Dissecting the Complexities of Learning With Infinite Hidden Markov Models
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract Learning to exploit the contingencies of a complex experiment is not an easy task for animals. Individuals learn in an idiosyncratic manner, revising their approaches multiple times as they are shaped, or shape themselves, and potentially end up with different strategies. Their long-run learning curves are therefore a tantalizing target for the sort of individualized quantitative characterizations that sophisticated modelling can provide. However, any such model requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden semi-Markov model, whose latent states are associated with specific components of behaviour. This model includes an infinite number of potential states and so has the capacity to describe substantially new behaviours by unearthing extra states; while dynamics in the model allow it to capture more modest adaptations to existing behaviours. We individually fit the model to data collected from more than 100 mice as they learned a contrast detection task over tens of sessions and around fifteen thousand trials each. Despite large individual differences, we found that most animals progressed through three major stages of learning, the transitions between which were marked by distinct additions to task understanding. We furthermore showed that marked changes in behaviour are much more likely to occur at the very beginning of sessions, i.e. after a period of rest, and that response biases in earlier stages are not predictive of biases later on in this task.
Authors 12
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Sebastian A. Bruijns corresponding
Max Planck Institute for Biological Cybernetics · University of Tübingen
Affiliation as printed
Max Planck Institute for Biological Cybernetics
University of Tübingen
Max Planck Institute for Biological Cybernetics, Tübingen, Germany
University of Tübingen, Germany
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Affiliation as printed
Champalimaud Foundation
Champalimaud Foundation, Portugal
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Affiliation as printed
Champalimaud Foundation
Champalimaud Foundation, Portugal
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Affiliation as printed
University College London
University College London, United Kingdom
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Affiliation as printed
Champalimaud Foundation
Champalimaud Foundation, Portugal
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Sainsbury Wellcome Centre · University College London
Affiliation as printed
Sainsbury Wellcome Centre, University College London
Sainsbury Wellcome Centre, University College London, United Kingdom
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Affiliation as printed
New York University
New York University, USA
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Affiliation as printed
Princeton University
Princeton University, USA
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Affiliation as printed
University of Washington
University of Washington, USA
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Affiliation as printed
University College London
University College London, United Kingdom
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Anne E Urai Aachen
Affiliation as printed
Leiden University
Leiden University, The Netherlands
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Max Planck Institute for Biological Cybernetics · University of Tübingen
Affiliation as printed
Max Planck Institute for Biological Cybernetics
University of Tübingen
Max Planck Institute for Biological Cybernetics, Tübingen, Germany
University of Tübingen, Germany
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