The role of binocular vision in the control and development of visually guided upper limb movements
Bibliographic record
Abstract
Vision provides a key sensory input for the performance of fine motor skills, which are fundamentally important to daily life activities, as well as skilled occupational and recreational performance. Binocular visual function is a crucial aspect of vision that requires the ability to combine inputs from both eyes into a unified percept. Summation and fusion are two aspects of binocular processing associated with performance advantages, including more efficient visuomotor control of upper limb movements. This paper uses the multiple processes model of limb control to explore how binocular viewing could facilitate the planning and execution of prehension movements in adults and typically developing children. Insight into the contribution of binocularity to visuomotor control also comes from examining motor performance in individuals with amblyopia, a condition characterized by reduced visual acuity and poor binocular function. Overall, research in this field has advanced our understanding of the role of binocular vision in the development and performance of visuomotor skills, the first step towards developing assessment tools and targeted rehabilitation for children with neurodevelopment disorders at risk of poor visuomotor outcomes. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".