to Brooks, his Creatures have no need for representations. Implementing an idea from ecological psychology, Brooks says that the activity layers in his robots connect “perception to action directly” (1991b, 144). A robot designed in this way need not represent the world because it is able to “use the world as its own model” (1991b, 139). The robot’s behavior evolves through a continuous loop: the body moves, which changes the stimulation its sensors receive, which directly causes new movement, and so on. Because nothing stands “in between” the sensory signals and the robot’s behavior, there is no need for something that plays the standard intermediating role of a representational state. The robot does not require, for instance, a model of its environment in order to navigate through hallways. The move-sense loop does its job without one.

Despite the success of Brooks’s robots in comparison to their computational ancestors, and the impact Brooks’s ideas have had on industry (e.g., Roomba vacuum cleaners), whether Brooks’s insights pave the way for a radical, representation-free, cognitive science, as some enactivists like Chemero (2009) and Hutto and Myin (2013) believe, is far from certain. A first question concerns whether the behavior of Brooks’s Creatures really proceeds without the benefit of representational states. The sensors with which the Creatures are equipped, after all, send signals to the various activity layers so that the layers can respond to objects in the environment. Moreover, the various layers communicate with each other in order to modulate each other’s activities. They are, in effect, signaling each other with messages that seem to have a semantics: “go ahead,” or “stop!”.

Skeptics about representation, such as Chemero (2009) and Hutto and Myin (2013) focus on the continuous contact that Brooks’s Creatures bear to their environments as a reason to deny a role for representation. Because a Creature is in constant contact with the world, it does not need to represent the world. But constant contact does not always obviate a need for representation. Consider, for instance, that an organism might be in constant contact with many features of its environment—sunlight, humidity, oxygen, the gravitational pull of the moon, and so on. Yet, surely it will be sensitive to only some of these features—only some of these features will shape the organism’s behavior. A natural way to describe how some features make a difference to an organism while others do not might appeal to representation—an organism detects some features, represents them, and not others. Whether detection of this sort must involve representation will depend on the theory of representation that one adopts. One might therefore see the success of Brooks’s challenge to representation, and the enactivists’ embrace of the challenge, as hostage to a theory of representation, the details of which will no doubt themselves be controversial.

Another response to Brooks’s work doubts whether something like the subsumption architecture, even granting that it makes no use of representations, can “scale up”—can produce the more advanced sorts of behavior that cognitive scientists typically investigate (Shapiro 2007). Matthen (2014) argues that once we move just a little beyond the capabilities of Brooks’s Creatures, explanations of behavior will require an appeal to representations. For instance, imagine an organism that knows how to move from point A to point B , and from point A to point C , and on the basis of this knowledge, “figures out” how to move from point B to point C (Matthen 2014). It would seem that such an organism must possess a representation of the relations between points A , B , and C for such a calculation to be possible.

Clark and Toribio (1994) describe some cognitive tasks as “representation-hungry.” Examples include imagining or thinking about non-existent entities (e.g., unicorns) or counterfactual states of affairs (what would happen if I sawed through the tree in this direction?). Of necessity, an organism cannot be in constant contact with non-existents. That human beings so readily and often entertain such thoughts poses a difficulty for enactivists like Chemero and Hutto and Myin who see in Brooks’s “world as its own model” slogan a foundation for all or most cognition. Because the world contains no unicorns, using the world as a model cannot explain thoughts about unicorns.

Around the turn of the century, some cognitive scientists (Beer 2000; 2003; Kelso 1995; Thelen and Smith 1993; Thelen et al. 2001) and philosophers (Van Gelder 1995; 1998) began to advocate for dynamical systems approaches to cognition. Van Gelder (1995; 1998) argued that the computer, as the defining metaphor for cognitive systems, should be replaced with something more like the Watt’s centrifugal governor. A centrifugal governor regulates the speed of a steam engine by modulating the opening of a steam valve. As the valve opens, a spindle to which flyballs are connected spins faster, causing the flyballs to rise, which then cause the steam valve to close, decreasing the speed at which the spindle spins, causing the flyballs to drop, thus opening the steam valve, and so on. Whereas a computational solution to maintaining engine speed might represent the engine’s current speed, compare it to a representation of the engine’s desired speed, and then calculate and correct for the difference, the centrifugal governor does its job without having to represent or calculate anything (although some have argued that representations are indeed present in the governor: Bechtel 1998; Prinz and Barsalou 2000).

The centrifugal governor is an example of a dynamical system. Typical of dynamical systems is behavior that changes continuously through time—the height of the flyballs, the speed of the spindle, and the size of the steam valve opening all change continuously through time, and the rate of change in each effects the rate of change of the others. Dynamical systems theory provides the mathematical apparatus—differential and difference equations—to model dynamical systems. It is to these equations that dynamical cognitive science looks for explanations of cognition.

Among the most-cited examples of a dynamical explanation of cognition is the Haken-Kelso-Bunz (HKB) model of coordination dynamics (Haken, Kelso, and Bunz 1985; Kelso 1995). This model, consisting of a single differential equation, captures the dynamics of coordinated finger wagging. Subjects are asked to wag their right and left index fingers either in-phase, where each finger moves toward and away from each other, or out-of-phase, like windshield wipers. As the rate of finger wagging increases, out-of-phase motion will “flip” to in-phase motion, but motion that starts in-phase will remain in-phase. In dynamical terms, the coordination of finger wagging has two attractors , or regions of stability, at slower speeds (in-phase and out-of-phase) but only one attractor at a higher speed (in-phase). The HKB model makes a number of predictions borne out by observation, for instance that there are only two stable wagging patterns at lower speeds, that erratic fluctuations in coordination will occur near the critical threshold at which out-of-phase wagging transforms to in-phase, and that deviations from out-of-phase wagging will take longer to correct near the speed of transformation to in-phase (see Chemero 2001 discussion).

Other influential examples of dynamical explanations of cognition have focused on the coordination of infants’ legs for stepping behavior (Thelen and Smith 1993), perseverative reaching behavior in infants (Thelen et al. 2001), and categorization in a simulated agent (Beer 2003). Authors of these studies have been explicit in their belief that traditional cognitive science should be replaced with the commitments of dynamical cognitive science. Among these commitments is a rejection of representation as a necessary component of cognition as well as a view of cognition as “unfolding” from the continuous interactions between an organism’s brain, body, and environment rather than as emerging from discrete, rule-guided, algorithmic steps. This latter commitment returns us to the theme of embodiment. As Thelen et al. explain:

To say that cognition is embodied means that it arises from bodily interactions with the world. From this point of view, cognition depends on the kinds of experiences that come from having a body with particular perceptual and motor capabilities that are inseparably linked and that together form the matrix within which reasoning, memory, emotion, language, and all other aspects of mental life are meshed“ (2001, 1).

Of course, computational cognitive scientists can accept as well that cognition ”arises from bodily interactions with the world,“ in the sense that the inputs to cognitive processes often arise from bodily interactions with the world. Thelen et al. (2001) must then mean something more than that. Presumably, the idea is that the body is like a component in a centrifugal governor, and cognition arises from the continuous interactions between the body, the brain, and the world. Spivey, another prominent dynamical cognitive scientist, puts matters like this: ”For the new psychology on the horizon, perhaps we are ready to discard the metaphor of the mind as computer…and replace it with a treatment of the mind as a natural continuous event“ (2007, 29), much as, presumably, how the regulation of a steam engine’s speed is the result of the continuous interactions of the components of a centrifugal governor.

One challenge facing dynamical approaches to cognition echoes that confronting roboticists like Brooks. Just as the principles underlying the subsumption architecture may not scale-up in ways that can explain more advanced cognitive capacities, so too one might wonder whether dynamical approaches to such capacities will succeed. Perhaps finger wagging and infant stepping behavior are not instances of cognition in the first place, or are so only in an attenuated sense (Shapiro 2007; 2013), in which case any lessons learned from their investigation have little relevance to cognitive science.

Or perhaps as dynamical cognitive scientists examine more explicitly cognitive phenomena, they will find themselves in need of tools associated with standard cognitive science. Spivey, a pioneer of dynamical systems approaches, is on friendly terms with the idea of representations. Dietrich and Markman (2001) have argued that even behavior like coordinated finger wagging depends on representation, although perhaps not a conception of representation as ”thick“ as one usually attributed to computationalism. Once again, it is evident that resolving some of the controversy surrounding the Replacement thesis hinges on the theory of representation that one adopts.

Another criticism of dynamical cognitive science questions whether the differential equations that are offered as explanations of cognitive phenomena are genuinely explanatory. Chemero (2001) and Beer (2003) insist that they are. The equations can be used to predict the behavior of organisms as well as to address counterfactuals about behavior (how would the organism have behaved if such and such had occurred?)—both hallmarks of explanation. Dietrich and Markman (2001), on the other hand, argue that the equations offer only descriptions of phenomena rather than explaining them (see also Eliasmith 1996; van Leeuwen 2005). Spivey, despite his devotion to dynamical cognitive science, shares this view. Dynamical systems theory, he thinks, does not explain cognition. Its utility consists in ” modeling how the mind works“ (2007, 33, his emphasis). He continues:

The emergence of mind takes place in the medium of patterns of activation across neuronal cell assemblies in conjunction with the interaction of their attached sensors (eyes, ears, etc.) and effectors (hands, speech apparatus, etc.) with the environment in which they are embedded. Make no mistake about it, that is the stuff of which human minds are made: brains, bodies, and environments. Trajectories through high-dimensional state spaces are merely convenient ways for scientists to describe, visualize, and model what is going on in those brains, bodies, and environments” (2007, 33, his emphasis).

However, as Zednik (2011) has noted (see also Clark 1997 and Bechtel 1998), the differential equations on which dynamical explanations depend contain terms that permit interpretation. This is what turns a piece of pure mathematics into applied mathematics, which routinely is understood as describing causal processes (Sauer 2010). As an instance of applied (rather than pure) mathematics, The Lotka Volterra equations, for instance, do indeed explain the dynamics of predator-prey populations when their terms are taken to refer to predation rate and reproductive rate. The equations reveal how predation affects the size of the prey population, and how depletion in the prey population affects the size of the predator population, and how reproduction restores the prey population. So, Spivey may be right that the “stuff” of minds consists in brains, bodies, and environments, but this does not preclude the differential equations that describe these interactions from being explanatory. They are explanatory because they describe how brains, bodies, and environments interact and the consequences ensuing from these interactions.

5. Constitution

Baking powder is a constituent of a scone, and its presence causes the scone to rise when baked. A hot oven is also a cause of the scone’s rising, but it is not a constituent of the scone. You eat baking powder when you eat a scone, but you do not eat a hot oven. According to computational cognitive science, the constituents of a cognitive system are brain processes, where these processes are performing computations. The causes of cognition will be whatever causes these brain processes—stimulation to the body from the environment, for instance. Many embodied cognition theorists believe that this account of the constituents of cognition is incorrect. The constituents of a cognitive system extend beyond the brain, to include the body and the environment. A difficulty for this view is justifying the claim that the body and world are better construed as constituents of cognition rather than causes. Why are they more like baking powder than a hot oven?

The previous discussion of dynamic cognitive science serves also to illustrate the Constitution theme. As the quotation above from Spivey indicated, dynamically-oriented cognitive scientists regard cognition to be the product of interactions between brain, body, and world. The continuous interactions between these things, Chemero writes, explains why “dynamically-minded cognitive scientists do not assume that an animal must represent the world to interact with it. Instead, they think of the animal and the relevant parts of the environment as together comprising a single, coupled system” (2001, 142). Chemero continues this idea: “It is only for convenience (and from habit) that we think of the organism and environment as separate; in fact, they are best thought of as comprising just one system…the animal and environment are not separate to begin with” (2001, 142).

Chemero’s description of the animal and environment as coupled is ubiquitous in dynamical cognitive science. Coupling is a technical notion. The behaviors of objects are coupled when the differential equations that describe the behavior of one contains a term that refers to the behavior of the other. The equations that apply to the centrifugal governor, for instance, contain terms referring to the height of the flyballs and the size of the steam valve opening. The Lotka Volterra equations contain terms that refer to the number of predators and the number of prey. The co-occurrence of terms in the equations that describe a dynamical system shows that the behavior of the objects to which they refer are co-dependent. They are thus usefully construed as constituents of a single system—a system held together by the interactions of parts whose relationships are captured in coupled differential equations.

In addition to the technical sense of coupling, philosophers often appeal to a looser sense when defending Constitution. Clark, for instance, discusses the process of writing. When writing, “[i]t is not always that fully formed thoughts get committed to paper. Rather, the paper provides a medium in which, this time via some kind of coupled neural-scribbling-reading unfolding, we are enabled to explore ways of thinking that might otherwise be unavailable to us” (2008, 126). Clark’s idea is that the cognitive system that produces writing extends beyond a subject’s brain, to include among its constituents the paper on which words are written. The paper and the acts of reading and writing are literally parts of the cognitive process, no less than neural processes, because of the continuous interactions between all of these things. If it were possible to provide differential equations that describe the production of writing, they would include terms referring to the behaviors of each of these things. Thus, the reasoning that brings us to the conclusion that the components of a centrifugal governor are constituents of a single system, and that predator and prey are constituents of single system, leads also to the conclusion that the constituents of many cognitive systems will include parts of the body and world.

The coupling concept underlies some arguments for extended cognition . When brain processes are coupled to processes in the body or world, either in the technical sense deriving from dyamical systems theory or in the less strict sense involving loops of dependency, the resulting “brain+” is itself a single cognitive system. It is a cognitive system that extends beyond the head because the constituents of the system are not brain-bound.

Adams and Aizawa (2008; 2009; 2010) have objected to coupling-inspired defenses of Constitution, and hence the idea of extended cognition, on the grounds that they commit a coupling-constitution fallacy: “The pattern of reasoning here involves moving from the observation that process X is in some way causally connected (coupled) to a process Y of type j to the conclusion that X is part of the process of type j ” (2009, 81). They argue that this reasoning leads to absurd results. For instance, “[i]t is the interaction of the spinning bowling ball with the surface of the alley that leads to all the pins falling. Still, the process of the ball’s spinning does not extend into the surface of the alley or the pins” (2009, 83). Similarly, Adams and Aizawa would claim, the process of cognition does not extend into the paper and scribblings involved in writing.

This response is unlikely to impress supporters of coupling arguments for Constitution. Firstly, coupling arguments require that process X be more than simply causally connected to process Y of type j for X to be part of the j process. Suppose that process Y of type j is the production of a written paragraph on a piece of paper. Let X be the sound of the pencil as it leaves graphite on the surface of the paper. The sound is causally connected to the production of writing, but defenders of Constitution need not regard it as a constituent in the system of that results in the written paragraph. The sound does not contribute to the “loop”—neural events, scribbling, reading—from which the paragraph emerges. So, not just any causal connections suffice for constituency in a process.

Second, Clark and other defenders of Constitution would not claim that the writing process itself occurs in the constituents of the cognitive system that produces writing. Certainly the bowling ball’s spinning does not extend into the floor of the alley, and of course the writing process does not extend into a piece of paper. But the Constitution thesis is not committed to such claims (Shapiro 2019a). Just as one can say that a neuron is a constituent of a brain even if cognition does not take place in a neuron, it might make sense to say that the floor of the alley is a constituent in a system that results in the ball’s spinning even if spinning does not take place in the floor, and the paper is a constituent in a system that produces writing even if the writing process does not take place in the paper. Such conclusions, even if ultimately unwarranted, do not fail for the reasons Adams and Aizawa muster.

Apart from coupling arguments, some philosophers, e.g., Clark and Chalmers (1998) and Clark (2008), have defended the idea that cognitive systems include constituents outside the brain by appeal to a parity principle, whereas Wilson (2004) invokes the idea of wide computationalism . The arguments are similar, both seeking to reveal how a functionalist commitment to mental states or processes licenses the possibility of cognitive processes that extend beyond the brain.

The parity principle says “[i]f, as we confront some task, a part of the world functions as a process which, were it done in the head, we would have no hesitation in recognizing as part of the cognitive process, then that part of the world is…part of the cognitive process” (Clark 2008, 222). As illustration, Clark and Chalmers (1998) compare the occurrent beliefs of Otto, who is afflicted with Alzheimer’s disease, to those of Inga, who has a normal biological brain. Otto keeps a notebook containing information of the sort that would be stored in the hippocampus of a normally functioning brain. When Inga wants to visit MoMA, she pulls from her biological memory the information that MoMA is on 53 rd St. which prompts her to take a subway to the destination. When Otto has the same desire, he consults his notebook in which is written “MoMA is on 53 rd St.”, which in turn induces his trip to that location. By stipulation, the representation of MoMA’s location in Otto’s notebook plays an identical functional role to the representation in Inga’s brain. Hence, by the parity principle, the notebook entry is a memory—an occurrent belief about the location of MoMA. The notebook is thus home to constituents of many of Otto’s cognitive processes.

In a similar vein, Wilson (2004) discusses a person who wishes to solve a multiplication problem involving two large numbers. Calculating the product “in the head” is a possibility, but solving the problem with the aid of pencil and paper would be much easier. In the latter case, Wilson claims that the brain “offloads” onto the paper some of the work that it would otherwise have to do on its own. Crucial to Wilson’s argument is the idea that solving the multiplication problem is a computational process and that computational processes are not confined to particular spatial regions. When the multiplication problem is solved “in the head” the computational processes occur within the brain alone. But some of the steps in the computation could as well take place outside the head, on a piece of paper, in which case a computational process might partly occur outside the head. There is, then, a parity in the two processes, whether the particular computations are internal or external to the agent. To the extent that this is plausible, one can find additional support for Constitution.

Most criticism of extended cognition has been aimed at Clark and Chalmers’s original proposal, although because Wilson’s position is similar, it is as much victim to these criticisms insofar as they succeed. Among the most vocal critics are Adams and Aizawa (2001; 2008; 2009; 2010), who argue that extended cognitive systems like those involving Otto and his notebook or a person doing multiplication with a paper and pencil, cannot actually be cognitive because they fail to satisfy two “marks” of the cognitive. The first mark is that “cognitive states must involve intrinsic, non-derived content” (Adams and Aizawa 2001, 48). The second is that cognitive systems must display processes of sufficient uniformity to fall within the domain of a single science (Adams and Aizawa 2010).

The intrinsic content criterion assumes a distinction between content that is derived from human thought, as the content of the word ‘martini’ is derived from thoughts about martinis, and content that arises “on its own” without having to depend on some other contentful state for its origin. The thought martini , for instance, presumably does not (or need not) derive from other contentful states but arises from some naturalistic process involving relationships between brain states and martinis (relationships that it is the business of a naturalistic theory of content to specify). Words, maps, signs, and so on possess derived, non-intrinsic content whereas thoughts have intrinsic, non-derived, original content. Granting this distinction and its importance for identifying genuinely cognitive states and processes, Adams and Aizawa dismiss the plausibility of extended cognition on the grounds that things like notebook entries and numerals written on paper do not have intrinsic content.

Clark (2010) responds to this objection, in part pressing Adams and Aizawa to clarify how much intrinsic content must be present in a system for the system to qualify as cognitive. After all, brains, if anything, are cognitive systems but not all activity occurring in a brain involves states or processes with intrinsic content. Accordingly, Clark wonders, why should the fact that some elements of the Otto+notebook system, because they lack intrinsic content, preclude the system from counting as cognitive?

Adams and Adams propose in response that “if you have a process that involves no intrinsic content, then the condition rules that the process is non-cognitive” (2010, 70). However, this response leaves open whether Otto+notebook constitutes a cognitive system. Because Otto’s brain does indeed contain states and processes that “involve” intrinsic content—states and processes by which the notebook entries are read and understood and used to guide behavior—Clark can readily accept Adams and Aizawa’s stipulation. Some of the Otto+notebook system involves intrinsic content, some does not, and the cognitive system as a whole incorporates both these elements.

The second mark of the mental that Adams and Aizawa take to preclude systems like Otto+notebook from counting as cognitive raises issues concerning the identification of scientific domains. If one supposes, reasonably enough, that the objects, processes, properties, etc. that fall into the domain of a particular science do so in virtue of sharing particular features, then one should expect the same for the domain of cognitive science. The parts, properties, and activities taking place in brains do seem to share important features, features that explain how it is possible to identify brains in newly discovered species, how they differ from igneous rocks, and so on. But now suppose that cognitive systems can be extended in ways that Clark, Chalmers and Wilson have argued. Such systems would now contain constituents that could not possibly fit into the domain of a single science. Extended systems might include notebooks, or pencil and paper, or tools of just about any sort. “[F]or this reason,” Adams and Aizawa argue, “a would-be brain-tool science would have to cover more than just a multiplicity of causal processes. It would have to cover a genuine motley” (2010, 76).

Rupert (2004) shares a similar concern, noting that the processes by which Otto and Inga locate MoMA differ so considerably that it makes no sense to treat them as of a kind—as within the domain of a single science. Additionally, Rupert argues, there is no good reason to regard the various implements that combine with brain activity to be constituents of a cognitive system rather than simply tools that cognitive systems use to ease the processing they require to complete some task. Instead of insisting that cognitive systems extend, Rupert asks, why not regard them as seeking ways to embed themselves among tools that make their jobs easier? An axe does not become part of a person when she uses it to chop down a tree, why does a notebook become part of cognitive system when a brain uses it to locate MoMA? A sensible conservatism, Rupert thinks, speaks in favor of seeing cognitive systems as embedded in environments that allow ready use of tools to reduce their workloads, rather than as constituted, in part, by such tools. The hypothesis that cognitive systems use tools “is significantly less radical” (2004, 7) than the hypothesis that tools are constituents of cognitive systems and would seem to provide adequate explanations for all the phenomena that initially motivated the idea of extended cognition.

From Clark’s perspective, however, there is nothing motley, as Adams and Aizawa claim, about the brain+ tool systems that he believes constitute a legitimate kind for scientific investigation. Moreover, the processes by which Otto and Inga locate MoMA are not, as Rupert insists, vastly different. Once one steps back from the physical particularities of the constituents of extended cognitive systems and focuses just on the functional, computational, roles they play, such systems are identical, or very similar, to wholly brain-bound cognitive systems.

Similarly, Clark would deny Rupert’s claim that the hypothesis of embedded cognition can equally well save the phenomena that the hypothesis of extended cognition was intended to capture and do so while requiring less revision of existing ideas about how cognitive systems operate. A brain, Clark claims, is “’cognitively impartial’: It does not care how and where key operations are performed” (2008, 136). Rupert’s conservatism in fact reflects a misunderstanding—it conceives of brains as having the function of cognizing, which is true in a sense, but more accurate would be a description of the brain’s function as directing the construction of cognizing systems—some (many?) of which include constituents outside the brain proper (see also Wilson and Clark 2009).

Finally, Shapiro (2019b; 2019c) has suggested that the parity and wide-computationalist defenses of Constitution do not sit well with other commitments of embodied cognition. As mentioned, such defenses rest on a functionalist theory of cognition (for more on functionalism, see the entry on functionalism ). Functionalism may well justify the claim that states and processes outside the brain can be identical to states and processes internal to a brain (can stand in a relation of parity towards them), which in turn grounds the possibility that cognitive systems can contain non-neural constituents. But, Shapiro argues, this strategy for defending extended cognition seems contrary to the central theme of embodied cognition. Motivating the embodied turn in cognitive science is the idea that bodies are somehow essential to cognition. But the parity and wide-computational arguments for extended cognition entail just the opposite—important for cognition are computational processes, and because computational processes are “hardware neutral”, one need not consider the specifics of bodies in order to describe them. Thus, it appears, arguments in favor of extended cognition succeed to the extent that bodies, qua bodies, do not matter to cognition.

6. The Reach of Embodied Cognition

In addition to the usual cognitive terrain—language, perception, memory, categorization—that embodied cognition encompasses, researchers have recruited the concepts and methods of embodied cognition for the purpose of investigating other psychological domains. In particular, embodied cognition finds application in the fields of social cognition and moral cognition.

Social cognition is the ability to understand and interact with other agents. A wide variety of cognitive capacities are involved in social cognition, such as attention, memory, affective cognition, and metacognition (Fiske and Taylor 2013). Traditionally, however, the philosophical discussion of social cognition has narrowly conceived of it in terms of mentalizing (also called theory of mind or mindreading ). Mentalizing refers to the attribution of mental states, often restricted to propositional attitudes, and typically for the purpose of explaining and predicting others’ behavior. Thus, although social cognition is enabled by and involves numerous and diverse cognitive processes, many philosophers have tended to think of it simply as involving the attribution of propositional attitudes in order to predict and explain behavior. For canonical expressions of this view of social cognition, see Davies and Stone (1995a) and (1995b). More recently, philosophers have begun to conceive of social cognition more broadly. See Andrews, Spaulding, and Westra (2020) for a survey of Pluralistic Folk Psychology.

Embodied cognition theorists have rejected this narrow construal of social cognition. Though they do not deny that neurotypical adult humans have the capacity to attribute beliefs and desires and to explain and predict behavior, they argue that this is a specialized and rarely used skill in our ordinary social interactions (Gallagher 2020; Gallagher 2008; Hutto and Ratcliffe 2007). Most social interactions require only basic underlying social cognitive capacities that are known as primary and secondary intersubjectivity (Trevarthen 1979).

Primary intersubjectivity is the pre-theoretical, non-conceptual, embodied understanding of others that underlies and supports the higher-level cognitive skills involved in mentalizing. It is “the innate or early developing capacity to interact with others manifested at the level of perceptual experience—we see or more generally perceive in the other person’s bodily movements, facial gestures, eye direction, and so on, what they intend and what they feel” (Gallagher 2005, 204). Primary intersubjectivity is present from birth, but it continues to serve as the basis for our social cognition in adulthood. It manifests as the capacity for facial imitation, the capacity to detect and track eye movement, detect intentional behavior, and “read” emotions from actions and expressive movements of others. Primary intersubjectivity consists in informational sensitivity and appropriate responsiveness to specific features of one’s environment. It does not, embodied cognition theorists argue, involve representing and theorizing about those features. It simply requires certain practical abilities that have been shaped by selective pressures, e.g., sensitivity to certain bodily cues and facial expressions.

Around one year of age, neurotypical children develop the capacity for secondary intersubjectivity. This development enables a subject to move from one-on-one, immediate intersubjectivity to shared attention. At this stage, children learn to follow gazes, point, and communicate with others about objects of shared attention. According to embodied cognition, the cognitive skills acquired through secondary intersubjectivity are not rich, meta-cognitive representations about other minds. Rather, children learn practical skills when getting others to attend to an object and when learning to attend to objects others are attending to. This allows for a richer understanding of other agents, but it is still meant to be a behavioral, embodied understanding rather than a representation of others’ propositional attitudes (Gallagher 2005, 207).

Although primary and secondary intersubjectivity are described in developmental terms, according to embodied cognition these intersubjective practices constitute our primary mode of social cognition even as adults (Fuchs 2012; Gallagher 2008). For example, Hutto claims, “Our primary worldly engagements are nonrepresentational and do not take the form of intellectual activity” (2008, 51). One can see in Hutto’s description of social cognition a tendency toward the Replacement theme insofar as he seeks to minimize or reject completely a role for representation in the human capacity for understanding others’ behavior. Mentalizing, it is argued, is a late developing, rarely used, specialized skill. Primary and secondary intersubjectivity are fundamental insofar as they are sufficient for navigating most typical social interactions and insofar as they enable the development of higher-level social cognition, like mentalizing. Although, see Spaulding (2010) for a critique of these arguments.

Mirror neurons may be an important mechanism of social cognition on this kind of view. Mirror neurons are neurons that activate both endogenously in producing a behavior and exogenously in observing that very same behavior. For instance, neurons in the premotor cortex and inferior parietal lobule activate when a subject uses, say, a whole-handed grasp to pick up a bottle. These very same neurons selectively activate when a subject observes a target using a whole-handed grasp to pick up an object. Neuroscientists have discovered similar patterns of activation in neurons in various parts of the brain, leading to the proposal that there are mirror neuron systems for action, fear, anger, pain, disgust, etc. Though the interpretation of these findings is subject to a great deal of controversy (Hickok 2009), many theorists propose that mirror neurons are a basic mechanism of social cognition (Gallese 2009; Goldman 2009; Goldman and de Vignemont 2009; Iacoboni 2009). The rationale is that mirror neurons explain how a subject understands a target’s mental states without needing complicated, high-level inferences about behavior and mental states. In observation mode, the subject’s brain activates as if the subject is doing, feeling, or experiencing what the target is doing, feeling, or experiencing. Thus, the observation of the target’s behavior is automatically meaningful to the subject. Mirror neurons are a possible mechanism for embodied social cognition. If the findings and interpretations are upheld, they substantiate the claim that we can understand and interact with others without engaging in mentalizing. For a survey of the reasons to be cautious about these interpretations of mirror neurons, see Spaulding (2011; 2013).

Embodied moral cognition takes moral sentimentalism as a starting point. Moral sentimentalism is the view that our emotions and desires are, in some way, fundamental to morality, moral knowledge, and moral judgments. A particular version of moral sentimentalism holds that emotions, moral attitudes, and moral judgments are generated by our “gut reactions,” and any moral reasoning that occurs is typically post-hoc rationalization of those gut reactions (Haidt 2001; Nichols 2004; Prinz 2004). Embodied moral cognition takes inspiration from this kind of moral sentimentalism. It holds that many of our moral judgments stem from our embodied, affective states rather than abstract reasoning.

Various sources of empirical evidence support this kind of view. Consider, for example, pathological cases, such as psychopaths or individuals with damage to the ventromedial prefrontal cortex (vmPFC). Such individuals are impaired in making moral judgments. Psychopaths feel little compunction about behaving immorally and sometimes have a hard time differentiating moral from conventional norms (Hare 1999). Individuals with damage to the vmPFC retain knowledge of abstract moral principles but struggle in making specific, everyday moral decisions (Damasio 1994). In both cases, individuals lack the physiological responses that accompany neurotypical moral decision-making. Lacking these “somatic markers” that guide moral judgments, these individuals behave in impulsive, selfish, and immoral ways (Damasio 1994). Embodied cognition would predict this connection between physiological responses (like increased heartrate and palm sweating) and moral decision-making.

Psychologists and neuroscientists have observed the influence of embodied cues on moral judgments in neurotypical individuals, as well. For instance, experimentally manipulated perception of one’s heartrate seems to influence one’s moral judgments, with perceptions of faster heartrates leading to feelings of higher moral distress and more just moral judgments (Gu, Zhong, and Page-Gould 2013). Relatedly, there is some evidence that eliciting a feeling of disgust leads to harsher moral judgments (Schnall et al. 2008). Perceptions of cleanliness seem to lead to less severe moral judgments (Schnall, Benton, and Harvey 2008). In each of these cases, perception of embodied cues seems to mediate moral judgments. Moral sentimentalists have observed that many people have strong aversive reactions to harmless actions that violate taboos, such as consensual protected sex between adult siblings, cleaning a toilet with the national flag, eating one’s pet that had been run over, etc. In these cases, the strong negative affective response precedes the moral judgment, and often people have a difficult time articulating why they think these victimless, harmless actions are morally wrong (Strejcek and Zhong 2014; Haidt 2001; Haidt, Koller, and Dias 1993; Cushman, Young, and Hauser 2006). From the perspective of embodied cognition, this ordering confirms the notion that we make moral judgments on the basis of embodied cues.

Dual process theories of moral psychology reject the moral sentimentalism claim that all moral judgments are made in the same way. Dual process theories maintain that we have two systems of moral decision-making: a system for Utilitarian reasoning that is driven by affect-less, abstract deliberation, and system for Deontological reasoning that is driven by automatic, intuitive, emotional heuristics like gut feelings (Greene 2014). Dual process theories are meant to explain the seemingly inconsistent moral intuitions ordinary folks have about moral dilemmas. For example, in a standard trolley case where an out-of-control trolley is heading toward five innocent, unaware individuals on the track ahead, most people have the intuition that we ought to throw the switch so that the trolley goes onto a spur of the track, thereby killing one person on the spur but saving five lives. However, in the footbridge variation of the trolley problem where saving the five lives requires pushing an individual off a footbridge to derail the trolley, most people have the intuition that we should not do this even though the consequences are the same as in the standard trolley dilemma. The dual process theory holds that in the former case, our reasoning is guided by a System 2 type of abstract reasoning. However, in the latter case, our moral reasoning is guided by an aversive physiological response triggered by imagining pushing an individual off a footbridge. The dual process view partially vindicates the moral sentimentalist position insofar as it posits a distinctive System 1 type of moral reasoning that is based on embodied gut instincts. However, it maintains that there is a separate system operating on different inputs and processes for more abstract moral reasoning.

Recently, theorists have challenged dual process theories’ strict dichotomy between reason and emotion (Huebner 2015; Maibom 2010; Woodward 2016). On the one hand, brain areas that are associated with emotions like fear, anger, and disgust are implicated in complex learning and inferential processing. On the other hand, individuals who are clearly impaired in moral decision-making—psychopaths and those with damaged vmPFC—also suffer deficits in other kinds of learning and inferential processing. Abstract reasoning is not, as it turns out, cut off from affective processes. Somatic markers, affective cues, and physiological responses are central to reasoning, learning, and decision-making. For the proponent of embodied moral cognition, this serves as further confirmation of the idea that all cognition, including moral cognition, is deeply shaped by embodied cues. Though see May (2018), May and Kumar (2018) and Railton (2017) for a moral rationalist take on these findings.

This article aims to convey a sense of the breadth of topics that fall within the field of embodied cognition, as well as the numerous controversies that have been of special philosophical interest. As with any nascent research program, there remain questions about how embodied cognition relates to its forebears, in particular computational cognitive science and ecological psychology. Some of the hardest philosophical questions arising within embodied cognition, such as those concerning representation, explanation, and the very meaning of ‘mind’, are of a sort that any theory of mind must address. Apart from philosophical challenges to the conceptual integrity of embodied cognition there loom psychological concerns about the replicability of some of the most-cited findings within embodied cognition; although, in fairness, worries about replicability have recently arisen in many areas of psychology (Goldhill 2019; Lakens 2014; Maxwell, Lau, and Howard 2015; Rabelo et al. 2015). Whatever the future of embodied cognition, careful study of its aims, methods, conceptual foundations, and motivations will doubtless enrich the philosophy of psychology.

