Orbitofrontal cortex and learning predictions of state transitions

Citation:

Chan, S. C. Y., Schuck, N. W., Lopatina, N., Schoenbaum, G., & Niv, Y. (Submitted). Orbitofrontal cortex and learning predictions of state transitions.

Abstract:

Learning the transition structure of the environment – the probabilities of transitioning from one environmental state to another – is a key prerequisite for goal-directed planning and model-based decision making. To investigate the role of the orbitofrontal cortex (OFC) in goal-directed planning and decision making, we used fMRI to assess univariate and multivariate activity in the OFC while humans experienced state transitions that varied in degree of surprise. In convergence with recent evidence, we found that OFC activity was related to greater learning about transition structure, both across subjects and on a trial-by-trial basis. However, this relationship was inconsistent with a straightforward interpretation of OFC activity as representing a state prediction error that would facilitate learning of transitions via error-correcting mechanisms. The state prediction error hypothesis predicts that OFC activity at the time of observing an outcome should increase expectation of that observed outcome on subsequent trials. Instead, our results showed that OFC activity was associated with increased expectation of the more probable outcome; that is, with more optimal predictions. Our findings add to the evidence of OFC involvement in learning state-to-state transition structure, while providing new constraints for algorithmic hypotheses regarding how these transitions are learned.

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