Practice makes perfect, but only with the right hand: Sensitivity to perceptual illusions with awkward grasps decreases with practice in the right but not the left hand
Bibliographic record
Abstract
It has been proposed that the visual mechanisms that control well-calibrated actions, such as picking up a small object with a precision grip, are neurally distinct from those that mediate our perception of the object. Thus, grip aperture in such situations has been shown to be remarkably insensitive to many size-contrast illusions. But most of us have practiced such movements hundreds, if not thousands of times. What about less familiar and unpracticed movements? Perhaps they would be less likely to be controlled by specialized visuomotor mechanisms and would therefore be more sensitive to size-contrast illusions. To test this idea, we asked right-handed subjects to pick up small objects using either a normal precision grasp (thumb and index finger) or an awkward grasp (thumb and ring finger), in the context of the Ponzo illusion. Even though this size-contrast illusion had no effect on the scaling of the precision grasp, it did have a significant effect on the scaling of the awkward grasp. Nevertheless, after three consecutive days of practice, even the awkward grasp became resistant to the illusion. In a follow-up experiment, we found that awkward grasps with the left hand (in right handers) did not benefit from practice and remained sensitive to the illusion. We conclude that the skilled target-directed movements are controlled by visual mechanisms that are quite distinct from those controlling unskilled movements, and that these specialized visuomotor mechanisms may be lateralized to the left hemisphere.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".