Unravelling Boléro: progressive aphasia, transmodal creativity and the right posterior neocortex
Bibliographic record
Abstract
Most neurological lesion studies emphasize performance deficits that result from focal brain injury. Here, we describe striking gains of function in a patient with primary progressive aphasia, a degenerative disease of the human language network. During the decade before her language deficits arose, Anne Adams (AA), a lifelong scientist, developed an intense drive to produce visual art. Paintings from AA's artistic peak revealed her capacity to create expressive transmodal art, such as renderings of music in paint, which may have reflected an increased subjective relatedness among internal perceptual and conceptual images. AA became fascinated with Maurice Ravel, the French composer who also suffered from a progressive aphasia, and painted his best-known work, 'Boléro', by translating its musical elements into visual form. Later paintings, achieved when AA was nearly mute, moved towards increasing photographic realism, perhaps because visual representations came to dominate AA's mental landscape during this phase of her illness. Neuroimaging analyses revealed that, despite severe degeneration of left inferior frontal-insular, temporal and striatal regions, AA showed increased grey matter volume and hyperperfusion in right posterior neocortical areas implicated in heteromodal and polysensory integration. The findings suggest that structural and functional enhancements in non-dominant posterior neocortex may give rise to specific forms of visual creativity that can be liberated by dominant inferior frontal cortex injury.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".