The Goldilocks Zone of Perceptual Learning
Bibliographic record
Abstract
BACKGROUND/AIMS: Lexically guided perceptual learning in speech is the updating of linguistic categories based on novel input disambiguated by the structure provided in a recognized lexical item. We test the range of variation that allows for perceptual learning by presenting listeners with items that vary from subtle within-category variation to fully remapped cross-category variation. METHODS: Experiment 1 uses a lexically guided perceptual learning paradigm with words containing noncanonical /s/ realizations from s/ʃ continua that correspond to "typical," "ambiguous," "atypical," and "remapped" steps. Perceptual learning is tested in an s/ʃ categorization task. Experiment 2 addresses listener sensitivity to variation in the exposure items using AX discrimination tasks. RESULTS: Listeners in experiment 1 showed perceptual learning with the maximally ambiguous tokens. Performance of listeners in experiment 2 suggests that tokens which showed the most perceptual learning were not perceptually salient on their own. CONCLUSION: These results demonstrate that perceptual learning is enhanced with maximally ambiguous stimuli. Excessively atypical pronunciations show attenuated perceptual learning, while typical pronunciations show no evidence for perceptual learning. AX discrimination illustrates that the maximally ambiguous stimuli are not perceptually unique. Together, these results suggest that perceptual learning relies on an interplay between confidence in phonetic and lexical predictions and category typicality.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".