Visual–vestibular integration during self-motion perception in younger and older adults.
Bibliographic record
Abstract
Younger adults integrate visual and vestibular cues to self-motion in a manner consistent with optimal integration; however, little is currently known about whether this process changes with older age. Our objective was to determine whether older adults, like younger adults, display evidence of optimal visual-vestibular integration, including reductions in bimodal variance (Visual + Vestibular) compared with unimodal variance (visual or vestibular alone), and reliability-based cue weighting. We used a motion simulator and a head-mounted display to introduce a 2-interval forced-choice heading estimation task. Older (65+ years) and younger adults (18-35 years) judged which of two movements was more rightward. Movements consisted of vestibular cues (passive movement in darkness), visual cues (optic flow), or both cues combined. The combined condition contained either congruent cues or incongruent cues (either a subtle 5° or larger 20° conflict). Results demonstrated that older adults had less reliable visual heading estimates than younger adults but comparable vestibular heading estimates. During combined, congruent conditions, both age groups exhibited reductions in combined variance, consistent with predicted optimal integration. During subtle cue conflicts, only younger adults exhibited combined variance consistent with predicted optimal integration, but both age groups displayed reliability-based cue weighting. During larger spatial conflicts, neither group demonstrated optimal reductions in variance. Younger adults displayed reliability-based cue weighting but older adults' heading estimates were biased toward the less reliable visual estimate. Older adults' tendency to incorporate spatially conflicting and unreliable visual cues into their self-motion percept may affect their performance on mobility-related tasks like walking and driving. (PsycINFO Database Record
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".