Cascading activation in phonological planning and articulation: Evidence from spontaneous speech errors
Bibliographic record
Abstract
Speaking involves both retrieving the sounds of a word (phonological planning) and realizing these selected sounds in fluid speech (articulation). Recent phonetic research on speech errors has argued that multiple candidate sounds in phonological planning can influence articulation because the pronunciation of mis-selected error sounds is slightly skewed towards unselected target sounds. Yet research to date has only examined these phonetic distortions in experimentally-elicited errors, leaving doubt as to whether they reflect tendencies in spontaneous speech. Here, we analyzed the pronunciation of speech errors of English-speaking adults in natural conversations relative to matched correct words by the same speakers, and found the conjectured phonetic distortions. Comparison of these data with a larger set of experimentally-elicited errors failed to reveal significant differences between the two types of errors. These findings provide ecologically-valid data supporting models that allow for information about multiple planning representations to simultaneously influence speech articulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".