Adaptation to haptic target size highlights the hierarchical nature of grasp planning
Bibliographic record
Abstract
We used a grasp-adaptation paradigm to test two models of grasp planning: a ‘classic’ model in which the target is coded separately as size and position, and an integrated model that postulates the target is coded as a set of egocentric ‘grasp points’. Participants (N=144) reached, without visual feedback, for virtual targets and grasped real (haptic) ones whose sizes were either the same, larger, or smaller. Two adaptation sequences were administered, each comprised of a series of baseline, adaptation, and then washout trials. On baseline and washout trials, the virtual and haptic objects were identical. On adaptation trials, the target's haptic size was either always larger or always smaller than its virtual size. Consistent with prior work, we observed strong aftereffects in the first (control) sequence. We administered a second sequence to test if the aftereffect would generalize to a novel target orientation and/or position while preserving target size. Notably, the classic model implies that the aftereffect should generalize under such circumstances. In contrast, the integrated model implies the aftereffect should fail to generalize, because a novel target position and/or orientation requires a novel set of grasp points. Surprisingly, generalization depended on the direction of the grasp aperture’s induced adjustment and, therefore, neither model can fully accommodate our findings. Specifically, the aftereffect induced by the larger haptic object generalized to novel target position and/or orientation, whereas the aftereffect induced by the smaller haptic object did not. In the latter condition, novel target orientations resulted in the strongest attenuation of aftereffects. Considering the asymmetric consequences of under- vs. over-sizing grasp aperture, our findings suggest grasp planning prioritizes haptic size when compensating for an under-sized grasp aperture. When under-sizing is not applicable, however, haptic integration is subsumed under de novo coding of the orientation of the hand opposition space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".