The Effects of Speed and Direction on Eye-hand Coordination for Moving Targets
Bibliographic record
Abstract
Grasping moving objects involves both spatial and temporal predictions. The hand is aimed at a location where it will meet the object, rather than the position at which the object is seen when the reach is initiated. Previous eye-hand coordination research from our lab, utilizing stationary objects, has shown that participants initial gaze tends to be directed towards the eventual location of the index finger. This experiment examined how object movement affects gaze and selection of grasp points. A computer-generated target (4 x 4 cm) was presented on either the left or right edge of a 24 in. monitor, and after a 1.5 s delay, travelled horizontally across the monitor at either a "slow" (5 cm/s) or "fast" (10 cm/s) speed. Participants reached to grasp the target upon hearing a tone presented either 2.5 s or 5 s after the target appeared. Results showed that when the target first appeared, participants anticipated the targets eventual movement by fixating ahead of its leading edge. Once target movement began, participants shifted their fixation to the leading edge of the target. Upon reach initiation, participants then fixated towards the top edge of the target. Final fixations tended towards the final index finger contact point on the target. ROI analysis, and examination of the extent to which the eyes reproduced the targets motion, revealed that it was direction that most influenced fixation locations and grasp points. Interestingly, it was found that participants fixated further ahead of the targets leading edge when the direction of motion was leftward, particularly at the slower speedpossibly the result of mechanical constraints of intercepting leftward moving targets with ones right hand. Our findings suggest differences between initial fixation locations (an anticipation effect), but similar preference for final fixation locations, when reaching to grasp moving versus stationary targets. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.008 |
| 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) | 0.002 | 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".