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Rapid Learning of Temporal Dependencies at Multiple Timescales.

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Title: Rapid Learning of Temporal Dependencies at Multiple Timescales.
Authors: Smith, Cybelle M.1 (AUTHOR), Thompson-Schill, Sharon L.1 (AUTHOR), Schapiro, Anna C.1 (AUTHOR)
Source: Journal of Cognitive Neuroscience. Nov2024, Vol. 36 Issue 11, p2343-2356. 14p.
Subjects: Rules of games, Statistical learning, Learning, Games, Human beings
Abstract: Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (n = 96) played a game where they learned to quickly click on a target image when it appeared in one of nine locations, in eight different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first versus the second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgments comparing the games based on their order properties. Participants showed context-dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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  Data: Rapid Learning of Temporal Dependencies at Multiple Timescales.
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  Data: <searchLink fieldCode="AR" term="%22Smith%2C+Cybelle+M%2E%22">Smith, Cybelle M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thompson-Schill%2C+Sharon+L%2E%22">Thompson-Schill, Sharon L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schapiro%2C+Anna+C%2E%22">Schapiro, Anna C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Cognitive+Neuroscience%22">Journal of Cognitive Neuroscience</searchLink>. Nov2024, Vol. 36 Issue 11, p2343-2356. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Rules+of+games%22">Rules of games</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Games%22">Games</searchLink><br /><searchLink fieldCode="DE" term="%22Human+beings%22">Human beings</searchLink>
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  Data: Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (n = 96) played a game where they learned to quickly click on a target image when it appeared in one of nine locations, in eight different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first versus the second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgments comparing the games based on their order properties. Participants showed context-dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1162/jocn_a_02232
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 2343
    Subjects:
      – SubjectFull: Rules of games
        Type: general
      – SubjectFull: Statistical learning
        Type: general
      – SubjectFull: Learning
        Type: general
      – SubjectFull: Games
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      – SubjectFull: Human beings
        Type: general
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      – TitleFull: Rapid Learning of Temporal Dependencies at Multiple Timescales.
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            NameFull: Thompson-Schill, Sharon L.
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            NameFull: Schapiro, Anna C.
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            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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