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How Mental Sets Can Prohibit Problem Solving

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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A mental set is a tendency to only see solutions that have worked in the past. This type of fixed thinking can make it difficult to come up with solutions and can impede the problem-solving process. For example, that you are trying to solve a math problem in algebra class. The problem seems similar to ones you have worked on previously, so you approach solving it in the same way. Because of your mental set, you may be unable to see a simpler solution that is unique to this problem.

When we are solving problems, we tend to fall back on solutions that have worked in the past. In many cases, this is a useful approach that allows us to quickly come up with answers. In some instances, however, this strategy can make it difficult to think of new ways of solving problems .

Mental sets can lead to rigid thinking and create difficulties in the problem-solving process .

Functional Fixedness

Functional fixedness is a specific type of mental set where people are only able to see solutions that involve using objects in their normal or expected manner. Mental sets are definitely useful at times. By using strategies that have worked before, we are often able to quickly come up with solutions. This can save time and, in many cases, the approach does yield a correct solution.

While in many cases it is beneficial to use our past experiences to solve issues we face, it can also make it difficult to see novel or creative ways of fixing current problems. For example, imagine your vacuum cleaner has stopped working. When it has stopped working in the past, a broken belt was the culprit. Since past experience has taught you the belt is a common issue, you immediately replace the belt again. But, this time the vacuum continues to malfunction.

However, when you ask a friend to come to take a look at the vacuum, they quickly realize one of the hose attachments was not connected, causing the vacuum to lose suction. Because of your mental set, you failed to notice a fairly obvious solution to the problem.

Impact of Past Experiences

In daily life, a mental set may prevent you from solving a relatively minor problem (like figuring out what is wrong with your vacuum cleaner). On a larger scale, mental sets can prevent scientists from discovering answers to real-world problems or make it difficult for a doctor to determine the cause of an illness.

For example, a physician might see a new patient with symptoms similar to certain cases they have seen in the past, so they might diagnose this new patient with the same illness. Because of this mental set, the doctor might overlook symptoms that would actually point to a different illness altogether. Such mental sets can obviously have a dramatic impact on the health of the patient and possible outcomes.

Necka E, Kubik T. How non-experts fail where experts do not: Implications of expertise for resistance to cognitive rigidity . Studia Psychologica . 2012;54(1):3-14.

Valee-Tourangeau F, Euden G, Hearn V. Einstellung defused: Interactivity and mental set . Quarterly Journal of Experimental Psychology . 2011;64(10):1889-1895. doi:10.1080/17470218.2011.605151

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

Mental set refers to a cognitive tendency or predisposition to approach problem-solving or decision-making situations in a particular way, based on previous experiences or familiar strategies. It involves a fixed mindset that influences how individuals perceive and interpret information, as well as how they apply problem-solving techniques.

Components of Mental Set

1. Fixed Patterns of Thinking: Mental set involves relying on established patterns of thinking or problem-solving techniques instead of considering alternative approaches. These patterns can be based on prior successful experiences or learned strategies.

2. Resistance to Change: Mental set can create resistance to changing one’s perspective or trying new problem-solving methods. Individuals may feel comfortable and find it difficult to deviate from their familiar mental framework, even if it may not be the most effective approach.

3. Influence of Context: Mental set is highly influenced by the specific context in which a problem or decision arises. The environment, previous experiences, and societal norms can shape an individual’s mental set and lead them to approach situations in a particular way.

Examples of Mental Set

1. Functional Fixedness: A person utilizing functional fixedness may see a household item, such as a screwdriver, only as a tool for tightening or loosening screws, without considering its potential alternative uses.

2. Expertise: Experts in a particular field often develop a mental set that allows them to quickly solve problems within their domain of knowledge. However, this expertise can also hinder their ability to think creatively or consider alternative perspectives.

3. Confirmation Bias: Confirmation bias is a type of mental set that involves seeking information or evidence that supports one’s preexisting beliefs or expectations while ignoring or dismissing contradictory information.

4. Routine Thinking: Engaging in routine thinking can become a mental set where individuals approach problems or decisions in the same way, without questioning established methods. This can limit their ability to find innovative solutions.

Overcoming Mental Set

1. Increased Awareness: Recognizing the existence of mental set and its potential limitations can help individuals be more open to trying new perspectives or problem-solving approaches.

2. Encouraging Divergent Thinking: Cultivating an environment that encourages diverse viewpoints and alternative strategies can help break free from the constraints of mental set and stimulate creative problem-solving.

3. Deconstructing Assumptions: Challenging and revisiting underlying assumptions can broaden one’s mindset and allow for a more flexible approach to problem-solving.

4. Seeking External Input: Consulting others with different perspectives or expertise can provide fresh insights and help overcome the limitations imposed by mental set.

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• Published: 14 December 2023

Restructuring processes and Aha! experiences in insight problem solving

• Jennifer Wiley   ORCID: orcid.org/0000-0002-2590-7392 1 &
• Amory H. Danek   ORCID: orcid.org/0000-0002-2849-8774 2  

Nature Reviews Psychology volume  3 ,  pages 42–55 ( 2024 ) Cite this article

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Insightful solution processes represent cases of problem solving in which the emergence of a new interpretation allows for an abrupt shift from bewilderment to clarity. One approach to researching insight problem solving emphasizes cognitive restructuring of the problem representation as a defining feature of the insightful solution process. By contrast, another approach emphasizes phenomenological Aha! experiences. In this Review, we summarize both approaches, considering the restructuring processes involved in finding a solution and the Aha! experiences that might accompany solutions. We then consider the extent to which Aha! experiences co-occur with restructuring, and the critical observation that sometimes they do not. We conclude by proposing avenues for future research that combine the methodologies used to study restructuring and Aha! experiences to better understand the cognitive and phenomenological underpinnings of insight problem solving and the connections between them.

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The authors thank I. K. Ash, P. J. Cushen, T. George, A. F. Jarosz, T. S. Miller and S. Ohlsson for discussion on these topics.

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Wiley, J., Danek, A.H. Restructuring processes and Aha! experiences in insight problem solving. Nat Rev Psychol 3 , 42–55 (2024). https://doi.org/10.1038/s44159-023-00257-x

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Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring

1 Institute of Psychology, University of Klagenfurt, 9020 Klagenfurt, Austria

Amory H. Danek

2 Department of Psychology, Heidelberg University, 69117 Heidelberg, Germany

Nemanja Vaci

3 Department of Psychology, Sheffield University, Sheffield S10 2BP, UK

Merim Bilalić

4 Department of Psychology, University of Northumbria at Newcastle, Newcastle upon Tyne NE1 8ST, UK

Associated Data

Technical details, such as data and code for the analysis, is available at https://osf.io/pwuhs/?view_only=7c52bda4e6fa481e826e5d7570b6ef34 (accessed on 25 April 2023).

Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical “Aha!” experience, the evidence is inconclusive. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers’ subjective experience of the solution process. In our previous paper, we used matchstick arithmetic problems to demonstrate that it is possible to objectively trace problem-solving processes by combining eye movements with new analytical and statistical approaches. Specifically, we divided the problem-solving process into ten (relative) temporal phases to better capture possible small changes in problem representation. Here, we go a step further to demonstrate that classical statistical procedures, such as ANOVA, cannot capture sudden representational change processes, which are typical for insight problems. Only nonlinear statistical models, such as generalized additive (mixed) models (GAMs) and change points analysis, correctly identified the abrupt representational change. Additionally, we demonstrate that explicit hints reorient participants’ focus in a qualitatively different manner, changing the dynamics of restructuring in insight problem solving. While insight problems may indeed require a sudden restructuring of the initial mental representation, more sophisticated analytical and statistical approaches are necessary to uncover their true nature.

1. Introduction

In cognitive science, the temporal dynamics of problem-solving processes have always been an important topic of investigation. Most problems are assumed to be solved gradually, by piecing together information in order to arrive at a solution ( Newell and Simon 1972 ). To investigate these problems, several tools have been developed, which allow for the observation of each step of the problem-solving process (e.g., Tower of Hanoi, Hobbits and Orcs problem). In the case of “insight problems”, the solution often comes seemingly out of nowhere ( Duncker 1945 ), despite the problem appearing unsolvable just a moment earlier. To be solved, insight problems are thought to require a fundamental, sudden change in the way the problem is perceived, a process referred to as restructuring or representational change ( Ohlsson 1992 ; Wertheimer 1925 ). The restructuring from the initial, incorrect mental representation to the correct one is the key component in modern theories such as representational change theory (RCT) ( Knoblich et al. 1999 ; Ohlsson 1984 , 1992 , 2011 ).

Although the sudden nature of the underlying restructuring process is a main theoretical assumption about insight, the evidence for this claim is inconclusive. Ohlsson ( 1992 ) even hypothesized that “the sudden appearance of the complete solution in consciousness is an illusion caused by our lack of introspective access to our cognitive processes (...)” (p. 17). To truly understand the temporal nature of insight, the cognitive component of insight (restructuring) must be examined with appropriate tools. Observing changes in solvers’ mental problem representation is a methodological and statistical challenge, which is addressed in the present work. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers’ subjective experience of the solution process. Using matchstick arithmetic problems, we demonstrate that it is possible to objectively trace problem-solving processes.

We first review the research on representational change, focusing on the experimental designs. After that, we describe a novel analytical approach that improves upon previous attempts. Finally, and arguably most importantly, we show that this analytical approach needs to be combined with appropriate statistical tools in order to work properly. We demonstrate the feasibility of this approach by re-analyzing eye-tracking data from an already published study ( Bilalić et al. 2019 ). The paper is accompanied by an online supplement , with technical details, such as data and code for the analysis, which is freely available at https://osf.io/pwuhs/?view_only=7c52bda4e6fa481e826e5d7570b6ef3 (accessed on 25 April 2023).

1.1. Temporal Dynamics of the Restructuring Process

In 1994, Durso and colleagues conducted an early study on the temporal dynamics of insight problem solving. They asked participants to rate the relatedness of word pairs in a word puzzle and found that, on average, solution-relevant pairs were rated as increasingly similar as participants approached a solution. The authors concluded that “[l]ike dynamite, the insightful solution explodes on the solver’s cognitive landscape with breathtaking suddenness, but if one looks closely, a long fuse warns of the impending reorganization” ( Durso et al. 1994, p. 98 ). Novick and Sherman ( 2003 ; Experiment 2) provided similar evidence. They asked participants to indicate within a short time window (250 ms after stimulus offset) whether presented anagrams were solvable. They found that, although participants could not find the solution within the allotted time, they were increasingly better at differentiating between solvable and unsolvable anagrams as the presentation time of the anagrams increased. The authors concluded that solvers gradually accumulate information relevant for solving the anagrams.

Several studies have focused on the concept of restructuring in insight problem solving, but have typically not measured the dynamics of the solving process (e.g., Ash et al. 2012 ; Ash and Wiley 2006 , 2008 ; Fleck and Weisberg 2013 ; MacGregor and Cunningham 2009 ). However, a number of studies have attempted to measure the temporal dynamics of restructuring, using different methods to acquire trace data. Some used repeated ratings of problem elements, either regarding their similarity ( Durso et al. 1994 ) or with regard to their relevance for the solution ( Cushen and Wiley 2012 ; Danek et al. 2020 ). Others recorded eye movements ( Ellis et al. 2011 ; Knoblich et al. 2001 ; Bilalić et al. 2019 ; Tseng et al. 2014 ) or employed solvability judgments ( Novick and Sherman 2003 ). In some of these studies, both incremental and sudden solution patterns were found ( Cushen and Wiley 2012 ; Danek et al. 2020 ; Novick and Sherman 2003 ), whereas other studies found only incremental patterns ( Durso et al. 1994 ).

1.2. Eye Movements and Matchstick Arithmetic Problems

Here, we will take a closer look at using eye movement recordings to measure the temporal dynamics of restructuring in insight problems (for a comprehensive overview on eye movements, please see Holmqvist et al. 2011 ). In general, eye movements provide an objective measure of cognitive processes, as they are closely linked to attention (e.g., Just and Carpenter 1976 ; Rayner 1995 ; Reingold et al. 2001 ). Specifically, eye fixations reveal when people pay attention to certain features of a problem and for how long. More importantly, eye tracking is particularly useful when participants might not remember or even concurrently report that they are paying attention to these elements ( Bilalić and McLeod 2014 ; Bilalić et al. 2008 , 2010 ; Kuhn and Land 2006 ; Kuhn et al. 2009 ). This is particularly relevant in the case of insight problems, where it is possible that people are not aware of the dynamics of their solution process.

We use the matchstick arithmetic problems introduced by Knoblich et al. ( 1999 ). Matchstick arithmetic problems are suitable for investigation with eye tracking, as was powerfully demonstrated by the seminal study of Knoblich et al. ( 2001 ). A matchstick arithmetic problem consists of a false arithmetic statement written using Roman numerals, arithmetic operators, and equal signs, all formed using matchsticks ( Knoblich et al. 1999 , 2001 ; see also Figure 1 below). The task is to transform the false arithmetic statement into a true statement by moving only a single stick. Four types of matchstick arithmetic problems have been defined with varying levels of difficulty, depending on the constraints that need to be relaxed and the tightness of the chunks that need to be decomposed. These problem types were theoretically derived from the representational change theory ( Ohlsson 1992 ) and have been empirically confirmed ( Knoblich et al. 1999 ; Öllinger et al. 2006 , 2008 ). The use of matchstick arithmetic problems enables us to build on a well-researched task domain. It is known which problem type should elicit the restructuring process ( Knoblich et al. 1999 ; Öllinger et al. 2006 , 2008 ), and it is possible to contrast it with a type which requires no restructuring. Additionally, based on Knoblich’s study (2001), predictions about eye movement patterns can be made. Furthermore, the matchstick arithmetic domain is well suited for eye tracking because each problem consists of individual matchsticks that do not overlap, allowing for precise differentiation of fixations. In other words, we can determine at any point in time which aspect of the problem is attended to.

