Fixation stability during the performance of a high-precision manipulation task
Bibliographic record
Abstract
Numerous studies have shown a stereotypical hand-eye temporal coordination pattern during the performance of reaching and grasping movements. Typically the eyes fixate on the object prior to the initiation of hand movement, which provides high acuity information about the object, such as its shape, orientation or material properties. The information extracted while fixating on the object is important for programming grip aperture and grasp forces, contributing to efficient execution of manipulation tasks. However, every attempt at steady fixation consists of small involuntary eye movements: microsaccades, ocular drift, and tremor. Fixation stability, quantified using bivariate contour elliptical area (BCEA), is reduced during monocular compared to binocular viewing during a visual fixation task (Gonzalez et al 2012). Since grasping is also disrupted during monocular viewing, in this study we examined fixation stability during the performance of a high-precision manual task. Fifteen visually-normal adults grasped a small bead, and placed it on a vertical needle while their eye movements were recorded binocularly. BCEA was calculated for fixations on the bead and the needle, in each viewing condition. In contrast to the hypothesis, fixation stability was significantly better with the dominant eye when fixating on the bead during monocular viewing while there was no difference between the binocular and non-dominant eye viewing conditions. A correlation analysis between BCEA and the performance of grasping and placement tasks showed that better fixation stability was associated with a significantly shorter duration of the grasping and placement actions during monocular viewing. These results indicate that fixation stability contributes to the performance of high-precision manipulation tasks when binocular vision is removed. Meeting abstract presented at VSS 2016
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".