Native and non-native perception of phonemic length contrasts in Japanese: Effect of identification training and exposure
Bibliographic record
Abstract
Japanese distinguishes between words by the presence or absence of several types of mora phonemes, often realized as a contrast in segment duration, e.g., /hato/ (pigeon) versus /hat:o/ (hat). Several studies suggest that such contrasts are difficult for English listeners to perceive [e.g., R. Oguma, in Japanese-Language Education Around the Globe, 2000, Vol. 10, pp. 43–55]. In this study we investigated the effect on the perceptual ability of both Japanese exposure and perceptual identification training. Three groups of subjects were tested: (1) English speakers with no Japanese experience; (2) English speakers who had spent 1–6 months in Japan; and (3) native Japanese speakers. Subjects participated in a forced-choice identification task in which they heard words and nonwords produced by Japanese speakers and identified which word they heard by choosing among items that minimally differed with respect to these contrasts. Additionally, subjects in the second group underwent five days of perceptual training during which they received immediate feedback, repeating trials until they responded correctly. Results suggest that although the overall performance was relatively high, identification accuracy improved with exposure to Japanese and with perceptual identification training. Implications of this work on theories of second-language learning will be discussed. [Work supported by TAO, Japan.]
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".