Is it the dominant or ipsilateral eye that contributes to online visuomotor control the most
Bibliographic record
Abstract
Using the dominant limb (Apker et al., 2015) or gathering visual information from the dominant eye (Manzone et al., 2018) is advantageous for the control of goal-directed action. Further, participants were better able to correct for an imperceptible target jump during right limb trajectories when brief visual samples were provided to the ipsilateral (i.e., right) eye relative to their initial hand position (i.e., rightward home position) compared to contralateral combinations (e.g., left eye, rightward home position; Loria et al., 2019). But, movements were only performed with the dominant right hand and all participants were right eye dominant. Therefore, it is unclear whether the ipsilateral combination of eye, home position and hand or the coupling of the dominant eye and hand contributes to the corrections. In the current study, participants performed left-handed and right-handed reaches toward an imperceptibly jumped target from home positions located to the left and right of the midline. All participants were right hand and eye dominant. After movement onset, only a brief visual sample was provided to the left and/or right eye. This created two ipsilateral conditions: one with the dominant eye and hand (i.e., ipsilateral dominant) and one without (i.e., ipsilateral non-dominant). Significant corrections toward the jumped target position were found in the ipsilateral dominant condition (i.e., right hand, eye, home) but not the ipsilateral non-dominant condition (i.e., left hand, eye, home). This suggests that it is indeed the coupling of only the dominant eye and hand in ipsilateral space that contributes to online visuomotor control.Acknowledgments: University of Toronto, Ontario Research Fund, Canadian Foundation for Innovation, National Sciences and Engineering Research Council
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".