Matchstick arithmetic problem. Participants are required to transform the false arithmetic statement to a true statement by moving a single matchstick. This problem requires restructuring, because the initial assumption that only the matchsticks from values can be manipulated needs to be changed. In this case, the operator “+” can be decomposed and its vertical matchstick moved to make another “=” sign (VI = VI = VI). The “+” sign is the critical element that needs to be changed for solution.

Knoblich et al. ( 2001 ) investigated constraint relaxation type problems, which are considered to require restructuring; see Figure 1 for an example. They found that for this problem (constraint relaxation type), both solvers and non-solvers examined the values in the beginning and spent most of their time doing so. This can be seen as an indication that participants were using an initial incorrect problem representation, triggered by previous knowledge, where only values can be changed. Only in the final third of the problem- solving period did later solvers change their mental representation, as demonstrated by their eye movements. Solvers started to pay attention more to the operators and less to the values. In contrast, non-solvers remained stuck in their initial representation, as they continued to attend to values rather than to operators. Similar results for the same problem were found by another eye-tracking study ( Tseng et al. 2014 ).

The Knoblich et al. ( 2001 ) study provides strong evidence for the claim that in problems that require constraint relaxation, a restructuring of the problem representation took place. However, it did not answer the question of whether this change was a sudden or a gradual one. In the final third of the allotted time, solvers paid attention to the important but previously ignored features, which could be interpreted as a result of sudden restructuring. It is nevertheless not that clear, since the final period may have lasted minutes, given that they took around five minutes to solve the problem. Thus, the restructuring might have been a continuous process over time. On the other hand, an eye-tracking study on anagrams by Ellis et al. ( 2011 ; see also Ellis and Reingold 2014 ) found that participants started disregarding the irrelevant problem elements several seconds before they came up with the solution. The viewing times on that problem elements were decreasing gradually. Most intriguingly, both participant groups, those who experienced pop-out insight-like solutions and those who did not, displayed the same gradual accumulation of solution knowledge.

1.3. Metacognitive Processes and Insight Problems

There is evidence that the problem-solving process benefits from hints (e.g., Bowden 1997 ; Bilalić et al. 2019 ; Ammalainen and Moroshkina 2021 ; Becker et al. 2021 ; Korovkin and Savinova 2021 ; Spiridonov et al. 2021 ). This is the case even when hints were unreportable; that is, hints even work when presented briefly below the threshold of consciousness. Ammalainen and Moroshkina ( 2021 ) found evidence that hints can influence the problem-solving ability, which can be both, positive and negative. In a positive way, hints which are helpful to find the solution increase solution rates. On the other hand, misleading hints can negatively affect solution rates by distracting problem solvers and leading to a decrease in their success rate. In our paper ( Bilalić et al. 2019 ), we also provided hints when participants were unable to find the correct solution after a certain time.

These hints serve two purposes: a practical and a theoretical one. On a practical level, they provide an additional check on the main assumption behind the restructuring process. On a theoretical level, they serve as explicit clues that tap into metacognitive processes ( Takeuchi et al. 2019 ; Metcalfe and Shimamura 1994 ). Hints make participants aware of important aspects in the problem, drawing their attention towards elements that may have been neglected. They also change participants’ knowledge about the problem, potentially affecting the way they solve insight problems ( Bowden 1997 ; Bilalić et al. 2019 ; Korovkin and Savinova 2021 ).

The present work is a re-analysis of our paper ( Bilalić et al. 2019 ). In our paper, we also combined solving of insight and non-insight problems with eye tracking. We presented first a non-insight matchstick problem and then the matchstick insight problem depicted here (see Figure 1 ) to 61 participants (5 male; M age = 22.8; SD age = 6.5). The study was designed to take into account the methodological issue discussed in the previous section. It built upon previous attempts that utilized more time periods and sometimes presented the last 5 or 10 s separately (see also Bilalić et al. 2008 , 2010 , 2014 ). In the 2019 study, we provided a more fine-grained temporal analysis of the solution process by using ten time periods of equal length for our eye movement analysis 1 (for more information, please refer to Bilalić et al. 2019 ). We demonstrated that the restructuring is a gradual process on the insight problem as the solvers started paying attention to the important aspects of the problem long before they found the solution. Here, we provide another set of data where the jump is sudden; that is, the solvers started paying attention to the important aspects immediately before they found the solution (as reported by Knoblich et al. 2001 ). This is done to illustrate (1) how classical ways of analyzing data, such as ANOVA, are inappropriate for discovering the sudden changes, and (2) how other non-linear approaches are required.

We expected that all participants would initially focus on the values. Solvers would shift their attention towards the critical element (the “+” operator), while non-solvers would remain fixated on the values. The first question of interest is whether the representational shift in eventual solvers will be sudden or rather incremental. The second question of interest is whether the explicit cue, that is, the hint, will produce a sudden rearrangement of attention towards the critical elements (here “+”, but also “=” because “=” is also an operator). In our design, we included hints for participants who had not solved the problem within five minutes. The hint provided at this point was ‘You can change the operators, too.’ We were interested in whether the hints change the dynamics of problem solving, specifically whether the solution process remains sudden even after receiving an explicit cue.

The problem proved difficult as only 34% found the solution. After the hint was provided, an additional 11% of participants were able to find the solution. We present the eye data analysis below, with a particular focus on the critical element of the problem, the plus sign (+). Additionally, when analyzing the impact of hints, we also focused on the equal sign (=) as the hints should also affect the attention drawn to this operator through metacognitive control. For analysis of other problem elements, please refer to the supplementary materials .

3.1. Is Insight Sudden or Incremental? (Solvers vs. Non-Solvers: First 5 Min Analysis)

In Figure 2 , raw data and means for each bin of the critical element for the first five minutes are presented. 2 The solving pattern follows the typical sudden pattern, where there is not much difference between eventual solvers and non-solvers with regard to the time spent on the critical element (+) until the end of the first five minutes. Solvers suddenly increase their dwell time just before announcing the solution, while non-solvers continue to observe the critical element sporadically until the end of the solving period.

Raw data and means for each bin of the critical element (+). The raw data represents every data point of each participant over the entire problem-solving period. The problem-solving period was divided in 10 proportional bins, each representing 10% of the total problem-solving time. The error bars represent the 68% confidence interval. This figure illustrates a nonlinear increase in the amount of time that solvers spend on the critical element. In the case of solvers, the 100% bin means the participant provided a solution.

The crucial question is how to analyze the temporal changes presented in Figure 2 . The traditional method, which we had chosen in our previous paper ( Bilalić et al. 2019 ), is to use an analysis of variance (ANOVA) where the bins and groups are factors that predict the amount of time spent on the critical element. However, ANOVA not only requires a completely balanced dataset, but it also ignores the clustered nature of data ( van Rij et al. 2020 ). Furthermore, it is based on linear regression, which is not suitable for capturing sudden attentional shifts, which are nonlinear in nature. In order to capture the sudden shift as depicted in Figure 2 (the 100% bin for the solvers), ANOVA would need to adjust the linear trend throughout the whole problem-solving period. In other words, a sudden trend may appear as an incremental one as ANOVA adjusts by increasing previous periods (see Figure 3 , left panel).

Estimated model means based on ( a ) ANOVA with linear term; ( b ) ANOVA with both linear and quadratic terms. Y-Axis: Time on the problem element (%). Please refer to supplementary material for the detailed analysis.

ANOVA can be expressed as linear regression, where an additional quadratic polynomial term is included next to the linear one, in an attempt to capture the shift. However, even in this case, the predicted shift by the ANOVA model would begin earlier, namely at the 80% bin, than it does in the raw data (see Figure 3 , right panel). The general limitation of linear regression, with or without polynomial terms, is that it heavily relies on previous trends. If the change is sudden, the previous time periods will also be adjusted accordingly.

One way around this problem is generalized additive (mixed) modeling (GAM). These models are specifically designed to handle nonlinear relationships, as they are data-driven and use non-linear mixed-effects regression ( van Rij et al. 2020 ). A key benefit of GAMs is that they do not require the user to specify the shape of the nonlinear regression line, as the model determines this based on the data. However, while GAMs have a high level of flexibility in modeling nonlinear changes in time series data, they only allow for the exploration of changes in the function and do not provide parametric estimates such as standard error of estimate or its impact on predictive accuracy of the model. More specifically, GAMs do not provide parametric estimates, which means that they do not give us a set of parameters that describe the shape of the nonlinear function. However, the present work intends to demonstrate the advantages and downsides of the available analysis tools in question, which is why GAMs are included here.

Arguably the most reliable way of checking the assumption of suddenness is the use of change point analysis, which looks for significant deviance from previous trends ( Raftery and Akman 1986 ). Unlike the standard regression analysis (ANOVA) and nonlinear GAMs, change point regression estimates the moment of the function inflection. In other words, it includes the possibility to estimate additional parameters, such as intercept and slope of regression, time point when the function changes, and how the intercept and/or slope of regression changes (see the figures of the MCP analysis for illustrations). This makes the technique particularly valuable in detecting increasing patterns as one would expect several points of change in the attentional pattern on the way towards the solution. In this instance, we use the one implemented in the Multiple Change Points package (MCP; Lindeløv 2020 ).

Below, we address the three main questions using both GAM and MCP analysis. In the supplemental material , we provide the model-estimated values for each case, which include the results and, in the case of the MCPs, how well the model fits the data and which model was used. We begin with the GAM analysis of solvers and non-solvers for the first five minutes to determine whether the insight is sudden or incremental. Figure 4 provides the estimated trend lines for both solvers and non-solvers, as well as the time periods (shaded in orange) where the difference between the two is statistically significant. The model estimates closely follow the raw data (see Figure 2 ), and the difference between solvers and non-solvers is indeed significant at the beginning of the solving phase, as well as at the 90% bin and the 100% bin.

GAM: the difference between two estimated trend lines for solvers and non-solvers of the critical element (+). This figure illustrates that the GAM also found a nonlinear increase in the amount of time that non-solvers spend on the critical element. The orange area determines where the differences between solvers and non-solvers were significant.

Figure 5 shows the results of the MCP analysis for the same data as the GAM above. Similarly to the GAM, the MCP analysis identified a change point around the 90% bin for the solvers, which captures an attentional shift they made. While some non-solvers also shifted their attention towards the “+” sign at the end, it was not as clear as in the case of the solvers.

MCP analysis of the critical element (+) for non-solvers and solvers. This figure illustrates every data point of each participant over the problem-solving period. Lines at the bottom of the figure illustrate the posterior density (estimated likelihood) of the change point for each MCMC chain. There is a nonlinear increase in the amount of time that solvers spend on the critical element.

3.2. Do Explicit Cues Rearrange Attentional Distribution? (An Immediate Change after the Hint)

Figure 6 illustrates the impact of providing an explicit hint to the non-solvers from the first five minutes (presented here as a single group; solvers from the first five minutes are not included in this graph). The attentional shift from values towards operators, “+” and “=”, is substantial immediately after the hint. The operator “=” is attended to twice as much immediately after the hint than before. The change for “+” is slightly less dramatic at first (only 4%), but by the 20% bin, the dwell time has doubled in comparison to before the hint was provided. Note that only non-solvers are shown here, since solvers did not receive any hints.

Raw data and means for each bin of the operators (“+” upper panel; “=” lower panel) in the period before and after the hint. The raw data represents every data point of each participant (non-solvers only) over the problem-solving periods. Each of both problem-solving periods (before and after the hint was provided) were divided in 10 proportional bins, each representing 10% of the total problem-solving time. It is necessary to view the problem-solving periods as distinct periods; therefore, each period is labeled from beginning to end (10% to 100%) to differentiate them. The error bars represent the 68% confidence interval. This figure illustrates the attentional shifts from values (mostly attended to before the hint) towards operators (attended to after the hint was given).

The GAM analysis effectively captures the attentional shift, as depicted in Figure 7 . However, it predicts that the change occurs prior to the hint being provided, starting already at the 90% bin, which is not a correct reflection of the actual data. While GAM is considerably more flexible than regressions with polynomial terms, the same problem of interdependence of neighboring phases remains. The shift caused by the explicit cue is so drastic that the GAM needs to adjust the increase to begin earlier in order to account for it.

GAM: estimated trend line for non-solvers of the critical element (+; upper panel) and the other operator (=; lower panel). This figure illustrates that the GAM also found a nonlinear increase in the amount of time that non-solvers spend on the critical element after receiving a hint. The orange area indicates where there is a significant shift in attention.

In contrast, the switch points of the MCP analysis correctly capture where the change in attention allocation happens (see Figure 8 ).

MCP analysis of the critical element (+; upper panel) and the other operator (=; lower panel). This figure illustrates that the switch points of the MCP analysis correctly captures where the shift in attention happens.

3.3. Does Metacognition Influence Insight Problem Solving? (Solvers vs. Non-Solvers after the Hint)

The final question we aimed to address was whether the explicit cue, and the additional knowledge about the problem associated with it, would alter the way the problem was solved. Figure 9 indicates that both solvers and non-solvers maintain the level of attention on the critical aspects throughout the problem-solving period, which is a direct consequence of the explicit cue. However, this was not sufficient for finding the solution. The eventual solvers initially shifted their attention to “=” around the 30% bin, but starting from the 50% bin, they increasingly focused on “+”. This means that at this point in time, the solvers may have realized that the “+” symbol was the critical element they needed to solve the problem. Consequently, they gathered more information about the symbol by attending to it more closely.

