Driven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry.

ABSTRACT: Although it is well known that "losses loom larger than gains," computational approaches to aversion-related decision making (ARDM) for psychiatric disorders is an underdeveloped area. Computational models of ARDM have been implemented primarily as state-dependent reinforcement learning models with bias parameters to quantify Pavlovian associations, and differential learning rates to quantify instrumental updating have been shown to depend on context, involve complex cost calculations, and include the consideration of counterfactual outcomes. Little is known about how individual differences influence these models relevant to anxiety-related conditions or addiction-related dysfunction. It is argued that model parameters reflecting 1) Pavlovian biases in the context of reinforcement learning

SUBMITTER: Paulus MP 

PROVIDER: S-EPMC7012695 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature