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.
