The reward positivity is sensitive to affective liking.

The EEG feature known as the Reward Positivity (RewP) is elicited by reward receipt and appears to reflect sensitively and specifically positive prediction errors during reinforcement learning. Yet, the RewP also is modulated by state and trait affect, suggesting that it has a more complex computational role than simple reinforcement surprise. We conducted a series of experiments aimed to investigate underlying affect processing reflected in the RewP during a reinforcement learning task. In the first experiment (N = 25), we manipulated the type of rewards a person could win (simple points or hedonically-appraised pictures). Although there were no differences in the amplitudes of the RewP for different types of rewards, there was a significant correlation between the individual rating of liking for the images and RewP amplitude. In a second experiment (N = 25), we manipulated reinforcement rates (easy vs. hard) and affective picture content (liked vs. ambivalent) to examine the potential interaction of prediction error and liking on RewP amplitude. We again found a significant relationship between liking and RewP amplitude, however, only in the hard condition. These findings suggest that the RewP reflects cortical computations of reward surprise as well as hedonic liking, identifying it as a possible nexus where multidimensional value is computed.

Decreases in the Late Positive Potential to Alcohol Images Among Alcohol Treatment Seekers Following Mindfulness-Based Relapse Prevention.

AIM: Heightened craving among individuals with alcohol use disorder (AUD) has been attributed to a hypersensitivity to alcohol cues in attentional brain networks. Active mindfulness training has been shown to help improve attentional control. Here, we examined alcohol cue-related hypersensitivity among individuals with AUD who received rolling group mindfulness-based relapse prevention (MBRP) in combination with transcranial direct current stimulation (tDCS), over right inferior frontal gyrus. METHODS: Participants (n = 68) viewed a series of emotionally negative, emotionally neutral and alcohol-related images. Following image presentation, participants were asked to rate their level of craving for the alcohol cues, and their level of negative affect evoked by neutral and negative cues. During the task, electroencephalogram (EEG) was recorded to capture an event-related component shown to relate to emotionally salient stimuli: the late positive potential (LPP). Participants who completed a follow-up EEG (n = 37) performed the task a second time after up to eight sessions of MBRP coupled with active or sham tDCS. RESULTS: We found that both craving ratings and the LPP significantly decreased in response to alcohol cues from pre- to post-treatment, but not for other image cues. The magnitude of alcohol image craving reductions was associated with the number of MBRP group sessions attended. Active tDCS was not associated with craving ratings, but it was associated with greater LPP amplitudes across image types. CONCLUSIONS: Taken together, these results suggest that disruption of alcohol-cue hypersensitivity in people with AUD may be a target mechanism of MBRP.

Reduced positive affect alters reward learning via reduced information encoding in the Reward Positivity.

Reward Positivity (RewP) is a feedback-locked event-related potential component that is specifically elicited by rewarding feedback and scales with positive reward prediction error, a hallmark of reinforcement learning models. The RewP is also diminished in depression, suggesting that it may be a novel marker of anhedonia. Here, we examined if a sad mood induction offered an opportunity to causally induce a mood-related alteration of the RewP and reward-related learning. In Experiment 1 (N = 50 total), participants were randomly assigned to previously established sad or neutral mood induction procedures before a probabilistic selection task. This manipulation failed to induce changes in affect, suggesting that standard methods are inadequate. In Experiment 2 (N = 50 total), participants were randomly assigned to newly developed happy versus sad mood manipulations, which successfully induced large changes in affect. While the RewP was unaffected by mood induction, positive mood moderated the relationship between prediction error encoding in the RewP and reward learning, such that low positive mood and low prediction error encoding resulted in poorer reward learning. These findings provide a mechanistic example of how reduced positive affect moderates reward learning via poorer information encoding in the RewP.