Healthy Older Observers Cannot Use Biological-Motion Point-Light Information Efficiently within 4 m of Themselves
Bibliographic record
Abstract
Healthy aging is associated with a number of perceptual changes, but measures of biological-motion perception have yielded conflicting results. Biological motion provides information about a walker, from gender and identity to speed, direction, and distance. In our natural environment, as someone approaches us (closer distances), the walker spans larger areas of our field of view, the extent of which can be underutilized with age. Yet, the effect of age on biological-motion perception in such real-world scenarios remains unknown. We assessed the effect of age on discriminating walking direction in upright and inverted biological-motion patterns, positioned at various distances in virtual space. Findings indicate that discrimination is worse at closer distances, an effect exacerbated by age. Older adults' performance decreases at distances as far away as 4 m, whereas younger adults maintain their performance as close as 1 m (worse at 0.5 m). This suggests that older observers are limited in their capacity to integrate information over larger areas of the visual field and supports the notion that age-related effects are more apparent when larger neural networks are required to process simultaneous information. This has further implications for social contexts where information from biological motion is critical.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".