From lips to hand: How images of lip postures can facilitate hand movements
Bibliographic record
Abstract
Humans often perform concurrent mouth and hand movements during physical activity (e.g., contracting orofacial features during a power lift). Indeed, a recent study has shown that the orbicularis oris, a lip muscle involved in lip puckering, shows increased activity during various hand gestures (Higginbotham et al., 2008). Given the bidirectional nature of the relationship between hand and mouth movements (e.g., Gentilucci et al., 2001), it is unclear if the observation of various lip / mouth postures can also facilitate hand movements (i.e., decrease reaction time (RT) relative to baseline). Twenty participants were exposed to images of lip and mouth postures, including lip puckered (kiss) or pressed (smile) or mouth open or closed. In the experimental condition, these images were randomly presented, followed by a fixation cross (green or blue) that prompted participants to perform either a precision or power grip. A control condition involved images devoid of orofacial features. Results indicated a significant decrease in power grip RTs with the "lip puckering" image, relative to its control image. In contrast, no significant reduction in hand grip RTs were found for the other lip/ mouth postures. These findings provide evidence that some lip postures can facilitate the initiation of a hand grip, which further demonstrate the tight coupling between these two effectors.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".