Cortical language areas are coupled via a soft hierarchy of model-based linguistic features
Bibliographic record
Abstract
Abstract Natural language comprehension is a complex task that relies on coordinated activity across a network of cortical regions. In this study, we propose that regions of the language network are coupled to one another through subspaces of shared linguistic features. To test this idea, we developed a model-based connectivity framework to quantify stimulus-driven, feature-specific functional connectivity between language areas during natural language comprehension. Using fMRI data acquired while subjects listened to spoken narratives, we tested three types of features extracted from a unified neural network model for speech and language: low-level acoustic embeddings, mid-level speech embeddings, and high-level language embeddings. Our modeling framework enabled us to quantify the proportions of stimulus features driving connectivity between regions: early auditory areas were coupled to intermediate language areas via lower-level acoustic and speech features; in contrast, higher-order language and default-mode regions were predominantly coupled through more abstract language features. We observed a clear progression of feature-specific connectivity from early auditory to lateral temporal areas, advancing from acoustic connectivity to speech- and finally to language-driven connectivity. Our findings suggest that higher-order language areas are coupled along increasingly higher level, more contextualized language features.
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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 | 0.002 |
| 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".