Bibliographic record
Abstract
Aging is associated with changes in how our brains combine sensory information when perceiving self-motion. Despite age-related changes in sensory integration, little is known about whether multisensory self-motion perception changes in older adults (OAs). Understanding such changes is important since OAs are particularly vulnerable to errors during self-motion, which can increase their risk of injury (e.g., when walking, driving). Vestibular cues are very important for self-motion perception, yet how vestibular perception changes with older age and age-related sensory declines is understudied. Therefore, in Chapter 2, I explored whether vestibular perceptual thresholds differ between healthy OAs (i.e., no sensory/cognitive decline) and younger adults (YAs), for two different motion types (heave and pitch). Thresholds were measured using two different perceptual tasks: 1) detection task, and 2) discrimination task. Postural stability was also assessed. OAs demonstrated higher (worse) detection thresholds than YAs for both motions. Larger postural sway in OAs was also associated with higher vestibular thresholds. Age-related hearing loss (ARHL) is highly prevalent in OAs and is associated with increased falls risk. Therefore, using the same paradigm as Chapter 2, in Chapter 3 I evaluated whether higher vestibular perceptual thresholds are observed in individuals with ARHL than those with normal hearing. Here, OAs with ARHL showed higher pitch discrimination thresholds than those with normal hearing. Hearing loss in the low-frequency ranges also predicted worse pitch detection. Given that older age (Chapter 2) and ARHL (Chapter 3) were shown to predict poorer self-motion perception, in Chapter 4 I evaluated whether self-motion perception could be improved with training. Specifically, I trained OAs and YAs on a visual-vestibular heading-discrimination task. While OAs showed poorer overall precision than YAs, both groups showed improved precision post-training for the sensory condition with the lowest pre-training precision (visual-only). A sub-group of OAs who initially could not perform the visual heading task demonstrated greatly improved performance post-training. Collectively, I show that while healthy aging and common age-related sensory declines may be associated with poorer self-motion perception, training can potentially be used to improve these abilities. Together, these results may have implications for informing fall/collision prevention strategies.
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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 | 0.000 | 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) | 0.003 | 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".