Raw data and means for each bin of the critical element (+; upper panel) and the other operator (=; lower panel) after the hint was provided. The raw data represent every data point of each participant over the remaining problem-solving period after the hint was given. The error bars represent the 68% confidence interval. This figure illustrates a nonlinear increase or decrease in the time solvers spend on the critical element.

This incremental pattern of solving is well captured by GAMs, as Figure 10 illustrates. While the non-solvers attended to the critical “+” operator consistently over the entire problem-solving period, but on a rather low level of 25% of their time, solvers gradually increased their attention towards it. Significant differences were found in the middle and the end of the problem-solving period. This was also the case for the other operator (=). Non-solvers attended to “=” in a consistent manner throughout the problem-solving period, while the solvers attended to “=” more in the middle of the problem-solving period and less at the very end of it, probably because they were then already focusing more on the “+” sign which needs to be changed for a solution.

GAM: the difference between two estimated trend lines for solvers and non-solvers of the critical element (+; upper panel) and the other operator (=; lower panel). This figure illustrates that the GAM also found a nonlinear increase in the time solvers spend on that particular element. The orange area in the figure indicates regions where there are statistically significant differences between the attention patterns of solvers and non-solvers.

Figure 11 illustrates that the attentional shifts after receiving a hint are effectively captured by the MCP analysis. Again, non-solvers attended to the “+” operator on a consistently low level throughout the entire problem-solving process, while solvers attended to the “+” operator more and more. The same trend is observed for the “=” operator. Non-solvers attended to it less, while solvers shifted their attention to it in the middle of the problem-solving process. Towards the end of the problem-solving process, the data suggest that solvers became aware that the “=” operator was not as important for solving the problem and began to focus more on the “+” operator.

MCP analysis of the critical element after the hint (+; upper panel) and the other operator (=; lower panel) for non-solvers and solvers. This figure demonstrates that the switch points of the MCP analysis correctly captures the incremental pattern of solving for the critical element (+). It also demonstrates that after the hint, the non-solvers attended to the noncritical element (=) more in the beginning but not the critical element (+). As the GAMs showed already, the non-solvers attended to the critical element (+) in the same way throughout the whole problem-solving period.

4. Discussion

We have demonstrated that recording eye movements is a valuable method for gaining insight into complex cognitive processes, including mental restructuring in insight problems. It is also an adequate tool for investigating attentional shifts after receiving hints. However, it is important to use eye movement recording with appropriate analytical approaches. Our results show that it is necessary to conduct a more fine-grained analysis of the eye movement data to capture the temporal dynamics of the problem-solving process. This is particularly relevant for insight problems such as the one used here, which are believed to feature a sudden change in eye movement patterns reflecting a change in mental representation.

We were able to identify the point at which solvers and non-solvers start to differ in their attentional patterns by dividing the problem-solving period into ten equal bins. The temporal resolution of the problem-solving period is one aspect, but it is also important to choose an appropriate statistical procedure. We have demonstrated that nonlinear statistical models, such as GAM and MCP, can effectively capture the sudden change that is a hallmark of insight problem solving. The GAM analysis can effectively capture the attentional shift; however, it predicts that the change occurs prior to the correct reflection of the actual data. While GAM is considerably more flexible than regressions with polynomial terms, the same problem of interdependence of neighboring phases remains. The shift caused by the explicit cue is so drastic that the GAM needs to adjust the increase to begin earlier to account for it. In contrast, the change points of the MCP analysis correctly capture where the change in attention allocation happens. A change point is a time point where the statistical properties of a time series change abruptly. However, in contrast to GAMs, one needs a priori knowledge about the number of change points and the form of the segments in between ( Lindeløv 2020 ). Therefore, one might decide from case to case which statistical procedure is appropriate.

Our example illustrates the importance of considering theoretical assumptions when choosing analytical and statistical procedures. The restructuring of mental representations is a key concept in theories of insight ( Knoblich et al. 1999 ; Ohlsson 1984 , 1992 , 2011 ). It is a nonlinear process in essence, which can be operationalized as a sudden burst of attention to the relevant aspects of a problem ( Bilalić et al. 2019 ). The shift inevitably deviates significantly from participants’ previous problem solving. Seen as a part of the overall problem-solving continuum, the sudden shift is difficult to capture with linear statistical procedures. Only truly nonlinear statistical procedures can appropriately capture the sudden nature of representational change.

Providing explicit hints typically alters the dynamics of problem solving. It is obvious that the given hints were effective, as participants’ patterns of attention show a drastic change, which is very well captured by both GAM and MCP. However, it is important to note that the eventual solvers, after receiving the hint, exhibit a gradual, incremental shift, with increasing attention to the main elements during the problem-solving period. In contrast, non-solvers display an immediate burst of refocusing following the hint, but subsequently, their attention to the important aspects diminishes.

Both the analytical procedure for capturing the temporal resolution and the nonlinear statistical procedures can be easily extended beyond eye movements to other tracing methods. For example, “importance-to-solution” ratings of individual problem elements that are made repeatedly during the solving process ( Durso et al. 1994 ; Cushen and Wiley 2012 ; Danek et al. 2020 ; Danek and Wiley 2020 ) often reveal patterns of sudden change which could be effectively captured by GAMs and MCPs. Similarly, “Feelings-of-Warmth” that are used to assess metacognitive knowledge about solution progress ( Kizilirmak et al. 2018 ; Hedne et al. 2016 ; Pétervári and Danek 2020 ) are another suitable candidate for nonlinear modeling with GAMs. Other tracing methods, such as mouse-tracing data ( Loesche et al. 2018 ; van Rij et al. 2020 ), think-aloud protocols ( Gilhooly et al. 2010 ; Schooler et al. 1993 ; Blech et al. 2020 ), or even self-reports ( Fedor et al. 2015 ), are also better modeled with GAMs than with commonly applied linear methods, even if they are more appropriate than the classical ANOVA.

5. Conclusions

Our results indicate that for insight problems, the restructuring process leaves a discernible trace of suddenness. Eye movements suggest that just prior to solving the problems, participants shift their focus from elements that constituted the initial problem representation to those crucial for the solution. Our results also demonstrate that receiving hints leads to attentional shifts towards critical aspects, which in turn facilitates the generation of a correct solution. However, in order to accurately capture the sudden shift in attention, a combination of the appropriate methodological approach and statistical procedure is necessary. These nonlinear processes are best captured by nonlinear statistical procedures, such as GAMs and MCPs.

Acknowledgments

The help and cooperation from participants is greatly appreciated, as is Matthew Bladen’s contribution in preparing the text.

