Visuomotor mental rotation is mediated by a serial process of response substitution
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 slower reaction times (RT) than standard tasks wherein the visual cue is spatially compatible with the movement goal. RTs are reduced when the standard and 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°; Neely & Heath, submitted). One caveat to this finding, however, is that past work examined standard and VMR responses in separate blocks of trials. Thus, RT differences between tasks not only reflect the computational demands of the transformations, but also the temporal cost of visuomotor inhibition. The present work used a randomized task design to isolate the magnitude of the RT difference between standard and VMR tasks across a range of equally spaced angles. Between-task difference scores were least for small (i.e., 30°) and perceptually familiar (i.e., 90 and 180°) angles relative to large and less familiar angles (i.e., 60, 120, 150, and 210°). This finding suggests RTs reflect the time required to prepare and inhibit an automatic motor response to the visual cue and then compute the requisite transformations for the response. Moreover, the rate at which the response is prepared is influenced by the angular disparity between standard and VMR responses and the perceptual familiarity of the transformation angle.Acknowledgments: This research was supported by NSERC as well a Graduate Thesis Award from The University of Western Ontario.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".