Hand Dominance Increases During Concurrent Bimanual Tracking: The Role of Gaze Contingencies and Visual Display
Bibliographic record
Abstract
ObjectiveTo examine the effect of dual tasking on hand dominance during a bimanual visuomotor task.BackgroundMany operators need to perform separate tasks with each hand. Yet, there is no comprehensive study examining whether the right-hand visuomotor advantage found in right handers remains stable, increases or attenuates when another task is performed concurrently with the other hand.MethodsTwenty-eight right-handed participants (mean age = 22) performed 2D visuomotor tracking under either unimanual (one target, one hand) or bimanual conditions (two targets, one for each hand). Various gaze contingencies and visual displays were tested. Tracking performance of each hand was evaluated through the mean cursor-target distance.ResultsA clear right-hand advantage was found under all unimanual conditions. Under bimanual conditions, tracking accuracy decreased for both hands albeit more extensively for the left hand than the right when gaze was free, thus amplifying the above right-hand advantage. Prioritization of the right hand was associated with a gaze preference toward this hand. However, this increase in manual asymmetry was greatly alleviated when participants were instructed to fixate straight ahead, a benefit obtained at no cost in terms of overall tracking performance.ConclusionsDuring bimanual/dual tracking, there is a natural tendency for right handers to prioritize their right hand. However, this effect is strongly reduced by fixating straight ahead.ApplicationPerforming separate tasks with the right and left hands is common when piloting an aircraft. Fixating straight ahead may be useful for pilots that seek to divide more equally the negative impact of dual/bimanual tasking.
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 | 0.004 |
| 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 | 0.001 |
| 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".