From expressionless to impressionist: Exploring the link between neurological disease and artistic style in painters
Bibliographic record
Abstract
This paper discusses the relationship between various types of neurological disease and stylistic changes in painters. By first outlining the hypothesized neuroanatomical bases of creativity, the discussion then relates localized brain damage to various stylistic changes in painters and previously non-artists. It also explores artistic style in the context of more global neurological damage, such as dementias and neurotransmitter imbalances.The literature suggests that focal neurological insults (such as strokes or head injuries) may more often lead to focal deficits in painters, such as the loss of visuospatial ability or partial hemineglect. More widespread neurological damage may be associated with more global stylistic changes; for example, dopamine replacement therapy for Parkinson’s disease has been shown to produce a more impressionist painting style in numerous recorded artists. In several case studies, brain damage actually led to the emergence of de novo artistic ability.While these changes in artistic style may not be rigidly predictable based on the limited literature available, this paper demonstrates that both artists and non-artists may experience significant changes in artistic style after neurological disease. Patient narratives also suggest that painting may serve as an empowering personal coping and communication strategy, aiding patients in navigating their complex illnesses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".