Multisensory Integration of Native and Nonnative Speech in Bilingual and Monolingual Adults
Bibliographic record
Abstract
Face-to-face speech communication is an audiovisual process during which the interlocuters use both the auditory speech signals as well as visual, oral articulations to understand the other. These sensory inputs are merged into a single, unified process known as multisensory integration. Audiovisual speech integration is known to be influenced by many factors, including listener experience. In this study, we investigated the roles of bilingualism and language experience on integration. We used a McGurk paradigm in which participants were presented with incongruent auditory and visual speech. This included an auditory utterance of 'ba' paired with visual articulations of 'ga' that often induce the perception of 'da' or 'tha', a fusion effect that is strong evidence of integration, as well as an auditory utterance of 'ga' paired with visual articulations of 'ba' that often induce the perception of 'bga', a combination effect that is weaker evidence of integration. We compared fusion and combination effects on three groups ( N = 20 each), English monolinguals, Spanish-English bilinguals, and Arabic-English bilinguals, with stimuli presented in all three languages. Monolinguals exhibited significantly stronger multisensory integration than bilinguals in fusion effects, regardless of the stimulus language. Bilinguals exhibited a nonsignificant trend by which greater experience led to increased integration as measured by fusion. These results held regardless of whether McGurk presentations were presented as stand-alone syllables or in the context of real words.
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.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".