Object and Task Features Influence Visual and Sensorimotor Integration in Grasping Tasks
Bibliographic record
Abstract
From simple tasks like reaching for your morning cup of coffee to more complex activities like driving or playing sports, reach-to-grasp movements are integral to many daily activities. Although seemingly simple, reaching to grasp an object is a complex behavior requiring multiple sensory inputs for successful execution. This behavior can be broken down into three different phases: planning, reaching, and grasping. During the planning phase, information about the object's location and on the features of the object and task are necessary for appropriate motor planning (Betti et al., 2018; Guo and Niemeier, 2024). We process visual information on the object's features, such as its shape and size, to identify potential grasp points and estimate its weight. The task features include information on which specific effector (right hand, left hand, or both) and grasp orientation (clockwise and counterclockwise) would be used (Guo and Niemeier, 2024). In the reaching and grasping phases, the brain integrates the visual information gathered during the planning phase with motor commands, enabling precise hand movements toward the object (Klein et al., 2023). This integration of visual and motor processes is known as visuomotor computation, where the visual properties of the object are aligned with the motor actions required to execute the grasp (Klein et al., 2023). This visuomotor integration ensures the appropriate trajectory for reaching, and the correct grip and load forces are applied to successfully lift and retrieve the object (Klein et al., 2023; Guo and Niemeier, 2024). Despite the importance of visuomotor integration of both object and task features, these features are often studied in isolation. For instance, in one study, researchers focused solely on how grasp orientation and the alignment of handles on objects like beer mugs influence grasping behavior (Bub and Masson, 2010 … Correspondence should be addressed to Rana Abdelhalim at rana.abdelhalim{at}mail.mcgill.ca.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".