Delayed Phonological Encoding in Stuttering: Evidence from Eye Tracking
Bibliographic record
Abstract
Stuttering is a multifactorial disorder that is characterized by disruptions in the forward flow of speech believed to be caused by differences in the motor and linguistic systems. Several psycholinguistic theories of stuttering suggest that delayed or disrupted phonological encoding contributes to stuttered speech. However, phonological encoding remains difficult to measure without controlling for the involvement of the speech-motor system. Eye-tracking is proposed to be a reliable approach for measuring phonological encoding duration while controlling for the influence of speech production. Eighteen adults who stutter and 18 adults who do not stutter read nonwords under silent and overt conditions. Eye-tracking was used to measure dwell time, number of fixations, and response time. Adults who stutter demonstrated significantly more fixations and longer dwell times during overt reading than adults who do not stutter. In the silent condition, the adults who stutter produced more fixations on the nonwords than adults who do not stutter, but dwell-time differences were not found. Overt production may have resulted in additional requirements at the phonological and phonetic levels of encoding for adults who stutter. Direct measurement of eye-gaze fixation and dwell time suggests that adults who stutter require additional processing that could potentially delay or interfere with phonological-to-motor encoding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".