Does a stop bias exist in infant consonant manner-of-articulation perception?
Bibliographic record
Abstract
Phoneme inventories are biased favoring stop over fricative consonants. A similar bias is evident in acquisition. For example, an asymmetrical pattern was observed when infant word learning was assessed using the switch task with stop-initial and fricative-initial minimal pair CVC nonsense syllables (Altvater-Mackensen & Fikkert, 2010). In this task, Dutch-learning fourteen-month-olds noticed a fricative to stop change but failed to detect a stop to fricative change. These findings were interpreted in terms of phonological representations emerging in early lexical development. In this study, we tested English and French infants aged 4-5 months to determine whether they show a perceptual bias favoring stop manner. We presented CVC nonsense syllables - /bas/ and /vas/- in a preference task using the look-to-listen procedure. The /b-v/ contrast is phonemic in English and French. Infants listened significantly longer to /bas/ than to /vas/ trials (p = .004). This perceptual preference cannot be explained in terms of phonological representations in young infants who are not yet producing stops or fricatives and have almost no receptive vocabulary. We will discuss this phonetic bias in light of adult data showing similar perceptual asymmetries and consider the implications for the development of infant speech processing and early word learning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".