An fMRI study dissociating distance measures computed by Broca's area in movement processing: clause boundary vs. identity
Bibliographic record
Abstract
Behavioral studies of sentence comprehension suggest that processing long-distance dependencies is subject to interference effects when Noun Phrases (NP) similar to the dependency head intervene in the dependency. Neuroimaging studies converge in localizing such effects to Broca's area, showing that activity in Broca's area increases with the number of NP interveners crossed by a moved NP of the same type. To test if NP interference effects are modulated by adding an intervening clause boundary, which should by hypothesis increase the number of successive-cyclic movements, we conducted an fMRI study contrasting NP interveners with clausal (CP) interveners. Our design thus had two components: (I) the number of NP interveners crossed by movement was parametrically modulated; (II) CP-intervention was contrasted with NP-intervention. The number of NP interveners parametrically modulated a cluster straddling left BA44/45 of Broca's area, replicating earlier studies. Adding an intervening clause boundary did not significantly modulate the size of the NP interference effect in Broca's area. Yet, such an interaction effect was observed in the Superior Frontal Gyrus (SFG). Therefore, the involvement of Broca's area in processing syntactic movement is best captured by memory mechanisms affected by a grammatically instantiated type-identity (i.e., NP) intervention.
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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 | 0.001 |
| 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".