Flexible Composition: MEG Evidence for the Deployment of Basic Combinatorial Linguistic Mechanisms in Response to Task Demands
Bibliographic record
Abstract
The present study investigates whether a minimal manipulation in task demands can induce core linguistic combinatorial mechanisms to extend beyond the bounds of normal grammatical phrases. Using magnetoencephalography, we measured neural activity evoked by the processing of adjective-noun phrases in canonical (red cup) and reversed order (cup red). During a task not requiring composition (verification against a color blob and shape outline), we observed significant combinatorial activity during canonical phrases only - as indexed by minimum norm source activity localized to the left anterior temporal lobe at 200-250 ms(cf. [1], [2]). When combinatorial task demands were introduced (by simply combining the blob and outline into a single colored shape) we observed significant combinatorial activity during reversed sequences as well. These results demonstrate the first direct evidence that basic linguistic combinatorial mechanisms can be deployed outside of normal grammatical expressions in response to task demands, independent of changes in lexical or attentional factors.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".