Entropy-based metrics for predicting choice behavior based on local response to reward

Ethan Trepka; Mehran Spitmaan; Bilal A. Bari; Vincent D. Costa; Jeremiah Y. Cohen; Alireza Soltani

doi:10.1038/s41467-021-26784-w

Nature Communications (Nov 2021)

Entropy-based metrics for predicting choice behavior based on local response to reward

Ethan Trepka,
Mehran Spitmaan,
Bilal A. Bari,
Vincent D. Costa,
Jeremiah Y. Cohen,
Alireza Soltani

Affiliations

Ethan Trepka: Department of Psychological and Brain Sciences, Dartmouth College
Mehran Spitmaan: Department of Psychological and Brain Sciences, Dartmouth College
Bilal A. Bari: The Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University School of Medicine
Vincent D. Costa: Department of Behavioral Neuroscience, Oregon Health and Science University
Jeremiah Y. Cohen: The Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University School of Medicine
Alireza Soltani: Department of Psychological and Brain Sciences, Dartmouth College

DOI: https://doi.org/10.1038/s41467-021-26784-w
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 16

Abstract

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Animals distribute their choices between alternative options according to relative reinforcement they receive from those options (matching law). Here, the authors propose metrics based on information theory that can predict this global behavioral rule based on local response to reward feedback.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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