• Adams, Fred, and Ken Aizawa, 2001, “The Bounds of Cognition,” Philosophical Psychology , 14(1): 43–64. doi:10.1080/09515080120033571
• –––, 2008, The Bounds of Cognition , Malden, MA: Blackwell.
• –––, 2009, “Why the Mind Is Still in the Head,” in Philip Robbins and Murat Aydede (eds.), The Cambridge Handbook of Situated Cognition , 1st edition, Cambridge, New York: Cambridge University Press, pp, 78–95.
• –––, 2010, “Defending the Bounds of Cognition,” in Richard Menary (ed.), The Extended Mind , Cambridge, MA: MIT Press, pp, 67–80.
• Andrews, Kristin, Shannon Spaulding, and Evan Westra, 2020, “Introduction to Folk Psychology: Pluralistic Approaches ,” Synthese , August, 1–16, doi:10.1007/s11229-020-02837-3
• Baggs, Edward, and Anthony Chemero, 2018, “Radical Embodiment in Two Directions,” Synthese , 198 (Supplement 9): 2175–2190. doi:10.1007/s11229-018-02020-9
• Barsalou, Lawrence W, 1999, “Perceptual Symbol Systems,” Behavioral and Brain Sciences , 22(4): 577–660. doi:10.1017/S0140525X99002149
• –––, 2008, “Grounded Cognition,” Annual Review of Psychology , 59(1): 617–45. doi:10.1146/annurev.psych.59.103006.093639
• Barsalou, Lawrence W., W. Kyle Simmons, Aron K. Barbey, and Christine D. Wilson, 2003, “Grounding Conceptual Knowledge in Modality-Specific Systems,” Trends in Cognitive Sciences , 7(2): 84–91. doi:10.1016/S1364-6613(02)00029-3
• Barsalou, Lawrence W., and Katja Wiemer-Hastings, 2005, “Situating Abstract Concepts,” in Diane Pecher and Rolf A. Zwaan (eds.), Grounding Cognition (1st edition), pp. 129–63, Cambridge: Cambridge University Press. doi:10.1017/CBO9780511499968.007
• Bechtel, William, 1998, “Representations and Cognitive Explanations: Assessing the Dynamicist’s Challenge in Cognitive Science,” Cognitive Science , 22(3): 295–318. doi:10.1207/s15516709cog2203_2.
• Beer, Randall D, 2000, “Dynamical Approaches to Cognitive Science,” Trends in Cognitive Sciences , 4(3): 91–99. doi:10.1016/S1364-6613(99)01440-0
• –––, 2003, “The Dynamics of Active Categorical Perception in an Evolved Model Agent,” Adaptive Behavior , 11(4): 209–43. doi:10.1177/1059712303114001
• Broadbent, Donald E., 1958, Perception and Communication , New York: Pergamon Press.
• Brooks, Rodney. A., 1991a, “New Approaches to Robotics,” Science , 253 (5025): 1227–32. doi:10.1126/science.253.5025.1227
• –––, 1991b, “Intelligence without Representation,” Artificial Intelligence , 47(1–3): 139–59. doi:10.1016/0004-3702(91)90053-M
• Buccino, Giovanni, Lucia Riggio, Gabor Melli, Ferdinand Binkofski, Vittorio Gallese, and Giacomo Rizzolatti, 2005, “Listening to Action-Related Sentences Modulates the Activity of the Motor System: A Combined TMS and Behavioral Study,” Cognitive Brain Research , 24(3): 355–63. doi:10.1016/j.cogbrainres.2005.02.020
• Chemero, Anthony, 2001, “Dynamical Explanation and Mental Representations,” Trends in Cognitive Sciences , 5(4): 141–42. doi:10.1016/S1364-6613(00)01627-2
• –––, 2009, Radical Embodied Cognitive Science , Cambridge, MA: MIT Press.
• –––, 2016, “Sensorimotor Empathy,” Journal of Consciousness Studies , 23(5–6): 138–52.
• –––, 2021, “Epilogue: What Embodiment Is,” in Nancy Dess (ed.), A Multidisciplinary Approach to Embodiment: Understanding Human Being , New York: Routledge, pp. 133–40.
• Chomsky, Noam, 1959, “On Certain Formal Properties of Grammars,” Information and Control , 2(2): 137–67. doi:10.1016/S0019-9958(59)90362-6
• –––, 1980, “On Cognitive Structures and Their Development: A Reply to Piaget,” in Massimo Piattelli-Palmarini (ed.), Language and Learning : The Debate between Jean Piaget and Noam Chomsky , Cambridge, MA: Harvard University Press.
• Clark, Andy, 1997, “The Dynamical Challenge,” Cognitive Science , 21(4): 461–81. doi:10.1207/s15516709cog2104_3
• –––, 2008, Supersizing the Mind: Embodiment, Action, and Cognitive Extension , Oxford, New York: Oxford University Press.
• –––, 2010, “Coupling, Constitution, and the Cognitive Kind: A Reply to Adams and Aizawa,” in Richard Menary (ed.), The Extended Mind , Cambridge, MA: MIT Press, pp. 81–100.
• Clark, Andy, and David J. Chalmers, 1998, “The Extended Mind,” Analysis , 58(1): 7–19.
• Clark, Andy, and Josefa Toribio, 1994, “Doing without Representing?” Synthese , 101(3): 401–31. doi:10.1007/BF01063896.
• Cushman, Fiery, Liane Young, and Marc Hauser, 2006, “The Role of Conscious Reasoning and Intuition in Moral Judgment: Testing Three Principles of Harm,” Psychological Science , 17(12): 1082–89.
• Damasio, Antonio R., 1994, “Descartes’ Error and the Future of Human Life,” Scientific American , 271(4): 144.
• Davies, Martin, and Tony Stone, 1995a, Folk Psychology: The Theory of Mind Debate , Oxford: Blackwell.
• –––, 1995b, Mental Simulation: Evaluations and Applications (Volume 4), Oxford: Blackwell.
• Dietrich, Eric, and Arthur B. Markman, 2001, “Dynamical Description versus Dynamical Modeling,” Trends in Cognitive Sciences , 5(8): 332. doi:10.1016/S1364-6613(00)01705-8
• Dove, Guy, 2009, “Beyond Perceptual Symbols: A Call for Representational Pluralism,” Cognition , 110(3): 412–31. doi:10.1016/j.cognition.2008.11.016
• –––, 2016, “Three Symbol Ungrounding Problems: Abstract Concepts and the Future of Embodied Cognition,” Psychonomic Bulletin & Review , 23(4): 1109–21.
• Edmiston, Pierce, and Gary Lupyan, 2017, “Visual Interference Disrupts Visual Knowledge,” Journal of Memory and Language , 92 (February): 281–92. doi:10.1016/j.jml.2016.07.002
• Eliasmith, Chris, 1996, “The Third Contender: A Critical Examination of the Dynamicist Theory of Cognition,” Philosophical Psychology , 9(4): 441–63. doi:10.1080/09515089608573194
• Fiske, Susan T., and Shelley E. Taylor, 2013, Social Cognition: From Brains to Culture , London: Sage.
• Fodor, Jerry A., 1987, Psychosemantics: The Problem of Meaning in the Philosophy of Mind , Cambridge, MA: MIT Press.
• Fuchs, Thomas, 2013, “The Phenomenology and Development of Social Perspectives,” Phenomenology and the Cognitive Sciences , 12(4): 655–683. doi:10.1007/s11097-012-9267-x
• Gallagher, Shaun, 2005, How the Body Shapes the Mind , Oxford, Oxford University Press.
• –––, 2008, “Inference or Interaction: Social Cognition without Precursors,” Philosophical Explorations , 11(3): 163–74.
• –––, 2020, Action and Interaction , Oxford: University Press.
• Gallagher, Shaun, and Daniel D. Hutto, 2008, “Understanding Others through Primary Interaction and Narrative Practice,” in Chris Sinha, Esa Itkonen, Jordan Zlatev, and Timothy P. Racine (eds.), The Shared Mind: Perspectives on Intersubjectivity , Amsterdam: John Benjamins, pp. 17–38.
• Gallese, Vittorio, 2009, “Mirror Neurons and the Neural Exploitation Hypothesis: From Embodied Simulation to Social Cognition,” in Jaimie A. Pineda (ed.), Mirror Neuron Systems , New York: Humana, pp. 163–90.
• Gibson, James J., 1966, The Senses Considered as Perceptual Systems , Boston: Houghton Mifflin.
• –––, 1979, The Ecological Approach to Visual Perception , Boston: Houghton Mifflin.
• Glenberg, Arthur M., and Michael P. Kaschak, 2002, “Grounding Language in Action,” Psychonomic Bulletin & Review , 9(3): 558–65. doi:10.3758/BF03196313
• Goldhill, Olivia, 2019, “The Replication Crisis Is Killing Psychologists’ Theory of How the Body Influences the Mind,” Quartz , 16 January 2019, [ Goldhill 2019 available online ].
• Goldman, Alvin I., 2009, “Mirroring, Mindreading, and Simulation,” in Jaimie A. Pineda (ed.), Mirror Neuron Systems , New York: Humana, pp. 311–30.
• Goldman, Alvin I., and Frederique de Vignemont, 2009, “Is Social Cognition Embodied?” Trends in Cognitive Sciences , 13(4): 154–59.
• Greene, Joshua D., “Beyond Point-and-Shoot Morality,” Ethics , 124(4): 695–726.
• Gu, Jun, Chen-Bo Zhong, and Elizabeth Page-Gould, 2013, “Listen to Your Heart: When False Somatic Feedback Shapes Moral Behavior,” Journal of Experimental Psychology: General , 142(2): 307.
• Haidt, Jonathan, 2001, “The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment,” Psychological Review , 108(4): 814–834.
• Haidt, Jonathan, Silvia Helena Koller, and Maria G Dias, 1993, “Affect, Culture, and Morality, or Is It Wrong to Eat Your Dog?” Journal of Personality and Social Psychology , 65(4): 613–628.
• Haken, Hermann, J. A. Scott Kelso, and Herbert Bunz, 1985, “A Theoretical Model of Phase Transitions in Human Hand Movements,” Biological Cybernetics , 51(5): 347–56. doi:10.1007/BF00336922
• Hare, Robert D., 1999, Without Conscience: The Disturbing World of the Psychopaths among Us , New York: Guilford Press.
• Hatfield, Gary, 1991, “Representation and Rule-Instantiation in Connectionist Systems,” in Terence Horgan and John Tienson (eds.), Connectionism and the Philosophy of Mind (Studies in Cognitive Systems), Dordrecht: Springer Netherlands, pp. 90–112. doi:10.1007/978-94-011-3524-5_5
• Heidegger, Martin, 1975, The Basic Problems of Phenomenology , translated by Albert Hofstadter, 1988, Bloomington: Indiana University Press.
• Hickok, Gregory, 2009, “Eight Problems for the Mirror Neuron Theory of Action Understanding in Monkeys and Humans,” Journal of Cognitive Neuroscience , 21(7): 1229–43. doi:10.1162/jocn.2009.21189
• Huebner, Bryce, 2015, “Do Emotions Play a Constitutive Role in Moral Cognition?” Topoi , 34(2): 427–40.
• Husserl, Edmund, 1929, Cartesian Meditations: An Introduction to Phenomenology , translated by Dorian Cairns, 2012, Dordrect: Springer Science & Business Media.
• Hutchins, Edwin, 1996, Cognition in the Wild (second printing), Cambridge, MA: MIT Press.
• Hutto, Daniel D., 2008, Folk Psychological Narratives: The Sociocultural Basis of Understanding Reasons , Cambridge, MA: MIT Press.
• Hutto, Daniel D., and Erik Myin, 2013, Radicalizing Enactivism: Basic Minds without Content , Cambridge, MA: MIT Press.
• Hutto, Daniel D., and M. Ratcliffe, 2007, Folk Psychology Re-Assessed , Dordrecht; London: Springer.
• Kelso, J. A. Scott, 1995, Dynamic Patterns: The Self-Organization of Brain and Behavior , Cambridge, MA: MIT Press.
• Lakens, Daniël, 2014, “Grounding Social Embodiment,” Social Cognition , 32 (Supplement): 168–83. doi:10.1521/soco.2014.32.supp.168
• Lakoff, George, and Mark Johnson, 1980, Metaphors We Live By , Chicago: University of Chicago Press.
• –––, 1999, Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought , New York: Basic Books.
• Leeuwen, Marco van, 2005, “Questions For The Dynamicist: The Use of Dynamical Systems Theory in the Philosophy of Cognition,” Minds and Machines , 15(3–4): 271–333. doi:10.1007/s11023-004-8339-2
• Macrine, Shelia, L., and Jennifer M. B. Fugate, 2022, Movement Matters: How Embodied Cognition Informs Teaching and Learning , Cambridge, MA: MIT Press.
• Mahon, Bradford Z., 2015, “What Is Embodied about Cognition?” Language, Cognition and Neuroscience , 30(4): 420–29. doi:10.1080/23273798.2014.987791
• Mahon, Bradford Z., and Alfonso Caramazza, 2008, “A Critical Look at the Embodied Cognition Hypothesis and a New Proposal for Grounding Conceptual Content,” Journal of Physiology-Paris , Links and Interactions Between Language and Motor Systems in the Brain, 102(1): 59–70. doi:10.1016/j.jphysparis.2008.03.004
• Maibom, Heidi, 2010, “What Experimental Evidence Shows Us about the Role of Emotions in Moral Judgement,” Philosophy Compass , 5(11): 999–1012.
• Marr, David, 1982, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information , San Francisco: W. H. Freeman.
• Martin, Taylor, and Daniel L. Schwartz, 2005a, “Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts,” Cognitive Science , 29(4): 587–625. doi:10.1207/s15516709cog0000_15
• –––, 2005b, “Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts,” Cognitive Science , 29(4): 587–625. doi:10.1207/s15516709cog0000_15
• Matthen, Mohan, 2014, “Debunking Enactivism: A Critical Notice of Hutto and Myin’s Radicalizing Enactivism,” Canadian Journal of Philosophy , 44(1): 118–28. doi:10.1080/00455091.2014.905251
• Maxwell, Scott E., Michael Y. Lau, and George S. Howard, 2015, “Is Psychology Suffering from a Replication Crisis? What Does ‘Failure to Replicate’ Really Mean?” American Psychologist , 70(6): 487–98. doi:10.1037/a0039400
• May, Joshua, 2018, Regard for Reason in the Moral Mind , Oxford: Oxford University Press.
• May, Joshua, and Victor Kumar, 2018, “Moral Reasoning and Emotion,” in Karen Jones, Mark Timmons and Aaron Zimmerman (eds.), Routledge Handbook on Moral Epistemology . London: Routledge, pp. 139–156.
• Menary, Richard, 2008, Cognitive Integration: Mind and Cognition Unbounded , Basingstoke, New York: Palgrave Macmillan.
• Merleau-Ponty, Maurice, 1962, Phenomenology of Perception , translated by Colin Smith, London: Routledge.
• Michaels, Claire, and Zsolt Palatinus, 2014, “A Ten Commandments for Ecological Psychology,” in Lawrence Shapiro (ed.), The Routledge Handbook of Embodied Cognition , New York: Routledge, Taylor & Francis Group, pp. 19–28.
• Newell, Allen, John C. Shaw, and Herbert A. Simon, 1958, “Elements of a Theory of Human Problem Solving,” Psychological Review , 65(3): 151–66. doi:10.1037/h0048495
• Nichols, Shaun, 2004, Sentimental Rules: On the Natural Foundations of Moral Judgment , Oxford: Oxford University Press.
• Noë, Alva, 2004, Action in Perception , Cambridge, MA: MIT Press.
• O’Regan, J. Kevin, and Alva Noë, 2001, “A Sensorimotor Account of Vision and Visual Consciousness,” Behavioral and Brain Sciences , 24(5): 939–73. doi:10.1017/S0140525X01000115
• Pouw, Wim T. J. L., Tamara van Gog, and Fred Paas, 2014, “An Embedded and Embodied Cognition Review of Instructional Manipulatives,” Educational Psychology Review , 26(1): 51–72. doi:10.1007/s10648-014-9255-5
• Prinz, Jesse J., 2004, Gut Reactions: A Perceptual Theory of Emotion , Oxford: Oxford University Press.
• Prinz, Jesse J., and Lawrence W. Barsalou, 2000, “Steering a Course for Embodied Representation,” in Eric Dietrich and Arthur Markman (eds.), Cognitive Dynamics: Conceptual Change in Humans and Machines , Cambridge, MA: MIT Press, pp. 51–77.
• Pulvermüller, Friedemann, 2005, “Brain Mechanisms Linking Language and Action,” Nature Reviews Neuroscience , 6(7): 576–82. doi:10.1038/nrn1706
• Rabelo, André L. A., Victor N. Keller, Ronaldo Pilati, and Jelte M. Wicherts, 2015, “No Effect of Weight on Judgments of Importance in the Moral Domain and Evidence of Publication Bias from a Meta-Analysis,” PLoS ONE , 10(8). doi:10.1371/journal.pone.0134808
• Railton, Peter, 2017, “Moral Learning: Conceptual Foundations and Normative Relevance,” Cognition , 167 (October): 172–90.
• Rey, Georges, 1983, “Concepts and Stereotypes,” Cognition , 15(1): 237–62. doi:10.1016/0010-0277(83)90044-6
• –––, 1985, “Concepts and Conceptions: A Reply to Smith, Medin and Rips,” Cognition , 19(3): 297–303. doi:10.1016/0010-0277(85)90037-X
• Rupert, Robert D., 2004, “Challenges to the Hypothesis of Extended Cognition,” The Journal of Philosophy , 101(8): 389–428.
• Sauer, Niko, 2010, “Causality and Causation: What We Learn from Mathematical Dynamic Systems Theory,” Transactions of the Royal Society of South Africa , 65(1): 65–68. doi:10.1080/00359191003680091
• Schnall, Simone, Jennifer Benton, and Sophie Harvey, 2008, “With a Clean Conscience: Cleanliness Reduces the Severity of Moral Judgments,” Psychological Science , 19(12): 1219–22.
• Schnall, Simone, Jonathan Haidt, Gerald L Clore, and Alexander H Jordan, 2008, “Disgust as Embodied Moral Judgment,” Personality and Social Psychology Bulletin , 34(8): 1096–1109.
• Shapiro, Lawrence, 2007, “The Embodied Cognition Research Programme,” Philosophy Compass , 2(2): 338–46. doi:10.1111/j.1747-9991.2007.00064.x
• –––, 2012, “Embodied Cognition,” in Eric Margolis, Richard Samuels and Stephen P. Stich (eds.), The Oxford Handbook of Philosophy of Cognitive Science , New York: Oxford University Press, pp. 118–147.
• –––, 2013, “Dynamics and Cognition,” Minds and Machines , 23(3): 353–75. doi:10.1007/s11023-012-9290-2
• –––, 2019a, Embodied Cognition , Second Edition, London; New York: Routledge.
• –––, 2019b, “Matters of the Flesh: The Role(s) of Body in Cognition,” in Matteo Colombo, Elizabeth Irvine and Mog Stapleton (eds.), Andy Clark and His Critics , New York, NY: Oxford University Press, pp. 69–80.
• –––, 2019c, “Flesh Matters: The Body in Cognition,” Mind & Language , 34(1): 3–20. doi:10.1111/mila.12203
• Spaulding, Shannon, 2010, “Embodied Cognition and Mindreading,” Mind & Language , 25(1): 119–40.
• –––, 2011, “A Critique of Embodied Simulation,” Review of Philosophy and Psychology , 2(3): 579–99.
• –––, 2013, “Mirror Neurons and Social Cognition,” Mind & Language , 28(2): 233–57.
• Spivey, Michael J., 2007, The Continuity of Mind (Oxford Psychology Series), Oxford, New York: Oxford University Press.
• Sternberg, Saul, 1969, “Memory-Scanning: Mental Processes Revealed by Reaction-Time Experiments,” American Scientist , 57(4): 421–57.
• Symes, Ed, Rob Ellis, and Mike Tucker, 2007, “Visual Object Affordances: Object Orientation,” Acta Psychologica , 124(2): 238–55. doi:10.1016/j.actpsy.2006.03.005
• Tettamanti, Marco, Giovanni Buccino, Maria Cristina Saccuman, Vittorio Gallese, Massimo Danna, Paola Scifo, Ferruccio Fazio, Giacomo Rizzolatti, Stefano F. Cappa, and Daniela Perani, 2005, “Listening to Action-Related Sentences Activates Fronto-Parietal Motor Circuits,” Journal of Cognitive Neuroscience , 17(2): 273–81. doi:10.1162/0898929053124965
• Thelen, Esther, Gregor Schöner, Christian Scheier, and Linda B. Smith, 2001, “The Dynamics of Embodiment: A Field Theory of Infant Perseverative Reaching,” Behavioral and Brain Sciences , 24(1): 1–34. doi:10.1017/S0140525X01003910
• Thelen, Esther, and Linda Smith (eds.), 1993, A Dynamic Systems Approach to Development: Applications , Cambridge, MA: MIT Press.
• Thompson, Evan, 2010, Mind in Life , Cambridge, MA: Harvard University Press.
• Trevarthen, Colwyn, 1979, “Communication and Cooperation in Early Infancy: A Description of Primary Intersubjectivity,” in Margaret Bullowa (ed.) Before Speech: The Beginning of Interpersonal Communication , Cambridge: Cambridge University Press, pp. 321–348.
• Tucker, Mike, and Rob Ellis, 1998, “On the Relations between Seen Objects and Components of Potential Actions,” Journal of Experimental Psychology: Human Perception and Performance , 24(3): 830–46. doi:10.1037/0096-1523.24.3.830
• –––, 2001, “The Potentiation of Grasp Types during Visual Object Categorization,” Visual Cognition , 8(6): 769–800. doi:10.1080/13506280042000144
• –––, 2004, “Action Priming by Briefly Presented Objects,” Acta Psychologica , 116(2): 185–203. doi:10.1016/j.actpsy.2004.01.004
• Van Gelder, Tim, 1995, “What Might Cognition Be, If Not Computation?” The Journal of Philosophy , 92(7): 345–81. doi:10.2307/2941061
• –––, 1998, “The Dynamical Hypothesis in Cognitive Science,” Behavioral and Brain Sciences , 21(5): 615–28. doi:10.1017/S0140525X98001733
• Varela, Francisco J., Evan Thompson, and Eleanor Rosch, 2017, The Embodied Mind, Revised Edition: Cognitive Science and Human Experience , Cambridge, MA: MIT Press.
• Ward, Dave, David Silverman, and Mario Villalobos, 2017, “Introduction: The Varieties of Enactivism,” Topoi , 36(3): 365–75. doi:10.1007/s11245-017-9484-6
• Wilson, Andrew D., and Sabrina Golonka, 2013, “Embodied Cognition Is Not What You Think It Is,” Frontiers in Psychology , 4, published online 12 February 2013. doi:10.3389/fpsyg.2013.00058
• Wilson, Margaret, 2002, “Six Views of Embodied Cognition,” Psychonomic Bulletin & Review , 9(4): 625–36. doi:10.3758/BF03196322
• Wilson, Robert A., 1994, “Wide Computationalism,” Mind , 103(411): 351–72. doi:10.1093/mind/103.411.351
• Wilson, Robert A., and Andy Clark, 2001, “How to Situate Cognition: Letting Nature Take Its Course,” in Philip Robbins and Murat Aydede (eds.) The Cambridge Handbook of Situated Cognition , 1st ed., Cambridge: Cambridge University Press, pp. 55–77. doi:10.1017/CBO9780511816826.004
• Woodward, James, 2016, “Emotion versus Cognition in Moral Decision-Making: A Dubious Dichotomy,” in S. Matthew Liao (ed.), Moral Brains: The Neuroscience of Morality , Oxford: Oxford University Press, pp. 87–116.
• Zahavi, Dan, 2005, Subjectivity and Selfhood: Investigating the First-Person Perspective , Cambridge, MA: MIT Press.
• Zednik, Carlos, 2011, “The Nature of Dynamical Explanation,” Philosophy of Science , 78(2): 238–63.

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• The Language of Thought Hypothesis , entry by Matthew Katz in the Internet Encyclopedia of Philosophy .
• The Computational Theory of Mind , entry by Marcin Milkowski in the Internet Encyclopedia of Philosophy .
• Functionalism , entry by Thomas Polger in the Internet Encyclopedia of Philosophy .

artificial intelligence | cognitive science | concepts | connectionism | externalism about the mind | functionalism | intentionality | language of thought hypothesis | mental content: causal theories of | mental content: teleological theories of | mental representation | mind: computational theory of | moral sentimentalism | neuroscience, philosophy of | other minds | phenomenology

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• Review Article
• Published: 22 August 2023

Insights into embodied cognition and mental imagery from aphantasia

• Emiko J. Muraki   ORCID: orcid.org/0000-0001-9534-6538 1 , 2 ,
• Laura J. Speed   ORCID: orcid.org/0000-0002-3147-3615 3 &
• Penny M. Pexman   ORCID: orcid.org/0000-0001-7130-0973 1 , 2  

Nature Reviews Psychology volume  2 ,  pages 591–605 ( 2023 ) Cite this article

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Mental representations allow humans to think about, remember and communicate about an infinite number of concepts. A key question within cognitive psychology is how the mind stores and accesses the meaning of concepts. Embodied theories propose that concept knowledge includes or requires simulations of the sensory and physical interactions of one’s body with the world, even when a concept is subsequently processed in a context unrelated to those interactions. However, the nature of these simulations is highly debated and their mechanisms underspecified. Insight into whether and how simulations support concept knowledge can be derived from studying related mental representations, such as mental imagery. In particular, research into the inability to form mental imagery, known as aphantasia, can advance understanding of mental imagery and mental simulations. In this Review, we provide an overview of embodied theories of cognition, review research in mental imagery and consider how simulation and mental imagery might overlap. We then synthesize the growing aphantasia literature and discuss how aphantasia can be used to test predictions derived from theories of embodied cognition.

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Pearson, J., Naselaris, T., Holmes, E. A. & Kosslyn, S. M. Mental imagery: functional mechanisms and clinical applications. Trends Cogn. Sci. 19 , 590–602 (2015).

Article   PubMed   PubMed Central   Google Scholar  

Zeman, A. Z. J. et al. Loss of imagery phenomenology with intact visuo-spatial task performance: a case of ‘blind imagination’. Neuropsychologia 48 , 145–155 (2010).

Article   PubMed   Google Scholar  

Zeman, A., Dewar, M. & Della Sala, S. Lives without imagery — congenital aphantasia. Cortex 73 , 378–380 (2015). This article is among the first to describe and characterize congenital aphantasia, the lifelong absence of mental imagery.

Wilson, M. Six views of embodied cognition. Psychon. Bull. Rev. 9 , 625–636 (2002).

Barsalou, L. W. Grounded cognition. Annu. Rev. Psychol. 59 , 617–645 (2008). This article describes the theoretical foundation of embodied cognition as it relates to several cognitive processes and reviews the empirical literature testing embodied theories of cognition.

Mahon, B. Z. & Caramazza, A. A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. J. Physiol.-Paris 102 , 59–70 (2008).

Ostarek, M. & Bottini, R. Towards strong inference in research on embodiment — possibilities and limitations of causal paradigms. J. Cogn. 4 , 1–21 (2020). This article critiques empirical evidence for embodied conceptual processing and proposes methodological approaches to better understand the causal relationship between sensory experience and conceptual processing.

Google Scholar  

Dawes, A. J., Keogh, R., Robuck, S. & Pearson, J. Memories with a blind mind: remembering the past and imagining the future with aphantasia. Cognition 227 , 105192 (2022).

Fodor, J. A. The Language of Thought Vol. 5 (Harvard Univ. Press, 1975).

Pylyshyn, Z. Computation and cognition: issues in the foundations of cognitive science. Behav. Brain Sci. 3 , 111–169 (1980).

Article   Google Scholar  

Anderson, J. R. Arguments concerning representations for mental imagery. Psychol. Rev. 85 , 249–277 (1978).

Pylyshyn, Z. W. What the mind’s eye tells the mind’s brain: a critique of mental imagery. Psychol. Bull. 80 , 1–24 (1973).

Zwaan, R. A. The immersed experiencer: toward an embodied theory of language comprehension. Psychol. Learn. Motiv. 44 , 35–62 (2004).

Meteyard, L., Cuadrado, S. R., Bahrami, B. & Vigliocco, G. Coming of age: a review of embodiment and the neuroscience of semantics. Cortex 48 , 788–804 (2012). This article describes the spectrum of theories related to embodied and non-embodied semantic representation, linking behavioural and neural evidence with the theories.

Collins, A. M. & Loftus, E. F. A spreading-activation theory of semantic processing. Psychol. Rev. 82 , 407 (1975).

Quillian, M. R. Word concepts: a theory and simulation of some basic semantic capabilities. Behav. Sci. 12 , 410–430 (1967).

Quillian, R. A revised design for an understanding machine. Mech. Transl. 7 , 17–29 (1962).

Patterson, K. & Lambon Ralph, M. A. in Neurobiology of Language Ch. 61 (eds Hickok, G. & Small, S. L.) 765–775 (Academic, 2016).

Barsalou, L. W., Santos, A., Simmons, W. K. & Wilson, C. D. in Symbols, Embodiment, and Meaning (eds De Vega, M., Glenberg, A. M. & Graesser, A. C.) 245–283 (Oxford Univ. Press, 2008).

Connell, L. What have labels ever done for us? The linguistic shortcut in conceptual processing. Lang. Cogn. Neurosci. 34 , 1308–1318 (2019).

Paivio, A. Imagery and Verbal Processes (Holt, Rinehart and Winston, 1971).

Paivio, A. Mental imagery in associative learning and memory. Psychol. Rev. 76 , 241–263 (1969).

Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. H. & Yap, M. J. Visual word recognition of single-syllable words. J. Exp. Psychol.-Gen. 133 , 283–316 (2004).

Barsalou, L. W. Perceptual symbol systems. Behav. Brain Sci. 22 , 577–660 (1999).

Glenberg, A. M. & Gallese, V. Action-based language: a theory of language acquisition, comprehension, and production. Cortex 48 , 905–922 (2012).

Brysbaert, M., Warriner, A. B. & Kuperman, V. Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46 , 904–911 (2014).

Lynott, D., Connell, L., Brysbaert, M., Brand, J. & Carney, J. The lancaster sensorimotor norms: multidimensional measures of perceptual and action strength for 40,000 English words. Behav. Res. Methods 52 , 1271–1291 (2020).

Pexman, P. M., Muraki, E., Sidhu, D. M., Siakaluk, P. D. & Yap, M. J. Quantifying sensorimotor experience: body-object interaction ratings for more than 9,000 English words. Behav. Res. Methods 51 , 453–466 (2019).

Balota, D. A. et al. The English Lexicon Project. Behav. Res. Methods 39 , 445–459 (2007).

Siakaluk, P. D., Pexman, P. M., Aguilera, L., Owen, W. J. & Sears, C. R. Evidence for the activation of sensorimotor information during visual word recognition: the body–object interaction effect. Cognition 106 , 433–443 (2008).

Sidhu, D. M., Kwan, R., Pexman, P. M. & Siakaluk, P. D. Effects of relative embodiment in lexical and semantic processing of verbs. Acta Psychol. 149 , 32–39 (2014).

Pecher, D., Zeelenberg, R. & Barsalou, L. W. Verifying different-modality properties for concepts produces switching costs. Psychol. Sci. 14 , 119–124 (2003).

Pecher, D., Zeelenberg, R. & Barsalou, L. W. Sensorimotor simulations underlie conceptual representations: modality-specific effects of prior activation. Psychon. Bull. Rev. 11 , 164–167 (2004).

Dove, G. Three symbol ungrounding problems: abstract concepts and the future of embodied cognition. Psychon. Bull. Rev. 23 , 1109–1121 (2016).

Louwerse, M. M. Symbol interdependency in symbolic and embodied cognition. Top. Cogn. Sci. 3 , 273–302 (2011).

Glenberg, A. M. & Kaschak, M. P. Grounding language in action. Psychon. Bull. Rev. 9 , 558–565 (2002).

Stanfield, R. A. & Zwaan, R. The effect of implied orientation derived from verbal context on picture recognition. Psychol. Sci. 12 , 153–156 (2001).

Morey, R. D. et al. A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE). Psychon. Bull. Rev. 29 , 613–626 (2021).

Winter, A. The action–sentence compatibility effect (ACE): meta-analysis of a benchmark finding for embodiment. Acta Psychol. 230 , 103712 (2022).

Muraki, E. J. & Pexman, P. M. Simulating semantics: are individual differences in motor imagery related to sensorimotor effects in language processing? J. Exp. Psychol. Learn. Mem. Cogn. 47 , 1939–1957 (2021).

Zwaan, R. A. & Pecher, D. Revisiting mental simulation in language comprehension: six replication attempts. PLoS ONE 7 , e51382 (2012).

Beveridge, M. E. L. & Pickering, M. J. Perspective taking in language: integrating the spatial and action domains. Front. Hum. Neurosci. 7 , 577 (2013).

Hargreaves, I. S., White, M., Pexman, P. M., Pittman, D. & Goodyear, B. G. The question shapes the answer: the neural correlates of task differences reveal dynamic semantic processing. Brain Lang. 120 , 73–78 (2012).

Tousignant, C. & Pexman, P. Flexible recruitment of semantic richness: context modulates body–object interaction effects in lexical-semantic processing. Front. Hum. Neurosci. 6 , 7 (2012).

van Dam, W. O., van Dijk, M., Bekkering, H. & Rueschemeyer, S.-A. Flexibility in embodied lexical-semantic representations. Hum. Brain Mapp. 33 , 2322–2333 (2012).

Reifegerste, J., Meyer, A. S., Zwitserlood, P. & Ullman, M. T. Aging affects steaks more than knives: evidence that the processing of words related to motor skills is relatively spared in aging. Brain Lang. 218 , 104941 (2021).

Simonsen, H. G., Lind, M., Hansen, P., Holm, E. & Mevik, B. H. Imageability of Norwegian nouns, verbs and adjectives in a cross-linguistic perspective. Clin. Linguist. Phon. 27 , 435–446 (2013).

Ibáñez, A. et al. Ecological meanings: a consensus paper on individual differences and contextual influences in embodied language. Preprint at OSF https://osf.io/ej5y3/ (2022).

Andrews, M., Vigliocco, G. & Vinson, D. Integrating experiential and distributional data to learn semantic representations. Psychol. Rev. 116 , 463–498 (2009).

Banks, B., Wingfield, C. & Connell, L. Linguistic distributional knowledge and sensorimotor grounding both contribute to semantic category production. Cogn. Sci. 45 , e13055 (2021).

Henningsen-Schomers, M. R., Garagnani, M. & Pulvermüller, F. Influence of language on perception and concept formation in a brain-constrained deep neural network model. Philos. Trans. R. Soc. B Biol. Sci. 378 , 20210373 (2023).

Barsalou, L. W. Challenges and opportunities for grounding cognition. J. Cogn. 3 , 31 (2020).

Ostarek, M. & Huettig, F. A task-dependent causal role for low-level visual processes in spoken word comprehension. J. Exp. Psychol. Learn. Mem. Cogn. 43 , 1215–1224 (2017).

Kuhnke, P., Beaupain, M. C., Arola, J., Kiefer, M. & Hartwigsen, G. Meta-analytic evidence for a novel hierarchical model of conceptual processing. Neurosci. Biobehav. Rev . 144 , 104994 (2022). This article reports a meta-analysis examining the role of modal regions in conceptual processing.

Gallese, V. Mirror neurons and the social nature of language: the neural exploitation hypothesis. Soc. Neurosci. 3 , 317–333 (2008).

Pulvermüller, F. Semantic embodiment, disembodiment or misembodiment? In search of meaning in modules and neuron circuits. Brain Lang. 127 , 86–103 (2013).

Barsalou, L. W. On staying grounded and avoiding quixotic dead ends. Psychon. Bull. Rev. 23 , 1122–1142 (2016).

Barsalou, L. W. Establishing generalizable mechanisms. Psychol. Inq. 30 , 220–230 (2019).

Bottini, R., Morucci, P., D’Urso, A., Collignon, O. & Crepaldi, D. The concreteness advantage in lexical decision does not depend on perceptual simulations. J. Exp. Psychol. Gen. 151 , 731–738 (2022).

Boulenger, V. et al. Word processing in Parkinson’s disease is impaired for action verbs but not for concrete nouns. Neuropsychologia 46 , 743–756 (2008).

Buccino, G. et al. Processing graspable object images and their nouns is impaired in Parkinson’s disease patients. Cortex 100 , 32–39 (2018).

García, A. M. et al. Parkinson’s disease compromises the appraisal of action meanings evoked by naturalistic texts. Cortex 100 , 111–126 (2018).

García, A. M. et al. How language flows when movements don’t: an automated analysis of spontaneous discourse in Parkinson’s disease. Brain Lang. 162 , 19–28 (2016).

Nistico, R. et al. The embodiment of language in tremor-dominant Parkinson’s disease patients. Brain Cogn. 135 , 103586 (2019).

Trumpp, N. M., Kliese, D., Hoenig, K., Haarmeier, T. & Kiefer, M. Losing the sound of concepts: damage to auditory association cortex impairs the processing of sound-related concepts. Cortex 49 , 474–486 (2013).

Kim, J. S., Elli, G. V. & Bedny, M. Knowledge of animal appearance among sighted and blind adults. Proc. Natl Acad. Sci. USA 116 , 11213–11222 (2019).

Lewis, M., Zettersten, M. & Lupyan, G. Distributional semantics as a source of visual knowledge. Proc. Natl Acad. Sci. USA 116 , 19237–19238 (2019).

Nanay, B. Multimodal mental imagery. Cortex 105 , 125–134 (2018).

Pearson, J. The human imagination: the cognitive neuroscience of visual mental imagery. Nat. Rev. Neurosci. 20 , 624–634 (2019). This article provides an overview of the neural bases of visual imagery.

Pearson, J. & Westbrook, F. Phantom perception: voluntary and involuntary nonretinal vision. Trends Cogn. Sci. 19 , 278–284 (2015).

Kwok, E. L., Leys, G., Koenig-Robert, R. & Pearson, J. Measuring thought-control failure: sensory mechanisms and individual differences. Psychol. Sci. 30 , 811–821 (2019).

Jeannerod, M. Neural simulation of action: a unifying mechanism for motor cognition. Neuroimage 14 , S103–S109 (2001).

Willems, R. M., Toni, I., Hagoort, P. & Casasanto, D. Neural dissociations between action verb understanding and motor imagery. J. Cogn. Neurosci. 22 , 2387–2400 (2009).

Connell, L. & Lynott, D. Do we know what we’re simulating? Information loss on transferring unconscious perceptual simulation to conscious imagery. J. Exp. Psychol. Learn. Mem. Cogn. 42 , 1218–1232 (2016).

Pearson, J. & Kosslyn, S. M. The heterogeneity of mental representation: ending the imagery debate. Proc. Natl Acad. Sci. USA   112 , 10089–10092 (2015).

Kosslyn, S. M., Ganis, G. & Thompson, W. L. Neural foundations of imagery. Nat. Rev. Neurosci. 2 , 635–642 (2001).

Keogh, R. & Pearson, J. The perceptual and phenomenal capacity of mental imagery. Cognition 162 , 124–132 (2017).

Pearson, J., Clifford, C. W. G. & Tong, F. The functional impact of mental imagery on conscious perception. Curr. Biol. 18 , 982–986 (2008).

Brascamp, J. W., Knapen, T. H. J., Kanai, R., Van Ee, R. & Van Den Berg, A. V. Flash suppression and flash facilitation in binocular rivalry. J. Vis. 7 , 12 (2007).

Chang, S., Lewis, D. E. & Pearson, J. The functional effects of color perception and color imagery. J. Vis. 13 , 4–4 (2013).

Kosslyn, S. M. Scanning visual images: some structural implications. Percept. Psychophys. 14 , 90–94 (1973).

Kosslyn, S. M. Information representation in visual images. Cognit. Psychol. 7 , 341–370 (1975).

Kosslyn, S. M. Can imagery be distinguished from other forms of internal representation? Evidence from studies of information retrieval times. Mem. Cognit. 4 , 291–297 (1976).

Parsons, L. M. Imagined spatial transformations of one’s hands and feet. Cognit. Psychol. 19 , 178–241 (1987).

Shepard, R. N. & Metzler, J. Mental rotation of three-dimensional objects. Science 171 , 701–703 (1971).

Shepard, S. & Metzler, D. Mental rotation: effects of dimensionality of objects and type of task. J. Exp. Psychol. Hum. Percept. Perform. 14 , 3–11 (1988).

Kosslyn, S. M. & Pomerantz, J. R. Imagery, propositions, and the form of internal representations. Cognit. Psychol. 9 , 52–76 (1977).

Albers, A. M., Kok, P., Toni, I., Dijkerman, H. C. & de Lange, F. P. Shared representations for working memory and mental imagery in early visual cortex. Curr. Biol. 23 , 1427–1431 (2013).

Stokes, M., Thompson, R., Cusack, R. & Duncan, J. Top-down activation of shape-specific population codes in visual cortex during mental imagery. J. Neurosci. 29 , 1565–1572 (2009).

Harrison, S. A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458 , 632–635 (2009).

Kosslyn, S. M. et al. The role of area 17 in visual imagery: convergent evidence from PET and rTMS. Science 284 , 167–170 (1999).

Cui, X., Jeter, C. B., Yang, D., Montague, P. R. & Eagleman, D. M. Vividness of mental imagery: individual variability can be measured objectively. Vis. Res. 47 , 474–478 (2007).

Dijkstra, N., Zeidman, P., Ondobaka, S., van Gerven, M. A. J. & Friston, K. Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Sci. Rep. 7 , 5677 (2017).

Bensafi, M., Rinck, F., Schaal, B. & Rouby, C. Verbal cues modulate hedonic perception of odors in 5-year-old children as well as in adults. Chem. Senses 32 , 855–862 (2007).

Djordjevic, J., Zatorre, R. J., Petrides, M., Boyle, J. A. & Jones-Gotman, M. Functional neuroimaging of odor imagery. NeuroImage 24 , 791–801 (2005).

Bunzeck, N., Wuestenberg, T., Lutz, K., Heinze, H. J. & Jancke, L. Scanning silence: mental imagery of complex sounds. NeuroImage 26 , 1119–1127 (2005).

Hubbard, T. L. Auditory imagery: empirical findings. Psychol. Bull. 136 , 302–329 (2010).

Schmidt, T. T., Ostwald, D. & Blankenburg, F. Imaging tactile imagery: changes in brain connectivity support perceptual grounding of mental images in primary sensory cortices. NeuroImage 98 , 216–224 (2014).

Schmidt, T. T. & Blankenburg, F. The somatotopy of mental tactile imagery. Front. Hum. Neurosci. 13 , 10 (2019).

Yoo, S.-S., Freeman, D. K., McCarthy, J. J. & Jolesz, F. A. Neural substrates of tactile imagery: a functional MRI study. NeuroReport 14 , 581–585 (2003).

Gerardin, E. Partially overlapping neural networks for real and imagined hand movements. Cereb. Cortex 10 , 1093–1104 (2000).

Hardwick, R. M., Caspers, S., Eickhoff, S. B. & Swinnen, S. P. Neural correlates of action: comparing meta-analyses of imagery, observation, and execution. Neurosci. Biobehav. Rev. 94 , 31–44 (2018).

Linke, A. C. & Cusack, R. Flexible information coding in human auditory cortex during perception, imagery, and STM of complex sounds. J. Cogn. Neurosci. 27 , 1322–1333 (2015).

Plailly, J., Delon-Martin, C. & Royet, J. P. Experience induces functional reorganization in brain regions involved in odor imagery in perfumers. Hum. Brain Mapp. 33 , 224–234 (2012).

Alemanno, F. et al. Action-related semantic content and negation polarity modulate motor areas during sentence reading: an event-related desynchronization study. Brain Res. 1484 , 39–49 (2012).

Bechtold, L., Ghio, M., Lange, J. & Bellebaum, C. Event-related desynchronization of mu and beta oscillations during the processing of novel tool names. Brain Lang. 177–178 , 44–55 (2018).

Moreno, I., de Vega, M. & León, I. Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions. Brain Cogn. 82 , 236–242 (2013).

Moreno, I. et al. Brain dynamics in the comprehension of action-related language. a time-frequency analysis of mu rhythms. NeuroImage 109 , 50–62 (2015).

Niccolai, V. et al. Grasping hand verbs: oscillatory beta and alpha correlates of action-word processing. PLoS ONE 9 , e108059 (2014).

van Elk, M., van Schie, H. T., Zwaan, R. A. & Bekkering, H. The functional role of motor activation in language processing: motor cortical oscillations support lexical-semantic retrieval. NeuroImage 50 , 665–677 (2010).

Cayol, Z., Rotival, C., Paulignan, Y. & Nazir, T. A. “Embodied” language processing: mental motor imagery aptitude predicts word-definition skill for high but not for low imageable words in adolescents. Brain Cogn. 145 , 105628 (2020).

McKelvie, S. J. & Demers, E. G. Individual differences in reported visual imagery and memory performance. Br. J. Psychol. 70 , 51–57 (1979).

Bonnet, C. et al. Kinesthetic motor-imagery training improves performance on lexical-semantic access. PLoS ONE 17 , e0270352 (2022).

Pecher, D., van Dantzig, S. & Schifferstein, H. N. J. Concepts are not represented by conscious imagery. Psychon. Bull. Rev. 16 , 914–919 (2009).

Speed, L. J. & Majid, A. An exception to mental simulation: no evidence for embodied odor language. Cogn. Sci. 42 , 1146–1178 (2018).

Keogh, R., Pearson, J. & Zeman, A. in Handbook of Clinical Neurology vol. 178 (eds. Barton, J. J. S. & Leff, A.) 277–296 (Elsevier, 2021). This chapter provides a review of the extremes of visual imagery and current research on aphantasia and hyperphantasia.

Dance, C. J., Ipser, A. & Simner, J. The prevalence of aphantasia (imagery weakness) in the general population. Conscious. Cogn. 10 , 103243 (2022).

Zeman, A. et al. Phantasia — the psychological significance of lifelong visual imagery vividness extremes. Cortex 130 , 426–440 (2020).

Milton, F. et al. Behavioral and neural signatures of visual imagery vividness extremes: aphantasia versus hyperphantasia. Cereb. Cortex Commun. 2 , tgab035 (2021).

Farah, M. J., Hammond, K. M., Levine, D. N. & Calvanio, R. Visual and spatial mental imagery: dissociable systems of representation. Cognit. Psychol. 20 , 439–462 (1988).

Marks, D. F. Visual imagery differences in the recall of pictures. Br. J. Psychol. 64 , 17–24 (1973).

Dance, C. J., Ward, J. & Simner, J. What is the link between mental imagery and sensory sensitivity? Insights from aphantasia. Perception 50 , 757–782 (2021).

Dawes, A. J., Keogh, R., Andrillon, T. & Pearson, J. A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia. Sci. Rep. 10 , 10022 (2020).

Keogh, R. Visual working memory in aphantasia: retained accuracy and capacity with a different strategy. Cortex 143 , 237–253 (2021).

Wicken, M., Keogh, R. & Pearson, J. The critical role of mental imagery in human emotion: insights from fear-based imagery and aphantasia. Proc. R. Soc. B Biol. Sci. 288 , 20210267 (2021).

Bainbridge, W. A., Pounder, Z., Eardley, A. F. & Baker, C. I. Quantifying aphantasia through drawing: those without visual imagery show deficits in object but not spatial memory. Cortex 135 , 159–172 (2021).

Monzel, M., Keidel, K. & Reuter, M. Imagine, and you will find — lack of attentional guidance through visual imagery in aphantasics. Atten. Percept. Psychophys. 83 , 2486–2497 (2021).

Keogh, R. & Pearson, J. The blind mind: no sensory visual imagery in aphantasia. Cortex 105 , 53–60 (2018).

Kay, L., Keogh, R., Andrillon, T. & Pearson, J. The pupillary light response as a physiological index of aphantasia, sensory and phenomenological imagery strength. eLife 11 , e72484 (2022).

Visser, I. et al. Improving the generalizability of infant psychological research: the ManyBabies model. Behav. Brain Sci. 45 , e35 (2022).

Takahashi, J. et al. Diversity of aphantasia revealed by multiple assessments of visual imagery, multisensory imagery, and cognitive style. Front. Psychol. 14 , 1174873 (2023).

Blajenkova, O., Kozhevnikov, M. & Motes, M. A. Object-spatial imagery: a new self-report imagery questionnaire. Appl. Cogn. Psychol. 20 , 239–263 (2006).

Palermo, L., Boccia, M., Piccardi, L. & Nori, R. Congenital lack and extraordinary ability in object and spatial imagery: an investigation on sub-types of aphantasia and hyperphantasia. Conscious. Cogn. 103 , 103360 (2022).

Jacobs, C., Schwarzkopf, D. S. & Silvanto, J. Visual working memory performance in aphantasia. Cortex 105 , 61–73 (2018).

Monzel, M., Vetterlein, A. & Reuter, M. Memory deficits in aphantasics are not restricted to autobiographical memory — perspectives from the dual coding approach. J. Neuropsychol. 16 , 444–461 (2022).

Fulford, J. et al. The neural correlates of visual imagery vividness — an fMRI study and literature review. Cortex 105 , 26–40 (2018).

Speed, L. J. & Majid, A. Grounding language in the neglected senses of touch, taste, and smell. Cogn. Neuropsychol. 37 , 363–392 (2020).

Hald, L. A., van den Hurk, M. & Bekkering, H. Learning verbs more effectively through meaning congruent action animations. Learn. Instr. 39 , 107–122 (2015).

