Tetris as homework: does videogame training improve spatial anatomy comprehension? (725.4)
Bibliographic record
Abstract
Spatial ability, particularly spatial visualization (Vz), is a significant predictor of success in human anatomy. There is evidence that Vz can be improved through training with videogames, such as Tetris. The present study investigates the relationship between videogame training, Vz, and visuospatial anatomy comprehension. Participants (n=27) completed the Mental Rotations Test (MRT) and the Spatial Anatomy Task (SAT) in order to assess baseline levels of Vz and visuospatial anatomy comprehension, respectively. According to MRT scores, the participants were semi‐randomized into a Control (n=12) or Training (n=15) group, with both low‐ and high‐Vz individuals in each group. Participants in the Training group played five, one‐hour sessions of Tetris over five consecutive days. At least one week after baseline testing, all participants again performed the MRT and SAT. Participants in both the Control and Training groups showed significant improvements on their post‐MRT and SAT; however, contrary to our hypothesis, videogame training did not improve Vz and visuospatial anatomy comprehension beyond that observed in the Control group. Moreover, this improvement was independent of participant sex differences. Additional subjects are being recruited to help us further explore the potential utility of videogame training for anatomically‐demanding fields.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".