# Reinforcement Learning and Decision Making in Depression in Adolescents and Young Adults: Insights from a New Model of the Probabilistic Reward Task

## TL;DR

This study explores how depression affects reward processing and decision-making in adolescents and young adults using a new computational model.

## Contribution

A novel computational model was applied to the Probabilistic Reward Task to uncover new insights into reinforcement learning in depression.

## Key findings

- Depressed participants showed reduced influence of learned response values on decision bias.
- Higher anhedonia was linked to slower evidence accumulation during decision-making.

## Abstract

Depression is a prevalent psychiatric condition that commonly emerges in adolescence and young adulthood and is associated with reward processing abnormalities. The Probabilistic Reward Task (PRT) is widely used to investigate the impact of depression on reward processing, but prior studies have not comprehensively addressed the reinforcement learning and decision-making mechanisms involved in the task. In 726 adolescents and young adults with varying levels of depression, we collected PRT data and applied a novel computational model with response-outcome learning and evidence accumulation processes to provide new insights into the cognitive processes implicated in depression. Compared to participants with no history of psychopathology, those with depressive disorders showed reduced impact of learned response values on decision bias toward the more frequently rewarded action. In addition, higher levels of anhedonia were associated with slower evidence accumulation during decision-making. Together, these findings improved our understanding of the reinforcement learning and decision-making mechanisms assessed by the PRT and their associations with depression.