Visuomotor strategies for grasping a rotating target.
Bibliographic record
Abstract
When performing a visually-guided grasp to a moving object, spatial and temporal predictions must be made so that 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. Last year at VSS, we presented a translational movement study, where we showed that participants fixated the leading edge of a moving target until they initiated their reach, at which point they shifted their gaze to the eventual index finger contact position on the object, above the centre of mass (COM) (Bulloch, Prime & Marotta, 2014). This year, we investigated rotating targets. Do participants track one position on the target while it rotates, or do they shift their gaze to new potential grasping sites as they become available throughout the tracking phase? A 6.2 cm x 10.2 cm computer-generated Efron block (Efron, 1969) was rotated about its COM at one of two speeds (50 deg/s and 30 deg/s). After a delay of 3.5 seconds, participants received a “go” tone and reached out and “grasped” the block. Upon contact with the screen, the block stopped its rotation. Gaze fixations during the delay, and during the reach itself, favoured the left half of the block, above the COM, suggesting that every time a new “top edge” of the block rotated clockwise into position, it became the salient fixation point. These results suggest that anticipated grasping positions on a rotating object are constantly changing due to kinematic constraints (e.g. wrist extension) as the block rotates. Meeting abstract presented at VSS 2015
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".