The influence of individual differences in language experience on lexical stress cue-weighting: native and non-native listeners
Bibliographic record
Abstract
Learning to process the prosody of a second language can be challenging, particularly when the languages present different prosodic structures, as is the case for English and French. Although previous studies suggested that French listeners are unable to process lexical stress, more recent work suggests that they can, although they might assign a different weight to F0 and duration as stress cues compared to native listeners. To determine if this is the case, forty-two English-French bilinguals participated in two experiments investigating the impact of individual differences in language experience on F0 and duration weight when perceiving lexical stress. Interestingly, participants' language experience could predict the weight assigned to F0 and duration as cues to lexical stress in the behavioral task from Experiment 1, but not the event-related potentials of Experiment 2. Together, these results suggest that prosodic learning involves learning to assign the (language-specific) appropriate weight to non-language-specific acoustic-prosodic cues.
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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 | 0.000 | 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 | 0.000 |
| 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".