Reinforcement-Learning-Informed Queries Guide Behavioral Change

Abstract

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.