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Humans monitor learning progress in curiosity-driven exploration
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514490/ https://www.ncbi.nlm.nih.gov/pubmed/34645800 http://dx.doi.org/10.1038/s41467-021-26196-w |
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author | Ten, Alexandr Kaushik, Pramod Oudeyer, Pierre-Yves Gottlieb, Jacqueline |
author_facet | Ten, Alexandr Kaushik, Pramod Oudeyer, Pierre-Yves Gottlieb, Jacqueline |
author_sort | Ten, Alexandr |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8514490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85144902021-10-29 Humans monitor learning progress in curiosity-driven exploration Ten, Alexandr Kaushik, Pramod Oudeyer, Pierre-Yves Gottlieb, Jacqueline Nat Commun Article 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. Nature Publishing Group UK 2021-10-13 /pmc/articles/PMC8514490/ /pubmed/34645800 http://dx.doi.org/10.1038/s41467-021-26196-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ten, Alexandr Kaushik, Pramod Oudeyer, Pierre-Yves Gottlieb, Jacqueline Humans monitor learning progress in curiosity-driven exploration |
title | Humans monitor learning progress in curiosity-driven exploration |
title_full | Humans monitor learning progress in curiosity-driven exploration |
title_fullStr | Humans monitor learning progress in curiosity-driven exploration |
title_full_unstemmed | Humans monitor learning progress in curiosity-driven exploration |
title_short | Humans monitor learning progress in curiosity-driven exploration |
title_sort | humans monitor learning progress in curiosity-driven exploration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514490/ https://www.ncbi.nlm.nih.gov/pubmed/34645800 http://dx.doi.org/10.1038/s41467-021-26196-w |
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