Reinforcement Learning and Decision Making in Depression in Adolescents and Young Adults: Insights from a New Model of the Probabilistic Reward Task
Ziwei Cheng, Amelia D Moser, Jenna Jones, Christopher D Schneck, David J Miklowitz, Daniel G Dillon, Roselinde H Kaiser · 2025
Read Reinforcement Learning and Decision Making in Depression in Adolescents and Young Adults: Insights from a New Model of the Probabilistic Reward Task by Ziwei Cheng, Amelia D Moser, Jenna Jones, Christopher D Schneck, David J Miklowitz, Daniel G Dillon, Roselinde H Kaiser (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12758101.txt.
Description
Cheng and co-authors model reward learning in depressed teens and young adults with a fresh take on a classic task. Depression often starts in youth and dulls reward response. The Probabilistic Reward Task probes that response, but its inner steps were not fully modeled. The team gathered task data from 726 young people with varied depression levels. Their new model joins value learning with an evidence build-up process. That split lets single steps of choice be read apart.
Depressed participants showed a weaker pull from learned values toward the richer option. People with no illness history leaned on values more. The model sharpens what reward learning loses in depression.
Catalog record
| CREATOR | Ziwei Cheng, Amelia D Moser, Jenna Jones, Christopher D Schneck, David J Miklowitz, Daniel G Dillon, Roselinde H Kaiser |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |