Stepping over obstacles: Are older adults' perceptual judgments consistent with their actions?
Bibliographic record
Abstract
Obstacle avoidance includes: stepping over, around or through obstacles. To effectively avoid collisions individuals must use both ventral (perception) and dorsal (action) visual streams. Visual perception guides action and over a lifespan, action capabilities change. The objective was to determine whether these changes were a result of perceptual changes. To test this, we had 15 participants over 60 years of age perform an obstacle avoidance task. At the start of the trial the participants stood approximately 5m away from an obstacle and were asked to elevate their foot to their perceived height of the obstacle. Following this judgment, participants were instructed to walk one meter while looking at the object and make a second perceptual judgment of the same obstacle. After this second judgment, they were asked to step over the obstacle with the same foot used during the two initial perceptual judgments. There were three obstacle heights used (1.5, 10, 20 cm), which were representative of real world obstacles such as a curb height, stair height, and transition from carpet to hardwood such that results can be directly related to behaviours in real settings. The participants performed two blocks (free to look at foot during perceptual judgement and not allowed to look at foot) of 18 (3 obstacle heights x 2 obstacle locations x 3 trials) randomized trials. Preliminary results showed that only three trials were unsuccessful. Of the successful trials participants appear to use a similar toe elevation height for both the 10 and 20cm obstacle. This inability to properly scale toe elevation to obstacle height was reflected in their variable perceptual judgments. This finding suggests that older adults' perceptions and actions are different from those of young adults (Patla & Goodale, 1996), suggesting that older adults do not correctly couple perception with action.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".