The eye of the beholder: Can patterns in eye movement reveal aptitudes for spatial reasoning?
Bibliographic record
Abstract
Mental rotation ability (MRA) is linked to academic success in the spatially complex Science, Technology, Engineering, Medicine, and Mathematics (STEMM) disciplines, and anatomical sciences. Mental rotation literature suggests that MRA may manifest in the movement of the eyes. Quantification of eye movement data may serve to distinguish MRA across individuals, and serve as a consideration when designing visualizations for instruction. It is hypothesized that high-MRA individuals will demonstrate fewer eye fixations, conduct shorter average fixation durations (AFD), and demonstrate shorter response times, than low-MRA individuals. Additionally, individuals with different levels of MRA will attend to different features of the block-figures presented in the electronic mental rotations test (EMRT). All participants (n = 23) completed the EMRT while metrics of eye movement were collected. The test required participants view pairs of three-dimensional (3D) shapes, and identify if the pair is rotated but identical, or two different structures. Temporal analysis revealed no significant correlations between response time, average fixation durations, or number of fixations and mental rotation ability. Further analysis of within-participant variability yielded a significant correlation for response time variability, but no correlation between AFD variability and variability in the number of fixations. Additional analysis of salience revealed that during problem solving, individuals of differing MRA attended to different features of the block images; suggesting that eye movements directed at salient features may contribute to differences in mental rotations ability, and may ultimately serve to predict success in anatomy. Anat Sci Educ 9: 357-366. © 2015 American Association of Anatomists.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".