James, K. H. & Swain, S. N. Only self-generated actions create sensori-motor systems in the developing brain. Dev. Sci. 14 , 673–678 (2011).

Muraki, E. J., Siddiqui, I. A. & Pexman, P. M. Quantifying children’s sensorimotor experience: child body–object interaction ratings for 3359 English words. Behav. Res. Methods 54 , 2864–2877 (2022).

Thill, S. & Twomey, K. E. What’s on the inside counts: a grounded account of concept acquisition and development. Front. Psychol. 7 , 402 (2016).

Pexman, P. M. The role of embodiment in conceptual development. Lang. Cogn. Neurosci. 34 , 1274–1283 (2019).

Cortese, M. J. & Fugett, A. Imageability ratings for 3,000 monosyllabic words. Behav. Res. Methods Instrum. Comput. 36 , 384–387 (2004).

Schock, J., Cortese, M. J. & Khanna, M. M. Imageability estimates for 3,000 disyllabic words. Behav. Res. Methods 44 , 374–379 (2012).

Juhasz, B. J. & Yap, M. J. Sensory experience ratings for over 5,000 mono- and disyllabic words. Behav. Res. Methods 45 , 160–168 (2013).

Brysbaert, M. & New, B. Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behav. Res. Methods 41 , 977–990 (2009).

Hoffman, P., Lambon Ralph, M. A. & Rogers, T. T. Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words. Behav. Res. Methods 45 , 718–730 (2013).

Shaoul, C. & Westbury, C. Exploring lexical co-occurrence space using HiDEx. Behav. Res. Methods 42 , 393–413 (2010).

Pounder, Z. et al. Only minimal differences between individuals with congenital aphantasia and those with typical imagery on neuropsychological tasks that involve imagery. Cortex 148 , 180–192 (2022).

Dance, C. J. et al. What is the relationship between aphantasia, synaesthesia and autism? Conscious. Cogn. 89 , 103087 (2021).

Ganczarek, J., Żurawska-Żyła, R. & Rolek, A. “I remember things, but I can’t picture them.” What can a case of aphantasia tell us about imagery and memory? Psychiatr. Psychol. Klin. 20 , 134–141 (2020).

Keogh, R. & Pearson, J. Attention driven phantom vision: measuring the sensory strength of attentional templates and their relation to visual mental imagery and aphantasia. Philos. Trans. R. Soc. B Biol. Sci. 376 , 20190688 (2021).

Königsmark, V. T., Bergmann, J. & Reeder, R. R. The Ganzflicker experience: high probability of seeing vivid and complex pseudo-hallucinations with imagery but not aphantasia. Cortex 141 , 522–534 (2021).

Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J. & Clubley, E. The Autism-spectrum Quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J. Autism Dev. Disord. 31 , 5–17 (2001).

Blomkvist, A. Aphantasia: in search of a theory. Mind Lang . https://doi.org/10.1111/mila.12432 (2022).

Blazhenkova, O. & Pechenkova, E. The two eyes of the blind mind: object vs. spatial aphantasia? Russ. J. Cogn. Sci. 6 , 51–62 (2019).

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Muraki, E.J., Speed, L.J. & Pexman, P.M. Insights into embodied cognition and mental imagery from aphantasia. Nat Rev Psychol 2 , 591–605 (2023). https://doi.org/10.1038/s44159-023-00221-9

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Embodied cognition and STEM learning: overview of a topical collection in CR:PI

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• Nora S. Newcombe 2  

Cognitive Research: Principles and Implications volume  2 , Article number:  38 ( 2017 ) Cite this article

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Embodied learning approaches emphasize the use of action to support pedagogical goals. A specific version of embodied learning posits an action-to-abstraction transition supported by gesture, sketching, and analogical mapping. These tools seem to have special promise for bolstering learning in science, technology, engineering, and mathematics (STEM) disciplines, but existing efforts need further theoretical and empirical development. The topical collection in Cognitive Research: Principles includes articles aiming to formalize and test the effectiveness of embodied learning in STEM. The collection provides guideposts, staking out the terrain that should be surveyed before larger-scale efforts are undertaken. This introduction provides a broader context concerning mechanisms that can support embodied learning and make it especially well suited to the STEM disciplines.

Recent cognitive theory, under the umbrella term embodied cognition , has emphasized the role that the body and environment play in cognitive processing (e.g., Barsalou, 1999 ; 2008 ; Clark, 1999 ; 2001 ; Shapiro, 2011 ). While there are several “flavors” of embodied cognitive theory, all challenge the conception of human cognition as amodal and abstract, uncoupled from the concrete world. Considering the role of the body in human cognitive function has led to basic insights in cognitive science regarding the role of embodied tools: gesture, action, and analogical mapping. These embodied tools could be leveraged to improve learning in several ways. An embodied framework for cognition provides an opportunity for science, technology, education, and mathematics (STEM) disciplines to include embodied learning tools to enhance pedagogy. STEM education initiatives may particularly benefit from incorporating embodied cognitive principles because STEM disciplines rely on representation systems that require sensory encoding (e.g., visualizations of data and information including maps, blueprints, graphs, charts), and are nevertheless dependent on highly abstract, formalized symbol systems (e.g., those used in math or chemistry). Students need a “way in” to linking sensory representations with abstractions.

The purpose of this topical collection is to bring together theoretical discussions of how embodiment can inform educational practices in the STEM disciplines with empirical examinations of whether or not such practices actually work. While much research remains to be done, we hope that this collection provides a theoretical and empirical framework on which a more embodied pedagogy can be built. In this overview, we begin with a brief primer on what we mean (and do not mean) by embodied cognitive theory, and then develop several themes that we see in this literature.

A brief primer on embodied cognition

J.J. Gibson ( 1979 ) speculated that, because perception is for action, cognitive science does not need a theory of representation. Perception instead could be understood in terms of sensory information—visual, kinesthetic, olfactory, et cetera. Many theories have arisen from Gibson’s theoretical starting point (i.e., that cognition may consist of nonsymbolic, sensorimotor processes as well as or even instead of symbolic representations). Generally, in the embodied view, the cognitive processes that distill and then operate on representations cannot be isolated from the sensory systems that create the representations. Arising from this general definition, however, are several flavors of embodied cognition (e.g., Clark, 1999 , 2001 ; Shapiro, 2011 ; Varela, Thompson, & Rosch, 1991 ; Wilson, 2002 ).

Theorists have distinguished between embodied cognition as incorporating the role of the body in providing sensory inputs to the cognitive system—a definition not incompatible with the computational account of cognition—and embodied cognition in which the body provides a constitutive component of cognition (c.f. Kiverstein, 2012 ; Marshall, 2014 ). Clark ( 1999 ) espouses a view of cognition which incorporates body-based sensorimotor information as constitutive of cognition, taking place outside the brain. Others support a radical embodied cognitive science account that is antirepresentationalist (e.g., Chemero, 2009 ; Gibson, 1979 ; but see Clark, 1999 ; Fodor, 1983 ). These latter definitions of embodied cognition challenge the foundations of the computational approach to cognition. For a deeper review of the differences between embodied theories, as well as what constitutes strong and weak embodiment, see DeSutter and Stieff ( 2017 ) Footnote 1 and Abrahamson and Bakker ( 2016 ) Footnote 2 .

Here, we adhere to the definition of embodied cognition as emphasizing the body’s role in forming cognitive representations. Construing cognition in this context portrays learners as poised to incorporate sensorimotor information. Their cognitive systems are affected, even constrained, by action and perception in ways traditional cognitive theorists had not considered. Embodied cognitive approaches to learning, therefore, predict that sensorimotor processes, including perception and action, should strengthen learning when included in a structured lesson, given their close and unique relationship to the cognitive system. How might this strengthening occur? We organize the remainder of this introduction in terms of possible mechanisms: 1) building analogies between sensorimotor and abstract domains; 2) using gesture as a linking and abstraction tool; 3) improving cognitive skills and abilities; 4) off-loading cognitive processes and representations into the body or environment; and 5) constructing and interpreting visual representations.

Building embodied analogies

Analogies aid learning by mapping an unfamiliar domain onto a familiar one to encourage inferences or reach new conclusions (Gentner, 1983 ). Analogies do not require embodied learning tools, but embodied learning tools follow the principle of analogical learning by mapping a familiar domain onto an unfamiliar one; by moving action to abstraction (Goldin-Meadow & Beilock, 2010 ). Embodied learning could allow learners to extend body-based representations, familiar and easily understandable in sensorimotor terms, to map onto more abstract (or less embodied) concepts. Action and gesture can both be used to promote analogical reasoning, but a subset of verbal analogies and metaphors can specifically capitalize on embodied concepts (Lakoff & Johnson, 1980 ).

The symbol grounding problem, as described by Glenberg ( 1997 ) and Barsalou ( 1999 ), states that abstract representations cannot be represented abstractly ad infinitum but must ultimately be represented in a grounded form that derives from the sensorimotor system. Grounding abstract symbols in sensory or body-based representations provides the learner a way to put information in a format that can be understood and used. Using analogies to support learning in the STEM disciplines has been highly developed in mathematics; in particular, see Tran, Smith, and Buschkuehl ( 2017 ) Footnote 3 .

Nathan and Walkington ( 2017 ) Footnote 4 propose a theory which they term a grounded and embodied theory of mathematical cognition (GEMC). They focus on the transduction of sensorimotor actions into cognitive states. In this way, concrete sensory representations can build analogies, which in turn support thinking about abstract mathematical proofs. In their theory and pilot study, Nathan and Walkington provide a demonstration that the specific information content in student gestures and directed actions supports meaningful insights—gestures are more than hand-waving—and must connect underlying concepts, pedagogical language, and student understanding.

Dove ( 2011 ) and Chatterjee ( 2010 ) have both offered continuum-based arguments which suggest that embodied, sensory representations provide concrete, perceptually grounded information, while more abstract, conceptual representations trade up in increased flexibility. In any case, embodied cognitive approaches to learning that capitalize on grounding abstract symbols in sensory modalities could be enhancing learning through the creation of an analogy. That is, an unfamiliar domain—abstract and not related directly to a sensory modality—is mapped onto a familiar domain—concrete, able to be directly perceived (c.f., Jamrozik, McQuire, Cardillo, & Chatterjee, 2016 ).

Applying these ideas to STEM learning, Hayes and Kraemer ( 2017 ) Footnote 5 link theories from cognitive neuroscience about semantic knowledge and body-based sensory representations, to propose that neural signatures can be queried to examine STEM learning. In their review, Hayes and Kraemer ( 2017 ) draw on theories of neural processing, including Hebbian learning and predictive coding, to propose that sensorimotor contingencies are mapped more directly onto neural targets, while abstractions are more flexible but more difficult to acquire and more fragile. The links between STEM learning and the role of embodied analogies are still being explored, but provide an important framework for future work in cognitive neuroscience.

The role of gesture

One way analogies can connect abstract concepts with concrete sensory representations is through gesture. Gestures are nonverbal representational movements, usually of the hands, and usually accompanying speech. Gesture plays a role in maintaining or recalling visual imagery, simulating action, and representing the speaker’s nonverbal thoughts (for a review, see Hostetter & Alibali, 2008 ). As Alibali and Nathan ( 2011 ) have suggested, gestures may be a way to ground abstract instructional information presented by a teacher in the physical world. Gesturing has been extensively studied as both the mechanism through which embodied concepts can be communicated, and as a way in which spatial, abstract, or physical information is encoded (Tversky, 2009 ). By linking linguistic and sensorimotor representations, gesture could be a powerful tool in augmenting STEM learning. Atit, Weisberg, Newcombe, and Shipley ( 2016 ) Footnote 6 demonstrate the nuanced relationship between gesture and language in the context of learning topographic maps, an important and complex representational format for the geosciences.

Gestures are inherently spatial, and thus might help build strong spatial analogies to nonspatial domains. Cooperrider, Gentner, and Goldin-Meadow ( 2016 ) Footnote 7 studied spontaneous student gestures during explanations of abstract concepts. Students were exposed to and instructed in causal systems (like positive and negative feedback loops) across domains, after which they were instructed to explain the differences between them. Intriguingly, and despite not being instructed to gesture, students were highly likely to describe abstract causal concepts in terms of spatial language and with spatial gestures (even when all spatial language was stripped from the instruction). These findings reveal the strength of spatial analogies for abstract concepts, and promote the importance of gesture for highlighting and emphasizing analogies.

Improving cognitive skills and abilities

Embodied learning has the potential to improve learning generally by supporting and improving the skills of the learner. Incorporating the framework of embodied cognition into STEM learning may also improve learning by bolstering cognitive skills, or providing additional or alternative strategies. By analyzing embodied representations—through actions or gestures—educators can more effectively measure student strategy choice.

One of the principle ways that embodied tools may enhance STEM learning is by improving spatial cognition (Clifton, Chang …Mazalek, 2016 ; DeSutter and Stieff 2017 ) 1, Footnote 8 . Spatial cognition encompasses the set of cognitive processes involved in reasoning about spatial problems. Spatial cognition has been strongly linked to entrance into STEM disciplines, and is also correlated with performance in those disciplines (Wai, Lubinski, & Benbow, 2009 ). Many STEM disciplines, including math (Battista, 1990 ), physics (Pallrand & Seeber, 1984 ), chemistry (Ping, Decatur, Larson, Zinchenko, & Goldin-Meadow, 2012 ), engineering (Hsi, Linn, & Bell, 1997 ), and the geosciences (Ishikawa & Kastens, 2005 ) have been shown to incorporate large amounts of spatial reasoning skills (for an overview see National Research Council, 2006 ).

Importantly, spatial skills have been shown to be malleable across age groups, genders, and socioeconomic status (Uttal et al., 2013 ). Embodied approaches to improving spatial ability have gained traction in adults (Burte, Gardony, Hutton, & Taylor, 2017 ; Chu & Kita, 2011 ) Footnote 9 . Among toddlers and infants, there is growing evidence that active exploration of objects promotes the development of mental rotation (Möhring & Frick, 2013 ). In that study, toddlers show decreased looking time to the same object rotated through any angle, but look longer at a mirror-image object (indicating surprise that is not the same object). Experimentation has also demonstrated that providing motoric experience with objects for infants at 14 months can improve their mental rotation (Frick & Wang, 2014 ).

On the basis of these findings, Burte et al. ( 2017 ) 9 report on a large-scale effort to import spatial skill training. They used embodied tasks in elementary schools and examined benefits to math learning. The spatial training, Think3D!, uses paper folding and origami tasks to emphasize and improve spatial thinking. After going through the intervention, elementary school students improved on specifically targeted math problems, which required visualization and which were in a real-world context. However, in this research, unlike in cases where embodied learning builds analogies, the improvement did not generalize to abstract concepts or problems.

Embedded instruction may make it easier to engage learners and shift their attention. Unlike embodied learning, embedded instruction places decontextualized information into a meaningful situation. In the geosciences, Jaeger, Wiley, and Moher ( 2016 ) Footnote 10 devised an embedded intervention to teach earthquakes to elementary school students. In the embedded condition, students experienced a simulated earthquake during learning—rumbling sounds were played, and computers became seismographs, which had to be read during simulated seismic activity. In a control condition, learning content and timing were the same, but maps of the earthquakes were studied, and no earthquake simulation was produced. Results revealed a significant interaction—students in the embedded condition learned more from pre- to post-test than students in the nonembedded condition. Creating a rich sensory experience to embed the learner in what can otherwise be a meaningless and abstract context can serve to deepen engagement, focus attention, and generate excitement (c.f., Johnson-Glenberg & Romanowicz, 2017 ) Footnote 11 .

Offloading cognition

Offloading refers to allowing the learner to store information without relying on the taxing mental resources involved in simulating movement, visual information, or anything that can be represented as such (e.g., conceptual and spatial information). By offloading, the learner may be able to use the extra cognitive resources to focus on problem solving, making inferences, or explaining to others. Embodied actions have long been thought to offload cognitive process onto the world or the body. Margaret Wilson’s ( 2002 ) synthesis of embodied theory identifies six tenets of embodied cognition and outlines the different research support for each, three of which emphasize offloading. First, Wilson states, cognition is for action. That is, systems that were once thought of as solely the domain of the brain (memory, attention, perception) evolved for an organism that perceives and acts in a three-dimensional world. Second, human beings offload cognitive work onto the environment. Human behavior often requires cognitive resources to address situations that are informationally rich. Building from the argument that cognition arises through the interaction of mind, body, and world, Wilson’s second tenet holds that manipulating the environment is a form of cognition. For instance, in trying to solve an algebra equation, a student might move like terms to one side of the equation by performing that step and writing the resulting equation. This offloads the mental (visuospatial, in this case) process from the brain onto the environment, and thus is a way of offloading cognition. Wilson’s third view is that offline cognition is body-based. When thinking about concepts or ideas that are not available, the sensory system simulates the relevant constructs in the same way as if they were present.

How might action help offload information? Kirsh and Maglio ( 1994 ) have distinguished between certain types of offloading. They introduce epistemic action in which an actor manipulates the environment to discover new information. Epistemic action is distinct from pragmatic action, in which an actor produces an action directly toward accomplishing a goal. To test whether people use epistemic actions, in which action offloads a mental process such as rotation, Kirsh and Maglio ( 1994 ) conducted research on the video game Tetris. They found that participants performed nonessential rotations and translations on Tetris pieces instead of rotating them mentally. Because they were nonessential to the goal of placing the piece in the next row, the authors argued that rotating the pieces allowed the participants to view new possible piece combinations that were not evident in other orientations. They interpreted this result as a sign that human beings naturally modify their environment to offload cognition into the world as a means of saving valuable cognitive resources.

Observational research in the geosciences has demonstrated that offloading cognition plays a role in problem solving among geology students (Kastens, Liben, & Agrawal, 2008 ). When students solved complex visual problems, requiring the integration of multiple viewpoints, the students were observed to rotate candidate solutions, juxtapose two potential solutions in space, and perform other actions which suggested they were using their physical environment as a way to arrange their cognitive procedures.

Although visual perception was Gibson’s ( 1979 ) focus, it typically is dismissed as fostering embodied forms of representation. In the sense of offloading cognition into the learner’s environment, however, using visual representations that map onto natural cognitive capacities should ease student difficulty. For example, algebraic notation requires replacing visual symbols with mathematical concepts. Meaning is conveyed through some perceptual features, (e.g., an above/below spatial configuration denotes division: \( \frac{24}{8} \) ), but not others (e.g., proximity does not change the meaning of an equation: 3 + 8 = 3 + 8). Marghetis, Landy, and Goldstone ( 2016 ) Footnote 12 devised a study to test whether participants who are familiar with algebraic notation perceive terms as solitary units. Participants’ knowledge of order of operations was assessed (3 * 8 + 4 * 2 does not equal 3 + 8 * 4 + 2), and then participants were asked to determine whether a number changed colors either within one term (i.e., across multiplication) or across terms (e.g., across addition). Results showed that participants who knew the order of operations performed the color change more quickly when it occurred within the same term. Additionally, participants’ speed of detection correlated with their accuracy on subsequent algebra tasks. The authors frame these results as revealing that algebraic knowledge manifests as perceptual training. Although it remains to be tested experimentally, teaching students to offload the difficult mental processes of commutative and associative properties into perceptual problems could support math learning more broadly.

Visual representations

Visual presentations of data are important for all forms of scientific communication. Designing effective graphs and knowing how to interpret them is critical. Research on effective visualization of data and visual communication of STEM concepts has capitalized on Gibsonian ideas of perception for action. Michal and Franconeri ( 2017 ) Footnote 13 show that the cognitive processes involved in graph interpretation manifest not in high-level cognition, but in knowing where to look. In an eye tracking study, participants looked at bar graphs and were asked to attend either to the size of the bar or the luminance. In single-dimension trials, the other dimension was the same for both bars. On these trials, most participants preferred to look first at one bar, their anchor point—light/dark or short/tall. In orthogonal trials, both dimensions varied, but participants were only asked about one dimension. Even when both dimensions changed, individual participants kept their preferred anchor points. This detailed analysis of what the authors call the visual routine of interpreting graphs reveals the inextricable role that visual perception plays in driving and supporting cognition.

Embodied cognitive tools provide a unique opportunity to augment STEM education by adding approaches that the cognitive system can readily incorporate and internalize. STEM disciplines are well suited to the inclusion of gesture, action, and body-based metaphors due to their reliance on arbitrary or abstract symbol systems, and their study of complex, dynamic phenomena. Recent interventions have demonstrated promising success, showing efficacy of action in the realm of physics education, and gesture in mathematics and the geosciences. Future research should delineate limitations of these approaches, and determine the role of embodied learning in technology (e.g., computer science), and engineering, understudied disciplines in interdisciplinary cognitive science.

DeSutter & Stieff (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Abrahamson & Bakker (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Tran, Smith, & Buschkuehl (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Nathan & Walkington (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Hayes & Kraemer (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Atit, Weisberg, Newcombe, & Shipley (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Cooperrider, Gentner, & Goldin-Meadow (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Clifton et al. (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Burte, Gardony, Hutton, & Taylor (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Jaeger, Wiley, & Moher (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Johnson-Glenberg & Romanowicz (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Marghetic, Landy, & Goldstone (2016) is part of the topical collection on Embodied Cognition and STEM Learning .

Michal & Franconeri (2017) is part of the topical collection on Embodied Cognition and STEM Learning .

Abrahamson, D., & Bakker, A. (2016). Making sense of movement in embodied design for mathematics learning. Cognitive Research: Principles and Implications, 1 (1), 33.

Google Scholar  

Alibali, M. W. & Nathan, M. J. (2011). Embodiment in mathematics teaching and learning: Evidence from students’ and teachers’ gestures. Journal of the Learning Sciences. doi: 10.1080/10508406.2011.611446

Atit, K., Weisberg, S. M., Newcombe, N. S., & Shipley, T. F. (2016). Learning to interpret topographic maps: Understanding layered spatial information. Cognitive Research: Principles and Implications, 1 (1), 2.

Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22 , 577–660.

PubMed   Google Scholar  

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59 , 617–645.

Article   PubMed   Google Scholar  

Battista, M. T. (1990). Spatial visualization and gender differences in high school geometry. Journal for Research in Mathematics Education, 21 (1), 47–60. http://www.silccenter.org/aigaion2/index.php/publications/show/1482

Burte, H., Gardony, A. L., Hutton, A., Taylor, H. A. (2017). Think3d!: Improving mathematics learning through embodied spatial training. Cognitive Research: Principles and Implications, 2 , 13

Chatterjee, A. (2010). Disembodying cognition. Language and Cognition, 2 , 79–116.

Article   PubMed   PubMed Central   Google Scholar  

Chemero, A. (2009). Radical embodied cognitive science . Cambridge, MA: MIT Press.

Chu, M., & Kita, S. (2011). The nature of gestures’ beneficial role in spatial problem solving. Journal of Experimental Psychology: General, 140 (1), 102.

Article   Google Scholar  

Clark, A. (1999). An embodied cognitive science? Trends in Cognitive Sciences, 3 (9), 345–351.

Clark, A. (2001). Mindware: An introduction to the philosophy of cognitive science . New York: Oxford University Press.

Clifton, P. G., Chang, J. S. K., Yeboah, G., Doucette, A., Chandrasekharan, S., Nitsche, M., …Mazalek, A. (2016). Design of embodied interfaces for engaging spatial cognition. Cognitive Research: Principles and Implications, 1 (1), 24.

Cooperrider, K., Gentner, D., & Goldin-Meadow, S. (2016). Spatial analogies pervade complex relational reasoning: Evidence from spontaneous gestures. Cognitive Research: Principles and Implications, 1 (1), 28.

DeSutter, D., & Stieff, M. (2017). Teaching students to think spatially through embodied actions: Design principles for learning environments in science, technology, engineering, and mathematics. Cognitive Research: Principles and Implications, 2 (1), 22.

Dove, G. (2011). On the need for embodied and dis-embodied cognition. Frontiers in Psychology, 1 , 1–13. doi: 10.3389/fpsyg.2010.00242 .

Fodor, J. A. (1983). The modularity of mind . Cambridge, MA: MIT Press.

Frick, A., & Wang, S. H. (2014). Mental spatial transformations in 14- and 16-month-old infants: Effects of action and observational experience. Child Development, 85 (1), 278–293.

Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7 , 155–170.

Gibson, J. J. (1979). The ecological approach to visual perception . Boston: Houghton-Mifflin.

Glenberg, A. M. (1997). What memory is for. Behavioral and Brain Sciences, 20 , 1–55.

Goldin-Meadow, S., & Beilock, S. L. (2010). Action’s influence on thought: The case of gesture. Perspectives on Psychological Science, 5 (6), 664–674.

Hayes, J. C., & Kraemer, D. J. (2017). Grounded understanding of abstract concepts: The case of STEM learning. Cognitive Research: Principles and Implications, 2 (1), 7.

Hostetter, A. B., & Alibali, M. W. (2008). Visible embodiment: Gestures as simulated action. Psychonomic Bulletin and Review, 15 , 495–514.

Hsi, S., Linn, M. C., & Bell, J. E. (1997). The Role of spatial reasoning in engineering and the design of spatial instruction. Journal of Engineering Education, 86 (2), 151–158.

Ishikawa, T., & Kastens, K. A. (2005). Why some students have trouble with maps and other spatial representations. Journal of Geoscience Education, 53 (2), 184–197.

Jaeger, A. J., Wiley, J., & Moher, T. (2016). Leveling the playing field: Grounding learning with embedded simulations in geoscience. Cognitive Research: Principles and Implications, 1 (1), 23.

Jamrozik, A., McQuire, M., Cardillo, E. R., & Chatterjee, A. (2016). Metaphor: Bridging embodiment to abstraction. Psychonomic Bulletin & Review, 23 (4), 1080–1089.

Johnson-Glenberg, M. C., & Romanowicz, C. (2017). Embodied science and mixed reality: How gesture and motion capture affect physics education. Cognitive Research: Principles and Implications, 2 (1), 24.

Kastens, K. A., Liben, L. S., & Agrawal, S. (2008). Epistemic actions in science education . Basel: Proceedings of Spatial Cognition.

Book   Google Scholar  

Kirsh, D., & Maglio, P. (1994). On distinguishing epistemic from pragmatic action. Cognitive Science, 18 , 513–549.

Kiverstein, J. (2012). The meaning of embodiment. Topics in Cognitive Science, 4 (4), 740–758.

Lakoff, G., & Johnson, M. (1980). Metaphors we live by . Chicago: University of Chicago Press.

Marghetis, T., Landy, D., & Goldstone, R. L. (2016). Mastering algebra retrains the visual system to perceive hierarchical structure in equations. Cognitive Research: Principles and Implications, 1 (1), 25.

Marshall, P. J. (2014). Neuroscience, embodiment, and development. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Volume 1 of the Handbook of child psychology and developmental science (7th ed.). Hoboken: Wiley. Editor-in-chief: R. M. Lerner.

Michal, A. L., & Franconeri, S. L. (2017). Visual routines are associated with specific graph interpretations. Cognitive Research: Principles and Implications, 2 (1), 20.

Möhring, W., & Frick, A. (2013). Touching up mental rotation: Effects of manual experience on 6-month-old infants’ mental object rotation. Child Development, 84 (5), 1554–1565.

Nathan, M. J., & Walkington, C. (2017). Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language. Cognitive Research: Principles and Implications, 2 (1), 9.

National Research Council. (2006). Learning to think spatially: GIS as a support system in the K-12 curriculum. Committee on the Support for the Thinking Spatially, National Research Council, Publisher: The National Academies Press, https://www.nap.edu/catalog/11019/learning-to-think-spatially .

Pallrand, G. J., & Seeber, F. (1984). Spatial ability and achievement in introductory physics. Journal of Research in Science Teaching, 21 (5), 507–516.

Ping, R., Decatur, M., Larson, S. W., Zinchenko, E. & Goldin-Meadow, S. (2012). Gesture-speech mismatch predicts who will learn to solve an organic chemistry problem . Paper presented at the Annual Meeting of the American Educational Research Association, April 2012, Vancouver.

Shapiro, L. (2011). Embodied cognition . New York: Routledge.

Tran, C., Smith, B., & Buschkuehl, M. (2017). Support of mathematical thinking through embodied cognition: Nondigital and digital approaches. Cognitive Research: Principles and Implications, 2 (1), 16.

Tversky, B. (2009). Spatial cognition: Situated and embodied. In P. Robbins & M. Aydede (Eds.), The Cambridge handbook of situated cognition . Cambridge: Cambridge University Press.

Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., …Newcombe, N. S. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139 (2) , 352–402.

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience . Cambridge: MIT Press.

Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101 (4), 817.

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin and Review, 9 , 625–636.

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Work on this project was funded by the NIH grant to SMW from the National Institute on Deafness and Other Communication Disorders, #F32-DC015203, and a grant to NSN and the Spatial Intelligence and Learning Center from the National Science Foundation, SBE-1041707.

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Weisberg, S.M., Newcombe, N.S. Embodied cognition and STEM learning: overview of a topical collection in CR:PI. Cogn. Research 2 , 38 (2017). https://doi.org/10.1186/s41235-017-0071-6

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Interactivity and Embodied Cues in Problem Solving, Learning and Insight: Further Contributions to a “Theory of Hints”

• First Online: 29 March 2017

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• Linden J. Ball 3 &
• Damien Litchfield 4  

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This chapter addresses the situated, embodied and interactive characteristics of problem solving by focusing on the cues that arise within a solver’s external environment. In examining the influence of external cues on problem solving we have been heavily influenced by Kirsh’s (The Cambridge handbook of situated cognition, Cambridge University Press, Cambridge, 2009) “theory of hints”. We extend this theory to include hints that derive from the communicative properties of other people’s eye movements, focusing on the role of eye gaze in directing attention and conveying information that can be beneficial for problem solving. A particularly interesting aspect of eye gaze is its capacity to facilitate the perceptual priming of motor simulations in an observer. This gives rise to the potential for an expert problem solver’s eye movements to cue imitative perceptual and attentional processing in less expert observers that can promote effective problem solving. We review studies that support the hypothesised role of gaze cues in scaffolding problem solving, focusing on examples from insight tasks and diagnostic radiography. Findings reveal that eye gaze can support a variety of decisions and judgments in problem solving contexts. In sum, knowing where another person looks provides hints that can act both implicitly and explicitly to cue attention and to shape thoughts and decisions.

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Eye tracking and the cognitive reflection test: Evidence for intuitive correct responding and uncertain heuristic responding

Mental state attribution and the gaze cueing effect.

Ball, L. J., & Ormerod, T. C. (2000a). Applying ethnography in the analysis and support of expertise in engineering design. Design Studies, 21, 403–421.

Article   Google Scholar  

Ball, L. J., & Ormerod, T. C. (2000b). Putting ethnography to work: The case for a cognitive ethnography of design. International Journal of Human-Computer Studies, 53, 147–168.

Bard, E. G., Anderson, A. H., Chen, Y., Nicholson, H. B. M., Havard, C., & Dalzel-Job, S. (2007). Let’s you do that: Sharing the cognitive burdens of dialogue. Journal of Memory and Language, 57, 616–641.

Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–660.

Google Scholar  

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.

Barsalou, L. W. (2010). Grounded cognition: Past, present, and future. Topics in Cognitive Science, 2, 716–724.

Battersby, W. S., Teuber, H. L., & Bender, M. B. (1953). Problem solving behavior in men with frontal and occipital brain injuries. Journal of Psychology, 35, 329–351.

Bayliss, A. P., Frischen, A., Fenske, M. J., & Tipper, S. P. (2007). Affective evaluations of objects are influenced by observed gaze direction and emotional expression. Cognition, 104, 644–653.

Becchio, C., Bertone, C., & Castiello, U. (2008). How the gaze of others influences object processing. Trends in Cognitive Sciences, 12, 254–258.

Brennan, S. E., Chen, X., Dickinson, C. A., Neider, M. B., & Zelinsky, G. J. (2008). Coordinating cognition: The costs and benefits of shared gaze during collaborative search. Cognition, 106, 1465–1477.

Brooks, R., & Meltzoff, A. N. (2005). The development of gaze following and its relation to language. Developmental Science, 8, 535–543.

Busby, J. S. (2001). Practices in design concept selection as distributed cognition. Cognition, Technology & Work, 3, 150–160.

Butterworth, G., & Jarret, N. (1991). What minds have in common in space: Spatial mechanisms serving joint visual attention in infancy. British Journal of Developmental Psychology, 9, 55–72.

Chapman, P. R., & Underwood, G. (1998). Visual search of driving situations: Danger and experience. Perception, 27, 951–964.

Charness, N., Reingold, E. M., Pomplun, M., & Stampe, D. M. (2001). The perceptual aspect of skilled performance in chess: Evidence from eye movements. Memory & Cognition, 29, 1146–1152.

Corkum, V., & Moore, C. (1995). Development of joint visual attention in infants. In C. Moore & P. J. Dunham (Eds.), Joint attention: Its origins and role in development (pp. 61–83). Hove, England: Erlbaum.

Dominowski, R., & Dallob, P. (1995). Insight and problem solving. In R. J. Sternberg & J. Davidson (Eds.), The nature of insight (pp. 33–62). Cambridge, MA: MIT Press.

Driver, J., Davis, G., Ricciardelli, P., Kidd, P., Maxwell, E., & Baron-Cohen, S. (1999). Gaze perception triggers reflexive visuospatial orienting. Visual Cognition, 6, 509–540.

Dunbar, K., & Blanchette, I. (2001). The in vivo/in vitro approach to cognition: The case of analogy. Trends in Cognitive Sciences, 5, 334–339.

Duncker, K. (1945). On problem solving. Psychological Monographs, 58 (5, No. 270), i–113.

Emery, N. J. (2000). The eyes have it: The neuroethology, function and evolution of social gaze. Neuroscience and Biobehavioral Reviews, 24, 581–604.

Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.

Flom, R., & Pick, A. D. (2007). Increasing specificity and the development of joint visual attention. In R. Flom, K. Lee, & D. Muir (Eds.), Gaze-following: Its development and significance (pp. 95–111). London: Erlbaum.

Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23, 523–552.

Gibbs, R. (2006). Embodiment in cognitive science . New York: Cambridge University Press.

Gibson, J., & Pick, A. (1963). Perception of another person’s looking behavior. American Journal of Psychology, 76, 386–394.

Gilhooly, K. J., & Murphy, P. (2005). Differentiating insight from non-insight problems. Thinking & Reasoning, 11, 279–302.

Grant, E. R., & Spivey, M. J. (2003). Eye movements and problem solving: Guiding attention guides thought. Psychological Science, 14, 462–466.

Hanna, J. E., & Brennan, S. E. (2007). Speakers’ eye gaze disambiguates referring expressions early during face-to-face conversation. Journal of Memory & Language, 57, 596–615.

Hutchins, E. (1995). Cognition in the wild . Cambridge, MA: MIT Press.

Hutchins, E. (2010). Enaction, imagination, and insight. In J. Stewart, O. Gapenne, & E. A. Di Paolo (Eds.), Enaction: Toward a new paradigm for cognitive science . Cambridge, MA: MIT Press.

Jarodzka, H., Baslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2012). Conveying clinical reasoning based on visual observation via eye movement modeling examples. Instructional Science , 40 , 813–827.

Kaplan, C., & Simon, H. A. (1990). In search of insight. Cognitive Psychology, 22, 374–419.

Kingstone, A., Smilek, D., Ristic, J., Friesen, C. K., & Eastwood, J. D. (2003). Attention researchers! It’s time to take a look at the real world. Current Directions in Psychological Science, 12, 176–184.

Kirsh, D. (2009). Problem solving and situated cognition. In P. Robbins & M. Aydede (Eds.), The Cambridge handbook of situated cognition . Cambridge: Cambridge University Press.

Kleinke, C. L. (1986). Gaze and eye contact: A research review. Psychological Bulletin, 100, 78–100.

Knoblich, G., Ohlsson, S., Haider, H., & Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. Journal of Experimental Psychology: Learning, Memory & Cognition, 25, 1534–1555.

Kokinov, B., Hadjiilieva, K., & Yoveva, M. (1997). Is a hint always useful? In M. G. Shafto & P. Langley (Eds.), Proceedings of the nineteenth annual conference of the Cognitive Science Society . Hillsdale, NJ: Lawrence Erlbaum Associates Inc.

Krupinski, E. A. (1996). Visual scanning patterns of radiologists searching mammograms. Academic Radiology, 3, 137–144.

Krupinski, E. A., Tillack, A. A., Richter, L., Henderson, J. T., Bhattacharyya, A. K., Scott, K. M., et al. (2006). Eye-movement study and human performance using telepathology virtual slides: Implications for medical education and differences with experience. Human Pathology, 37, 1543–1556.

Landrum, R. E. (1990). Maier’s two-string problem revisited: Evidence for spontaneous transfer. Psychological Reports, 67, 1079–1088.

Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth a thousand words. Cognitive Science, 11, 65–99.

Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life . Cambridge, MA: Cambridge University Press.

Book   Google Scholar  

Litchfield, D., Ball, L. J., Donovan, T., Manning, D. J., & Crawford, T. (2010). Viewing another person’s eye movements improves identification of pulmonary nodules in chest x-ray inspection. Journal of Experimental Psychology: Applied, 16, 251–262.

Litchfield, D., & Ball, L. J. (2011). Using another’s gaze as an explicit aid to insight problem solving. Quarterly Journal of Experimental Psychology, 64, 649–656.

Lobmaier, J. S., Fischer, M. H., & Schwaninger, A. (2006). Objects capture perceived gaze direction. Experimental Psychology, 53, 117–122.

Lobmaier, J. S., Tiddeman, B. P., & Perret, D. I. (2008). Emotional expression modulates perceived gaze direction. Emotion, 8, 573–577.

Maier, N. R. F. (1931). Reasoning in humans: II. The solution of a problem and its appearance in consciousness. Journal of Comparative & Physiological Psychology, 12, 181–194.

Nalanagula, D., Greenstein, J. S., & Gramopadhye, A. K. (2006). Evaluation of the effect of feedforward training displays of search strategy on visual search performance. International Journal of Industrial Ergonomics, 36, 289–300.

Neider, M. B., Chen, X., Dickinson, C. A., Brennan, S. E., & Zelinsky, G. J. (2010). Coordinating spatial referencing using shared gaze. Psychonomic Bulletin & Review, 17, 718–724.

Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.

Ormerod, T. C., & Ball, L. J. (in press). Qualitative methods in cognitive psychology. In C. Willig & W. Stainton-Rogers (Eds.), Handbook of qualitative research in psychology (2nd ed.). London: Sage Publications Ltd.

Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. In M. T. Keane & K. J. Gilhooly (Eds.), Advances in the psychology of thinking (Vol. 1, pp. 1–44). London: Harverster Wheatsheaf.

Pedone, R., Hummel, J. E., & Holyoak, K. J. (2001). The use of diagrams in analogical problem solving. Memory & Cognition, 29, 214–221.

Pomplun, M., Ritter, H., & Velichkovsky, B. (1996). Disambiguating complex visual information: Towards communication of personal views of a scene. Perception, 25, 931–948.

Proffitt, D. R. (2006). Embodied perception and the economy of action. Perspectives on Psychological Science, 1, 110–112.

Reid, F. J. M., Culverhouse, P. F., Jagodzinski, A. P., Parsons, R., & Burningham, C. (2000). The management of electronics engineering design teams: Linking tactics to changing conditions. Design Studies, 21, 75–97.

Sadasivian, D., Greenstein, J. S., Gramopadhye, A. K., & Duchowski, A. T. (2005). Use of eye movements as feedforward training for a synthetic aircraft inspection task. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2005 (pp. 289–300). New York, NY: ACM Press.

Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: Bodies and minds moving together. Trends in Cognitive Sciences, 10, 70–76.

Sheperd, S. V. (2010). Following gaze: Gaze-following behavior as a window into social cognition. Frontiers in Integrative Neuroscience, 4, 1–13.

Simon, H. A. (1981). The sciences of the artificial (2nd ed.). Cambridge MA: MIT Press.

Smith, R. W., & Kounios, J. (1996). Sudden insight: All or none processing revealed by speed-accuracy decomposition. Journal of Experimental Psychology; Learning, Memory & Cognition, 22, 1443–1462.

Spivey, M. (2007). The continuity of mind . Oxford: Oxford University Press.

Stein, R., & Brennan, S. E. (2004). Another person’s eye gaze as a cue in solving programming problems. Proceedings of ICMI 2004: Sixth international conference on multimodal interfaces (pp. 9–15). New York, NY: ACM Press.

Suchman, L. (1987). Plans and situated actions: The problem of human-machine communication . Cambridge, MA: Cambridge University Press.

Symons, L. A., Lee, K., Cedrone, C. C., & Nishimura, M. (2004). What are you looking at? Acuity for triadic eye gaze. The Journal of General Psychology, 131, 451–469.

Tabachneck-Schijf, H. J. M., Leonardo, A. M., & Simon, H. A. (1997). CaMeRa: A computational model of multiple representations. Cognitive Science, 21, 305–350.

Thomas, L. E., & Lleras, A. (2007). Moving eyes and moving thought: On the spatial compatibility between eye movements and cognition. Psychonomic Bulletin & Review, 14, 663–668.

Thomas, L. E., & Lleras, A. (2009a). Covert shifts of attention function as an implicit aid to insight. Cognition, 111, 168–174.

Thomas, L. E., & Lleras, A. (2009b). Swinging into thought: Directed movement guides insight in problem solving. Psychonomic Bulletin & Review, 16, 719–723.

Tomasello, M. (1999). The cultural origins of human cognition . Harvard, MA: Harvard University Press.

Van De Weil, M. W. J., Boshuizen, H. P. A., & Schmidt, H. G. (2000). Knowledge restructuring in expertise development: Evidence from pathophysiological representations of clinical cases by students and physicians. European Journal of Cognitive Psychology, 12, 323–356.

Van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25, 785–791.

Velichkovsky, B. M. (1995). Communicating attention: Gaze position transfer in cooperative problem solving. Pragmatics & Cognition, 3, 199–222.

