Inference-Based Decisions in a Hidden State Foraging Task: Differential Contributions of Prefrontal Cortical Areas

Pietro Vertechi, Eran Lottem, Dario Sarra, Beatriz Godinho, Isaac Treves, Tiago Quendera, Matthijs Nicolai Oude Lohuis, Zachary F. Mainen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Essential features of the world are often hidden and must be inferred by constructing internal models based on indirect evidence. Here, to study the mechanisms of inference, we establish a foraging task that is naturalistic and easily learned yet can distinguish inference from simpler strategies such as the direct integration of sensory data. We show that both mice and humans learn a strategy consistent with optimal inference of a hidden state. However, humans acquire this strategy more than an order of magnitude faster than mice. Using optogenetics in mice, we show that orbitofrontal and anterior cingulate cortex inactivation impacts task performance, but only orbitofrontal inactivation reverts mice from an inference-based to a stimulus-bound decision strategy. These results establish a cross-species paradigm for studying the problem of inference-based decision making and begins to dissect the network of brain regions crucial for its performance.

Original languageAmerican English
Pages (from-to)166-176.e6
Issue number1
StatePublished - 8 Apr 2020

Bibliographical note

Funding Information:
We would like to thank Shira Lottem for her help with the graphic art of the human task. This work was supported by the European Research Council (Advanced Investigator Grant 671251 to Z.F.M.) and Champalimaud Foundation (Z.F.M.).

Publisher Copyright:
© 2020 The Authors


  • PFC
  • cross-species task
  • foraging
  • inference
  • state representation


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