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Generalization guides human exploration in vast decision spaces

Wu, Charley M., Schulz, Eric, Speekenbrink, Maarten, Nelson, Jonathan D. and Meder, Björn (2018) Generalization guides human exploration in vast decision spaces Nature Human Behaviour, 2 (12). pp. 915-924.

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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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Psychology
Authors :
NameEmailORCID
Wu, Charley M.
Schulz, Eric
Speekenbrink, Maarten
Nelson, Jonathan D.j.d.nelson@surrey.ac.uk
Meder, Björn
Date : 12 November 2018
DOI : 10.1038/s41562-018-0467-4
Copyright Disclaimer : © The Author(s), under exclusive licence to Springer Nature Limited 2018
Uncontrolled Keywords : Computational science; Human behaviour
Depositing User : Clive Harris
Date Deposited : 25 Jan 2019 12:59
Last Modified : 13 May 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/850284

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