Model-based foraging using latent-cause inference

Nora C. Harhen, Catherine A. Hartley, Aaron M. Bornstein

Research output: Contribution to conferencePaperpeer-review


Foraging has been suggested to provide a more ecologically-valid context for studying decision-making. However, the environments used in human foraging tasks fail to capture the structure of real world environments which contain multiple levels of spatio-temporal regularities. We ask if foragers detect these statistical regularities and use them to construct a model of the environment that guides their patch-leaving decisions. We propose a model of how foragers might accomplish this, and test its predictions in a foraging task with a structured environment that includes patches of varying quality and predictable transitions. Here, we show that human foraging decisions reflect ongoing, statistically-optimal structure learning. Participants modulated decisions based on the current and potential future context. From model fits to behavior, we can identify an individual’s structure learning ability and relate it to decision strategy. These findings demonstrate the utility of leveraging model-based reinforcement learning to understand foraging behavior.

Original languageEnglish (US)
Number of pages7
StatePublished - 2021
Event43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria
Duration: Jul 26 2021Jul 29 2021


Conference43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021
CityVirtual, Online


  • decision-making
  • foraging
  • reinforcement learning
  • structure learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction


Dive into the research topics of 'Model-based foraging using latent-cause inference'. Together they form a unique fingerprint.

Cite this