Supplementary Materials

The following supporting information can be downloaded at: https://osf.io/pwuhs/?view_only=7c52bda4e6fa481e826e5d7570b6ef34 .

Funding Statement

This research was funded by Talent Austria der OeAD-GmbH, finanziert aus Mitteln des österreichischen Bundesministeriums für Wissenschaft, Forschung und Wirtschaft (BMWFW), grant number ICM-2017-07423 given to the first author.

Author Contributions

Conceptualization, M.B. and M.G.; methodology, M.B. and M.G.; software, M.G.; validation, M.B., N.V., A.H.D., and M.G.; formal analysis, M.G. and N.V.; investigation, M.G.; data curation, M.G.; writing—original draft preparation, M.G.; writing—review and editing, M.B., A.H.D., N.V., and M.G.; visualization, M.G., N.V., and M.B.; project administration, M.G. and M.B.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Since this is a re-analysis of our paper ( Bilalić et al. 2019 ), please refer to the original paper for the Institutional Review Board Statement.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

1 The length of time taken to solve (or not solve) a problem is different from person to person, meaning that one cannot compare the eye tracking data directly between people. For example, some may need only 45 s to solve the problem, whereas others need four minutes to find a solution. In consequence, the data must be transformed in order to be able to compare the data between people properly. While the problem-solving period can be extended by adding more time phases, it is important to note that the duration should not be prolonged beyond a certain point. Utilizing too many time frames may leave too little data (e.g., a 10-second trial should not be divided into 100 bins, as each bin will have the duration of only 100 ms). This can lead to distorted eye movement patterns, masking the underlying effects present before the data were binned. On the other hand, choosing too few bins may not capture the full temporal dynamics of the problem-solving process. In either case, ANOVA is not suitable for analyzing a large number of problem-solving periods, unlike GAM and multiple change point analysis, which can easily accommodate a large number of time frames. MCP analysis is another adequate tool for this type of analysis as it can capture the shift of attention. However, in contrast to GAMs, one needs a priori knowledge about the number of change points and the form of the segments in between ( Lindeløv 2020 ).

2 Please note that the data presented here are simulated to represent a sudden shift, which is difficult to capture by classical analyses. The original data in Bilalić et al. ( 2019 ) indicate a gradual shift.

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6.8: Blocks to Problem Solving

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Sometimes, previous experience or familiarity can even make problem solving more difficult. This is the case whenever habitual directions get in the way of finding new directions – an effect called fixation.

Functional Fixedness

Functional fixedness concerns the solution of object-use problems. The basic idea is that when the usual way of using an object is emphasised, it will be far more difficult for a person to use that object in a novel manner. An example for this effect is the candle problem : Imagine you are given a box of matches, some candles and tacks. On the wall of the room there is a cork- board. Your task is to fix the candle to the cork-board in such a way that no wax will drop on the floor when the candle is lit. – Got an idea?

Explanation: The clue is just the following: when people are confronted with a problem

and given certain objects to solve it, it is difficult for them to figure out that they could use them in a different (not so familiar or obvious) way. In this example the box has to be recognized as a support rather than as a container.

A further example is the two-string problem: Knut is left in a room with a chair and a pair of pliers given the task to bind two strings together that are hanging from the ceiling. The problem he faces is that he can never reach both strings at a time because they are just too far away from each other. What can Knut do?

Solution: Knut has to recognize he can use the pliers in a novel function – as weight for a pendulum. He can bind them to one of the strings, push it away, hold the other string and just wait for the first one moving towards him. If necessary, Knut can even climb on the chair, but he is not that small, we suppose…

Mental Fixedness

Functional fixedness as involved in the examples above illustrates a mental set - a person’s tendency to respond to a given task in a manner based on past experience. Because Knut maps an object to a particular function he has difficulties to vary the way of use (pliers as pendulum's weight). One approach to studying fixation was to study wrong-answer verbal insight problems. It was shown that people tend to give rather an incorrect answer when failing to solve a problem than to give no answer at all.

A typical example: People are told that on a lake the area covered by water lilies doubles every 24 hours and that it takes 60 days to cover the whole lake. Then they are asked how many days it takes to cover half the lake. The typical response is '30 days' (whereas 59 days is correct).

These wrong solutions are due to an inaccurate interpretation, hence representation, of the problem. This can happen because of sloppiness (a quick shallow reading of the problemand/or weak monitoring of their efforts made to come to a solution). In this case error feedback should help people to reconsider the problem features, note the inadequacy of their first answer, and find the correct solution. If, however, people are truly fixated on their incorrect representation, being told the answer is wrong does not help. In a study made by P.I. Dallop and R.L. Dominowski in 1992 these two possibilities were contrasted. In approximately one third of the cases error feedback led to right answers, so only approximately one third of the wrong answers were due to inadequate monitoring. [6] Another approach is the study of examples with and without a preceding analogous task. In cases such like the water-jug task analogous thinking indeed leads to a correct solution, but to take a different way might make the case much simpler:

Imagine Knut again, this time he is given three jugs with different capacities and is asked to measure the required amount of water. Of course he is not allowed to use anything despite the jugs and as much water as he likes. In the first case the sizes are 127 litres, 21 litres and 3 litres while 100 litres are desired. In the second case Knut is asked to measure 18 litres from jugs of 39, 15 and three litres size.

In fact participants faced with the 100 litre task first choose a complicate way in order tosolve the second one. Others on the contrary who did not know about that complex task solved the 18 litre case by just adding three litres to 15.

Pitfalls to Problem Solving

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now. Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Link to Learning

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and non-industrialized cultures (German & Barrett, 2005).

Common obstacles to solving problems

The example also illustrates two common problems that sometimes happen during problem solving. One of these is functional fixedness : a tendency to regard the functions of objects and ideas as fixed (German & Barrett, 2005). Over time, we get so used to one particular purpose for an object that we overlook other uses. We may think of a dictionary, for example, as necessarily something to verify spellings and definitions, but it also can function as a gift, a doorstop, or a footstool. For students working on the nine-dot matrix described in the last section, the notion of “drawing” a line was also initially fixed; they assumed it to be connecting dots but not extending lines beyond the dots. Functional fixedness sometimes is also called response set , the tendency for a person to frame or think about each problem in a series in the same way as the previous problem, even when doing so is not appropriate to later problems. In the example of the nine-dot matrix described above, students often tried one solution after another, but each solution was constrained by a set response not to extend any line beyond the matrix.

Functional fixedness and the response set are obstacles in problem representation , the way that a person understands and organizes information provided in a problem. If information is misunderstood or used inappropriately, then mistakes are likely—if indeed the problem can be solved at all. With the nine-dot matrix problem, for example, construing the instruction to draw four lines as meaning “draw four lines entirely within the matrix” means that the problem simply could not be solved. For another, consider this problem: “The number of water lilies on a lake doubles each day. Each water lily covers exactly one square foot. If it takes 100 days for the lilies to cover the lake exactly, how many days does it take for the lilies to cover exactly half of the lake?” If you think that the size of the lilies affects the solution to this problem, you have not represented the problem correctly. Information about lily size is not relevant to the solution, and only serves to distract from the truly crucial information, the fact that the lilies double their coverage each day. (The answer, incidentally, is that the lake is half covered in 99 days; can you think why?)

