Delayed Stream Segregation in Older Adults
Bibliographic record
Abstract
In Brief Objective: To determine whether the time course for the buildup of auditory stream segregation differs between younger and older adults. Design: Word recognition thresholds were determined for the first and last keywords in semantically anomalous but syntactically correct sentences (e.g., “A rose could paint a fish”) when the target sentences were masked by speech-spectrum noise, 3-band vocoded speech, 16-band vocoded speech, intact and colocated speech, and intact and spatially separated speech. A significant reduction in thresholds from the first to the last keyword was interpreted as indicating that stream segregation improved with time. Results: The buildup of stream segregation is slowed for both age groups when the masker is intact, colocated speech. Conclusions: Older adults are more disadvantaged; for them, stream segregation is also slowed even when a speech masker is spatially separated, conveys little meaning (3-band vocoding), and vocal fine structure cues are impoverished but envelope cues remain available (16-band vocoding). Older and younger adults repeated words in nonsense sentences. Speech-spectrum noise was the control masker; there were four comparison two-talker speech maskers (co-located, spatially separated, 3-band and 16-band noise-vocoded). Performance was measured as the 50% correct SNR threshold for sentence-initial and sentence-final keywords. For both age groups, performance improved across keywords when the target and masker were co-located and acoustically similar (intact two-talker speech), but not when the masker was speech-spectrum noise. Unlike younger adults, older adults’ performance also improved with sentence position when the masker was noise-vocoded or spatially separated, indicating age-related slowing in stream segregation in these conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".