Exploring individual differences in native phonetic perception and their link to nonnative phonetic perception.
Bibliographic record
Abstract
Adults differ considerably in their perception of both native and nonnative phonemes. For instance, when presented with continua of native phonemes on two-alternative forced choice (2AFC) or visual analog scaling (VAS) tasks, some people show sudden changes in responses (i.e., steep identification slopes) and others show gradual changes (i.e., shallow identification slopes). Moreover, some adults are more successful than others at learning unfamiliar phonemes. The predictors of these individual differences and the relationships between them are poorly understood. It also remains unclear to what extent different tasks (2AFC vs. VAS) may reflect distinct individual differences in perception. In two experiments, we addressed these questions by examining the relationships between individual differences in performance on native and nonnative phonetic perception tasks. We found that shallow 2AFC identification slopes were not related to shallow VAS identification slopes but were related to inconsistent VAS responses. Additionally, our results suggest that consistent native perception may play a role in promoting successful nonnative perception. These findings help characterize the nature of individual differences in phonetic perception and contribute to our understanding of how to measure such differences. This work also has implications for encouraging successful acquisition of new languages in adulthood. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".