The use of exemplars differs between native and non-native listening
Bibliographic record
Abstract
Abstract This study compares the role of exemplars in native and non-native listening. Two English identity priming experiments were conducted with native English, Dutch non-native, and Spanish non-native listeners. In Experiment 1, primes and targets were spoken in the same or a different voice. Only the native listeners showed exemplar effects. In Experiment 2, primes and targets had the same or a different degree of vowel reduction. The Dutch, but not the Spanish, listeners were familiar with this reduction pattern from their L1 phonology. In this experiment, exemplar effects only arose for the Spanish listeners. We propose that in these lexical decision experiments the use of exemplars is co-determined by listeners’ available processing resources, which is modulated by the familiarity with the variation type from their L1 phonology. The use of exemplars differs between native and non-native listening, suggesting qualitative differences between native and non-native speech comprehension processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".