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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Anonymized participant data and model simulation data are available at https://github.com/charleywu/gridsearch.
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We thank P. Todd, T. Pleskac, N. Bramley, H. Singmann and M. Moussaïd for helpful feedback. This work was supported by the International Max Planck Research School on Adapting Behavior in a Fundamentally Uncertain World (C.M.W.), by the Harvard Data Science Initiative (E.S.), and DFG grants ME 3717/2-2 to B.M. and NE 1713/1-2 to J.D.N. as part of the New Frameworks of Rationality (SPP 1516) priority programme. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Wu, C.M., Schulz, E., Speekenbrink, M. et al. Generalization guides human exploration in vast decision spaces. Nat Hum Behav 2, 915–924 (2018). https://doi.org/10.1038/s41562-018-0467-4
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