TY - JOUR

T1 - A probability distribution over latent causes, in the orbitofrontal cortex

AU - Chan, Stephanie C.Y.

AU - Niv, Yael

AU - Norman, Kenneth A.

N1 - Funding Information:
This work was supported by National Science Foundation/National Institutes of Health Collaborative Research in Computational Neuroscience Grant NSF IIS-1009542, National Institutes of Health Grant 2T32MH065214, and U.S. Army Research Office Grant W911NF1410101. We thank sciencewithme.com for the animal illustrations.
Publisher Copyright:
© 2016 the authors.

PY - 2016/7/27

Y1 - 2016/7/27

N2 - 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.

AB - 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.

KW - Bayes’rule

KW - Context

KW - Posterior distribution

KW - Schemas

KW - State representation

KW - Ventromedial prefrontal cortex

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U2 - 10.1523/JNEUROSCI.0659-16.2016

DO - 10.1523/JNEUROSCI.0659-16.2016

M3 - Article

C2 - 27466328

AN - SCOPUS:84979766862

SN - 0270-6474

VL - 36

SP - 7817

EP - 7828

JO - Journal of Neuroscience

JF - Journal of Neuroscience

IS - 30

ER -