PT  - JOURNAL ARTICLE
AU  - Miller, Kevin J.
AU  - Botvinick, Matthew M.
AU  - Brody, Carlos D.
TI  - From predictive models to cognitive models: An analysis of rat behavior in the two-armed bandit task
AID  - 10.1101/461129
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 461129
4099  - http://biorxiv.org/content/early/2018/11/02/461129.short
4100  - http://biorxiv.org/content/early/2018/11/02/461129.full
AB  - 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 largely 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 data-first 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 model revealed suggests that behavior on 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.