Evidence for the use of rotational optic flow cues for locomotor steering in healthy older adults
Bibliographic record
Abstract
Optic flow is a powerful visual cue for the control of locomotion. Considerable research has focused on how healthy young people use and perceive optic flow. However, little is known on how older adults use this type of visual motion to control walking. The purpose of this study is to investigate the ability of young and older adults to adjust their physical walking trajectory in response to a rotation of the optic flow presented in a virtual environment. Ten healthy young adults (mean age 23.49 ± 4.72 yr) and 10 healthy older adults (mean age 76.22 ± 3.11 yr) participated in the study. Subjects were instructed to walk straight in a virtual environment viewed within a head-mounted display unit as they walked overground for 5 m, while the focus of expansion was gradually rotated to the left or the right by 40°. All subjects responded with a similar strategy by rotating their head and body in the direction away from the orientation of the perturbation. The younger subjects achieved almost complete corrections and had very small net heading errors. In contrast, the older adults had delayed and smaller reorientations, particularly in the head, thus showing significantly larger heading errors compared with younger subjects. We conclude that older adults retain the ability to use optic flow to control their walking trajectory, although smaller, delayed head rotations and larger heading errors may indicate an age-dependent effect on sensorimotor coordination.
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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 | 0.003 |
| 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) | 0.003 | 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".