Age Differences in Visual-Auditory Self-Motion Perception during a Simulated Driving Task
Bibliographic record
Abstract
Recent evidence suggests that visual-auditory cue integration may change as a function of age such that integration is heightened among older adults. Our goal was to determine whether these changes in multisensory integration are also observed in the context of self-motion perception under realistic task constraints. Thus, we developed a simulated driving paradigm in which we provided older and younger adults with visual motion cues (i.e., optic flow) and systematically manipulated the presence or absence of congruent auditory cues to self-motion (i.e., engine, tire, and wind sounds). Results demonstrated that the presence or absence of congruent auditory input had different effects on older and younger adults. Both age groups demonstrated a reduction in speed variability when auditory cues were present compared to when they were absent, but older adults demonstrated a proportionally greater reduction in speed variability under combined sensory conditions. These results are consistent with evidence indicating that multisensory integration is heightened in older adults. Importantly, this study is the first to provide evidence to suggest that age differences in multisensory integration may generalize from simple stimulus detection tasks to the integration of the more complex and dynamic visual and auditory cues that are experienced during self-motion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".