Vision‐Based Spatial Abilities and Pictures of Objects Recognized from Haptic Perception
Bibliographic record
Abstract
Objective. As a basis for an application in anatomy education, vision‐based spatial abilities have been previously correlated with a drawing score based on haptic perception of objects. The objective of the current study was to determine whether a picture could replace a drawing in correlating haptic perception of objects with vision‐based spatial abilities tests. Methods. A cohort of 48 medical graduates was enrolled in a prospective study. Spatial abilities were measured with a redrawn Vandenberg and Kuse Mental Rotations Tests in two (MRTA) and three (MRTC) dimensions and a Surface Development Test (SDT). Twenty‐five objects constructed from various shaped parts glued together were identified on a picture by participants after haptic perception. The maximum score was 24 for each of MRTA and MRTC, 60 for SDT, and 25 for the picture score. Descriptive statistics included median and lower and upper quartiles. Spearman's correlation coefficient was used to compare the picture score to MRTA, MRTC and SDT scores. Results. The picture score [18 (12, 21)] was correlated with MRTA [14 (9, 17)], MRTC [9.5 (6.5, 12)] and SDT [44.5 (36, 53)] scores with a correlation of 0.427 (p = 0.0025), 0.539 (p < 0.0001) and 0.429 (p = 0.0024), respectively. Conclusions Vision‐based spatial abilities tests were correlated with pictures of objects recognized from haptic perception. Individual differences in spatial abilities as related to haptic perception have implications for education in the anatomy laboratory. This study was supported by an internal grant from the Department of Surgery, University of Sherbrooke, Sherbrooke, QC, Canada.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".