Effects of material properties and object orientation on precision grip kinematics
Bibliographic record
Abstract
Successfully picking up and handling objects requires taking into account their physical properties (e.g., material) and position relative to the body. Such features are often inferred by sight, but it remains unclear to what extent observers vary their actions depending on the perceived properties. To investigate this, we asked participants to grasp, lift and carry cylinders to a goal location with a precision grip. The cylinders were made of four different materials (Styrofoam, wood, brass and an additional brass cylinder covered with Vaseline) and were presented at six different orientations with respect to the participant (0°, 30°, 60°, 90°, 120°, 150°). Analysis of their grasping kinematics revealed differences in timing and spatial modulation at all stages of the movement that depended on both material and orientation. Object orientation affected the spatial configuration of index finger and thumb during the grasp, but also the timing of handling and transport duration. Material affected the choice of local grasp points and the duration of the movement from the first visual input until release of the object. We find that conditions that make grasping more difficult (orientation with the base pointing toward the participant, high weight and low surface friction) lead to longer durations of individual movement segments and a more careful placement of the fingers on the object.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".