Lower limb selection in a preferential reaching task: The influence of lateralization and balance control
Bibliographic record
Abstract
Previous investigation and research (Bryden et al., 2001) has shown that when objects of interest are placed in either left or right hemispace, adult preferred hand use is found to typically decrease as they perform actions in the hemispace that is contralateral to their preferred hand. The purpose of this investigation was to explore the effects of changes to balance control during a similar lower limb reaching task. We hypothesized that the preferred limb would be that which is closest to the illuminated light, and would therefore be selected most often to complete these reaching tasks. Participants (n=10, 18-25 years) performed a preferential lower limb reaching task while seated, standing on flat surface, and standing on a foam surface. Seven Fit Lights were arranged in a semicircular configuration (0, +30, +60, +90, degrees) on the ground directly in front of the participant. Each light illuminated 5 times in a random order, and the participants were instructed to reach using their preferred foot to extinguish the light. Foot kinematics were recorded using the NDI Optotrak System. A two-way (condition x light location) repeated measure ANOVA was conducted. Results indicated no significant differences in limb selection for the seated and standing conditions, however the foam pad condition resulted in participants having a strong preference to balance consistently on their dominant foot, while using their non-dominant foot to reach the illuminated lights. In conclusion, balance control has a moderate influence on lower limb selection as the participants are challenged to complete the limb selection task with a higher degree of balance manipulation.
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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 | 0.000 | 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".