Examining the Effect of Age on Visual–Vestibular Self-Motion Perception Using a Driving Paradigm
Bibliographic record
Abstract
Previous psychophysical research has examined how younger adults and non-human primates integrate visual and vestibular cues to perceive self-motion. However, there is much to be learned about how multisensory self-motion perception changes with age, and how these changes affect performance on everyday tasks involving self-motion. Evidence suggests that older adults display heightened multisensory integration compared with younger adults; however, few previous studies have examined this for visual-vestibular integration. To explore age differences in the way that visual and vestibular cues contribute to self-motion perception, we had younger and older participants complete a basic driving task containing visual and vestibular cues. We compared their performance against a previously established control group that experienced visual cues alone. Performance measures included speed, speed variability, and lateral position. Vestibular inputs resulted in more precise speed control among older adults, but not younger adults, when traversing curves. Older adults demonstrated more variability in lateral position when vestibular inputs were available versus when they were absent. These observations align with previous evidence of age-related differences in multisensory integration and demonstrate that they may extend to visual-vestibular integration. These findings may have implications for vehicle and simulator design when considering older users.
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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 | 0.001 |
| 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) | 0.003 | 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".