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Temporal Decoding of Reward Processing using MEG

Poster Session B - Sunday, March 8, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballroom

Chris Pirrung¹ (), Chris Baker¹; ¹Laboratory of Brain and Cognition, National Institutes of Health

The Reward Positivity (RewP) is a sensitive, specific marker of reward. The RewP is likely generated by a distributed network of brain regions that do not contribute homogenous information. Given the broad set of contributors to this signal, using a univariate approach such as ERP analysis may not capture the full scope of this signal. MVPA has shown increased sensitivity to neural signals and allowed for successful classification of perceptual stimuli based on evoked neural activity. MEG data suggest that we can use MVPA to temporally decode rewarding and non-rewarding feedback in individual participants. Participants completed a reinforcement learning task in which they received a blue or yellow screen that indicated monetary reward. Feedback-color mapping changed between runs. MVPA was first used to classify color at each timepoint, irrespective of feedback. This model accurately predicted color from 70-400ms. Feedback was accurately predicted from 200-700ms in a model including both blue- and yellow-reward trials. A cross-decoding model, in which the training set included rewards of one color and the testing set included rewards of the other color, made logical systematic errors at early timepoints (70-250ms) in line with color processing, but successfully predicted feedback at later timepoints (350-700ms). This suggests that we are successfully decoding the response to the cognitive content of the feedback, not just the perceptual attributes. This method could allow for differential decoding of both perceptual and cognitive attributes of reward. Additionally, the sensitivity of the method could allow for better understanding of individual differences in reward processing.

Topic Area: THINKING: Decision making