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Abstract

Humans have a remarkable capacity for flexible decision-making, deliberating among actions by modeling their likely outcomes. This capacity allows us to adapt to the specific features of diverse circumstances. In real-world decision-making, however, people face an important challenge: There are often an enormous number of possibilities to choose among, far too many for exhaustive consideration. There is a crucial, understudied prechoice step in which, among myriad possibilities, a few good candidates come quickly to mind. How do people accomplish this? We show across nine experiments (N = 3,972 U.S. residents) that people use computationally frugal cached value estimates to propose a few candidate actions on the basis of their success in past contexts (even when irrelevant for the current context). Deliberative planning is then deployed just within this set, allowing people to compute more accurate values on the basis of context-specific criteria. This hybrid architecture illuminates how typically valuable thoughts come quickly to mind during decision-making.

Keywords

consideration sets value planning reinforcement learning decision-making open data open materials preregistered

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Generating Options and Choosing Between Them Depend on Distinct Forms of Value Representation

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