Separating phonological and semantic processing in auditory sentence processing: A high‐resolution event‐related brain potential study
Bibliographic record
Abstract
Phonological and semantic processing was studied using high-resolution event-related brain potentials (ERPs) during a sentence-matching task to investigate the spatial distribution of the phonological mismatch negativity (PMN) and the N400 response. It was hypothesized that the two components were spatially separable and that the activity matched prior localization knowledge. Participants examined visual-auditory sentence pairs that related within a semantic hierarchy (e.g., visual: "The man is teaching in the classroom"; Auditory: "The man is in the em leader school/barn"). Semantic congruency was varied for the final words of the spoken sentences. Incongruent words mismatched expectation in terms of both the initial phonological features (unexpected sound) and semantic features (unexpected meaning). In addition, the category-exemplar probability of the final words was either high or low, with low probability words being more difficult to anticipate. Low probability words were predicted to selectively affect PMN activity. We found that incongruent words elicited a PMN (287 msec) and a N400 (424 msec), for both the high and low probability words. As expected, low probability congruent words elicited a small PMN but no N400. In contrast, high probability congruent words elicited neither a detectible PMN nor a N400. The primary PMN sources were in left inferior frontal and inferior parietal lobes. The primary N400 source activation occurred along the left perisylvian cortex, consistent with prior N400 source localization work. From these results, it was concluded that the PMN and N400 were localized to separate cortical language (and memory) regions and had different source activation patterns.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".