title: A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex.
abstract: The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes.
  
  
  
  Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas.
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544 s.chan@alum.mit.edu.
authors.forename: Stephanie C Y
authors.identifier: 0000-0003-2590-7832
authors.initials: SC
authors.lastname: Chan
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544.
authors.forename: Yael
authors.identifier: 0000-0002-0259-8371
authors.initials: Y
authors.lastname: Niv
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544.
authors.forename: Kenneth A
authors.identifier: 0000-0002-5887-9682
authors.initials: KA
authors.lastname: Norman
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000293:Adolescent; D000328:Adult; D015984:Causality; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D017397:Prefrontal Cortex / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D055815:Young Adult
keywords: Bayes' rule; context; posterior distribution; schemas; state representation; ventromedial prefrontal cortex