Discrimination of Second Language Vowel Contrasts and the Role of Phonological Short-Term Memory and Nonverbal Intelligence
Bibliographic record
Abstract
Although extensive research has focused on the perceptual abilities of second language (L2) learners, a significant gap persists in understanding how cognitive functions like phonological short-term memory (PSTM) and nonverbal intelligence (IQ) impact L2 speech perception. This study sets out to investigate the discrimination of L2 English monophthongal vowel contrasts and to assess the effect of PSTM and nonverbal IQ on L2 speech perception. The participants consisted of adult monolingually-raised Greek speakers, who completed an AX discrimination test, a digit span test, and a nonverbal intelligence test. A control group of English speakers also completed the AX test. Data were analyzed using Bayesian regression models. The results revealed that Greek speakers exhibited below chance discrimination for the majority of L2 vowel contrasts, consistently underperforming in comparison to the control group. Intriguingly, the study did not provide substantial evidence in favor of more accurate discrimination of L2 contrasts by Greek participants with high PSTM compared to those with low PSTM. However, the study yielded compelling evidence indicating that Greek participants with higher IQ demonstrated superior accuracy in discriminating most L2 contrasts compared to their lower IQ counterparts. The limited influence of PSTM on speech perception suggests the need for further exploration, considering the potential impact of test methodologies and the intricate interplay of other confounding factors. Furthermore, the study uncovers a noteworthy relationship between nonverbal IQ and L2 speech perception, likely linked with the association of high IQ with enhanced attentional capacities, information processing abilities, and learning skills-all of which are pivotal for accurate speech perception.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".