Are cognitive researchers ignoring their senses? The problem of sensory deficit in cognitive aging research
Bibliographic record
Abstract
Sensory impairments are common in older adult populations and have notable impacts on aging outcomes. Relationships between sensory and cognitive functions have been clearly established, though the mechanisms underlying those relationships are not fully understood. Given the growing burden of dementia, older adults with sensory deficits are an important and growing population to study in cognitive aging research. Yet, cognitive research sometimes excludes those with uncorrected significant/severe sensory deficits and often poorly or inconsistently assesses those deficits. Observational and interventional studies that exclude participants with sensory deficits will be limited in their generalizability to the narrower subset of the older adult population without vision or hearing impairment and may be missing an opportunity to study a growing population of older adults at higher risk of cognitive impairment. Strategies exist for adapting cognitive testing instruments, and inroads could be made into collecting normative data to inform ongoing research. Bringing together psychometricians with researchers who specialize in vision and hearing impairments could launch highly innovative research on both measurement methods and cognitive disease etiology, as sensory organs provide readily accessible neuronal and vascular beds that may show pathology earlier and elucidate innovative screening opportunities for early signs of cognitive disease.
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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 | 0.531 | 0.659 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.019 | 0.052 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".