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.
