Do our hands see what our eyes see? Investigating spatial and haptic abilities
Bibliographic record
Abstract
Spatial abilities (SAs) are cognitive resources used to mentally manipulate representations of objects to solve problems. Haptic abilities (HAs) represent tactile interactions with real-world objects transforming somatic information into mental representations. Both are proposed to be factors in anatomy education, yet relationships between SAs and HAs remain unknown. The objective of the current study was to explore SA-HA interactions. A haptic ability test (HAT) was developed based on the mental rotations test (MRT) with three-dimensional (3D) objects. The HAT was undertaken in three sensory conditions: (1) sighted, (2) sighted with haptics, and (3) haptics. Participants (n = 22; 13 females, 9 males) completed the MRT and were categorized into high spatial abilities (HSAs) (n = 12, mean± standard deviation: 13.7 ± 3.0) and low spatial abilities (LSAs) (n = 10, 5.6 ± 2.0) based on score distributions about the overall mean. Each SA group's HAT scores were compared across the three sensory conditions. Spearman's correlation coefficients between MRT and HAT scores indicated a statistically significant correlation in sighted condition (r = 0.553, p = 0.015) but were not significant in the sighted with haptics (r = 0.0.078, p = 0.212) and haptics (r = 0.043, p = 0.279) conditions. These data suggest HAs appear unrelated to SAs. With haptic exploration, LSA HAT scores were compensated; comparing HSA with LSA: sighted with haptics [median (lower and upper quartiles): 12 (12,13) vs. 12 (11,13), p = 0.254], and haptics [12 (11,13) vs. 12 (10,12), p = 0.381] conditions. Migrations to online anatomy teaching may unwittingly remove important sensory modalities from the learner. Understanding learner behaviors and performance when haptic inputs are removed from the learning environment represents valuable insight informing future anatomy curriculum and resource development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".