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9, 625–636.

Zwaan, R. A. (1999). Embodied cognition, perceptual symbols, and situation models. Discourse Processes, 28, 81–88.

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Acknowledgements

This chapter is based on a paper presented at the Symposium on Distributed Cognition , University of Kingston, Kingston-upon-Thames, UK, held in July 2010, and sponsored and organised by Kingston Psychology and the Distributed Language Group. We are grateful for David Kirsh’s invaluable feedback on this presentation, which inspired the connection between his sketch of a theory of hints and our interest in embodied and interactive eye movement cues in problem solving. We are also grateful to Stephen Cowley and Frédéric Vallee-Tourangeau for follow-up discussions on the work we presented at the symposium as well as for their critical comments on the present chapter.

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Ball, L.J., Litchfield, D. (2017). Interactivity and Embodied Cues in Problem Solving, Learning and Insight: Further Contributions to a “Theory of Hints”. In: Cowley, S., Vallée-Tourangeau, F. (eds) Cognition Beyond the Brain. Springer, Cham. https://doi.org/10.1007/978-3-319-49115-8_6

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Embodied Cognition: What It Is & Why It's Important

Learn what embodied cognition is and why it is important..

Posted February 20, 2012 | Reviewed by Abigail Fagan

I recently conducted the following interview with Dr. Andrew Wilson and Dr. Sabrina Golonka, authors of the popular blog PyschScienceNotes, about embodied cognition . I reached out to them to offer everyone some basics on what embodied cognition is, what it is not, and why it is important.

Embodied cognition is the latest sexy topic in cognitive science. There is, however, a great deal of confusion about exactly what it means and how to study it. A lot of studies have hit the headlines claiming to be examples of embodiment, but if you look a little deeper, they are really just business as usual with a few bells and whistles. The view of embodiment we would like to defend is a fairly radical view, with far-ranging implications for how we do cognitive science and what we will end up thinking the brain (for example) is up to.

What embodied cognition is not

Traditional theories in psychology place all the responsibility for generating our behaviour in the brain; perception is the input to a computational, representational system that mentally transforms the input into motor commands. Many researchers treat embodied cognition as the idea that the contents of these mental states/representations can be influenced by the states of our bodies.

For example, Eerland, Guadalupe & Zwann (2011) recently suggested that leaning to the left makes you underestimate the magnitude of things, because this state of the body biases you towards the left end of the mental number line they claim we use to generate estimates of magnitude ( more here ). You can also demonstrate the connection between 'mind' and 'body' goes the other way; Miles, Lind & Macrae (2010) showed that thinking about the future made you sway forwards and thinking about the past made you sway backwards; they suggest this sway is caused by the underlying metaphor of 'past is behind us' and 'the future is ahead of us' ( more here ).

There are numerous problems with these studies; the primary problem, however, is that in both cases the mental content is assumed to the same as it would be if you were doing non-embodied cognition. This research remains business as usual, with a couple of embodied bells and whistles—all the hard work of generating behaviour is done in the brain, it's just that this work can be biased by what the body is up to. However, the hypothesis that the body can play a role in cognition is actually much more radical than this. By expanding the resources available to solve a task from simply the brain to include the body (with its perceptual and motor systems), we've opened up the possibility that we can solve a task in a very different way than a brain by itself might solve the task.

What embodied cognition is

The kind of embodied cognition we advocate ( more here ) is the claim that the brain, while important, is not the only resource we have available to us to generate behaviour. Instead, the form of our behaviour emerges from the real-time interaction between a nervous system in a body with particular capabilities and an environment that offers opportunities for behaviour and information about those opportunities. The reason this is quite a radical claim is that it changes the job description for the brain; instead of having to represent knowledge about the world and using that knowledge to simply output commands, the brain is now a part of a broader system that critically involves perception and action as well. The actual solution an organism comes up with for a given task includes all these elements.

Our favourite example of this right now is 'the outfielder problem' ( more here ). This is the question of how a baseball outfielder can catch a fly ball—how do they manage to get to the right place at the right time? The disembodied, computational solution notes that in order to predict where the ball will land, you need a model of the projectile motion of the ball and some information about its initial conditions as it came off the bat (speed, direction, etc). Perception provides this input, the brain uses a representation that implements the model to predict the landing location and then commands the body to move the right location.

The embodied solution instead begins with a task analysis: What resources are available to a mobile, perceiving-acting organism to solve the task? Do we need to compute anything, or can we simply directly use perceptual information to guide our interception? It turns out that careful consideration of the task reveals two potential informational strategies that are consequences of the parabolic arc the fly ball takes through the air.

The first works if you are in a direct line with the arc of the fly ball; if you run so as to make the (actually accelerating) ball appear to move at constant velocity, it turns out you will end up in the right place at the right time. If you are off the direct line, you can move so as to make the (actually curved) trajectory of the ball appear linear. There is no prediction or internal model required; instead, the outfielder solves the prediction problem by moving in a particular way.

Evidence strongly supports this latter prospective control strategy—people move in the kinds of curved paths it predicts (McBeath, Shaffer & Kaiser, 1995), as do dogs (Shaffer, Krauchunas, Eddy & McBeath, 2004). In effect, the work done by a computation in the disembodied account is instead done by the way you move in the embodied account; embodiment does work to solve problems that was typically assumed to be all done in the head.

Our other favourite way to describe the difference between the traditional, non-embodied computational approach and embodied cognition is to point to two very different kinds of robots ( more here ). Robots have an advantage in that we know exactly what went into making them—we know if they are computing sophisticated solutions, or simply moving under the control of simple rules. We can then look to see what their behaviour is like and relate that to biological organisms like ourselves.

Honda's ASIMO literally implements a traditional cognitive, computational approach. Everything it does is the output of complex internal programmes which control everything he does. Honda are fond of trotting him out to dance, run, and climb stairs; he can do all this, but it's very fragile. Minor disruptions throw him entirely (e.g. a minor error in foot placement and he falls; hide his pre-set landmarks with a little clutter and he completely fails to navigate his way across a room). He's slow, and inefficient; if you knock him, he needs time to recompute his behaviour or else he falls, and he often doesn't have the time.

Contrast this to Boston Dynamics "Big Dog." They wanted a robot that could walk over rough, uncertain terrain while carrying heavy loads, and they knew that the computational strategy was too slow and cumbersome. So they instead built a robot with springy legs and joints that mimic the kind of dynamical systems seen in animal quadrupeds. Big Dog has very little brain; the specific movements he produces at any given time emerge from the interaction between his moving legs, the surface he's on, and any other forces acting on him. If you knock Big Dog, he doesn't need to recompute his behaviour; he simply responds to the new force and the details are left up to his anatomy (his leg moves where it goes because that's the way it's built). He looks very biological when he does it, too.

Why is embodied cognition important for people who rely on communication?

The fascinating insight of embodied cognition is that behaviour is not simply the output of someone's isolated brain. Communication is an interesting example; how well a given person's conversational style works depends on the environment in which they find themselves. If you're interested in changing someone's communication behaviour, the implication is that you don't necessarily need to change anything about them; you might instead focus on changing the environment. In addition, embodiment done right gives you a way to talk rigorously about conversations as events spread across two (or more) people; it's not two distinct people each adding their piece to the conversation, but a negotiation extended over time and relying on a flow of information that each person is helping to shape.

Further suggested reading

There are a lot of books on embodied cognition but of course, this is partly because there are so many different flavours. There are a few recent works that get embodiment right, though. They all review recent empirical work that support this view of embodied cognition.

Louise Barrett, Beyond the Brain: How Body and Environment Shape Animal and Human Minds (2011)

This book is an excellent review of all the key pieces to embodied cognition, including James J Gibson's ecological approach to perception and action, dynamical systems theory and summaries of some key empirical research such as work by Esther Thelen and Linda Smith on the A-not-B error and a fascinating chapter on how the Portia spider uses embodied action to hunt other spiders using mimicry and deception , as well as some robot crickets that contain only very simple body parts but produce all kinds of adaptive complex behaviour. Highly readable and accessible to a general audience.

Rolf Pfeifer & Josh C Bongard, How The Body Shapes the Way We Think: A New View of Intelligence (2012) .

Pfeifer makes robots that have very simple internal control systems but interesting perceptual and action capabilities. He's demonstrated numerous examples of how the kind of body you give a robot can alter how it behaves, without ever changing the internal states. Robot studies are, as we noted, excellent ways to demonstrate that a behaviour is emerging from simple embodied solutions rather than sophisticated internal models because you can build a robot without the latter and still have it produce the behaviour.

(There's also a recent pre-print in arXiv.org of a paper by Pfeifer that reviews animal and robotic case studies demonstrating various aspects of embodiment.)

Antony Chemero, Radical Embodied Cognitive Science (2009) .

Chemero is a philosopher of science who follows the implications of embodied cognition to deny that the brain trades in mental representations. He also relies heavily on Gibson and dynamical systems theory. This is a slightly more specialised book, but provides a lot of useful specifics about what an embodied cognitive science research programme should look like.

Dr. Andrew D. Wilson is a Lecturer in Motor Control at the University of Leeds. His research is on the embodied, perceptual control of action with a particular interest in learning, and how these change across the lifespan.

Dr. Sabrina Golonka is a Lecturer in Psychology at Leeds Metropolitan University. Her research takes an ecological and embodied approach to language and categorization.

Jeff Thompson, Ph.D., is an adjunct associate research scientist in the Department of Psychiatry at Columbia University Medical Center and the New York State Psychiatric Institute.

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Article Contents

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Embodied Cognition in Practice: Exploring Effects of a Motor-Based Problem-Solving Intervention

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Regina T Harbourne, Sarah E Berger, Embodied Cognition in Practice: Exploring Effects of a Motor-Based Problem-Solving Intervention, Physical Therapy , Volume 99, Issue 6, June 2019, Pages 786–796, https://doi.org/10.1093/ptj/pzz031

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Embodied cognition interests physical therapists because efforts to advance motor skills in young infants can affect learning. However, we do not know if simply advancing motor skill is enough to support advances in cognition.

The objective was to examine the effect of 2 interventions on the developing motor skill of sitting and problem solving and to describe the feasibility of using eye-tracking technology to explore visual and motor interaction.

This was a longitudinal, randomized comparison of interventions.

Twenty infants with developmental delay and/or cerebral palsy, ranging in age from 8 to 34 months (mean [SD] = 15 [6.9] months), participated in an intervention emphasizing motor-based problem solving, and an intervention focused on advancing motor skill through assistance for attaining optimal movement patterns. Outcome measures were the Gross Motor Function Measure sitting subsection and the Early Problem Solving for Infants test. Active touch and looks were measured with eye-tracking technology.

Participants in both groups made significant motor gains from baseline, with no difference between intervention groups on Gross Motor Function Measure change scores. Participants in the problem-solving group showed significant gains in Early Problem Solving for Infants scores over the participants in the optimal movement patterns group. Overall, participants increased active touch of toys and increased concurrent looking with active touching.

This exploratory study was small, with variation in participants’ skills. The sampled behaviors for analysis were a small portion of the overall function of the participant.

An intervention using motor-based problem solving could improve infants’ problem-solving skill. The use of eye-tracking could help to understand embodied cognition as infants develop, but the challenges of embedding the method in natural settings require further work.

Listen to the author interview at https://academic.oup.com/ptj/pages/podcasts

Since the 1920s when Piaget situated infants’ sensorimotor actions as the foundation of his hierarchical theory of cognitive development, the development of cognition and action have been theoretically linked. According to Piaget's view, self-initiated actions served as the means by which infants learned to reason about the world, as well as a way to glean insight into the cognitive underpinnings of infancy. For example, Piaget's manual search task was interpreted as indicative of object permanence (or lack thereof). In that tradition, recent work has shown that the kinematics of 10-month-old infants’ reaching reflects prospective planning and intent for goal-directed actions, 1 and that 18-month-old infants’ executive functioning is grounded in prospective motor control (velocity of infants’ goal-directed reach), which predicts their performance on working memory and simple inhibition tasks. 2

The acquisition of new motor skills creates new opportunities for learning. Infants’ new-found ability to sit independently, for example, frees their arms so they can reach, grasp, and manually explore objects. 3 , 4 The onset of independent sitting and the acquisition of skills for manual exploration of objects facilitates increased attention to objects and object perception and understanding. 5 , 6 Similarly, active self-produced reaching prompts increased social engagement in the form of a preference for faces, 7 , 8 and the onset of independent walking prompts increased engagement in joint attention with caregivers, marking the onset of social cognition. 9

Conversely, early motor delays in infancy often are associated with delays in cognition. 10–12 Preterm infants with an inability to minimize extraneous movements of their body in sitting show a reduction in focused attention to a play task. 13 Infants with delayed locomotion show delays in spatial cognitive skills, but then show advancement of cognition when self-mobility begins. 14–16 It has been postulated that the association could also stem from the close interaction of movement and cognition that contributes to change in both systems over time. 17

On an embodied cognition account of learning, cognitive development in infancy happens as a result of the infant acting on and within an environment. 18 , 19 Typically developing crawling and walking infants with at least 6 weeks of experience in that posture showed spatial memory for hidden objects, whereas novices in those postures did not. 20 For children with early motor delays or dysfunction, motor skill acquisition appears directly connected to observed change in several areas of cognition. For example, early improvements in motor control during sitting for infants at risk for cerebral palsy (CP) contribute to gains in sustained focused attention to objects. 21 Preterm infants provided with early motor intervention for reaching interact with objects for longer periods and with greater skill, allowing early learning about objects. 22 However, a knowledge gap exists regarding the effect of differing motor interventions on cognitive change in infancy.

Conceptualizations of motor behavior typically include big, observable, gross locomotor actions, like crawling or walking, or precise, targeted, fine movements like reaching and grasping. An action system that is often overlooked is the visual system. 23 Just as the onset of independent sitting and reaching create opportunities for learning about the world by enabling infants to act on it, gaining control over eye movements enables infants to explore patterns, edges, motion, object constancies, and so forth. 24 Like other movements, eye movements can also index cognition 25 : looking times to visual stimuli reflect cognitive processes, such as perceptual discrimination, categorization, and learning. 26 Premature infants exhibit less time focused on a target object, and are slower to fixate attention on the object of interest than infants born at term. 27 Young children with cerebral palsy exhibit slower tracking of objects than age-matched peers, but spontaneously improve with age. 28 Infants with motor delays in achieving sitting independence exhibit longer look times to objects when compared with infants with typical development who are learning to sit, reflecting a longer time needed to process visual information from a natural scene while performing a new and challenging motor task. 29 However, eye-tracking studies of infants with delays are rare and none report eye movements in an effort to understand changes in cognition occurring with motor change.

Early intervention services, including physical therapy, are mandated for infants with significant motor and/or cognitive delays, 30 but the key ingredients of the therapeutic approaches are not well defined, making comparisons between interventions difficult. 31 In a recent systematic review, no “standard-of-care” approach could be identified, with components of standard care varying so much among studies as to defy replication. 31 Motor intervention for infants with movement delays or disorders can be provided in the home or clinic, but the amount (dosage) and type of intervention vary across regions and settings, and individual approaches of the therapist shape the nature of the therapy provided. 32 For example, 2 popular forms of current motor intervention are motivated by opposing assumptions about the advancement of early motor skills.

The first type, a motor-based, problem-solving (PS) approach, emphasizes child-initiated movement and environmental enrichment during intervention. 32 , 33 Practitioners encourage infants to solve increasingly difficult problems, set up the environment for small increments of movement that infants can use to solve a movement problem, and train caregivers to do the same. Errors and self-correction for goal-directed action provide input for building new skills in a dynamic way. In contrast, a body-weight-support approach provides assisted positioning in optimal alignment (OP). The premise is that supporting infants’ movement within normal patterns provides a template for the emergence of independent motor skill. Families can be encouraged to use special seating supports to assist sitting, and infants are assisted with initiating movement to take crawling and standing steps as needed, rather than being required to “figure things out.”

Our overall goal was to compare the short-term efficacy of the PS and OP types of motor intervention, initiated at the onset of sitting, and examine changes in both motor and cognitive areas. This randomized exploratory trial was undertaken to determine whether differences in physical therapy intervention with emphasis either on motor-based problem solving or assistance with optimal movement strategies would result equally in changes in motor skill, as well as changes in problem solving.

A subset of the group participated in an examination of attention and cognition through eye-tracking methodology. For these 8 participants, we asked whether we could detect change in visual and manual exploration because improved eye-hand use might underlie problem-solving abilities. The eye-tracking component of the study was done to determine the feasibility of the tool in a naturalistic setting with infants with motor delays.

Participants

A power analysis using G*power 34 was conducted using an effect size of 0.85 for our primary outcome measure, which was based on a previous related study examining motor skills and play of older children with neuromotor disorders. 35 A planned sample of 24 children with neuromotor disorders (12 in each therapy group), were estimated to provide 80% power to detect a true standardized effect size of 0.85, a large effect 36 for the difference in the improvement of Early Problem Solving for Infants (EPSI) scores both within and between the 2 treatment groups over the 12-week treatment period, assuming a 1-sided .05 α level.

Randomization was by blocks of 6, with envelopes created by a blinded associate, and opened after the initial baseline assessment for each participant. We recruited 28 infants from local early interventionists, but 8 were excluded due to our inclusion criteria (Fig.  1 ). Twenty infants with motor delays (age range 9–22 months) were finally included in this study. All participants concurrently received physical therapy once weekly as part of their home-based early intervention program. Participants with a diagnosis of CP, or a birth/developmental history indicative of CP (prematurity, intraventricular hemorrhage, periventricular leukomalacia) were included in this study. Participants were developmentally delayed, indicated by a score of at least 1 SD below the mean on the Peabody Gross Motor assessment. Participants entered the study when they were able to sit propping using their hands for at least 3 seconds. Exclusion criteria were: blindness, dislocated hip, and ability at baseline to transition in and out of sitting independently. After an initial screening for eligibility, participants were randomized into groups. Eleven participants were in the PS group, and nine in the OP group. Table  1 lists participants, ages, Gross Motor Function Classification Scale (GMFCS) level, and diagnoses.

Recruitment and group assignment flow diagram.

Participant Demographics a

a ASD = autism spectrum disorder; CP = cerebral palsy; devel = developmental; GMFCS = Gross Motor Functioning Classification Scale; ns = non-significant; OP = optimal patterning approach; PS = problem-solving approach.

Participants were assessed and received the study intervention either in their home or in an infant lab that approximated a home environment. Families were able to choose the location for their convenience. Families were present during all assessment and intervention activities, and included parents, siblings, and other interested family members. No restrictions were placed on the family regarding those present in order to maintain a naturalistic environment.

Outcome Measures

Participants were tested on the Gross Motor Function Measure (GMFM) 37 and on the EPSI assessment. 38 , 39 The EPSI measures attention capacity and object exploration movements to quantify the child's functional use of toys (defined as problem solving), and tracks these skills over time. Both the GMFM and EPSI are used to track children as compared to themselves, marking change over time to determine progress and response to intervention. Because all participants entered the study at a stage when they were developing sitting, and were expected to advance primarily in sitting, we used the sitting subsection of the GMFM as our primary outcome measure for motor change, although the entire GMFM was given and scored. The sitting subsection of the GMFM quantifies the child's ability to attain, maintain, and transition out of the sitting position, as well as reach and play using a variety of sitting strategies. The EPSI allows scoring of looks, explores, functions, and solutions while interacting with toys that afford exploring and manipulating functional parts in many ways, which are counted during the presentation of the toys from an adult. A standard set of toys, presented for 2 minutes each, includes nesting cups, a 4-door pop-up toy, and a ball/container/lever toy. The EPSI was scored in the standardized manner, which excludes the counting of looks when infants explore with hands. “Solutions” are achieved when the child can perform all the functions of the toy (nests all cups together in order); however, none of the participants achieved the solutions for any toy. Thus, we summed “explores” and “functions” as our outcome measure, per the standard instructions for this measure. Solutions are not necessary to quantify the problem-solving construct using this test. We used the most current method of scoring the EPSI, with 1 point for each explore, and 2 points for each function to create a summative score (see the IGDIs website: http://igdi.ku.edu/epsi-updates/ ). Definitions of EPSI items are also on the website ( http://igdi.ku.edu/wp-content/uploads/2018/10/EPSI-definitions-New.pdf ).

Intervention

All participants received physical therapy twice weekly for 12 weeks, for a total of 24 sessions, with each session lasting ∼1 hour. Each participant had a single therapist trained in the approach they were providing. Participants continued to receive their usual early intervention services during the intervention. Table  2 lists the key ingredients for each approach. The PS group's overriding goal was to enable the participant to incrementally solve small motor problems by environmental adaptation, enabling participant initiation of a movement strategy, and providing suggestions through light handling for variable motor options. The OP group's overriding goal was to assist the participant in initiating movement that was within normal or optimal movement patterns, and use direct handling to block patterns of movement that would result in errors or malalignment. Intervention was provided by 4 therapists trained by the first author (R.T.H.), with periodic on-site checks to ensure the fidelity of each approach was being maintained. Each participant was consistently seen by 1 experienced pediatric therapist who had received training from the primary investigator. The primary caregiver for the child attended each session and participated with all activities, and individualized strategies were suggested for home carryover. Neither group received a formal or written home program. Because the parents were present and participatory during each therapy session, modeling and verbal suggestions were provided in each session to scaffold the participant to the next level of skill. All participants were at the stage of development in which they were learning to sit, and not completely mobile, so they all needed regular carrying and handling during daily activities, which were discussed and practiced during therapy sessions.

Key Ingredients of the 2 Interventions

Data collection occurred prior to the start of intervention and at the end of intervention, 3 months later. We videotaped all assessments to be scored later by trained coders, who were blinded to group assignment and assessment number, to an ICC reliability of .90 or greater. The EPSI was conducted in the sitting position, with the infant supported from behind by the parent if needed. Toys were presented by the examiner, during a 6-minute play session. If necessary, the examiner redirected the infant's attention to the toys to attain the requisite 2 minutes per toy. The GMFM assessor was trained in reliable administration of the test.

Head-mounted eye-tracker

For 8 participants, additional data were collected using a head mounted eye-tracking system, allowing quantification of the precise location of eye focus to areas of interest on a toy. The eye-tracker was used during play with 1 of the EPSI toys. The eye-tracker consists of a head-mounted unit with a scene camera and an eye camera affixed to a lightweight adjustable frame. The frame fits onto a soft, stretchable cap, and the camera placement is universally adjustable with Velcro strapping and a bendable frame. Placement of the eye-tracker was done quickly by 1 researcher, while an assistant played with the objects in front of the participant. The eye-tracker uses an infrared reflection from the cornea, as well as feature detection of the pupil, to calculate the location of eye focus on the scene in front of the wearer. The device shows the point of eye fixation within the visual field of the wearer. Calibration and specifics of the eye-tracker are detailed in the eAppendix (available at https://academic.oup.com/ptj ). For the purpose of this study, we used the “pop-up” EPSI toy to measure look and touch variables because the toy afforded a fixed distance and salient areas of interest for eye focusing and hand touching that could be coded during infant play. The synchronized combined videos of the behavior, scene, and eye focus were used for behavioral coding (Fig.  2 ). Five of the participants in the eye-tracker group were in the PS group, and 3 were in the OP group.

Left, Infant playing with toy from Early Problem Solving Indicator with eye-tracker in place (top and side views). Right, Focused eye gaze of the infant (target) on an area of interest (door of pop-up toy), showing active touch while concurrently looking.

Data coding of visual and manual exploration

A primary coder scored the eye-tracking video using Datavyu digital video coding software ( datavyu.org ). This system allows coders to view videotaped sessions frame by frame, via keyboard control, and to record durations and frequencies of behaviors of interest. We identified 12 discrete areas of interest on the pop-up toy used in the EPSI: 4 buttons, 4 doors, and 4 pictures. Data were coded in 2 passes. The first pass through the video captured touching. For each touch to 1 of the locations, a coder identified the video frame marking the onset of the touch and the video frame marking the offset of the touch. The second pass captured looking, which was defined as the point of gaze cross-hairs from the eye-tracking video superimposed on 1 of the locations. A coder identified the video frame marking the onset of the look and the video frame marking the offset of the look. With these parameters, we calculated duration of each touch and duration of each look . We also classified concurrent touching and looking by identifying the frames where touch and look overlapped. In addition, we coded whether each touch was active or passive . Touches were operationalized as active if they were self-initiated and included some kind of haptic exploration such as rubbing, scratching, or fingering. Touches were passive if the experimenter placed participants’ hands on the toy or if participants rested their hands on the toy without haptic exploration.

To ensure interrater reliability, a second coder coded approximately 10% of all outcome measures. 40 This level of reliability was chosen as the standard for our eye-tracker as used in a similar setting and procedure (sitting infant with toys in a natural setting). 40 Our data were uniquely structured whereby we coded every video frame for where participants looked and touched, rather than for discrete behaviors that we had to judge as having happened or not/categorizing according to criteria. That changed the “philosophy” around the variables in that participants were almost always looking at and touching the toy that we presented to them; the code was to identify where. Those variables were coded in independent passes (codes for 1 variable not visible when coding the other variable).There was very little subjectivity to this variable, because the question of interest was about when those behaviors co-occurred, which was calculated by software, rather than by coder.

For duration variables, Pearson correlations ranged from 0.78 to 0.99; P values were less than .01. For categorical variables, interrater agreement ranged from 0.84 to 0.97; P values for all Cohen κ coefficients were less than .01.

Statistical Analysis

For our primary questions, our planned analysis compared the scores of the sitting subsection of the GMFM between groups (PS vs OP intervention) using change scores from preintervention to postintervention. We also used preintervention-to-postintervention change scores to compare groups on the EPSI (PS vs OP intervention). Independent t tests with an α level of .05, and a Bonferroni correction for multiple comparisons, resulted in a final α level of significance at .025.

The eye-tracker data were not statistically analyzed, but were examined for feasibility and proof of concept to determine added value for examining the problem-solving process of the participants during play.

Role of Funding Source

Neither funding source (Section on Pediatrics, American Physical Therapy Association, and PA Dept of Health C.U.R.E grants) had any role in the design of the study, analysis, or interpretation of the results. Funding supported only personnel for intervention, data collection, and analysis.

Equality of variance between groups was established for all variables. There were no significant differences between groups on any of the outcome measures at baseline.

Our 2 primary aims were to examine whether there were differences between the 2 intervention groups on the: (1) GMFM sitting scores, and (2) EPSI weighted scores. Due to variation in severity of the participants’ motor and cognitive skills, we used change scores from the baseline session to the postintervention data collection to reflect participants’ individual trajectories. An independent samples t test of GMFM sitting subsection scores revealed no difference in amount of change between the 2 intervention groups ( t (18) = 0.69; P  = .50); the PS group mean [SD] change score was 10.2% [5.3%]; the OP group mean change score was 7.2% [6.8%]. A paired samples t test of GMFM sitting subsection scores for the full sample revealed a significant increase over the 12 weeks from baseline to postintervention ( t (2,19) = −6.5; P  = .00). The mean score change was 9% from pretesting to posttesting, considered higher than the minimal clinically important change of 5% to 7% on the GMFM.

Independent sample t tests on EPSI change scores increased significantly more for the participants in the PS group than in the OP group ( t (18) = 2.48; P  = .02). The mean [SD] change in EPSI for the PS group was 21.0 [22.5], and the mean change for the OP group was 0.33 [11.9]. Notably, 5 out of the 9 participants in the OP group had decreased EPSI scores from baseline to postintervention, whereas only 2 of the participants in the PS group had lower scores at postintervention compared with their baseline scores.

Eye-Tracker Findings

Because of the small number of participants with eye-tracker data (n = 8), we did not perform statistical comparisons between groups. We focused on visual and manual exploration of 1 of the toys in the EPSI test. Descriptive data for each of the participants are presented graphically (Fig.  3 ) to compare baseline measures with postintervention measures.

Change in active and passive touch (percent of time with toy) contingent with looking, from preintervention to postintervention for individual participants. ASD = autism spectrum disorder; CP = cerebral palsy; NS = non-significant.

Participants from both the PS and OP groups increased active touching with concurrent looking from preintervention to postintervention (Fig.  2 ). Several problems arose during data collection, which resulted in collecting fewer data than we wanted. Infants with motor problems who are learning to sit have poor sitting posture, contributing to difficulties in calibrating the device. Some participants were distracted by the eye camera, pulling on it and requiring readjustment. Overall, the use of the eye-tracker in a naturalistic setting proved to be challenging. Although the eye-tracker is mobile, multiple factors make it difficult to use in homes including access to electric outlets, connecting wires that are difficult to conceal, distractions from family and home events, and inadequate lighting. In spite of these difficulties, we did manage to collect data that could prove to be valuable. The positive aspects are: accurate quantification of looking times, which are indicative of cognitive skill; very clear view of hand activity and manipulation of objects; a window into the attention strategies of young children, which are difficult to quantify using other means; and an accurate method to determine exactly what part of the environment the child is attending to during exploratory play.

Our comparison of the effects of 2 types of motor intervention on short-term changes in motor and cognitive skills yielded differences in the cognitive rather than in the motor area. Although both groups of participants changed significantly in the advancement of motor skill, they differed in advancing cognitive (problem-solving) skills. Early intervention services for preterm infants have been effective in advancing both motor and cognitive skills, but the relationship between cognitive and motor as regards intervention efforts is not well studied, 10 particularly in children with significant motor deficits. We followed recent expert recommendations for advancing both motor and cognitive skills in infants with CP or at risk for CP by incorporating child-initiated, variable movement; task-specific practice; and environmental adaptations into the PS intervention. 31 , 33 , 41–44 Our findings support these recommendations for early diagnosis and intervention for infants with CP. 41

Our first aim was to examine motor skill differences between groups receiving different motor intervention. Participants in both PS and OP interventions advanced in gross motor skills as measured by the sitting subsection of the GMFM. Motor skill was held constant with all participants entering the study with early sitting skills but an inability to get in and out of sitting. Commonalities between approaches included increased dosage of time specifically challenging the movement system, and increased attention to advancing motor development through family training and engagement. As expected, both groups made significant gains in early motor skills, specifically sitting. Thus, a higher dosage of movement therapy can promote significant motor skill change, at least in the short term.

Our second aim, determining change in cognitive processes as related to action systems, found differences based on intervention type. Although participants in both intervention groups improved in the motor outcome, we did not see a comparable improvement in cognitive skill as measured by the problem-solving test. The participants in the OP group, in which optimal solutions to movement problems were presented and guided, showed very little change in problem-solving skill over the course of the intervention. In fact, it is of concern that many of the participants in the OP group lost ground in the EPSI measure. One explanation for this regression is that these participants were less exposed to problem solving within their attempts at play and movement. They could have learned to wait for assistance, or simply had less practice in the self-initiated movement necessary for problem solving. In contrast, the participants in the PS group, who were allowed to initiate movement, make errors in that movement, and use suboptimal strategies to achieve the goal of interacting with a target object, improved significantly in problem solving as measured by the EPSI. The connection between problem solving with objects and solving motor problems could be related to supporting behavioral flexibility within the context of intervention.

The development of infants’ balance control has been used as a “model system” for understanding behavioral flexibility. 45 , 46 In this model, infants’ experiences of solving an array of everyday balance control problems enable them to generalize what they learn about keeping balance to novel situations. A tenet of this model is that trial and error and exploration are the mechanisms by which learning occurs. This suggests that for participants in the PS intervention, the opportunity to practice solving their own motor problems facilitated overall improvement in problem-solving strategies on the EPSI test. The traditional “learning to learn” models of behavioral flexibility also have as a core tenet the idea that learning is limited to a particular problem space. However, the participants in the PS group appeared to have generalized across problem spaces—they practiced learning to sit and myriad other movements during intervention, and subsequently applied their newly acquired strategies to a cognitive task. This pattern seems to more accurately reflect embodied cognition. Previous studies have found relations between improved motor skill and faster information and holistic face processing, increased ability to inhibit, spatial memory, and a host of other cognitive achievements. 29 , 47–50 The differences between the PS and OP groups similarly reflect the inextricable link between motor and cognitive skills as well as providing additional insight into the possible mechanism underlying that link. Across a variety of contexts, the acquisition of new skills involves variability in strategy use. 51 , 52 The ingredients that made up the intervention for the PS group provided experiences for the participants in the PS group that more closely resembled the everyday experiences of infants who are typically developing: engagement in a variety of movements, in a variety of contexts, embedded with problem solving. Child initiation of exploratory and investigative movement, as in the PS intervention, might not seem goal-directed to an adult, but it importantly serves the early learner to build problem-solving skills.

As a proof-of-concept undertaking, eye-tracking technology provided detailed information about the complex interaction of eyes and hands and during unassisted play. Because active movement feeds the cognition-action system, quantifying active movement of hands and eyes, which provide specific information necessary for learning about an object, could help to understand how infants learn. In addition to the examination of active vs passive touch, eye-tracking allowed measurement of concurrent looking and touching. In examining the individual participants’ results from the eye-tracker regarding active and passive touch concurrent with looking, the general pattern of increasing active touch with concurrent looking was the norm, except for the participant who was suspected of being on the autism spectrum and who showed less contingent looking while touching. Using the eye-tracker for repeated measures could be a marker for assisting in early diagnosis, or for monitoring progress. The challenges of the technique will need to be weighed against the benefits stemming from the information gained.

There are several limitations to this study, including the small sample size. Although group assignment was randomized, and the groups appeared equal on our measures at baseline, the diagnostic characteristics of the participants could have affected the outcomes. At this early age children are notably delayed, but not necessarily diagnosed; thus, it is impossible to know the developmental path they might take based on their unique biologic characteristics. A larger sample size would ensure that we had a large representative mix of children for comparing interventions. Another limitation was the small amount of behavioral data used for the play assessment and eye-tracking measures. More in-depth study is warranted to examine strategies of visual-motor skill and its relationship to overall motor skill and cognition. We also allowed families to choose the setting for assessment and intervention, which could have affected the results.

Conclusions

As therapists engage in promoting motor development, the question remains whether simply advancing motor skills causes or facilitates the development of cognitive skills. The findings of this study suggest that different motor interventions, with a different focus, can lead to different outcomes when looking broadly across the developmental spectrum and considering cognition. Our findings suggest that advancing motor skills without including strategies that build problem solving, self-initiated movement, and object exploration could have a negative effect on cognitive advancement, or at least reduce the potential effect that building motor capacity can bring. In addition, play-based assessments seem especially promising for tracking the development of problem-solving skills during intervention, although more information is needed on the relationship of play-based tools to traditional cognitive tests for infants. Finally, the use of eye-tracking might provide insights to better understand the advancement of cognition-action skills during development if the challenges of the technique can be overcome, with the possibility to use this new technology during naturalistic play to inform diagnosticians and interventionists of an important component of learning, the visual motor system.

Concept/idea/research design: R.T. Harbourne, S.E. Berger

Writing: R.T. Harbourne, S.E. Berger

Data collection: R.T. Harbourne

Data analysis: R.T. Harbourne, S.E. Berger

Project management: R.T. Harbourne, S.E. Berger

Fund procurement: R.T. Harbourne, S. E. Berger

This study was funded by an Academy of Pediatric Physical Therapy, American Physical Therapy Association research grant awarded to Regina Harbourne; a Commonwealth Universal Research Enhancement grant, Pennsylvania Department of Public Health “Project for Cognitive Advancement in Infants with Neuromotor Disorders: the CAN-DO project,” awarded to Regina Harbourne; and a College of Staten Island, Office of Technology Student-Faculty Technology Support Award awarded to Sarah Berger.

This research was approved by Duquesne University's Institutional Review Board.

This clinical trial was registered retrospectively in a clinical trial registry (NCT02673658) because the authors viewed this work as exploratory and had used the data to support a larger randomized controlled trial for early intervention.

The authors completed the ICJME Form for Disclosure of Potential Conflicts of Interest and reported no conflicts of interest.

This study was presented at a platform presentation at the International Conference for Infant Studies, July 3, 2018, Philadelphia, PA.

Claxton LJ , Keen R , McCarty ME . Evidence of motor planning in infant reaching behavior . Psych Sci . 2003 ; 14 : 354 – 356 .

Google Scholar

Gottwald JM , Achermann S , Marciszko C , Lindskog M , Gredebäck G . An embodied account of early executive-function development: prospective motor control in infancy is related to inhibition and working memory . Psych Sci . 2016 ; 27 : 1600 – 1610 .

Harbourne RT , Lobo MA , Karst GM , Galloway JC . Sit happens: Does sitting development perturb reaching development, or vice versa? . Infant Behav Dev . 2013 ; 36 : 438 – 450 .

Soska KC , Adolph KE . Postural position constrains multimodal object exploration in infants . Infancy . 2014 ; 19 : 138 – 161 .

Eppler MA . Development of manipulatory skills and the deployment of attention . Infant Behav Dev . 1995 ; 18 : 391 – 405 .

Soska KC , Adolph KE , Johnson SP . Systems in development: motor skill acquisition facilitates three-dimensional object completion . Dev Psych . 2010 ; 46 : 129 – 138 .

Libertus K , Needham A . Reaching experience increases face preference in 3-month-old infants . Dev Science . 2011 ; 14 : 1355 – 1364 .

Libertus K , Needham A . Face preference in infancy and its relation to motor activity . Inter J Behav Dev . 2014 ; 38 : 529 – 538 .

Clearfield MW . Learning to walk changes infants’ social interactions . Infant Behav Dev . 2011 ; 34 : 15 – 25 .

Spittle A , Orton J , Anderson PJ , Boyd R , Doyle LW . Early developmental intervention programmes provided post hospital discharge to prevent motor and cognitive impairment in preterm infants . Cochrane Database Syst Rev . (11) 2015 ; CD005495 . doi:10.1002/14651858.CD005495.pub4 .

Rosenbaum P , Paneth N , Leviton A et al. . A report: the definition and classification of cerebral palsy April 2006 . Dev Med Child Neurol Suppl . 2007 ; 109 : 8 – 14 .

Piek JP , Dawson L , Smith LM , Gasson N . The role of early fine and gross motor development on later motor and cognitive ability . Hum Mov Sci . 2008 ; 27 : 668 – 681 .

Berger SE , Harbourne RT , Guallpa Lliguichuzhca CL . Sit still and pay attention! Trunk movement and attentional resources in infants with typical and delayed development . Phys Occup Ther Pediatr . 2019 ; 39 : 48 – 59 .

Campos JJ , Anderson DI , Barbu-Roth MA , Hubbard EM , Hertenstein MJ , Witherington D . Travel broadens the mind . Infancy . 2000 ; 1 : 149 – 219 .

Anderson DI , Campos JJ , Witherington DC et al. . The role of locomotion in psychological development . Front Psych . 2013 ; 4 : 440 .

Lynch A , Ryu JC , Agrawal S , Galloway JC . Power mobility training for a 7-month-old infant with spina bifida . Ped Phys Ther . 2009 ; 21 : 362 – 368 .

Lobo MA , Galloway JC . Enhanced handling and positioning in early infancy advances development throughout the first year . Child Devel . 2012 ; 83 : 1290 – 1302 .

Needham A , Libertus K . Embodiment in early development . Wiley Interdiscip Rev Cogn Sci . 2011 ; 2 : 117 – 123 .

Smith L , Gasser M . The development of embodied cognition: Six lessons from babies . Artificial Life . 2005 ; 11 : 13 – 29 .

Clearfield MW . The role of crawling and walking experience in infant spatial memory . J Exp Child Psych . 2004 ; 89 : 214 – 241 .

Surkar SM , Edelbrock C , Stergiou N , Berger S , Harbourne R . Sitting postural control affects the development of focused attention in children with cerebral palsy . Ped Phys Ther . 2015 ; 27 : 16 – 22 .

Heathcock JC , Lobo M , Galloway JC . Movement training advances the emergence of reaching in infants born at less than 33 weeks of gestational age: a randomized clinical trial . Phys Ther . 2008 ; 88 : 310 – 322 .

Adolph KE , Berger SE . Motor development . In: Kuhn D , Siegel RS , eds. Cognition, Perception, and Language , 6th ed. New York, NY : Wiley ; 2006 : Handbook of Child Psychology (eds Damon W, Lerner RM); vol 2:161–213 .

Johnson SP , Hannon EE . Perceptual development . In: Liben LS , Müller U , Lerner RM , eds. Handbook of Child Psychology and Developmental Science: Cognitive Processes , 7th ed. Hoboken, NJ : Wiley ; 2015 : Vol 2 : 63 – 112 .

Google Preview

Hayhoe M , Ballard D . Eye movements in natural behavior . Trends Cogn Sci . 2005 ; 9 : 188 – 194 .

Aslin RN . What's in a look? . Dev Sci . 2007 ; 10 : 48 – 53 .

Downes M , Kelly D , Day K , Marlow N , de Haan M . Visual attention control differences in 12-month-old preterm infants . Infant Behav Dev . 2018 ; 50 : 180 – 188 .

Ego A , Lidzba K , Brovedani P et al. . Visual-perceptual impairment in children with cerebral palsy: a systematic review . Dev Med Child Neuro . 2015 ; 57 : 46 – 51 .

Harbourne RT , Ryalls B , Stergiou N . Sitting and looking: A comparison of stability and visual exploration in infants with typical development and infants with motor delay . Phys Occ Ther Ped . 2014 ; 34 : 197 – 212 .

Federal Regulations, U.S . Department of Education, IDEA, Part C, 34 CFR Part 303 . Public Law 108-446: Individuals with Disabilities Education Improvement Act of 2004, 118 Stat 2647-2808. 2004 .

Morgan C , Darrah J , Gordon AM , Harbourne R , Spittle A , Johnson R , Fetters L . Effectiveness of motor interventions in infants with cerebral palsy: a systematic review . Dev Med Child Neuro . 2016 ; 58 : 900 – 909 .

Morgan C , Novak I , Dale RC , Badawi N . Optimising motor learning in infants at high risk of cerebral palsy: a pilot study . BMC Ped . 2015 ; 15 : 30 .

