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Mental Set in Psychology: Definition, Examples, and Impact on Problem-Solving

A seemingly simple task, like threading a needle, can become an insurmountable challenge when our minds are stubbornly set in their ways, illustrating the powerful influence of mental set on our ability to solve problems. It’s a peculiar quirk of human cognition that can either be our greatest ally or our most formidable foe. Imagine, for a moment, you’re faced with a jigsaw puzzle. You’ve been piecing together the sky for hours, your fingers expertly fitting blue piece after blue piece. Then, suddenly, you’re presented with a section of vibrant green foliage. Your brain, still in “sky mode,” might struggle to shift gears. Welcome to the fascinating world of mental set in psychology!

Now, don’t get your neurons in a knot just yet. We’re about to embark on a mind-bending journey through the labyrinth of mental set, exploring its nooks and crannies, and maybe even finding a way to outsmart our own brains. Buckle up, folks – it’s going to be a wild ride!

What on Earth is Mental Set, Anyway?

Let’s cut to the chase: mental set is like that one friend who always insists on taking the same route to the coffee shop, even when there’s a parade blocking the street. It’s a cognitive tendency to approach problems in a particular way, based on our past experiences and learned problem-solving methods. In other words, it’s our brain’s way of saying, “Hey, this worked before, so let’s stick with it!”

But here’s the kicker: while mental set can be a real time-saver in familiar situations, it can also be as stubborn as a mule when we need to think outside the box. It’s a bit like trying to open a pickle jar with a spoon – sometimes, you need to change your approach to get results.

In the grand scheme of cognitive psychology, mental set plays a starring role. It’s not just some obscure concept gathering dust in a psychology textbook; it’s a key player in how we tackle challenges and make decisions every single day. From solving complex mathematical equations to figuring out why your cat suddenly decided the laundry basket is its new favorite bed, mental set is always there, quietly influencing our thought processes.

But don’t just take my word for it. Let’s dive deeper into the rabbit hole and explore the nitty-gritty of mental set in psychology. Trust me, by the time we’re done, you’ll be seeing mental sets everywhere – and maybe even learning how to outsmart your own!

Mental Set: More Than Just a Stubborn Mindset

Alright, let’s roll up our sleeves and get down to brass tacks. What exactly is mental set in psychology? Well, imagine your brain as a Swiss Army knife. Each tool represents a different problem-solving approach. Mental set is like having your favorite tool always at the ready – it’s quick, it’s familiar, but it might not always be the best tool for the job.

In more formal terms, mental set is a predisposition to approach problems in a specific way, based on our past experiences and learned strategies. It’s like having a default setting in your brain’s problem-solving software. This concept is closely related to Problem Space Psychology: Exploring Cognitive Approaches to Problem-Solving , where we examine how our minds navigate the landscape of potential solutions.

Now, let’s break it down further. Mental set has a few key components:

1. Past experiences: Your brain loves patterns. It’s constantly looking for similarities between current situations and past ones.

2. Learned strategies: These are your go-to methods for solving problems. They’re like your brain’s favorite recipes.

3. Cognitive efficiency: Mental set helps your brain save energy by not reinventing the wheel every time you face a problem.

4. Rigidity: Sometimes, mental set can be as flexible as a brick wall, making it hard to see alternative solutions.

But hold your horses! Before you start thinking mental set is just another fancy term for stubbornness, let’s clear the air. Mental set is different from other cognitive biases. While confirmation bias makes you seek out information that supports your existing beliefs, mental set influences how you approach problem-solving. And unlike functional fixedness, which limits you to using objects in traditional ways, mental set affects your overall problem-solving strategy.

Now, let’s take a quick trip down memory lane. The concept of mental set didn’t just pop up overnight like a mushroom after rain. It has its roots in Gestalt psychology, a school of thought that emerged in the early 20th century. These psych pioneers were all about how our brains perceive and organize information. They realized that sometimes, our perception can be so fixed that it prevents us from seeing alternative solutions – and voila! The idea of mental set was born.

