How do words compete? Quantifying lexical competition with acoustic distance
Bibliographic record
Abstract
Research on speech perception and lexical access often uses the activation and competition metaphor to describe the process of spoken word recognition. One way of expressing competition associated with a given word is its phonological neighborhood density, which is a calculation of similarity. The present study uses acoustic distance as an alternative to phonological neighborhood density to measure lexical competition during speech perception. The quantification of a word's lexical competition is given by what is termed its acoustic distinctiveness, which is taken as its average acoustic distance to all other words in the lexicon. A variety of possible abstract acoustic representations for items in the lexicon are analyzed. Statistical modeling shows that acoustic distinctiveness has a similar effect as phonological neighborhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density, regardless of the abstract representation used. Acoustic distinctiveness, however, does not explain all the same things as phonological neighborhood density. Potential theoretical implications of acoustic distinctiveness's usefulness in statistical and psycholinguistic models are discussed.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".