Phonological aspects of word recognition as revealed by high-resolution spatio-temporal brain mapping
Bibliographic record
Abstract
We describe, for the first time, the use of high-resolution event-related brain potentials (hrERP) to identify the spatio-temporal characteristics of neural systems involved in phonological analysis. Subjects studied a visual word/non-word that was followed by the brief presentation of a prime letter (e.g. House, M) with the instruction to anticipate the word/non-word formed by replacing the word's first letter with the prime letter. After the prime letter, an auditory target word/non-word was presented that either matched/mismatched expectations (e.g., Mouse/Barn). ERPs were recorded to the onset of the auditory targets and scalp topographical maps were derived for the phonological mismatch negativity (PMN). The PMN reflected phonological analysis and examination of the peak topography revealed that the response was characterized by a prominent frontal, right-asymmetrical distribution. Spatial de-blurring (using current source density maps) indicated that the PMN scalp topography resulted primarily from an active left anterior source. The current results provide the initial evidence for the localization of the intra-cranial generator(s) involved in phonological analysis.
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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 | 0.001 |
| 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 | 0.000 |
| 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".