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Opening Burton's Clock: Psychiatric insights from computational cognitive models

Computational psychiatry is a nascent field that seeks to use computational tools from neuroscience and cognitive science to understand psychiatric illness. In this chapter, we make the case for computational cognitive models as a bridge between the cognitive and affective deficits experienced by those with a psychiatric illness and the neurocomputational dysfunctions that underlie these deficits. We first review the history of computational modelling in psychiatry and conclude that a key moment of maturation in this field occurred with the transition from qualitative comparison between computational models and human behaviour to formal quantitative model fitting and model comparison. We then summarise current research at one of the most exciting frontiers of computational psychiatry: reinforcement learning models of mood disorders. We review state-of-the-art applications of such models to major depression and bipolar disorder, and outline important open questions to be addressed by the coming wave of research in computational psychiatry.