title: A Mathematical Framework for Statistical Decision Confidence.
abstract: Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations.
authors.affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, U.S.A., and Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1083, Hungary hangya.balazs@koki.hu.
authors.forename: Balázs
authors.initials: B
authors.lastname: Hangya
authors.affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, U.S.A. joshua21@gmail.com.
authors.forename: Joshua I
authors.initials: JI
authors.lastname: Sanders
authors.affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, U.S.A. kepecs@cshl.edu.
authors.forename: Adam
authors.initials: A
authors.lastname: Kepecs
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002755:Choice Behavior; D003657:Decision Making; D006801:Humans; D009415:Nerve Net; D011336:Probability