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  Generalization guides human exploration in vast decision spaces

Wu, C., Schulz, E., Speekenbrink, M., Nelson, J., & Meder, B. (2018). Generalization guides human exploration in vast decision spaces. Nature Human Behaviour, 2(12), 915-924. doi:10.1038/s41562-018-0467-4.

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Wu, CM, Author           
Schulz, E1, Author           
Speekenbrink, M, Author
Nelson, JD, Author
Meder, B, Author
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1External Organizations, ou_persistent22              

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 Abstract: From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using various bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, in which the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across various different probabilistic and heuristic models, we find evidence that Gaussian process function learning—combined with an optimistic upper confidence bound sampling strategy—provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.

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 Dates: 2018-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.1038/s41562-018-0467-4
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Title: Nature Human Behaviour
Source Genre: Journal
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Publ. Info: London : Nature Research
Pages: - Volume / Issue: 2 (12) Sequence Number: - Start / End Page: 915 - 924 Identifier: ISSN: 2397-3374
CoNE: https://pure.mpg.de/cone/journals/resource/2397-3374