The Effects of Bilingualism on Speech Evoked Brainstem Responses Recorded in Quiet and in Noise
Bibliographic record
Abstract
The main objective of the present study was to investigate the effect of sensory enrichment, such as bilingualism, on the subcortical processing in quiet and adverse listening conditions such as in the presence of noise. More specifically, the aim of this investigation was to identify some neural biomarkers at brainstem level distinguishing bilinguals from monolinguals. Forty-one 18- to 25-year-old adults participated in the study: 19 monolinguals and 22 bilinguals. Their language fluency was assessed with the Language Experience and Proficiency (LEAP) questionnaire. Auditory Brainstem Responses (ABRs) were recorded using click and speech /da/ stimuli in quiet and also in noise for the latter. No significant differences between the two groups were observed for click-evoked ABR. The speech-evoked ABR transient waves (V, C) and the periodic region (D and F) latencies were longer for the monolinguals compared to the bilingual group. The Frequency Following Responses (F0 and F1) of the speech-evoked ABR were similar for the two groups in quiet and in noise. Results suggested that monolinguals need more time to process speech stimuli than their bilingual peers. Early in the auditory system, the neural responses related to speech processing in the absence or the presence of background noise seem to be less resilient when compared to those of adults who are fluent in two languages. Bilingualism could stimulate the automatic sound processing abilities of the auditory system in a way that makes it highly efficient. Furthermore, this study demonstrated the applications of speech-ABR and its potential usefulness as a clinical biomarker.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".