Listen to your mother: Highly familiar voices facilitate perceptual segregation
Bibliographic record
Abstract
We studied the effect of voice familiarity on the ability to segregate one voice from a competing speaker. Specifically, we examine the utility of arguably the most familiar voice of all—the mother’s voice—in facilitating segregation, and compared it to the effect of a voice that listeners had been familiarized with in the laboratory. We tested 19 older adolescents (still living at home) on a version of the coordinate-response-measure procedure (CRM; Bolia et al., 2001), with mixtures of two voices, at three signal-to-noise ratios (Ő3 dB, 0 dB, + 3 dB). Performance was better when the mother’s voice was the target, compared both to novel and lab-familiar targets. At the most disadvantageous target-to-masker ratio (Ő3 dB), listeners were also better able to ignore their mother’s voice so as to comprehend a stranger’s voice more effectively, demonstrating that extremely familiar voice information facilitates segregation. This pattern of results is similar to that observed with older people (aged 44–59) when their spouse’s voice was present in a two-voice CRM mixture (Johnsrude et al., 2013). The new results demonstrate the importance of long-term (rather than short-term) familiarity and show that it aids sound segregation for adolescents as well as older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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) | 0.002 | 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".