Individual differences in perceptual adaptation to phonetic categories: Categorization gradiency and cognitive abilities
Bibliographic record
Abstract
We examine whether listeners flexibly adapt to unfamiliar speech patterns such as those encountered in foreign-accented English vowels. In these cases, the relative informativity of acoustic dimensions (spectral quality vs. duration) can be changed such that the most informative dimension (spectral quality) is no longer informative, but the role of the secondary cue (duration) is enhanced. We further test whether listeners’ adaptive strategies are related to individual differences in utilizations of secondary cues (measured by categorization gradiency) and cognitive abilities. Native English listeners (N = 36) listened to continuum of vowels /ɛ/ and /æ/ (as in head and had) varying spectral and duration values to complete a perceptual adaptation task, a visual analogue scaling (VAS) task, and were given cognitive ability tasks examining executive function capacities. Results showed that listeners mostly used spectral quality to signal vowel category at baseline, but rapidly adapted by up-weighting reliance on duration when spectral quality was no longer informative. The VAS task showed substantial individual differences in categorization gradiency with more gradient listeners using a secondary cue more, but gradiency was not linked to degree of adaptation. Finally, results of cognitive ability tasks revealed that individual differences in inhibitory control, but not the other cognitive abilities, correlated with the amount of perceptual adaptation.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".