Gradients versus dichotomies: how strength of semantic context influences event-related potentials and lexical decision times

Electrophysiological alterations during action semantic processing in Parkinson's disease

Assessments of action semantics consistently reveal markers of Parkinson's disease (PD). However, neurophysiological signatures of the domain remain under-examined in this population, especially under conditions that allow patients to process stimuli without stringent time constraints. Here we assessed event-related potentials and time-frequency modulations in healthy individuals (HPs) and PD patients during a delayed-response semantic judgment task involving related and unrelated action-picture pairs. Both groups had shorter response times for related than for unrelated trials, but they exhibited discrepant electrophysiological patterns. HPs presented significantly greater N400 amplitudes as well as theta enhancement and mu desynchronization for unrelated relative to related trials. Conversely, N400 and theta modulations were abolished in the patients, who further exhibited a contralateralized cluster in the mu range. None of these patterns were associated with the participants' cognitive status. Our results suggest that PD involves multidimensional neurophysiological disruptions during action-concept processing, even under task conditions that elicit canonical behavioral effects. New constraints thus emerge for translational neurocognitive models of the disease.

Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming

When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) - a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related "prime" word. This so-called "N400 semantic priming effect" is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model's trial-by-trial estimates of target word probability accounted for significant variance in trial-by-trial N400 amplitude. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing.