Head engagement during visuomotor tracking is determined by postural demands and aging
Bibliographic record
Abstract
Abstract Vision is important for various tasks, from visually tracking moving objects to maintaining balance. People obtain visual information through eye movements performed either alone or in combination with head movements. Even when isolated eye movements can accommodate the amplitude of the desired gaze shift, humans still perform head movements, as they provide additional sensory signals that can be integrated with retinal input resulting in improved gaze estimates. However, head movements also create mechanical torques and attenuate vestibular processing that could disturb balance. We, therefore, here examined whether head engagement is determined by postural requirements when performing a visual tracking task. Young participants visually tracked a target moving horizontally along different amplitudes, while they were seated, standing on a firm and an unstable surface. Our results showed stronger head engagement when standing than sitting, but no systematic differences were found between firm and unstable surfaces. To further explore the interplay between head engagement and postural demands, we conducted a second experiment where young and older participants performed a similar task, but now they were either allowed to move their head or instructed to limit their head movements. Both tracking accuracy and postural sway increased when engaging the head. When asked to limit head movements, both age groups engaged their head minimally, but head movements were more pronounced in more challenging postures. When allowed to move their head naturally, younger participants engaged their head more when standing than sitting, but older adults reduced their head movements with more demanding postures. We suggest that head movements in younger adults facilitate visual tracking, while limited head movements in older adults preserve balance.
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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 | 0.000 | 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) | 0.002 | 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".