RUNNING HEAD: STRATEGY SELECTION AS RATIONAL METAREASONING Strategy selection as rational metareasoning

Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy’s performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decisionmaking to arithmetic by capturing the variability of people’s strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and four new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the two poles of the debate about human rationality by integrating heuristics and biases with learning and rationality.