Causal Inference About Good and Bad Outcomes
Hayley M. Dorfman, Rahul Bhui, Brent L. Hughes, Samuel J. Gershman
Psychological Science · 2019 · Volume 30 · Issue 4
Abstract
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.
Keywords
reinforcement learning, Bayesian inference, decision making, attribution, agency, open data, open materials, preregistered
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