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A neural signature of reward

View ORCID ProfileLuke J. Chang, Xiao Li, Kenny Nguyen, Maxwell Ranger, Yelyzaveta Begunova, View ORCID ProfilePin-Hao A. Chen, Jaime J. Castrellon, View ORCID ProfileGregory R. Samanez-Larkin, David H. Zald, Dominic S. Fareri, Mauricio R. Delgado, Livia Tomova
doi: https://doi.org/10.1101/2022.08.23.504939
Luke J. Chang
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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  • For correspondence: luke.j.chang@dartmouth.edu
Xiao Li
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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Kenny Nguyen
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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Maxwell Ranger
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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Yelyzaveta Begunova
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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Pin-Hao A. Chen
2Department of Psychology, Neurobiology and Cognitive Science Center, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
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Jaime J. Castrellon
3Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Gregory R. Samanez-Larkin
4Department of Psychology and Neuroscience, Duke University, Durham, NC, 27705, USA
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David H. Zald
5Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
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Dominic S. Fareri
6Gordon F. Derner School of Psychology, Adelphi University, Garden City, NY, 11530, USA
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Mauricio R. Delgado
7Department of Psychology, Rutgers University, Newark, NJ, 07102, USA
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Livia Tomova
8Department of Psychology, University of Cambridge, Cambridge, UK
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Abstract

Using a neurometric approach, we identify and validate a neural signature of reward encoded in a distributed pattern of brain activity using data collected from 21 different studies (N = 2,691). Our model can discriminate between receiving rewards from punishments in completely independent data with 99% accuracy and includes weights located in regions containing a high density of D2/D3 receptors. The model exhibits strong generalizability across a range of tasks probing reward, and a high degree of specificity for reward compared to non-reward constructs. We demonstrate several applications of how this model can infer psychological states of positive affect in the absence of self report. The model is sensitive to changes in brain activity following causal manipulations of homeostatic states, can uncover individual preferences for loss-aversion, and can be used to identify positive affective experiences when watching a television show. Our results suggest that there is a shared neural signature of reward elicited across these different task contexts.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/cosanlab/reward_signature

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 24, 2022.
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A neural signature of reward
Luke J. Chang, Xiao Li, Kenny Nguyen, Maxwell Ranger, Yelyzaveta Begunova, Pin-Hao A. Chen, Jaime J. Castrellon, Gregory R. Samanez-Larkin, David H. Zald, Dominic S. Fareri, Mauricio R. Delgado, Livia Tomova
bioRxiv 2022.08.23.504939; doi: https://doi.org/10.1101/2022.08.23.504939
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A neural signature of reward
Luke J. Chang, Xiao Li, Kenny Nguyen, Maxwell Ranger, Yelyzaveta Begunova, Pin-Hao A. Chen, Jaime J. Castrellon, Gregory R. Samanez-Larkin, David H. Zald, Dominic S. Fareri, Mauricio R. Delgado, Livia Tomova
bioRxiv 2022.08.23.504939; doi: https://doi.org/10.1101/2022.08.23.504939

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