problem solving cognitive training

Cognitive Behavioral Therapy

Solving problems the cognitive-behavioral way, problem solving is another part of behavioral therapy..

Posted February 2, 2022 | Reviewed by Ekua Hagan

• What Is Cognitive Behavioral Therapy?
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• Problem-solving is one technique used on the behavioral side of cognitive-behavioral therapy.
• The problem-solving technique is an iterative, five-step process that requires one to identify the problem and test different solutions.
• The technique differs from ad-hoc problem-solving in its suspension of judgment and evaluation of each solution.

As I have mentioned in previous posts, cognitive behavioral therapy is more than challenging negative, automatic thoughts. There is a whole behavioral piece of this therapy that focuses on what people do and how to change their actions to support their mental health. In this post, I’ll talk about the problem-solving technique from cognitive behavioral therapy and what makes it unique.

The problem-solving technique

While there are many different variations of this technique, I am going to describe the version I typically use, and which includes the main components of the technique:

The first step is to clearly define the problem. Sometimes, this includes answering a series of questions to make sure the problem is described in detail. Sometimes, the client is able to define the problem pretty clearly on their own. Sometimes, a discussion is needed to clearly outline the problem.

The next step is generating solutions without judgment. The "without judgment" part is crucial: Often when people are solving problems on their own, they will reject each potential solution as soon as they or someone else suggests it. This can lead to feeling helpless and also discarding solutions that would work.

The third step is evaluating the advantages and disadvantages of each solution. This is the step where judgment comes back.

Fourth, the client picks the most feasible solution that is most likely to work and they try it out.

The fifth step is evaluating whether the chosen solution worked, and if not, going back to step two or three to find another option. For step five, enough time has to pass for the solution to have made a difference.

This process is iterative, meaning the client and therapist always go back to the beginning to make sure the problem is resolved and if not, identify what needs to change.

Advantages of the problem-solving technique

The problem-solving technique might differ from ad hoc problem-solving in several ways. The most obvious is the suspension of judgment when coming up with solutions. We sometimes need to withhold judgment and see the solution (or problem) from a different perspective. Deliberately deciding not to judge solutions until later can help trigger that mindset change.

Another difference is the explicit evaluation of whether the solution worked. When people usually try to solve problems, they don’t go back and check whether the solution worked. It’s only if something goes very wrong that they try again. The problem-solving technique specifically includes evaluating the solution.

Lastly, the problem-solving technique starts with a specific definition of the problem instead of just jumping to solutions. To figure out where you are going, you have to know where you are.

One benefit of the cognitive behavioral therapy approach is the behavioral side. The behavioral part of therapy is a wide umbrella that includes problem-solving techniques among other techniques. Accessing multiple techniques means one is more likely to address the client’s main concern.

Salene M. W. Jones, Ph.D., is a clinical psychologist in Washington State.

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10 Best Problem-Solving Therapy Worksheets & Activities

Cognitive science tells us that we regularly face not only well-defined problems but, importantly, many that are ill defined (Eysenck & Keane, 2015).

Sometimes, we find ourselves unable to overcome our daily problems or the inevitable (though hopefully infrequent) life traumas we face.

Problem-Solving Therapy aims to reduce the incidence and impact of mental health disorders and improve wellbeing by helping clients face life’s difficulties (Dobson, 2011).

This article introduces Problem-Solving Therapy and offers techniques, activities, and worksheets that mental health professionals can use with clients.

Before you continue, we thought you might like to download our three Positive Psychology Exercises for free . These science-based exercises explore fundamental aspects of positive psychology, including strengths, values, and self-compassion, and will give you the tools to enhance the wellbeing of your clients, students, or employees.

This Article Contains:

What is problem-solving therapy, 14 steps for problem-solving therapy, 3 best interventions and techniques, 7 activities and worksheets for your session, fascinating books on the topic, resources from positivepsychology.com, a take-home message.

Problem-Solving Therapy assumes that mental disorders arise in response to ineffective or maladaptive coping. By adopting a more realistic and optimistic view of coping, individuals can understand the role of emotions and develop actions to reduce distress and maintain mental wellbeing (Nezu & Nezu, 2009).

“Problem-solving therapy (PST) is a psychosocial intervention, generally considered to be under a cognitive-behavioral umbrella” (Nezu, Nezu, & D’Zurilla, 2013, p. ix). It aims to encourage the client to cope better with day-to-day problems and traumatic events and reduce their impact on mental and physical wellbeing.

Clinical research, counseling, and health psychology have shown PST to be highly effective in clients of all ages, ranging from children to the elderly, across multiple clinical settings, including schizophrenia, stress, and anxiety disorders (Dobson, 2011).

Can it help with depression?

PST appears particularly helpful in treating clients with depression. A recent analysis of 30 studies found that PST was an effective treatment with a similar degree of success as other successful therapies targeting depression (Cuijpers, Wit, Kleiboer, Karyotaki, & Ebert, 2020).

Other studies confirm the value of PST and its effectiveness at treating depression in multiple age groups and its capacity to combine with other therapies, including drug treatments (Dobson, 2011).

The major concepts

Effective coping varies depending on the situation, and treatment typically focuses on improving the environment and reducing emotional distress (Dobson, 2011).

PST is based on two overlapping models:

Social problem-solving model

This model focuses on solving the problem “as it occurs in the natural social environment,” combined with a general coping strategy and a method of self-control (Dobson, 2011, p. 198).

The model includes three central concepts:

• Social problem-solving
• The problem
• The solution

The model is a “self-directed cognitive-behavioral process by which an individual, couple, or group attempts to identify or discover effective solutions for specific problems encountered in everyday living” (Dobson, 2011, p. 199).

Relational problem-solving model

The theory of PST is underpinned by a relational problem-solving model, whereby stress is viewed in terms of the relationships between three factors:

• Stressful life events
• Emotional distress and wellbeing
• Problem-solving coping

Therefore, when a significant adverse life event occurs, it may require “sweeping readjustments in a person’s life” (Dobson, 2011, p. 202).

• Enhance positive problem orientation
• Decrease negative orientation
• Foster ability to apply rational problem-solving skills
• Reduce the tendency to avoid problem-solving
• Minimize the tendency to be careless and impulsive

D’Zurilla’s and Nezu’s model includes (modified from Dobson, 2011):

• Initial structuring Establish a positive therapeutic relationship that encourages optimism and explains the PST approach.
• Assessment Formally and informally assess areas of stress in the client’s life and their problem-solving strengths and weaknesses.
• Obstacles to effective problem-solving Explore typically human challenges to problem-solving, such as multitasking and the negative impact of stress. Introduce tools that can help, such as making lists, visualization, and breaking complex problems down.
• Problem orientation – fostering self-efficacy Introduce the importance of a positive problem orientation, adopting tools, such as visualization, to promote self-efficacy.
• Problem orientation – recognizing problems Help clients recognize issues as they occur and use problem checklists to ‘normalize’ the experience.
• Problem orientation – seeing problems as challenges Encourage clients to break free of harmful and restricted ways of thinking while learning how to argue from another point of view.
• Problem orientation – use and control emotions Help clients understand the role of emotions in problem-solving, including using feelings to inform the process and managing disruptive emotions (such as cognitive reframing and relaxation exercises).
• Problem orientation – stop and think Teach clients how to reduce impulsive and avoidance tendencies (visualizing a stop sign or traffic light).
• Problem definition and formulation Encourage an understanding of the nature of problems and set realistic goals and objectives.
• Generation of alternatives Work with clients to help them recognize the wide range of potential solutions to each problem (for example, brainstorming).
• Decision-making Encourage better decision-making through an improved understanding of the consequences of decisions and the value and likelihood of different outcomes.
• Solution implementation and verification Foster the client’s ability to carry out a solution plan, monitor its outcome, evaluate its effectiveness, and use self-reinforcement to increase the chance of success.
• Guided practice Encourage the application of problem-solving skills across multiple domains and future stressful problems.
• Rapid problem-solving Teach clients how to apply problem-solving questions and guidelines quickly in any given situation.

Success in PST depends on the effectiveness of its implementation; using the right approach is crucial (Dobson, 2011).

Problem-solving therapy – Baycrest

The following interventions and techniques are helpful when implementing more effective problem-solving approaches in client’s lives.

First, it is essential to consider if PST is the best approach for the client, based on the problems they present.

Is PPT appropriate?

It is vital to consider whether PST is appropriate for the client’s situation. Therapists new to the approach may require additional guidance (Nezu et al., 2013).

Therapists should consider the following questions before beginning PST with a client (modified from Nezu et al., 2013):

• Has PST proven effective in the past for the problem? For example, research has shown success with depression, generalized anxiety, back pain, Alzheimer’s disease, cancer, and supporting caregivers (Nezu et al., 2013).
• Is PST acceptable to the client?
• Is the individual experiencing a significant mental or physical health problem?

All affirmative answers suggest that PST would be a helpful technique to apply in this instance.

Five problem-solving steps

The following five steps are valuable when working with clients to help them cope with and manage their environment (modified from Dobson, 2011).

Ask the client to consider the following points (forming the acronym ADAPT) when confronted by a problem:

• Attitude Aim to adopt a positive, optimistic attitude to the problem and problem-solving process.
• Define Obtain all required facts and details of potential obstacles to define the problem.
• Alternatives Identify various alternative solutions and actions to overcome the obstacle and achieve the problem-solving goal.
• Predict Predict each alternative’s positive and negative outcomes and choose the one most likely to achieve the goal and maximize the benefits.
• Try out Once selected, try out the solution and monitor its effectiveness while engaging in self-reinforcement.

If the client is not satisfied with their solution, they can return to step ‘A’ and find a more appropriate solution.

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Positive self-statements

When dealing with clients facing negative self-beliefs, it can be helpful for them to use positive self-statements.

Use the following (or add new) self-statements to replace harmful, negative thinking (modified from Dobson, 2011):

• I can solve this problem; I’ve tackled similar ones before.
• I can cope with this.
• I just need to take a breath and relax.
• Once I start, it will be easier.
• It’s okay to look out for myself.
• I can get help if needed.
• Other people feel the same way I do.
• I’ll take one piece of the problem at a time.
• I can keep my fears in check.
• I don’t need to please everyone.

5 Worksheets and workbooks

Problem-solving self-monitoring form.

Answering the questions in the Problem-Solving Self-Monitoring Form provides the therapist with necessary information regarding the client’s overall and specific problem-solving approaches and reactions (Dobson, 2011).

Ask the client to complete the following:

• Describe the problem you are facing.
• What is your goal?
• What have you tried so far to solve the problem?
• What was the outcome?

Reactions to Stress

It can be helpful for the client to recognize their own experiences of stress. Do they react angrily, withdraw, or give up (Dobson, 2011)?

The Reactions to Stress worksheet can be given to the client as homework to capture stressful events and their reactions. By recording how they felt, behaved, and thought, they can recognize repeating patterns.

What Are Your Unique Triggers?

Helping clients capture triggers for their stressful reactions can encourage emotional regulation.

When clients can identify triggers that may lead to a negative response, they can stop the experience or slow down their emotional reaction (Dobson, 2011).

The What Are Your Unique Triggers ? worksheet helps the client identify their triggers (e.g., conflict, relationships, physical environment, etc.).

Problem-Solving worksheet

Imagining an existing or potential problem and working through how to resolve it can be a powerful exercise for the client.

Use the Problem-Solving worksheet to state a problem and goal and consider the obstacles in the way. Then explore options for achieving the goal, along with their pros and cons, to assess the best action plan.

Getting the Facts

Clients can become better equipped to tackle problems and choose the right course of action by recognizing facts versus assumptions and gathering all the necessary information (Dobson, 2011).

Use the Getting the Facts worksheet to answer the following questions clearly and unambiguously:

• Who is involved?
• What did or did not happen, and how did it bother you?
• Where did it happen?
• When did it happen?
• Why did it happen?
• How did you respond?

2 Helpful Group Activities

While therapists can use the worksheets above in group situations, the following two interventions work particularly well with more than one person.

Generating Alternative Solutions and Better Decision-Making

A group setting can provide an ideal opportunity to share a problem and identify potential solutions arising from multiple perspectives.

Use the Generating Alternative Solutions and Better Decision-Making worksheet and ask the client to explain the situation or problem to the group and the obstacles in the way.

Once the approaches are captured and reviewed, the individual can share their decision-making process with the group if they want further feedback.

Visualization

Visualization can be performed with individuals or in a group setting to help clients solve problems in multiple ways, including (Dobson, 2011):

• Clarifying the problem by looking at it from multiple perspectives
• Rehearsing a solution in the mind to improve and get more practice
• Visualizing a ‘safe place’ for relaxation, slowing down, and stress management

Guided imagery is particularly valuable for encouraging the group to take a ‘mental vacation’ and let go of stress.

Ask the group to begin with slow, deep breathing that fills the entire diaphragm. Then ask them to visualize a favorite scene (real or imagined) that makes them feel relaxed, perhaps beside a gently flowing river, a summer meadow, or at the beach.

The more the senses are engaged, the more real the experience. Ask the group to think about what they can hear, see, touch, smell, and even taste.

Encourage them to experience the situation as fully as possible, immersing themselves and enjoying their place of safety.

Such feelings of relaxation may be able to help clients fall asleep, relieve stress, and become more ready to solve problems.

We have included three of our favorite books on the subject of Problem-Solving Therapy below.

1. Problem-Solving Therapy: A Treatment Manual – Arthur Nezu, Christine Maguth Nezu, and Thomas D’Zurilla

This is an incredibly valuable book for anyone wishing to understand the principles and practice behind PST.

Written by the co-developers of PST, the manual provides powerful toolkits to overcome cognitive overload, emotional dysregulation, and the barriers to practical problem-solving.

Find the book on Amazon .

2. Emotion-Centered Problem-Solving Therapy: Treatment Guidelines – Arthur Nezu and Christine Maguth Nezu

Another, more recent, book from the creators of PST, this text includes important advances in neuroscience underpinning the role of emotion in behavioral treatment.

Along with clinical examples, the book also includes crucial toolkits that form part of a stepped model for the application of PST.

3. Handbook of Cognitive-Behavioral Therapies – Keith Dobson and David Dozois

This is the fourth edition of a hugely popular guide to Cognitive-Behavioral Therapies and includes a valuable and insightful section on Problem-Solving Therapy.

This is an important book for students and more experienced therapists wishing to form a high-level and in-depth understanding of the tools and techniques available to Cognitive-Behavioral Therapists.

For even more tools to help strengthen your clients’ problem-solving skills, check out the following free worksheets from our blog.

• Case Formulation Worksheet This worksheet presents a four-step framework to help therapists and their clients come to a shared understanding of the client’s presenting problem.
• Understanding Your Default Problem-Solving Approach This worksheet poses a series of questions helping clients reflect on their typical cognitive, emotional, and behavioral responses to problems.
• Social Problem Solving: Step by Step This worksheet presents a streamlined template to help clients define a problem, generate possible courses of action, and evaluate the effectiveness of an implemented solution.

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While we are born problem-solvers, facing an incredibly diverse set of challenges daily, we sometimes need support.

