Eye-hand coordination: Differential effects of object shape and surface properties on fixation and grasp locations
Bibliographic record
Abstract
Previous research has highlighted the importance of object shape and center of mass (COM) position in grasp location selection. For example, during a stable grasp, a subject’s "grasp line" will fall close to or across an object’s COM, with grasp locations shifting as a function of small COM changes. However, this research has typically utilized objects where the COM and the horizontal centre are very close in proximity, and has also ignored the characterization of fixation locations. In Experiment 1, we explored fixation and grasp location sensitivity to COM changes across object shape. The COM of objects was dissociated from the object’s horizontal centre at three different distances (0.5cm, 1cm and 1.5cm). Results showed that orientation of COM was the largest influence on grasp locations, with no differences in grasp positions as a function of COM distance. Fixation locations were found to be more sensitive to changes in COM. Fixations to objects with COM distances of 1cm and 1.5cm were significantly further away from fixation locations to objects with the COM closest to the object’s horizontal center (0.5cm). In Experiment 2, we kept the shape of the objects constant but varied the visual characteristics of the objects with gradients of luminosity (greyscale), numerosity (more to less stars), or area coloured (1/2, 1/3[sup]rd[/sup], or 1/6[sup]th[/sup] of the object). Results showed a significant influence of the surface properties on grasp locations but not fixation positions. A rightward grasp bias was observed when grasping the greyscale and star objects. On the other hand, objects with 1/3[sup]rd[/sup] and 1/6[sup]th [/sup]of the surface area covered, biased grasp positions away from the coloured areas. Results from the current studies demonstrate how changes in object shape or changes in the local elements within a shape can differentially affect fixation and grasp locations when picking up an object. Meeting abstract presented at VSS 2013
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".