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. Converging with recent evidence, we found that OFC activity was related to greater learning about transition structure. However, the observed 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 same 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. These results suggest that the OFC is involved in updating or reinforcing a learned transition model on a trial-by-trial basis, specifically for the currently observed cue-outcome associations. 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. Significance Statement The orbitofrontal cortex (OFC) has been implicated in model-based decision making—the kind of decisions that result from planning using an “environment model” of how current actions affect our future states. However, the widely suggested role of the OFC in representing expected values of future states is not sufficient to explain why the OFC would be critical for planning in particular. A new line of evidence implicates the OFC in learning about transition structure of the environment – a key component of the “environment model” used for planning. We investigate this function, adding to the growing literature on the role of the OFC in learning and decision making, while unveiling new questions about the algorithmic role of OFC in goal-directed planning.