Mental rotation: Cross-task training and generalization.
Bibliographic record
Abstract
It is well established that performance on standard mental rotation tasks improves with training (Peters et al., 1995), but thus far there is little consensus regarding the degree of transfer to other tasks which also involve mental rotation. In Experiment 1, we assessed the effect of mental rotation training on participants' Mental Rotation Test (MRT) scores. Twenty-eight participants were randomly assigned to one of three groups: a "One-Day Training," "Spaced Training," or "No Training" group. Participants who received training achieved higher scores on the MRT, an advantage that was still evident after 1 week. Distribution of training did not affect performance. Experiment 2 assessed generalization of mental rotation training to a more complex mental rotation task, laparoscopic surgery. Laparoscopic surgical skills were assessed using Fundamentals of Laparoscopic Surgery (FLS) tasks. Thirty-four participants were randomly assigned to a "Full Mental Rotation Training, MRT and FLS," "MRT and FLS," or "FLS-only" group. MRT results from Experiment 1 were replicated and mental rotation training was found to elicit higher scores on the MRT. Further, mental rotation training was found to generalize to certain laparoscopic surgical tasks. Participants who obtained mental rotation training performed significantly better on mental-rotation dependent surgical tasks than participants who did not receive training. Therefore, surgical training programs can use simple computer or paper-based mental rotation training instead of more expensive materials to enhance certain aspects of surgical performance of trainees.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".