Effect of priming on age-related differences in release from informational masking.
Bibliographic record
Abstract
In a previous study, [Freyman et al. (2004)] showed that presenting listeners with all but the last word of a target nonsense sentence immediately prior to presenting the full sentence in a noisy background, produced a greater release from masking when the masker was two-talker nonsense speech than when it was speech-spectrum noise, thereby demonstrating that an auditory prime could produce a release from informational masking. In Experiment 1 of this study we showed that auditory priming produced an equivalent amount of release from informational masking in good-hearing younger and older adults. To investigate the extent to which this release from informational masking was due to the semantic content of the prime, in Experiment 2 we noise-vocoded the prime (using three bands) to remove semantic content, while retaining the prime’s amplitude envelope. This manipulation eliminated any release from informational masking. In Experiment 3, when the speech masker, but not the prime was vocoded, the performance of both age groups improved equivalently. These results indicate that younger and older adults benefit equally from semantic priming, and that both age groups make equivalent use of amplitude fluctuations in a masker in an informational masking paradigm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".