Visuomotor mental rotation: Reaction time is determined by the complexity of sensorimotor transformations supporting the response
Bibliographic record
Abstract
In the visuomotor mental rotation (VMR) task, participants execute a center-out reaching movement to a location that deviates from a visual cue by a predetermined angle. Seminal work from Georgopoulos and Massey (1987) revealed a linear increase in reaction time (RT) as a function of increasing instruction angle, for angles of 5, 10, 15, 35, 70, 105 and 140°. This finding led to the mental rotation model, which asserts that response preparation is mediated by the imagined rotation of the movement vector (Georgopoulos and Massey, 1987). We recently demonstrated that the mental rotation model does not account for RT in all VMR tasks. Specifically, we revealed a RT advantage for the 180° instruction angle relative to 90° (Neely and Heath, 2009, in press). We interpreted this as evidence that 180° is mediated by a vector inversion strategy; however, we were unable to determine whether 90° invoked a mental rotation strategy. The goal of the present work was to examine 90° and 180° in concert with a set of intermediary angles to determine whether 180° is a special case of VMR. To that end, we evaluated two independent sets of instruction angles: 0, 5, 10, 15, 35, 70, 105 and 140° (Experiment One) and 0, 30, 60, 90, 120, 150, 180, and 210° (Experiment Two). The results revealed a linear increase in RT as a function of instruction angle for Experiment One. In contrast, the results for Experiment Two revealed a non-linear relationship between RT and instruction angle; specifically, we observed a RT advantage for 180°, followed by 30° and 90°. Such results provide convergent evidence that response planning in the VMR task is not universally mediated by a mental rotation strategy. Rather, we contend that RT is determined by the complexity of the visuomotor transformations supporting the voluntary response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".