Toward a Neurobiologically Plausible Model of Language-Related, Negative Event-Related Potentials

Abstract

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences.

Animacy modulates gender agreement computation in Hindi: An ERP study

“…This issue does not arise in case of natural or semantic gender since it is linked to the biological sex of the referent. The explanation offered here for the latency shift of the ERP effect with regards to dialectal complexities and requirement of more resources for comprehension is in consonance with the account offered for processing complexities by Bornkessel-Schlesewsky & Schlesewsky (2019).…”

Event-related potentials reflect prediction errors and pop-out during comprehension of degraded speech

“…The N400 can, therefore, be characterised as an index of the amount of mismatch between a semantic prediction and the incoming stimulus -i.e. a semantic prediction error (Bornkessel-Schlesewsky & Schlesewsky, 2019;Paczynski & Kuperberg, 2012). Indeed, prediction error minimisation accounts of global brain function, such as free energy (Friston, 2010), propose that all evoked activity in the brain reflects this mismatch of prediction and stimulus, i.e.…”

Language Processing as a Precursor to Language Change: Evidence From Icelandic

“…From this perspective, we would expect to observe more pronounced N400 effects for higher precision predictions. This approach constitutes a promising conceptual framework for interpreting the N400 effects in the current experiment (for a comparison to other current interpretations of the N400, see Bornkessel-Schlesewsky and Schlesewsky, 2019).…”

“…We have recently proposed that N400 effects reflect precisionweighted prediction errors (Bornkessel-Schlesewsky and Schlesewsky, 2019) in the sense of a predictive-coding account of brain function (cf. Friston, 2005Friston, , 2010.…”

“…Crucially, predictions differ in regard to their precision, which is defined as the inverse of variance and thus essentially reflects the degree of (un)certainty (Feldman and Friston, 2010). Prediction precision has been shown to modulate mismatch negativity (MMN) effects (Todd et al, 2014) and, as posited in Bornkessel-Schlesewsky and Schlesewsky (2019), there is evidence to suggest that the same holds for N400 effects in language. From this perspective, we would expect to observe more pronounced N400 effects for higher precision predictions.…”