People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, and this pattern should reverse when hidden causes are likely to generate good outcomes. To test this hypothesis, we conducted two experiments ( N = 80, N = 255) in which we explicitly manipulated the behavior of hidden agents. This gave rise to both kinds of learning asymmetries in the same paradigm, as predicted by a novel Bayesian model. These results provide a mechanistic framework for understanding how causal attributions contribute to biased learning.

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The capacity for strategic thinking about the payoff-relevant actions of conspecifics is not well understood across species. We use game theory to make predictions about choices and temporal dynamics in three abstract competitive situations with chimpanzee participants. Frequencies of chimpanzee choices are extremely close to equilibrium (accurate-guessing) predictions, and shift as payoffs change, just as equilibrium theory predicts. The chimpanzee choices are also closer to the equilibrium prediction, and more responsive to past history and payoff changes, than two samples of human choices from experiments in which humans were also initially uninformed about opponent payoffs and could not communicate verbally. The results are consistent with a tentative interpretation of game theory as explaining evolved behavior, with the additional hypothesis that chimpanzees may retain or practice a specialized capacity to adjust strategy choice during competition to perform at least as well as, or better than, humans have.

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models can explain exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to real-world choice problems. We investigate the factors guiding exploratory behavior in a dataset consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. In particular, customers seem to engage in uncertainty-directed exploration and use feature-based generalization to guide their exploration. Our results provide evidence that people use sophisticated strategies to explore complex, real-world environments.