Mental Set: The Good, The Bad, and The Downright Puzzling

Now that we’ve got the basics down, let’s dive into the different flavors of mental set. Yep, you heard that right – mental set comes in more varieties than your local ice cream shop!

First up, we’ve got positive mental sets. These are the golden children of the mental set family. They’re like having a superpower that helps you solve problems faster than a speeding bullet. For instance, if you’re a whiz at sudoku puzzles, you’ve probably developed a positive mental set for number-based logic problems. Your brain has a toolkit of strategies ready to go, making you the Usain Bolt of puzzle-solving.

But wait, there’s a flip side to this coin. Enter negative mental sets, the troublemakers of the bunch. These are the mental sets that make you bang your head against the wall, trying the same ineffective strategy over and over again. It’s like trying to fit a square peg in a round hole, and insisting that if you just push hard enough, it’ll work eventually.

Let’s bring this down to earth with some real-life examples. Have you ever been driving home from work, only to find yourself pulling into your driveway when you meant to go to the grocery store? That’s a mental set in action! Your brain was on autopilot, following its usual routine. Or how about when you’re trying to open a new app on your smartphone, but you keep tapping the spot where the icon was on your old phone? Yep, you guessed it – mental set strikes again!

But the real mind-benders come from famous psychological experiments. Take the Luchins Water Jar problem, for example. Participants were given a series of water jars and asked to measure out specific amounts of water. The first few problems could be solved using the same complex method. But when a simpler solution was introduced later, many participants stuck with the complex method they’d been using. Talk about being set in your ways!

Another classic is the Nine-Dot Problem. Participants are asked to connect nine dots arranged in a square using four straight lines without lifting their pen. The trick? You need to extend your lines beyond the imaginary square formed by the dots. But many people get stuck trying to solve it within the square – a perfect example of how mental set can limit our thinking.

These experiments aren’t just psychological party tricks. They illuminate how our minds work and Mental Operations in Psychology: Defining Cognitive Processes shape our problem-solving abilities. They show us that sometimes, our greatest strength – our ability to recognize patterns and apply learned strategies – can also be our greatest weakness.

When Mental Set Meets Problem-Solving: A Comedy of Errors (Sometimes)

Picture this: you’re faced with a problem. Your brain, ever the eager beaver, immediately starts rifling through its filing cabinet of past experiences and solutions. “Aha!” it exclaims, pulling out a dusty file. “This worked before, so it’ll definitely work now!” And off you go, confidently applying your tried-and-true method. Sometimes, this works like a charm. Other times… well, let’s just say it can lead to some pretty comical situations.

Mental set influences our approach to problems in a big way. It’s like having a GPS in your brain, always trying to route you down familiar paths. This can be super helpful when you’re dealing with routine tasks or problems similar to ones you’ve solved before. It’s why experienced chefs can whip up a meal without even glancing at a recipe, or why seasoned drivers can navigate through traffic while belting out their favorite tunes.

But here’s where things get interesting. When we’re faced with novel or complex problems, our trusty mental set can sometimes lead us astray. It’s like trying to use a map of New York to navigate Tokyo – sure, they’re both big cities, but the similarities pretty much end there.

This is where the drawbacks of mental set rear their ugly heads. We might become so fixated on using a familiar strategy that we completely miss a simpler or more effective solution. It’s like trying to open a door by repeatedly pushing when all you need to do is pull. Frustrating? You bet. But it’s also a perfectly normal part of how our brains work.

So, how do we overcome these limiting mental sets? Well, it’s not about completely rewiring your brain (though wouldn’t that be convenient?). Instead, it’s about developing strategies to recognize when your mental set might be hindering rather than helping. Here are a few tips:

1. Take a step back: Sometimes, all you need is a fresh perspective. Try looking at the problem from different angles.

2. Question your assumptions: Ask yourself, “Why am I approaching the problem this way? Is there another way I haven’t considered?”

3. Seek input from others: Sometimes, a fresh pair of eyes can spot solutions we’ve overlooked.

4. Practice mindfulness: Being aware of your thought processes can help you recognize when you’re stuck in a mental rut.

5. Embrace the unfamiliar: Regularly exposing yourself to new experiences and ideas can help keep your thinking flexible.

Remember, overcoming limiting mental sets isn’t about completely abandoning your tried-and-true methods. It’s about developing the flexibility to know when to stick with what works and when to try something new. It’s a balancing act, but with practice, you can become a mental acrobat!

Mental Set: The Swiss Army Knife of Psychology

Now that we’ve got a handle on what mental set is and how it affects problem-solving, let’s take a whirlwind tour through its various roles in different psychological contexts. Buckle up, folks – we’re about to see just how versatile this concept really is!

First stop: cognitive psychology. Here, mental set is like the star quarterback of the team. It plays a crucial role in how we process information, make decisions, and solve problems. Researchers in this field are constantly exploring how mental set influences our thinking patterns and cognitive biases. It’s all part of understanding the intricate Mental Map Psychology: Understanding How Our Minds Navigate the World .

Next up: learning and education. In the classroom, mental set can be both a blessing and a curse. On one hand, it helps students quickly apply learned concepts to familiar problems. On the other hand, it can sometimes prevent them from thinking creatively or approaching new types of problems effectively. That’s why educators are always on the lookout for ways to help students develop flexible thinking skills.

But wait, there’s more! Mental set also plays a significant role in clinical psychology and therapy. Many psychological issues, from anxiety to depression, involve rigid thinking patterns – essentially, unhelpful mental sets. Therapists often work with clients to identify and challenge these limiting thought patterns, helping them develop more flexible and adaptive ways of thinking.

Last but not least, let’s talk about creativity and innovation. You might think mental set would be the arch-nemesis of creative thinking, but it’s not that simple. While rigid mental sets can indeed stifle creativity, positive mental sets can actually enhance it. Think of artists who have mastered their craft – their mental set allows them to effortlessly execute techniques, freeing up mental space for more creative expression.

Breaking Free: Outsmarting Your Own Brain

Alright, we’ve seen how mental set can sometimes be as stubborn as a mule. But fear not! We’re not doomed to be forever trapped by our own thinking patterns. With a little effort and some clever strategies, we can learn to recognize our mental sets and even break free from limiting ones.

First things first: recognizing your own mental sets. This is like trying to spot your own nose – it’s right there, but you rarely notice it. Here are some signs that you might be stuck in a mental set:

1. You keep trying the same solution even when it’s not working. 2. You feel frustrated or stuck when facing a new type of problem. 3. You dismiss alternative solutions without really considering them. 4. You find yourself saying, “But this is how we’ve always done it!”

Sound familiar? Don’t worry, we’ve all been there. The good news is, once you can recognize your mental sets, you’re halfway to overcoming them.

Now, let’s talk about some methods to challenge and break those limiting mental sets:

1. Practice divergent thinking: Try to come up with multiple solutions to a problem, even if some seem silly at first. 2. Seek out new experiences: Exposing yourself to new ideas and situations can help broaden your perspective. 3. Play devil’s advocate: Challenge your own assumptions by arguing against them. 4. Use analogies: Try relating the problem to something completely different – you might stumble upon a novel solution. 5. Take breaks: Sometimes, stepping away from a problem can help you see it with fresh eyes when you return.

But hey, it’s not all about breaking mental sets. Sometimes, they can be pretty darn useful. The key is learning how to harness positive mental sets for improved performance. For example, athletes often develop mental sets that help them perform under pressure. The trick is to cultivate helpful mental sets while maintaining the flexibility to adapt when needed.

