How multiple learning systems contribute to naturalistic patch foraging

Optimal patch foraging requires the ability to discern valuable resources and harvest them efficiently. This process requires integrating across multiple learning systems, including perceptual, structural, and value-based mechanisms. We explored how these varied learning systems contribute to foraging efficiency through a novel virtual foraging videogame. Human participants (N=60) navigated a virtual world with four neighborhoods (patches) populated with houses, searching for “bitter Oomplets,” characterized by the union of specific body colors and textures. Each neighborhood had a different overall probability of containing bitter Oomplets and, within each, specific house locations predicted the relative rate of bitter Oomplets. Participants proficiently learned to discriminate Oomplet types and strategically allocate their time to higher-probability neighborhoods. However, they did not learn the spatial predictors within neighborhoods. Importantly, we found no interaction between these two distinct learning abilities in determining foraging ability, suggesting that the impact of the perceptual and reward learning systems are independent of one another in human foraging efficiency.