Priming and sentence context support listening to noise-vocoded speech by younger and older adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Older adults are known to benefit from supportive context in order to compensate for age-related reductions in perceptual and cognitive processing, including when comprehending spoken language in adverse listening conditions. In the present study, we examine how younger and older adults benefit from two types of contextual support, predictability from sentence context and priming, when identifying target words in noise-vocoded sentences. In the first part of the experiment, benefit from context based on primarily semantic knowledge was evaluated by comparing the accuracy of identification of sentence-final target words that were either highly predictable or not predictable from the sentence context. In the second part of the experiment, benefit from priming was evaluated by comparing the accuracy of identification of target words when noise-vocoded sentences were either primed or not by the presentation of the sentence context without noise vocoding and with the target word replaced with white noise. Younger and older adults benefited from each type of supportive context, with the most benefit realized when both types were combined. Supportive context reduced the number of noise-vocoded bands needed for 50% word identification more for older adults than their younger counterparts.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it