Bilingual phonology in dichotic perception: A case study of Malayalam and English voicing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it