Harbourne RT , Willett S , Kyvelidou A , Deffeyes J , Stergiou N . A comparison of interventions for children with cerebral palsy to improve sitting postural control: a clinical trial . Phys Ther . 2010 ; 90 : 1881 – 1898 .

Faul F , Erdfelder E , Lang AG , Buchner A . G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences . Behav Res Meth . 2007 ; 39 : 175 – 191 .

Ryalls BO , Harbourne RT , Kelly-Vance L , Wickstrom J , Stergiou N , Kyvelidou A . A perceptual motor intervention improves play behavior in children with moderate to severe cerebral palsy . Front Psych . 2016 ; 7 : 643 .

Cohen J . A power primer . Psychol Bull . 1992 ; 112 : 155 .

Palisano R , Rosenbaum P , Walter S , Russell D , Wood E , Galuppi B . Development and reliability of a system to classify gross motor function in children with cerebral palsy . Dev Med Child Neuro . 1997 ; 39 : 214 – 223 .

Greenwood CR , Walker D , Carta JJ , Higgins S . Developing a general outcome measure of growth in the cognitive abilities of children 1 to 4 years old: The Early Problem Solving Indicator . School Psych Rev . 2006 ; 35 : 535 – 551 .

Walker D , Carta JJ , Greenwood CR , Buzhardt JF . The use of individual growth and developmental indicators for progress monitoring and intervention decision making in early education . Exceptionality . 2008 ; 16 : 33 – 47 .

Yu C , Smith LB . Hand-Eye coordination predicts joint attention . Child Dev . 2017 ; 88 : 2060 – 2078 .

Novak I , Mcintyre S , Morgan C et al. . A systematic review of interventions for children with cerebral palsy: state of the evidence . Dev Med Child Neuro . 2013 ; 55 : 885 – 910 .

Blauw-Hospers CH , de Graaf-Peters VB , Dirks T , Bos AF , Hadders-Algra M . Does early intervention in infants at high risk for a developmental motor disorder improve motor and cognitive development? . Neurosci Biobehav Rev . 2007 ; 31 : 1201 – 1212 .

Novak I , Morgan C , Adde L . et al. . Early, accurate diagnosis and early intervention in cerebral palsy: advances in diagnosis and treatment . JAMA Ped . 2017 ; 171 : 897 – 907 .

Hamer EG , Hielkema T , Bos AF et al. . Effect of early intervention on functional outcome at school age: Follow-up and process evaluation of a randomised controlled trial in infants at risk . Early Hum Devel . 2017 ; 106 : 67 – 74 .

Adolph KE . Learning to learn in the development of action . In Lockman J , Reiser J , Nelson CA eds. Action as an Organizer of Perception and Cognition During Learning and Development: Minnesota Symposium on Child Development ; vol 33 . Hillsdale, NJ : Erlbaum ; 2005 : 91 – 122 .

Adolph KE , Robinson SR . Motor development . In: Liben LS , Müller U , eds. Cognitive Processes , 7th ed. Hoboken, NJ : Wiley ; 2015 : Handbook of Child Psychology and Developmental Science (ed Lerner RM); vol 2:114–157 .

Berger SE . Demands on finite cognitive capacity cause infants' perseverative errors . Infancy . 2004 ; 5 : 217 – 238 .

Berger S . Locomotor expertise predicts infants’ perseverative errors . Dev Psych . 2010 ; 46 : 326 – 336 .

Cashon CH , Ha O , Allen CL , Barna AC . A U-shaped relation between sitting ability and upright face processing in infants . Child Dev . 2013 ; 84 : 802 .

Berger SE , Chin B , Basra S , Kim H . Step-by-step: A microgenetic study of the development of strategy choice in infancy . Brit J Dev Psych . 2015 ; 33 : 106 – 122 .

Lemaire P , Siegler RS . Four aspects of strategic change: Contributions to children's learning of multiplication . J Exp Psych Gen . 1995 ; 124 : 83 – 97 .

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Integrating Embodied Cognition and Information Processing: A Combined Model of the Role of Gesture in Children's Mathematical Environments

Children learn and use various strategies to solve math problems. One way children's math learning can be supported is through their use of and exposure to hand gestures. Children's self-produced gestures can reveal unique, math-relevant knowledge that is not contained in their speech. Additionally, these gestures can assist with their math learning and problem solving by supporting their cognitive processes, such as executive function. The gestures that children observe during math instructions are also linked to supporting cognition. Specifically, children are better able to learn, retain, and generalize knowledge about math when that information is presented within the gestures that accompany an instructor's speech. To date, no conceptual model provides an outline regarding how these gestures and the math environment are connected, nor how they may interact with children's underlying cognitive capacities such as their executive function. In this review, we propose a new model based on an integration of the information processing approach and theory of embodied cognition. We provide an in-depth review of the related literature and consider how prior research aligns with each link within the proposed model. Finally, we discuss the utility of the proposed model as it pertains to future research endeavors.

Introduction

Hand gestures are used in a variety of mathematical contexts by children and adults alike. These gestures include both directed, meaningful movements intended to convey information, as well as less formal shifting of the hands that occurs naturally alongside conversation. Children use gestures to represent information, enhance conversation, and even support their own cognition (for a review, see Goldin-Meadow, 2009 ). Children's self-produced gestures (e.g., Broaders et al., 2007 ; Cook et al., 2008 ) as well as the gestures they see teachers use during instruction (e.g., Singer and Goldin-Meadow, 2005 ) have been shown to support their math learning. Given that children's early math knowledge has been consistently linked with their later math achievement (Claesens and Engel, 2013 ; Geary et al., 2013 ; Watts et al., 2014 ; Geary and Vanmarle, 2016 ), factors related to children's math understanding, like gesture, are important to understand.

One way that gestures can support mathematical learning is through their ability to aid children's cognition (Goldin-Meadow et al., 2001 ; Cook et al., 2012 ). Specifically, gesture can be linked to different components of domain-general abilities, such as executive function (EF). EF refers to the cognitive control processes that coordinate sub-processes such as attention shifting, working memory, and inhibitory control (e.g., Bull and Lee, 2014 ). Given the association between children's EF and math abilities (see Clements et al., 2016 for a review), as well as gestures supporting math through EF, we propose that it is important to study these factors together.

In this review, we discuss how children's use of and exposure to hand gestures during math contexts can shape their learning. Within the first section, we provide background information on two prevalent, but separate theories; information processing approach and theory of embodied cognition. We assert that combining these theories allows for a better understanding of the dynamic relations of gesture, EF, and children's mathematical learning. Thus, we propose a new model based on a combination of central tenets from these two theories. Next, we review relevant literature, and discuss how these results may be interpreted within the proposed model. In the final sections, we present opportunities for future empirical work. This paper serves as a unique examination of prior research through the lens of a unified model.

Overview of Gestures and Gesture Theories

Gestures are dynamic hand and body movements which accompany language. They can occur spontaneously or intentionally, and oftentimes provide different yet complementary information to a person's speech (Church and Goldin-Meadow, 1986 ; McNeill, 1992 ; Church, 1999 ). A speaker's gestures can facilitate listener's comprehension (for a meta-analysis, see Hostetter, 2011 ) and improve overall communication compared to speech alone (Church et al., 2000 ). Therefore, information provided by speakers' gestures are useful to those who see them.

Self-produced gestures can serve an important, internal purpose for the user as well. Hand gestures allow a speaker to simultaneously process their thoughts and put them into communicative form (McNeill, 1992 ; Krauss, 1998 ). People continue to gesture even when no one is watching (Krauss et al., 1995 ; Alibali et al., 2001 ). Research has shown that congenitally blind speakers use gestures even when they are communicating with a blind listener (Iverson and Goldin-Meadow, 1998 ), suggesting even those who have never seen gestures modeled in communication will use them too. Thus, gestures appear to support internal mechanisms of communication and cognition.

Due to the prevalence of gestures across ages, contexts, and domains, numerous theoretical models have been created to account for their communicative and cognitive functions. Each theory understandably overlaps in part with another; however, each one also provides complementary information explaining new contexts, factors, and functions. For example, frameworks that focus on what we can uncover about the speaker (Goldin-Meadow, 2003 ), or where these gestures emerge from (Gesture as Simulated Action framework, GSA; Hostetter and Alibali, 2008 ) both provide insight into how gestures relate to and shape underlying cognitive processes. Furthermore, the GSA framework builds upon another foundational idea that these cognitive processes are rooted within the environment (Embodied cognition, Barsalou, 1999 ). Gestures have also previously been considered under theories of cognition, such as Cognitive Load Theory (Sweller, 1988 ). This framework provides an explanation that self-produced gestures reduce “cognitive load,” a mechanism that is often considered as one of the main roles of gestures. Each of these, as well as other gesture-related frameworks, provide unique and compelling explanations of the distinctive roles of gestures as they relate to a particular set of circumstances. However, these models do not consider the specific role of gestures in mathematical contexts. Our goal was to create a model based on the growing literature regarding the benefits of gestures, both produced and seen by children, during math environments.

A New Model of Gesture for Math Learning

We propose that the literature is best supported by a model that integrates two previously established frameworks. First is the information processing approach, commonly used within math research to represent how information moves through each component of human cognition during problem solving and learning. Second is the theory of embodied cognition, the basis of many gesture theories. This framework provides our model's infrastructure, as it articulates the importance of human-cognition being situated within a body, further encompassed in an active, stimuli-ridden environment.

Information Processing Approach

One way to conceptualize how children solve math problems and learn math related content is the Information Processing Approach (IP; e.g. Pellegrino and Goldman, 1987 ). This is not a single theory, rather an umbrella term for approaches which explains human cognition as a system that processes stimuli input from the environment and delivers a variety of outputs. The IP model suggests that learning occurs via a flow of information through a series of memory stores and processes. These distinctive elements in the IP approach can be conceptualized as the subcomponents of EF (adapted from Lutz and Huitt, 2003 ). Input is received from stimuli in the environment by way of the sensory registry. Attention is directed to fixate on relevant information, which progresses to working memory, a short-term store where information is held and processed for use in further cognitive tasks (Gathercole, 1998 ). Working memory is responsible for determining what information is important, choosing and enacting problem-solving strategies, and coming to a solution. Ultimately, information will be either be forgotten or encoded and stored in long-term memory for retrieval at a later time.

Although the IP framework can be broadly applied to represent children's math problem solving and learning, there are ways in which it could be further specified. First, a framework that focuses on both visual and auditory math-specific input could help to better understand how this input is relevant for children's math abilities and learning. Second, when investigating the role of gesture for children's early mathematics, it is important for a framework to include the body itself. While the IP model describes the cognitive processes, it does not explain any co-occurring physical behaviors. Thus, this framework cannot adequately account for the gesture-specific benefits that may occur within a math-related context. The question remains open as to how to model the role the learner's body, and the different types of math stimuli (words and gestures) within the environment.

Embodied Cognition

One theory that provides insight into these two components is embodied cognition (EC). While EC has been conceptualized in various ways, each adaptation generally emphasizes the body and stimuli within the surrounding environment as important to cognition (e.g., Barsalou, 1999 ; Clark, 1999 ; Shapiro, 2019 ). Here, we outline Wilson's ( 2002 ) presentation of EC. Specifically, she conveys six central claims of EC, three of which outline the importance of considering cognition as a situated process, and the other three focus on the importance of the body as a tool for cognition.

The first claim stipulates that cognition is situated. In other words, cognitive processing occurs in parallel with the task-relevant inputs and outputs from the environment. Thus, cognition cannot be separated from interplay between perception of the environment and subsequent actions taken. The second claim is that cognition is “time pressured,” where cognitive processing requires real-time responses to the stimuli in their environment. Lastly, Wilson's fourth claim states that the environment is an important part of the cognitive system. Though similar to the first claim, Wilson outlines that since the reception of stimuli, cognitive processes, and behavioral responses are cyclical in nature, each of these components cannot be considered alone.

Wilson's claims three, five, and six all focus on the role of the individual's body in cognition. Claim three emphasizes that humans tend to off-load cognitive work externally in strategic ways. Wilson provides finger counting as an example, indicating this gesture can be used as a representation of relevant numeric information (e.g., linking number words to objects to keep track of quantity). Thus, offloading is a critical cognitive function that helps the speaker reason and express thoughts. The fifth claim states that cognition's primary function is for action. Meaning, a person's perception of the world as well as their concepts and memory are both “for” and “formulated by” their behaviors. Lastly, claim six says that off-line cognition is “body-based.” Wilson's conceptualization of off-line cognitive processes involves any that are separable from the time-sensitive environment. Importantly, though they are distinct from the environment itself, the processes within the mind are inevitably tied to cognitive mechanisms that were originally designed for external behaviors, such as sensory processing and motor control.

The critical takeaway from Wilson's presentation of EC is that both the body and environment are integral to cognition. Her representation of EC underscores how embodied practices can result in an offloading of cognitive load. Based on how EC provides the important contribution of the body and the environment, and a focus on how cognition may be offloaded, we propose a model combining central tenets from both IP and EC.

The Proposed Model

The proposed model contains aspects from EC and IP, and specific contextual elements of gestures within the mathematical environment ( Figure 1 ).

Combined model of information processing and embodied cognition.

The proposed model is unique in its applicability to different math domains. For example, during a lesson on addition, math input could include a teacher's speech and gestures in reference to an equation on the chalkboard, while the output could be children's verbal and gestural response and explanations. In another context where a younger child is counting a set of objects, their math-input could be instructions and countable objects, and their output may include them pointing and counting out loud. Thus, there are numerous opportunities for applying the model for research by specifying the components (learner, input, and output) within a math environment.

Notably, our model also does not differentiate between perceived speech and gestures. Instead, it includes a unified representation of math-input. We base this combined representation on research showing that simultaneous presentation of these two observed modalities can be beneficial for children (Congdon et al., 2017 ). However, children's math-output is differentiated in the model because the literature (reviewed in subsequent sections) suggests children's gesture and speech often contain different but complementary information. For example, recent work supports separation of math output by modality, given that temporal-synchrony of self-produced gestures and speech does not relate to learning and retention for children in the same way that observed gestures do (Wakefield et al., 2021 ).

Incorporating gesture as input and output separately allows the model to be adapted in two critical ways. First, it can be applied to different mathematical domains (e.g., cardinality, algebra, fractions, etc.), such that the input and output can vary by content. Second, the model can be used to understand a broad range of differences in children's general EF abilities and math knowledge specifically. This is of particular importance given children are found to be adaptive in their responses to math problems (Siegler et al., 1996 ), and that the strategies children display may differ between their speech and gesture (Goldin-Meadow et al., 1993b ).

Consider the example of a child solving the problem 3+2; if they have the answer memorized, they may quickly answer “5!” using a direct retrieval strategy of relevant math knowledge. A second child, who has only learned about addition principles generally, would likely respond differently to the same problem. They may use a backup strategy (i.e., any method other than retrieval), such as holding up three fingers then extending two more while counting on “4…5! The answer is 5!.” The proposed model highlights how these children's individual differences in math knowledge could impact their use of self-produced gestures, and would allow researchers to explore the theoretical implications of how these strategies connect to their subsequent math abilities and later learning.

In addition to understanding the connection to math knowledge, an additional goal of the proposed model is to explain how gestures may be beneficial for EF and its subcomponents. EF includes three separate, but interrelated processes; attention shifting, inhibitory control, and working memory (Miyake et al., 2000 ). While EF is often discussed as a multidimensional construct, there is also evidence of unidimensionality in early childhood (Wiebe et al., 2008 ; Hughes et al., 2010 ). This makes it difficult to determine empirically whether the benefits of gesture for children relate to EF broadly, or to one specific sub-component. For example, it is common within the gesture literature to discuss gestures as providing a reduction in “cognitive load” (Goldin-Meadow et al., 2001 ) or linking them to executive function (EF) broadly (O'Neill and Miller, 2013 ). As such, connections between sub-components of EF and gesture are represented in the current review based on how they are discussed within their respective studies. The implications of this approach are reviewed in the discussion.

In sum, when studying the role of gesture in math environments, we propose a combined model of the IP approach and theory of EC. By establishing the pattern of information flow from the IP model within specific embodied locales and conventions of EC, this new model provides a dynamic representation of the cognitive impact of gestures in a mathematical environment. The connections between children's domain-specific knowledge (stored in long-term memory) to their self-produced gestures are illustrated within the model itself, as is an additional pathway between math-input and children's EF. Thus, both types of gestures are connected with children's cognition. Each of these connections will be considered through a review of the current literature.

Goals of the Current Review

There are two primary goals of the current review. First, we will review empirical work on the relations between children's mathematics ability and EF; mathematics ability and gesture use; as well as their gesture use and EF. Each of these will be discussed in terms of how this research fits within the proposed model. The second goal is to address any remaining gaps within the literature in order to demonstrate how the proposed model lays the foundation for future research to examine the mechanistic role that gesture may play in children's math learning.

Search Methodology

The present review is focused on connecting three separate, but related, bodies of literature: the gestures which children see and use, their EF abilities, and their mathematics knowledge. In the present review, less time is spent on connections between children's mathematics and EF abilities, given the numerous reviews of this topic that are readily available (for reviews, see Bull and Espy, 2006 ; Bull and Lee, 2014 ; and Cragg and Gilmore, 2014 ). Each of the subsequent components (gesture and math, gesture and EF) were investigated in a review conducted with APA PsycInfo database and Google Scholar in February through March of 2020. Follow-up searches were conducted in June-July of 2020. Relevant articles that came to our attention after our two initial searches were also included. In order to be included, studies must have been (1) published in English; (2) reports of original research, conceptualization of theory, or related reviews of literature published in peer-reviewed journals; (3) focused primarily on outcomes with children. Each search was conducted with separate keyword searches. Math and executive function were searched along with keyword combinations including but not limited to children, learning, and review. Math and gesture were searched along with keyword combinations including but not limited to children, learning, education, instruction, teacher, and review. Lastly, gesture and executive function were searched along with keyword combinations including but not limited to children, individual differences, working memory, attention, inhibition, childhood, and review.

Math and Executive Function

Numerous studies have demonstrated a relation between children's mathematics ability and their EF (for reviews, see Bull and Espy, 2006 ; Bull and Lee, 2014 ; Cragg and Gilmore, 2014 ; Jacob and Parkinson, 2015 ; Peng et al., 2016 ). Broadly, this relation is consistent across different mathematical areas, including early numerical tasks, arithmetic problems, word problems, and standardized math measures (e.g., Lee et al., 2009 , Bull et al., 2011 ; Van der Ven et al., 2012 ). It is critical to note that in both empirical and applied settings, EF has been conceptualized in numerous ways with researchers using a variety of assessment measures. As a result, empirical work on relations between math and EF are extensive and this literature has been previously reviewed as noted. Therefore, the focus of this section is to briefly summarize this research to demonstrate how representation of EF within the proposed model provides a specific operational system that is firmly connected to math contexts throughout childhood.

Cross-sectional correlational research has shown that different sub-components of EF are related to children's mathematical performance. For example, research indicates that working memory abilities are related to a range of mathematical tasks, such as early numeracy abilities (Kroesbergen et al., 2009 ), arithmetic achievement (Navarro et al., 2011 ), problem solving more broadly (Swanson, 2004 ), written and verbal calculation (Andersson, 2008 ), as well as mathematical word problem accuracy (Andersson, 2007 ; Zheng et al., 2011 ). Similar findings have shown connections between children's inhibitory control abilities and their math performance and achievement (Espy et al., 2004 ; Brock et al., 2009 ; Gilmore et al., 2013 ). There is additional evidence that inhibitory control, attention shifting, and working memory independently account for separate variance in children's math ability (Bull and Scerif, 2001 ). Further, when different sub-components of EF were examined in parallel, unique contributions of each on children's math ability were still prevalent (e.g., Bull and Scerif, 2001 ; St Clair-Thompson and Gathercole, 2006 ; Kroesbergen et al., 2009 ). Thus, evidence demonstrates relations between all three sub-components of EF and mathematics in children, lending support to including these factors within our model.

As children's mathematical knowledge develops, the impact of EF ability on their learning and performance differs. For children, it appears that working memory is of particular importance. Specifically, both children's symbolic (Caviola et al., 2012 ) and non-symbolic math abilities (Xenidou-Dervou et al., 2013 ) are positively related to their working memory. Importantly, children appear to rely more on their working memory than adults while solving math problems (Cragg et al., 2017 ). This may be due in part to how children's enactment of strategies is a more active and less efficient process and so their ability to enact a problem-solving strategy may be more of a direct result of their EF abilities compared to adults. Further, different EF abilities may allow an individual to enact different mathematical strategies (Imbo and Vandierendonck, 2007 ). For example, first grade children with higher working memory abilities were found to use more correct and sophisticated strategies on arithmetic problems compared to children with lower working memory capacity (Geary et al., 2012 ). These findings suggest that the relevance, contribution, and demand of working memory and broader EF abilities may shift depending on both the mathematical content and children's task knowledge, which can impact overall task performance. Thus, individual variation in EF abilities is a critical component to include in a model of children's math performance and learning, which is reflected in the centralized location of the proposed model.

Lastly, longitudinal studies have shown that children's EF is not only predictive of later mathematics performance (Alloway and Alloway, 2010 ; Monette et al., 2011 ), but also of their growth in mathematical abilities (Geary, 2011 ; Clark et al., 2013 ; LeFevre et al., 2013 ). For example, in a study following children from kindergarten to third grade, working memory related to children's early and later number competencies, which contributed to their math achievement (Krajewski and Schneider, 2009 ). However, training studies have shown mixed results. Some studies have found that EF training can improve children's numerical knowledge (Holmes et al., 2009 ; St Clair-Thompson et al., 2010 ; Holmes and Gathercole, 2014 ; Ramani et al., 2017 , 2019 ). For example, training WM improved kindergarten children's counting skills, and WM games that included both numerical and non-numerical information improved children's counting and numerical comparison skills (Kroesbergen et al., 2014 ). However, others have found that providing children with EF training does not necessarily improve their mathematical knowledge (Jaeggi et al., 2012 ; Shipstead et al., 2012 ; Karbach et al., 2015 ). These findings suggest varying levels of efficacy in EF training on improvements in mathematics and provide the first window of opportunity for future research using the proposed model.

Overall, there is consistent cross-sectional and longitudinal evidence of relations between EF and mathematical achievement in children. These connections are found in a variety of mathematics domains, and the individual differences in EF abilities can influence children's mathematical performance. However, experimental evidence demonstrating that training EF can improve children's mathematical knowledge is less consistent, although numerous studies have shown promising results.

Gesture and Math

In this section, we review literature on two types of gestures included in our model. First, we outline literature pertaining to gestures used by other people, such as a teacher or experimenter, to explain or teach math concepts; included in the model's “Math Input” section. Second, literature regarding children's self-produced gestures is reviewed; included in the model's “Math Output” section. Studies in these areas establish two critical functions (represented by connected arrows in Figure 1 ). One function highlights how children's self-produced gestures may convey math information (stored within their memory), which assists with their cognitive processing (EF). A second connection between children's gesture math-output connects back to their math input, which allows for the possibility that children's gestures elicit math information from their environment. Each of these are functions are reviewed and discussed.

Math Input: Observing Other People's Gestures

Individuals who observe a speaker's gestures during a mathematical context can extract useful information (Goldin-Meadow et al., 1992 ; Alibali et al., 1997 ; Kelly and Church, 1998 ). No training is required to gather this information, as children are readily able to attend to information found uniquely in gesture (Kelly and Church, 1997 ). Therefore, gestures that occur within math environments are straightforward in their presentation yet are critical to understand.

Experimental studies have shown that watching gestures can support learning and generalization of math concepts. For example, Graham ( 1999 ) had 2–4-year-old children ( n = 85) watch a puppet point while counting objects. When asked about the puppet's performance, children spoke about the puppet's gestures suggesting that from a young age, children are explicitly able to recognize gesture strategies (pointing) in a math environment. Alibali and DiRusso ( 1999 ) used a similar paradigm with preschoolers ( n = 20; M age = 4.67), where a subset of children was asked to count aloud while watching a puppet gesture to keep track of the objects. These children made fewer counting errors compared to children who had no gesture supports (either their own, or the puppets). These studies illustrate how young children can benefit from receiving gestures as part of their math input.

Research has also examined how gesture input could benefit other domains of math. Valenzeno et al. ( 2003 ) worked with 25 preschool-age children (M age = 4.5 years) who watched videos of teachers providing instruction on symmetry in a speech alone, or in gesture plus speech. Children who saw the gesture plus speech instructions had higher posttest scores for this math-concept, compared to children who received instruction in speech alone. Thus, children who received math-input with gesture showed greater improvement in math knowledge compared to their peers who received speech alone.

Additionally, children are also able to detect information that is uniquely communicated through gestural math-input. Specifically, Goldin-Meadow et al. ( 1999 ) asked a group of teachers to give children ( n = 49, M age = 9.83 years) lessons on mathematical equivalence 1 . Teachers were not specifically told to gesture, though they did gesture spontaneously during instruction. These gestures contained relevant problem-solving strategies, such as a v-handshape to group two numbers together visually that should be summed, or gesticulating a flat palm under one side of a problem and then the other to indicate equality. These gestures either reinforced the information in the teacher's speech (gesture-speech match) or contained different, but complementary information (gesture-speech mismatch). Overall, children were more likely to reiterate their teacher's speech if it was accompanied by a gesture. Critically, children were also found to be able to recognize information that was solely presented within a teacher's gesture. This suggests that children both notice and process the mathematical information presented uniquely by gestures.

Children's ability to perceive information from gesture is further supported by evidence from a bilingual sample (Church et al., 2004 ). In this study, 51 Spanish-speaking first grade students (M age = 7.0 years) were assigned either to a Spanish-speaking classroom in school, or to an English-speaking classroom. Students watched a video of an English-speaking teacher providing instructions either with or without gestures. These gestures were gesture-speech mismatches, such that they contained unique but complementary information to speech. Overall, children in both classrooms benefited from the inclusion of gesture during instruction, and Spanish-speaking children's learning in particular increased from 0 to 50%. This suggests an additional benefit of including gestures as math-input. Specifically, gestures may be a more universally accessible representation of math information, as its manual format is not tied to a language.

Singer and Goldin-Meadow ( 2005 ) continued to build off this line of inquiry using the mathematical equivalence paradigm. Specifically, 3rd and 4th grade children ( n = 160) were taught problem-solving strategies either with no gesture, gesture-speech matches, or gesture-speech mismatches. Children were more likely to learn when their teacher's math-input contained one problem-solving strategy in speech, while simultaneously presenting a different strategy in gesture. This finding extends previous work by suggesting that the addition of gesture to speech is unique in its ability to present two math concepts simultaneously (one in each modality), which in turn facilitates learning. Therefore, the inclusion of gestures as an accessible, beneficial form of math-input is cemented in the model.

It is additionally important to review research on how gesture input may impact children's math knowledge. Cook et al. ( 2013 ) asked 7–10-year-old children ( n = 184) to watch a video where an instructor provided a lesson on math equivalence either with or without gestures. Children completed both an immediate and delayed posttest to test for general learning and transfer. Compared to children who received instruction in speech alone, children who received gestural math-input performed better on both the immediate and later posttests, including a transfer of knowledge to new problems. Thus, children appear to gain knowledge quicker and to generalize knowledge better when that information is provided with supporting gestures, as opposed to speech alone. These findings provide insight into how the inclusion of gestural math-input could impact children's own math-output, such as their response to a later math test.

Additional work expanded on these results with a computerized avatar (Cook et al., 2017 ). Sixty-five children (M age = 9.0) watched as a computer avatar provided instruction on mathematical equivalence, either with or without accompanying gestures. Children who saw the gesturing avatar learned more, solved problems quicker, and were more likely to generalize their knowledge. Thus, children benefited from the addition of gesture regardless of whether their instructor was human or a computer avatar. These results reveal how gestural math-input can be expanded to include technology-based instruction to advance children's learning and generalization of knowledge. This emphasizes the connections within the proposed model regarding math-input to children's overall math understanding.

Together, these findings suggest that children notice, and process mathematical information provided in instructor's gesture. These gestures are found to enhance children's learning and support broader understanding through concept transfer and generalization. This literature is consistent with the proposed model; children receive math input from their instructor's gestures and speech, which supports their problem solving and later learning in the form of math-output.

However, it is also critical to understand the mechanisms by which gestures provide these supports. One study assessed this issue by manipulating whether task-objects were in view, and thus referenceable, by their subjects (Ping and Goldin-Meadow, 2008 ). Specifically, kindergarten and first-grade students ( n = 61, 5–7 years old) participated in Piagetian conservation tasks where they were shown two objects (e.g., two glasses with equal liquid) and were asked if they were equal. One of the objects was manipulated, (e.g., poured into a shorter glass), such that children's understanding of conservation could be assessed when asked to explain if they were equal now. Children then received instruction on conservation, either in speech alone or gesture-plus-speech, as well as either with or without the objects present. On average, children were more likely to learn from instruction that contained gesture-plus-speech, even when the objects themselves were not present. In other words, gestural math-input was helpful beyond the scope of referencing specific, concrete objects within children's environment. Thus, the function of gesture as math-input goes beyond simple attention direction or grounding of speech in the physical environment and has broader implications for children's learning.

Overall, the literature suggests that the gestures which children observe as math-input can directly support their math learning, which reinforces these connections in the proposed model. Children are better able to learn, retain, and generalize new information about math when their instructor uses both gestures and speech, compared to speech alone. When children cannot access math-information in their teacher's speech, gestures become even more important. These benefits extend beyond a simple direction of attention, as gestures continue to be beneficial even when the relevant items are not present.

Math Output: Children's Self-Produced Gestures

In the following section, we review literature on the self-produced gestures children use in math contexts to scaffold their own knowledge and learning. These gestures occur spontaneously (e.g., Crowder and Newman, 1993 ) or as resulting from explicit instruction (e.g., Alibali and Goldin-Meadow, 1993 ). In the proposed model, children's own gestures are linked to supporting their ongoing cognitive functions, while also producing a form of math output. This output can then be observed by teachers to continue to inform the child's math environment (e.g. Gibson et al., 2019 ). Each of these functions of children's self-produced gestures are examined in turn.

Self-produced gestures have been shown to reduce cognitive load during math contexts. This benefit of gesture was examined by Goldin-Meadow et al. ( 2001 ), who asked participants to solve and explain age-appropriate math problem (e.g., math equivalence problems for children, harder problems for adults). They were also asked to remember a string of letters or words while providing the explanation for their solution. Gesture was manipulated directly, such that participants were given instruction regarding whether gestures were permitted, or if they should keep their hands on the table. Both adults and children were able to remember significantly more of their list when they used gestures during their math explanations. This finding supports the inclusion of children's self-produced gestures within the model. Furthermore, the authors suggest that the observed cognitive benefit may be due in part to gestures' utility in reducing memory demands, which may additionally link self-produced gestures to the memory processes in children's minds. Thus, this study is discussed briefly a second time in relation to working memory.

Another study investigated how self-produced gestures may further support children's performance on a math task. Specifically, Gordon et al. ( 2019 ) investigated how preschool children's own gestures may support their knowledge and performance on a cardinality task. Results indicated that children's cardinality knowledge was positively related to their spontaneous gesture use, even while controlling for age. This relation was not just driven by children who had mastered cardinality; indeed, the same positive relation between gesture and cardinality knowledge existed for the subsample of children who were still learning principles of cardinality. Children were also found to gesture the most during parts of the task that were most difficult for them, subjectively, based on their current cardinality knowledge. This emphasizes the connection in the model between children's own gestures, their math knowledge in long-term memory, facilitated by the problem-solving abilities within other components of EF.

Based on the advantages of self-produced gestures, additional work considers how providing explicit gesture instruction or encouragement to children to may impact their performance or learning in math environments. Broaders et al. ( 2007 ) examined this phenomenon in two studies with 3rd and 4th grade children who were asked to solve math equivalence problems. In the first study, children were asked to explain their solutions to these problems either using specifically with gesture, specifically without any gesture, or heard no mention of gesture. Children who were told to gesture conveyed different information in this modality (i.e., gesture-speech mismatch), such that their math-output contained new and relevant information. Therefore, instructing the use of gesture can lead children to express math knowledge with their hands that may not otherwise be communicated with their speech. The authors also sought to address whether children who received this instruction would be more receptive to learning by testing a new set of 3rd and 4th graders using a similar protocol for their second study. Results indicated that instructing children to use gesture not only taps into their implicit math-knowledge, but also makes them more likely to learn. Taken together, these results highlight how a combination of direct instruction (math-input) and the resulting self-produced gesture (math-output) could impact later math learning; the overall goal of the proposed model.

To further parse apart the benefits of instructed gestures, Goldin-Meadow et al. ( 2009 ) investigated whether specific types of gestures were more advantageous than others. Third and fourth graders completed math equivalence problems and were assigned to one of three training groups: no-gesture, correct-gesture, or partially correct gesture 2 . Overall, children learned more when any gesture was used, regardless of whether the information it contained was mathematically correct. However, children who received correct-gesture training solved more problems correctly compared to the partially correct gesture group. This suggests that gestures which contain specific, correct math information are superior to other gestural types. Furthermore, children were able to verbalize the grouping strategy used in gesture without any direct instruction, indicating that children learned a strategy from their own gestures. Taken together, these results indicate that while any gesture may benefit children, instructing specific gestures that align with math-concepts could allow children to extract and learn that information. This further supports the proposed model; children's self-produced gestures, while labeled as a form of “math-output,” have connections to and from the knowledge storage and EF processes within their minds. Thus, by providing instruction to children to self-produce a specific type of gesture, they may be able to tap into and build on task-relevant knowledge.

New research involving fMRI methods builds on the mounting evidence that providing instruction to children to use gesture improves their mathematics ability. Wakefield et al. ( 2019 ) worked with 7–9-year-old children who had engaged in the same mathematical equivalence training outlined in previous research (Cook et al., 2008 ; Goldin-Meadow et al., 2009 ). Children solved a series of these problems, then received training to express an equalizer strategy in either speech alone or speech plus gesture. Only children who had gotten all problems wrong initially then successfully solved at least half the problems after training were included in the final sample ( n = 20). A week later, this sub-sample of children completed a short training refresher before participating in an fMRI session where they solved new mathematical equivalence problems. Results showed differences in neural activation during problem solving by training condition, such that children in the speech and gesture condition had greater activation of the motor regions of their brains compared to speech-alone. This indicates that training math concepts through self-produced gestures may have lasting neural impacts, even though children were unable to use gesture during the fMRI reading itself. Thus, the neurological research is consistent in its support for the pathways generated by the behavioral research for the proposed model.

However, it is essential to address whether these benefits are unique to gesture, or if any movement or action could render the same benefits. For example, could children use a bodily strategy consisting solely of actions and have the same mathematical benefits? Novack et al. ( 2014 ) explored this idea with 3rd grade children using the math equivalence paradigm. Children were taught to use either a physical action on objects, a concrete gesture which mimicked that action, or an abstract gesture while solving the problem. While each of these strategies lead to more learning, only children who used gestures were able to generalize their knowledge to successfully complete later problems. Therefore, given that it is gesture rather physical action that best assists learning and knowledge transfer, the current model provides a unique vantage point to delve further into how gesture confers these benefits.

Building off this line of work, Congdon et al. ( 2018 ) investigated how individual differences in children's math knowledge influenced their learning from gesture or action strategies. First grade children's initial measurement knowledge was assessed, after which they received one of four trainings for a measurement task. Half of the conditions used a physical stick above a ruler aligned with zero, the other half shifted over to align with a different whole number. Conditions were further split by action or gesture-based trainings; Action-based trainings with physical manipulatives to show children how the ruler segments could be used to count, and gesture-based training using a “pinching” gesture to highlight the relevant segments of the ruler. Children who used simpler strategies incorrectly during the initial measurement assessment benefited from the action training, but not the gesture training. However, children who initially used a more complex, but incorrect, strategy at pretest learned from both the training with actions and gestures. This finding highlights the importance of recognizing how and when gestures could be applied, as well as how individual differences in children's own math knowledge may influence the benefits of gesture. In particular, encouraging the use of gesture may help a child who has reached the particular level of underlying math knowledge, yet hinder another less-advanced child at the same time. Thus, our model centralizes the importance of gesture while also highlighting the importance of not separating the utility of the tool from its intended user.

In educational settings, it is also important to understand how children's self-produced gestures can provide information to an observer, and how this observer could provide additional math-relevant input. In their seminal work, Church and Goldin-Meadow ( 1986 ) examined 5–8-year-old children's speech-gesture mismatches to investigate whether these movements indexed their transitional knowledge. In the first study ( n = 28), children participated in a series of Piagetian conservation tasks where an experimenter made visual transformations of two equivalent objects. Throughout the task, children were asked if the objects had the same amount and to provide an explanation after the transformation. Children were categorized as a conserver (e.g., recognized the key concept of conservation), partial conserver, or a non-conserver based on their explanations. Children's speech and gesture use were coded during their explanation to determine if they were a match or a mismatch 3 . On average, children who had more mismatches showed more complex knowledge in their gestures than their speech. Based on this finding, the authors conducted a second study where half of the children received direct instruction on the concept of equivalence while the other half were given the opportunity to physically manipulate the objects. Children who had more speech-gesture mismatches in their explanations were more likely to learn new information after training and benefited from the opportunity to play and manipulate the objects afterwards. In contrast, those children with more matches than mismatches did not show any additional benefits from explicit training or more informal contact with the objects.

These findings were further expanded upon by Perry et al. ( 1988 ), who sought to explore how spontaneous self-produced gestures used in math contexts could index children's “readiness” to learn new information. In a series of studies, they asked 9–12-year-old children to solve problems and explain their solutions related to concepts of mathematical equivalence and Piagetian conservation. In general, children's speech and gestures were more likely to match during the conceptually easier mathematical task (conservation), but more likely to mismatch during the more difficult mathematical task (mathematical equivalence). Additionally, the amount and the type of mismatches produced by children provided an index of their “readiness” to learn. Specifically, the authors suggest that children's math-output (gesture and speech) provides insight into their math knowledge, as well as whether they may be able to receive new math-input from their environment. Indeed, children's gesture and speech mismatches have been linked to their zone of proximal development (Goldin-Meadow et al., 1993a ). In other words, their gestures may be used by adults to specifically calibrate future math-input to a child's individual level of understanding.

To further understand how children's self-produced gestures mark their conceptual knowledge, Garber et al. ( 1998 ) assessed the speech gesture mismatches produced by 4th grade children in their explanations of mathematical equivalence problems. Children subsequently were asked to judge the acceptability of a variety of other commonly used problem-solving strategies, some of which were incorrect. Overall, children gave the highest rating to strategies which contained conceptual elements that they had only indicated in their gestures during their initial explanations of how to solve equivalence problems. Thus, these children not only expressed knowledge uniquely in their gestures, but this knowledge was accessible when presented to them later as additional mathematical input. Therefore, by watching the gestures that children produce as a type of mathematical output, it is possible to map out what math concepts they may already have some knowledge of implicitly. Taken together, these studies findings are consistent with the proposed model; that the gestures which children produce as a form of math output are linked to the knowledge stored within their long-term memory.

These markers of conceptual knowledge are found for other domains of math knowledge too. Specifically, Gunderson et al. ( 2015 ) studied 3–5-year-old children's mismatches in the context of cardinality, an early math concept which involves an extended learning process. Children who were still in the process of learning about this concept were more than twice as accurate in their gesture responses compared to their speech. Moreover, the gestures children produced were more accurate when the information in their gestures was a mismatch with their speech. Therefore, even young children who are in the process of learning a basic numerical concept provide unique information in their gestures that is not otherwise found in their speech. This finding supports that the current model may be extended to consider mathematics more broadly, as the patterns and information in gestural mismatches appear in the form of gestural math-output with younger children as well.

There is also evidence of this phenomenon in manual languages, such as American Sign Language (ASL). Goldin-Meadow et al. ( 2012 ) examined how the gestures produced by ASL-signing deaf children ( n = 40) in the previously explained mathematical equivalence paradigm predicted whether they would benefit from explicit instruction on those problems. In general, the children who produced more gesture-sign mismatches were more likely to succeed after instruction than those who did not. This adds to the evidence by suggesting that mismatches occur even within the same modality, and strengthens the claim that it is critical to observe children's gestures as a form of math-output regardless of the modality of their language. Additionally, this finding highlights that the proposed integrated model may be extended for populations who use manual languages as well, though future research is required to further support each proposed connection.

In addition to studying whether the knowledge children express in gesture can be made available to them, it is also important to understand whether an external observer is able to recognize the utility of children's gestures. In other words, how does the literature support the connection within the model between children's self-produced gestures and the math-input they receive? One such study investigated this connection by recruiting a set of teachers ( n = 8) to work with 3rd and 4th graders ( n =38) on mathematical equivalence problems (Goldin-Meadow and Singer, 2003 ). Specifically, each child completed a pre-test of six problems, and explained their solutions to an experimenter. The teacher watched this pretest to gain insight on the child's knowledge, but was given no information or instruction regarding gestures. Each teacher then provided instruction on a set of problems before the child completed another, comparable posttest. Results showed that teachers were more likely to have variation in their instructions (e.g., give additional strategies) to children who had used more gesture-speech mismatches during their initial explanations. Therefore, children's own gestures (math-output) inadvertently shaped their own learning environment by evoking further explanation and support from the teacher (math-input). Not only does this happen spontaneously, but research shows that when adults are instructed to watch children's gestures, it can amplify the amount of information they were able to glean from children's gestures (Kelly et al., 2002 ). Even when the instruction was subtle, included different domains of knowledge, or different aged children, these results held. Thus, it is both possible to pick up on the information children possess implicitly by watching their gestures, and respond to these gestures in ways that may specifically scaffold the children's knowledge. These findings strengthen the connection within the integrated model between children's own math-output informing new math-input.

