Effect of Visual Information on Dominant and Non-dominant Hands During Bimanual Drawing with a Robotic Platform
Bibliographic record
Abstract
In a stable bimanual trajectory tracing task with interlimb spatial and temporal synchrony, blocking the visual information from one hand may alter the performance of either hand. In this paper, we investigate the effect of visual information on motor behaviour of dominant and non-dominant hands during a bimanual task, with a focus on motor lateralization theory's anticipation for a more pronounced distortion on one hand due to visual information withdrawal. To address this question, four bimanual circle tracing experiments were designed with two rehabilitation robotic arms with real time visual feedback. Two experiments were conducted under the free-visual condition whereas the visual feedback from one hand was blocked for the other two. The in-depth analysis of the metrics extracted from 685 circles, drawn by 6 participants, revealed that non-dominant hand, when visible, generally performs worse than the dominant hand, for instance it exhibits less circularity. In their invisible modes, the performance of the dominant and non-dominant hands displayed inconsistent difference across the participants. Moreover, both hands showed a higher pace when partial visual information was available. Our findings using this robotic framework as a systematic tool on developing new paradigms are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".