Adaptive locomotion and self-sampled vision: The effects of age and task complexity
Bibliographic record
Abstract
This study investigated the effects of environment complexity and aging on adaptive locomotion under voluntary sampled vision. Eight young and eight old adults, independent walkers with normal or corrected-to-normal vision, volunteered to participate. They walked at a self-selected pace and pointed with the dominant foot at the center of a target flush to the ground followed by the post targeting task (PTT): walking or stair climbing. Intermittent vision was sampled as needed during approach via a hand-held momentary switch connected to a pair of Plato LCD goggles (Translucent Technology, Toronto, ON). A six-camera Visualeyez motion analysis system (PTI, Burnaby, BC) captured kinematic data. The approach footfall variability revealed a two-phase approach with adjustments spread out over the last three steps regardless of experimental manipulations. Older adults decreased the approach velocity as the PTT complexity increased and had, overall, more accurate target pointing as compared to young adults. Intermittent vision resulted in longer cumulative distance over the three adaptive steps and larger cumulative distance variability for the complex PTT only. Intermittent vision condition resulted in longer last step length and swing duration as well as increased last step length and last step stance duration variability. Vision was necessary only for 42% of the approach. Visual samples coincided with the last two steps; the common sampling strategy had one or two samples.
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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 | 0.003 |
| 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) | 0.001 | 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".