Speaking of flexibility, that’s really the ultimate goal here. Developing cognitive flexibility is like giving your brain a good stretch. It’s about being able to switch between different mental sets as the situation demands. This skill is crucial for Problem Solving Techniques in Psychology: Effective Strategies for Overcoming Challenges .

Some ways to develop cognitive flexibility include:

1. Learning new skills: This forces your brain to create new neural pathways. 2. Trying new problem-solving techniques: Don’t always rely on your go-to methods. 3. Engaging in creative activities: Art, music, and writing can all help foster flexible thinking. 4. Mindfulness meditation: This can help you become more aware of your thought processes.

Remember, the goal isn’t to completely eliminate mental sets – they’re a natural and often helpful part of how our brains work. Instead, it’s about developing the awareness and skills to use them effectively, while also knowing when to set them aside and try something new.

The Final Thread: Tying It All Together

Whew! We’ve covered a lot of ground, haven’t we? From threading needles to breaking mental blocks, we’ve explored the ins and outs of mental set in psychology. Let’s take a moment to recap and reflect on this fascinating aspect of human cognition.

Mental set, at its core, is our tendency to approach problems in a particular way based on our past experiences and learned strategies. It’s like having a default setting in our brain’s problem-solving software. Sometimes this default setting serves us well, helping us quickly solve familiar problems. Other times, it can be as helpful as a screen door on a submarine, preventing us from seeing novel solutions.

Understanding mental set is crucial in psychology because it sheds light on how we think, learn, and solve problems. It’s not just some abstract concept – it has real-world implications in areas ranging from education and therapy to creativity and innovation. By recognizing the role of mental set in our thinking, we can better understand both our strengths and our limitations.

But the story of mental set doesn’t end here. As with many areas in psychology, there’s still much to explore. Future research might delve deeper into the neurological basis of mental set, or investigate how cultural differences influence the formation and persistence of mental sets. We might see more studies on how to effectively teach cognitive flexibility, or explore the role of mental set in emerging fields like artificial intelligence.

So, what does all this mean for you in your daily life? Well, the next time you find yourself stuck on a problem, remember the lessons of mental set. Take a step back, question your assumptions, and consider alternative approaches. You might be surprised at the solutions you come up with when you break free from your usual thinking patterns.

Remember, your brain is an incredibly powerful tool, but like any tool, it works best when you know how to use it effectively. By understanding concepts like mental set, you’re essentially learning to be a better operator of your own mind. And in a world that’s constantly throwing new challenges our way, that’s a skill that’s more valuable than ever.

So go forth, dear reader, and flex those mental muscles! Challenge your assumptions, embrace new perspectives, and don’t be afraid to think outside the box. After all, life is full of puzzles waiting to be solved – and with a flexible mindset, you’re well-equipped to tackle them all.

And who knows? The next time you’re faced with threading a needle, you might just surprise yourself with an innovative new approach. Just remember to keep your fingers out of the way!

References:

1. Luchins, A. S. (1942). Mechanization in problem solving: The effect of Einstellung. Psychological Monographs, 54(6), i-95.

2. Duncker, K. (1945). On problem-solving. Psychological Monographs, 58(5), i-113.

3. Sternberg, R. J., & Sternberg, K. (2016). Cognitive psychology (7th ed.). Cengage Learning.

4. Goldstein, E. B. (2018). Cognitive psychology: Connecting mind, research, and everyday experience (5th ed.). Cengage Learning.

5. Eysenck, M. W., & Keane, M. T. (2020). Cognitive psychology: A student’s handbook (8th ed.). Psychology Press.

6. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

7. Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. HarperCollins.

8. Beck, A. T. (1979). Cognitive therapy and the emotional disorders. Penguin.

9. Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

10. Reisberg, D. (2016). Cognition: Exploring the science of the mind (6th ed.). W. W. Norton & Company.

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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.

SuHP / Getty Images

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."

Investigating the effect of mental set on insight problem solving

Affiliation.

• 1 Parmenides Center for the Study of Thinking, Munich, Germany. [email protected]
• PMID: 18683624
• DOI: 10.1027/1618-3169.55.4.269

Mental set is the tendency to solve certain problems in a fixed way based on previous solutions to similar problems. The moment of insight occurs when a problem cannot be solved using solution methods suggested by prior experience and the problem solver suddenly realizes that the solution requires different solution methods. Mental set and insight have often been linked together and yet no attempt thus far has systematically examined the interplay between the two. Three experiments are presented that examine the extent to which sets of noninsight and insight problems affect the subsequent solutions of insight test problems. The results indicate a subtle interplay between mental set and insight: when the set involves noninsight problems, no mental set effects are shown for the insight test problems, yet when the set involves insight problems, both facilitation and inhibition can be seen depending on the type of insight problem presented in the set. A two process model is detailed to explain these findings that combines the representational change mechanism with that of proceduralization.

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

Amory h danek, nemanja vaci, merim bilalić.

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Correspondence: [email protected]

Received 2023 Mar 16; Revised 2023 Apr 24; Accepted 2023 Apr 26; Collection date 2023 May.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

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.

Keywords: insight problems, representational change, problem-solving, generalized additive models, change points analysis

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 .

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

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).

Conflicts of Interest

The authors declare no conflict of interest.

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.

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 ).

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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• Yao Xiang 2  

Mental set, also known as psychological set, refers to a psychological tendency or prepared state formed by certain mental activities. During the process of problem-solving, individuals may tend to adopt attitudes or behaviors to adopt attitudes or behaviors consistent with their past experiences, which is also referred to as cognitive inertia. Mental set is typically formed by integrating psychological states, habits, or attitudes developed during past cognitive processing and judgments, which can influence subsequent psychological activities such as perception, memory, thinking, and emotional responses, as well as behavioral activities.

Mental set has both positive and negative effects on problem-solving. Under conditions of a stable environment, mental set enhances an individual’s perception, problem-solving speed, and quality, allowing the individual to engage in activities skillfully or even automatically, thereby saving time and energy. However, when the context changes, mental...

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Exploring the Concept of Mental Set in Psychology

Have you ever found yourself stuck in a particular way of thinking, unable to see alternative solutions to a problem? This could be a result of a mental set, a cognitive framework that influences our perception and problem-solving abilities.

In this article, we will delve into the concept of mental set in psychology, defining its types such as functional fixedness and Einstellung, and exploring how it can impact our thought processes. We will also discuss the factors that contribute to mental set and provide strategies for overcoming or changing this mindset.

Join us on this journey to better understand the complexities of mental set and how it shapes our cognition.

• Mental set refers to a person’s tendency to approach problems and situations in a particular way based on their past experiences and knowledge.
• There are three main types of mental set: functional fixedness, einstellung, and problem-solving set, each influencing perception and problem-solving differently.
• 1 What Is Mental Set?
• 2 How Is Mental Set Defined in Psychology?
• 3.1 Functional Fixedness
• 3.2 Einstellung
• 3.3 Problem-Solving Set
• 4.1 Influence on Perception
• 4.2 Influence on Problem-Solving
• 5.1 Experience and Past Knowledge
• 5.2 Cultural Influences
• 5.3 Cognitive Biases
• 6.1 Encouraging Flexible Thinking
• 6.2 Exposing Oneself to New Experiences
• 6.3 Challenging Assumptions and Biases
• 7.1 What is mental set in psychology?
• 7.2 How does mental set affect problem solving?
• 7.3 Can mental set be changed?
• 7.4 What are some common examples of mental set?
• 7.5 How does mental set relate to cognitive rigidity?
• 7.6 How can mental set be beneficial?

What Is Mental Set?

Mental set , a concept in cognitive psychology, refers to a framework or attitude that shapes how individuals approach and solve problems.

Having a mental set influences the way people perceive and interpret information, ultimately affecting their problem-solving strategies. For example, if someone has a tendency to always approach tasks in a certain way, they may overlook alternative solutions that could be more effective. An individual’s past experiences, cultural background, and personal beliefs all contribute to the formation of their mental set.

In cognitive psychology, mental set plays a crucial role in understanding the limitations and biases that can arise during problem-solving. Researchers have found that people with a fixed mental set may struggle to adapt when faced with new challenges, while those with a more flexible mindset are often able to explore diverse solutions.

How Is Mental Set Defined in Psychology?

In psychology, mental set is described as a cognitive framework that influences problem-solving approaches and decision-making processes.

It refers to the tendency of individuals to approach a new problem or situation based on past experiences or previously successful strategies. This ingrained mental pattern, while being efficient in familiar contexts, can sometimes hinder creative thinking and innovative solutions. For example, if a person is used to solving mathematical problems in a certain way, they may apply the same approach to a different type of problem, leading to a mental block.

What Are the Types of Mental Set?

Various types of mental sets exist in cognitive psychology, including functional fixedness , Einstellung , and problem-solving set , each influencing cognitive performance differently.

Functional fixedness is a mental set that limits a person to using an object only in the way it is traditionally used, preventing them from seeing alternative uses. Einstellung refers to the tendency to approach problems in a certain way, even if there are better solutions available. Problem-solving sets are frameworks or strategies individuals use when approaching new challenges, which can either aid or hinder problem-solving abilities.

Functional Fixedness

Functional fixedness is a mental set that limits problem-solving by restricting individuals to traditional uses of objects or concepts.

This cognitive bias can hinder creativity and innovation as it prevents people from exploring unconventional solutions. For example, in a classic experiment, participants were given a candle, matches, and a box of tacks, and asked to attach the candle to a wall. Many struggled to think beyond the typical use of the box as simply a container, overlooking its potential as a platform for mounting the candle. This demonstrates how functional fixedness can lead to overlooking simple yet effective solutions.

Einstellung

Einstellung refers to the phenomenon where pre-existing ideas or solutions prevent individuals from considering alternative problem-solving approaches.

This cognitive bias can be a significant obstacle in effectively solving problems as it locks one’s mind into a particular way of thinking, leading to overlooking potentially better solutions. Einstellung can be compared to a mental ‘rut’ that hinders creativity and innovation. For instance, if someone always uses a specific method to solve math problems, they may struggle to adapt when presented with a new, more efficient technique. This rigid adherence to familiar methods can result in suboptimal outcomes and missed opportunities for growth.

Problem-Solving Set

A problem-solving set is a mental framework that guides individuals in approaching and solving complex problems based on past experiences and cognitive biases.

This set of strategies and mental models significantly influences the way individuals navigate decision-making processes and exhibit cognitive flexibility. For example, when an individual encounters a familiar problem, their problem-solving set may automatically prompt them to apply a previously successful solution, streamlining the resolution process.

Relying too heavily on a problem-solving set can sometimes lead to ineffective outcomes. In situations where a problem has unique aspects that differ from past experiences, individuals may struggle to adapt their existing mental framework, resulting in suboptimal solutions or being stuck in a cycle of unproductive approaches.

How Does Mental Set Affect Perception and Problem-Solving?

Mental set plays a crucial role in shaping both perception and problem-solving by influencing how individuals interpret information and approach challenges.

When individuals have a particular mental set, they tend to focus on specific aspects of a situation while ignoring potential alternatives. This can lead to cognitive biases, where decisions are based on preconceived notions rather than objective analysis. For example, if someone has a rigid mental set that success only comes through hard work, they may ignore innovative solutions or opportunities for collaboration. This narrow view can limit creativity and hinder effective problem-solving.

Influence on Perception

Mental set can significantly impact perception by filtering information through cognitive biases and shaping interpretations at the cognitive unconscious level.

Our mental set essentially acts as a lens through which we view the world, influencing how we perceive and make sense of our experiences. Cognitive biases, such as confirmation bias or anchoring effect, can lead us to selectively pay attention to information that aligns with our pre-existing beliefs, reinforcing our mental set. For example, if someone has a pervasive belief that all politicians are corrupt, they may interpret every political action through this lens, further solidifying their negative perception.

Influence on Problem-Solving

In problem-solving, mental set can either enhance or hinder cognitive flexibility, affecting the ability to generate innovative solutions and navigate complex challenges.

Individuals with a rigid mental set tend to approach problems with preconceived notions and fixed strategies, often leading to stalemate situations. For example, imagine a team working on a project where one member holds a strong belief in a traditional methodology, restricting the group’s exploration of new, more efficient approaches. On the contrary, individuals with high cognitive flexibility can adapt quickly and switch between different problem-solving strategies based on the demands of the situation, opening up new possibilities for creative solutions.

What Are the Factors That Contribute to Mental Set?

Various factors contribute to the formation of mental sets, including personal experiences, cultural norms, and cognitive biases that shape individuals’ problem-solving strategies.

Personal experiences play a crucial role in the development of mental sets as individuals tend to approach new problems based on past situations they have encountered. For example, someone who has faced success using a particular method in the past may default to that same approach when encountering a similar problem.

The cultural upbringing of an individual can influence their mental sets; cultural norms and values can dictate what is considered a valid solution to a problem, thus impacting the decision-making process.

Experience and Past Knowledge

One of the key factors contributing to the formation of mental sets is individuals’ experiences and past knowledge, which influence problem-solving approaches and cognitive strategies.

Our past experiences shape the way we approach new challenges, guiding our thought processes and decision-making. For instance, consider a seasoned chess player who, due to years of practice, quickly recognizes patterns and potential moves during a game, demonstrating a well-established mental set. On the other hand, a novice in the game might struggle to see beyond immediate options, lacking the depth of experience to rely on.

Cultural Influences

Cultural influences play a significant role in shaping individuals’ mental sets by providing cognitive frameworks and norms that impact problem-solving strategies and decision-making processes.

One of the essential aspects of cultural influences on mental sets is the way in which societal values dictate what is considered an appropriate approach to various challenges. For instance, in a collectivist society, the emphasis may be on reaching consensus and maintaining harmony, leading individuals to prioritize collaboration and compromise in problem-solving.

On the other hand, in individualistic cultures, independence and personal achievement might be highly valued, shaping mental sets towards assertiveness and self-reliance when addressing issues. These contrasting examples highlight how cultural norms can shape the cognitive frameworks that individuals rely on to navigate complex situations.

Cognitive Biases

Cognitive biases are inherent in the formation of mental sets, influencing individuals’ decision-making strategies and problem-solving approaches based on heuristic shortcuts and perceptual sets.

This means that our minds can sometimes trick us into making decisions or solving problems in a certain way, even if a different approach might be more effective. One common example of a cognitive bias is confirmation bias, where people tend to search for, interpret, or prioritize information that confirms their existing beliefs or hypotheses.

Another prevalent bias is the anchoring bias, where individuals rely too heavily on the first piece of information they receive when making decisions. This can lead to overlooking other relevant facts that could potentially alter the decision-making process.

How Can Mental Set Be Overcome or Changed?

Overcoming or changing a mental set requires adopting flexible thinking, exposing oneself to new experiences, and challenging assumptions and biases that may limit problem-solving abilities.

One effective strategy for fostering flexible thinking is to engage in activities that involve looking at problems from multiple perspectives. This can include discussing ideas with individuals who have different backgrounds or opinions, which can help broaden your own viewpoint.

• Seeking out new experiences can also be beneficial in breaking out of a fixed mental set. Whether it’s trying a new hobby, traveling to unfamiliar places, or learning a new skill, stepping outside of your comfort zone can stimulate creativity and encourage adaptability.
• Critically evaluating assumptions involves questioning the beliefs and preconceptions that influence your thoughts and decisions. To challenge these biases, one can engage in exercises like journaling to reflect on their reasoning processes or seek feedback from others to gain different insights.

Encouraging Flexible Thinking

Encouraging flexible thinking is a crucial step in overcoming mental sets, as it allows individuals to adapt their problem-solving strategies and consider alternative approaches.

Flexible thinking enables individuals to break out of fixed patterns and explore new possibilities. One effective technique to promote cognitive flexibility is the practice of ‘mental set switching,’ where individuals intentionally shift their focus to view a problem from different angles. By engaging in this exercise, individuals can enhance their ability to generate creative solutions by expanding their perspective.

Real-world examples of the importance of flexible thinking can be seen in industries such as technology and design. Companies like Apple and Google foster environments that encourage employees to think outside the box, leading to innovative products and solutions. Embracing flexibility in thinking not only enhances problem-solving skills but also promotes adaptability in an ever-changing world.

Exposing Oneself to New Experiences

Exposing oneself to new experiences is a powerful way to challenge existing mental sets, broaden perspectives, and enhance cognitive performance through diverse learning opportunities.

Seeking new experiences not only helps in breaking the monotony of routine but also stimulates the brain to create new neural connections, boosting creativity and problem-solving skills. To actively expose oneself to diverse experiences, individuals can try engaging in activities outside their comfort zone, such as traveling to unfamiliar places, trying new cuisines, learning a new language, or taking up a hobby they’ve never considered before. Attending workshops, seminars, or volunteering for different causes can introduce fresh perspectives and insights that fuel personal growth and mental agility.

Challenging Assumptions and Biases

Challenging assumptions and biases is essential for reshaping mental sets and improving problem-solving strategies by promoting critical thinking and reducing the impact of cognitive rigidity.

By questioning assumptions, individuals can break free from entrenched patterns of thought and open themselves up to new perspectives. For example, in a business setting, challenging the assumption that a certain marketing strategy will be successful can lead to exploring innovative alternatives that might yield better results.

Critical thinking encourages individuals to analyze information objectively, consider different viewpoints, and weigh evidence before drawing conclusions. This process not only enhances problem-solving abilities but also fosters a more flexible and open-minded approach to decision-making.

Frequently Asked Questions

What is mental set in psychology.

Mental set refers to the tendency of individuals to approach problems or situations in a particular way based on past experiences and strategies that have been successful in the past.

How does mental set affect problem solving?

A person’s mental set can either facilitate or hinder problem solving. When a problem is similar to one that has been successfully solved in the past, mental set can be beneficial. However, if a problem requires a new approach, mental set may limit one’s ability to think creatively.

Can mental set be changed?

Yes, mental set can be changed by consciously approaching problems with a new perspective and being open to different strategies and solutions. This is known as breaking set.

What are some common examples of mental set?

Some common examples of mental set include using the same route to get to work every day, always solving a math problem using a specific formula, and approaching a problem at work in the same way as it has been done in the past.

How does mental set relate to cognitive rigidity?

Cognitive rigidity is a state of inflexibility in thinking and problem solving. Mental set can contribute to cognitive rigidity by limiting a person’s ability to see alternative solutions or approaches to a problem.

How can mental set be beneficial?

Mental set can be beneficial by allowing individuals to quickly and efficiently solve problems that are similar to ones they have encountered before. It can also help to reduce decision-making time and cognitive load.

Dr. Henry Foster is a neuropsychologist with a focus on cognitive disorders and brain rehabilitation. His clinical work involves assessing and treating individuals with brain injuries and neurodegenerative diseases. Through his writing, Dr. Foster shares insights into the brain’s ability to heal and adapt, offering hope and practical advice for patients and families navigating the challenges of cognitive impairments.

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