From predictive models to cognitive models: Separable behavioral processes underlying reward learning in the rat

Abstract

Cognitive models are a fundamental tool in computational neuroscience, embodying in software precise hypotheses about the algorithms by which the brain gives rise to behavior. The development of such models is often a hypothesis-first process, drawing on inspiration from the literature and the creativity of the individual researcher to construct a model, and afterwards testing the model against experimental data. Here, we adopt a complementary approach, in which richly characterizing and summarizing the patterns present in a dataset reveals an appropriate cognitive model, without recourse to an a priori hypothesis. We apply this approach to a large behavioral dataset from rats performing a dynamic reward learning task. The revealed model suggests that behavior in this task can be understood as a mixture of three components with different timescales: a quick-learning reward-seeking component, a slower-learning perseverative component, and a very slow “gambler’s fallacy” component.