Counterfactual Reasoning Underlies the Learning of Priors in Decision Making

Summary

Accurate decisions require knowledge of prior probabilities (e.g., prevalence or base rate), but it is unclear how prior probabilities are learned in the absence of a teacher. We hypothesized that humans could learn base rates from experience making decisions, even without feedback. Participants made difficult decisions about the direction of dynamic random dot motion. Across blocks of 15–42 trials, the base rate favoring left or right varied. Participants were not informed of the base rate or choice accuracy, yet they gradually biased their choices and thereby increased accuracy and confidence in their decisions. They achieved this by updating knowledge of base rate after each decision, using a counterfactual representation of confidence that simulates a neutral prior. The strategy is consistent with Bayesian updating of belief and suggests that humans represent both true confidence, which incorporates the evolving belief of the prior, and counterfactual confidence, which discounts the prior.

Highlights

• •

  People can learn base rates without feedback and apply them to make better decisions

• •

  The estimate of base rate is updated based on the confidence in each decision

• •

  The form of confidence used is counterfactual, as if the base rate were uninformative

• •

  The study extends the Bayesian framework from choice to prior probability estimation

Abstract

Zylberberg et al. show that human decision makers can learn environmental biases from sequences of difficult decisions, without feedback about accuracy, by calculating the belief that the decisions would have been correct in an unbiased environment—a form of counterfactual confidence.