Gaze and hand behaviors during haptic abilities testing—An update to multimedia learning theory
Bibliographic record
Abstract
The Cognitive Theory of Multimedia Learning (CTML) suggests humans learn through visual and auditory sensory channels. Haptics represent a third channel within CTML and a missing component for experiential learning. The objective was to measure visual and haptic behaviors during spatial tasks. The haptic abilities test (HAT) quantifies results in several realms, accuracy, time, and strategy. The HAT was completed under three sensory conditions using sight (S), haptics (H), and sight with haptics (SH). Subjects (n = 22, 13 females (F), 20-28 years) completed the MRT (10.6 ± 5.0, mean ± SD) and were classified as high or low spatial abilities scores with respect to mean MRT: high spatial abilities (HSA) (n = 12, 6F, MRT = 13.7 ± 3.0), and low spatial ability (LSA) groups (n = 10, 7F, MRT = 5.6 ± 2.0). Video recordings gaze and hand behaviors were compared between HSA and LSA groups across HAT conditions. The HSA group spent less time fixating on mirrored objects, an erroneous answer option, of HAT compared to the LSA group (11.0 ± 4.7 vs. 17.8 ± 7.3 s, p = 0.020) in S conditions. In haptic conditions, HSA utilized a hand-object interaction strategy characterized as palpation, significantly less than the LSA group (23.2 ± 16.0 vs. 43.1 ± 21.5 percent, p = 0.022). Before this study, it was unclear whether haptic sensory inputs appended to the mental schema models of the CTML. These data suggest that if spatial abilities are challenged, LSA persons both benefit and utilize strategies beyond the classic CTML framework by using their hands as a third input channel. This data suggest haptic behaviors offer a third type of sensory memory resulting in improved cognitive performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".