Individual Differences in Categorical Judgment of L2 Stops: A Link to Proficiency and Acoustic Cue-Weighting
Bibliographic record
Abstract
This study investigated individual differences in Korean adult learners’ categorical perception of L2 English stops with an aim to explore the relationship of gradient categorizations to perceptual sensitivity to acoustic cues and L2 proficiency. Korean young adult L2 learners of English ( N = 49) participated in two speech perception tasks (visual analog scaling and forced-choice identification) in which they listened to English voiced and voiceless stops and Korean lax and aspirated stops with Voice Onset Time (VOT) and F0 manipulated to form a continuum. It was found that in both L1 and L2 stop perception, listeners’ gradient category judgment was associated with greater reliance on language-specific redundant cues (i.e., F0 in L2 English and VOT in L1 Korean) and that in the perception of L2 stops, categorical listeners who tended to be less sensitive to F0 were the ones with a higher level of L2 English proficiency. The results suggest that the categorical manner of judging L2 stops reflects learners’ better knowledge of L2-specific acoustic cue-weightings, based on which less relevant acoustic information is effectively suppressed.
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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 | 0.001 | 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".