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  Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning

Elteto, N., Nemeth, D., Janacsek, K., & Dayan, P. (2021). Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning. Poster presented at 43rd Annual Conference of the Cognitive Science Society (CogSci 2021).

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Elteto, N1, 2, Author              
Nemeth, D, Author
Janacsek, K, 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: Long perceptuo-motor sequences underlie skills from walking to language learning, and are often learned gradually and unconsciously in the face of noise. We used a non-parametric Bayesian n-gram model (Teh, 2006) to characterize the multi-day evolution of human subjects’ implicit representation of a serial reaction time task sequence with second-order contingencies. The reaction time for an element in the sequence depended on zero, one and more preceding elements at the same time, predicting frequency, repetition and higher-order learning effects. Our trial-level dynamic model captured these coexistent facilitation effects by seamlessly combining information from shorter and longer windows onto past events. We show how shifting their priors over window lengths allowed subjects to grow and refine their internal sequence representations week by week.

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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: 3307 Identifier: -