Reading music and words: The anatomical connectivity of musicians’ visual cortex
Bibliographic record
Abstract
Musical score reading and word reading have much in common, from their historical origins to their cognitive foundations and neural correlates. In the ventral occipitotemporal cortex (VOT), the specialization of the so-called Visual Word Form Area for word reading has been linked to its privileged structural connectivity to distant language regions. Here we investigated how anatomical connectivity relates to the segregation of regions specialized for musical notation or words in the VOT. In a cohort of professional musicians and non-musicians, we used probabilistic tractography combined with task-related functional MRI to identify the connections of individually defined word- and music-selective left VOT regions. Despite their close proximity, these regions differed significantly in their structural connectivity, irrespective of musical expertise. The music-selective region was significantly more connected to posterior lateral temporal regions than the word-selective region, which, conversely, was significantly more connected to anterior ventral temporal cortex. Furthermore, musical expertise had a double impact on the connectivity of the music region. First, music tracts were significantly larger in musicians than in non-musicians, associated with marginally higher connectivity to perisylvian music-related areas. Second, the spatial similarity between music and word tracts was significantly increased in musicians, consistently with the increased overlap of language and music functional activations in musicians, as compared to non-musicians. These results support the view that, for music as for words, very specific anatomical connections influence the specialization of distinct VOT areas, and that reciprocally those connections are selectively enhanced by the expertise for word or music reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".