The use of visual information in non-native speech sound discrimination across the first year of life
Bibliographic record
Abstract
Infants are able to match seen and heard speech even in non-native languages, and familiarization to audiovisual speech appears to affect subsequent auditory-only discrimination of non-native speech sounds (Danielson et al., 2013; 2014). However, the robustness of these behaviors appears to change rapidly within the first year of life. In this current set of studies, conducted with six-, nine-, and 10-month-old English-learning infants, we examine the developmental trajectory of audiovisual speech perception of non-native speech sounds. In the first place, we show that the tendency to detect a mismatch between heard and seen speech sounds in a non-native language changes across this short period in development, in tandem with the trajectory of auditory perceptual narrowing (Werker & Tees, 1984; Kuhl et al., 1992; inter alia). Furthermore, we demonstrate that infants’ familiarization to matching and mismatching audiovisual speech affects their auditory speech perception differently at various ages. While six-month-old infants’ auditory speech perception appears to be malleable in the face of prior audiovisual familiarization, this tendency declines with age. The current set of studies is one of the first to utilize traditional looking-time measurements while also employing pupillometry as a correlate of infants’ acoustic change detection (Hochmann & Papeo, 2014).
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".