Dissociation of the N400 component between linguistic and non-linguistic processing: A source analysis study
Bibliographic record
Abstract
The N400 component is commonly associated with the detection of linguistic incongruity. A few studies have shown that the N400 can also be elicited by non-linguistic stimuli. Different spatiotemporal patterns were observed between the typical Linguistic N400 and the Non-linguistic N400, suggesting distinct brain generators. The aim of this study was to investigate the presence of an N400 in response to linguistic and non-linguistic stimuli, and to specify anatomical sources of both N400s using a novel analysis method: the Bayesian Model Averaging (BMA) distributed source model. Picture-word and environmental soundpicture associations, either congruent or incongruent, were presented to ten young healthy adults while highdensity ERP recordings were made. Standard electrophysiological analyses confirmed that the N400 was not specific to linguistic incongruity but was also elicited by environmental sound-picture incongruities. Different topographic distributions were obtained for the Linguistic N400 and Non-linguistic N400. BMA analysis showed that the Linguistic N400 generators were mostly located in the left superior temporal gyrus, whereas the sources of the Non-linguistic N400 were identified mostly in the right middle and superior temporal gyri. Detection of linguistic incongruities recruited cerebral areas commonly associated with language processing, whereas non-linguistic incongruities recruited right cerebral regions usually associated with auditory processing of non-linguistic stimuli. The Linguistic and Non-linguistic N400s appear to be elicited by similar cognitive mechanisms assumed by different cerebral areas depending on the type of material to be processed. The present findings support the existence of parallel pathways for the processing of linguistic and non-linguistic incongruities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".