Ho, M. K., Cohen, J. D., & Griffiths, T.
(2023, March 30). PsyArXiv
https://doi.org/10.31234/osf.io/aqxws
Abstract
Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g., “functional fixedness”) that lead them astray. How can our capacity for behavioral flexibility be reconciled with our susceptibility to conceptual inflexibility? We propose that these tendencies reflect avoidance of two cognitive costs: the cost of representing task details and the cost of switching between families of representations. To test this hypothesis, we developed a novel paradigm that affords participants opportunities to choose different families of simplified representations to plan. In two pre-registered online studies (N = 377; N = 294), we found that participants’ optimal behavior, suboptimal behavior, and reaction time are explained by a computational model that formalizes people’s avoidance of representational complexity and switching. These results demonstrate how the selection of simplified, rigid representations leads to the otherwise puzzling combination of flexibility and inflexibility observed in problem solving.
General Discussion
Here, we evaluated the hypothesis that functional fixedness reflects the avoidance of complexity and switching costs during planning. To do so, we developed a novel paradigm in which participants navigated mazes that could be represented simply as blocks or more complexly as blocks and notches. Experiments revealed that people simplify problems(for instance, by adopting a blocks-only construal strategy if navigating through notches was unnecessary) and that they persist in these strategies (for instance, continuing to ignore notches even when attending to a notch would lead to a better solution). Additionally, our computational analyses using the value-guided construal framework (Ho et al., 2022)confirmed that the avoidance of complexity and switching costs explains observed patterns of optimal behavior, suboptimal behavior, and reaction times under different experimental manipulations. Overall, these results support our proposal and help clarify the computational principles that underlie functional fixedness.
Summary:
The authors argue that people often simplify problems in order to make them more manageable, but this can lead to rigidity and suboptimal solutions.
The authors conclude that rational simplification is a common cognitive mechanism that can lead to both flexibility and rigidity in planning. They argue that the model provides a useful framework for understanding how people simplify problems and make decisions.
Here are some of the key takeaways from the article:
- People often simplify problems in order to make them more manageable.
- This can lead to rigidity and suboptimal solutions.
- The tendency to simplify problems is a cognitive mechanism that can be explained by the limited capacity for representing task details.
- The model provides a useful framework for understanding how people simplify problems and make decisions.
