The neural dynamics of reward value and risk coding in the human orbitofrontal cortex.

The orbitofrontal cortex is known to carry information regarding expected reward, risk and experienced outcome. Yet, due to inherent limitations in lesion and neuroimaging methods, the neural dynamics of these computations has remained elusive in humans. Here, taking advantage of the high temporal definition of intracranial recordings, we characterize the neurophysiological signatures of the intact orbitofrontal cortex in processing information relevant for risky decisions. Local field potentials were recorded from the intact orbitofrontal cortex of patients suffering from drug-refractory partial epilepsy with implanted depth electrodes as they performed a probabilistic reward learning task that required them to associate visual cues with distinct reward probabilities. We observed three successive signals: (i) around 400 ms after cue presentation, the amplitudes of the local field potentials increased with reward probability; (ii) a risk signal emerged during the late phase of reward anticipation and during the outcome phase; and (iii) an experienced value signal appeared at the time of reward delivery. Both the medial and lateral orbitofrontal cortex encoded risk and reward probability while the lateral orbitofrontal cortex played a dominant role in coding experienced value. The present study provides the first evidence from intracranial recordings that the human orbitofrontal cortex codes reward risk both during late reward anticipation and during the outcome phase at a time scale of milliseconds. Our findings offer insights into the rapid mechanisms underlying the ability to learn structural relationships from the environment.

The hippocampus codes the uncertainty of cue-outcome associations: an intracranial electrophysiological study in humans.

Learning to predict upcoming outcomes based on environmental cues is essential for adaptative behavior. In monkeys, midbrain dopaminergic neurons code two statistical properties of reward: a prediction error at the outcome and uncertainty during the delay period between cues and outcomes. Although the hippocampus is sensitive to reward processing, and hippocampal-midbrain functional interactions are well documented, it is unknown whether it also codes the statistical properties of reward information. To address this question, we recorded local field potentials from intracranial electrodes in human hippocampus while subjects learned to associate cues of slot machines with various monetary reward probabilities (P). We found that the amplitudes of negative event-related potentials covaried with uncertainty at the outcome, being maximal for P = 0.5 and minimal for P = 0 and P = 1, regardless of winning or not. These results show that the hippocampus computes an uncertainty signal that may constitute a fundamental mechanism underlying the role of this brain region in a number of functions, including attention-based learning, associative learning, probabilistic classification, and binding of stimulus elements.

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans.

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies.