Left hemisphere advantage in the visual processing of graspable objects
Bibliographic record
Abstract
n a series of grasping studies using visual illusions we showed that regardless of handedness, right hand but not left hand grip apertures are accurately scaled to the size of an object. Grip apertures in the left hand, reflected the perceived (illusory) and not the real size of the target (Gonzalez et al., 2006, 2008). We advanced the hypothesis that the left hemisphere is specialized for visually-guided grasping and that this specialization was independent of handedness. Grasping requires the integration of a visual and a motor component, however. Here we investigate if there are hemispheric asymmetries in encoding visual properties of objects that could later explain the left hemisphere/right hand advantages in visually-guided grasping. We presented pictures of graspable objects, defined as objects that could be picked up with one hand (e.g. coin, apple, etc) and non-graspable objects, defined as tangible objects that could not be picked up with one hand (i.e. car, piano, etc) selectively to the right (RVF) or left (LVF) visual fields. Graspable/non-graspable objects and RVF/LVF presentation were randomized over the trials. Right- and left-handed participants quickly pressed a button with the index finger if the object on the screen was of a graspable object and pressed a different button with the middle finger if it was of a non-graspable object. Responding fingers and starting hands were counterbalanced among participants. Overall, reaction times were shorter for graspable objects than for non-graspable objects. Furthermore, there was a RVF (left hemisphere) advantage (shorter reaction times) but only for graspable objects. No visual field difference was detected for non-graspable objects. These findings were the same for right and left-handers. The results strongly suggest that the processing of graspable objects regardless of handedness is more efficient in the left hemisphere and this might help explain asymmetries in visually-guided grasping.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".