Expertise as mental set: The effects of domain knowledge in creative problem solving

• Published: July 1998
• Volume 26 , pages 716–730, ( 1998 )

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Experts generally solve problems in their fields more effectively than novices because their wellstructured, easily activated knowledge allows for efficient search of a solution space. But what happens when a problem requires a broad search for a solution? One concern is that subjects with a large amount of domain knowledge may actually be at a disadvantage, because their knowledge may confine them to an area of the search space in which the solution does not reside. In other words, domain knowledge may act as a mental set, promoting fixation in creative problem-solving attempts. A series of three experiments in which an adapted version of Mednick’s (1962) remote associates task was used demonstrates conditions under which domain knowledge may inhibit creative problem solving.

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Wiley, J. Expertise as mental set: The effects of domain knowledge in creative problem solving. Memory & Cognition 26 , 716–730 (1998). https://doi.org/10.3758/BF03211392

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Title: overcoming the mental set effect in programming problem solving.

Abstract: This paper adopts a cognitive psychology perspective to investigate the recurring mistakes in code resulting from the mental set (Einstellung) effect. The Einstellung effect is the tendency to approach problem-solving with a preconceived mindset, often overlooking better solutions that may be available. This effect can significantly impact creative thinking, as the development of patterns of thought can hinder the emergence of novel and creative ideas. Our study aims to test the Einstellung effect and the two mechanisms of its overcoming in the field of programming. The first intervention was the change of the color scheme of the code editor to the less habitual one. The second intervention was a combination of instruction to "forget the previous solutions and tasks" and the change in the color scheme. During the experiment, participants were given two sets of four programming tasks. Each task had two possible solutions: one using suboptimal code dictated by the mental set, and the other using a less familiar but more efficient and recommended methodology. Between the sets, participants either received no treatment or one of two interventions aimed at helping them overcome the mental set. The results of our experiment suggest that the tested techniques were insufficient to support overcoming the mental set, which we attribute to the specificity of the programming domain. The study contributes to the existing literature by providing insights into creativity support during problem-solving in software development and offering a framework for experimental research in this field.

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Does Your Mental Set Need A Tune-Up?

The term “mental set” generally refers to the tendency to fall back on past solutions rather than create new ones. While a mental set may not be automatically good or bad, you can adjust your mental set to be more flexible through various practices. These practices can include developing problem-solving skills, practicing mindfulness and meditation, setting and achieving goals, and engaging in positive self-talk. Another effective method of enhancing your mental set may be to work with a licensed therapist in person or online.

What is a mental set?

In general, everyone problem-solves, whether they’re at work, at home, or out in the world. There can be many different ways to solve a problem, but sometimes, there is a tendency to fall back on solutions that have worked in the past instead of innovating new ways. This is often called a “mental set.”

One study explains, “A mental set generally refers to the brain’s tendency to stick with the most familiar solution to a problem and stubbornly ignore alternatives. This tendency is likely driven by previous knowledge (the long-term mental set) or is a temporary by-product of procedural learning (the short-term mental set).” 

While a mental set may not be inherently good or bad, it can be helpful to remain aware of it. For individuals interested in enhancing their mental set to be more flexible, there are mental habits that can be easily incorporated.

• Mindfulness and meditation 
• Goal-setting and positive self-talk
• Stress management
• Professional help

Mindfulness and meditation

Mindfulness and meditation can be used separately or together to improve mental well-being and flexibility. Even though these practices are often recommended for stress management, they can also enhance a mental set through increased problem-solving skills and open-mindedness. 

Mindfulness is usually referred to as the practice of staying in the present moment without judging what is happening around you. Specifically, this can refer to paying attention to your thoughts and emotions without judging them. By not judging emotions as they occur, it can free us from the idea that a feeling is “right” or “wrong” or “good” or “bad”.

One significant benefit of mindfulness (especially for enhancing a mental set) is that it can be practiced anywhere. Since the idea is to be fully engaged in the moment, that moment can be out at a restaurant, hanging out at home, or chatting with a friend. Training your mind to tune out distractions through mindfulness can help you see solutions more clearly when solving problems.

Mindfulness can also promote mental flexibility and open-mindedness. Individuals who practice mindfulness often find it easier to switch between perspectives and tasks. This can directly correlate to an enhanced mental set because someone already accustomed to looking at a problem from multiple angles will likely approach new issues the same way. 

Similarly, meditation and mindfulness can reduce the influence of personal bias or the tendency to fall back on old patterns. Because both practices usually emphasize acknowledging each thought as it appears, it can become easier for individuals to “sort” through their thoughts and determine which might lead to the most effective solution.

Meditation is another practice that can strengthen problem-solving skills and enhance the mental set. Individuals practicing meditation often become more comfortable focusing on something specific. Many meditators use their breath, while others may use a mantra or thought. Regardless of what they focus on, the intentional focus over time can train the mind to concentrate. 

Individuals who meditate may find it easier to create multiple solutions to a problem instead of remaining stuck on one. Meditation can also make individuals more empathetic and compassionate. Staying in touch with emotional states and experiences typically makes it more likely for an individual to be open-minded about the emotions and experiences of those around them. These traits can lead to better collaboration and overall problem-solving skills.

Goal-setting and positive self-talk 

If you want to be more open-minded and make problem-solving easier, setting goals and engaging in positive self-talk can both be important. Setting goals and speaking positively to oneself can help you overcome negative thought patterns and prioritize solutions. 

Forbes quotes Carol Dweck, Stanford psychologist and author of Mindset: The New Psychology of Success, as saying, “Your beliefs and thoughts play a pivotal role in success. Whether conscious or subconscious, they strongly affect what we want and whether we succeed in getting it. Much of what we think we understand of our personality comes from our ‘mindset.’ This both propels us and prevents us from fulfilling our potential.” A mental set can be similar to a mindset in that both can be positive or negative when overcoming challenges, but it depends on the perspective. 

Goal setting can be approached from a variety of perspectives, but SMART goals are often especially popular. SMART goals are normally specific, measurable, achievable, relevant, and time-bound so that they can be easily tracked for progress. Goals should align with personal values and aspirations when possible, potentially making the individual more likely to complete them. 

Accomplishing goals often leads to improved confidence and increased motivation, which can both be beneficial in enhancing the mental set. Goals can encourage open-mindedness because individuals must usually evaluate different methods of accomplishing the goal before starting the journey. Considering different approaches can help them be more open to other solutions when problem-solving.

Self-talk typically refers to the way an individual speaks to themselves. Those who engage in positive self-talk tend to be likelier to use uplifting and empowering language to encourage themselves. By repeatedly choosing positive ways to describe themselves, they can replace negative thought patterns with positive ones. Positive self-talk is also usually linked to high self-esteem and confidence, which can enhance mental flexibility and help achieve goals, both of which can be important for enhancing mental sets. 

Finally, positive self-talk can increase open-mindedness because individuals may be more likely to question negative beliefs they may be holding onto. Replacing negative self-talk with positive self-talk can result in approaching challenges with a growth or solution-oriented mindset. 

Mindset shift

Sometimes, tools may not be enough to enhance the mental set, and a total mindset shift may be necessary. A mindset can be indicative of an individual’s approach to problem-solving and is usually a fixed mindset or a growth mindset. 

Fixed mindsets typically involve the belief that abilities are innate and cannot be changed, while a growth mindset usually believes that abilities can be developed through effort and learning. Individuals with a growth mindset are often more open-minded and tend to improve their problem-solving skills constantly.

Some may find it helpful to approach a problem with a different mindset altogether. If past solutions and fixed mindsets aren’t working,  Forbes recommends trying one of the following:

• “Beginner's mindset—one that remains open to learning and seeing things with a fresh set of eyes every time. Evolving as a leader requires this openness to change, to dynamic interpretations of situations that would otherwise be quickly and easily dismissed. There's also a sense of playfulness that I believe comes with approaching business challenges in this way, allowing for creative solutions that might be overlooked by a more stringent, traditional way of thinking.”
• “In addition, applying the growth mindset—which, by definition, is one where the entrepreneur embraces challenges and leverages pitfalls or setbacks along the way as learning experiences rather than limitations—is especially critical for leaders to accept because hurdles to success are inevitable when starting a new business venture.”

Professional help for enhancing your mental set

Seeking the help of a professional can be a beneficial next step for those wishing to enhance their mental set. Online therapy can connect you with a therapist who may help you identify challenges or biases, create strategies to overcome them and enhance your mental set for future problems. Whether you are looking to overcome a specific issue or improve your overall mental set, a professional can be valuable in achieving those goals. 

Online therapy can be especially convenient because of its availability and flexibility. Individuals who have a busy schedule or do not have geographical availability to mental health services near them can schedule appointments and speak to their therapist using the internet. Additionally, online therapy usually offers appointments outside regular business hours to ensure patients can reach mental health support at a time that is convenient for them. 

One study investigated adherence to online and face-to-face (F2F) interventions and found, “Past studies have consistently found that online treatments can save the therapists time and support relapse prevention after F2F therapy. Additional strengths of online interventions over F2F interventions are that they are deliverable from remote locations, need less time commitment, and provide more flexibility for therapists and patients. Another advantage may be that the risk of stigma due to a mental disorder and seeking treatment is reduced.” These benefits can make online therapy an effective choice for those looking to enhance their mental set.

What are the symptoms of being addicted to social media?

Social media addiction symptoms may be similar to those traditionally associated with substance use . These may include mood changes, preoccupation with social media use, tolerance, withdrawal, and relapse. Someone addicted to social media sites may experience disruption in other areas of their life, like ignoring real-life relationships or having problems at work or school. 

What causes social media addiction?

Some experts feel that social media addiction may be related to dopamine in the brain . Generally, humans are social animals, and the ease of connecting on social media apps may have made us vulnerable to this kind of behavioral addiction. Interacting or scrolling through social media apps can cause large amounts of dopamine to flood our brains and trigger our internal reward system, similar to the effects of alcohol or heroin. When we log off a social media site, our brains can experience a dopamine deficit as they try to adapt to the high levels of dopamine released by excessive social media usage. 

What are the side effects of social media?

The side effects of social media and social media apps are still being studied, but some recent research shows that anxiety and depression are the most common. This research also found that more females are addicted to social media than males and passive activity, like reading posts, was more likely to lead to depression than active use, like making posts.

How do I fix my social media addiction?

There are a few things you can do to try to reduce your dependence on addictive social media. Take a break and try to notice the parts of your life you might ignore with excessive time spent on social media. Set boundaries for yourself about how much time you want to spend on social media and hold yourself accountable. Use any newfound free time to try a new hobby or other things you enjoy. Limit the accounts that you follow, blocking any that make you feel bad about yourself. 

When you’re back online, turn off notifications. That way, you won’t be tempted to check your socials every time you get a notification. If you feel that social media is the only way you have to connect with people, find other ways to keep in touch with friends and family and form real life relationships. In-person meetups or classes are great ways to meet new people, but if you have social anxiety or prefer to meet with people online, you can do virtual meetups, too.

If you have tried these things and they’re not working or if you want to talk to someone who can offer you advice and support and your addiction to online social networking sites, consider reaching out to an online therapist. 

Is social media addiction a disorder?

Social media addiction is not a recognized disorder in the DSM-5, but researchers worldwide are studying it, so that may change. 

What is the most harmful effect of social media?

The most harmful effect of social media platforms may be the mental health issues they can cause, including increased risk of depression and anxiety, low self-esteem, greater chance of relationship issues, increased potential for stress, increased isolation-aligned behaviors, and potential for a lowered body image.

How does social media affect students' lives?

Problematic social media use can have a significant impact on students’ lives. One recent study found that the negative effects of social media on students may include reduced learning and research capabilities, time wastage, reduction in real human contact, loss of motivation, and low grades. 

How long does social media addiction last?

Social media affects people differently, and everyone will respond to treatment in their own way, so it is difficult to determine how long social media addiction will last. Some recent research shows that weekly sessions of CBT over 10 weeks may be sufficient for internet addiction or 15 weeks for group therapy . 

Who is affected by social media addiction?

Anyone can experience social media addiction, but some research shows it may be particularly damaging to teens and young adults. This may be because of the difference between their brains . Social media does activate the brain’s reward center in adults, but adults tend to have a fixed sense of self that is less affected by feedback from peers and adults have a more mature brain, which helps regulate the emotional response to social rewards.

What does social media do to your brain?

Our brains release dopamine when we make connections with other people , which encourages us to do it again. With excessive social media use, the constant connection and validation can leave us vulnerable to overconsumption, which releases large amounts of dopamine in the brain, stimulating our reward pathways. This effect is similar to what happens when people use meth, alcohol, or heroin, and it amplifies the feel-good properties that attract us to other people in the first place. 

• Can't Concentrate? Tips For Focusing Medically reviewed by Kayce Bragg , LPCS, LAC, LCPC, LPC, NCC
• Five Strategies To Help You Learn How To Stand Up For Yourself Medically reviewed by Laura Angers Maddox , NCC, LPC
• Relationships and Relations

The influence of mental set on problem-solving

• PMID: 13304282
• DOI: 10.1111/j.2044-8295.1956.tb00563.x
• Problem Solving*