The "eyes" have it: Restricting eye movements during imagination decreases the accuracy of action imagination
Bibliographic record
Abstract
Not only can we perform a wide variety of actions, but we can also simulate or imagine ourselves performing those actions. When we perform actions, our eyes and hands typically move in a coordinated way. Research on the role that the execution of eye movements plays during imagination has lead to contradicting results. Interestingly, several studies have demonstrated a positive influence of eye movements on performance in motor imagery tasks. Recent work from our lab has revealed that the movement times of imagined actions are similar to those of executed movements (i.e., conform to Fitts' Law). Although hand movements are restricted in these studies, eye movements have not been restricted which opens the possibility that unrestricted eye movements might be supporting the generation of the Fitts' relationship during the imagination of hand movements. The present study was conducted to investigate if Fitts' relationship in a motor imagery task emerges while controlling for eye movements. Participants imagined reciprocal aiming movements in two conditions: 1) no instructions regarding eye movements and 2) instructions to fixate on a central circle. Participants also executed the movements. Although the movement times in the fixation-imagination condition were higher than in no fixation-imagination and in execution, the Fitts' relationship emerged in each condition. These findings suggest that the imagination of action can occur without eye movements, but that the associated eye movements might assist with maintaining the accuracy of the imagination process.Acknowledgments: This work was supported by operating grants from the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".