The native-language benefit for talker identification is robust in 7.5-month-old infants.
Bibliographic record
Abstract
Adults recognize talkers better when the talkers speak a familiar language than when they speak an unfamiliar language. This language familiarity effect (LFE) demonstrates the inseparable nature of linguistic and indexical information in adult spoken language processing. Relatively little is known about children's integration of linguistic and indexical information in speech. For example, to date, only one study has explored the LFE in infants. Here, we sought to better understand the maturation of speech processing abilities in infants by replicating this earlier study using a more stringent experimental design (eliminating a potential voice-language confound), a different test population (English- rather than Dutch-learning infants), and a new language pairing (English vs. Polish rather than Dutch vs. Italian or Japanese). Furthermore, we explored the language exposure conditions required for infants to develop an LFE for a formerly unfamiliar language. We hypothesized based on previous studies (including the perceptual narrowing literature) that infants might develop an LFE more readily than would adults. Although our findings replicate those of the earlier study-demonstrating that the LFE is robust in 7.5-month-olds-we found no evidence that infants need less language exposure than do adults to develop an LFE. We concluded that both infants and adults need extensive (potentially live) exposure to an unfamiliar language before talker identification in that language improves. Moreover, our study suggests that the LFE is likely rooted in early emerging phonology rather than shared lexical knowledge and that infants already closely resemble adults in their processing of linguistic and indexical information. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".