A Mathematical Framework for Statistical Decision Confidence

Hangya, B., Sanders, J. L., Kepecs, A. (September 2016) A Mathematical Framework for Statistical Decision Confidence. Neural Comput, 28 (9). pp. 1840-1858. ISSN 1530-888X (Electronic)0899-7667 (Linking)

URL: http://www.ncbi.nlm.nih.gov/pubmed/27391683
DOI: 10.1162/NECO_a_00864

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.

Item Type: Paper
Subjects: organism description > animal behavior > decision making
bioinformatics > computational biology > statistical analysis
CSHL Authors:
Communities: CSHL labs > Kepecs lab
Depositing User: Matt Covey
Date: September 2016
Date Deposited: 22 Jul 2016 18:47
Last Modified: 18 Sep 2018 18:23
PMCID: PMC5378480
Related URLs:
URI: http://repository.cshl.edu/id/eprint/32974

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