Bilingual phonology in dichotic perception: A case study of Malayalam and English voicing
Bibliographic record
Abstract
Listeners often experience cocktail-party situations, encountering multiple ongoing conversations while tracking just one. Capturing the words spoken under such conditions requires selective attention and processing, which involves using phonetic details to discern phonological structure. How do bilinguals accomplish this in L1-L2 competition? We addressed that question using a dichotic listening task with fluent Malayalam-English bilinguals, in which they were presented with synchronized nonce words, one in each language in separate ears, with competing onsets of a labial stop (Malayalam) and a labial fricative (English), both voiced or both voiceless. They were required to attend to the Malayalam or the English item, in separate blocks, and report the initial consonant they heard. We found that perceptual intrusions from the unattended to the attended language were influenced by voicing, with more intrusions on voiced than voiceless trials. This result supports our proposal for the feature specification of consonants in Malayalam-English bilinguals, which makes use of privative features, underspecification and the “standard approach” to laryngeal features, as against “laryngeal realism”. Given this representational account, we observe that intrusions result from phonetic properties in the unattended signal being assimilated to the closest matching phonological category in the attended language, and are more likely for segments with a greater number of phonological feature specifications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".