Motor Expertise Differentiates the Representational Structure of Graded Error Monitoring in Midfrontal Theta

Error monitoring has been widely characterized as a graded process that scales with the magnitude of prediction errors. Yet it remains unclear whether this rule is fixed or can be reshaped by expertise. Using a graded-deviation paradigm in table tennis experts and novices, we examined how long-term training refines the neural coding of prediction-outcome discrepancies. Experts demonstrated higher accuracy and stability as deviation angles increased (from 0° to 30°). EEG analyses revealed that frontal-midline theta power scaled with deviation magnitude in both groups but exhibited distinct representational structures: experts showed a three-step graded organization, whereas novices displayed a binary pattern. Multivariate decoding further confirmed that only the frontal-midline region carried discriminable information about error magnitude, with decoding accuracy significantly higher in experts. These findings suggest that expertise is associated with differences in the gain structure of error coding, with experts showing a more parametric mapping of prediction-outcome discrepancies. Frontal-midline theta thus provides a neural substrate for expertise-dependent tuning of predictive precision, indicating that graded error monitoring differs between individuals with different levels of motor expertise.