Problem-Solving Therapy aims to reduce stress and associated mental health disorders and improve wellbeing by improving our ability to cope. PST is valuable in diverse clinical settings, ranging from depression to schizophrenia, with research suggesting it as a highly effective treatment for teaching coping strategies and reducing emotional distress.

Many PST techniques are available to help improve clients’ positive outlook on obstacles while reducing avoidance of problem situations and the tendency to be careless and impulsive.

The PST model typically assesses the client’s strengths, weaknesses, and coping strategies when facing problems before encouraging a healthy experience of and relationship with problem-solving.

Why not use this article to explore the theory behind PST and try out some of our powerful tools and interventions with your clients to help them with their decision-making, coping, and problem-solving?

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

• Cuijpers, P., Wit, L., Kleiboer, A., Karyotaki, E., & Ebert, D. (2020). Problem-solving therapy for adult depression: An updated meta-analysis. European P sychiatry ,  48 (1), 27–37.
• Dobson, K. S. (2011). Handbook of cognitive-behavioral therapies (3rd ed.). Guilford Press.
• Dobson, K. S., & Dozois, D. J. A. (2021). Handbook of cognitive-behavioral therapies  (4th ed.). Guilford Press.
• Eysenck, M. W., & Keane, M. T. (2015). Cognitive psychology: A student’s handbook . Psychology Press.
• Nezu, A. M., & Nezu, C. M. (2009). Problem-solving therapy DVD . Retrieved September 13, 2021, from https://www.apa.org/pubs/videos/4310852
• Nezu, A. M., & Nezu, C. M. (2018). Emotion-centered problem-solving therapy: Treatment guidelines. Springer.
• Nezu, A. M., Nezu, C. M., & D’Zurilla, T. J. (2013). Problem-solving therapy: A treatment manual . Springer.

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Cognitive Training: A Field in Search of a Phenomenon

Fernand gobet.

1 Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science

Giovanni Sala

2 Department of Epidemiology of Aging, National Center for Geriatrics and Gerontology, Obu, Japan

3 Department of Psychology, University of Liverpool

Considerable research has been carried out in the last two decades on the putative benefits of cognitive training on cognitive function and academic achievement. Recent meta-analyses summarizing the extent empirical evidence have resolved the apparent lack of consensus in the field and led to a crystal-clear conclusion: The overall effect of far transfer is null, and there is little to no true variability between the types of cognitive training. Despite these conclusions, the field has maintained an unrealistic optimism about the cognitive and academic benefits of cognitive training, as exemplified by a recent article (Green et al., 2019). We demonstrate that this optimism is due to the field neglecting the results of meta-analyses and largely ignoring the statistical explanation that apparent effects are due to a combination of sampling errors and other artifacts. We discuss recommendations for improving cognitive-training research, focusing on making results publicly available, using computer modeling, and understanding participants’ knowledge and strategies. Given that the available empirical evidence on cognitive training and other fields of research suggests that the likelihood of finding reliable and robust far-transfer effects is low, research efforts should be redirected to near transfer or other methods for improving cognition.

The last two decades have witnessed a considerable interest in cognitive training. Not only is cognitive training a multibillion-dollar industry ( Ahuja, 2019 ), but its techniques are also used by large organizations, such as the U.S. military, and companies such as Cogmed, Lumosity, and Posit Science are often featured in the news. According to its proponents, cognitive training enhances children’s educational achievements, improves adults’ decision-making abilities, and alleviates the effects of aging on cognition. To try to support these claims, independent researchers and companies directly involved with cognitive training have conducted a substantial number of experiments.

The hypothesis that general cognitive abilities can be improved by cognitive-training tasks of fairly short duration is certainly counterintuitive to anyone familiar with the accumulated literature on intelligence and cognition. Considerable research indicates that fluid intelligence and working memory (WM) capacity cannot be improved through cognitive interventions (e.g., Deary, 2001 ; Shipstead et al., 2012 ). Likewise, substantial empirical evidence shows that learning and skill acquisition are domain-specific (e.g., Gobet, 2016 ; Sagi & Tanne, 1994 ; Simon & Chase, 1973 ). Showing positive effects of cognitive training would invalidate claims about the inflexibility of intelligence, WM capacity, learning, and expertise. There is no doubt that this would constitute a paradigm shift in psychology ( Hurley, 2013 ), as is made clear by cognitive-training researchers. For example, Jaeggi et al. (2008) stated that “thus, in contrast to many previous studies, we conclude that it is possible to improve Gf without practicing the testing tasks themselves, opening a wide range of applications” (p. 6829), and Green and Bavelier (2003) concluded that “therefore, although video-game playing may seem to be rather mindless, it is capable of radically altering visual attentional processing” (p. 536).

An objective consideration of the evidence shows that these optimistic predictions have not been borne out by the data. The best way to evaluate the empirical evidence is to carry out meta-analyses, and we discuss the conclusions of several recent meta-analyses that covered WM training, video-game playing, chess playing, music, and exergame. Despite this contradicting evidence, researchers in the field maintain a high level of confidence that cognitive training is effective to improve general cognitive abilities, as exemplified recently by an article written by a group of 48 cognitive-training researchers ( Green et al., 2019 ). Given that this article assembles many of the leading researchers in the field, we discuss it at some length in the second part of our article.

We argue that one of the reasons for this misplaced optimism is that the field, by and large, has ignored the role of study artifacts. We therefore spend a fair amount of space to communicate two critically important points mostly ignored in the literature about cognitive training. First, variability in the effect sizes obtained by different types of interventions does not necessarily imply that there are true differences between them: These differences might be simply due to the effects of sampling error and other kinds of artifacts. Second, before any conclusion can be reached about the variability of moderating variables, it is imperative to evaluate whether this variability is genuine (true heterogeneity) or due to random error.

Defining Terms

Before diving into the details of our arguments, it is important to define key terms. Cognitive training refers to interventions using cognitive tasks or intellectually demanding activities, the goal of which is to enhance general cognitive ability ( Sala & Gobet, 2017b , 2019 ). Thus, our definition includes not only “brain-training” tasks (i.e., tasks practicing basic cognitive abilities to enhance performance on other cognitive tasks, including everyday activities; Simons et al., 2016 ) but also activities such as music learning and video-game playing. 1 This definition is fairly standard; for example, Strobach and Karbach’s (2016) book on cognitive training also includes a broader variety of activities than those covered by brain training, and so do numerous articles on the topic ( Buschkuehl & Jaeggi, 2010 ; Katz et al., 2018 ; Simons et al., 2016 ; Taatgen, 2016 ).

The question of “transfer” is a central question in cognitive-training research. In line with the literature (e.g., Donovan et al., 1999 ), we define near transfer as the generalization of acquired skills across two (or more) domains that are closely related to each other (e.g., studying algebra to be better in geometry) and far transfer as the generalization of acquired skills across domains that are only loosely related to each other (e.g., studying algebra to improve in Chinese). 2 Although this definition of transfer is qualitative and there are undoubtedly some ambiguous cases, in most cases, it is fairly easy to decide between near and far transfer. Everybody agrees that using a 3-back task after 2-back training is near transfer and that testing the effect of this training with an IQ test is far transfer. In addition, it is possible to use a more graded classification, such as “nearest transfer” (tasks that are the same as or similar to those used during training) and “less near transfer” (e.g., tasks that are different but still are aimed at improving performance in memory tasks; see Sala & Gobet, 2020b ).

Our broad definition of cognitive training allows one to ask whether cognitive-training methods, taken as a group, provide broad cognitive and academic benefits (far transfer). We note that many researchers in the field would argue that this question is not legitimate. For example, Green et al. (2019) took as a starting point that “each individual type of behavioral intervention for cognitive enhancement (by definition) differs from all others in some way, and thus will generate different patterns of effects on various cognitive outcome measures” (p. 4). We believe that this hypothesis should be tested empirically rather than being accepted by fiat. In fact, as we show below, we have tested it and found that with respect to far transfer, it is incorrect.

On the Importance of Sampling Error and Other Artifacts

In the first chapter of their book, Schmidt and Hunter (2015) presented a table summarizing the results of 30 studies on the link between job satisfaction and organizational commitment. They invited the reader to reach a conclusion about the strength of this link and about the variables that might moderate it and to draw implication for theory. The correlations ranged from –.10 to .56. Out of the 30 studies, 19 found a significant correlation, and 11 did not. Schmidt and Hunter discussed several patterns apparent in the data. For example, if only younger workers are considered, 19 out of the remaining 23 studies showed a significant correlation. Another pattern is that a significant correlation was found for 83% of the studies carried out in large organizations but only 50% in small organizations. Hence, the data seem to support the theory that organizational commitment grows over a 10-year period but then plateaus.

In fact, the data were generated by a Monte Carlo run in which the correlations were randomly sampled from a distribution with a population correlation of .33 and sample size was randomly selected from a distribution with a mean of 40. The organizational characteristics were allocated random values for each study. Therefore, the variation in the results were due to only chance (i.e., sampling error), and the large departures from the mean were obtained with small samples. According to Schmidt and Hunter (2015) , this is a common situation in the psychological literature, and one should be aware that “‘conflicting results in the literature’ may be entirely artifactual” (p. 6). In addition, “many of the interactions hypothesized to account for differences in findings in different studies are nonexistent; that is, they are apparitions composed of the ectoplasm of sampling error and other artifacts” (p. 7).

It is our contention that by and large, the literature on cognitive training has underestimated the role of sampling error and other artifacts, which include issues with measurement, range restriction, and typographical errors, among others. Specifically, many researchers assume that distinct types of interventions will have different effects on far transfer—some interventions will have a positive effect, and others will not. But this is a hypothesis that researchers can test empirically while keeping in mind that the variability in results could be in reality artifactual. We tested this hypothesis in the meta-analyses and second-order meta-analysis that we discuss below and found that the hypothesis is incorrect empirically: The variability is artifactual. Thus, beyond random fluctuations, there are no differences between the different types of intervention: Their effect on far-transfer tasks is null when sampling error, publication bias, and type of control group are taken into account. We get the same results when meta-analyses are carried out within one domain (e.g., action video games vs. nonaction video games) or between domains (i.e., the second-order meta-analysis comparing the effects of WM training, video-game playing, etc.). Thus, rather than limiting researchers to piecemeal conclusions (e.g., Intervention 1 does not lead to far transfer; Intervention 2 does not lead to far transfer), we show that it is possible to reach a conclusion that applies to the broad category of cognitive training. Reaching broad generalizations supported by empirical evidence is the hallmark of scientific progress ( Braithwaite, 1960 ; Chow, 1987 ).

We give this preview of our results because the importance of sampling error and other artifacts has been systematically overlooked in the cognitive-training field. Assuming that different treatments lead to different effects was a plausible hypothesis at the beginning of the research, but it is not anymore. However, the field has, on the whole, clung to this hypothesis, and many of the points we discuss next hinge on the failure to recognize the role played by sampling error.

Meta-Analytic Evidence

The rationale behind meta-analysis.

Disagreements often occur in quantitative empirical research, and meta-analysis is considered one of the most effective tools for resolving them. Meta-analysis offers a set of statistical methods for integrating research findings on a particular topic across studies ( Borenstein et al., 2009 ; Schmidt & Hunter, 2015 ). It has three main objectives: (a) to estimate the magnitude of an overall effect and its confidence intervals, (b) to quantify the consistency of the literature (i.e., whether there is variability in the findings across studies), and (c) to reveal the role of potential moderators.

The overall effect size is calculated by averaging the effect sizes (e.g., standardized mean differences between two groups) obtained from the primary studies. Each effect size is weighted on precision (i.e., inverse of the sampling error variance), 3 which is primarily, sometimes solely, a function of sample size. The larger the sample, the bigger the weight of the effect in the analysis will be.

An essential piece of information offered by meta-analysis is the degree of between-studies true variance (τ 2 ). In brief, the variance observed in any population of effect sizes can be decomposed, at the very least, into true variance and artifactual variance (e.g., variance because of sampling error and measurement error). Whereas the former warrants an explanation, the latter does not. Specifically, τ 2 estimates the between-studies variance in the population of the effect sizes that is not due to sampling error. A low or null τ 2 suggests that no moderating variable affects the magnitude of the effects across the primary studies. If τ 2 ≈ 0, then it can be inferred that there is only one true effect in the literature. The accuracy of this overall effect is provided by its standard error, which is a function of the number of observations included in the meta-analytic model. By contrast, a high τ 2 indicates that the magnitude of the effect is moderated by some variables (e.g., type of control group). Accounting for between-studies true variance, when it exists, is fundamental to providing reliable and interpretable meta-analytic estimates.

Note that unless one has strong a priori predictions about the type of moderators that might play a role, it is necessary to first test whether there is true heterogeneity in the data. If this is not the case, then no moderator analysis should be carried out to not capitalize on sampling error ( Schmidt & Hunter, 2015 ). If there is true heterogeneity, one should test whether specific moderators are statistically significant. Only in this case is it appropriate to carry out a detailed moderator analysis. A final caveat is that testing a large number of potential moderators is inappropriate because this capitalizes on chance (Type I error).

What do meta-analyses tell researchers about cognitive training?

As noted above, we have carried out several meta-analyses about cognitive training. 4 We have repeatedly found that the true far-transfer effect size, when estimated from the comparison of treatment versus active control group, is close to zero. This outcome has been found for WM training ( Aksayli et al., 2019 ; Sala, Aksayli, Tatlidil, Gondo, & Gobet, 2019 ; Sala & Gobet, 2020b ), video-game playing ( Sala et al., 2018 ), exergames ( Sala et al., 2021 ), and music training ( Sala & Gobet, 2017c , 2020a , 2020b ). The exception is chess ( Sala & Gobet, 2016 ), for which too few studies with an active control group have been carried out; however, the few available studies with an active control group suggest a lack of far transfer (e.g., Sala & Gobet, 2017a ).

These meta-analyses were carried out with different methods. Sala, Aksayli, Tatlidil, Tatsumi, et al. (2019) redid them with the same method. Table 1 presents a summary of these meta-analyses and the results of adjustments enabled by second-order meta-analyses (see the following section) for the experimental results not corrected for publication bias and including both active and passive control groups. Table 2 presents the corresponding meta-analyses when the studies are corrected for publication bias and include only active control groups—a better estimate of the true effect of cognitive training. As we show, the estimated effect sizes of the first-order meta-analyses are small in Table 1 (range = 0.04–0.19) and essentially zero in Table 2 (range = −0.03 to 0.02). In both tables, the amount of true heterogeneity is very small.

First- and Second-Order Meta-Analyses With the Uncorrected (Naive) Overall Effect Sizes, Far Transfer Only

Note: Data From Sala, Aksayli, Tatlidil, Tatsumi, et al. (2019) . k i k i = number of samples; g ¯ i = first-order overall effect size; S g i 2 = variance of the observed g s; τ 2 = amount of true heterogeneity; adjusted g ¯ i = adjusted first-order overall effect size; TD = typically developing; LD = learning disabilities; VG = video games; WM = working memory.

First- and Second-Order Meta-Analyses With the Corrected Overall Effect Sizes (Only Active Control Groups), Far Transfer Only

Note: Data From Sala, Aksayli, Tatlidil, Tatsumi, et al. (2019) . k i = number of samples; g ¯ i = first-order overall effect size; S g i 2 = variance of the observed g s; τ 2 = amount of true heterogeneity; adjusted g ¯ i = adjusted first-order overall effect size; TD = typically developing; LD = learning disabilities; VG = video games; WM = working memory.

Thus, the meta-analyses allowed us to quantify, with respect to far-transfer effects, the extent to which the literature is mixed and could explain any between-studies true variance. An important conclusion was that the results are not inconsistent and thus do not depend on differences in methodologies between researchers. That is, once baseline differences were controlled for, the only appreciable source of true variance (which is often quite low) is the type of control group. In other words, the debate about the literature being mixed and the results inconsistent is just much ado about nothing. Far-transfer effects do not exist. Cognitive-training researchers seem to incorrectly equate sampling-error variance and true variance: Terms such as “τ 2 ,” “true variance,” or “true heterogeneity” rarely appear in cognitive-training reviews. In addition, it seems that cognitive-training researchers fail to understand that it is absolutely normal that significantly positive effects are sometimes found (e.g., when comparing treatment groups with active control groups on far-transfer measures) even if the true effect is zero. Specifically, by chance, we expect a portion (5%) of the measurements to be statistically significant ( p < .05, one-tailed). Effect sizes in a given literature are mathematically bound to differ because of sampling error. Variability across and within the studies is the rule, not the exception.

A step further: second-order meta-analysis

Second-order meta-analysis is a procedure designed by Schmidt and Oh (2013) for integrating findings of first-order (i.e., conventional) meta-analyses. This technique estimates a grand mean of the first-order overall effect sizes and, most notably, the between-meta-analyses true variance. Second-order meta-analysis represents the current highest level of cumulative knowledge in quantitative research.

In Sala, Aksayli, Tatlidil, Tatsumi, et al. (2019) , we applied second-order meta-analysis to cognitive-training data (for results about far transfer, see Tables 1 and ​ and2). 2 ). The analysis included 14 statistically independent first-order meta-analyses (332 samples, 1,555 effect sizes, and 21,968 participants) of near- and far-transfer effects in different populations (e.g., children, adults, and older adults). As shown in Tables 1 and ​ and2, 2 , the training programs covered were WM training, action- and nonaction-video-game training, music training, chess training, and exergame training. The key results were as follows. First, near transfer occurs even when placebo effects are controlled for and seems to be moderated by the age of the participants. Second, far transfer is negligible (uncorrected overall effect) or null (when placebo effects and publication bias are ruled out). Third, within-studies (ω 2 ) and between-studies true variance (τ 2 ) are small to null with far transfer. Fourth, second-order sampling error (i.e., the residual sampling error from first-order meta-analyses) explains all the between-meta-analyses variance with far transfer. That is, we found no evidence of either within-studies, between-studies, or between-meta-analyses true variance. These results strongly corroborate the idea that although near transfer is real and the magnitude of its effect is moderated by the population examined, the observed far transfer is due to factors that are unspecific (i.e., it occurs regardless of the type of training regimen or population), such as placebos. (This conclusion is buttressed by the results of Kassai et al., 2019 , who carried out a meta-analysis on training components of children’s executive-functions skills, a type of training not covered by our second-order meta-analysis.)

Other cognitive-training programs

For some cognitive-training programs, there are not enough studies to perform a proper meta-analysis. Examples include the ACTIVE trial, commercial brain-training games (e.g., Neuroracer, Lumosity, and BrainHQ), and multidomain training programs ( Binder et al., 2016 ; Buitenweg et al., 2017 ; Duyck & Op de Beeck, 2019 ). To date, none of these regimens have shown compelling evidence, or any evidence at all, of training-induced far transfer to either cognitive tests or real-life skills (for reviews, see Sala & Gobet, 2019 ; Simons et al., 2016 ). These studies are thus in line with the findings reviewed above.

Active versus passive control groups

Recently, Au et al. (2020) questioned the use of active control groups as currently used in the cognitive-training literature. These authors carried out a meta-analysis and a meta-meta-analysis on the effects of cognitive interventions, focusing on the differences between passive and active control groups. They took their results as showing that there is no meaningful performance difference between the two types of control groups. This is clearly different from the conclusions obtained in our meta-analyses with respect to far transfer. Why did they obtain different results? We believe that these differences result from several suboptimal (to incorrect) decisions made by Au et al.

Most importantly, the meta-meta-analysis was performed in a less than optimal way. Statistically dependent meta-analyses—that is, meta-analyses including the same primary studies—were put together in the same model. 5 This procedure violates the assumption of independence. This often leads to underestimating sampling error variance and, hence, overestimating true variance, which results in errors in calculating effect sizes and confidence intervals ( Schmidt & Hunter, 2015 ; Schmidt & Oh, 2013 ). In addition, only meta-analyses published until 2016 were included, which has the consequence of ignoring a substantial amount of evidence. Finally, Au et al. (2020) mixed different types of information: (a) different types of training, including cognitive-training interventions, mnemonics ( Floyd & Scogin, 1997 ; Verhaeghen et al., 1992 ), and serious games ( Wouters et al., 2013 ), and (b) near-transfer (e.g., Uttal et al., 2013 ) and far-transfer (e.g., Lampit et al., 2014 ) outcomes (there is little to no placebo effect in near transfer in our meta-analyses, too). In conclusion, Au et al.’s results do not represent any compelling evidence that the choice of control group (passive or active) is irrelevant to the results in the cognitive-training literature.

Technical issues aside, the most relevant aspect of the problem is defining what qualifies as an active control group. Simons et al. (2016) highlighted that active controls should be designed to isolate the variable of interest (i.e., the effect of the training program) as accurately as possible. This means that to rule out placebo effects, active control groups should be engaged in activities that are cognitively demanding and trigger positive expectations on their effectiveness in the participants ( Boot et al., 2013 ). Therefore, control activities should differ from the cognitive-training program regarding only the key element that is hypothesized to enhance the target cognitive skill or skills. For example, the far-transfer effects of WM training regimens could be tested by employing adaptive visual-search tasks (e.g., Guye & von Bastian, 2017 ; Hering et al., 2017 ). Although cognitively demanding and perceived as effective training, these tasks lack the “WM training component.” Using nonadaptive WM training tasks is, in our opinion, a slightly less desirable choice.

Meta-analyses and reviews about cognitive training often do not apply Simons et al.’s (2016) criterion for defining a control activity as active (e.g., Au et al., 2020 ; Teixeira-Santos et al., 2019 ). Rather, control groups engaged in any alternative activity (e.g., non-cognitively demanding filler tasks) are considered as active. This less stringent (suboptimal) criterion is another source of discrepancy between meta-analyses in the literature.

Finally, note that our meta-analyses do not show that placebo effects occur in all cognitive-training programs. For example, they are not present in either action- or nonaction-video-game training ( Sala et al., 2018 ). However, we did find that placebos always occur in WM training when it comes to far transfer ( Sala & Gobet, 2020b ). These placebos are around 0.15 to 0.20 standardized mean difference at best and often affected by publication bias.

Publication bias and laboratory bias

In our second-order meta-analysis, we estimated a small publication-bias effect (0.05–0.10 standardized mean differences). Publication bias thus seems to be a minor issue in the cognitive-training literature. In fact, this finding appears to be in line with the current state of the art in psychology ( Stanley et al., 2018 ). Of more interest are probably the anomalous effects reported by two laboratories involved in cognitive-training studies, effects that were identified by meta-analyses ( Bediou et al., 2018 ; Sala, Aksayli, Tatlidil, Gondo, & Gobet, 2019 ). The effect sizes reported by these laboratories, which are unusually large compared with those found by other laboratories, are a nonnegligible source of variability in the cognitive-training literature, and an important task for further research will be to understand the reason for these discrepancies.

First, the Padua laboratory (Borella and colleagues) has carried out more than 10 studies implementing a particular WM training regimen in older adults (Categorization Working Memory Span [CWMS] task; for more details, see Borella et al., 2017 ). In nearly all of these studies, medium to large effect sizes were found in both near- and far-transfer measures. The other studies in the field that used the CWMS task reported small to null overall effect sizes ( Sala, Aksayli, Tatlidil, Gondo, & Gobet, 2019 ). This marked difference between the findings of the Padua laboratory and the ones reported by other laboratories is probably due to the peculiar type of active control group employed by the former. Rather than a cognitively demanding activity, the control subjects were often asked to fill in biographical questionnaires. This type of filler task does not meet the standards of an active task. A study that employed the CWMS training regimen and compared its effects against a cognitively active control task (adaptive visual-search training) found small near-transfer effects and no far-transfer effect ( Hering et al., 2017 ).

Second, Green and Bavelier’s studies about the benefits of playing action video games reported much greater effects than all the other studies in the field ( Bediou et al., 2018 ). This anomaly—which is captured in the asymmetry of the distribution of the effect sizes—is, in all probability, due to the fact that some effect sizes were suppressed from the primary studies (Bavelier’s personal communication reported in Boot et al., 2011 ) or have been incorrectly reported as coming from different samples. These issues have been documented in several articles by Simons and Boot ( Boot et al., 2011 ; Hilgard et al., 2019 ) and have led to a series of corrections of Green and Bavelier’s findings (e.g., Green & Bavelier, 2019 , 2020 ).

Between-individuals differences in far transfer

A common argument against meta-analytic evidence is that it does not account for within-studies individual differences. In a very general sense, this argument is correct. Meta-analysis does not provide any detailed information regarding within-studies, between-subjects differences. Meta-analysis is designed for estimating the magnitude and consistency of overall effects. Nonetheless, this does not mean that meta-analytic evidence is unreliable. In fact, the combination of null overall far-transfer effects and null between-studies true variability suggests that between-individuals, within-studies differences seem to matter very little in cognitive training. That being said, we think that it is useful to discuss how some authors come to the conclusion that individual differences do show up in cognitive-training data despite a lack of clear-cut effects.

Jaeggi et al. (2011) presented the argument that there are between-individuals differences in far transfer (even if the mean difference between trainees and control subjects is close to zero) because there is a correlation between gains in the trained task and gains in the transfer tasks in the experimental group. The idea is that the more one improves on the training task (e.g., n -back), the more one benefits from the training in terms of far transfer (e.g., improvement in the Raven’s matrices).

This argument is incorrect statistically. Positive correlations between gains occur every time within-sessions (i.e., same time point) covariances are bigger than between-sessions covariances. However, there is no good reason why this should be considered as evidence in favor of a training effect (for all the details, see Tidwell et al., 2014 ).

Another common incorrect argument relies on the negative correlation occurring between far-transfer pretest scores and pretest/posttest gains. This correlation is sometimes presented as evidence of an individual-based compensatory effect (e.g., Karbach et al., 2015 ). Put simply, a given cognitive-training regimen is believed to be particularly effective for individuals who performed poorly at baseline assessment (i.e., Subject × Treatment interaction). However, such negative correlations are likely to be, at least in part, statistical artifacts due to regression to the mean ( Smoleń et al., 2018 ). Therefore, correlations between pretest/posttest gains and pretest scores alone cannot be considered as evidence for true individual differences in training-induced transfer effects.

Beyond the above statistically incorrect inferences, we note that postulating between-individuals differences when the overall far-transfer effect is zero leads to absurd conclusions, especially if no true between- or within-studies variance is observed. In fact, if a subgroup of participants outperforms the control participants (true positive effect size), that means that the other subgroup is outperformed by the control participants (true negative effect size) because the mean effect is zero. Now, why should cognitive-training programs exert a true negative effect (i.e., damage) on cognition? It is obvious that if the overall effect is zero, then the training has no impact on one’s domain-general cognitive skills regardless of any covariate. On the other hand, if researchers assume that the training is effective (i.e., true positive effect size) for a subgroup of individuals and ineffective yet not detrimental (i.e., true null effect size) for the other group, then they would observe an attenuated but still positive overall effect size. This scenario is, however, inconsistent with the empirical data (the observed overall effect is zero).

Finally, the above correlation-based arguments seem odd. It is well known that correlations do not constitute any evidence of causality. Only the inclusion of a control group can isolate the variable of interest (i.e., training-induced far-transfer effects). For example, Smoleń et al. (2018) showed that modeling correlation with structural models may, in principle, provide some evidence of a true compensatory effect (i.e., beyond regression to the mean). However, it is necessary to include a control group to demonstrate that such an effect is caused by training programs. More prosaically, it is unclear why time and resources should be invested to enroll an entire control group if correlations were enough to establish a causality link between a person’s performance in training tasks and cognitive enhancement. We must conclude that, in the current state of the art, appealing to putative individual differences in cognitive training appears more like an attempt to make far-transfer null effects worth some optimism and further research rather than a proper scientific hypothesis.

What Is Wrong With the Cognitive-Training Hypothesis?

As is clear from the empirical evidence reviewed in the previous sections, the likelihood that cognitive training provides broad cognitive and academic benefits is very low indeed; therefore, resources should be devoted to other scientific questions—it is not rational to invest considerable sums of money on a scientific question that has been essentially answered by the negative. In a recent article, Green et al. (2019) took the exact opposite of this decision—they strongly recommended that funding agencies should increase funding for cognitive training. This obviously calls for comments.

The aim of Green et al.’s (2019) article was to provide methodological recommendations and a set of best practices for research on the effect of behavioral interventions aimed at cognitive improvement. Among others, the addressed issues include the importance of distinguishing between different types of studies (feasibility, mechanistic, efficacy, and effectiveness studies), the type of control groups used, and expectation effects. Many of the points addressed in detail by Green et al. reflected sound and well-known research practices (e.g., necessity of running studies with sufficient statistical power, need for defining the terminology used, and importance of replications; see also Simons et al., 2016 ).

However, the authors made disputable decisions concerning central questions. These include whether superordinate terms such as “cognitive training” and “brain training” should be defined, whether a discussion of methods is legitimate while ignoring the empirical evidence for or against the existence of a phenomenon, the extent to which meta-analyses can compare studies obtained with different methodologies and cognitive-enhancement methods, and whether multiple measures should be used for a latent construct such as intelligence.

Lack of definitions

Although Green et al. (2019) emphasized that “imprecise terminology can easily lead to imprecise understanding and open the possibility for criticism of the field,” they opted to not provide an explicit definition of “cognitive training” (p. 4). Nor did they define the phrase “behavioral interventions for cognitive enhancement,” used throughout their article. Because they specifically excluded activities such as video-game playing and music (p. 3), we surmised that they used “cognitive training” to refer to computer tasks and games that aim to improve or maintain cognitive abilities such as WM. The term “brain training” is sometimes used to describe these activities, although it should be mentioned that Green et al. objected to the use of the term.

Note that researchers investigating the effects of activities implicitly or explicitly excluded by Green et al. (2019) have emphasized that the aim of those activities is to improve cognitive abilities and/or academic achievement, for example, chess ( Jerrim et al., 2017 ; Sala et al., 2015 ), music ( Gordon et al., 2015 ; Schellenberg, 2006 ), and video-game playing ( Bediou et al., 2018 ; Feng et al., 2007 ). For example, Gordon et al.’s (2015) abstract concluded by stating that “results are discussed in the context of emerging findings that music training may enhance literacy development via changes in brain mechanisms that support both music and language cognition” (p. 1).

Green et al. (2019) provided a rationale for not providing a definition. Referring to “brain training,” they wrote:

We argue that such a superordinate category label is not a useful level of description or analysis. Each individual type of behavioral intervention for cognitive enhancement (by definition) differs from all others in some way, and thus will generate different patterns of effects on various cognitive outcome measures. (p. 4)

They also noted that even using subcategories such as “working-memory training” is questionable. They did note that “there is certainly room for debate” (p. 4) about whether to focus on each unique type of intervention or to group interventions into categories.

In line with common practice (e.g., De Groot, 1969 ; Elmes et al., 1992 ; Pedhazur & Schmelkin, 1991 ), we take the view that definitions are important in science. Therefore, in this article, we have proposed a definition of “cognitive training” (see “Defining Terms” section above), which we have used consistently in our research.

Current state of knowledge and meta-analyses

A sound discussion of methodology in a field depends on the current state of knowledge in this field. Whereas Green et al. (2019) used information gleaned from previous and current cognitive-training research to recommend best practices (e.g., use of previous studies to estimate the sample size needed for well-powered experiments), they also explicitly stated that they will not discuss previous controversies. We believe that this is a mistake because, as just noted, the choice of methods is conditional on the current state of knowledge. In our case, a crucial ingredient of this state is whether cognitive-training interventions are successful—specifically, whether they lead to far transfer. One of the main “controversies” precisely concerns this question, and thus it is unwise to ignore it.

Green et al. (2019) were critical of meta-analyses and argued that studies cannot be compared:

For example, on the basic research side, the absence of clear methodological standards has made it difficult-to-impossible to easily and directly compare results across studies (either via side-by-side contrasts or in broader meta-analyses). This limits the field’s ability to determine what techniques or approaches have shown positive outcomes, as well as to delineate the exact nature of any positive effects – e.g., training effects, transfer effects, retention of learning, etc. (p. 3)

These comments wholly underestimate what can be concluded from meta-analyses. Like many other researchers in the field, Green et al. (2019) assumed that (a) the literature is mixed and, consequently, (b) the inconsistent results depend on differences in methodologies between researchers. However, assuming that there is some between-studies inconsistency and speculating on where this inconsistency stems from is not scientifically apposite (see “The Importance of Sampling Error and Other Artifacts” section above). Rather, quantifying the between-studies true variance (τ 2 ) should be the first step to take.

Using latent factors

In the section “Future Issues to Consider With Regard to Assessments,” Green et al. (2019 , pp. 16–17) raised several issues with using multiple measures for a given construct such as WM. This practice has been recommended by authors such as Engle et al. (1999) to reduce measurement error. Several of Green et al.’s arguments merit discussion.

A first argument is that using latent factors—as in confirmatory factor analysis—might hinder the analysis of more specific effects. This argument is incorrect because the relevant information is still available to researchers (see Kline, 2016 ; Loehlin, 2004 ; Tabachnik & Fidell, 1996 ). By inspecting factor loadings, one can examine whether the preassessment/postassessment changes (if any) affect the latent factor or only specific tests (this is a longitudinal-measurement-invariance problem). Green et al. (2019) seemed to equate multi-indicator composites (e.g., summing z scores) with latent factors. Composite measures are the result of averaging or summing across a number of observed variables and cannot tell much about any task-specific effect. A latent factor is a mathematical construct derived from a covariance matrix within a structural model that includes a set of parameters that links the latent factor to the observed variables. That being said, using multi-indicator composites would be an improvement compared with the current standards in the field.

A second argument is that large batteries of tests induce motivational and/or cognitive fatigue in participants, especially with particular populations. Although this may be true, for example with older participants, large batteries have been used in several cognitive-training studies, and participants were able to undergo a large variety of testing (e.g., Guye & von Bastian, 2017 ). Nevertheless, instead of assessing many different constructs, it may be preferable to focus on one or two constructs at a time (e.g., fluid intelligence and WM). Such a practice would help reduce the number of tasks and the amount of fatigue.

Another argument concerns carryover and learning effects. The standard solution is to randomize the presentation order of the tasks. This procedure, which ensures that bias gets close to zero as the number of participants increases, is generally efficient if there is no reason to expect an interaction between treatment and order ( Elmes et al., 1992 ). If this is the case, another approach can be used: counterbalancing the order of the tasks. However, complete counterbalancing is difficult with large numbers of tasks, and in this case, one often has to be content with incomplete counterbalancing using a Latin square (for a detailed discussion, see Winer, 1962 ).

A final point made by Green et al. (2019) is that using large batteries of tasks increases the rate of Type I errors. Although this point is correct, it is not an argument against multi-indicator latent factors. Rather, it is an argument in favor because those do not suffer from this bias. In addition, latent factors aside, there are many methods designed for correcting α (i.e., the significance threshold) for multiple comparisons (e.g., Bonferroni, Holm, false-discovery rate). Increased Type I error rates are a concern with researchers who ignore the problem and do not apply any correction.

One reasonable argument is that latent factor analysis requires large numbers of participants. The solution is offered by multilab trials. The ACTIVE trial—the largest experiment carried out in the field of cognitive training—was, indeed, a multisite study ( Rebok et al., 2014 ). Another multisite cognitive-training experiment is currently ongoing ( Mathan, 2018 ).

To conclude this section, we emphasize two points. First, it is well known that in general, single tests possess low reliability. Second, multiple measures are needed to understand whether improvements occur at the level of the test (e.g., n -back) or at the level of the construct (e.g., WM).

Some methodological recommendations

We are not as naive as to believe that our analysis will deter researchers in the field to carry out much more research on the putative far-transfer benefits of cognitive training despite the lack of any empirical evidence. We thus provide some advice about the directions that should be taken so that not all resources are spent in search of a chimera.

Making methods and results accessible, piecemeal publication, and objective report of results

We broadly agree with the methodological recommendations made by Green et al. (2019) , such as reporting not only p values but also effect sizes and confidence intervals, and the need for well-powered studies. We add a few important recommendations (for a summary of the recommendations throughout this article, see Table 3 ). To begin with, it is imperative to put the data, analysis code, and other relevant information online. In addition to providing supplementary backup, this allows other researchers to closely replicate the studies and to carry out additional analyses (including meta-analyses)—important requirements in scientific research. By the same token and in the spirit of Open Science, researchers should reply to requests from meta-analysts asking for summary data and/or the original data. In our experience, response rate is currently 20% to 30% at best (e.g., Sala et al., 2018 ). Although we understand that it may be difficult to answer such replies positively when data were collected 20 years or more ago, there is no excuse for data collected more recently.

Key Recommendations for Researchers

Just like other questionable research practices, piecemeal publication should be avoided ( Hilgard et al., 2019 ). If dividing the results of a study into several articles cannot be avoided, the articles should clearly and unambiguously indicate the fact that this has been done and should reference the articles sharing the results.

There is one point made by Green et al. (2019) with which we wholeheartedly agree: the necessity of reporting results correctly and objectively without hyperbole and incorrect generalization. The field of cognitive training is littered with exaggerations and overinterpretations of results (see Simons et al., 2016 ). A fairly common practice is to focus on the odd statistically significant result even though most of the tests turn out nonsignificant. This is obviously capitalizing on chance and should be avoided at all costs.

In a similar vein, there is a tendency to overinterpret results of studies using neuroscience methods. A striking example was recently offered by Schellenberg (2019) , who showed that in a sample of 114 journal articles published in the last 20 years on the effects of music training, causal inferences were often made although the data were only correlational; neuroscientists committed this logical fallacy more often than psychologists. There was also a rigid focus on learning and the environment and a concurrent neglect of alternative explanations, such as innate differences. Another example consists in inferring far transfer when neuroimaging effects are found but not behavioral effects. However, such an inference is illegitimate.

The need for detailed analyses and computational models

As a way forward, Green et al. (2019) recommended well-powered studies with large numbers of participants. In a similar vein, and focusing on the n -back-task training, Pergher et al. (2020) proposed large-scale studies isolating promising features. We believe that such an atheoretical approach is unlikely to succeed. There is an indefinite space of possible interventions (e.g., varying the type of training task, the cover story used in a game, the perceptual features of the material, the pace of presentation, ad infinitum), which means that searching this space blindly and nearly randomly would require a prohibitive amount of time. Strong theoretical constraints are needed to narrow down the search space.

There is thus an urgent need to understand which cognitive mechanisms might lead to cognitive transfer. As we showed above in the section on meta-analysis, the available evidence shows that the real effect size of cognitive training on far transfer is zero. Prima facie, this outcome indicates that theories based on general mechanisms, such as brain plasticity ( Karbach & Schubert, 2013 ), primitive elements ( Taatgen, 2013 ), and learning to learn ( Bavelier et al., 2012 ), are incorrect when it comes to far transfer. We reach this conclusion by a simple application of modus tollens: (a) Theories based on general mechanisms such as brain plasticity, primitive elements, and learning to learn predict far transfer. (b) The empirical evidence shows that there is no far transfer. Therefore, (c) theories based on general mechanisms such as brain plasticity, primitive elements, and learning to learn are incorrect.

Thus, if one believes that cognitive training leads to cognitive enhancement—most likely limited to near transfer—one has to come up with other theoretical mechanisms than those currently available in the field. We recommend two approaches to identify such mechanisms, which we believe should be implemented before large-scale randomized controlled trials are carried out.

Fine analyses of the processes in play

The first approach is to use experimental methods enabling the identification of cognitive mechanisms. Cognitive psychology has a long history of refining such methods, and we limit ourselves to just a few pointers. A useful source of information consists in collecting fine-grained data, such as eye movements, responses times, and even mouse location and mouse clicks. Together with hypotheses about the processes carried out by participants, these data make it possible to rule out some mechanisms while making others more plausible. Another method is to design experiments that specifically test some theoretical mechanisms. Note that this goes beyond establishing that a cognitive intervention leads to some benefits compared with a control group. In addition, the aim is to understand the specific mechanisms that lead to this superiority.

It is highly likely that the strategies used by the participants play a role in the training, pretests, and posttests used in cognitive-training research ( Sala & Gobet, 2019 ; Shipstead et al., 2012 ; von Bastian & Oberauer, 2014 ). It is essential to understand these strategies and the extent to which they differ between participants. Are they linked to a specific task or a family of tasks (near transfer), or are they general across many different tasks (far transfer)? If it turns out that such general strategies exist, can they be taught? What do they tell researchers about brain plasticity and changing basic cognitive abilities such as general intelligence?

Two studies that investigated the effects of strategies are mentioned here. Laine et al. (2018) found that instructing participants to employ a visualization strategy when performing n -back training improved performance. In a replication and extension of this study, Forsberg et al. (2020) found that the taught visualization strategy improved some of the performance measures in novel n -back tasks. However, older adults benefited less, and there was no improvement in WM tasks structurally different from n -back tasks. In the uninstructed participants, n -back performance correlated with the type of spontaneous strategies and their level of detail. The types of strategies also differed as a function of age.

A final useful approach is to carry out a detailed task analysis (e.g., Militello & Hutton, 1998 ) of the activities involved in a specific regimen of cognitive training and in the pretests and posttests used. What are the overlapping components? What are the critical components and those that are not likely to matter in understanding cognitive training? These components can be related to information about eye movements, response times, and strategies and can be used to inspire new experiments. The study carried out by Baniqued et al. (2013) provides a nice example of this approach. Using task analysis, they categorized 20 web-based casual video games into four groups (WM, reasoning, attention, and perceptual speed). They found that performance in the WM and reasoning games was strongly associated with memory and fluid-intelligence abilities, measured by a battery of cognitive tasks.

Cognitive modeling as a method

The second approach we propose consists of developing computational models of the postulated mechanisms, which of course should be consistent with what is known generally about human cognition (for a similar argument, see Smid et al., 2020 ). To enable an understanding of the underlying mechanisms and be useful in developing cognitive-training regimens, the models should be in a position to simulate not only the tasks used as pretests and posttests but also the training tasks. This is what Taatgen’s (2013) model is doing: It first simulates improvement in a complex verbal WM task over 20 training sessions and then simulates how WM training reduces interference in a Stroop task compared with a control group. (We would, of course, query whether this far-transfer effect is genuine.) By contrast, Green, Pouget, & Bavelier’s (2010) neural-network and diffusion-to-bound models simulate the transfer tasks (a visual-motion-direction discrimination task and an auditory-tone-location discrimination task) but do not simulate the training task with action video-game playing. Ideally, a model of the effect of an action video game should simulate actual training (e.g., by playing Call of Duty 2 ), processing the actual stimuli involved in the game. To our knowledge, no such model exists. Note that given the current developments in technology, modeling such a training task is not unrealistic.

The models should also be able to explain data at a micro level, including eye movements and verbal protocols (to capture strategies). There is also a need for the models to use exactly the same stimuli as those used in the human experiments. For example, the chunk hierarchy and retrieval structures model of chess expertise ( De Groot et al., 1996 ; Gobet & Simon, 2000 ) receives as learning input the kind of board positions that players are likely to meet in their practice. When simulating experiments, the same stimuli are used as those employed with human players, and close comparison is made between predicted and actual behavior along a number of dimensions, including percentage of correct responses, number and type of errors, and eye movements. In the field of cognitive training, Taatgen’s (2013) model is a good example of the proper level of granularity for understanding far transfer. Note that, ideally, the models should be able to predict possible confounds and how modifications to the design of training would circumvent them. Indeed, we recommend that considerable resources be invested in this direction of research with the aim of testing interventions in silico before testing them in vivo ( Gobet, 2005 ). Only those interventions that lead to benefits in simulations should be tested in trials with human participants. In addition to embodying sound principles of theory development and testing, such an approach would also lead to considerable savings of research money in the medium and long terms.

Searching for small effects

Green et al. (2019 , p. 20) recognized the possibility that large effects are unlikely and that one should be content with small effects. They are also open to the possibility of using unspecific effects, such as expectation effects. It is known that many educational interventions bring a modest effect ( Hattie, 2009 ), and thus, the question arises as to whether cognitive-training interventions are more beneficial than alternative ones. We argue that many other interventions are cheaper and/or have specific benefits when they directly match educational goals. For example, games related to mathematics are more likely to improve one’s mathematical knowledge and skills than n -back tasks and can be cheaper and more fun.

If cognitive training leads only to small and unspecific effects, one faces two implications, one practical and one theoretical. Practically, the search for effective training features has to operate blindly, which is very inefficient. This is because current leading theories in the field are incorrect, as noted above, and thus there is no theoretical guidance. Thus, effectiveness studies are unlikely to yield positive results. Theoretically, if the effectiveness of training depends on small details of training and pre/post measures, then the prospects of generalization beyond specific tasks are slim to null. This is unsatisfactory scientifically because science progresses by uncovering general laws and finding order in apparent chaos (e.g., the state of chemistry before and after Mendeleev’s discovery of the periodic table of elements).

