An electroencephalographic analysis of mental visuomotor rotation
Bibliographic record
Abstract
Recent experimental evidence (Neely & Heath, 2010) suggests that visuomotor mental rotation does not rely on a rotation process that occurs just prior to response initiation, but instead relies on a more complex and cognitive substitution process related to the sensorimotor transformations mediating the response. Here, we sought to provide further evidence for this contention by using event-related brain potentials (ERP) to demonstrate that an increase in the degree of visuomotor mental rotation resulted in a related increase in the amplitude of the P300 – an ERP component that has an amplitude sensitive to the magnitude of cognitive processing. Our behavioral results are in line with previous work – the amount of spherical variability in movement endpoints increased in relation to increases in the degree of visuomotor mental rotation. Not surprisingly, our ERP data revealed differences in visual processing between the presentation of unrotated and rotated targets. However, as predicted, the amplitude of the P300 scaled to the degree of visuomotor mental rotation – a result that supports the Neely and Heath's findings and suggests that visuomotor mental rotation relies on a cognitive response substitution process as opposed to a rotation of the movement vector just prior to movement onset.
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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) | 0.001 | 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".