Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making.

ABSTRACT: Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat

SUBMITTER: Moughrabi N 

PROVIDER: S-EPMC9835092 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature