Motivated for near impossibility: How task type and reward modulate task enjoyment and the striatal activation for extremely difficult task

Murayama, Kou; Sakaki, Michiko; Meliß, Stefanie; Yomogida, Yohei; Matsumori, Kausu; Sugiura, Ayaka; Matsumoto, Madoka; Matsumoto, Kenji

doi:10.1101/828756

Cited by 3 publications

(2 citation statements)

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“…A considerable literature has debated the role of external incentives, with different authors arguing that extrinsic rewards help [3] or hinder [46] the intrinsic motivation to learn. Our findings suggest that the answer is more complex, as external objectives both enhance and impair different aspects of our participants' study strategy.…”

Section: Discussionmentioning

confidence: 99%

Humans monitor learning progress in curiosity-driven exploration

Ten¹,

Kaushik²,

Oudeyer³

et al. 2020

Preprint

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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, or PC) and learning progress (LP), 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 random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

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Section: Discussionmentioning

confidence: 99%

Humans monitor learning progress in curiosity-driven exploration

Ten¹,

Kaushik²,

Oudeyer³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Humans monitor learning progress in curiosity-driven exploration

Ten¹,

Kaushik²,

Oudeyer³

et al. 2020

Preprint

View full text Add to dashboard Cite

Curiosity-driven learning is foundational to human cognition. Byenabling 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 drivingpeople to set intrinsic goals, when they are free to explore multiplelearning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures(e.g. percent correct, or PC) and learning progress (LP), that could beused as intrinsic utility functions to efficiently organize exploration.Such intrinsic utilities constitute computationally cheap but smartheuristics to prevent people from laboring in vain on random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideasby means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales/

show abstract

Humans monitor learning progress in curiosity-driven exploration

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Motivated for near impossibility: How task type and reward modulate task enjoyment and the striatal activation for extremely difficult task

Cited by 3 publications

References 72 publications

Humans monitor learning progress in curiosity-driven exploration

Humans monitor learning progress in curiosity-driven exploration

Humans monitor learning progress in curiosity-driven exploration

Humans monitor learning progress in curiosity-driven exploration

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