Testing the storage of prosody-induced phonetic detail via auditory lexical decision
Bibliographic record
Abstract
Abstract This article reports the results of an auditory lexical decision task, testing the processing of phonetic detail of English noun/verb conversion pairs. The article builds on recent findings showing that the frequent occurrence in certain prosodic environments may lead to the storage of prosody-induced phonetic detail as part of the lexical representation. To investigate this question with noun/verb conversion pairs, ambicategorical stimuli were used that exhibit systematic occurrence differences with regard to prosodic environment, as indicated by either a strong verb-bias, e.g., talk (N/V) or a strong noun-bias, e.g., voice (N/V). The auditory lexical decision task tests whether acoustic properties reflecting either the typical or the atypical prosodic environment impact the processing of recordings of the stimuli. In doing so assumptions about the storage of prosody-induced phonetic detail are tested that distinguish competing model architectures. The results are most straightforwardly accounted for within an abstractionist architecture, in which the acoustic signal is mapped onto a representation that is based on the canonical pronunciation of the word.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".