Vollständiger Abstract
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Reward valuation and reward and punishment learning are important constructs for understanding and intervening on mental illness. These constructs are frequently measured with behavioral tasks such as intertemporal choice tasks, measuring delay discounting, and the Iowa Gambling Task (IGT), measuring reward and punishment learning. Delay discounting refers to how people value delayed outcomes, with steep discounting (i.e., delayed outcomes holding little value) considered a form of impulsive decision-making. Reward and punishment learning refers to how people adjust their decision behavior in response to rewards and punishments, with deficits indicating atypical reward and punishment processing related to clinical outcomes such as substance abuse. In this study, we tested the association between delay discounting and computational measures of reward and punishment learning in adults (N = 299) that completed an intertemporal choice task and the play-or-pass IGT. We fit a joint model to the task data to simultaneously estimate parameters from the hyperbolic discounting model (for the intertemporal choice task) and a reinforcement learning model (for the play-or-pass IGT). Our results indicate that steep delay discounting is associated with lower performance on the IGT, specifically through lower punishment learning. That is, people who do not value delayed outcomes also show less learning from punishment, resulting in worse performance in the IGT. Implications of these findings are discussed in the context of improving assessment of reward valuation and reward and punishment learning as constructs relevant to mental illness.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Jeremy Haynes, Peter Kvam, Thomas Olino
- Quelle
- Computational Psychiatry
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2379-6227
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Zitierfähiger Nachweis
Jeremy Haynes, Peter Kvam, Thomas Olino (2026). Associations Between Steep Delay Discounting and Punishment Learning. Computational Psychiatry. https://doi.org/10.5334/cpsy.172
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