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)
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 School of Biological Sciences > Publications |
| Depositing User: | Matt Covey |
| Date: | September 2016 |
| Date Deposited: | 22 Jul 2016 18:47 |
| Last Modified: | 01 Mar 2024 15:45 |
| PMCID: | PMC5378480 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/32974 |
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