Hemispheric Specialization for the Visual Control of Action Is Independent of Handedness
Bibliographic record
Abstract
The idea that visually guided action is independent of visual perception has been supported by neurological, neuropsychological, and behavioral studies. In healthy subjects, evidence for this distinction has come from psychophysical studies of the effects of visual illusions on perceptual judgments and object-directed grasping. This evidence is limited, however, by the fact that virtually all studies have involved right-handed subjects using their dominant hand, which is presumably controlled by the left hemisphere. There is tentative evidence from earlier neurological studies that the left hemisphere may in fact play a special role in the integration of visual and motor information during grasping. We designed two experiments to test this idea. The first experiment involved pictorial illusions, which are known to have robust effects on perceptual judgments but little influence on grasping. Right- and left-handed subjects reached out and grasped objects embedded in two different visual illusions with either their dominant or their nondominant hand. For both right- and left-handed subjects, precision grasping with the left hand, but not with the right, was affected by the illusions. In a follow-up experiment, we examined precision grasping in a more natural setting and showed that left-handed subjects use their nondominant (right) hand significantly more as compared with right-handed subjects. We conclude that visuomotor mechanisms encapsulated in the left hemisphere play a crucial role in the visual control of action and that this hemispheric specialization evolved independently of handedness.
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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 | 0.001 |
| 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 | 0.001 |
| 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".