Do social preferences matter in lexical retuning?
Bibliographic record
Abstract
In perceiving spoken language, listeners not only recognize and comprehend the intended meaning of the speaker’s words and phrases; they also assess the social dimensions of the speaker. In the present study, we ask whether perceptual learning — a process by which listeners adapt to novel pronunciations — is modulated by listeners’ social preferences. To this end, we use a novel accent exhibiting a back vowel lowering chain shift in pleasant and unpleasant conditions in a lexically guided perceptual learning paradigm to test whether listeners adapt less to the unpleasant guise. Experiment 1 confirms that listeners disprefer the unpleasant guise. Using a lexical decision task as a measure of lexical adaptation, Experiment 2 indicates that listeners in the pleasant and unpleasant guises learned the back vowel shift compared to listeners in a control group. These results suggest that exposure to a voice with lower social prestige does not inhibit lexical adaptation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".