Visualization of viewing strategies for grasping a rotating target
Bibliographic record
Abstract
Grasping a rotating target necessitates that visuomotor strategies be repeatedly updated as new grasp sites come into play. Our previous investigations have shown that we do not simply track a single location on a rotating target prior to grasping it. Rather, gaze fixations continually relocate to the top edge of the target. Tracking strategies vary depending upon the speed of the target's rotation, with faster speeds resulting in fixations closer to the centre of mass of the target (Leferink & Marotta, 2015). In order to gain further understanding of viewing strategies, visualizations were employed to evaluate at which orientations of the target did gaze fixation relocate, and how speed and direction of target rotation influenced the distributions of gaze on the target. A 2D rectangular target was rotated at one of 4 speeds around its centre of mass, in either direction. After a delay of 3.5s, participants reached out and 'grasped' the target. A heat map analysis indicated the likelihood of fixating the target through probabilistic distributions of gaze location, from which the differences between the distributions at certain time points and across the conditions were evaluated. Characterizations of various viewing strategies suggest that distributions of gaze location were influenced by kinematic and oculomotor constraints differently across the target conditions. Slower rotational speeds allowed for the tracking of a 'graspable' position on the target, whereas faster speeds increased the task difficulty resulting in gaze locating more frequently near the target's centre of mass. An interaction between the direction and speed of target rotation showed that the direction of rotation was more influential during slower speeds of rotation. The results also suggest that a common viewing strategy for grasping a rapidly moving target is to utilize parafoveal vision in order to adapt to oculomotor constraints. Meeting abstract presented at VSS 2017
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".