In sum, prior research provides evidence that self-produced gestures may benefit children's own learning and problem solving. These studies support the proposed, integrated model in several specific ways. First, they emphasize the modeled connection between math-input in children's environment and the subsequent impacts the input has on their math performance and learning. Second, literature which uniquely considers spontaneous or instructed self-produced gestures allows for additional insight to be added to the model, such as the how individual differences in children's knowledge made lead to differences in children's use of gestures, or differences in the benefits of gesture use itself. The same results are not reported with similar methods which employ physical action, which suggests that these mechanisms are unique to gesture. Additionally, prior research underscores the importance of centralizing the child within the model, given that a learner's own math knowledge and cognitive abilities can change the utility of gesture. Lastly, there is evidence suggesting that children's gestures are an indicator of their knowledge, and that this form of math-output that can be used as a tool by adults. This crucial collection of studies provides the connection within our model between children's gestures as math-output impacting the mathematical input they receive from others. Taken together, these studies highlight the necessity of a model where children's self-produced gestures in math environments can be studied further.

Gesture and Executive Function (EF)

Given the multi-faceted role of gesture in children's math environments, it is critical to examine how the current literature supports the model's proposed connections between gestures and children's EF. Research outside the domain of mathematics has linked gesture specifically to EF from an early age (e.g., gesture, language, and EF; Kuhn et al., 2014 ). As previously discussed, individual's gestures may show information about implicit knowledge that is not found in their speech (Broaders et al., 2007 ; Pine et al., 2007 ). By shifting this information outside the mind and onto the hands, gesture is commonly proposed as a mechanism by which the user can “lighten their cognitive load” (Goldin-Meadow et al., 2001 ; Wagner et al., 2004 ). The idea of cognitive load is often presented as an offloading of related memory resources. While previous work has not drawn explicit connections to components of EF, more recent work has begun to delineate how gesture may be related to each subcomponents of EF. Thus, in this section, we review the literature regarding gesture, and their implied or direct connections made to the subcomponents of EF presented within the integrated model.

Working Memory

Working memory is a limited capacity sub-system of EF where information is temporarily held and processed during problem solving. On average, children use more gestures when faced with an explicit working memory demand (Delgado et al., 2011 ). The mechanistic connections between working memory and gesture are commonly discussed within the math and gesture literature.

For example, the aforementioned study by Goldin-Meadow et al. ( 2001 ) examined how children's memory could be impacted if they used gesture during some parts of the common math-equivalence task, but then were told to keep their hands still during other parts. Results indicated that participants performed better on the memory task when they were able to use gesture. This suggests the use of gesture allowed for a reduction of working memory load, compared when participants had to speak without gesturing. The authors suggest the use of gesture allowed for a reduction in working memory demands, allowing for a greater allocation of cognitive resources for the memory task, thereby improving performance. This same finding was found with adults. Using an updated, age-appropriate set of math problems to solve and explain as well as a harder set of memory items, adults were told they were allowed to use gesture only on some of their explanations. Similar to the children, the adults' performance was better when they were able to use gesture compared to when they only used speech, suggesting that both children and adults who use gesture while they speak would benefit in a reduction of working memory demands (Goldin-Meadow et al., 2001 ; Wagner et al., 2004 ). Thus, the current model reflects the direct connection between children's gestures and their working memory.

Ping and Goldin-Meadow ( 2010 ) further explored the mechanisms underlying how gestures benefit working memory. In this study, 2nd and 3rd grade children (M age 8.75 years) watched as an experimenter perform Piagetian conservation transformations. Children were asked to remember a list of words, then turned around to explain conservation to a new experimenter at another table. The new table was either empty or had the same conservation objects. This manipulation was critical as it allowed the researchers to test whether the cognitive benefits of gesture were based in its bodily capacity to link to a specific object or location (e.g., Ballard et al., 1997 ; Glenberg and Robertson, 1999 ). However, children who used gestures during their conservation explanations performed better on the memory task even when the items were absent and could not be directly indexed by gesture. Therefore, the working memory benefits of gesture are not tied to any specific object or spatial relation within the external environment.

More recent research with adults emphasizes the specific connection between gesture and working memory. For example, adults who are asked to use gesture may experience differential working memory benefits depending on their initial working memory abilities (Marstaller and Burianová, 2013 ). Additional studies have shown that people who have either lower visuospatial or verbal working memory capacity tend to produce more gestures on average (Chu et al., 2014 ; Gillespie et al., 2014 ; Pouw et al., 2016 ), and those who have higher than average visuospatial working memory abilities seem to be better equipped to detect information conveyed in gesture (Wu and Coulson, 2014a , b ; Özer and Göksun, 2020 ). Thus, the connection between gesture and working memory are well-established.

The results of these studies are represented in the proposed model. Specifically, the proposed model reflects the bidirectional flow of information processing between children's own gestures and their working memory. This highlights the critical question of whether individual working memory abilities change how children receive gesture based math-input, as well as whether an individual's propensity to gesture could be impacted by their working memory abilities. In other words, would a child's initial working memory ability explain variability in their subsequent use of gesture within a math task?

Currently, there is not enough work available to answer this question. However, one recent study sought to address the related issue of whether the flow of information processing should vary based on a child's initial domain-general cognitive abilities. Specifically, recent research with preschoolers ( n = 81) found that their spontaneous gestures and working memory were related to their performance on an age-appropriate math task (Gordon et al., 2021 ). However, children's gestures were not significantly related to their working memory after controlling for age. This work leaves room for future research to investigate this dynamic relation in further detail.

Additional work has informed the connection in our model between gestures and attention, another sub-component of EF. Research with infants indicates that they attend to pointing gestures before 6-months of age (Rohlfing et al., 2012 ). Shortly after 1 year, they begin to make their own attention-directing gestures to convey and request information from other people in their environment (Tomasello et al., 2007 ; Kovács et al., 2014 ), suggesting at least a basic understanding of the attentional function of gesture. Therefore, within the proposed model, children could be expected to both use and recognize the utility of gesture as a tool for attention.

However, the primary function of gesture is not only to drive attention. For example, one of the previously described studies exposed children to math gestures that contained task-relevant information, but also that directed their attention to irrelevant components of the math problem (Goldin-Meadow et al., 2009 ). Results showed children who saw these partially-correct gestures still learn more than children who received no gestures at all, suggesting that even though their attention may have been drawn to less relevant components, the gestures still helped. Nevertheless, attention has still been added as its own separate component within the proposed model, given that children in this study still learned the most when they received a gesture that contained both the task-relevant strategy information and directed their attention to the relevant parts of the problem. Therefore, we still believed it is important to include within our model that gestural math-input can direct children's attention towards relevant information within their environment.

Recent research lends additional support to retaining attention in some way within the proposed model. Specifically, Wakefield et al. ( 2018 ) investigated how gestural input could change children's visual attention during math instruction. Eight- to ten-year-old children ( n = 50) participated in the math equivalence paradigm and watched videos of a teacher's instruction in speech alone or in speech and gesture. Children's eye movements were captured using eye-tracking technology, and their learning progress as well as concept transfer was assessed. Children who received both speech and gesture instruction spent time looking at both the problem and the gestures. Additionally, children who received instructions with both speech and gesture were more likely to follow along visually 4 . Following along was uniquely predictive of learning for those in the speech and gesture condition. Therefore, gesture as math input appears to moderate the impact of visual attention on learning and provides additional support for the inclusion of a connection between gesture input and attention within the proposed model.

The current model also ties children's self-produced gestures to their attention. There are limited empirical examples that directly test how children's own gestures drive their attention in ways that impact their math output and learning. However, Alibali and Kita ( 2010 ) assessed whether prohibiting children's gestures would result in a shift of focus away from task-relevant information, which provides equal insight into this part of the model. In this study, researchers asked whether prohibiting 50 children (M age = 6 years, 5 months) from gesturing in the standard Piagetian conservation task would cause them to shift focus away from the perceptual-motor information which is commonly expressed in gesture. At first, all children were allowed to explain the conservation task with gesture, and then half the children were prohibited from gesturing for the second round of explanations by wearing a muff on their hands. On average, children were more likely to focus on information that was not perceptually present when they did not have access to gesture. When they were allowed to gesture, their focus shifted to the perceptually present information instead. Taken together, the results indicate that children's own gestures highlighted information within their own environment, and this information could be used in further cognitive processing related to children's later output. Therefore, while the main mechanism underlying gesture is not attention, it is still an essential component of EF that is tied to gesture. As such, the connection between gesture and attention within the proposed model are supported.

It is important to recognize that the proposed model does not include one connection built within the literature. Specifically, it has been suggested that individual speakers have a threshold for producing gestures, and that it may be possible for speakers to take advantage of this threshold (either directly or implicitly) to reap the cognitive benefits of gesture (Alibali and Nathan, 2012 ), suggesting a possible connection between attention sub-component of EF to gesture directly. The GSA framework provides a theoretical outlines how self-produced gestures are a consequence of a speaker's activation of own motor system involved in both planning and producing speech (Hostetter and Alibali, 2008 ). Based on a review of the empirical and theoretical supports, there is not enough support within the literature to draw a direct line from children's own math gestures to their own attention. As such, the proposed model only represents a flow of information routed by proxy of children's broader EF processes.

Inhibitory Control

Although inhibitory control is an important component of EF, it is currently not included in our proposed model. This is, in part, because less is known about how gesture may impact or be impacted by a children's inhibitory control. Here, we briefly review two studies outside of the scope of the mathematics to highlight the potential for future research.

First, O'Neill and Miller ( 2013 ) examined preschool children's gestures (M age = 47 months) during a Dimensional Change Card Sort task. Children ( n = 41) were asked first to sort cards based on a given rule (e.g., sort cards by color), then midway switched to sorting the cards by a new rule (e.g., sort by shape). To sort successfully, children must inhibit the first rule to sort by the new rule. In general, children who gestured more had higher performance. Similar to math tasks, children who used specific task-relevant gestures had higher performance compared to children who used less relevant gestures. In particular, the majority of differences were noted after the rule shift, which is when children would have needed to inhibit the old rule to implement the new rule.

Additional work with preschoolers using the same card sort task assessed whether a direct, causal relation existed between preschoolers' gestures and their scores on another version of the Dimensional Change Card Sort task (Rhoads et al., 2018 ). Specifically, preschoolers received training to use gesture as a support during the task to retain the specific dimensions they were using to sort. On average, children who were instructed to gesture showed improved sorting accuracy, and these instructions appeared to be particularly beneficial for younger children. These results suggest that instructing children to use gesture may boost their overall EF performance, or even lead to specific improvements in their inhibitory control abilities.

While these results occur outside of the domain of mathematics, they suggest that children's gestures may help to keep new rules in mind, inhibit an old rule, or some combination of the two. While the proposed model provides a breakdown of EF, and the information that flows between the sub-systems of attention shifting and working memory, further research needs to be conducted to better understand how to incorporate the third component of EF, inhibitory control, into the model.

In the current paper, we narrow our focus from the function of gesture across learning contexts broadly (e.g. Goldin-Meadow and Wagner, 2005 ) and present a new model regarding the role of gesture in math environments. The processes involved in math learning are well-modeled by the Information Processing Approach, however this approach is not able to fully explain the underlying mechanisms of gesture. Thus, we include tenets of Wilson's ( 2002 ) presentation of EC by modeling the mind within the body, and by extension the surrounding environment. This allows for a consideration of gestures as a form of math-input from the environment, as well as a form of math-output from children's own bodies. After the model presentation, we review the relevant literature on each of the model components. First, we briefly summarize the literature between math and EF, providing additional motivation for operationalization of IP into the sub-components of EF. Next, we review literature pertaining to gesture both as a form of math-input and math-output. Lastly, we summarize studies pertaining to the cognitive benefits of gestures, and how these relate to the sub-components of EF. Here, we outline the strengths and weaknesses of the proposed model and make recommendations for future research.

One strength of the proposed model is its direct expression of the connections that have been made separately, or alluded to, in previous literature. For example, the integrated model ties findings from studies of EF and math to those of gestures in a math context. In doing so, this new model presents a more holistic representation of the connections between gesture, and EF, and math. Specifically, given that EF and math abilities have been robustly linked throughout childhood (e.g., Bull and Espy, 2006 ), new studies should account for whether differences in EF's subcomponents change the contribution of gesture.

A related strength of the proposed model is its direct attribution of benefits of gesture directly to the specific, and separate components of EF (e.g., working memory, attention, inhibitory control) when necessary. The benefits of gesture are commonly described in terms of promoting conceptual change or providing cognitive supports. For example, self-produced gestures are often said to reduce cognitive burden, “thereby freeing up effort that can be allocated to other tasks” (Goldin-Meadow, 1999 , p. 427). This reduction of “cognitive burden” or influence on is still broadly used to represent the complicated, tangle of cognitive processes that are relevant to discussing how, when, and why gestures are beneficial (e.g., Novack and Goldin-Meadow, 2017 ). While there is evidence of unidimensionality across EF constructs in infancy and early childhood (Wiebe et al., 2008 ; Hughes et al., 2010 ), much of the literature emphasizes the number of distinct and separate components of EF in later childhood and adulthood (see Baggetta and Alexander, 2016 for a review). Thus, this model is the first of its kind to outline the connections between gestures, math, and the potential of developing, multidimensional components of EF for children.

Another strength of the model is its capacity to represent how individual differences may impact the role gesture. A recent meta-analysis investigating the role of observed and produced gestures in comprehension found that while gestures are generally beneficial to comprehension, they are most beneficial when a learner produces gesture themselves (Dargue et al., 2019 ). Indeed, there are even times where gestures do not promote learning (see Goldin-Meadow, 2010 for a review). Therefore, the proposed model is unique in that it highlights how variation at the core of the model (e.g., the learner's EF abilities, math-knowledge stored in their long-term memory, and other factors which shape these capacities) will change when and for whom gestures will promote learning.

In addition to these strengths, there are several areas for future research that this model helps to identify. Specifically, the proposed model is primarily informed by gesture instruction and gesture use during two mathematical concepts, the mathematical-equivalence paradigm (Perry et al., 1988 ) and Piagetian conservation tasks (Church and Goldin-Meadow, 1986 ). To date, many math-gesture researchers have chosen to use these paradigms as they have been shown to produce natural and relevant gestures. Therefore, our model is heavily informed by studies which have repeatedly tested their questions within the same specific mathematical domain. As such, our model is limited in its scope in terms of representing gesture in a broader array of math contexts, ages, and levels of cognition. Future studies may examine how this model reflects gesture in mathematics more broadly. For example, while some studies have been conducted on early mathematical skills (counting and cardinality), more research on the benefits of gestures for foundational math knowledge is of particular importance given that children's early abilities are strongly linked to their later math achievement (Claesens and Engel, 2013 ; Geary et al., 2013 ; Watts et al., 2014 ). As such, it is imperative to understand how and when to use gesture in the mathematics classroom to best maximize academic achievement.

An additional consideration for the current model is how well it aligns with other proposed frameworks of gesture. While our model allows for gesture-speech mismatches produced or witnessed by children in a math environment, we do not center our model around them. However, we do not believe that the decentralization of gesture-speech mismatches in the proposed model conflicts with prior literature. For example, literature considering self-produced speech-gesture mismatches find that they are indicative of student's readiness to learn new information (Church and Goldin-Meadow, 1986 ). On the other hand, watching a teacher's mismatches may actually drive student's learning, compared to those who receive matching or no gestures (Singer and Goldin-Meadow, 2005 ). Thus, we argue it is not just that speech-gesture mismatches contribute to conceptual change that is important. Instead, our model prominently features the distinct pathways by which these mismatches could impact children's cognition, and how this impact may vary depending on the source.

One gap in the current model is its ability to address the neural underpinnings of gesture (e.g., Wakefield et al., 2013 , 2019 ). This line of work is imperative and may provide additional insight regarding how each related brain region could play a role in learning. However, the proposed model does not provide a basis for studies considering detailed neurophysiological components. This is not to say that the results of these studies could not be thought of in parallel with the behavioral measures outlined within the proposed model. Rather, it is our goal to provide an accurate representation both in terms of the model's primary objective, as well as its scope. As such, while support for the proposed model may be further strengthened by neuroscience methods, it is possible that a more precise neural model of gesture use may be needed.

The implications and opportunities for future research within this domain are broad. Specifically, there are several questions remaining to be answered: How do the individual differences in children's EF impact their use gestures during math tasks? Additionally, how does children's level of math knowledge impact their EF, self-produced gesture, or the interaction between the two? Do the types and rates of gesture vary as a function of problem difficulty, based on these individual differences? How does the nature of these relations change as children's math knowledge grows, and the specific content they are learning changes? Although each of these questions are motivated from the substantial research on children's gesture, mathematics, and executive function, key information is still missing.

As discussed previously, one gap in the literature is how children's inhibitory control may be linked to the mechanisms and benefits of gestures. Math contexts are particularly useful to study how children employ their inhibition abilities. For example, during problems solving children can inhibit old, ineffective, or incorrect strategies in lieu of new or correct strategies they have learned more recently (Siegler, 1996 ). Thus, future research could analyze how gesture may be used to support strategy inhibition during these critical learning periods. Additionally, children's spontaneous gestures could provide insight into their inhibition. If a child produces old, ineffective strategy knowledge in speech but newer strategy knowledge in gesture, this mismatch could imply that supporting their inhibitory control abilities would allow them to use the strategy knowledge they displayed in their gestures.

Additional research could be conducted to better support the proposed model's connection between the math knowledge stored within children's long-term memory and their self-produced gestures. The proposed model follows the current literature in that math knowledge can be displayed in children's self-produced gestures (e.g., Garber et al., 1998 ), and an assessment of this “implicit” knowledge can be used to determine whether children are ready to learn (Broaders et al., 2007 ), thereby leading to additional math-input. Thus, a unidirectional arrow leads from the information stored in children's long-term memory to their gestures, but these gestures loop out into the environment to inform their math-input. Thus, future research could directly investigate how this information changes depending on if children's gestures were spontaneous or the result of instruction. For example, while these types of gestures may appear to display similar information, it is possible that the underlying reason why gestures are generated in these circumstances could vary. Additionally, a child's propensity to gesture could differ based on the instructions they receive, and therefore the types and rates of self-produced gestures could also be expected to differ.

Relatedly, future research could examine the differences between when children receive specific instruction to use gestures themselves, compared to when they are just broadly exposed to gesture and mimic these movements independently. In other words, if a child is exposed to a particular type of gesture in a math context, what could we expect from them in later math settings? Would the presenter of that gesture matter in terms of whether it was a parent, teacher, or even a peer? In the event that children are told about the specific benefits of using gesture as a tool for math, would children use it in a way that helps them? Or would they over-employ gesture in ways that hinders performance? These are just a few of the many questions that the proposed model is uniquely suited to address. In particular, it allows future researchers to question how gesture-based math-input may facilitate learning, while simultaneously considering children's EF, math knowledge, and their own gestures and math-output.

The proposed model fuses central components of embodied cognition and information processing theories to highlight connections drawn in previous studies investigating gestures, EF, and math learning. Each component of this new model is outlined in a thorough review of the prior literature, through a combined lens of these two theories. Although there are several existing models of gestures and math learning, our model offers specific, novel avenues for future research. In particular, it provides a cohesive, theory driven representation of the role of gestures as they pertain to children's cognition within a math environment. In sum the proposed model provides future researchers with a theoretical foundation from which they may continue to understand the relations between gestures, EF, and children's math learning.

Author Contributions

RG and GR developed the conceptual ideas. RG performed the literature search and drafted the manuscript. GR revised the manuscript and provided critical feedback for important intellectual content. All authors approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1 An example of a problem involving the concept of math equivalence: in the problem 4 + 5 + 6 = _ + 6, children must recognize that the equals sign represents that one side should be equal to the other side, and that the problem requires them to solve for the blank within the equation.

2 Children in the no-gesture group only had the opportunity to verbalize the relevant equivalence strategy. Children in the correct gesture group learned to use their fingers to group the two, specific numbers that should be added to get to the correct answer. Children in the partially correct gesture condition still used a grouping gesture but with two numbers that would not sum to the correct answer.

3 In the original work, when children's speech and gesture contained different information, it was labeled as “discordant”, and if they contained the same information it was termed “concordant”. However, these terms are used less commonly today, and we have replaced them to be consistent in our terminology across reference of this concept.

4 For example, the instructor says “one side equal to the other side” while pointing the specific sides of the problem referenced in speech, and the child switches their gaze to the indicated components of the problem.

Funding. This material is based upon work supported by the Faculty-Student Research Award (FSRA) from the Graduate School at the University of Maryland, College Park, and the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1840340.

• Alibali M. W., DiRusso A. A. (1999). The function of gesture in learning to count: more than keeping track . Cogn. Dev . 14 , 37–56. 10.1016/S0885-2014(99)80017-3 [ CrossRef ] [ Google Scholar ]
• Alibali M. W., Flevares L. M., Goldin-Meadow S. (1997). Assessing knowledge conveyed in gesture: do teachers have the upper hand? . J. Educ. Psychol . 89 , 183–193. 10.1037/0022-0663.89.1.183 [ CrossRef ] [ Google Scholar ]
• Alibali M. W., Goldin-Meadow S. (1993). Gesture–speech mismatch and mechanisms of learning: what the hands reveal about a child's state of mind . Cognit. Psychol . 25 , 468–523 10.1006/cogp.1993.1012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Alibali M. W., Heath D. C., Myers H. J. (2001). Effects of visibility between speaker and listener on gesture production: some gestures are meant to be seen . J. Mem. Lang . 44 , 169–188. 10.1006/jmla.2000.2752 [ CrossRef ] [ Google Scholar ]
• Alibali M. W., Kita S. (2010). Gesture highlights perceptually present information for speakers . Gesture 10 , 3–28. 10.1075/gest.10.1.02ali [ CrossRef ] [ Google Scholar ]
• Alibali M. W., Nathan M. J. (2012). Embodiment in mathematics teaching and learning: evidence from learners' and teachers' gestures . J. Learn. Sci . 21 , 247–286. 10.1080/10508406.2011.611446 [ CrossRef ] [ Google Scholar ]
• Alloway T. P., Alloway R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment . J. Exp. Child Psychol. 106 , 20–29. 10.1016/j.jecp.2009.11.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Andersson U. (2007). The contribution of working memory to children's mathematical word problem solving . Appl. Cogn. Psychol. 21 , 1201–1216. 10.1002/acp.1317 [ CrossRef ] [ Google Scholar ]
• Andersson U. (2008). Working memory as a predictor of written arithmetical skills in children: The importance of central executive functions . Br. J. Educ. Psychol. 78 , 181–203. 10.1348/000709907X209854 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baggetta P., Alexander P. A. (2016). Conceptualization and operationalization of executive function . Mind Brain Educ . 10 , 10–33. 10.1111/mbe.12100 [ CrossRef ] [ Google Scholar ]
• Ballard D. H., Hayhoe M. M., Pook P. K., Rao R. P. N. (1997). Deictic codes for the embodiment of cognition . Brain Behav. Sci. 20 , 723–742. 10.1017/S0140525X97001611 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barsalou L. W. (1999). Perceptual symbol systems . Behav. Brain Sci . 22 , 577–660. 10.1017/S0140525X99002149 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Broaders S. C., Cook S. W., Mitchell Z., Goldin-Meadow S. (2007). Making children gesture brings out implicit knowledge and leads to learning . J. Exp. Psychol. 136 , 539–550. 10.1037/0096-3445.136.4.539 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brock L. L., Rimm-Kaufman S. E., Nathanson L., Grimm K. J. (2009). The contributions of ‘Hot’ and ‘cool’ executive function to children's academic achievement, learning-related behaviors, and engagement in kindergarten . Early Child. Res. Q . 24 , 337–349. 10.1016/j.ecresq.2009.06.001 [ CrossRef ] [ Google Scholar ]
• Bull R., Espy K. A. (2006). Working memory, executive functioning, and children's mathematics , in Working Memory and Education , eds Pickering SJ. (Burlington, MA: Academic Press; ), 94–123. 10.1016/B978-012554465-8/50006-5 [ CrossRef ] [ Google Scholar ]
• Bull R., Espy K. A., Wiebe S. A., Sheffield T. D., Nelson J. M. (2011). Using confirmatory factor analysis to understand executive control in preschool children: sources of variation in emergent mathematic achievement . Dev. Sci . 14 , 679–692. 10.1111/j.1467-7687.2010.01012.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bull R., Lee K. (2014). Executive functioning and mathematics achievement . Child Dev. Perspect . 8 , 36–41. 10.1111/cdep.12059 [ CrossRef ] [ Google Scholar ]
• Bull R., Scerif G. (2001). Executive functioning as a predictor of children's mathematics ability: Inhibition, switching, and working memory . Dev. Neuropsychol . 19 , 273–293. 10.1207/S15326942DN1903_3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Caviola S., Mammarella I. C., Cornoldi C., Lucangeli D. (2012). The involvement of working memory in children's exact and approximate mental addition . J. Exp. Child Psychol . 112 , 141–160. 10.1016/j.jecp.2012.02.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chu M., Meyer A., Foulkes L., Kita S. (2014). Individual differences in frequency and saliency of speech-accompanying gestures: the role of cognitive abilities and empathy . J. Exp. Psychol. 143 , 694–709. 10.1037/a0033861 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Church R. B. (1999). Using gesture and speech to capture transitions in learning . Cogn. Dev . 14 , 313–342. 10.1016/S0885-2014(99)00007-6 [ CrossRef ] [ Google Scholar ]
• Church R. B., Ayman-Nolley S., Mahootian S. (2004). The role of gesture in bilingual education: does gesture enhance learning? . Int. J. Biling. Educ. Biling . 7 , 303–319. 10.1080/13670050408667815 [ CrossRef ] [ Google Scholar ]
• Church R. B., Goldin-Meadow S. (1986). The mismatch between gesture and speech as an index of transitional knowledge . Cognition 23 , 43–71. 10.1016/0010-0277(86)90053-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Church R. B., Kelly S. D., Lynch K. (2000). Immediate memory for mismatched speech and representational gesture across development . J. Nonverbal Behav . 24 , 151–174. 10.1023/A:1006610013873 [ CrossRef ] [ Google Scholar ]
• Claesens A., Engel M. (2013). How important is where you start? Early mathematics knowledge and later school success . Teachers College Record . 115 , 1–29. Available online at: https://psycnet.apa.org/record/2013-18273-005 [ Google Scholar ]
• Clark A. (1999). An embodied cognitive science? . Trends Cogn. Sci. 3 , 345–351. 10.1016/S1364-6613(99)01361-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clark C. A., Sheffield T. D., Wiebe S. A., Espy K. A. (2013). Longitudinal associations between executive control and developing mathematical competence in preschool boys and girls . Child Dev . 84 , 662–677. 10.1111/j.1467-8624.2012.01854.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clements D. H., Sarama J., Germeroth C. (2016). Learning executive function and early mathematics: directions of causal relations . Early Child. Res. Q . 36 , 79–90. 10.1016/j.ecresq.2015.12.009 [ CrossRef ] [ Google Scholar ]
• Congdon E. L., Kwon M. K., Levine S. C. (2018). Learning to measure through action and gesture: children's prior knowledge matters . Cognition 180 , 182–190. 10.1016/j.cognition.2018.07.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Congdon E. L., Novack M. A., Brooks N., Hemani-Lopez N., O'Keefe L., Goldin-Meadow S. (2017). Better together: Simultaneous presentation of speech and gesture in math instruction supports generalization and retention . Learn. Instr . 50 , 65–74. 10.1016/j.learninstruc.2017.03.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook S. W., Duffy R. G., Fenn K. M. (2013). Consolidation and transfer of learning after observing hand gesture . Child Dev . 84 , 1863–1871. 10.1111/cdev.12097 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook S. W., Friedman H. S., Duggan K. A., Cui J., Popescu V. (2017). Hand gesture and mathematics learning: lessons from an Avatar . Cogn. Sci . 41 , 518–535. 10.1111/cogs.12344 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook S. W., Mitchell Z., Goldin-Meadow S. (2008). Gesturing makes learning last . Cognition 106 , 1047–1058. 10.1016/j.cognition.2007.04.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook S. W., Yip T. K., Goldin-Meadow S. (2012). Gestures, but not meaningless movements, lighten working memory load when explaining math . Lang. Cogn. Process 27 , 594–610. 10.1080/01690965.2011.567074 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cragg L., Gilmore C. (2014). Skills underlying mathematics: the role of executive function in the development of mathematics proficiency . Trends Neurosci. Educ . 3 , 63–68. 10.1016/j.tine.2013.12.001 [ CrossRef ] [ Google Scholar ]
• Cragg L., Keeble S., Richardson S., Roome H. E., Gilmore C. (2017). Direct and indirect influences of executive functions on mathematics achievement . Cognition 162 , 12–26. 10.1016/j.cognition.2017.01.014 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Crowder E. M., Newman D. (1993). Telling what they know: the role of gesture and language in children's science explanations . Pragmat. Cogn. 1 , 341–376. 10.1075/pc.1.2.06cro [ CrossRef ] [ Google Scholar ]
• Dargue N., Sweller N., Jones M. P. (2019). When our hands help us understand: a meta-analysis into the effects of gesture on comprehension . Psychol. Bull . 145 , 765–784. 10.1037/bul0000202 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delgado B., Gómez J. C., Sarriá E. (2011). Pointing gestures as a cognitive tool in young children: experimental evidence . J. Exp. Child Psychol . 110 , 299–312. 10.1016/j.jecp.2011.04.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Espy K. A., McDiarmid M. M., Cwik M. F., Stalets M. M., Hamby A., Senn T. E. (2004). The contribution of executive functions to emergent mathematic skills in preschool children . Dev. Neuropsychol . 26 , 465–486. 10.1207/s15326942dn2601_6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garber P., Alibali M. W., Goldin-Meadow S. (1998). Knowledge conveyed in gesture is not tied to the hands . Child Dev . 69 , 75–84. 10.2307/1132071 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gathercole S. E. (1998). The development of memory . J. Child Psychol. Psychiatry 39 , 3–27. 10.1017/S0021963097001753 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Geary D. C. (2011). Cognitive predictors of achievement growth in mathematics: a 5-year longitudinal study . Dev. Psychol . 47 , 1539–1552. 10.1037/a0025510 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Geary D. C., Hoard M. K., Nugent L. (2012). Independent contributions of the central executive, intelligence, and in-class attentive behavior to developmental change in the strategies used to solve addition problems . J. Exp. Child Psychol . 113 , 49–65. 10.1016/j.jecp.2012.03.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Geary D. C., Hoard M. K., Nugent L., Bailey D. H. (2013). Adolescents' functional numeracy is predicted by their school entry number system knowledge . PLoS ONE 8 :e54651. 10.1371/journal.pone.0054651 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Geary D. C., Vanmarle K. (2016). Young children's core symbolic and nonsymbolic quantitative knowledge in the prediction of later mathematics achievement . Dev. Psychol . 52 , 2130–2144. 10.1037/dev0000214 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gibson D. J., Gunderson E. A., Spaepen E., Levine S. C., Goldin-Meadow S. (2019). Number gestures predict learning of number words . Dev. Sci. 22 :e12791. 10.1111/desc.12791 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gillespie M., James A. N., Federmeier K. D., Watson D. G. (2014). Verbal working memory predicts co-speech gesture: evidence from individual differences . Cognition 132 , 174–180. 10.1016/j.cognition.2014.03.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gilmore C., Attridge N., Clayton S., Cragg L., Johnson S., Marlow N., et al.. (2013). Individual differences in inhibitory control, not non-verbal number acuity, correlate with mathematics achievement . PLoS ONE 8 :e67374. 10.1371/journal.pone.0067374 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glenberg A. M., Robertson D. A. (1999). Indexical understanding of instructions . Discourse Proc. 28 , 1–26. 10.1080/01638539909545067 [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S. (1999). The role of gesture in communication and thinking . Trends Cogn. Sci. 3 , 419-429. 10.1016/S1364-6613(99)01397-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S. (2003). Hearing Gesture: How Our Hands Help Us Think. Cambridge, MA: Harvard University Press. 10.1037/e413812005-377 [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S. (2009). How gesture promotes learning throughout childhood . Child Dev. Perspect . 3 , 106–111. 10.1111/j.1750-8606.2009.00088.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S. (2010). When gesture does and does not promote learning . Lang. Cogn. 2 , 1–19. 10.1515/langcog.2010.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Alibali M. W., Church R. B. (1993a). Transitions in concept acquisition: using the hand to read the mind . Psychol. Rev . 100 , 279–297. 10.1037/0033-295X.100.2.279 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Cook S. W., Mitchell Z. A. (2009). Gesturing gives children new ideas about math . Psychol. Sci . 20 , 267–272. 10.1111/j.1467-9280.2009.02297.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Kim S., Singer M. (1999). What the teacher's hands tell the student's mind about math . J. Educ. Psychol . 91 , 720–730. 10.1037/0022-0663.91.4.720 [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Nusbaum H., Kelly S. D., Wagner S. (2001). Explaining math: gesturing lightens the load . Psychol. Sci . 12 , 516–522. 10.1111/1467-9280.00395 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Nusbaum H. C., Garber P., Church R. B. (1993b). Transitions in learning: evidence for simultaneously activated strategies . J. Exp. Psychol. 19 , 92–107. 10.1037/0096-1523.19.1.92 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Shield A., Lenzen D., Herzig M., Padden C. (2012). The gestures ASL signers use tell us when they are ready to learn math . Cognition 123 , 448–453. 10.1016/j.cognition.2012.02.006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Singer M. A. (2003). From children's hands to adults' ears: gesture's role in the learning process . Dev. Psychol . 39 , 509–520. 10.1037/0012-1649.39.3.509 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Wagner S. M. (2005). How our hands help us learn . Trends Cogn. Sci. 9 , 234–241. 10.1016/j.tics.2005.03.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldin-Meadow S., Wein D., Chang C. (1992). Assessing knowledge through gesture: using children's hands to read their minds . Cogn. Instr . 9 , 201–219. 10.1207/s1532690xci0903_2 [ CrossRef ] [ Google Scholar ]
• Gordon R., Chernyak N., Cordes S. (2019). Get to the point: preschoolers' spontaneous gesture use during a cardinality task . Cogn. Dev . 52 :100818. 10.1016/j.cogdev.2019.100818 [ CrossRef ] [ Google Scholar ]
• Gordon R., Scalise N. R., Ramani G. B. (2021). Give yourself a hand: the role of gesture and working memory in preschoolers' numerical knowledge . J. Exp. Child Psychol. [Epub ahead of print]. [ PubMed ] [ Google Scholar ]
• Graham T. A. (1999). The role of gesture in children's learning to count . J. Exp. Child Psychol . 74 , 333–355. 10.1006/jecp.1999.2520 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gunderson E. A., Spaepen E., Gibson D., Goldin-Meadow S., Levine S. C. (2015). Gesture as a window onto children's number knowledge . Cognition 144 , 14–28. 10.1016/j.cognition.2015.07.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holmes J., Gathercole S. E. (2014). Taking working memory training from the laboratory into schools . Educ. Psychol . 34 , 440–450. 10.1080/01443410.2013.797338 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holmes J., Gathercole S. E., Dunning D. L. (2009). Adaptive training leads to sustained enhancement of poor working memory in children . Dev. Sci . 12 , F9–F15. 10.1111/j.1467-7687.2009.00848.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hostetter A. B. (2011). When do gestures communicate? A meta-analysis . Psychol. Bull. 137 :297. 10.1037/a0022128 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hostetter A. B., Alibali M. W. (2008). Visible embodiment: Gestures as simulated action . Psychon. Bull. Rev. 15 , 495–514. 10.3758/PBR.15.3.495 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hughes C., Ensor R., Wilson A., Graham A. (2010). Tracking executive function across the transition to school: a latent variable approach . Dev. Neuropsychol . 35 , 20–36. 10.1080/87565640903325691 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Imbo I., Vandierendonck A. (2007). The development of strategy use in elementary school children: working memory and individual differences . J. Exp. Child Psychol . 96 , 284–309. 10.1016/j.jecp.2006.09.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Iverson J. M., Goldin-Meadow S. (1998). Why people gesture when they speak . Nature 396 , 228–228. 10.1038/24300 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacob R., Parkinson J. (2015). The potential for school-based interventions that target executive function to improve academic achievement: a review . Rev. Educ. Res . 85 , 512–552. 10.3102/0034654314561338 [ CrossRef ] [ Google Scholar ]
• Jaeggi S. M., Buschkuehl M., Jonides J., Shah P. (2012). Cogmed and working memory training – current challenges and the search for underlying mechanisms . J. Appl. Res. Mem. Cogn . 1 , 211–213. 10.1016/j.jarmac.2012.07.002 [ CrossRef ] [ Google Scholar ]
• Karbach J., Strobach T., Schubert T. (2015). Adaptive working-memory training benefits reading, but not mathematics in middle childhood . Child Neuropsychol . 21 , 285–301. 10.1080/09297049.2014.899336 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kelly S. D., Church R. B. (1997). Can children detect conceptual information conveyed through other children's nonverbal behavior's . Cogn. Instr . 15 , 107–134. 10.1207/s1532690xci1501_4 [ CrossRef ] [ Google Scholar ]
• Kelly S. D., Church R. B. (1998). A comparison between children's and adults' ability to detect conceptual information conveyed through representational gestures . Child Dev . 69 , 85–93. 10.1111/j.1467-8624.1998.tb06135.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kelly S. D., Singer M., Hicks J., Goldin-Meadow S. (2002). A helping hand in assessing children's knowledge: instructing adults to attend to gesture . Cogn. Instr . 20 , 1–26. 10.1207/S1532690XCI2001_1 [ CrossRef ] [ Google Scholar ]
• Kovács A. M., Tauzin T., Téglás E., Gergely G., Csibra G. (2014). Pointing as epistemic request: 12-month-olds point to receive new information . Infancy , 19 , 543–557. 10.1111/infa.12060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krajewski K., Schneider W. (2009). Exploring the impact of phonological awareness, visual-spatial working memory, and preschool quantity-number competencies on mathematics achievement in elementary school: findings from a 3-year longitudinal study . J. Exp. Child Psychol. 103 , 516–531. 10.1016/j.jecp.2009.03.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krauss R. M. (1998). Why do we gesture when we speak? Curr. Dir. Psychol. Sci . 7 , 54–54. 10.1111/1467-8721.ep13175642 [ CrossRef ] [ Google Scholar ]
• Krauss R. M., Dushay R. A., Chen Y., Rauscher F. (1995). The communicative value of conversational hand gesture . J. Exp. Soc. Psychol . 31 , 533–552. 10.1006/jesp.1995.1024 [ CrossRef ] [ Google Scholar ]
• Kroesbergen E. H., Van Luit J. E. H., Van Lieshout E. C. D. M., Van Loosbroek E., Van de Rijt B. A. M. (2009). Individual differences in early numeracy: the role of executive functions and subitizing . J. Psychoeduc. Assess . 27 , 226–236. 10.1177/0734282908330586 [ CrossRef ] [ Google Scholar ]
• Kroesbergen E. H., van't Noordende J. E., Kolkman M. E. (2014). Training working memory in kindergarten children: Effects on working memory and early numeracy . Child Neuropsychol. 20 , 23–37. 10.1080/09297049.2012.736483 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhn L. J., Willoughby M. T., Wilbourn M. P., Vernon-Feagans L., Blair C. B., Family Life Project Key Investigators (2014). Early communicative gestures prospectively predict language development and executive function in early childhood . Child Dev . 85 , 1898–1914. 10.1111/cdev.12249 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee K., Ng E. L., Ng S. F. (2009). The contributions of working memory and executive functioning to problem representation and solution generation in algebraic word problems . J. Educ. Psychol . 101 , 373–387. 10.1037/a0013843 [ CrossRef ] [ Google Scholar ]
• LeFevre J. A., Berrigan L., Vendetti C., Kamawar D., Bisanz J., Skwarchuk S. L., et al.. (2013). The role of executive attention in the acquisition of mathematical skills for children in Grades 2 through 4 . J. Exp. Child Psychol . 114 , 243–261. 10.1016/j.jecp.2012.10.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lutz S., Huitt W. (2003). Information processing and memory: theory and applications , in Educational Psychology Interactive (Valdosta, GA: Valdosta State University; ), 1–17. Available online at: http://www.edpsycinteractive.org/papers/infoproc.pdf (accessed February, 2020). [ Google Scholar ]
• Marstaller L., Burianová H. (2013). Individual differences in the gesture effect on working memory . Psychon. Bull. Rev. 20 , 496–500. 10.3758/s13423-012-0365-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McNeill D. (1992). Hand and Mind: What Gestures Reveal About Thought . University of Chicago press. [ Google Scholar ]
• Miyake A., Friedman N. P., Emerson M. J., Witzki A. H., Howerter A., Wager T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis . Cogn. Psychol . 41 , 49–100. 10.1006/cogp.1999.0734 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Monette S., Bigras M., Guay M. C. (2011). The role of the executive functions in school achievement at the end of Grade 1 . J. Exp. Child Psychol . 109 , 158–173. 10.1016/j.jecp.2011.01.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Navarro J. I., Aguilar M., Alcalde C., Ruiz G., Marchena E., Menacho I. (2011). Inhibitory processes, working memory, phonological awareness, naming speed, and early arithmetic achievement . Span. J. Psychol . 14 , 580–588. 10.5209/rev_SJOP.2011.v14.n2.6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Novack M. A., Congdon E. L., Hemani-Lopez N., Goldin-Meadow S. (2014). From action to abstraction: using the hands to learn math . Psychol. Sci . 25 , 903–910. 10.1177/0956797613518351 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Novack M. A., Goldin-Meadow S. (2017). Gesture as representational action: a paper about function . Psychon. Bull. Rev . 24 , 652–665. 10.3758/s13423-016-1145-z [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• O'Neill G., Miller P. H. (2013). A show of hands: relations between young children's gesturing and executive function . Dev. Psychol . 49 , 1517–1528. 10.1037/a0030241 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Özer D., Göksun T. (2020). Visual-spatial and verbal abilities differentially affect processing of gestural vs. spoken expressions . Lang. Cogn. Neurosci . 35 , 896–914. 10.1080/23273798.2019.1703016 [ CrossRef ] [ Google Scholar ]
• Pellegrino J. W., Goldman S. R. (1987). Information processing and elementary mathematics . J. Learn. Disabil. 20 , 23–32. 10.1177/002221948702000105 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peng P., Namkung J., Barnes M., Sun C. (2016). A meta-analysis of mathematics and working memory: moderating effects of working memory domain, type of mathematics skill, and sample characteristics . J. Educ. Psychol . 108 , 455–473. 10.1037/edu0000079 [ CrossRef ] [ Google Scholar ]
• Perry M., Church R., Goldin-Meadow S. (1988). Transitional knowledge in the acquisition of concepts . Cogn. Dev . 3 , 359–400. 10.1016/0885-2014(88)90021-4 [ CrossRef ] [ Google Scholar ]
• Pine K. J., Lufkin N., Kirk E., Messer D. (2007). A microgenetic analysis of the relationship between speech and gesture in children: Evidence for semantic and temporal asynchrony . Lang. Cogn. Process . 22 , 234–246. 10.1080/01690960600630881 [ CrossRef ] [ Google Scholar ]
• Ping R., Goldin-Meadow S. (2010). Gesturing saves cognitive resources when talking about nonpresent objects . Cogn. Sci . 34 , 602–619. 10.1111/j.1551-6709.2010.01102.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ping R. M., Goldin-Meadow S. (2008). Hands in the air: using ungrounded iconic gestures to teach children conservation of quantity . Dev. Psychol . 44 , 1277–1287. 10.1037/0012-1649.44.5.1277 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pouw W. T., Mavilidi M. F., Van Gog T., Paas F. (2016). Gesturing during mental problem solving reduces eye movements, especially for individuals with lower visual working memory capacity . Cogn. Process . 17 , 269–277. 10.1007/s10339-016-0757-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ramani G. B., Daubert E. N., Scalise N. R. (2019). Role of play and games in building children's foundational numerical knowledge , in Cognitive Foundations for Improving Mathematical Learning , eds Geary D. C., Berch D. B., Koepke K. M. (Cambridge, MA: Elsevier Academic Press; ), 69–90. 10.1016/B978-0-12-815952-1.00003-7 [ CrossRef ] [ Google Scholar ]
• Ramani G. B., Jaeggi S. M., Daubert E. N., Buschkuehl M. (2017). Domain-specific and domain-general training to improve kindergarten children's mathematics . J. Numerical Cogn. 3 , 468–495. 10.5964/jnc.v3i2.31 [ CrossRef ] [ Google Scholar ]
• Rhoads C. L., Miller P. H., Jaeger G. O. (2018). Put your hands up! Gesturing improves preschoolers' executive function . J. Exp. Child Psychol . 173 , 41–58. 10.1016/j.jecp.2018.03.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rohlfing K. J., Longo M. R., Bertenthal B. I. (2012). Dynamic pointing triggers shifts of visual attention in young infants . Dev. Sci. 15 , 426–435. 10.1111/j.1467-7687.2012.01139.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shapiro L. (2019). Embodied Cognition. New York, NY: Routledge. 10.4324/9781315180380 [ CrossRef ] [ Google Scholar ]
• Shipstead Z., Redick T. S., Engle R. W. (2012). Is working memory training effective? Psychol. Bull . 138 , 628–654. 10.1037/a0027473 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Siegler R. S. (1996). Emerging Minds: The Process of Change in Children's Thinking . New York, NY: Oxford University Press. 10.1093/oso/9780195077872.001.0001 [ CrossRef ] [ Google Scholar ]
• Siegler R. S., Adolph K. E., Lemaire P. (1996). Strategy choices across the life span , in Implicit Memory and Metacognition , ed Bossert O. (New York, NY: Oxford University Press; ) 79–121. [ Google Scholar ]
• Singer M. A., Goldin-Meadow S. (2005). Children learn when their teacher's gestures and speech differ . Psychol. Sci . 16 , 85–89. 10.1111/j.0956-7976.2005.00786.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• St Clair-Thompson H., Stevens R., Hunt A., Bolder E. (2010). Improving children's working memory and classroom performance . Educ. Psychol . 30 , 203–219. 10.1080/01443410903509259 [ CrossRef ] [ Google Scholar ]
• St Clair-Thompson H. L., Gathercole S. E. (2006). Executive functions and achievements in school: shifting, updating, inhibition, and working memory . Quart. J. Exp. Psychol . 59 , 745–759. 10.1080/17470210500162854 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swanson H. L. (2004). Working memory and phonological processing as predictors of children's mathematical problem solving at different ages . Mem. Cognit . 32 , 648–661. 10.3758/BF03195856 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sweller J. (1988). Cognitive load during problem solving: effects on learning . Cogn. Sci . 12 , 257–285. 10.1207/s15516709cog1202_4 [ CrossRef ] [ Google Scholar ]
• Tomasello M., Carpenter M., Liszkowski U. (2007). A new look at infant pointing . Child Dev. 78 , 705–722. 10.1111/j.1467-8624.2007.01025.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valenzeno L., Alibali M. W., Klatzky R. (2003). Teachers' gestures facilitate students' learning: a lesson in symmetry . Contemp. Educ. Psychol . 28 , 187–204. 10.1016/S0361-476X(02)00007-3 [ CrossRef ] [ Google Scholar ]
• Van der Ven S. H., Kroesbergen E. H., Boom J., Leseman P. P. (2012). The development of executive functions and early mathematics: a dynamic relationship . Br. J. Educ. Psychol . 82 , 100–119. 10.1111/j.2044-8279.2011.02035.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wagner S. M., Nusbaum H., Goldin-Meadow S. (2004). Probing the mental representation of gesture: is handwaving spatial? J. Mem. Lang . 50 , 395–407. 10.1016/j.jml.2004.01.002 [ CrossRef ] [ Google Scholar ]
• Wakefield E. M., Congdon E. L., Novack M. A., Goldin-Meadow S., James K. H. (2019). Learning math by hand: The neural effects of gesture-based instruction in 8-year-old children . Attent. Percept. Psychophys . 81 , 2343–2353. 10.3758/s13414-019-01755-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wakefield E. M., James T. W., James K. H. (2013). Neural correlates of gesture processing across human development . Cogn. Neuropsychol . 30 , 58–76. 10.1080/02643294.2013.794777 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wakefield E. M., Novack M. A., Congdon E. L., Franconeri S., Goldin-Meadow S. (2018). Gesture helps learners learn, but not merely by guiding their visual attention . Dev. Sci . 21 :e12664. 10.1111/desc.12664 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wakefield E. M., Novack M. A., Congdon E. L., Howard L. H. (2021). Individual differences in gesture interpretation predict children's propensity to pick a gesturer as a good informant . J. Exp. Child Psychol . 205 :105069. 10.1016/j.jecp.2020.105069 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Watts T. W., Duncan G. J., Siegler R. S., Davis-Kean P. E. (2014). What's past is prologue: Relations between early mathematics knowledge and high school achievement . Educ. Res . 43 , 352–360. 10.3102/0013189X14553660 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wiebe S. A., Espy K. A., Charak D. (2008). Using confirmatory factor analysis to understand executive control in preschool children: I. latent structure . Dev. Psychol . 44 , 575–587. 10.1037/0012-1649.44.2.575 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilson M. (2002). Six views of embodied cognition . Psychon. Bull. Rev . 9 , 625–636. 10.3758/BF03196322 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu Y. C., Coulson S. (2014a). Co-speech iconic gestures and visuo-spatial working memory . Acta Psychol . 153 , 39–50. 10.1016/j.actpsy.2014.09.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu Y. C., Coulson S. (2014b). A psychometric measure of working memory capacity for configured body movement . PLoS ONE 9 :e84834. 10.1371/journal.pone.0084834 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Xenidou-Dervou I., De Smedt B., van der Schoot M., van Lieshout E. C. (2013). Individual differences in kindergarten math achievement: the integrative roles of approximation skills and working memory . Learn. Individ. Differ . 28 , 119–129. 10.1016/j.lindif.2013.09.012 [ CrossRef ] [ Google Scholar ]
• Zheng X., Swanson H. L., Marcoulides G. A. (2011). Working memory components as predictors of children's mathematical word problem solving . J. Exp. Child Psychol . 110 , 481–498. 10.1016/j.jecp.2011.06.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]

