Reaction Time, Speech Recognition, and Verbal Memory Performance: Nonnative Versus Native English Speakers
Bibliographic record
Abstract
Purpose: Previous studies have debated differences in spoken language processing between nonnative and native English speakers, often yielding varying and sometimes contradictory results. To address these discrepancies, we employed a comprehensive battery of tasks to compare auditory, speech, and memory processing between nonnative and native English language speakers (NELS). Method: The study included 70 university students aged 18–35 years, comprising 29 nonnative and 41 native monolingual NELS of both genders. Participants were assessed on auditory and visual reaction time (RT), speech recognition, and verbal memory tasks under two conditions: quiet and multitalker babble noise (0 dB). Results: In sentence recognition ( p < .001) and word memory tests, the nonnative group performed worse than the native group under both quiet ( p = .001) and noisy ( p = .002) conditions. Both groups demonstrated a significant reduction in word recognition scores and slower RTs for auditory and visual modules in noise compared to quiet conditions ( p < .001). Conclusions: The study yielded two primary findings. First, background noise negatively impacts all levels of auditory processing, from RT to speech recognition and memory recall. Second, nonnative students experience more difficulty in speech perception and memory performance in their second language, English, in both quiet and noisy conditions. These findings underscore the importance of providing additional support to facilitate learning and academic growth for nonnative English language students.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".