Anatomy, technology, art, and culture: toward a realistic perspective of the brain
Bibliographic record
Abstract
In the 15th century, brain illustration began to change from a schematic system that involved scant objective rendering of the brain, to accurate depictions based on anatomical dissections that demanded significant artistic talent. Notable examples of this innovation are the drawings of Leonardo da Vinci (1498-1504), Andreas Vesalius' association with the bottega of Titian to produce the drawings of Vesalius' De humani corporis fabrica (1543), and Christopher Wren's illustrations for Thomas Willis' Cerebri Anatome (1664). These works appeared during the Renaissance and Age of Enlightenment, when advances in brain imaging, or really brain rendering, reflected not only the abilities and dedications of the artists, but also the influences of important cultural and scientific factors. Anatomy and human dissection became popular social phenomena as well as scholarly pursuits, linked with the world of the fine arts. The working philosophy of these artists involved active participation in both anatomical study and illustration, and the belief that their discoveries of the natural world could best be communicated by rendering them in objective form (that is, with realistic perspective). From their studies emerged the beginning of contemporary brain imaging. In this article, the authors examine how the brain began to be imaged in realism within a cultural and scientific milieu that witnessed the emergence of anatomical dissection, the geometry of linear perspective, and the closer confluence of art and science.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".