Exposure to an unfamiliar language bolsters talker learning
Bibliographic record
Abstract
Listeners are better at identifying talkers who speak their native language than talkers who speak a foreign language, showing that phonological knowledge of a language facilitates talker identification. However, research with infants indicates that language comprehension is not necessary for improving talker identification. In this study, we asked whether language exposure alone could improve talker learning. Two groups of English-monolingual adults were recruited: one group from Montréal, Québec, who receive regular French exposure, and the other from Storrs, Connecticut who receive no French exposure. In Experiment 1, we used a four-alternative forced choice task (4AFC) to train listeners about the voices of four English talkers and four French talkers. Results show that Montréal participants were faster at learning French voices than Storrs participants, showing that exposure to a foreign language is sufficient in boosting talker learning in that language. However, in Experiment 2, a 2AFC was used to train participants, and no significant differences were found between groups. These findings show that varying the training paradigm in laboratory analogs of talker identification can induce different types of talker learning. Taken together, our results suggest that phonological sensitivity contributes to listeners’ talker identification abilities, but only under certain training contexts.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".