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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), Wien, Austria.

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 Creators:
Bruijns, S1, 2, Author           
Dayan, P1, 2, Author           
Affiliations:
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: 2021-072021-11
 Publication Status: Published in print
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Title: 43rd Annual Conference of the Cognitive Science Society (CogSci 2021)
Place of Event: Wien, Austria
Start-/End Date: 2021-07-26 - 2021-07-29

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Title: 43rd Annual Conference of the Cognitive Science Society (CogSci 2021): Workshop 2 Using Games to Understand Intelligence
Source Genre: Proceedings
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Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: 2-F-196 Start / End Page: 3429 Identifier: ISBN: 978-1-7138-3525-7