Scholarlink

<p>The striosome compartments in the striatum gate cortical inputs to dopamine neurons, which then feed back to the striosomes and surrounding matrix. This loop underlies decision-making, learning, and movement. Dopamine signals strongly correlate with reward prediction errors (RPEs) in certain tasks. However, many dopaminergic responses, such as to high costs, novelty, aversive stimuli, and real-time movement guidance, do not align with RPE. Separately, information theory explains how dopamine responds to uncertainty and encodes when rewards are expected to occur. Here, we show that apparent RPE correlations arise mathematically from the information gain of the policy of actions (policy-IG). Policy-IG quantifies how much newly arriving information changes choice. In simple reward tasks, policy-IG reduces to classic RPEs, but it also predicts dopamine responses to aversive events, nonlinear reward scaling, novelty, movement, state valuation, and moment-by-moment decision control. Thus, RPE could be a special case of this more general function. We show that policy-IG is mathematically related to a range of information-centric, Bayesian, active inference, and casual association models, allowing those models to formally incorporate the experimental RPE literature in their support. Simulating impaired policy-IG replicates basal ganglia disorder features, suggesting policy-IG as a target for therapies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction error correlates in the striosome-dopamine circuit emerge from information gain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月27日

查看全文
  • Dirk W. Beck,
  • Alexander Friedman

摘要

The striosome compartments in the striatum gate cortical inputs to dopamine neurons, which then feed back to the striosomes and surrounding matrix. This loop underlies decision-making, learning, and movement. Dopamine signals strongly correlate with reward prediction errors (RPEs) in certain tasks. However, many dopaminergic responses, such as to high costs, novelty, aversive stimuli, and real-time movement guidance, do not align with RPE. Separately, information theory explains how dopamine responds to uncertainty and encodes when rewards are expected to occur. Here, we show that apparent RPE correlations arise mathematically from the information gain of the policy of actions (policy-IG). Policy-IG quantifies how much newly arriving information changes choice. In simple reward tasks, policy-IG reduces to classic RPEs, but it also predicts dopamine responses to aversive events, nonlinear reward scaling, novelty, movement, state valuation, and moment-by-moment decision control. Thus, RPE could be a special case of this more general function. We show that policy-IG is mathematically related to a range of information-centric, Bayesian, active inference, and casual association models, allowing those models to formally incorporate the experimental RPE literature in their support. Simulating impaired policy-IG replicates basal ganglia disorder features, suggesting policy-IG as a target for therapies.