Causal Inference About Good and Bad Outcomes
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 13, 2019
- Source ID
- 10.1177/0956797619828724
Entities
People
- Brent L. Hughes
- Hayley M Dorfman
- Rahul Bhui
- Samuel J Gershman
Organizations
- Harvard University
- National Institutes of Health
- Office of Naval Research
- University of California