The Electrophysiology of Basic Phrase Building
Bibliographic record
Abstract
A defining trait of linguistic competence is the ability to combine elements into increasingly complex structures to denote, and to comprehend, a potentially infinite number of meanings. Recent magnetoencephalography (MEG) work has investigated these processes by comparing the response to nouns in combinatorial (blue car) and non-combinatorial (rnsh car) contexts. In the current study we extended this paradigm using electroencephalography (EEG) to dissociate the role of semantic content from phonological well-formedness (yerl car). We used event-related potential (ERP) recordings in order to better relate the observed neurophysiological correlates of basic combinatorial operations to prior ERP work on comprehension. We found that nouns in combinatorial contexts (blue car) elicited a greater centro-parietal negativity between 180-400ms, independent of the phonological well-formedness of the context word. We discuss the potential relationship between this 'combinatorial' effect and classic N400 effects. We also report preliminary evidence for an early anterior negative deflection immediately preceding the critical noun in combinatorial contexts, which we tentatively interpret as an electrophysiological reflex of syntactic structure initialization.
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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 | 0.000 |
| 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) | 0.002 | 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".