A straightforward explanation can be proposed for the pattern of results found in our meta-analyses with respect to far transfer—small to zero effect sizes, low or null true between-studies variance. Positive effect sizes are just what can be expected by chance, features of design (i.e., active vs. passive control groups), regression to the mean, and sometimes publication bias. (If you believe that explanations based on chance are not plausible, consider Galton’s board: It perfectly illustrates how a large number of small effects can lead to a normal distribution. Likewise, in cognitive training, multiple variables and mechanisms lead to some experiments having a positive effect, others a negative effect, with most experiments centered around the mean of the distribution.) Thus, the search for robust and replicable effects is unlikely to be successful.

Note that the issue with cognitive training is not the lack of replications and the lack of reproducibility, which plague large swathes of psychology: The main results have been replicated often and form a highly coherent pattern when results are put together in (meta-)meta-analyses. Pace Pergher et al. (2020) , we do not believe that variability of methods is an issue. On the contrary, the main outcomes are robust to experimental variations. Indeed, results obtained with many different training and evaluation methods converge (small-to-zero effect sizes and low true heterogeneity) and thus satisfy a fundamental principle in scientific research: the principle of triangulation ( Mathison, 1988 ).

Funding agencies

Although Green et al.’s (2019) article is explicitly about methodology, it does make recommendations for funding agencies and lobbies for more funding: “We feel strongly that an increase in funding to accommodate best practice studies is of the utmost importance” (p. 17). On the one hand, this move is consistent with the aims of their article in that several of the suggested practices, such as using large samples and performing studies that would last for several years, would require substantial amounts of money to be carried out. On the other hand, lobbying for an increase in funding is made without any reference to results showing that cognitive training might not provide the hoped-for benefits. The authors only briefly discussed the inconsistent evidence for cognitive training, concluding that “our goal here is not to adjudicate between these various positions or to rehash prior debates” (p. 3). However, in general, rational decisions about funding require an objective evaluation of the state of the research. Obviously, if the research is about developing methods for cognitive enhancement, funders must take into consideration the extent to which the empirical evidence supports the hypothesis that the proposed methods provide domain-general cognitive benefits. As we showed in the “Meta-Analytical Evidence” section, there is little to null support for this hypothesis. Thus, our advice for funders is to base their decisions on the available empirical evidence and on the conclusions reached by meta-analyses.

The Broader View

As discussed earlier, our meta-analyses clearly show that cognitive training does not lead to any far transfer in any of the cognitive-training domains that have been studied. In addition, using second-order meta-analysis made it possible to show that the between-meta-analyses true variance is due to second-order sampling error and thus that the lack of far transfer generalizes to different populations and different tasks. Taking a broader view suggests that our conclusions are not surprising and are consistent with previous research. In fact, they were predictable. Over the years, it has been difficult to document far transfer in experiments ( Singley & Anderson, 1989 ; Thorndike & Woodworth, 1901 ), industrial psychology ( Baldwin & Ford, 1988 ), education ( Gurtner et al., 1990 ), and research on analogy ( Gick & Holyoak, 1983 ), intelligence ( Detterman, 1993 ), and expertise ( Bilalić et al., 2009 ). Indeed, theories of expertise emphasize that learning is domain-specific ( Ericsson & Charness, 1994 ; Gobet & Simon, 1996 ; Simon & Chase, 1973 ). When putting this substantial set of empirical evidence together, we believe that it is possible to conclude that the lack of training-induced far transfer is an invariant of human cognition ( Sala & Gobet, 2019 ).

Obviously, this conclusion conflicts with the optimism displayed in the field of cognitive training, as exemplified by Green et al.’s (2019) article discussed above. However, it is in line with skepticism recently expressed about cognitive training ( Moreau, 2021 ; Moreau et al., 2019 ; Simons et al., 2016 ). It also raises the following critical epistemological question: Given that the overall evidence in the field of cognitive training strongly suggests that the postulated far-transfer effects do not exist, and thus the probability of finding such effects in future research is very low, should one conclude that the reasonable course of action is to stop performing cognitive-training research on far transfer?

We believe that the answer to this question is “yes.” Given the clear-cut empirical evidence, the discussion about methodological concerns is irrelevant, and the issue becomes searching for other cognitive-enhancement methods. However, although the hope of finding far-transfer effects is tenuous, the available evidence clearly supports the presence of near-transfer effects. In many cases, near-transfer effects are useful (e.g., with respect to older adults’ memory), and developing effective methods for improving near transfer is a valuable—and importantly, realistic—avenue for further research.

Acknowledgments

We thank Walter Boot, Daniel Simons, Laura Bartlett, Angelo Pirrone, and Whitney Zhang for comments on earlier drafts of this article. We dedicate this article to the memory of Frank L. Schmidt (1944–2021), who tirelessly encouraged researchers to use meta-analysis to summarize data and emphasized the dangers of ignoring sampling error, measurement error, and other kinds of artifacts.

1. Because our definition focuses on cognitive tasks, it does not include mostly physical activities, such as sport. In addition, note that the term “cognitive training” is also used in a different line of research in which the interest is in testing the limits of cognitive plasticity in ageing, for example by training younger and older participants to use mnemonics (e.g., Kliegl et al., 1989 ).

2. For a broader conceptualization of transfer, see Barnett and Ceci (2002) and Klahr and Chen (2011) .

3. When a random-effect meta-analysis is performed, the effect sizes are weighted on the inverse of the sum of their sampling error and the between-studies true variance (τ 2 ).

4. The article listed in this section contain extensive discussions of the meta-analyses carried out by other authors.

5. Au and colleagues (2020) violated the assumption of statistical independence by grouping meta-analyses with overlapping samples into a number of clusters. Although the clusters’ overall effect sizes were statistically independent to each other, these effect sizes and their sampling error variances were incorrectly calculated as a result of the aforementioned violation.

Transparency

Action Editor: Laura A. King

Editor: Laura A. King

Author Contributions

F. Gobet conceived the idea of the article. F. Gobet and G. Sala wrote the manuscript. Both authors approved the final manuscript for submission.

The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.

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How Can Cognitive Training Improve my Problem-Solving Skills?

Learn How Cognitive Training Can Benefit Your Problem-Solving Abilities

The most striking quality about powerhouses like Bill Gates or Elon Musk is their extraordinary ability to innovate creative solutions. Admittedly it’s easy to feel like they’re somehow different from us—somehow more naturally inspired  and resourceful than the average person. But believe it or not, you can also train your brain to think like a genius . In fact, you can accomplish amazing feats just by sharpening your problem-solving skills and learning to navigate challenges.

It all starts with cognitive training.

No matter who you are, your problem-solving abilities depend on the core cognitive skills you use every day. Cognitive training helps you hone those skills so you can accomplish more in the day-to-day of your professional life. Let’s take a closer look at those skills.

Cognitive training makes you better at solving problems alone or in a team.

How Does Problem-Solving Depend on Cognitive Skills?

Essentially, cognitive skills are the building blocks of all your thought processes.  In fact, they’re used so frequently that most of the time you aren’t even aware of them—much like breathing. However, once you start noticing and actively working on them, you’ll become more capable and effective than ever.

Cognitive skills related to information-processing and problem-solving :

• Direction and Orientation:  These skills make it easier to understand relationships in the environment. They allow you to give, take, and prioritize directions.
• Classification and Categorization:  These skills allow you to group and reorganize ideas, emotions, objects, actions, and time periods. They help you deal with complicated tasks.
• Environmental Awareness:  This is how well you see how different things are related. It helps you develop mental templates for dealing with similar situations.
• Analysis and Synthesis:  These skills break down ideas and challenges into smaller, more manageable pieces. They keep you from becoming overwhelmed by too much information.
• Motor Integration:  This skill makes it easier for you to set aside emotions and plan tasks out ahead of time.
• Concrete Sequencing:  This skill makes you more aware of what is and isn’t working when carrying out a plan. It helps you to think logically and identify potential problems.
• Pattern Recognition:  This skill helps you identify patterns and allows you to manage complicated or repetitive information.

How Will Cognitive Training Help?

Now, what can you do to develop those problem-solving skills and improve your on-the-job performance?

At Critical Thinking for Success, we start out by administering proficiency tests that identify which skills have the most room for growth . This lets us hone in on the skills that will benefit you the most. Once we’ve determined where to focus our efforts, we develop an individualized training regimen just for you . Furthermore, this training regimen involves daily exercises as well as in-person sessions.

Additionally, for each cognitive skill there are a variety of   games, puzzles, and other exercises to help your development.  For example, we use games and puzzles where moving one piece changes the rest of the board, forcing you to visualize a plan before you execute it rather jumping right into blind experimentation. This way, you train your brain to take an analytical approach and assess the inner logic and relationships behind a problem before you try to tackle it.

At our in-person sessions, we’ll review your progress with those exercises, as well as how you’ve been doing in your personal and professional life. We’ll discuss ways you can apply your cognitive training and suggest strategies for using them to face upcoming real-life challenges.

As you progress, you’ll see yourself having an easier time coming up with effective and creative solutions to the problems you face, and getting more done with less stress. That’ll make you more resourceful, more capable of handling whatever comes your way, and all-around a better business professional.

Ready to boost  your problem-solving skills and take the next step toward accomplishing your dreams?

Call Critical Thinking for Success at 847-845-0422 . Let’s set up a consultation, and discuss how we can train your brain to be a problem-solving machine!

Author:  Critical Thinking for Success

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Robert Kauffman Obituary

Robert Louis Kauffman – A Man Who Loved Helping People March 23, 1943 – April 14, 2024

Robert Louis Kauffman, 81 years old, of Gurnee, IL, affectionately known as Bob, departed from his life here on earth after a valiant battle with pancreatic cancer. Bob was a caring family man-devoted husband to Kit (Cathleen Mika), loving father to Katherine Specht (Brian), Tim (Ashley), and the late Jennifer; adoring grandfather of Jackson, Emily, Alison, Riley, and Joey; and loyal brother, uncle, and cousin to many Kauffman relatives.

Bob was born in Bremen, Indiana to Donald Kauffman and Madeline (Darscheid), the third of six children including brothers Dick (Rita), Keith (Marcy), Kathy Snyder (David), Carol Balentine and Susan O’Connor (Knute).

Bob was raised with strong Midwest farming roots that taught him the meaning and value of hard work and service to others from a young age which became the foundation of his life’s work. His formative education began at

Our Lady of the Lake Seminary in Syracuse, Indiana, where he explored his strong faith, but soon realized that the priesthood was not his calling. He continued his higher education at St. Joseph’s College in Rensselaer, Indiana, where he received a Bachelor of Sociology, and went on to receive his Master of Social Work at the University of Illinois in Chicago. From there his professional journey began, working as a counselor at a pharmaceutical company where his passion for helping people to deal with various life problems really began.

As his career evolved, Bob continued to expand his knowledge of how to help people. His personal life experiences also shaped him in profound ways, most notably the birth of his first child Jennifer who subsequently died at an early age. After recovering from this devastating loss, Bob was able to help others going through similar life-altering experiences with compassion and love.

Bob’s knowledge of the brain and human nature continued to open doors to new opportunities in his life. As a psychotherapist Bob counseled thousands of individuals over the course of his long career, helping people to improve their lives by offering strategies for happiness and self-fulfillment.

Bob was also a successful businessman and launched several entrepreneurial endeavors, most recently the company Critical Thinking for Success in 2009 which further expanded his impact in helping others. Through the use of neurofeedback, biofeedback, and cognitive therapy techniques, Bob was able to address a range of conditions from anxiety, depression, PTSD, brain injuries, and professional coaching for enhanced job performance.

Bob worked with a wide range of individuals including athletes, professional musicians, corporate CEO’s, and the U.S. Marines. His techniques were designed to address specific issues to improve lives. Bob also published his first book, “The Incredible Journey of Loving Ourselves” in 2022, providing a personal guide for finding our best selves. Bob firmly believed that self-improvement and learning was ongoing, and one was never too old to change the direction of their life with guidance, support, and self-love.

Bob was actively engaged with a broad network of family and friends, and especially enjoyed spending time with his grandkids. He was an avid fisherman and regularly took a group of friends up to his favorite Wine Lake fishing spot in Ontario Canada. He enjoyed movies, basketball, telling a good story or joke, and playing competitive games of poker and euchre with family and friends. He was also a humanitarian, serving on the advisory board of A Safe Haven Foundation in Chicago, a non-profit organization that strives to restore hope and opportunity to individuals in crisis by providing treatment, housing, support services, and career opportunities.

While Bob’s physical presence will no longer be with us, his lasting legacy of self-love and self-improvement will continue to bring comfort and hopefully inspire everyone who knew Bob to carry on his mission of making a difference in people’s lives.

On Monday May 20, 2024, at 1:00 pm., a funeral mass will be held at Holy Name Cathedral, 735 N. State Street, Chicago, IL (validated parking entrance at 14 W. Superior).

A Celebration of Life will follow at Plumber’s Hall, 1340 W. Washington Boulevard, Chicago, IL from 3:00 – 6:00 pm. Limited on-site parking is free (Washington gate entrance). Additional on-site parking is provided at a cost of $4.00 per hour, (1371 W. Randolph gate entrance).

Please feel free to attend both or either events of the memorial day.

It is Bob’s request, and of the family, that in lieu of flowers a donation be made to A Safe Haven Foundation: www.asafehaven.org.

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• December 19, 2023

20 Cognitive Behavioral Therapy (CBT) Techniques with Examples

Muhammad Sohail

Table of contents.

Cognitive Behavioral Therapy (CBT) stands as a powerful, evidence-based therapeutic approach for various mental health challenges. At its core lies a repertoire of techniques designed to reframe thoughts, alter behaviors, and alleviate emotional distress. This article explores 20 most commonly used cbt techniques. These therapy techniques are scientifcally valid, diverse in their application and effectiveness, serve as pivotal tools in helping individuals navigate and conquer their mental health obstacles.

Cognitive Restructuring or Reframing:

This is the most talked about of all cbt techniques. CBT employs cognitive restructuring to challenge and alter negative thought patterns. By examining beliefs and questioning their validity, individuals learn to perceive situations from different angles, fostering more adaptive thinking patterns.

John, feeling worthless after a rejected job application, questions his belief that he’s incompetent. He reflects on past achievements and reframes the situation, realizing the rejection doesn’t define his abilities.

Guided Discovery:

In guided discovery, therapists engage individuals in an exploration of their viewpoints. Through strategic questioning, individuals are prompted to examine evidence supporting their beliefs and consider alternate perspectives, fostering a more nuanced understanding and empowering them to choose healthier cognitive pathways.

During therapy, Sarah explores her fear of failure. Her therapist asks, “What evidence supports your belief that you’ll fail? Can we consider alternate outcomes?” Guided by these questions, Sarah acknowledges her exaggerated fears and explores more balanced perspectives.

Journaling and Thought Records:

Writing exercises like journaling and thought records aid in identifying and challenging negative thoughts. Tracking thoughts between sessions and noting positive alternatives enables individuals to monitor progress and recognize cognitive shifts.

James maintains a thought journal. Between sessions, he records negative thoughts about social situations. He then challenges these thoughts, jotting down positive alternatives and notices a shift in his mindset.

Activity Scheduling and Behavior Activation:

By scheduling avoided activities and implementing learned strategies, individuals establish healthier habits and confront avoidance tendencies, fostering behavioral change.

Emily, struggling with social anxiety, schedules coffee outings with friends. By implementing gradual exposure, she confronts her fear and eventually feels more comfortable in social settings.

Relaxation and Stress Reduction Techniques:

CBT incorporates relaxation techniques like deep breathing, muscle relaxation, and imagery to mitigate stress. These methods equip individuals with practical skills to manage phobias, social anxieties, and stressors effectively.

David practices deep breathing exercises when faced with work stress. By incorporating this technique into his routine, he manages work-related anxiety more effectively.

Successive Approximation:

Breaking overwhelming tasks into manageable steps cultivates confidence through incremental progress, enabling individuals to tackle challenges more effectively.

Maria, overwhelmed by academic tasks, breaks down her study sessions into smaller, manageable sections. As she masters each segment, her confidence grows, making the workload seem more manageable.

Interoceptive Exposure:

This technique targets panic and anxiety by exposing individuals to feared bodily sensations, allowing for a recalibration of beliefs around these sensations and reducing avoidance behaviors.

Tom, experiencing panic attacks, deliberately induces shortness of breath in a controlled setting. As he tolerates this discomfort without avoidance, he realizes that the sensation, though distressing, is not harmful.

Play the Script Until the End:

Encouraging individuals to envision worst-case scenarios helps alleviate fear by demonstrating the manageability of potential outcomes, reducing anxiety.

Facing fear of public speaking, Rachel imagines herself stumbling during a presentation. By playing out this scenario mentally, she realizes that even if it happens, it wouldn’t be catastrophic.

Shaping (Successive Approximation):

Shaping involves mastering simpler tasks akin to the challenging ones, aiding individuals in overcoming difficulties through gradual skill development.

Chris, struggling with public speaking, begins by speaking to small groups before gradually addressing larger audiences. Each step builds his confidence for the next challenge.

Contingency Management:

This method utilizes reinforcement and punishment to promote desirable behaviors, leveraging the consequences of actions to shape behavior positively.

To encourage healthier eating habits, Sarah rewards herself with a favorite activity after a week of sticking to a balanced diet.

Acting Out (Role-Playing):

Role-playing scenarios allow individuals to practice new behaviors in a safe environment, facilitating skill development and desensitization to challenging situations.

Alex, preparing for a job interview, engages in role-playing with a friend. They simulate the interview scenario, allowing Alex to practice responses and manage anxiety.

Sleep Hygiene Training:

Addressing the link between depression and sleep problems, this technique provides strategies for improving sleep quality, a critical aspect of mental well-being.

Lisa, struggling with sleep, follows sleep hygiene recommendations. She creates a calming bedtime routine and eliminates screen time before sleep, noticing improvements in her sleep quality.

Mastery and Pleasure Technique:

Encouraging engagement in enjoyable or accomplishment-driven activities serves as a mood enhancer and distraction from depressive thoughts.

After feeling low, Mark engages in gardening (a mastery activity) and then spends time painting (a pleasure activity). He finds joy in these activities, which uplifts his mood.

Behavioral Experiments:

This technique involves creating real-life experiments to test the validity of certain beliefs or assumptions. By actively exploring alternative thoughts or behaviors, individuals gather concrete evidence to challenge and modify their existing perspectives.

Laura believes people judge her negatively. She experiments by initiating conversations at social gatherings and observes that most interactions are positive, challenging her belief.

Externalizing:

Externalizing helps individuals separate themselves from their problems by giving those issues an identity or persona. This technique encourages individuals to view their problems as separate entities, facilitating a more objective approach to problem-solving.

Adam, dealing with anger issues, visualizes his anger as a separate entity named “Fury.” This helps him view his emotions objectively and manage them more effectively.

Acceptance and Commitment Therapy (ACT):

ACT combines mindfulness strategies with commitment and behavior-change techniques. It focuses on accepting difficult thoughts and emotions while committing to actions aligned with personal values, promoting psychological flexibility.

Sarah practices mindfulness exercises to accept her anxiety while committing to attend social events aligned with her values of connection and growth.

Imagery-Based Exposure:

This technique involves mentally visualizing feared or distressing situations, allowing individuals to confront and manage their anxieties in a controlled, imaginative setting.

Jack, afraid of flying, visualizes being on a plane, progressively picturing the experience in detail until he feels more comfortable with the idea of flying.

Mindfulness-Based Stress Reduction (MBSR):

MBSR incorporates mindfulness meditation and awareness techniques to help individuals manage stress, improve focus, and enhance overall well-being by staying present in the moment.

Rachel practices mindfulness meditation daily. By focusing on the present moment, she reduces work-related stress and enhances her overall well-being.

Systematic Desensitization:

Similar to exposure therapy, systematic desensitization involves pairing relaxation techniques with gradual exposure to anxiety-inducing stimuli. This process helps individuals associate relaxation with the feared stimuli, reducing anxiety responses over time.

Michael, with a fear of heights, gradually exposes himself to elevators first, then low floors in tall buildings, gradually working up to higher levels, reducing his fear response.

Narrative Therapy:

Narrative therapy focuses on separating individuals from their problems by helping them reconstruct and retell their life stories in a more empowering and positive light, emphasizing strengths and resilience.

Emily reevaluates her life story by focusing on instances where she overcame challenges, emphasizing her resilience and strength rather than her setbacks.

Each of these CBT techniques plays a unique role in helping individuals transform their thoughts, behaviors, and emotions. While some focus on cognitive restructuring, others emphasize behavioral modification or stress reduction. Together, they form a comprehensive toolkit empowering individuals to navigate their mental health challenges and foster positive change in their lives.

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Improve cognitive function with fun brain training

Cognitive abilities can often decline with age. Because away from the rigors of school and new mental challenges, your brain might no longer encounter activities that require the same level of flexibility and focus. These cognitive declines can result in difficulty learning new things, solving problems, and remembering important details . 

Curious about what you can do to keep your mind sharp? Keep reading to explore the science behind cognitive training, the benefits of brain training, and practical tips for incorporating brain games into your daily life.

Understanding cognitive function

Cognitive function is essential for daily life. Broadly speaking, it's how your brain interacts with the world around you. It involves a variety of different, but related, cognitive skills, including learning, thinking, reasoning, remembering, problem-solving, decision-making, and multitasking.

As you age, your brain naturally shrinks, decreasing the number of synapses and receptors powering your cognitive functions. The rate of this decrease varies by person—and it can be affected by other factors, such as family history or cognitive impairment—but the brain is capable of change, especially as it ages. 

The science behind cognitive training

Cognitive training is a type of brain exercise that involves challenging the brain with a diverse set of mentally stimulating tasks. It's not unlike physical training. Both physical and mental training help you develop greater coordination, flexibility, stamina, and concentration . But instead of using weights or other equipment to build your muscles, cognitive training involves the use of games and activities that work out your mind. 

Research suggests that training cognitive skills can improve brain function . And the key to successful cognitive training lies in understanding how the brain works. The brain is composed of billions of neurons that form complex networks. These networks are responsible for processing information and creating new memories. When these neural networks are strengthened, which is what cognitive training is designed to do, cognitive function can be improved. 

In other words, the more you train your brain, the more you're strengthening the brain functions associated with working memory, sustained attention, clear communication, processing speed, mental math, and more.

The benefits of fun brain games for cognitive training

Brain games are a popular form of cognitive training. They are designed to challenge the brain in an engaging way. The reason for this is twofold: An effective brain game needs to improve cognitive function while also being enjoyable to play. A lengthy, exhausting brain training program isn't doing anyone any favors if nobody wants to use it.

From short-term memory games to logic puzzles and more, brain games come in all different shapes, sizes, and formats. Some people prefer pen-and-paper challenges, while others turn to apps and websites. The benefit of this variety is that you can train your brain anywhere at any time, whether you're at home or on the go, in a waiting room or on a plane. 

However, one thing to keep in mind is that not all brain games have the same cognitive benefits. If you're serious about improving cognitive functions, you'll want to focus on brain games that are backed by actual science and rooted in practical skills—as opposed to those that are simply fun. 

With the Elevate app, you can access and learn how to play 40+ entertaining games created in collaboration with experts in neuroscience and cognitive learning and based on scientific research. And the games cover a wide range of skills, including writing , reading, memory , mental math , vocabulary , and more.

Tips and techniques for cognitive training

In addition to brain games, there are other strategies for cognitive training. The most basic and important one: Taking care of your body. Regular exercise, a healthy diet, and adequate sleep are essential. Exercise increases blood flow to the brain, which improves cognitive function. A healthy diet provides the brain with the necessary nutrients for optimal function. And adequate sleep is essential for memory consolidation and processing.

Trying new activities is another way to engage in cognitive training. Learning a new language or discovering how to play an instrument, for example, challenges the brain and can help improve cognitive function. Studies have shown that learning to play an instrument can positively affect non-musical cognitive abilities . So if you're looking for a new challenge and a way to improve your cognitive abilities, consider trying something completely new.

And when it comes to protecting your brain and its cognitive abilities, prioritize your mental health as well. Considering the link between stress and underperforming cognitive function , checking in with your emotions, taking breaks, and trying meditation can help you manage your anxiety and help prevent cognitive decline. 

You can take the first steps toward prioritizing your mental health by downloading the Balance app —your entire first year is completely free.

Cognitive training for personal and professional growth

Cognitive training isn't just for boosting brain power. It can also be used to reach personal and professional goals. For students, cognitive training can improve academic performance, helping them to better understand and retain information. It can also help with test-taking skills, such as problem-solving, short-term memory recall, processing speed, and multitasking. 

For professionals, cognitive training can help improve communication, decision-making, and problem-solving skills , giving them a competitive edge in the workplace. Additionally, cognitive training can help with career advancement by increasing confidence and preparing individuals for new roles or responsibilities.

Elevate's brain games are specifically designed to help you build practical, real-world skills that can boost productivity, earning power, and self-confidence. And with a fully personalized program , achievements, and daily streaks, the app makes cognitive training a quick, easy, and fun part of your daily routine, ensuring you stay sharp for years to come.

Start strengthening your cognitive skills today

Cognitive decline is a natural part of aging, but there are practical steps you can take to keep your mind focused and flexible. Most importantly, look after your mental and physical health. You can't keep your brain sharp if you're neglecting your overall well-being. 

Beyond taking care of yourself, brain games are one of the best ways to strengthen cognitive function—and certainly the most fun. With the Elevate app, you can make cognitive training a part of your daily life with a program that's personalized to how you learn. You'll get customized, interactive, and fun brain workouts featuring 40+ games backed by science and designed to improve your communication, mental math, memory skills, and more. 

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Enhancing your cognitive abilities: an introduction to brain training

• Learn what brain training is, its benefits, and how you can easily get started training your brain. 

The best brain games for seniors: keeping your mind sharp and active

• Discover the benefits of fun brain games, which are an effective way to keep your mind sharp and active. 

Unlock your cognitive potential with memory training exercises

• Discover how memory training exercises can improve your cognitive function.

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What is Brain Training?

Brain training is an active behavior that stimulates neuronal activity in the brain. Our brain training games are much more than just a computer game. Trusted by doctors and clinicians around the world, our brain training programs are developed, tested, and have been analyzed through rigorous scientific research for over 20 years. The brain is responsible for controlling our entire body and as we consider brain function as a health factor, we must put in effort to be more brain responsible.

Brain Training Apps

Brain training apps offer a convenient and easy way to exercise the mind. With activities such as problem solving, memory recall, focus training, word games and more, users are able to challenge their cognitive abilities while having fun at the same time. These apps can be tailored to suit the user's specific needs and preferences, making it easier for them to stay motivated and engaged with the activities. So, why not give your brain a workout? There's no better time than now to get your neurons firing and start exercising those brain cells!

Brain Health

The importance of a healthy brain cannot be understated in addition to physical health. It is essential to maintaining our advanced cognitive functioning, emotional wellbeing, and overall quality of life. Keeping the brain active allows for better problem-solving skills, enhanced memory, improved concentration and focus, and attempts to prevent cognitive decline.

Regular exercise, maintaining a balanced diet, getting enough sleep and engaging in brain training activities are all key components of a healthy brain. Other factors may be contributing to brain health problems and regular monitoring of scores over time can show meaningful changes as they may occur. By using CogniFit you can be more brain healthy and supplement your physical exercise and regularly train your brain!

Cognitive Health

Improving cognitive health starts with understanding the importance of a healthy brain and how to maintain it. Regular exercise is key in ensuring that the brain remains alert and sharp, as physical activity helps improve blood flow to the brain which is essential for its functioning.

Eating a balanced diet will also help keep the brain in top shape, as adequate nutrition is essential for helping the brain perform its best. Additionally, getting enough sleep and managing stress levels are important in keeping cognitive health at its peak. Lastly, engaging in activities such as puzzles and games can help keep the brain active and sharp and identify the earliest signs of problems like memory loss.

Are you ready to give your brain the care it deserves? With these tools at your fingertips, you will be well on your way towards achieving optimal cognitive health. Don’t let your time go to waste – get started being CogniFit today!

Brain Training Benefits

• Improved memory recall
• Enhanced executive functions
• Increased cognitive function and processing speed
• Enhanced critical thinking skills
• Improved focus and concentration
• Lower risk of age-related cognitive decline

Brain training can also be beneficial in reducing the risk of age-related mental decline. As we age, our brain’s ability to function at a high-level decreases over time due to the natural process of aging and the decrease in blood flow to the brain. (Shah et al., 2017)

Through regular cognitive activities such as those provided by brain training apps, we are able to maintain or even improve our cognitive health. Cognitive exercises help keep neurons firing and engage both short-term and long-term memory.

Additionally, research has shown that engaging in mentally stimulating activities like puzzles or word games can delay age-related mental decline by up to seven years! With all these benefits it is clear why engaging in regular brain training is so important for maintaining a healthy mind.

Brain training is an effective tool for improving overall cognitive functioning. It helps enhance memory recall, problem-solving abilities, focus, concentration and creativity skills. Put it to the test with a cognitive test .

Cognitive training

Crossword puzzles have been a popular form of cognitive training since the early 1900s. They are a great way to exercise your brain and increase mental sharpness, as they require players to think critically and solve complex problems. Crossword puzzles help with memory recall, problem-solving skills, focus and concentration, creativity, and even language learning. Studies have shown that solving crossword puzzles can help reduce age-related decline in cognitive function by up to seven years!

Crosswords consist of grids filled with clues, which when solved correctly will create an interconnected set of words across the whole puzzle. In order to complete these puzzles, you must use your cognitive skills in order to decipher the clues and figure out what words fit into each clue.

