Is there an Own-Age Advantage in Talker Recognition?
Bibliographic record
Abstract
Adults are far better at identifying adult talkers than child talkers (e.g., Cooper et al., 2020). Why is this the case? Are child talkers acoustically less distinguishable (e.g., Lee et al., 1999)? Or perhaps adults better identify adult talkers, while children better identify child talkers (e.g., see Anastasi & Rhodes, 2005, for evidence that an own-age advantage exists in face recognition). Here, we test adults (N=72) and 6.5-year-olds (N=71) on a voice identification task featuring single word recordings by 8 children and 8 adults. While all listeners successfully identified all talkers above chance (ps<.05), adults significantly outperformed children with both adult (Madult=0.66 vs. Mchild=0.54; p<.001) and child talkers (Madult=0.64 vs. Mchild=0.57; p<.05). Thus, we find no evidence of an own-age advantage in talker recognition. Rather, we find additional evidence that adults are more skilled than children in voice identification tasks, suggesting that talker recognitions skills take time to fully mature.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.010 |
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; both teacher heads agree on what is shown here.
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".