Objects drawn from haptic perception and vision‐based spatial abilities
Bibliographic record
Abstract
Haptic perception is used in the anatomy laboratory with the handling of three-dimensional (3D) prosections, dissections, and synthetic models of anatomical structures. Vision-based spatial ability has been found to correlate with performance on tests of 3D anatomy knowledge in previous studies. The objective was to explore whether haptic-based spatial ability was correlated with vision-based spatial ability. Vision-based spatial ability was measured in a study group of 49 medical graduates with three separate tests: a redrawn Vandenberg and Kuse Mental Rotations Tests in two (MRT A) and three (MRT C) dimensions and a Surface Development Test (SDT). Haptic-based spatial ability was measured using 18 different objects constructed from 10 cubes glued together. Participants were asked to draw these objects from blind haptic perception, and drawings were scored by two independent judges. The maximum score was 24 for each of MRT A and MRT C, 60 for SDT, and 18 for the drawings. The drawing score based on haptic perception [median = 17 (lower quartile = 16, upper quartile = 18)] correlated with MRT A [14 (9, 17)], MRT C [9 (7, 12)] and SDT [44 (36, 52)] scores with a Spearman's rank correlation coefficient of 0.395 (p = 0.0049), 0.507 (p = 0.0002) and 0.606 (p < 0.0001), respectively. Spatial abilities assessed by vision-based tests were correlated with a drawing score based on haptic perception of objects. Future research should investigate the contribution of haptic-based and vision-based spatial abilities on learning 3D anatomy from physical models.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".