Grasp orientation varies with local object features
Bibliographic record
Abstract
In the current investigation, task-irrelevant local features of an object were manipulated during a reach-to-grasp task. Specifically, 3 cm diameter circular discs, were grasped with surface features manipulated from trial-to-trial. Local feature conditions were a) solid white, b) solid black; lines oriented through clock positions: c) 8 and 2; d) 10 and 4; e) 12 and 6, f) 9 and 3.The lines were 1 mm thick and spaced 3 mm apart.The position of the wrist, index finger and thumb were tracked using the OptoTrak (Northern Digital Inc.). Main dependent measures were grip aperture, peak grip aperture, grip orientation; movement kinematics: Peak Acceleration and velocity. The finding most relevant to the current experiment was the sensitivity of grip formation to the presence and angle of the lines. In control conditions (i.e., solid white and black) individuals generally grasped the object at 35° from horizontal. While for conditions where line features were involved, grip angles increased as a function of line condition, to the order of i) 8 and 2: 40°; ii) 12 and 6: 41°; iii) 10 and 4: 45°; iv) 9 and 3: 49°. Therefore, orientations involving a greater angle magnitude from horizontal resulted a greater grasp angle. Overall, the findings suggest that the control of grasping may also be dictated by local features (often formed into global composites). When considered within the canon of dedicated streams for perception and action, this would suggest a dorsal stream that is 'feature tuned' to the local features of the object.Acknowledgments: NSERC, CFI, BCKDF
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".