Repository logo
 

Counterfactual Reasoning Underlies the Learning of Priors in Decision Making.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Zylberberg, Ariel 
Wolpert, Daniel M 
Shadlen, Michael N 

Abstract

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.

Description

Keywords

Adult, Decision Making, Female, Humans, Learning, Male, Motion Perception, Photic Stimulation, Problem Solving, Random Allocation

Journal Title

Neuron

Conference Name

Journal ISSN

0896-6273
1097-4199

Volume Title

99

Publisher

Elsevier BV
Sponsorship
Wellcome Trust (097803/Z/11/Z)
Royal Society (RP120142)
Human Frontier Science Program (HFSP) (RGP0067/2011)
Relationships
Is derived from: