Listening in aging adults: From discourse comprehension to psychoacoustics.
Bibliographic record
Abstract
Older adults, whether or not they have clinically significant hearing loss, have more trouble than their younger counterparts understanding speech in everyday life. These age-related difficulties in speech understanding may be attributed to changes in higher-level cognitive processes such as language comprehension, memory, attention, and cognitive slowing, or to lower-level sensory and perceptual processes. A complicating factor in determining how these sources might contribute to age-related declines in speech understanding is that they are highly correlated. Experimenters have typically focused either on cognitive declines or sensory declines in artificially optimized test conditions. In contrast, our approach focuses on the complex interactions between age-related changes in cognitive and perceptual factors that affect spoken language comprehension, especially in nonideal, realistic conditions. In this article, we describe our attempts to systematically investigate sensory-cognitive interactions in controlled experimental situations. We begin by looking at experimental conditions that closely approximate everyday listening, and show that older adults do indeed experience deficits in spoken language comprehension relative to younger adults in these conditions. We then review further experiments designed to isolate more precisely the cognitive and perceptual sources of these age-related differences and how they vary with listening condition. In large part, we find that age-related changes in speech understanding are a consequence of auditory declines.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".