PT  - JOURNAL ARTICLE
AU  - Charley M. Wu
AU  - Eric Schulz
AU  - Maarten Speekenbrink
AU  - Jonathan D. Nelson
AU  - Björn Meder
TI  - Exploration and generalization in vast spaces
AID  - 10.1101/171371
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 171371
4099  - http://biorxiv.org/content/early/2018/08/10/171371.short
4100  - http://biorxiv.org/content/early/2018/08/10/171371.full
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.