Pursuit eye movements enhance decision making and hitting accuracy in a go/no-go manual interception task
Bibliographic record
Abstract
It is well established that eye and hand movements are closely coupled in space and time—the eye saccades to and fixates on task-relevant object locations and leads the hand during reaching, grasping, or pointing movements. However, the relation between smooth pursuit and hand movements in response to dynamic visual targets is less well understood. Here, we investigate the relation between smooth pursuit and interception performance in a go/no-go manual interception task. We focus on two aspects of performance: the decision whether or not to move the hand, and the accuracy of the interception. Observers (n=10) viewed a small target moving along a linear path that either projected into a strike zone (hit) or went past it (miss); observers were instructed to intercept only in hit trials, and to not move their hand in miss trials. The target was shown for the full path to the strike zone, or for ¼, ½, or ¾ of the full trajectory. Eye movements were manipulated in separate blocks by instructing observers to maintain fixation on a stationary fixation cross, randomly positioned at three locations along the trajectory, or to move their eyes freely. Across conditions, better and faster pursuit (lower 2D position error, higher eye velocity) and more accurate fixation were linked to better decision and hitting accuracy. Importantly, engaging in smooth pursuit eye movements as compared to fixation resulted in a significant performance improvement across all observers. This pursuit benefit was of the same magnitude (8% average improvement in pursuit vs. fixation) with regard to decision making and hitting accuracy. These results underline the critical importance of smooth pursuit in guiding movement decisions and execution. They also imply that efference-copy signals, generated by the pursuit system, act early on the perceptual system to inform the decision whether or not to initiate a movement. Meeting abstract presented at VSS 2018
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".