Different perspectives: Spatial ability influences where individuals look on a timed spatial test
Bibliographic record
Abstract
Learning in anatomy can be both spatially and visually complex. Pedagogical investigations have begun exploration as to how spatial ability may mitigate learning. Emerging hypotheses suggests individuals with higher spatial reasoning may attend to images differently than those who are lacking. To elucidate attentional patterns associated with different spatial ability, eye movements were measured in individuals completing a timed electronic mental rotation test (EMRT). The EMRT was based on the line drawings of Shepherd and Metzler. Individuals deduced whether image pairs were rotations (same) or mirror images (different). It was hypothesized that individuals with high spatial ability (HSA) would demonstrate shorter average fixation durations during problem solving and attend to different features of the EMRT than low spatial ability (LSA) counterparts. Moreover, question response accuracy would be associated with fewer fixations and shorter average response times, regardless of spatial reasoning ability. Average fixation duration in the HSA group was shorter than LSA (F(1,8) = 7.99; P = 0.022). Importantly, HSA and LSA individuals looked to different regions of the EMRT images (Fisher Exact Test: 12.47; P = 0.018); attending to the same locations only 34% of the time. Correctly answered questions were characterized by fewer fixations per question (F(1, 8) = 18.12; P = 0.003) and shorter average response times (F(1, 8) = 23.89; P = 0.001). The results indicate that spatial ability may influence visual attention to salient areas of images and this may be key to problem solving processes for low spatial individuals. Anat Sci Educ 10: 224-234. © 2016 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".