Event:

Much work has been devoted to formal characterizations of the ways that humans and other animals make decisions in mostly stable, near-asymptotic, regimes in behavioural tasks. Although this has been highly revealing, it hides aspects of learning that happen on the way to this asymptote. I will discuss three attempts that we have been making to use parametric and non-parametric models to capture more of the course of learning: in a spatial alternation task for rats, the International Brain Lab task for mice, and an alternating serial reaction time task for humans. Many incompletely addressed challenges arise - for instance separating individual differences in outcomes into differences between models, differences between parameters within a model, or path dependence from stochasticity in early choices and outcomes.