The Speech Focus Position Effect on Jaw–Finger Coordination in a Pointing Task
Bibliographic record
Abstract
PURPOSE: This article investigates jaw-finger coordination in a task involving pointing to a target while naming it with a CVCV (e.g., /papa/) versus CVCV (e.g., /papa/) word. According to the authors' working hypothesis, the pointing apex (gesture extremum) would be synchronized with the apex of the jaw-opening gesture corresponding to the stressed syllable. METHOD: Jaw and finger motions were recorded using Optotrak (Northern Digital, Waterloo, Ontario, Canada). The effects of stress position on jaw-finger coordination were tested across different target positions (near vs. far) and different consonants in the target word (/t/ vs. /p/). Twenty native Portuguese Brazilian speakers participated in the experiment (all conditions). RESULTS: Jaw response starts earlier, and finger-target alignment period is longer for CVCV words than for CVCV ones. The apex of the jaw-opening gesture for the stressed syllable appears synchronized with the onset of the finger-target alignment period (corresponding to the pointing apex) for CVCV words and with the offset of that period for CVCV words. CONCLUSIONS: For both stress conditions, the stressed syllable occurs within the finger-target alignment period because of tight finger-jaw coordination. This result is interpreted as evidence for an anchoring of the speech deictic site (part of speech that shows) in the pointing gesture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".