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<p>Learning the contingencies of a task is difficult. Individuals learn in an idiosyncratic manner, revising their approach multiple times as they explore and adapt. Quantitative characterization of these learning curves requires a model that can capture both new behaviors and slow changes in existing ones. Here we suggest a dynamic infinite hidden semi-Markov model, whose latent states are associated with specific components of behavior. This model can describe new behaviors by introducing new states and capture more modest adaptations through dynamics in existing states. We tested the model by fitting it to behavioral data of &gt;100 mice learning a contrast-detection task. Although animals showed large interindividual differences while learning this task, most mice progressed through three stages of task understanding, new behavior often arose at session onset, and early response biases did not predict later ones. We thus provide a new tool for comprehensively capturing behavior during learning.</p>

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Infinite hidden Markov models can dissect the complexities of learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月30日

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  • Sebastian A. Bruijns,
  • Petrina Y. P. Lau,
  • Nathaniel J. Miska,
  • Jean-Paul Noel,
  • Alejandro Pan-Vazquez,
  • Noam Roth,
  • Karolina Z. Socha,
  • Anne E. Urai,
  • Kcénia Bougrova,
  • Inês C. Laranjeira,
  • Petrina Y. P. Lau,
  • Guido T. Meijer,
  • Nathaniel J. Miska,
  • Jean-Paul Noel,
  • Alejandro Pan-Vazquez,
  • Noam Roth,
  • Karolina Z. Socha,
  • Anne E. Urai,
  • Peter Dayan

摘要

Learning the contingencies of a task is difficult. Individuals learn in an idiosyncratic manner, revising their approach multiple times as they explore and adapt. Quantitative characterization of these learning curves requires a model that can capture both new behaviors and slow changes in existing ones. Here we suggest a dynamic infinite hidden semi-Markov model, whose latent states are associated with specific components of behavior. This model can describe new behaviors by introducing new states and capture more modest adaptations through dynamics in existing states. We tested the model by fitting it to behavioral data of >100 mice learning a contrast-detection task. Although animals showed large interindividual differences while learning this task, most mice progressed through three stages of task understanding, new behavior often arose at session onset, and early response biases did not predict later ones. We thus provide a new tool for comprehensively capturing behavior during learning.