Algorithmically defined aspects of reinforcement learning correlate with psychopathology symptoms and change with symptom improvement following cognitive-behavioral therapy (CBT). Separate work in nonclinical samples has shown that varying the structure and statistics of task environments can change learning. Here, we combine these literatures, drawing on CBT-based guided restructuring of thought processes and computationally defined mechanistic targets identified by reinforcement-learning models in depression, to test whether and how verbal queries affect learning processes. Using a parallel-arm design, we tested 1,299 online participants completing a probabilistic reward-learning task while receiving repeated queries about the task environment (11 learning-query arms and one active control arm). Querying participants about reinforcement-learning-related task components altered computational-model-defined learning parameters in directions specific to the target of the query. These effects on learning parameters were consistent across depression-symptom severity, suggesting new learning-based strategies and therapeutic targets for evoking symptom change in mood psychopathology.

中文翻译：

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强化学习知情查询指导行为改变

强化学习的算法定义方面与精神病理学症状相关，并随着认知行为治疗（CBT）后症状改善而变化。非临床样本的单独研究表明，改变任务环境的结构和统计数据可以改变学习。在这里，我们结合这些文献，利用基于 CBT 的思维过程引导重组和抑郁症强化学习模型识别的计算定义的机械目标，来测试言语查询是否以及如何影响学习过程。使用并行臂设计，我们测试了 1,299 名在线参与者完成概率奖励学习任务，同时接收有关任务环境的重复查询（11 个学习查询臂和一个主动控制臂）。向参与者询问与强化学习相关的任务组件，会按照特定于查询目标的方向改变计算模型定义的学习参数。这些对学习参数的影响在抑郁症状严重程度之间是一致的，这表明新的基于学习的策略和治疗目标可以引起情绪精神病理学中的症状变化。