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Showing posts with label Problem-Solving. Show all posts
Showing posts with label Problem-Solving. Show all posts

Thursday, February 8, 2024

People's thinking plans adapt to the problem they're trying to solve

Ongchoco, J. D., Knobe, J., & Jara-Ettinger, J. (2024).
Cognition, 243, 105669.

Abstract

Much of our thinking focuses on deciding what to do in situations where the space of possible options is too large to evaluate exhaustively. Previous work has found that people do this by learning the general value of different behaviors, and prioritizing thinking about high-value options in new situations. Is this good-action bias always the best strategy, or can thinking about low-value options sometimes become more beneficial? Can people adapt their thinking accordingly based on the situation? And how do we know what to think about in novel events? Here, we developed a block-puzzle paradigm that enabled us to measure people's thinking plans and compare them to a computational model of rational thought. We used two distinct response methods to explore what people think about—a self-report method, in which we asked people explicitly to report what they thought about, and an implicit response time method, in which we used people's decision-making times to reveal what they thought about. Our results suggest that people can quickly estimate the apparent value of different options and use this to decide what to think about. Critically, we find that people can flexibly prioritize whether to think about high-value options (Experiments 1 and 2) or low-value options (Experiments 3, 4, and 5), depending on the problem. Through computational modeling, we show that these thinking strategies are broadly rational, enabling people to maximize the value of long-term decisions. Our results suggest that thinking plans are flexible: What we think about depends on the structure of the problems we are trying to solve.


Some thoughts:

The study is based on the idea that people have "thinking plans" which are essentially roadmaps that guide our thoughts and actions when we are trying to solve a problem. These thinking plans are not static, but rather can change and adapt depending on the specific problem we are facing.

For example, if we are trying to solve a math problem, our thinking plan might involve breaking the problem down into smaller steps, identifying the relevant information, and applying the appropriate formulas. However, if we are trying to solve a social problem, our thinking plan might involve considering the different perspectives of the people involved, identifying potential solutions, and evaluating the consequences of each solution.

The study used computational modeling to simulate how people would solve different types of problems. The model showed that people's thinking plans were flexible and adapted to the specific problem at hand. The model also showed that these thinking plans were broadly rational, meaning that they helped people to make decisions that were in their best interests.

The findings of the study have important implications for education and other fields that are concerned with human decision-making. The study suggests that it is important to teach people how to think flexibly and adapt their thinking plans to different situations. It also suggests that we should not expect people to always make the "right" decision, as the best course of action will often depend on the specific circumstances.

Tuesday, September 14, 2021

Reconstructing the Einstellung effect

Binz, M., & Schulz, E. (2021, August 10).
https://doi.org/10.31234/osf.io/yhcf4

Abstract

The Einstellung effect was first described by Abraham Luchins in his doctoral thesis published in 1942. The effect occurs when a repeated solution to old problems is applied to a new problem even though a more appropriate response is available. In Luchins' so-called water jar task, participants had to measure a specific amount of water using three jars of different capacities. Luchins found that subjects kept using methods they had applied in previous trials, even if a more efficient solution for the current trial was available: an Einstellung effect. Moreover, Luchins studied the different conditions that could possibly mediate this effect, including telling participants to pay more attention, changing the number of tasks, alternating between different types of tasks, as well as putting participants under time pressure. In the current work, we reconstruct and reanalyze the data of the various experimental conditions published in Luchins' thesis. We furthermore show that a model of resource-rational decision-making can explain all of the observed effects. This model assumes that people transform prior preferences into a posterior policy to maximize rewards under time constraints. Taken together, our reconstructive and modeling results put the Einstellung effect under the lens of modern-day psychology and show how resource-rational models can explain effects that have historically been seen as deficiencies of human problem-solving.

From the Discussion

It is typically assumed that the best solution for any particular problem is necessarily the shortest, and thus previous research has largely characterized the Einstellung effect as maladaptive behavior.  In the present paper, we have challenged this assumption and provided a resource-rational interpretation of the effect. We did so with the help of an information-theoretic model of decision-making.  The central premise of this  model is to transform prior preferences into posterior policies in a way that trade of expected utility with the time it takes to make a decision. The resulting model incorporates three basic principles: (1) people prefer simple solutions, i.e.,they attempt to spend as little physical effort as possible, (2) they avoid costly computations, i.e., those that require high mental effort, and (3) they adapt to their environment,  i.e., they learn about statistics of the problem they interact with.We found that these simple principles are sufficient to capture the rich characteristics found in Luchins’ data. An additional ablation analysis  confirmed  that  all  of  these  principles  are necessary to reproduce the entire set of phenomena reported in Luchins’ thesis.

Friday, July 19, 2019

Cognitive skills and decision-making are related to distinct facets of trait mindfulness

Tung Bui, Neil Dittmann, Kaleb Hobgood, and Neil Schmitzer-Torbert
PsyArXiv Preprints
Lasted edited June 1, 2019

Abstract

Objective: Mindfulness has been an active area of research focused on the potential links to health. Recent work has also established that trait mindfulness is also related to cognition and decision-making. The present study tested the relationship between dimensions of trait mindfulness and measures of perception, cognition, and decision-making.

Method: Forty-three undergraduate males and 126 online participants (54 females) completed a perceptual accuracy task, Stroop task, and surveys assessing five facets of trait mindfulness, problem solving, decentering, and mental health measures (stress, depression, anxiety).

Results: Overall, only a subset of mindfulness facets were related to performance on the perceptual accuracy and Stroop tasks, partially replicating previous reports. Similarly, a subset of mindfulness dimensions was related to ethical decision-making and problem-solving success. In contrast, measures of poor mental health (stress, worry, depression) were non-specifically related to the majority of mindfulness facets. Relationships between mental health measures, but not measures of cognition and decision-making, were mediated by decentering. One exception was perceptual accuracy, which was related to several mindfulness facets, and mediated by decentering.

Conclusions: Our findings indicate that separable dimensions of mindfulness are specifically related to distinct cognitive skills and decision-making, and that these relationships are largely distinct from those between mindfulness and psychological health.

The research is here.