Motor planning in a cup manipulation task: Does the task context influence end-state comfort?
Bibliographic record
Abstract
It is generally understood that each hand is specialized for certain aspects of movement, the preferred hand for limb trajectory dynamics (i.e., mobilizing) and the non-preferred hand for positional control (i.e., stabilizing). In this study we questioned whether differences between the hands persist in motor planning, in particular, with respect to end-state comfort. Adults typically demonstrate uncomfortable start postures to facilitate comfortable end postures, with a recent study revealing more end-state comfort with the right-hand regardless of hand preference (Janssen et al., 2011). As well, the kinematics of a reach-to-grasp movement can be affected by task context; however, the kinematics of end-state comfort have yet to be examined. Therefore, right- and left-handed adults (Mean age= 25.1) were asked to pick up a cup (i.e., upright or overturned) as if to pour water in four conditions (i.e., pantomime, pantomime using image/cup as guide, grasp cup) with the preferred and non-preferred hand. Results revealed right-handers displayed end-state comfort in all conditions with both hands, whereas a subset of left-handers failed to demonstrate end-state comfort. These results contrast with those previously reported (right-hand advantage) in the literature. Although no kinematic differences were revealed according to hand preference or hand use, analyses indicated reaction time was fastest in the pantomime condition. As individuals received full set of instructions prior to ‘go’ signal, they did not need to adjust their movements based on object presented (i.e., cup or picture). Results will be discussed in light of the extant literature pertaining to handedness and end-state comfort.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".