Gesture production during stuttered speech: insights into the nature of gesture–speech integration
Bibliographic record
Abstract
Introduction Some of our clearest insights into how the mind constructs language come from the investigation of challenges to the sensory, motor, and/or neural mechanisms of the human brain. The study of stroke and diseases of the central nervous system has led to enormous, but still incomplete, knowledge about the neural architecture of human language. Investigation of the sign languages that spontaneously arise among individuals who are deaf has revolutionized psycholinguistic theory by demonstrating that human language capacity transcends sensory and motor modality. The studies we describe here follow in this long research tradition. We have been investigating the gesture–speech relationship in individuals with chronic stuttering in order to gain insights into the nature of the relationship between the two in spontaneous expression. Stuttering, the involuntary and excessive repetition of syllables, sounds, and sound prolongations while speaking, is highly disruptive to the production of speech. This provides us with an opportunity to observe what happens to the temporal patterning of gesture against the backdrop of a fractionated speech stream. Our studies have garnered striking evidence that gesture production is, and moreover must be, integrated with speech production at a deep, neuromotor planning level prior to message execution. The expressive harmony of gesture patterning relative to speech patterning is so tightly maintained throughout the frequent and often lengthy speech disruptions caused by stuttering that it suggests a principle of co-expression governing gesture–speech execution (Mayberry, Jaques & Shenker 1999).
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".