Translating a Reinforcement Learning Task into a Computational Psychiatry Assay: Challenges and Strategies

Abstract

Computational psychiatry applies advances from computational neuroscience to psychiatric disorders. A core aim is to develop tasks and modeling approaches that can advance clinical science. Special interest has centered on reinforcement learning (RL) tasks and models. However, laboratory tasks in general often have psychometric weaknesses and RL tasks pose special challenges. These challenges must be addressed if computational psychiatry is to capitalize on its promise of developing sensitive, replicable assays of cognitive function. Few resources identify these challenges and discuss strategies to mitigate them. Here, we first overview general psychometric challenges associated with laboratory tasks, as these may be unfamiliar to cognitive scientists. Next, we illustrate how these challenges interact with issues specific to RL tasks, in the context of presenting a case example of preparing an RL task for computational psychiatry. Throughout, we highlight how considering measurement issues prior to a clinical science study can inform study design.


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