Articulation and grasping: On the relationship between speech production and manual movement control in adults
Bibliographic record
Abstract
Many investigations, particularly in children with speech and language impairments, have highlighted the motor nature of speech. Children with Specific Language Impairment (SLI), for example, show both speech-motor and generalized motor abnormalities (DiDonato Brumbach & Goffman, 2014). The connection between fine motor control and speech functions is likely mediated by brain lateralized networks in the left hemisphere that support both speech production and fine motor control of the right hand (Gonzalez et al., 2014). The goal of this study is to examine the interaction between speech production and motor skills in individuals with different hand preference. Thirty-four undergrad students (15 left-handers) participated in a study in which they were asked to perform a variety of speech and manual tasks. The speech tasks include a picture naming task, a diadochokinetic (DDK) task, and a tongue twister task. The motor tasks involve building a Lego brick model and the Peabody Pegboard Test (Desrosiers etal., 1995). Participants' speech and actions were recorded and the audio and videos were acoustically and perceptually analyzed. Preliminary analysis revealed a positive relationship between people’s Pegboard performance using their right hand and their DDK performance as well as a negative relationship between Lego building time and tongue twister error rate, again only when people using their right hand. The results indicate the complex relationship between speech articulation, fine manual movement control, and handedness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".