The effects of phonotactic probability on auditory recognition of pseudo-words
Bibliographic record
Abstract
A number of speech perception studies have been carried out to investigate how we process audio signals containing real words. However, comparatively fewer studies have been conducted looking at how we process audio signals containing phonotactically valid pseudo-words. Many traditional metrics, such as lexical frequency, used as predictors in this kind of analysis are difficult or impossible to calculate for pseudo-words, but other metrics like phonotactic probability can be calculated for such pseudo-words. Phonotactic probability is the likelihood that a particular sequence of phones occurs in a particular language, and it can be easily calculated using existing pronunciation and lexical frequency data. The present study uses the CMU Pronouncing Dictionary and the Google Ngram datasets to calculate phonotactic probability measures for each stimulus in an existing set of English auditory lexical decision responses and then statistically models the influence of phonotactic probability on the participants’ reaction times to the stimuli. This modeling shows a general trend of response times decreasing as phonotactic probability increases. The results are then framed in the greater picture of speech perception and word recognition overall.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".