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Humans monitor learning progress in curiosity-driven exploration

Author

Listed:
  • Alexandr Ten

    (INRIA Bordeaux Sud-Ouest)

  • Pramod Kaushik

    (INRIA Bordeaux Sud-Ouest)

  • Pierre-Yves Oudeyer

    (INRIA Bordeaux Sud-Ouest)

  • Jacqueline Gottlieb

    (Columbia University)

Abstract

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

Suggested Citation

  • Alexandr Ten & Pramod Kaushik & Pierre-Yves Oudeyer & Jacqueline Gottlieb, 2021. "Humans monitor learning progress in curiosity-driven exploration," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26196-w
    DOI: 10.1038/s41467-021-26196-w
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    References listed on IDEAS

    as
    1. Lisa K. Son & Rajiv Sethi, 2006. "Metacognitive Control and Optimal Learning," Economics Working Papers 0065, Institute for Advanced Study, School of Social Science.
    2. Johnny King L. Lau & Hiroki Ozono & Kei Kuratomi & Asuka Komiya & Kou Murayama, 2020. "Shared striatal activity in decisions to satisfy curiosity and hunger at the risk of electric shocks," Nature Human Behaviour, Nature, vol. 4(5), pages 531-543, May.
    3. Celeste Kidd & Steven T Piantadosi & Richard N Aslin, 2012. "The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-8, May.
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