What do comparisons between younger and older adult listeners tell us about speech processing?
Bibliographic record
Abstract
The perception of speech, the recognition of words, and the understanding of spoken language involve the dynamic and interactive processing of cues provided by the incoming signal and information stored in memory. Even when accuracy is high, the relative contributions of bottom-up and top-down processes may explain variations in the speed and effort required when listening to speech. Listening is fast when the quality of the incoming signal is optimal, but it is slowed as signal quality is reduced. Likewise, listening can be speeded when expectations constrain the likely alternatives or when priming implicitly facilitates the recognition of the signal, whereas it can be slowed if the context is incongruent with the signal or if context is used to resolve ambiguities or repair misperceptions in a compensatory fashion. Within-subjects comparisons on off-line and on-line measures in different listening conditions, including simulations of auditory aging and hearing loss, are used to investigate how listening effort varies and how listening is speeded or slowed depending on signal-driven and knowledge-driven factors. Comparisons between younger and older participants are used to evaluate how long-standing reductions in auditory temporal processing and compensatory changes in brain organization may alter how signal-driven and knowledge-driven processes interact.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".