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• > The Cambridge Handbook of Situated Cognition
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Book contents

• The Cambridge Handbook of Situated Cognition
• Copyright page
• Acknowledgments
• Contributors
• Part I Backdrop
• Part II Conceptual Foundations
• Part III Empirical Developments
• Chapter 11 Situated Perception and Sensation in Vision and Other Modalities
• Chapter 12 Spatial Cognition
• Chapter 13 Remembering
• Chapter 14 Situating Concepts
• Chapter 15 Problem Solving and Situated Cognition
• Chapter 16 The Dynamic Interactions between Situations and Decisions
• Chapter 17 Situating Rationality
• Chapter 18 Situativity and Learning
• Chapter 19 Language in the Brain, Body, and World
• Chapter 20 Language Processing Embodied and Embedded
• Chapter 21 Situated Semantics
• Chapter 22 Is Consciousness Embodied?
• Chapter 23 Emotions in the Wild
• Chapter 24 The Social Context of Cognition
• Chapter 25 Cognition for Culture
• Chapter 26 Neuroethology

Chapter 15 - Problem Solving and Situated Cognition

from Part III - Empirical Developments

Published online by Cambridge University Press:  05 June 2012

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• Problem Solving and Situated Cognition
• By David Kirsh
• Edited by Philip Robbins , Washington University, St Louis , Murat Aydede , University of Florida
• Book: The Cambridge Handbook of Situated Cognition
• Online publication: 05 June 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511816826.015

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HYPOTHESIS AND THEORY article

Insights into conscious cognitive information processing.

• 1 Department of Behavioral and Clinical Neuroscience, Ruhr-University Bochum, Bochum, Germany
• 2 Unité de Formation et de Recherche des Sciences de la Vie (UFR 927), Institut de Biologie Paris-Seine, Sorbonne Université, Paris, France

For over a century, the neuro- and pathophysiological, behavioral, and cognitive correlates of consciousness have been an active field of theoretical considerations and empirical research in a wide range of modern disciplines. Conscious cognitive processing of information cannot be observed directly, but might be inferred from step-like discontinuities in learning performance or sudden insight-based improvements in problem solving behavior. It is assumed that a sudden step of knowledge associated with insight requires a creative reorganization of mental representations of task- or problem-relevant information and the restructuration of the task, respectively problem to overcome an cognitive dead-end or impasse. Discontinuities in learning performance or problem solving after an insight event can be used as time-tags to capture the time window in which conscious cognitive information processing must have taken place. According to the platform theory of conscious cognitive information processing, the reorganization and restructuration processes, require the maintenance of task- or problem-relevant information in working memory for the operation of executive functions on these mental representations. Electrophysiological evidence suggests that the reorganization and restructuration processes in working memory, that precede insight-based problem solutions are accompanied by an increase in the power of gamma oscillations in cortical areas including the prefrontal cortex. Empirical evidence and theoretical assumptions argue for an involvement of gap junction channels and connexin hemichannels in cortical gamma-oscillations and working memory processes. Discontinuities in learning or problem solving performance might be used as time-tags to investigate the implication of gap junction channels and hemichannels in conscious cognitive processing.

Conceptual issues with consciousness

Detailed information on the neurophysiological and molecular mechanisms, as well as regarding the behavioral correlates of consciousness is still scarce ( Dere et al., 2021 ; Zlomuzica and Dere, 2022 ). This gap of knowledge is even more astonishing in that the keyword “consciousness” entered into the online scientific publication database PubMed returns more than 59.000 hits. Nevertheless, there is no general definition in sight that would be unanimously accepted by all the different disciplines ( Dere et al., 2021 ; Zlomuzica and Dere, 2022 ). Owed to this conceptual vacuum, the measurement of cognitive, behavioral and neurophysiological correlates of consciousness in animals and humans has been an extremely challenging task. This an untenable situation, if one considers that altered consciousness (e.g., lack of insight into illness, tunnel vision, altered attention, perception and biased processing of disease-relevant stimuli) is a frequent symptom (and sometimes obstacle for successful treatment) among mental, neurological, and psychiatric diseases ( Bob et al., 2016 ; Dere et al., 2021 ; Zlomuzica et al., 2022 ; Zlomuzica and Dere, 2022 ; Martin, 2023 ; Stefanelli, 2023 ). However, due to the conceptual difficulty indicated above, alterations in consciousness as a clinical symptom is usually neglected (except in severe cases subsumed under the term disorders of consciousness) where the patient is no longer responsive or oriented in terms of time, location and personal information ( Edlow et al., 2021 ).

Conscious cognitive information processing

In order to resolve the definition issue and to pave the way to an empiric approach to this phenomenon, one should focus on the adaptive function of consciousness in everyday life and ask, which situations would probably induce a conscious cognitive information processing in the brain. It is reasonable to assume that people engage in conscious cognitive information processing when they are confronted with novel situations in which they cannot apply learned behavior, habits or scripts to master the situation. Such situations can occur, when a problem emerges (for example when environmental reinforcement contingencies suddenly change) and there is no masterplan at hand to cope with such an irregularity.

In contrast, one does not necessarily need conscious cognitive information processing to drive a car, prepare a meal or take a shower. Here, a mental autopilot driven by scripts, habits, reflexive and genetically predetermined behavior, and automatic motor programs would be sufficient to maintain such “unconscious” behavior. However, when something unexpected happens, a new problem is posed, conscious cognitive information processing is required to resolve the situation. Another situation that is likely to require conscious cognitive information processing is mental time travel ( Breeden et al., 2016 ; Dere et al., 2019 ). People engage in mental time travel to plan for the future, anticipate difficulties and problems and prepare effective coping strategies for such anticipated or imagined problems. Mental time travel into an anticipated or imagined future cannot be successfully implemented without conscious cognitive information processing.

An operational definition of conscious cognitive information processing has been proposed by Dere et al. (2021) . The central statement of this definition is that conscious cognitive information processing is initiated, whenever novel situations or problems are encountered that need to be resolved, or when mental time travel is performed to reconstruct past experiences that can be exploited to anticipate, imagine and prepare for future events in order to maximize the probability to experience rewarding situations and to minimize the probability of experiencing aversive situations ( Dere et al., 2021 ).

During conscious cognitive information processing, representations of perceived interoceptive and/or exteroceptive stimuli, as well as related semantic concepts, memories, and experiences are effortfully maintained in working memory to be actively manipulated (e.g., reorganized or restructured) in order to generate a novel creative output. According to this definition conscious cognitive information processing critically depends on the complexity of the situation with which the brain is dealing with and the complexity of the information that is actively manipulated on the working memory workbench ( Dere et al., 2021 ).

Platform theory of conscious cognitive information processing

The above definition is part of a larger theoretical framework, designed as the platform theory of conscious cognitive information processing, that attempts to explain how conscious cognitive information processing guides and controls flexible, respectively, adaptive behavior in humans and probably animals ( Dere et al., 2019 , 2021 ; Zlomuzica and Dere, 2022 ). The platform theory of conscious cognitive information processing proposes a hierarchical model of perception, memory, cognition and conscious cognitive information processing that is composed of a central executive/online processing platform that serves as a conscious cognitive operation control center that organizes, monitors and orchestrates subordinate operation and storage units called platforms (see Figure 1 for a summary of the platform functions). Conscious cognitive information processing requires the maintenance of mental representations of internal and external stimuli as well as related semantic concepts, memories, and experiences on a working memory platform that is endowed with a multitude of sophisticated executive functions ( Breeden et al., 2016 ; Dere et al., 2019 , 2021 ; Zlomuzica and Dere, 2022 ). The subordinate platforms serve, for example, as storage media for semantic and episodic knowledge or planned actions, activities, and the working-off of daily agendas.

Figure 1 . Scheme of the different components of the platform theory of conscious cognitive information processing. The central executive/online platform is a consciousness control center that organizes and monitors conscious mental operations by the orchestration of subordinated operation and storage units called “platforms.” The main function of the central executive/online platform is the generation of a daily schedule or agenda with goals to archive and the allocation of different grades of consciousness to mental representations or contents that are required to accomplish these tasks. The central executive/online platform selects mental representations (internal or external stimuli, stored content from the off-line memory platform) to be maintained, used, manipulated, or to be placed on the steady-state or stand-by platforms. The steady-state, stand-by, and off-line memory platforms do not execute cognitive operations, but rather provide information and content that is important to accomplish tasks or solve problems. The contents of declarative and non-declarative memory including episodic, semantic, and procedural information are stored in the off-line memory platform and are accessible to the central executive/online and mental time travel platforms. The central executive/online platform controls and presets attentional stimuli filters and receives input from sensory systems, as well as from motivation, emotion, vegetative, and motor systems. Intense stimuli can by-pass the attentional filters and directly access the central executive/online platform. The central executive/online platform recruits the mental time travel platform to re-construct past experiences, anticipate future events, or simulate and calculate the outcomes of different actions in imagined scenarios by feeding this platform with information from external and internal perceptions, as well as stored content from the off-line memory platform ( Breeden et al., 2016 ; Dere et al., 2019 , 2021 ; Zlomuzica and Dere, 2022 ).

It has been proposed that the stream of consciousness including conscious perception, conscious processing of information and metacognition relies on the synchronization of neuronal network activity across distant cortical regions ( Alkire et al., 2008 ; Koch et al., 2016 ; Tian et al., 2020 ; Valencia and Froese, 2020 ). These far-reaching synchronization processes are always neuronal mass phenomena with large amplitudes (otherwise the signal could not be filtered out from the background noise), that only vary by the oscillation frequency ( Meador et al., 2002 ; Northoff, 2017 ). It is difficult to believe that such mass phenomena can represent specific content or cognitive processes. It is more reasonable to assume that they represent more global network synchronization states that enable specific conscious cognitive information processing in local neuronal circuits which in contrast are likely to have an neuronal activity pattern that is sequential, reverberatory, and asynchronous to the global neuronal network synchronization in which these local neuronal circuits are embedded ( Singer, 1998 ; Dere et al., 2021 ; Zlomuzica and Dere, 2022 ; Zlomuzica et al., 2023 ). The activity of local neuronal circuits as singular phenomena cannot be detected with electrophysiological methods such as the EEG and MEG or neuroimaging approaches and require intracerebral and intracellular recordings.

According to the platform theory of conscious cognitive information processing, conscious cognitive information processing and adaptive behavior is supported by local neuronal circuit and neuronal network oscillations shaped by gap junction channels and connexin hemichannels ( Dere et al., 2021 ; Zlomuzica et al., 2022 , 2023 ; Zlomuzica and Dere, 2022 ). The platform theory of conscious cognitive information processing also proposes a molecular mechanism for conscious cognitive operations in everyday life, including the execution of planned actions, and the accomplishment of behavioral goals and agendas. Specifically, it is proposed that pacemaker cells or neural circuit pulsars embedded into local neuronal circuits and more far-reaching networks ( Le Bon-Jego and Yuste, 2007 ; Wittner and Miles, 2007 ; Kocsis et al., 2022 ), connexin hemichannels, and gap junctions jointly generate local neural circuit (and network) oscillations ( Draguhn et al., 1998 ; Coulon and Landisman, 2017 ; Mäkinen et al., 2018 ; Traub et al., 2018 ), which are the basis for the maintenance of mental representations in working memory ( Allen et al., 2011 ), to enable the performance of conscious cognitive operations on these mental representations, and the initiation of behavioral changes, as consequences of these conscious cognitive operations ( Dere et al., 2021 ).

What is the “added value” of the platform theory of conscious cognitive information processing compared to other empirical and theoretical approaches to consciousness?

There is a great deal of research that attempts to correlate very general “states” of consciousness with corresponding “levels” of consciousness. These states include coma, vegetative state (unresponsive wakefulness syndrome), minimally conscious state, sleep and the awake state ( Laureys et al., 2010 ). However, since coma and vegetative state are usually the consequence of a severe disease, injury or poisoning, they are essentially manifestations of brain dysfunction or breakdown, rather than states of consciousness. The inability to maintain consciousness after brain damage or the absence of consciousness in a diseased brain should not be considered as a state of consciousness or a condition that should be investigated to understand the nature of consciousness in an intact and operational brain. If one follows such a rationale until its end, death could be also regarded as a state of consciousness that is associated with an “absolute zero” level of consciousness.

Attempts have also been made to determine the neural basis of consciousness through imaging and electrophysiological measurements during the regaining or recovery of consciousness, for example after anesthesia or upon awakening ( Edlow et al., 2021 ). The regaining of consciousness has been related to the “re-boot” of resting state networks including the default mode network, fronto-parietal network, and the salience network ( Li et al., 2023 ). At most, these measurements can be used to distinguish between a waking receptive state (and the brain networks required) and a non-receptive state, but they do not say much about brain processes that are associated with conscious cognitive information processing (see also Chenot et al., 2024 ). To use a metaphor, it’s like measuring the lowering of a rolled up ceiling recessed projector screen to project content onto it. It does not give much information about the projector or the content that is being projected.

Yet, another line of research probes the awake brain to tease apart conscious and unconscious processing. This seems to be a more convincing and promising approach which is also followed by the platform theory of conscious cognitive information processing. According to this line of research, conditions, respectively, experimental situations have to be created, in which conscious cognitive information processing is likely to be initiated and therefore can be measured. In other words, the “brain” can be actively put in a situation which “inevitably” initiates conscious cognitive information processing and can be “observed” or “measured” during conscious cognitive information processing. In the context of the platform theory of conscious cognitive information processing, consciousness is treated as an observable variable and it is proposed that conscious cognitive information processing is initiated in situations where the owner of the brain is confronted with a novel problem for which there is no prefabricated solution available. Consequently, it is assumed that, at the moment of an insight-based solution, the brain was engaged with conscious cognitive information processing, which is different from unconscious information processing. In other words, the changes in the electro- and neurochemical activity of different areas of the brain that accompany this special moment of insight will certainly tell us much more about the neurophysiological mechanisms of conscious cognitive information processing than the comparison of functional network activity during pathological brain states including the unresponsive wakefulness syndrome and minimally conscious state ( Panda et al., 2022 ).

Furthermore, there are reductionist approaches that attempt to identify electrophysiological correlates for the conscious subjective perception of visual stimuli (in contrast to basic activity that is evoked by mere sensory stimulation) in order to gain insight into neurophysiological mechanisms and principles which might also help to understand more complex phenomena, including conscious cognitive information processing. Animal research in this area has suggested that visual awareness is reflected by power modulation of high-frequency local field potentials (in the gamma oscillation range) in the lateral prefrontal cortex, temporal and parietal cortex, where spiking activity is found to be perceptually modulated ( Panagiotaropoulos et al., 2012 ; Tseng et al., 2016 ). The gamma oscillations in this brain network might be related to the maintenance of “conscious visual perceptions” in working memory (or online platform) for further conscious cognitive information processing in order to generate a novel creative output. In this regard these visual consciousness approaches emphasize the importance of working memory for the generation and maintenance of conscious cognitive information processing, in a similar way to the platform theory of conscious cognitive information processing ( Dere et al., 2021 ).

One of the most influential theories of consciousness is Bernhard J. Baars’ Global Workspace theory (updated in Baars et al., 2021 ). It attempts to explain how a serial, integrated and very limited stream of consciousness emerges from a system of specialized “receiving processes” that are “unconsciously” working in parallel. The most peripheral “receiving processes” transmit information from primarily sensory pathways to downstream “receiving processes” that process and interpret these sensory information. The content entering the global workspace is then projected to secondary cortical sensory association areas. It is proposed that only the most significant information is allowed to enter the global workspace, which in turn can be regarded as a fleeting memory area with a limited storage capacity (in the range of seconds). It is assumed that only information that is passively stored (for a few seconds only) within the global workspace is conscious. “Consciousness” is regarded as a passive response (the information in the global workspace is not subject to cognitive operations) to a significant and intense sensorial stimulation. Formulated pointedly, the global workspace theory merely describes a sensory gripping reflex without specifying the neuronal or molecular mechanisms that underlie this function ( Zlomuzica and Dere, 2022 ). The global workspace is therefore not well suited to maintain a larger amount of information which might be required to solve complex problems. It is therefore not clear how this model can explain conscious cognitive information processing, complex problem solving or mental time travel into the past and future in any way better that the platform theory of conscious cognitive information processing (see also Zlomuzica and Dere, 2022 ).

Another popular theory of consciousness is the integrated information theory, since it provides a mathematical model to actually measure the level of consciousness in any neuronal network or other system that is potentially capable to generate consciousness ( Tononi et al., 2016 ). In the framework of this theory it is assumed that consciousness is a subjective, immediate, direct, and unified process. It further attempts to define the basic properties of a physical system capable of generating consciousness. According to this theory, consciousness necessities an interconnected set of elements with reentrant feed-back loops, in which the single elements have a mutual physical cause-effect powers on each other leading to the integration of information. The integrated information theory equates integrated information with consciousness, suggests that the degree of consciousness (in both quantity and quality) is measurable by determining the amount of intrinsic cause-effect power via phi metrics and extends its claims beyond human consciousness to animal and artificial consciousness. The integrated information theory attempts to define the necessities and operation mode of physical substrates of consciousness in the process of generation of integrated information and system-immanent intrinsic modulation. Testing the compatibility of this theory with the platform theory of conscious cognitive information processing would require measuring the postulated cause-effect system-immanent intrinsic-modulation during the execution of a conscious cognitive information processing or insight event. It is currently difficult to imagine what such a proof-of-principle experiment should look like. In a first step, one could try to detect corresponding cause-effect activity patterns or self-modulating systems in an organic model system such as brain slice preparations from animals or brain organoids. As promising and intuitively plausible as this theory may seem at first glance, the essential assumptions of the theory are essentially claims that have no empirical basis and are hardly amenable to experimental verification in a healthy, intact and awake brain.

Conscious cognitive information processing in animals

Despite the importance of conscious cognitive processing for the mental life and adaptive behavior of humans (and with limitations in animals), and in view of the devastating consequences of impaired conscious cognitive processing in patients with mental disorders and neuropsychiatric diseases, there is no valid animal model of conscious cognitive information processing available, that could be exploited for psychopharmacological, comparative and translational research ( Zlomuzica et al., 2022 , 2023 ; Dere and Zlomuzica, 2023 ). Animal models of conscious cognitive information processing have to deal with important issues of construct, face and predictive validity ( Dere et al., 2021 ; Zlomuzica and Dere, 2022 ). In a recent review, the few available paradigms to measure animal consciousness have been reviewed and the conclusion was reached, that all available tests have major shortcomings and are not well suited to serve as routine tests for neurobiological and pre-clinical research ( Zlomuzica and Dere, 2022 ). Instead the combination of a number of sophisticated cognitive tests in a behavioral test battery and the calculation of individual composite performance scores might be a better approximation to the measurement of conscious cognitive information processing. However, the proposed test battery is very time consuming, requires considerable equipment, training, and expertise with behavioral testing. These factors will probably hinder the application this test battery on a larger scale. There might be an “cheaper” and more direct path to conscious cognitive information processing in animals and humans.

Classic and contemporary definitions of the insight phenomenon by cognitive psychologists conceptualize insight as a sudden comprehension, realization, or creative problem solution that is based on a reorganization of mental representations of relevant information that is opposed to incremental trial-and-error learning ( Hebb, 1949 ; Thorpe, 1956 ; Kounios and Beeman, 2014 ; but see Bowden et al., 2005 for a different view). A sudden insight into the nature of a problem that leads to an instantaneous step of knowledge is likely to be accompanied by a sudden and strong emotional arousal ( Shen et al., 2017 ) resulting in the formation of an episodic memory ( Dere et al., 2006 , 2010 ; Kinugawa et al., 2013 ; Pause et al., 2013 ), which probably from this point on is the mnemonic basis of the persisting improvement in task performance.

In Wolfgang Köhler’s insight learning theory ( Köhler, 1917 , 1925 ), insight is defined as an instantaneous type of understanding of relations and reinforcement contingencies that can emerge without prior trial-and-error learning and that leads to a solution to a current problem. Learning success based on insight has been proposed to be conceptually incompatible with learning based on operant conditioning or reinforcement learning which is associated with a gradual buildup of a reward value or reinforcement signal for the correct response or sequence of responses accumulated through experiences ( Thorndike, 1911 ; Schultz et al., 1997 ).

Insight and conscious cognitive information processing

Event-related potential studies suggest that complex perceptions and cognitive processes can occur in the range of milliseconds. It has been proposed that a sudden step of knowledge might also be the result of a latent process that that runs unconsciously in the background and suddenly reaches consciousness while escaping metacognitive monitoring up to this point of time (see Tolman, 1948 ; Qiu et al., 2008 ).

There is an ongoing debate on whether insight is the result of conscious information processing (in the form of progress monitoring) after it has been realized that conventional solution attempts will not create the goal configuration or attenuate the problem space, e.g., in the 9-dot problem ( MacGregor et al., 2001 ) or an unconscious process in which self-imposed constraints on a problem or misleading presuppositions are discarded, and chunked items in the problem are decomposed and redistributed ( Knoblich et al., 2001 ).

There are also many anecdotical stories of sudden “inspirations” of how great researchers and scholars suddenly had an idea which immediately solved a problem that they had been trying to solve for a long time (think of the apple falling on Newton’s head or Archimedes idea to calculate material density through water displacement). The truth content of these romanticized stories of great scientists and inventors might be questionable. Nevertheless, many people report similar experiences of “inspiration” defined as a non-religious or mystical thought or idea that suddenly arises, is recognized as a solution to a problem, and seems to be detached from the actual context and current stream of thoughts or thinking.

It is not plausible to assume that an inspiration comes out of “nothing,” i.e., without preparatory cognitions. It seems for example more plausible to assume that conscious cognitive processing of information took place in advance, but the end product of this reasoning has not been recognized as the solution (and therefore has been “put aside”). It is also possible that in the course of reasoning, initially a small fragment of the solution was missing, but has been added at a later point in time. Phenomenologically, these processes might be felt as a flash of inspiration propelled by an metacognitive illusion ( Dere et al., 2021 ). Therefore, it seems to be more reasonable to assume that an “inspiration” is probably the intrusion of an insight-based solution (or promising part of the solution after a period of “incubation”), that has not been acknowledged as such by the time it was generated. For the remainder of this article insight is defined as the end product of an ongoing conscious cognitive information process, which should not be equated with the term “inspiration.”

Examples for insight-based problem solving in humans, non-human primates, and laboratory rats and mice

Robert W. Weisberg proposed in his integrated theory of insight in problem solving, that insight depends on conscious cognitive operations that aim to restructure problem-relevant information, in a way that the new information structure (comparable to individual puzzle pieces that have been put together), enables a direct solution to the problem ( Weisberg, 2015 ). Insight can be regarded as the endpoint or manifestation of a hidden problem solving mechanism and has been studied, for example, with ill-structured innovation tasks, including the hook bending paradigm or the tower of Hanoi. These tasks have in common that they require a multi-step solution, whereby the solution path is unknown and only information about the goal or target configuration is provided. Children who were at least 7 years old usually arrived suddenly at the solution to the hook bending problem, suggesting an insight-like problem solving mechanism ( Defeyter and German, 2003 ; Cutting et al., 2014 ). The hook bending problem has also been posed to non-human primates and large brained birds, which both showed task performance (e.g., very few failures after the task has been solved for the first time) that was interpreted in the sense of tool innovation that was made possible through sudden insight into the problem ( Weir, 2002 ; Bird and Emery, 2009 ; Laumer et al., 2018 ).

The Gestalt- and comparative psychologist Wolfgang Köhler investigated the phenomenon of insight in nonhuman primates ( Köhler, 1917 , 1925 ). The food-deprived chimpanzee Sultan had to realize that two small sticks in the cage could be inserted into each other to be able to pull a banana within reach that had been placed outside the cage. After several unsuccessful attempts to reach the banana with one of the two sticks, Sultan managed to put the two small sticks together by accident. Sultan immediately took the stick and pulled the banana towards him. However, this behavior cannot be clearly interpreted as insight because the solution was found more or less by chance. Nevertheless, Sultan understood that he needed a longer stick and when he saw it, he considered the problem solved and went straight to implementing it ( Köhler, 1917 ). However, it must be noted that the experiment was probably not designed in an ecologically valid way. In the wild, respectively, nature there are usually no sticks that can be stuck together, just like there are no magic wands. Therefore, Sultan would possibly not have been able to find the solution right away through prior conscious cognitive processing of all the information available. However, it is quite possible that Sultan, as a result of conscious cognitive processing of information, came to the conclusion that only a stick twice as long would make it possible to pull the banana.

As previously mentioned, insight can emerge from reorganizing or restructuring information ( MacGregor et al., 2001 ; Weisberg, 2015 ). However, this cognitive process can be impaired if a so-called functional fixation is present ( McCaffrey, 2012 ). This means, for example, that a tool is very strongly associated with a certain type of use or activity, so that a potential unconventional use of it to solve a problem is masked. Consequently, it has been shown that great apes have difficulties to find a solution to a new problem, when the available tools have been strongly associated with a different type of use or the solution of another problem ( Ebel et al., 2020 ). Again task design seems to be highly critical for the usefulness or sensitivity of a task or paradigm to detect problem solving based on insight.

The first study into the question of whether lower mammals such as rats are able to solve problems through insight was published by Helson (1927) . In the first phase of an elegant discrimination learning experiment, Helson trained one pair of rats (first pair) to prefer a food-rewarded light (60 W illumination) over a non-rewarded dark compartment (15 W illumination) and another pair of rats (second pair) to show the opposite preference. In the second phase of the experiment the illumination intensity of the two compartments was changed to 150 W and 60 W for the first pair of rats and to 15 W and 1 cd for the second pair of rats, while the reward contingency or task rule was not changed. The first pair would receive a reward for choosing the lighter compartment, while the second pair would receive a reward for the darker compartment. After the change of the intensity values of the stimuli presented the rats continued to prefer the light, respectively, dark compartment, suggesting that their decision was guided by structure–function relationships rather than simple stimulus–reward associations. The latter would require the rats to stay with the initially rewarded stimulus rather than to switch immediately to a novel stimulus that has not been paired with a reward. Helson concluded that the adaptive behavior of the rats was based on insight into the structure or general rule of the task ( Helson, 1927 ).

Behavioral correlates of insight as a manifestation of conscious cognitive information processing

Insight is a singular phenomenon that cannot be reliably reproduced over multiple teaching sessions and it can only be examined at the level of an individual and not in groups (but see Zlomuzica and Dere, 2022 ). Conscious cognitive processing of information cannot be observed directly but can perhaps be inferred from discontinuities in learning or problem solving behavior. As indicated above conscious cognitive information processing is initiated whenever a novel problem is posed that cannot be solved through learned, instinctive, or reflective behavior ( Dere et al., 2021 ; Zlomuzica and Dere, 2022 ) and it is realized that there is no ready-made solution available.

A new problem might be initially addressed through trial-and-error learning ( Thorndike, 1911 ). This type of problem solving strategy depends on randomly generated actions or sequences of actions. One of which happens to be correct and brings about the desired solution to the problem. Trial-and-error learning is generally characterized by gradual, incremental or continuous learning ( Thorndike, 1911 ). Even after a correct action or sequence of actions has been executed and the problem has been temporarily solved, it is possible that the correct response to the problem is not remembered shortly after. Just imagine that you incidentally managed to solve a “computer or software problem,” there is no guarantee that you will remember the sequence of actions that you have performed the next time when you are confronted with the same or a similar problem. The trial-and-error learning process may thus be more tedious than a quicker solution based on insight. The latter by definition is characterized by discontinuous or step-like learning and a sudden “step of knowledge” which stamps in permanently the correct response to the problem. The main point of this review is the hypothesis that learning and associated abrupt changes in performance through insight are likely to be the consequence of previous conscious cognitive information processing and that sudden and persistent changes in learning performance (discontinuities in learning performance) can be used as time tags indicating the time windows in which conscious cognitive information processing must have taken place. Equally, it can be assumed that an individual who exhibits merely continuous learning without abrupt increases in performance has not initiated conscious cognitive information processing to solve the problem.

Translated into an experimental learning paradigm this sudden insight would be reflected by an equally sudden and stable improvement in performance ( Gallistel et al., 2004 ). The analysis of the performance dynamics of individual participants or experimental animals during the acquisition of a task can help to identify the time point of this discontinuity in performance and thus the time point when conscious cognitive processing has been initiated. On the other hand the analysis of performance dynamics can also differentiate between slow and fast incremental continuous learning, that is the distinction between superior and inferior learners that do not engage in conscious cognitive processing and thus do not show discontinuities in learning performance ( Figure 2 ).

Figure 2 . Discontinuous and continuous learning performance. (A) Schematic drawing of a simple spatial working memory test to identify continuous and discontinuous learning performance. Depicted is a food-rewarded radial eight-arm maze. Red circles represent food rewards. The test starts with a free choice trial, in which a food deprived animal is allowed to make 4 choices. An example of a sequence of choices is indicated by the numbers. After the 4 th choice the animal is trapped at the central platform for a retention interval (e.g., 10 s). Thereafter all doors are unblocked and the animal is allowed to make 4 more choices. The number of correct choices in 4-choice bins (target choices of arms which are still baited) is recorded. (B) Line and scatter plots represent idealized learning curves (discontinuous learning, fast and slow incremental learning) of individual mice. Discontinuous learning is characterized by a sudden and stable improvement in the number of correct target choices within 4-choice or corner visit bins. Continuous learning is characterized by gradual incremental improvements in performance. The speed of learning equals the slope of the curve while a learning impairment is indicated by low asymptotic performance.

In a recent experiment by Rosenberg and colleagues, mice that had to solve a complex labyrinth navigation problem showed “sudden insight” that is a sharp discontinuity during learning, when the mice figured out to take a long but direct path with more than 6 correct binary choices to the goal. This sudden improvement in task performance persisted for the remainder of the experiment ( Rosenberg et al., 2021 ). Rosenberg et al. (2021) developed a “sudden insight statistics” procedure with which the slope of an individual learning curve can be analyzed in a way that enables the distinction between individuals that show a discontinuous learning curve from those that show continuous or incremental learning. This work demonstrates that the insight phenomenon can be quantified at the level of individual behavior and statistically analyzed (see also Reddy, 2022 ).

In conclusion, insight and associated discontinuities in learning or problem solving behavior can be regarded as the successful endpoint, respectively, outcome of conscious cognitive information processing, that might be used to time tag conscious cognitive processing and to investigate the underlying neurophysiological processes and substrates. This is especially relevant for experiments where learning behavior and electrophysiological readouts are measured simultaneously and continuously. It should be noted that has also been suggested that discontinuities in learning behavior do not necessarily reflect insight-based problem solving, but might be the consequence of other cognitive processes ( Van Steenburgh et al., 2012 ). However, this uncertainty might be eliminated by demonstrating the specificity of a putative neuro- or electrophysiological readout or correlate of discontinuous learning. For example, in the context of electrophysiological measurements, one could identify neuronal oscillations of certain frequency bands and power that correlate temporally with jumps in performance and that are not found in a comparable form or intensity during continuous learning. Future research will determine whether the neurobiological basis of conscious cognitive processing can be tackled by the simultaneous measurement of insight-like discontinuities in learning performance and in-vivo electrophysiological recordings and/or optogenetic manipulations with high temporal resolution.

The neurophysiological basis of discontinuous learning based on sudden insight

In a visual face and place recognition experiment with non-human primates, it has been found that discontinuous learning performance or sudden steps of knowledge coincides with a transient peak in neuronal network synchronization between the reward-sensitive areas of the prefrontal cortex and inferotemporal cortex known for the processing of images ( Csorba et al., 2022 ). However, the authors also reported that the amplitude of cross-region synchronization increased gradually across task performance, so that the maximal synchronization was build up slowly and not suddenly. These findings suggest that discontinuous or step-like learning performance based on insight might require the synchronization of neuronal activity in the brain areas that are involved in the processing of task-relevant information. Given that the synchronization of cross-regional neuronal activity was build up gradually, it is tempting to speculate whether conscious cognitive information processing that eventually leads to an sudden improvement in task performance is reflected by a gradual and cumulative increase in the synchronization of neuronal activity in the brain regions involved in the mental representation, maintenance and processing of task-relevant information.

Further evidence for the involvement of the prefrontal cortex in discontinuous learning performance that might be based on an insight-like decision process comes from a rule-shifting task with rats ( Durstewitz et al., 2010 ). In this experiment, rats were first trained to acquire a cue-based response strategy to obtain rewards in an operant chamber. Thereafter, the rats experienced a change in the reinforcement contingencies that required them to abandon the old strategy and gradually acquire a different egocentric response strategy by gathering evidence through trial-and-error. The authors suggested that novel rule learning can be regarded as an evidence-based decision process, that might be accompanied by moments of sudden insight at the instant, when there is enough information gathered that undoubtedly indicates that the reinforcement contingencies in the learning situation have changed permanently (a similar interpretation can be found in Bowden et al., 2005 ). It was found that transitions in behavioral performance of rats during rule learning in a set-shifting task were temporally correlated with abrupt transitions in the firing activity of “rule-selective” neural ensembles in the prefrontal cortex ( Durstewitz et al., 2010 ). This study suggests that behavioral and neural dynamics can be correlated in gradual or incremental learning situations, where an inefficient response gradually decreases, while an efficient response gradually increases. However, it remains to be determined whether the detected correlation between behavioral and neural dynamics in this experiment indeed reflects an insight-like cognitive process, or whether it is the manifestation of the execution of different responses, respectively the active inhibition of the inefficient response. It should be also considered that, one important criterion for the appreciation that a change in behavior occurred after insight, is that the performance leap is permanent, illustrated by a step-like learning curve (without a transition phase), which was probably not the case in the study by Durstewitz et al. (2010) .

A combined scalp electroencephalogram and functional magnetic resonance imaging study by Jung-Beeman and colleagues searched for the neural correlates of insight-like verbal problem solving. In this experiment, participants had to state whether the correct solutions were reached by insight or not. Insight-based solutions were found to be preceded (0.3 s prior to insight solutions) by a sudden burst of neural oscillations in the gamma-frequency range in the anterior superior temporal gyrus of the right hemisphere. Insight solutions also associated with increased neuronal activation in the anterior superior temporal gyrus as compared to non-insight solutions ( Jung-Beeman et al., 2004 ).

Further support for the implication of gamma-frequency neuronal oscillations in insight-like problem solving was provided by an electroencephalogram study by Rosen and Reiner (2017) . Here, participants were asked to solve a spatial puzzle (which can be solved either incrementally or by insight), and had to indicate whether they found the solution by sudden insight or in another way. Participants who have reported solutions based on insight showed an increase in gamma and beta frequency activity in frontal areas and with respect to alpha frequency activity in right temporal areas as compared to participants who reported an solution based on incremental learning ( Rosen and Reiner, 2017 ). Interestingly, the incremental group exhibited a decrease in gamma and beta activity during the task performance as compared to baseline recordings, suggesting that different solution strategies are mediated by different neuronal network operations rather than on the basis of a gradual difference. The latter alternative would mean that both an insight-based solution and a solution based on incremental learning would require gamma activity, but an insightful solution would be of example only possible if a certain intensity or power threshold is exceeded. In conclusion, these findings suggest that the assumed reorganization of information and the restructuration of the spatial puzzle problem prior to the insight-based solution was associated with gamma frequency neural oscillations in the right frontal cortex.

Another electroencephalographic study searched for neural activity that might be specific for different phases of insight-based problem solving, including a mental impasse (when it is realized that routine solutions are useless), the reorganization of the relevant information to have a different access to the problem, and, finally the sudden insight into the problem and its solution based on subjective ratings ( Sandkühler and Bhattacharya, 2008 ). According to the authors the states of mental impasse or sudden insight were correlated with the power of gamma-frequency activity at parietal-occipital regions, while the reorganization of task-relevant information seemed to be associated with a decreased power in the alpha-frequency range in the right prefrontal area ( Sandkühler and Bhattacharya, 2008 ).

In the studies reported above, the categorization of insight vs. non-insight solutions has been made by subjective or introspective reports requiring metacognition (hereinafter referred to as insight-like problem solving), rather than on the basis of more objective criterions, such as discontinuous learning performance as suggested above. Therefore, it remains to be determined whether the findings reported above can be replicated when a behavioral criterion for insight-like problem solving is used as a specifier for problem solving based on insight.

The studies discussed so far suggest an involvement of cortical brain regions in insight-like problem solving (prefrontal cortex, anterior superior temporal gyrus, parieto-occipital regions showing neural activity in the gamma-frequency range). However, there is also evidence for a subcortical contribution to insight-like problem solving. In an functional magnetic resonance experiment, participants signaled correct solutions in a remote associates task (inferring the commonality in word triplets) with a button press, which has been used as a time-tag for sudden insight-like solutions ( Tik et al., 2018 ). Insight-like solutions were not only accompanied by neuronal activation in the left anterior middle temporal gyrus, but also by bilateral activations in the thalamus, hippocampus, ventral tegmental area, nucleus accumbens, and caudate nucleus. The authors conclude that the subjective feeling of relief and the emotional arousal induced after the sudden emergence of the solution is accompanied by a reward signal in the mesolimbic dopamine system ( Tik et al., 2018 ). It would be interesting to know, whether insight-based solutions of more complex problem configurations (e.g., after conscious cognitive information processing) would likewise be accompanied by a subcortical dopaminergic reward signal. Such a reward signal is normally associated with strong emotional arousal, that is known to be required to trigger episodic memory formation for the preservation of the correct solution ( Dere et al., 2006 , 2008 , 2010 ; Pause et al., 2013 ). This episodic memory for the problem solving event is very likely the mnemonic basis for the persistent improvement in task performance that follows after problem solving based on insight.

The role of gap junction channels and hemichannels for discontinuous learning based on insight

Electrophysiological evidence suggests that the reorganization and restructuration processes in working memory, that precede insight-based problem solutions are accompanied by an increase in the power of gamma oscillations in cortical areas including the prefrontal cortex. In the following, I will review evidence for the involvement of gap junction channels and connexin hemichannels in cortical gamma-oscillations ( Cunningham et al., 2004 ; Traub and Whittington, 2010 , 2022 ; Traub et al., 2020 ) and working memory processes.

Gap junction channels composed of connexin proteins allow an intercellular coupling between neighboring cells. The connexins Cx36 and Cx45 are expressed in neurons, while Cx43 and Cx30 are expressed in astrocytes (see Dere, 2013 for an overview). Intercellular electrotonic and metabolic coupling allows the bidirectional diffusion of ions, cations, metabolites, second messengers, cyclic nucleotides, oligonucleotides, and small molecules with a molecular mass up to 1–2 kDa ( Dere, 2013 ). Coupling and uncoupling of adjacent cells via gap junctions is activity-dependent, shows plasticity similar to the modulation of synaptic strength, and is not restricted to cells of the same type ( Yang et al., 1990 ; Landisman et al., 2002 ; Alev et al., 2008 ). In this way, neuronal depolarization can theoretically directly propagate across neurons and generate action potentials. Intercellular communication and signal transmission via gap junctions operates almost without a temporal delay and is therefore much faster as compared to synaptic neurotransmission that operates at least in the order of milliseconds ( Dere and Zlomuzica, 2012 ; Dere, 2013 ).

Connexin hemichannels and gap junctions have been shown to shape neuronal network oscillations throughout the brain ( Coulon and Landisman, 2017 ). These network oscillations appear to be critically involved in working memory, cognition, and behavior ( Allen et al., 2011 ; Maciunas et al., 2016 ; Walrave et al., 2016 ; Tao et al., 2021 ; Linsambarth et al., 2022 ). There is evidence that theta and gamma oscillations in hippocampal neuronal networks are mediated by gap junction channels composed of Cx36, Cx45, or both ( Hormuzdi et al., 2001 ; Schmitz et al., 2001 ; Meier et al., 2002 ; Buhl et al., 2003 ; Zlomuzica et al., 2010 ). Electrophysiological studies with Cx36 deficient mice revealed slower hippocampal theta oscillations ( Allen et al., 2011 ), reduced overall power, and synchrony of hippocampal gamma oscillations in vitro ( Hormuzdi et al., 2001 ), and in vivo ( Buhl et al., 2003 ). Furthermore, the administration of gap junction blockers induced a complete suppression of hippocampal gamma oscillations in Cx36-deficient mice ( Buhl et al., 2003 ). Furthermore, Cx45-deficent mice showed changes in kainite-induced gamma oscillation in the CA1 and CA3 region together with impaired performance in an object recognition task ( Zlomuzica et al., 2010 ).

The behavioral phenotyping mCx36 deficient mice in our lab revealed changes in motor coordination and balancing performance, increased locomotion and running speed in a novel environment, changes in Y-maze exploration, increased anxiety-related behavior; changes in novel object exploration and impaired delay-dependent object and object-place recognition ( Frisch et al., 2005 ; Zlomuzica et al., 2012 ). The behavioral phenotypes of Cx36 and Cx45 deficient mice suggest altered learning and memory performance (including behavioral and emotional habituation to normal environments in Cx36-deficient mice) that might be related to changes in theta and gamma oscillations and conscious cognitive information processing.

Given that intercellular communication via gap junctions shows activity-dependent plasticity and might contribute to contribute to the formation of functional cell assemblies ( Traub et al., 2020 ), it would be extremely interesting to find a way that would allow the monitoring of the cellular coupling/uncoupling status of cells in the prefrontal cortex, before and after behavioral discontinuities during learning performance and problem solving. It is tempting to speculate that the reorganization of information and restructuration of the problem faced, might be associated with the sudden coupling or uncoupling of neuronal circuits and/or networks via gap junctions that ultimately leads out of an impasse or cognitive dead end and towards a sudden insight.

The platform theory of conscious cognitive information processing holds that the conscious experience of a mental representation requires working memory. The neuronal mechanism underlying working memory is thought to be a sustained reverberatory neuronal activity in the neural circuit that contains the mental representation. This maintained mental representation can then be used and manipulated by the central executive/online platform ( Dere et al., 2021 ). The platform theory of conscious cognitive information processing further proposes that the reverberation in the neuronal circuits could be maintained by the help of gap junction channels between neurons, astrocytes, as well as undocked connexin hemichannels ( Rash et al., 2001 ; Nagy et al., 2004 ; Dere et al., 2021 ). Successive neurons in the neural circuit can be either directly coupled via gap junctions or might be both coupled to the same astrocyte. Continuous reverberation and sustained excitability in neuronal circuits depends on the fast redistribution of ions and metabolites between the cytoplasm and extracellular space. These adjustments might be ensured by connexin hemichannels ( Dere et al., 2021 ). Connexin hemichannels are undocked connexin channels in the plasma membrane that are involved in paracrine communication. They provide a conduit between the intracellular and extracellular space, allowing the passage and exchange of ions and metabolites between the cytosol and extracellular milieu ( Dere and Zlomuzica, 2012 ; Dere, 2013 ). Therefore, gap junctions and connexin hemichannels have biophysical characteristics that are well suited to support working memory through fast and continuous propagation of neuronal depolarization between neurons in the neuronal circuit (probably mediated via gap junction channels with astrocytes) and via the preservation of excitability of the individual neurons for prolonged periods through the regulation of intra and extracellular ion homeostasis (ensured by the unopposed hemichannels; Dere et al., 2021 ).

The empirical evidence and theories presented so far can be summarized into 10 basic assumptions which might be helpful for future research into the neurophysiological mechanisms of conscious cognitive information processing.

1. Insight is the endpoint of conscious cognitive information processing, and, at the behavioral level, leads to a sudden step-like discontinuity in learning or problem solving performance.

2. Insight is based on a creative reorganization of mental representations of task-relevant information and the restructuration of the problem to overcome an impasse.

3. The reorganization and restructuration process requires the maintenance of task-relevant information in working memory and the operation of executive functions on these mental representations (ensured the central executive on the online platform).

4. The reorganization and restructuration processes, which precede sudden insight are correlated with an increase in the power of gamma oscillations in the prefrontal cortex, anterior superior temporal gyrus, parieto-occipital regions.

5. Gap junctions and connexin hemichannels have been implicated in cortical network oscillations and might be involved in the oscillation changes that are associated with conscious cognitive processing and insight.

6. On a subjective level, insight is perceived as a sudden comprehension, realization, or creative problem solution.

7. The subjective feeling of relief and the emotional arousal experienced by the sudden emergence of the solution might be accompanied by a reward signal in the mesolimbic dopamine system.

8. Insight is associated with strong emotional arousal that triggers episodic memory formation creating a memory for the insight event that includes context and content (solution path) information.

9. The generated insight memory is the basis of the persistent improvement in task or problem performance that is recollected whenever the same or similar problems are encountered.

10. Discontinuities in learning or problem solving performance might be used as time-tags to investigate the implication of gap junction channels and hemichannels in conscious cognitive processing.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ED: Conceptualization, Methodology, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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Alev, C., Urschel, S., Sonntag, S., Zoidl, G., Fort, A. G., Höher, T., et al. (2008). The neuronal connexin-36 interacts with and is phosphorylated by CaMKII in a way similar to CaMKII interaction with glutamate receptors. Proc. Natl. Acad. Sci. USA 105, 20964–20969. doi: 10.1073/pnas.0805408105

PubMed Abstract | Crossref Full Text | Google Scholar

Alkire, M. T., Hudetz, A. G., and Tononi, G. (2008). Consciousness and anesthesia. Science 322, 876–880. doi: 10.1126/science.1149213

Allen, K., Fuchs, E. C., Jaschonek, H., Bannerman, D. M., and Monyer, H. (2011). Gap junctions between interneurons are required for normal spatial coding in the hippocampus and short-term spatial memory. J. Neurosci. 31, 6542–6552. doi: 10.1523/JNEUROSCI.6512-10.2011

Baars, B. J., Geld, N., and Kozma, R. (2021). Global workspace theory (GWT) and prefrontal cortex: recent developments. Front. Psychol. 12:749868. doi: 10.3389/fpsyg.2021.749868

Crossref Full Text | Google Scholar

Bird, C. D., and Emery, N. J. (2009). Insightful problem solving and creative tool modification by captive nontool-using rooks. Proc. Natl. Acad. Sci. USA 106, 10370–10375. doi: 10.1073/pnas.0901008106

Bob, P., Pec, O., Mishara, A. L., Touskova, T., and Lysaker, P. H. (2016). Conscious brain, metacognition and schizophrenia. Int. J. Psychophysiol. 105, 1–8. doi: 10.1016/j.ijpsycho.2016.05.003

Bowden, E. M., Jung-Beeman, M., Fleck, J., and Kounios, J. (2005). New approaches to demystifying insight. Trends Cogn. Sci. 9, 322–328. doi: 10.1016/j.tics.2005.05.012

Breeden, P., Dere, D., Zlomuzica, A., and Dere, E. (2016). The mental time travel continuum: on the architecture, capacity, versatility and extension of the mental bridge into the past and future. Rev. Neurosci. 27, 421–434. doi: 10.1515/revneuro-2015-0053

Buhl, D. L., Harris, K. D., Hormuzdi, S. G., Monyer, H., and Buzsáki, G. (2003). Selective impairment of hippocampal gamma oscillations in connexin-36 knock-out mouse in vivo. J. Neurosci. 23, 1013–1018. doi: 10.1523/JNEUROSCI.23-03-01013.2003

Chenot, Q., Hamery, C., Truninger, M., Langer, N., De Boissezon, X., and Scannella, S. (2024). Investigating the relationship between resting-state EEG microstates and executive functions: a null finding. Cortex 178, 1–17. doi: 10.1016/j.cortex.2024.05.019

Coulon, P., and Landisman, C. E. (2017). The potential role of gap junctional plasticity in the regulation of state. Neuron 93, 1275–1295. doi: 10.1016/j.neuron.2017.02.041

Csorba, B. A., Krause, M. R., Zanos, T. P., and Pack, C. C. (2022). Long-range cortical synchronization supports abrupt visual learning. Curr. Biol. 32, 2467–2479.e4. doi: 10.1016/j.cub.2022.04.029

Cunningham, M. O., Whittington, M. A., Bibbig, A., Roopun, A., LeBeau, F. E. N., Vogt, A., et al. (2004). A role for fast rhythmic bursting neurons in cortical gamma oscillations in vitro. Proc. Natl. Acad. Sci. USA 101, 7152–7157. doi: 10.1073/pnas.0402060101

Cutting, N., Apperly, I. A., Chappell, J., and Beck, S. R. (2014). The puzzling difficulty of tool innovation: why can’t children piece their knowledge together? J. Exp. Child Psychol. 125, 110–117. doi: 10.1016/j.jecp.2013.11.010

Defeyter, M. A., and German, T. P. (2003). Acquiring an understanding of design: evidence from children's insight problem solving. Cognition 89, 133–155. doi: 10.1016/S0010-0277(03)00098-2

Dere, E. (2013). Gap junctions in the brain: physiological and pathological roles . San Diego, CA: Academic Press.

Google Scholar

Dere, E., Kart-Teke, E., Huston, J. P., and De Souza Silva, M. A. (2006). The case for episodic memory in animals. Neurosci. Biobehav. Rev. 30, 1206–1224. doi: 10.1016/j.neubiorev.2006.09.005

Dere, E., Pause, B., and Pietrowsky, R. (2010). Emotion and episodic memory in neuropsychiatric disorders. Behav. Brain Res. 215, 162–171. doi: 10.1016/j.bbr.2010.03.017

Dere, E., and Zlomuzica, A. (2012). The role of gap junctions in the brain in health and disease. Neurosci. Biobehav. Rev. 36, 206–217. doi: 10.1016/j.neubiorev.2011.05.015

Dere, E., and Zlomuzica, A. (2023). Editorial: special issue on the neuroscience of consciousness. Behav. Brain Res. 437:114166. doi: 10.1016/j.bbr.2022.114166

Dere, D., Zlomuzica, A., and Dere, E. (2019). Fellow travellers in cognitive evolution: co-evolution of working memory and mental time travel? Neurosci. Biobehav. Rev. 105, 94–105. doi: 10.1016/j.neubiorev.2019.07.016

Dere, D., Zlomuzica, A., and Dere, E. (2021). Channels to consciousness: a possible role of gap junctions in consciousness. Rev. Neurosci. 32, 101–129. doi: 10.1515/revneuro-2020-0012

Dere, E., Zlomuzica, A., Huston, J. P., and De Souza Silva, M. A. (2008). “Chapter 2.2: animal episodic memory” in Handbook of episodic memory . eds. E. Dere, A. Easton, L. Nadel, and J. P. Huston, vol. 18 (Amsterdam: Elsevier Science), 155–184.

Draguhn, A., Traub, R. D., Schmitz, D., and Jefferys, J. G. (1998). Electrical coupling underlies high-frequency oscillations in the hippocampus in vitro. Nature 394, 189–192. doi: 10.1038/28184

Durstewitz, D., Vittoz, N. M., Floresco, S. B., and Seamans, J. K. (2010). Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66, 438–448. doi: 10.1016/j.neuron.2010.03.029

Ebel, S., Völter, C., and Call, J. (2020). Prior experience mediates the usage of food items as tools in great apes (pan paniscus, pan troglodytes, Gorilla gorilla, and Pongo abelii ). J. Comp. Psychol. 135, 64–73. doi: 10.1037/com0000236

Edlow, B. L., Claassen, J., Schiff, N. D., and Greer, D. M. (2021). Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat. Rev. Neurol. 17, 135–156. doi: 10.1038/s41582-020-00428-x

Frisch, C., De Souza-Silva, M. A., Söhl, G., Güldenagel, M., Willecke, K., Huston, J. P., et al. (2005). Stimulus complexity dependent memory impairment and changes in motor performance after deletion of the neuronal gap junction protein connexin36 in mice. Behav. Brain Res. 157, 177–185. doi: 10.1016/j.bbr.2004.06.023

Gallistel, C. R., Fairhurst, S., and Balsam, P. (2004). The learning curve: implications of a quantitative analysis. Proc. Natl. Acad. Sci. USA 101, 13124–13131. doi: 10.1073/pnas.0404965101

Hebb, D. O. (1949). The organization of behaviour: A neuropsychological theory . New York: Wiley.

Helson, H. (1927). Insight in the white rat. J. Exp. Psychol. 10, 378–396. doi: 10.1037/h0070577

Hormuzdi, S. G., Pais, I., LeBeau, F. E., Towers, S. K., Rozov, A., Buhl, E. H., et al. (2001). Impaired electrical signaling disrupts gamma frequency oscillations in connexin 36-deficient mice. Neuron 31, 487–495. doi: 10.1016/S0896-6273(01)00387-7

Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol. 2:E97. doi: 10.1371/journal.pbio.0020097

Kinugawa, K., Schumm, S., Pollina, M., Depre, M., Jungbluth, C., Doulazmi, M., et al. (2013). Aging-related episodic memory decline: are emotions the key? Front. Behav. Neurosci. 7:2. doi: 10.3389/fnbeh.2013.00002

Knoblich, G., Ohlsson, S., and Raney, G. E. (2001). An eye movement study of insight problem solving. Mem. Cogn. 29, 1000–1009. doi: 10.3758/BF03195762

Koch, C., Massimini, M., Boly, M., and Tononi, G. (2016). Neural correlates of consciousness: progress and problems. Nat. Rev. Neurosci. 17, 307–321. doi: 10.1038/nrn.2016.22

Kocsis, B., Martínez-Bellver, S., Fiáth, R., Domonkos, A., Sviatkó, K., Schlingloff, D., et al. (2022). Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation. Cell Rep. 40:111149. doi: 10.1016/j.celrep.2022.111149

Köhler, W. (1917). Intelligenzprüfungen an Anthropoiden . Berlin: Royal Prussian Society of Sciences.

Köhler, W. (1925). The mentality of apes . Oxford, England: Harcourt, Brace.

Kounios, J., and Beeman, M. (2014). The cognitive neuroscience of insight. Annu. Rev. Psychol. 65, 71–93. doi: 10.1146/annurev-psych-010213-115154

Landisman, C. E., Long, M. A., Beierlein, M., Deans, M. R., Paul, D. L., and Connors, B. W. (2002). Electrical synapses in the thalamic reticular nucleus. J. Neurosci. 22, 1002–1009. doi: 10.1523/JNEUROSCI.22-03-01002.2002

Laumer, I. B., Call, J., Bugnyar, T., and Auersperg, A. M. I. (2018). Spontaneous innovation of hook-bending and unbending in orangutans ( Pongo abelii ). Sci. Rep. 8:16518. doi: 10.1038/s41598-018-34607-0

Laureys, S., Celesia, G. G., Cohadon, F., Lavrijsen, J., León-Carrión, J., Sannita, W. G., et al. (2010). European task force on disorders of consciousness. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. 8:68. doi: 10.1186/1741-7015-8-68

Le Bon-Jego, M., and Yuste, R. (2007). Persistently active, pacemaker-like neurons in neocortex. Front. Neurosci. 1, 123–129. doi: 10.3389/neuro.01.1.1.009.2007

Li, H., Zhang, X., Sun, X., Dong, L., Lu, H., Yue, S., et al. (2023). Functional networks in prolonged disorders of consciousness. Front. Neurosci. 17:1113695. doi: 10.3389/fnins.2023.1113695

Linsambarth, S., Carvajal, F. J., Moraga-Amaro, R., Mendez, L., Tamburini, G., Jimenez, I., et al. (2022). Astroglial gliotransmitters released via Cx43 hemichannels regulate NMDAR-dependent transmission and short-term fear memory in the basolateral amygdala. FASEB J. 36:e22134. doi: 10.1096/fj.202100798RR

MacGregor, J. N., Ormerod, T. C., and Chronicle, E. P. (2001). Information processing and insight: a process model of performance on the nine-dot and related problems. J. Exp. Psychol. Learn. Mem. Cogn. 27, 176–201. doi: 10.1037/0278-7393.27.1.176

Maciunas, K., Snipas, M., Paulauskas, N., and Bukauskas, F. F. (2016). Reverberation of excitation in neuronal networks interconnected through voltage-gated gap junction channels. J. Gen. Physiol. 147, 273–288. doi: 10.1085/jgp.201511488

Mäkinen, M. E., Ylä-Outinen, L., and Narkilahti, S. (2018). GABA and gap junctions in the development of synchronized activity in human pluripotent stem cell-derived neural networks. Front. Cell. Neurosci. 12:56. doi: 10.3389/fncel.2018.00056

Martin, S. (2023). Why using “consciousness” in psychotherapy? Insight, metacognition and self-consciousness. New Ideas Psychol. 70:101015. doi: 10.1016/j.newideapsych.2023.101015

McCaffrey, T. (2012). Innovation relies on the obscure: a key to overcoming the classic problem of functional fixedness. Psychol. Sci. 23, 215–218. doi: 10.1177/0956797611429580

Meador, K. J., Ray, P. G., Echauz, J. R., Loring, D. W., and Vachtsevanos, G. J. (2002). Gamma coherence and conscious perception. Neurology 59, 847–854. doi: 10.1212/WNL.59.6.847

Meier, C., Petrasch-Parwez, E., Habbes, H. W., Teubner, B., Güldenagel, M., Degen, J., et al. (2002). Immunohistochemical detection of the neuronal connexin36 in the mouse central nervous system in comparison to connexin36-deficient tissues. Histochem. Cell Biol. 117, 461–471. doi: 10.1007/s00418-002-0417-z

Nagy, J. I., Dudek, F. E., and Rash, J. E. (2004). Update on connexins and gap junctions in neurons and glia in the mammalian nervous system. Brain Res. Brain Res. Rev. 47, 191–215. doi: 10.1016/j.brainresrev.2004.05.005

Northoff, G. (2017). "paradox of slow frequencies"—are slow frequencies in upper cortical layers a neural predisposition of the level/state of consciousness (NPC)? Conscious. Cogn. 54, 20–35. doi: 10.1016/j.concog.2017.03.006

Panagiotaropoulos, T. I., Deco, G., Kapoor, V., and Logothetis, N. K. (2012). Neuronal discharges and gamma oscillations explicitly reflect visual consciousness in the lateral prefrontal cortex. Neuron 74, 924–935. doi: 10.1016/j.neuron.2012.04.013

Panda, R., Thibaut, A., Lopez-Gonzalez, A., Escrichs, A., Bahri, M. A., Hillebrand, A., et al. (2022). Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 11:e77462. doi: 10.7554/eLife.77462

Pause, B. M., Zlomuzica, A., Kinugawa, K., Mariani, J., Pietrowsky, R., and Dere, E. (2013). Perspectives on episodic-like and episodic memory. Front. Behav. Neurosci. 7:33. doi: 10.3389/fnbeh.2013.00033

Qiu, J., Li, H., Yang, D., Luo, Y., Li, Y., Wu, Z., et al. (2008). The neural basis of insight problem solving: an event-related potential study. Brain Cogn. 68, 100–106. doi: 10.1016/j.bandc.2008.03.004

Rash, J. E., Yasumura, T., Dudek, F. E., and Nagy, J. I. (2001). Cell-specific expression of connexins and evidence of restricted gap junctional coupling between glial cells and between neurons. J. Neurosci. 21, 1983–2000. doi: 10.1523/JNEUROSCI.21-06-01983.2001

Reddy, G. (2022). A reinforcement-based mechanism for discontinuous learning. Proc. Natl. Acad. Sci. USA 119:e2215352119. doi: 10.1073/pnas.2215352119

Rosen, A., and Reiner, M. (2017). Right frontal gamma and beta band enhancement while solving a spatial puzzle with insight. Int. J. Psychophysiol. 122, 50–55. doi: 10.1016/j.ijpsycho.2016.09.008

Rosenberg, M., Zhang, T., Perona, P., and Meister, M. (2021). Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration. eLife 10:e66175. doi: 10.7554/eLife.66175

Sandkühler, S., and Bhattacharya, J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS One 3:e1459. doi: 10.1371/journal.pone.0001459

Schmitz, D., Schuchmann, S., Fisahn, A., Draguhn, A., Buhl, E. H., Petrasch-Parwez, E., et al. (2001). Axo-axonal coupling. A novel mechanism for ultrafast neuronal communication. Neuron 31, 831–840. doi: 10.1016/S0896-6273(01)00410-X

Schultz, W., Dayan, P., and Montague, P. R. (1997). A neural substrate of prediction and reward. Science 275, 1593–1599. doi: 10.1126/science.275.5306.1593

Shen, W., Tong, Y., Yuan, Y., Zhan, H., Liu, C., Luo, J., et al. (2017). Feeling the insight: uncovering somatic markers of the “aha” experience. Appl. Psychophysiol. Biofeedback 43, 13–21. doi: 10.1007/s10484-017-9381-1

Singer, W. (1998). Consciousness and the structure of neuronal representations. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 353, 1829–1840. doi: 10.1098/rstb.1998.0335

Stefanelli, R. (2023). Theories of consciousness and psychiatric disorders—a comparative analysis. Neurosci. Biobehav. Rev. 152:105204. doi: 10.1016/j.neubiorev.2023.105204

Tao, X. D., Liu, Z. R., Zhang, Y. Q., and Zhang, X. H. (2021). Connexin43 hemichannels contribute to working memory and excitatory synaptic transmission of pyramidal neurons in the prefrontal cortex of rats. Life Sci. 286:120049. doi: 10.1016/j.lfs.2021.120049

Thorndike, E. L. (1911). Animal Intelligence: Experimental Studies . Lewiston, NY, United States: Macmillan Press.

Thorpe, W. H. (1956). Learning and instinct in animals . Cambridge, MA: Harvard University Press.

Tian, Y., Margulies, D. S., Breakspear, M., and Zalesky, A. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432. doi: 10.1038/s41593-020-00711-6

Tik, M., Sladky, R., Luft, C. D. B., Willinger, D., Hoffmann, A., Banissy, M. J., et al. (2018). Ultra-high-field fMRI insights on insight: neural correlates of the Aha!-moment. Hum. Brain Mapp. 39, 3241–3252. doi: 10.1002/hbm.24073

Tolman, E. C. (1948). Cognitive maps in rats and men. Psychol. Rev. 55, 189–208. doi: 10.1037/h0061626

Tononi, G., Boly, M., Massimini, M., and Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17, 450–461. doi: 10.1038/nrn.2016.44

Traub, R. D., and Whittington, M. A. (2010). Cortical oscillations in health and disease . New York: Oxford Univ. Press.

Traub, R. D., and Whittington, M. A. (2022). Processing of cell assemblies in the lateral entorhinal cortex. Rev. Neurosci. 33, 829–847. doi: 10.1515/revneuro-2022-0011

Traub, R. D., Whittington, M. A., Gutiérrez, R., and Draguhn, A. (2018). Electrical coupling between hippocampal neurons: contrasting roles of principal cell gap junctions and interneuron gap junctions. Cell Tissue Res. 373, 671–691. doi: 10.1007/s00441-018-2881-3

Traub, R. D., Whittington, M. A., Maier, N., Schmitz, D., and Nagy, J. I. (2020). Could electrical coupling contribute to the formation of cell assemblies? Rev. Neurosci. 31, 121–141. doi: 10.1515/revneuro-2019-0059

Tseng, P., Chang, Y. T., Chang, C. F., Liang, W. K., and Juan, C. H. (2016). The critical role of phase difference in gamma oscillation within the temporoparietal network for binding visual working memory. Sci. Rep. 6:32138. doi: 10.1038/srep32138

Valencia, A. L., and Froese, T. (2020). What binds us? Inter-brain neural synchronization and its implications for theories of human consciousness. Neurosci. Conscious 2020:niaa010. doi: 10.1093/nc/niaa010

Van Steenburgh, J. J., Fleck, J. I., Beeman, M., and Kounios, J. (2012). Insight. The Oxford handbook of thinking and reasoning . United Kingdom: Oxford University Press.

Walrave, L., Vinken, M., Albertini, G., De Bundel, D., Leybaert, L., and Smolders, I. J. (2016). Inhibition of Connexin43 Hemichannels impairs spatial short-term memory without affecting spatial working memory. Front. Cell. Neurosci. 10:288. doi: 10.3389/fncel.2016.00288

Weir, A. A. S. (2002). Shaping of hooks in new Caledonian crows. Science 297:981. doi: 10.1126/science.1073433

Weisberg, R. W. (2015). Toward an integrated theory of insight in problem solving. Think. Reason. 21, 5–39. doi: 10.1080/13546783.2014.886625

Wittner, L., and Miles, R. (2007). Factors defining a pacemaker region for synchrony in the hippocampus. J. Physiol. 584, 867–883. doi: 10.1113/jphysiol.2007.138131

Yang, X. D., Korn, H., and Faber, D. S. (1990). Long-term potentiation of electrotonic coupling at mixed synapses. Nature 348, 542–545. doi: 10.1038/348542a0

Zlomuzica, A., and Dere, E. (2022). Towards an animal model of consciousness based on the platform theory. Behav. Brain Res. 419:113695. doi: 10.1016/j.bbr.2021.113695

Zlomuzica, A., Plank, L., and Dere, E. (2022). A new path to mental disorders: through gap junction channels and hemichannels. Neurosci. Biobehav. Rev. 142:104877. doi: 10.1016/j.neubiorev.2022.104877

Zlomuzica, A., Plank, L., Kodzaga, I., and Dere, E. (2023). A fatal alliance: glial connexins, myelin pathology and mental disorders. J. Psychiatr. Res. 159, 97–115. doi: 10.1016/j.jpsychires.2023.01.008

Zlomuzica, A., Reichinnek, S., Maxeiner, S., Both, M., May, E., Wörsdörfer, P., et al. (2010). Deletion of connexin45 in mouse neurons disrupts one-trial object recognition and alters kainate-induced g-oscillations in the hippocampus. Physiol. Behav. 101, 245–253. doi: 10.1016/j.physbeh.2010.05.007

Zlomuzica, A., Viggiano, D., Degen, J., Binder, S., Ruocco, L. A., Sadile, A. G., et al. (2012). Behavioral alterations and changes in Ca/calmodulin kinase II levels in the striatum of connexin36 deficient mice. Behav. Brain Res. 226, 293–300. doi: 10.1016/j.bbr.2011.08.028

Keywords: incremental learning, discontinuous performance, conscious cognitive information processing, platform theory of conscious cognitive information processing, animal consciousness, behavioral correlates of consciousness

Citation: Dere E (2024) Insights into conscious cognitive information processing. Front. Behav. Neurosci . 18:1443161. doi: 10.3389/fnbeh.2024.1443161

Received: 03 June 2024; Accepted: 15 July 2024; Published: 25 July 2024.

Reviewed by:

Copyright © 2024 Dere. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ekrem Dere, [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Effect of the Embodied Guidance in the Insight Problem Solving: An Eye Movement Study

Affiliations.

• 1 Department of Psychology, School of Education, Guangzhou University, Guangzhou, China.
• 2 Jiangcun Primary School, Guangzhou, China.
• 3 Shunde Experiment Middle School, Foshan, China.
• PMID: 30534097
• PMCID: PMC6275308
• DOI: 10.3389/fpsyg.2018.02257

Insight is an important cognitive process in creative thinking. The present research applied embodied cognitive perspective to explore the effect of embodied guidance on insight problem solving and its underlying mechanisms by two experiments. Experiment 1 used the matchstick arithmetic problem to explore the role of embodied gestures guidance in problem solving. The results showed that the embodied gestures facilitate the participants' performance. Experiment 2 investigated how embodied attention guidance affects insight problem solving. The results showed that participants performed better in prototypical guidance condition. Experiment 2a adopted the Duncker's radiation problem to explore how embodied behavior and prototypical guidance influence problem solving by attention tracing techniques. Experiment 2b aimed to further examine whether implicit attention transfer was the real cause which resulted in participants over-performing in prototypical guidance condition in Experiment 2a. The results demonstrated that overt physical motion was unnecessary for individuals to experience the benefits of embodied guidance in problem solving, which supported the reciprocal relation hypothesis of saccades and attention. In addition, the questionnaire completed after experiments showed that participants did not realize the relation between guidance and insight problem solving. Taken together, the current study provided further evidence for that embodied gesture and embodied attention both facilitated the insight problem solving and the facilitation is implicit.

Keywords: attention guidance; creativity; embodied effect; eye movement track; insight problem solving.

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Matchstick arithmetic problems type diagram.

The detailed procedures of Experiment…

The detailed procedures of Experiment 1.

Accuracy and reaction time of…

Accuracy and reaction time of the participants under different guidance conditions (error bars:…

Ratio of accuracy under different…

Ratio of accuracy under different guidance condition (error bars: 95% confidence interval).

A schematic diagram of the…

A schematic diagram of the Duncker’s radiation problem.

Digital tracking task of prototypical…

Digital tracking task of prototypical guidance condition.

Digital tracking task of non-prototypical…

Digital tracking task of non-prototypical guidance condition.

The procedures of Experiment 2a.

The digital tracking task of…

The digital tracking task of attention-tracing and attention-transfer condition.

The digital tracking task of attention-fixation condition.

The average saccade counts of…

The average saccade counts of two phases under different guidance conditions (error bars:…

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• Using another's gaze as an explicit aid to insight problem solving. Litchfield D, Ball LJ. Litchfield D, et al. Q J Exp Psychol (Hove). 2011 Apr;64(4):649-56. doi: 10.1080/17470218.2011.558628. Epub 2011 Feb 22. Q J Exp Psychol (Hove). 2011. PMID: 21347990 Clinical Trial.
• Action's Influence on Thought: The Case of Gesture. Goldin-Meadow S, Beilock SL. Goldin-Meadow S, et al. Perspect Psychol Sci. 2010 Nov;5(6):664-74. doi: 10.1177/1745691610388764. Perspect Psychol Sci. 2010. PMID: 21572548 Free PMC article. Review.
• Looking for Creativity: Where Do We Look When We Look for New Ideas? Salvi C, Bowden EM. Salvi C, et al. Front Psychol. 2016 Feb 15;7:161. doi: 10.3389/fpsyg.2016.00161. eCollection 2016. Front Psychol. 2016. PMID: 26913018 Free PMC article. Review.
• Unconscious processing of prototype heuristics in scientific innovation problem-solving. Ling Y, Tan L, Zhang L, Cao G. Ling Y, et al. Front Psychol. 2023 Feb 23;14:1056045. doi: 10.3389/fpsyg.2023.1056045. eCollection 2023. Front Psychol. 2023. PMID: 36910757 Free PMC article.
• The Effect of Working Memory Updating Ability on Spatial Insight Problem Solving: Evidence From Behavior and Eye Movement Studies. Xing Q, Lu Z, Hu J. Xing Q, et al. Front Psychol. 2019 Apr 24;10:927. doi: 10.3389/fpsyg.2019.00927. eCollection 2019. Front Psychol. 2019. PMID: 31068884 Free PMC article.
• Adam H., Galinsky A. D. (2012). Enclothed cognition. J. Exp. Soc. Psychol. 48 918–925. 10.1016/j.jesp.2012.02.008 - DOI
• Ball L. J., Litchfield D. (2017). Interactivity and Embodied Cues in problem Solving, Learning and Insight: Further Contributions to a “Theory of Hints”. Cognition Beyond the Brain. London: Springer, 223–239. 10.1007/978-1-4471-5125-8_12 - DOI
• Beilock S. L., Goldin-Meadow S. (2010). Gesture changes thought by grounding it in action. Psychol. Sci. 21 1605–1610. 10.1177/0956797610385353 - DOI - PMC - PubMed
• Belopolsky A. V., Theeuwes J. (2009). When are attention and saccade preparation dissociated? Psychol. Sci. 20 1340–1347. 10.1111/j.1467-9280.2009.02445.x - DOI - PubMed
• Bowden E. M., Jung-Beeman M. (2007). Methods for investigating the neural components of insight. Methods 42 87–99. 10.1016/j.ymeth.2006.11.007 - DOI - PubMed

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Frog Hop Number Line

Action Based Learning Frog Hop Number Line

Get kids hopping with the ABL Frog hop number mat! Hop from numbers 0-20!  These concepts aid the brain in following the flow of words, sequencing patterns in math and reading, solving problems, and sorting information. Available in Mat or Wall Mount.

• Action Based Learning Classroom and Lab Activity Guide - with progressions and activities easily adapted for all levels of elementary students
• First course on ABL Online Academy for free! [ ABL-321 Activities in the Classroom Math]  provides activities specifically for Elementary Level Math. This self paced course demonstrates the core  activities   that are the most critical and the most effective ways to improve math comprehension at the elementary level, according to science! Become an expert on ABL math strategies, how to anchor learning, close learning gaps, and improve student achievement through purposeful movement!   

ABC Pathways Mat Package

Students walk the alphabet, A-Z to learn letters kinesthetically!

Movement Activities on the mat are endless, allow the student to learn the letters of the alphabet kinesthetically. Walking the ABC Pathways mat, the student is able to kinesthetically trace each letter of the alphabet, while strengthening gross motor skills, eye tracking, and cross lateralization - 3 foundations critical to learning.

The ABC Pathways mat is designed to prepare our students brains to be ready for learning in the classroom.  The patented design works to relax, and refocus the brain, giving the child a chance to strengthen their self regulation skills, and reset to a peaceful state. As research shows, a child acting out, is a child calling out for help to cope with their current emotional state. Walking the ABC Pathways figure 8 pattern, creates a meditative rhythm that works to calm the brain waves and soothe the child.  The movement is designed to help the child reset and refocus.  ABC Pathways mat is used in Action Based Learning Labs, Classrooms, Hallways, and Libraries to give the student an opportunity to practice self regulation on the patented ABC Pathways Figure 8 symbol.

#1 HIGHEST RATED Action Based Learning Tool! Includes:

• Activity Guide & Instructional Manual
• XL Double Sided Teacher Key Card (11 x 17 Laminate Card)
• 24 Student Cue Cards (8.5x11 Never Tear Cards)

ABL Learn N Move Mat

Action Based Learning Learn N Move Mat provides kinesthetic math  activities for elementary students. Students participate in active math by learning numbers, addition, subtraction, multiplication, and division with the learn n move mat. Sign language and spanish is also printed on the mat! Kinesthetic Math Activity guide included. 

Dimensions: 60x60    |     Indoor/Outdoor Use | Weight 14 lbs

Heavy Duty Commercial Grade G floor material made for high frequency use

Portable  | Easy to Clean  |  MADE IN THE USA | Expect 10+ years of use!

Letter Learning Wall Station

Action Based Learning Letter Learning Wall Station

Choices are endless with the letter learning wall station! Students can use the kinesthetic letter board to review spelling words, work with a partner to spell words (each partner spells the word one letter at a time taking turns), spell answers to questions, spell vocabulary words when given a definition, etc

These concepts aid the brain in encoding the stroke of each symbol of letters and numbers, letter recognition, letter formation, following words from left to right, focusing on reading for longer periods of time.

Dimensions: 48x96

Geo Color Hop Mat

Children and students can learn their colors and shapes by hopping on our Geo Color Hop Mat!

Dimensions: 60x60

ABL Keyboard Mat

The mat uses eye tracking skills which are important for reading and copying information from the board to paper. It also practices using peripheral vision to increase eye fitness. Children can learn their numbers, letters, punctuation, and more!

⭐️ Let’s learn the Spanish Alphabet today. Foundation 11:Problem Solving. Embodied cognition. #SpanishLab Action Based Learning @Kidsfit_ABL pic.twitter.com/A2ooQaXapr — Mrs. Tamaura (@MrsErikaTamaura) November 16, 2022

ABL Math Telephone Mat

Kinesthetic Math and Telephone Mat

Learn numbers kinesthetically!  Use numbers 0-9 to learn cardinal direction, and simple math equations. Now made with 100% polyvinyl G-Floor vinyl mats. Can be used both inside and outdoors. 

The Math Telephone Mat encourages motor development, and improves:  Hand-eye Coordination, Visual Motor Skills, Attention and Focus, Gross Motor Skills, Fine Motor Development, Executive Functioning Skills, & Motor Planning