Dissociating neural subsystems for grammar by contrasting word order and inflection
Bibliographic record
Abstract
An important question in understanding language processing is whether there are distinct neural mechanisms for processing specific types of grammatical structure, such as syntax versus morphology, and, if so, what the basis of the specialization might be. However, this question is difficult to study: A given language typically conveys its grammatical information in one way (e.g., English marks "who did what to whom" using word order, and German uses inflectional morphology). American Sign Language permits either device, enabling a direct within-language comparison. During functional (f)MRI, native signers viewed sentences that used only word order and sentences that included inflectional morphology. The two sentence types activated an overlapping network of brain regions, but with differential patterns. Word order sentences activated left-lateralized areas involved in working memory and lexical access, including the dorsolateral prefrontal cortex, the inferior frontal gyrus, the inferior parietal lobe, and the middle temporal gyrus. In contrast, inflectional morphology sentences activated areas involved in building and analyzing combinatorial structure, including bilateral inferior frontal and anterior temporal regions as well as the basal ganglia and medial temporal/limbic areas. These findings suggest that for a given linguistic function, neural recruitment may depend upon on the cognitive resources required to process specific types of linguistic cues.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".