The "eye" in imagination: Restricting eye movements influence imagined actions
Bibliographic record
Abstract
Humans use their eyes and hands in a coordinated way during the performance of goal-directed action – the eyes usually precede the hand to the target to assist in error detection and correction. A version of this eye-hand coupling may also be maintained when we imagine performing these actions. That is, even if the body is not moving during motor imagery, the eyes tend to maintain a movement pattern consistent with the pattern seen during actual performance. These eye movements may functionally support (or be the expression of) the accurate imagination of the body movement. To test this hypothesis, we examined the role of eye movements in imagined and executed actions in a Fitts' reciprocal pointing task. Participants were asked to imagine and execute reciprocal aiming movements in two conditions. In one of these conditions, no instructions were given regarding eye movements. In the other condition, participants were asked to fixate their eyes on a central circle. The primary finding was that the eye fixation instructions only affected movement times in the imagination task. Specifically, whereas movement times in the execution task were similar when participants could freely move their eyes or were instructed to centrally fixate, movement times in the imagination task were longer when participants were instructed to fixate than when they could freely move their eyes. Therefore, only imagined actions were affected by restricting the eye movements indicating a functional role for eye movements during imagination.Acknowledgments: NSERC
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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 | 0.004 |
| 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) | 0.003 | 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".