Vision and touch used for catching small balls with a power grip and large balls with a precision grip supports dual visuomotor channel theory
Bibliographic record
Abstract
Abstract The dual visuomotor channel theory of grasping posits that distinct neural pathways mediate hand shaping in response to a target’s extrinsic (e.g., location) and intrinsic (e.g., size and shape) properties. To evaluate this theory, we examined grasp behavior in human participants as they caught balls of four diameters (2.5–9 cm) thrown toward them. Hand shaping during catching was compared with that observed during the pickup of stationary balls and the interception of rolling balls. Kinematic measures included digit opposition distance (thumb pad to index finger pad) and prehension span (digit pad to palm distance), obtained using electromagnetic sensors, 3D video capture, and frame-by-frame video analysis. Participants displayed significantly greater hand opening when catching thrown balls than when interacting with static or rolling balls. Nonetheless, the maximum pregrasp aperture (MPA), contact grasp aperture (CGA), and terminal grasp aperture (TGA) scaled proportionally with ball size across all conditions. Ball size further influenced grasp type: small thrown balls were caught with power grips, while larger balls were caught with precision grips. In contrast, precision grips were used consistently when picking up stationary balls or grasping intercepting rolling ones. In the catching condition, grasp type and the trajectory of digit closure were also affected by the location of ball to hand contact. These findings support the dual visuomotor channel theory by demonstrating that anticipatory hand opening reflects target location, whereas grip selection reflects target size. Moreover, the modulation of grasp type and digit closure by tactile contact suggests that somatosensory input may operate within a dual-channel framework analogous to that of vision.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".