Year: 2022
Type: preprint
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.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Toward a computational understanding of Mianzi across cultures: a computational neuroscience approach(1/3), Toward a Computational Understanding of Mianzi across Cultures: a Computational Neuroscience Approach( III ), CAREER: Neural and Computational Basis of Guilt in Decision-Making, Toward a Computational Understanding of Mianzi across Cultures: a Computational Neuroscience Approach( II ), G6671297155, G7489393043
OpenAlex ID: W4293235834