The Visuomotor Mental Rotation Task: Visuomotor Transformation Times Are Reduced for Small and Perceptually Familiar Angles
Bibliographic record
Abstract
In the visuomotor mental rotation (VMR) task, participants point to a location that deviates from a visual cue by a predetermined angle. This task elicits longer reaction times (RT) relative to tasks wherein the visual cue is spatially compatible with the movement goal. The authors previously reported that visuomotor transformations are faster and more efficient when VMR responses elicit a degree of dimensional overlap (i.e., 0° and 5°) or when the transformation involves a perceptually familiar angle (i.e., 90° or 180°; K. A. Neely & M. Heath, 2010b). One caveat to this finding is that standard and VMR responses were completed in separate blocks of trials. Thus, between-task differences not only reflect the temporal demands of the visuomotor transformations, but also reflect the temporal cost of response inhibition. The goal of this study was to isolate the time cost of visuomotor transformations in the VMR task. The results demonstrated that visuomotor transformations are more efficient and effective when the response entails a degree of dimensional overlap between target and response (i.e., when the angular disparity between the responses is small) or when the transformation angle is perceptually familiar.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".