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  Exploring learning trajectories with dynamic infinite hidden Markov models

Bruijns, S., & Dayan, P. (2021). Exploring learning trajectories with dynamic infinite hidden Markov models. Poster presented at 43rd Annual Conference of the Cognitive Science Society (CogSci 2021).

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Bruijns, S1, 2, Author              
Dayan, P1, 2, Author              
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1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Learning the contingencies of a complex experiment is hard, and animals likely revise their strategies multiple times during the process. Individuals learn in an idiosyncratic manner and may even end up with different asymptotic strategies. Modeling such long-run acquisition 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 Markov model whose latent states capture behaviours. We fit this model to data collected from mice who learnt a contrast detection task over tens of sessions and thousands of trials. Different stages of learning are quantified via the number and psychometric nature of prevalent behavioural states. Our model indicates that initial learning proceeds via drastic changes in behavior (i.e. new states), whereas later learning consists of adaptations to existing states, even if the task structure changes notably at this time.

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 Dates: 2021-07
 Publication Status: Published online
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Title: 43rd Annual Conference of the Cognitive Science Society (CogSci 2021)
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Start-/End Date: 2021-07-26

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Title: CogSci 2021 Virtual: Comparative Cognition, Cognitive Animals
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3429 Identifier: -