Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

作者：Brittany Liebenow, Rachel Jones, Emily K. DiMarco, Jonathan D. Trattner, Joseph B. Humphries, L. Paul Sands, Kasey P. Spry, Christina K. Johnson, Evelyn Farkas, Angela Jiang, Kenneth T. Kishida · 发表于：Frontiers in Psychiatry · 年份：2022 · DOI：10.3389/fpsyt.2022.886297 · 被引用次数：15 · 研究领域：Neurotransmitter Receptor Influence on Behavior、Mental Health Research Topics、Neural and Behavioral Psychology Studies

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of tr...