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Abstract Large language models (LLMs) can perform reasoning computations both internally within their latent space and externally by generating explicit token sequences like chains of thought. Significant progress in enhancing reasoning abilities has been made by scaling test-time compute. However, understanding and quantifying model-internal reasoning abilities – the inferential “leaps” models make between individual token predictions – remains crucial.
In the present study we investigated the nature and chronometry of empathy for pain influences on perceptual and motor processes. Thus, event-related brain potentials (ERPs), response force (RF) and oscillatory electroencephalography (EEG) activity were measured while participants were presented with pictures of body parts in painful or neutral situations. Their task consisted in either judging the painfulness of the stimuli or counting the body parts displayed.
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