Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Tracking human skill learning with a hierarchical Bayesian sequence model

View ORCID ProfileNoémi Éltető, View ORCID ProfileDezső Nemeth, View ORCID ProfileKarolina Janacsek, View ORCID ProfilePeter Dayan
doi: https://doi.org/10.1101/2022.01.27.477977
Noémi Éltető
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Noémi Éltető
  • For correspondence: noemi.elteto@tuebingen.mpg.de
Dezső Nemeth
2Lyon Neuroscience Research Center, Université de Lyon, Lyon, France
3Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
4Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Dezső Nemeth
Karolina Janacsek
5Centre for Thinking and Learning, Institute for Lifecourse Development, Universtiy of Greenwich, London, United Kingdom
3Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Karolina Janacsek
Peter Dayan
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany
6University of Tübingen, Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Peter Dayan
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a non-parametric, hierarchical Bayesian sequence model to the reaction times of human participants’ responses over ten sessions, each comprising thousands of trials, in a serial reaction time task involving higher-order dependencies. The model, adapted from the domain of language, forgetfully updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighing the windows proportionally to their predictive power. As the model defines a prior over window depths, we were able to determine the extent to which the internal predictions of individual participant depended on how many previous elements.

Already in the first session, the model showed that participants had begun to rely on two previous elements (i.e., trigrams), thereby successfully adapting to the most prominent higher-order structure in the task. The extent to which local statistical fluctuations influenced participants’ responses waned over subsequent sessions, as subjects forgot the trigrams less and evidenced skilled performance. By the eighth session, a subset of participants shifted their prior further to consider a context deeper than two previous elements. Finally, participants showed resistance to interference and slow forgetting of the old sequence when it was changed in the final sessions. Model parameters for individual subjects covaried appropriately with independent measures of working memory. In sum, the model offers the first principled account of the adaptive complexity and nuanced dynamics of humans’ internal sequence representations during long-term implicit skill learning.

Author summary A central function of the brain is to predict. One challenge of prediction is that both external events and our own actions can depend on a variably deep temporal context of previous events or actions. For instance, in a short motor routine, like opening a door, our actions only depend on a few previous ones (e.g., push the handle if the key was turned). In longer routines such as coffee making, our actions require a deeper context (e.g., place the moka pot on the hob if coffee is ground, the pot is filled and closed, and the hob is on). We adopted a model from the natural language processing literature that matches humans’ ability to learn variable-length relationships in sequences. This model explained the gradual emergence of more complex sequence knowledge and individual differences in an experiment where humans practiced a perceptual-motor sequence over 10 weekly sessions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted January 27, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Tracking human skill learning with a hierarchical Bayesian sequence model
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Tracking human skill learning with a hierarchical Bayesian sequence model
Noémi Éltető, Dezső Nemeth, Karolina Janacsek, Peter Dayan
bioRxiv 2022.01.27.477977; doi: https://doi.org/10.1101/2022.01.27.477977
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Tracking human skill learning with a hierarchical Bayesian sequence model
Noémi Éltető, Dezső Nemeth, Karolina Janacsek, Peter Dayan
bioRxiv 2022.01.27.477977; doi: https://doi.org/10.1101/2022.01.27.477977

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Animal Behavior and Cognition
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9155)
  • Bioengineering (6797)
  • Bioinformatics (24052)
  • Biophysics (12149)
  • Cancer Biology (9562)
  • Cell Biology (13814)
  • Clinical Trials (138)
  • Developmental Biology (7653)
  • Ecology (11729)
  • Epidemiology (2066)
  • Evolutionary Biology (15534)
  • Genetics (10663)
  • Genomics (14346)
  • Immunology (9502)
  • Microbiology (22876)
  • Molecular Biology (9113)
  • Neuroscience (49080)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2576)
  • Physiology (3851)
  • Plant Biology (8347)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2299)
  • Systems Biology (6202)
  • Zoology (1302)