Time limits in testing: An analysis of eye movements and visual attention in spatial problem solving
Bibliographic record
Abstract
Individuals with an aptitude for interpreting spatial information (high mental rotation ability: HMRA) typically master anatomy with more ease, and more quickly, than those with low mental rotation ability (LMRA). This article explores how visual attention differs with time limits on spatial reasoning tests. Participants were assorted to two groups based on their mental rotation ability scores and their eye movements were collected during these tests. Analysis of salience during testing revealed similarities between MRA groups in untimed conditions but significant differences between the groups in the timed one. Question-by-question analyses demonstrate that HMRA individuals were more consistent across the two timing conditions (κ = 0.25), than the LMRA (κ = 0.013). It is clear that the groups respond to time limits differently and their apprehension of images during spatial problem solving differs significantly. Without time restrictions, salience analysis suggests LMRA individuals attended to similar aspects of the images as HMRA and their test scores rose concomitantly. Under timed conditions however, LMRA diverge from HMRA attention patterns, adopting inflexible approaches to visual search and attaining lower test scores. With this in mind, anatomical educators may wish to revisit some evaluations and teaching approaches in their own practice. Although examinations need to evaluate understanding of anatomical relationships, the addition of time limits may induce an unforeseen interaction of spatial reasoning and anatomical knowledge. Anat Sci Educ 10: 528-537. © 2017 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".