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.
Bibliographical noteFunding 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.).
© 2020 The Authors
- cross-species task
- state representation