RT Journal Article SR Electronic T1 Exploration and generalization in vast spaces JF bioRxiv FD Cold Spring Harbor Laboratory SP 171371 DO 10.1101/171371 A1 Charley M. Wu A1 Eric Schulz A1 Maarten Speekenbrink A1 Jonathan D. Nelson A1 Björn Meder YR 2018 UL http://biorxiv.org/content/early/2018/08/10/171371.abstract AB 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 a variety of bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, where the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across a variety of 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 novel insights about human behaviour in complex environments.