Angle-Based Drawing Accuracy Analysis and Mental Models of Three-Dimensional Space
Bibliographic record
Abstract
Drawing from a still-life is a complex visuomotor task. Nevertheless, experts depict three-dimensional subjects convincingly with two-dimensional images. Studies of drawing have historically relied on human critics’ judgement of the drawings, the professional reputations and self-reported experience of the drawers. To extend that work, we developed an objective measurement of the accuracy of a perspective drawing, based on a comparison of the drawing with a ground truth photograph of the subject taken from the same viewpoint. If we measure the angles at intersecting edges in the drawings we can calculate both local errors and each person’s mean percentage magnitude error across angles in the still life. This gives a continuous objective measure of drawing accuracy that correlates well with years of art experience. Drawing expertise may depend to some extent on more accurate internal models of 3D space. To explore this possibility we had adults with a range of drawing experience draw a still life. Participants also made perceptual judgements of still lifes, both from direct observation and from an imagined side view. A conventional mental rotation task failed to differentiate drawing expertise. However, those who drew angles more accurately were also significantly better judges of slant, i.e., the pitch of edges in the still life. Those with the most drawing experience were significantly better judges of spatial extent, i.e., which landmarks were leftmost, rightmost, nearest, farthest etc. The ability to visualize in three dimensions the orientation and relationships of components of a still life predicts drawing accuracy and expertise.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".