Understanding Learning Trajectories With Infinite Hidden Markov Models
Sebastian Bruijns, Peter Dayan, Max Planck Institute for Biological Cybernetics, Germany; The International Brain Laboratory, The International Brain Laboratory, Germany
Posters 1 Poster
Pacific Ballroom H-O
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
Learning the contingencies of a complex experiment is not easy. Individuals learn in an idiosyncratic manner, revising their strategies multiple times as they are shaped, or shape themselves. They may even end up with different asymptotic strategies. This long-run learning is therefore a tantalizing target for the sort of quantitatively individualized characterization that descriptive models can provide. However, any such model requires a flexible and extensible structure which can capture the rapid introduction of radically new behaviours as well as slow changes in existing ones. We suggest a dynamic input-output infinite hidden semi-Markov model whose latent states are associated with specific behavioural patterns. This model encompasses a countably infinite number of potential states, and so can capture new behaviours by introducing states; equally, dynamical evolution of the behavioural pattern specified by a single state allows tracking of slow adaptations in existing behaviours. We fit this model to around 10,000 trials per mouse as they learned to perform a contrast detection task over multiple stages. We quantify different stages of learning via the number and psychometric characteristics of behavioural states, providing comprehensive insight into the highly individualised learning trajectories of animals.