Individual differences in the empathic experience of pain: An EEG and machine learning approach.

Observing pain in others often elicits vicarious responses commonly considered as indices of empathy. However, the extent to which these responses reflect genuine empathic engagement remains subject of debate, with little research on how they may vary among individuals exhibiting low empathy traits like callousness or emotional detachment. To investigate this, we recorded EEG activity from 37 healthy participants to determine if neural responses to second-hand pain correlate with self-reported empathy and callousunemotional traits, while further testing the predictive utility of these signatures using single-trial machine learning classification. Although painful stimuli elicited distinct responses at the group level - specifically larger late positive potentials (LPP; 500-900 ms) and decreased theta and alpha power (650-1300 ms) over centroparietal brain regions -, machine learning classification of pain versus no-pain trials did not exceed chance accuracy, suggesting weak or heterogeneous neural differentiation at the single-trial level. Furthermore, pain-related EEG activity did not correlate with subjective pain ratings or empathy. Instead, the data revealed that callous and uncaring traits predicted attenuated LPP amplitudes, and unemotional traits were associated with stronger theta desynchronisation. Together, these findings suggest that neural markers of vicarious pain do not necessarily index empathic engagement but rather seem to reflect individual differences in emotional sensitivity.