Long-term memory for voices frees up cognitive resources and enhances speech perception in noise
Bibliographic record
Abstract
When masked by competing speech, utterances are more intelligible when spoken by a familiar voice than by a novel one. If familiar voices are less cognitively demanding to perceive, a concurrent task, which consumes resources, should disrupt perception of speech less if the attended voice is familiar. Furthermore, if the intelligibility benefit occurs because listening to a familiar voice is less cognitively demanding, then the concurrent task should reduce the familiar voice benefit. Participants (N=30) heard two concurrent closed-set sentences in two different voices (familiar-novel or novel-novel) and reported the content of one (target) while ignoring the other (masker). Simultaneously, participants tracked the location of four moving dots on a screen (multiple object tracking, dual task) or ignored the visual input (single task). Word report was highest when the target voice was familiar, lowest when the masker voice was familiar, and intermediate when both voices were novel. Concurrent MOT performance reduced intelligibility of the novel, more than the familiar, voice, but did not reduce the magnitude of the benefit, suggesting that familiar voices require fewer cognitive resources to process than unfamiliar ones but that this reduction may not directly facilitate the familiar-voice benefit.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".