Whole-Brain Dimensions of Intrinsic Connectivity Capture Modality-Specific and Heteromodal Language Representations
Bibliographic record
Abstract
Comprehension of spoken and written language involves a hierarchical sequence of modality-specific and heteromodal processes. While these have been localized to different regions, modality-selective responses extend beyond them, implicating large-scale network organization in language comprehension. Dimensions of whole-brain connectivity, derived from intrinsic activity, have been proposed as a general organizing framework for cognition. Here, we test their utility in accounting for the spatial distribution of task-evoked activity during language comprehension. We investigated brain activity in human males and females in response to psycholinguistic variables linked to input processing and meaning in a sentence comprehension task presented both visually and auditorily. Macroscale patterns of brain activity were similar across modalities for sentence-level and semantic variables, but effects of orthographic and phonological distance were negatively correlated between modalities. The first dimension, separating heteromodal and unimodal cortices, showed no differences across modalities for sentence processing and semantic variables and opposite effects of word length and orthographic/phonological distance for spoken and written words, supporting the notion that higher-order processing requires heteromodal resources different to those linked to input processing. The second dimension, separating auditory-motor and visual processes, showed an asymmetry in the recruitment of the unimodal systems-listening to long and semantically dissimilar words involved stronger recruitment of primary auditory-motor regions and low visual engagement. These findings show that the language system is organized according to large-scale axes of intrinsic connectivity, with psycholinguistic processes varying systematically along whole-brain dimensions. This supports the view that language comprehension reflects general principles of cortical organization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".