Why do older adults have difficulty following conversations?
Bibliographic record
Abstract
Age-related declines in understanding conversation may be largely a consequence of perceptual rather than cognitive declines. B. A. Schneider, M. Daneman, D. R. Murphy, and S. Kwong-See (2000) showed that age-related declines in comprehending single-talker discourse could be eliminated when adjustments were made to compensate for the poorer hearing of older adults. The authors used B. A. Schneider et al.'s methodology to investigate age-related differences in comprehending 2-person conversations. Compensating for hearing difficulties did not eliminate age-related differences when the 2 talkers were spatially separated by 9 degrees or 45 degrees azimuth, but it did when the talkers' contributions came from one central location. These findings suggest that dialogue poses more of a problem for older than for younger adults, not because of the additional cognitive requirements of having to follow 2 talkers rather than 1, but because older adults are not as good as younger adults at making use of the auditory cues that are available for helping listeners perceptually segregate the contributions of 2 spatially separated talkers.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".