Processing Noncanonical Sentences in Broca's Region: Reflections of Movement Distance and Type
Bibliographic record
Abstract
Various noncanonical sentence constructions are derived from basic sentence structures by a phrase displacement called Movement. The moved phrase (filler) leaves a silent copy at the extracted position (gap) and is reactivated when the hearer/reader passes over the gap. Consequently, memory operations are assumed to occur to establish the filler-gap link. For languages that have a relatively free word order like German, a distinct linguistic operation called Scrambling is proposed. Although Movement and Scrambling are assumed to be different linguistic operations, they both involve memory prone filler-gap processes. To clarify whether filler-gap memory processes in Scrambling and Movement differ neuroanatomically, we designed a functional magnetic resonance imaging study and compared the effect of memory load parameterized by filler-gap distance in the 2 sentence types. Here, we show that processing of the 2 sentence types commonly relies on a left hemispheric network consisting of the inferior frontal gyrus, middle part of the middle temporal gyrus, and intraparietal sulcus. However, we found differences for the 2 sentence types in the linearity of filler-gap distance effect. Thus, the present results suggest that the same neural substrate supports the memory processes of sentences constructed by Movement and Scrambling, although differentially modulated by memory load.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".