This type of cognitive training helps stimulate both short-term, long-term, and working memory as well as provide creative ways to think about how each clue can be solved. Additionally, crossword puzzles also help improve language proficiency by providing an opportunity for users to learn new words or review previously learned ones while working their way through the puzzle.

Overall, crossword puzzles are a great form of mental exercise for anyone looking for a fun yet challenging activity. Solving them regularly can help keep the brain active and sharp while also reducing your risk of age-related mental decline. So why not give it a try? Pick up a newspaper or download one online today – it’s time to get those neurons firing!

Trusted by Researchers and Doctors

Cognitive training is gaining traction among doctors and researchers as a reliable method to promote mental wellbeing. Studies have shown that engaging in regular cognitive exercises can produce measurable benefits, such as improve memory, reduce brain age scores, increased focus and concentration, and improve mental skills.

These results are being noticed by doctors, who recognize the value of brain training for their patients. In fact, many medical professionals suggest incorporating cognitive exercises into their patient’s care plans to help improve overall mental health. This is especially true for older adults who may be at greater risk for cognitive impairment or those with neurological conditions that require special attention. Not only does cognitive training provide the opportunity to stay mentally sharp and alert, but it can also help reduce stress levels and even improve mood.

With a variety of activities ranging from problem solving and memory recall to language learning, users are able to keep their brains active while having fun at the same time. Furthermore, brain training apps allow users to tailor the activities to their own needs and preferences. This makes it simpler for them to stay motivated and engaged with the exercises which helps lead to better results in the long run.

Doctors are taking notice of this trend as well which is why they are increasingly recommending brain training apps as part of comprehensive care plans for their patients. With all these benefits in mind it’s easy to see why cognitive training has become such an important part of healthcare today! Doctors understand that staying mentally sharp is just as important as physical fitness when it comes to maintaining one's overall health and wellbeing – so don’t let your time go to waste – get started being CogniFit today!

What are examples of brain training?

Brain training exercises come in a variety of forms, such as puzzles, word games, quizzes, cool math games, cognitive tasks and more. Puzzles like our mini crossword puzzles are popular for their ability to stimulate both short-term and long-term memory as well as provide creative ways to think about how each clue can be solved. Additionally, they also help improve language proficiency by providing an opportunity for users to learn new words or review previously learned ones.

More research has been published supporting the psychology and data regarding improvement related from playing video games, the use of a musical instrument, socialization, and other active vs passive sedentary behaviors like watching TV. ( https://www.ncbi.nlm.nih.gov/ PMCID: PMC6182813 ) Keep your brain healthy with a free trial today and try our mind games. Individual differences will be marked with your own Brain Age Grade.

The Power of Personalized Brain Training

Our personalized brain training programs challenge players to answer questions and progress through difficulty levels in over 21 different cognitive domains. They help enhance neuronal connectivity while also improving cognitive functioning and processing speed. (Lebowitz et al., 2012)

Cognitive tasks like brain training and cognitive assessment can also be used for identifying problems early in your brain plasticity. These activities are designed to test participants' intelligence and mental agility by having them complete weekly goals or practice and train areas they feel they may be struggling.

All these types of brain training exercises have one thing in common: they help keep the mind active by engaging both short-term and long-term memory which in turn helps improve overall brain speed and cognitive strengths. So why not give it a try? Pick up your cell phone, tablet and download the CogniFit app today – it’s time to get those neurons firing!

Brain Games and Brain Training Programs

Do you want to keep your mind sharp? Lets take your brain structure and cognitive health seriously and develop new memories by trying three games right now for free. If you feel like you want to keep your brain healthy and active then sign up for CogniFit today and get your baseline scores from our Cognitive Assessment Battery and watch how your scores change over time. You might be surprised what just a few hours of brain training might do for you!

As heart disease has the stethoscope to monitor, CogniFit is like a thermometer for your brain. Measure how well over 22 different cognitive domains are functioning at any given time. Our AI systems will identify your weakest areas and recommend training for the areas that could use improvement. Explore our neuropsychological testing collection.

More Than Just Computer Games

Popular video games based in repetitive tasks are virtually the opposite of science based systematic brain training tasks. Human cognition is far too complex and repeating simple tasks over and over do not create significant changes in the brain that would transfer to other real life tasks.

Repetition is the antithesis of cognitive training, since only by presenting the brain with new information and new challenges can we expect new connections between neurons to be formed. Thus, repetition is a sure way to reduce the value of cognitive training.

Serious brain training tasks are not just tasks given over and over again, but rather a highly individualized system that aims to optimize the challenge for each user. This is not something that a simple computer game can provide, nor can we expect that consecutive testing will do the job. Using a simple "one size fits all" approach is inadequate.

In addition, training requires immediate feedback concerning success and failure and on-going dificulty personalization for the next session. This aspect of learning (and cognitive training is just another instance of learning) has been known to scientists for a very long time indeed.

CogniFit products incorporate all these missing features into the brain training regimen.

Brain Training FAQs

Do brain exercises really work.

Many people are concerned with this question and at CogniFit we feel that the answer to this should come from the science that we have been conducting for over 20 years. Scientific research on the efficacy of brain exercises over the extend of hundreds of research publications provides comprehensive evidence that cognitive training can significantly improve cognitive abilities.

What is the best way to train the brain?

The best way to begin training your brain is to create an account at CogniFit, after creating an account you will receive your baseline scores and our AI algorithm will help you detect areas for improvement!

What is brain training supposed to do?

Brain training is supposed to stimulate the neurons in your brain to activate and connect new neuronal pathways. By performing a certain behavior you "train," the cells in your brain to perform better on suggested tasks and there is a phenomenon called "transference," which explains the transference effect of cognitive skill progression into improved daily living activities.

What age is best for brain training?

We recommend people start brain training from the age of 7. Basically anybody that can use a smartphone, tablet, or computer can access our simple and fun brain training programs to improve and monitor their scores over time and learn more about how to improve brain health as we age.

Shah, T. M., Weinborn, M., Verdile, G., Sohrabi, H. R., & Martins, R. N. (2017). Enhancing Cognitive Functioning in Healthly Older Adults: a Systematic Review of the Clinical Significance of Commercially Available Computerized Cognitive Training in Preventing Cognitive Decline. Neuropsychology Review, 27(1), 62–80. https://doi.org/10.1007/s11065-016-9338-9.

Lebowitz, M., Dams-O’Connor, K., & Cantor, J. (2012). Feasibility of computerized brain plasticity-based cognitive training after traumatic brain injury.. Journal of rehabilitation research and development. https://doi.org/10.1682/JRRD.2011.07.0133.

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Cognitive Training in Mental Disorders: Update and Future Directions

Information & authors, metrics & citations, view options, conclusions, introduction.

How is Cognitive Training Defined?

Mental illness, neuroplasticity, and cognitive training.

Neural Targets for Cognitive Training in Mental Illness

Current state of knowledge, schizophrenia., attention deficit hyperactivity disorder (adhd)., anxiety disorders., mood disorders., substance use disorders., autism spectrum disorders (asds)., other disorders., predictors and moderators of response to cognitive training, learner characteristics, age and neurodevelopmental effects., cognitive function and brain reserve., motivation and emotional state., features of training, training approaches..

Intensity and progression of training.

Adjunctive cognitive-enhancing interventions, physical exercise., pharmacological agents., brain neuromodulation., trial design issues.

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Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

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From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

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Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

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Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Lumos Labs conducted a randomized study of Lumosity brain training and published the results in a peer-reviewed research journal.

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After 10 weeks, Lumosity users improved more than the control group on our assessments of working memory, short term memory, processing speed, problem solving, fluid reasoning, and overall cognitive function.

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Future research should address the risk of inadvertent experimenter bias and the risk of attrition bias in this study, as both the Lumosity and crossword groups had approximately 50% attrition rate. As with all scientific research, there is also a risk of publication bias.

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Enhancing Cognitive Abilities with Comprehensive Training: A Large, Online, Randomized, Active-Controlled Trial

Affiliations.

• 1 Department of Research and Development, Lumos Labs, San Francisco, California, United States of America.
• 2 Department of Psychology, Wheaton College, Norton, Massachusetts, United States of America.
• 3 Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan, United States of America; Department of Pediatrics, Wayne State University School of Medicine, Detroit, Michigan, United States of America.
• PMID: 26333022
• PMCID: PMC4557999
• DOI: 10.1371/journal.pone.0134467

Background: A variety of studies have demonstrated gains in cognitive ability following cognitive training interventions. However, other studies have not shown such gains, and questions remain regarding the efficacy of specific cognitive training interventions. Cognitive training research often involves programs made up of just one or a few exercises, targeting limited and specific cognitive endpoints. In addition, cognitive training studies typically involve small samples that may be insufficient for reliable measurement of change. Other studies have utilized training periods that were too short to generate reliable gains in cognitive performance.

Methods: The present study evaluated an online cognitive training program comprised of 49 exercises targeting a variety of cognitive capacities. The cognitive training program was compared to an active control condition in which participants completed crossword puzzles. All participants were recruited, trained, and tested online (N = 4,715 fully evaluable participants). Participants in both groups were instructed to complete one approximately 15-minute session at least 5 days per week for 10 weeks.

Results: Participants randomly assigned to the treatment group improved significantly more on the primary outcome measure, an aggregate measure of neuropsychological performance, than did the active control group (Cohen's d effect size = 0.255; 95% confidence interval = [0.198, 0.312]). Treatment participants showed greater improvements than controls on speed of processing, short-term memory, working memory, problem solving, and fluid reasoning assessments. Participants in the treatment group also showed greater improvements on self-reported measures of cognitive functioning, particularly on those items related to concentration compared to the control group (Cohen's d = 0.249; 95% confidence interval = [0.191, 0.306]).

Conclusion: Taken together, these results indicate that a varied training program composed of a number of tasks targeted to different cognitive functions can show transfer to a wide range of untrained measures of cognitive performance.

Trial registration: ClinicalTrials.gov NCT-02367898.

Trial registration: ClinicalTrials.gov NCT02367898 .

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Conflict of interest statement

Competing Interests: Lumos Labs, Inc. funded the research through the development of its software tools. JLH, DAS, KK, FF and MS are employed at Lumos Labs, the company that produces the cognitive training program Lumosity that is used in this study. These authors hold stock options in the company. RAN works as a consultant for Lumos Labs. MET is on the Scientific Advisory Board of Lumos Labs and holds stock options in the company. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Fig 1. CONSORT flow chart of participants…

Fig 1. CONSORT flow chart of participants in the study.

Fig 2. Change in composite score (Grand…

Fig 2. Change in composite score (Grand Index) for the cognitive training treatment and crossword…

Fig 3. Change in individual assessments of…

Fig 3. Change in individual assessments of cognitive ability.

Error bars represent confidence intervals bootstrapped…

Fig 4. Change in composite score (Grand…

Fig 4. Change in composite score (Grand Index) by number of active days in treatment…

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10 Brain Exercises That Boost Memory

You already know physical fitness supports good health, but you may not realize exercising your mind is equally important to keep your brain in top shape.

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The old adage “use it or lose it” applies not only to our physical health but also to our cognitive health. We know that regular physical exercise is important, especially as we get older and want to reduce our risk of developing diseases and other health issues associated with aging. For instance, strength exercises can help build muscle and boost bone density, per the Mayo Clinic ; balance exercises can help prevent falls, per  MedlinePlus ; and regular moderate-to-vigorous exercise can help maintain your range of motion to keep you limber, according to the National Institute on Aging (NIA) .

Similarly, your brain's cognitive reserve — its ability to withstand neurological damage due to aging and other factors without showing signs of slowing or memory loss — can also benefit from exercise, both physical and cognitive. Just as weight workouts add lean muscle to your body and help you retain muscle in your later years, the NIA notes that following a brain-healthy lifestyle and performing regular, targeted brain exercises may help increase your brain’s cognitive reserve, though more research is needed to confirm the effects.

A Whole-Body Approach to a Healthy Brain

So what types of exercises might benefit your brain? Research suggests that when it comes to keeping your mind sharp, exercising your body as well as your mind and sticking to healthy habits is the ideal formula.

Authors of a  study published in July 2019 in  The Journal of the American Medical Association followed about 196,400 participants ages 60 and older who didn’t have cognitive impairment or dementia when they joined the study for eight years. They gathered data on participants’ lifestyle habits, such as current smoking status, regular physical activity, healthy diet, and alcohol consumption. Ultimately, researchers found that a healthy lifestyle was associated with a lower dementia risk among participants, regardless of genetic risk for Alzheimer’s disease and related dementias.

Another study, published in Neurology in July 2020 , found that people who participate in multiple healthy behaviors significantly reduce their risk for Alzheimer’s disease, the most common form of dementia. For about six years, the study tracked five healthy lifestyle behaviors — nonsmoking, regular physical activity, low to moderate alcohol consumption, adherence to a Mediterranean-style diet, and engagement in activities that boost cognitive skills — in nearly 2,800 adults and found that those who followed at least four of the behaviors were about 60 percent less likely to develop Alzheimer’s disease.

“Approaches to brain health include a well-balanced diet low in fat, low in cholesterol, and high in antioxidants,” says  Robert Bender, MD , section chief of the Geriatric and Memory Center at Broadlawns Medical Center in Des Moines, Iowa. Foods high in antioxidants include nuts, fruits (especially berries), veggies, chocolate, and herbs and spices, past research  notes.

In addition to good nutrition, regular exercise can promote vascular health to help protect brain tissue. Avoiding ruts and boredom is also critical. “The brain wants to learn new things,” says Dr. Bender, adding that some researchers believe people are more vulnerable to dementia when they pay less attention to the things around them. “When the brain is passive, it has a tendency to atrophy,” he adds. Therefore, sedentary and relatively passive activities, such as sitting in front of a TV for hours a day, can be detrimental to brain health over time.

Physical exercise can also be particularly beneficial for the brain. In a small study published in September 2018 in the journal  Proceedings of the National Academy of Sciences , researchers found that a single 10-minute period of low-intensity pedaling on a stationary bike was associated with increased activity in the brain’s hippocampus, the part of the brain responsible for creating new memories and remembering facts and events.

And a small study published in July 2019 in the  Journal of the International Neuropsychological Society found that a single moderate-intensity workout session immediately before a cognitive task resulted in greater brain activation. The researchers measured the brain activity of 26 healthy adults ages 55 to 85 on two separate days. On one day, they had participants rest for 30 minutes before identifying famous and nonfamous names; on a separate day, they had participants pedal a stationary bike for 30 minutes before doing the same activity. The result: There was significantly greater brain activation after exercise. This finding led researchers to conclude that exercise can immediately change the way our brains function, which added to existing scientific evidence that physical activity helps strengthen brain function and memory.

10 Brain Exercises to Boost Memory and Cognitive Function

In addition to following the aforementioned healthy lifestyle habits, you can also keep your mind and memory sharp with exercises to train your brain — and you don’t have to break the bank to do so. While there are scores of computer games and apps that promise to enhance cognitive function, there isn’t any definitive research that shows these products have significant neurological benefits for older adults. A meta-analysis of eight clinical trials published in February 2020 in the Cochrane Database of Systematic Reviews found that while computer cognition training was associated with small, short-term cognitive benefits, there’s not enough high-quality research to support the use of brain games for preventing dementia or improving long-term cognitive function.

Health experts recommend sticking to brain training that involves real-world activities instead. Exercises to strengthen brain function should offer novelty and challenge. “Almost any silly suggestion can work,” says  David Eagleman, PhD , a neuroscientist and adjunct professor of psychology and public mental health and population sciences at the Wu Tsai Neurosciences Institute at Stanford University in California. “Drive home via a different route. Brush your teeth with your opposite hand. The brain works through associations, [which is why it’s easier to memorize lyrics than it is to try to remember the same words without music], so the more senses you involve, the better.”

Your morning newspaper is a great place to start. “Simple games like Sudoku and word games are good, as well as comic strips where you find things that are different from one picture to the next,” says  John E. Morley, MD , a professor of medicine in the division of geriatric medicine at St. Louis University in Missouri. In addition to word games, Dr. Morley recommends the following exercises to sharpen your mental skills. (Keep in mind that there’s a lack of high-quality research in this area; these recommendations are based on Morley’s clinical experience.)

• Test your recall. Make a list — grocery items, things to do, or anything else that comes to mind — and memorize it. An hour or so later, see how many items you can recall. Make the list as challenging as possible for the greatest mental stimulation. One small past study suggested that writing and organizing lists helped older adults recall word lists more effectively.
• Let the music play.  Learn to play a musical instrument or join a choir. Learning new and complex skills is good for the aging brain, and a  past review published in The Gerontologist suggested that musical activities (like playing a musical instrument, singing in a choir, or taking piano lessons) showed particular promise for healthy brain aging, though research is limited.
• Do math in your head.  Figure out problems without the aid of a pencil, paper, or computer. One small study, published in Advances in Experimental Medicine and Biology in 2021 , suggested that solving math problems had a positive effect on participants’ cognition. You can make this exercise more difficult — and athletic — by walking at the same time.
• Take a cooking class. Learn how to cook a new cuisine. Cooking uses a number of senses — smell, touch, sight, and taste — that involve different parts of the brain. Plus, you’ll use cognitive skills like planning the meal, problem-solving, crafting a grocery list, multi-tasking, and organizing, according to the Cleveland Clinic .
• Learn a foreign language.  The listening and hearing involved in learning a new language stimulates the brain. Plus, being bilingual was associated with a lower risk of developing dementia in one  meta-analysis published in October 2020 in Psychonomic Bulletin & Review .
• Create word pictures.  Visualize the spelling of a word in your head, and then try to think of other words that begin (or end) with the same two letters.
• Draw a map from memory.  After returning home from visiting a new place, try to draw a map of the area. Repeat this exercise each time you go somewhere new. One  past study , which focused on London taxi drivers (who are expected to memorize the complex layout of the city), found that drivers who successfully memorized the city map showed permanent changes to brain structure and better cognitive function.
• Challenge your taste buds.  When eating, try to identify individual ingredients in your meal, including subtle herbs and spices.
• Refine your hand-eye coordination.  Take up a new hobby that involves fine motor skills, and can help you keep your hand-eye coordination sharp. Per  Harvard Health Publishing , this could include racquet sports, tai chi, knitting, drawing, painting, or playing video games.
• Learn a new sport.  Start doing an athletic exercise. A  review published in Frontiers in Psychology in December 2019 noted that boosting your balance, strength, and aerobic capacity — that is, your body’s ability to use oxygen for energy — can help protect your brain as you age. Morley specifically suggests yoga, golf, or tennis as exercises that boost brain health, while Harvard Health Publishing  recommends swimming for its brain-boosting benefits.

Soon people will realize they can take steps to keep their brains healthy, just as they know they can prevent heart disease by taking certain actions, says Bender: “In the coming decade, I predict brain wellness to be right up there with heart health, now that there’s proof that living a brain-healthy lifestyle works!”

Additional reporting by Lisa Rapaport .

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What Is Cognitive Training?

Just a few years ago, experts believed that the brain was like a sealed black box, and you were stuck with whatever nature gave you at birth. Now it has become evident that our brains can keep adapting and developing new abilities throughout our lifetime. This ability to reorganize and create new pathways is called neuroplasticity, and it’s the science behind cognitive training, a tool which can be utilized by educators and health care professionals to supplement and help enhance their therapeutic interactions with their clients. Research has shown that systematic brain training with the help of a “brain coach” can potentially result in the improvement of a number of cognitive skills including attention, working memory, problem solving abilities, reading and, in some cases, psychosocial functioning.

Cognitive training is used by psychologists, neuropsychologists, speech therapists, occupational therapists, psychiatrists, and other clinical rehabilitation medicine specialists as a technique within their treatment program to help improve an individual’s ability to function after a brain injury or other neurological event, such as a stroke. The exercises are used as a tool to help achieve targeted therapeutic goals, such as enhancing self-esteem, training frustration tolerance, and developing problem solving strategies. It can also be used in the school setting, where it may potentially ameliorate problems associated with learning difficulties. The goal is to improve memory, attention, perception, reasoning, planning, judgment, general learning, and overall executive functioning. Some research has shown that developing these cognitive abilities can lead, in turn, to improvements in self-awareness, self-confidence, and emotional stability.

Various meta-cognitive coaching strategies that focus on developing coping skills or positive thinking can be applied interactively during cognitive training. The benefit is that the individual through trial and error can learn to apply these new strategies and approaches in order to problem solve how to enhance their cognitive performance. For example, a trainer might help a client to develop the habit of writing down and prioritizing daily tasks or to improve the skills needed to organize and categorize household items or grocery lists by learning how to pause and quickly take notes during an exercise.

There are a number of reasons why the computer is an ideal training partner for exercising the mind. The computer makes it easy for the trainer to customize training and to track progress. It not only provides a wide variety of different types of exercises, both visual and auditory, but also automatically becomes more challenging as the client progresses. Clients will be continually directed to develop their cognitive skills to the maximum of their capabilities. The computer is also non-judgmental and never loses patience! And, perhaps best of all, clients associate computer “games” with fun.

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Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism

Jin Liu Hyesang Chang Daniel A. Abrams Julia Boram Kang Lang Chen , Santa Clara University Follow Miriam Rosenberg-Lee Vinod Menon

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Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

© 2023, Liu, Chang et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Liu, J., Chang, H., Abrams, D. A., Kang, J. B., Chen, L., Rosenberg-Lee, M., & Menon, V. (2023). Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism. eLife, 12, e86035. https://doi.org/10.7554/eLife.86035

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The Role of the Workforce System in Addressing the Opioid Crisis: A Literature Review

Publication info, research methodology, description, other products.

This literature review describes findings from studies on various employment and training interventions to 1) assist individuals in recovery, 2) provide assistance to employers preventing opioid use disorder and creating a recovery-friendly workplace, and 3) develop the health care workforce to address the opioid crisis. The review was developed as part of an implementation evaluation of six Dislocated Worker Demonstration Grants to address the National Health Emergency (NHE) of the opioid crisis. Products form the study also include a resource guide, final report, and four short briefs on promising strategies.

As this review notes, the evidence base for employment interventions specifically aimed at or tested with people with opioid use disorder is limited and, that, while some of the approaches have been rigorously tested, others have not yet been evaluated but are seen as potentially promising practices. The research reviewed covers such approaches as:

• Intensive case management, as found in various models, such as the individual placement and support (IPS) model, a counseling model based on the interpersonal cognitive problem solving (ICPS) method; and a strategy based on the customized employment support (CES) vocational model;
• Use of "contingency management," a treatment approach that provides privileges or rewards to participants who exhibit desired behaviors;
• "Lighter-touch" employment or vocational services for people receiving substance use disorder treatment;
• Workplace prevention initiatives, employee assistance programs, recovery-friendly workplace initiatives, and modifications in workplace drug testing; and
• Innovative methods to increase the reach and breadth of training for health care professionals, strategies to support provider training on using medication-assisted treatment, and use of nontraditional providers (such as peer recovery specialists).

The literature notes that, overall, the research on employment-related interventions for people with opioid use disorder is still in its infancy, and for that reason, opportunities for building evidence should be capitalized upon by any organization providing services to address it, and in so doing, lay the groundwork for more rigorous studies.

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Solving word problems involving triangles and implications on training pre-service mathematics teachers

• William Guo , 
• School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD 4702, Australia
• Academic Editor: Zlatko Jovanoski
• Received: 18 June 2024 Revised: 02 July 2024 Accepted: 05 July 2024 Published: 09 July 2024
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Triangles and trigonometry are always difficult topics for both mathematics students and teachers. Hence, students' performance in solving mathematical word problems in these topics is not only a reflection of their learning outcomes but also an indication of teaching effectiveness. This case study drew from two examples of solving word problems involving triangles by pre-service mathematics teachers in a foundation mathematics course delivered by the author. The focus of this case study was on reasoning implications of students' performances on the effective training of pre-service mathematics teachers, from which a three-step interactive explicit teaching-learning approach, comprising teacher-led precise and inspiring teaching (or explicit teaching), student-driven engaged learning (or imitative learning), and student-led and teacher-guided problem-solving for real-world problems or projects (or active application), was summarized. Explicit teaching establishes a solid foundation for students to further their understanding of new mathematical concepts and to conceptualize the technical processes associated with these new concepts. Imitative learning helps students build technical abilities and enhance technical efficacy by engaging in learning activities. Once these first two steps have been completed, students should have a decent understanding of new mathematical concepts and technical efficacy to analyze, formulate, and finally solve real-world applications with assistance from teachers whenever required. Specially crafted professional development should also be considered for some in-service mathematics teachers to adopt this three-step interactive teaching-learning process.

• pre-service mathematics teacher ,
• word problem ,
• triangles ,
• problem-solving ,
• explicit teaching ,
• imitated learning ,
• active applications ,
• professional development

Citation: William Guo. Solving word problems involving triangles and implications on training pre-service mathematics teachers[J]. STEM Education, 2024, 4(3): 263-281. doi: 10.3934/steme.2024016

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• Figure Problem 1. The first word problem assigned to the pre-service mathematics teachers
• Figure 1. A sketch of isosceles triangle for the first word problem
• Figure Problem 2. The second word problem assigned to the pre-service mathematics teachers
• Figure 2. A reworked sketch for the second problem with derived angles (in red)
• Figure 3. The first reworked sketch for solving the second problem through right triangles
• Figure 4. The second reworked sketch for solving the second problem through right triangles
• Figure 5. The third reworked sketch for solving the second problem through right triangles

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This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Programmers have spent decades writing code for AI models , and now, in a full circle moment, AI is being used to write code. But how does an AI code generator compare to a human programmer?

A study published in the June issue of IEEE Transactions on Software Engineering evaluated the code produced by OpenAI’s ChatGPT in terms of functionality, complexity and security. The results show that ChatGPT has an extremely broad range of success when it comes to producing functional code—with a success rate ranging from anywhere as poor as 0.66 percent and as good as 89 percent—depending on the difficulty of the task, the programming language, and a number of other factors.

While in some cases the AI generator could produce better code than humans, the analysis also reveals some security concerns with AI-generated code.

Yutian Tang is a lecturer at the University of Glasgow who was involved in the study. He notes that AI-based code generation could provide some advantages in terms of enhancing productivity and automating software development tasks—but it’s important to understand the strengths and limitations of these models.

“By conducting a comprehensive analysis, we can uncover potential issues and limitations that arise in the ChatGPT-based code generation... [and] improve generation techniques,” Tang explains.

To explore these limitations in more detail, his team sought to test GPT-3.5’s ability to address 728 coding problems from the LeetCode testing platform in five programming languages: C, C++, Java, JavaScript, and Python .

“A reasonable hypothesis for why ChatGPT can do better with algorithm problems before 2021 is that these problems are frequently seen in the training dataset.” —Yutian Tang, University of Glasgow

Overall, ChatGPT was fairly good at solving problems in the different coding languages—but especially when attempting to solve coding problems that existed on LeetCode before 2021. For instance, it was able to produce functional code for easy, medium, and hard problems with success rates of about 89, 71, and 40 percent, respectively.

“However, when it comes to the algorithm problems after 2021, ChatGPT’s ability to generate functionally correct code is affected. It sometimes fails to understand the meaning of questions, even for easy level problems,” Tang notes.

For example, ChatGPT’s ability to produce functional code for “easy” coding problems dropped from 89 percent to 52 percent after 2021. And its ability to generate functional code for “hard” problems dropped from 40 percent to 0.66 percent after this time as well.

“A reasonable hypothesis for why ChatGPT can do better with algorithm problems before 2021 is that these problems are frequently seen in the training dataset,” Tang says.

Essentially, as coding evolves, ChatGPT has not been exposed yet to new problems and solutions. It lacks the critical thinking skills of a human and can only address problems it has previously encountered. This could explain why it is so much better at addressing older coding problems than newer ones.

“ChatGPT may generate incorrect code because it does not understand the meaning of algorithm problems.” —Yutian Tang, University of Glasgow

Interestingly, ChatGPT is able to generate code with smaller runtime and memory overheads than at least 50 percent of human solutions to the same LeetCode problems.

The researchers also explored the ability of ChatGPT to fix its own coding errors after receiving feedback from LeetCode. They randomly selected 50 coding scenarios where ChatGPT initially generated incorrect coding, either because it didn’t understand the content or problem at hand.

While ChatGPT was good at fixing compiling errors, it generally was not good at correcting its own mistakes.

“ChatGPT may generate incorrect code because it does not understand the meaning of algorithm problems, thus, this simple error feedback information is not enough,” Tang explains.

The researchers also found that ChatGPT-generated code did have a fair amount of vulnerabilities, such as a missing null test, but many of these were easily fixable. Their results also show that generated code in C was the most complex, followed by C++ and Python, which has a similar complexity to the human-written code.

Tangs says, based on these results, it’s important that developers using ChatGPT provide additional information to help ChatGPT better understand problems or avoid vulnerabilities.

“For example, when encountering more complex programming problems, developers can provide relevant knowledge as much as possible, and tell ChatGPT in the prompt which potential vulnerabilities to be aware of,” Tang says.

• What to Do When the Ghost in the Machine Is You ›
• How Coders Can Survive—and Thrive—in a ChatGPT World ›
• Coding Assistant - ChatGPT ›

Michelle Hampson is a freelance writer based in Halifax. She frequently contributes to Spectrum's Journal Watch coverage, which highlights newsworthy studies published in IEEE journals.

That's yesterday's news, try it with version 4o, it's free.

"struggles due to training limitations" isn't that EVERYONE's problem with EVERYTHING.

"I could be an awesome guitar playing, but I struggle due to training limitations."

"I could be a great Opera singer, but I struggle due to training limitations."

"I could be a great jockey, but I am 6'4"...." Ok, well maybe not everything.

ChatGPT sucks at coding because it's not an AI - it's a big ass word predictor.

I actually think the key here is writing good test suits to ensure AI does the right thing...

Here is the full argument: https://medium.com/@samuel.sperling/software-2-1-ai-is-coding-now-why-test-mastery-is-your-new-job-security-31a65e792f7f

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