Multiple object handling: exploring strategies for cumulative grasping and transport using a single hand
Bibliographic record
Abstract
Humans can cumulatively grasp multiple objects and then transport them using a single hand (e.g., when clearing up tableware). This skill, which we refer to as 'multiple object handling', helps minimise the number of actions required to transport objects. However, it also presents unique challenges for the sensorimotor system, including the use of grips other than finger-thumb opposition (almost always used for grasping single objects). In the present work we explored the strategies used for multiple object handling, particularly focussing on object selection and grip choice. Participants were presented with pairs of objects, asked to grasp one of them and then, without placing that object down, grasp the second object and transport both to a designated location. We examined the order in which participants selected objects and the grips used for grasping and holding them. Results provide preliminary evidence for a typical approach to multiple object handling. We observed that when two objects were grasped cumulatively for transport, finger-thumb opposition was almost always used to grasp the first object, which was then frequently held using an atypical grip (e.g., finger-finger or finger-palm opposition). Finger-thumb opposition was almost always used once again to grasp the subsequent object. Participants preferred to grasp objects with lower mass or surface area first, potentially facilitating this approach. In sum, this work provides insight into a technique commonly used for efficient object transport.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".