Distinct visual resolution supports aperture shaping in natural and pantomime-grasping.
Bibliographic record
Abstract
Pantomime-grasping is a "simulated" motor response wherein an individual grasps to an area dissociated from a physical target. The task has been used in the apraxia literature as a proxy for natural grasping (i.e., physically grasping a target); however, it is important to recognize that the task's decoupled spatial relations between stimulus and response renders the top-down processing of target features (e.g., size) that accumulating evidence has shown to be mediated by visual information functionally distinct from natural grasping. Here, we examined whether the visual information supporting pantomime-grasps exhibits a visual resolution power commensurate with natural grasps. Participants were presented with a target and nontarget that differed in size below the perceptual threshold (i.e., 0.5 mm or ∼1.3%) and were asked to make a perceptual judgment about the target (i.e., "smaller" or "larger" than the nontarget) before and after completing natural and pantomime-grasps. Results showed that perceptual judgments "before" and "after" natural and pantomime-grasps did not reliably distinguish between target and nontarget. Natural grasp peak grip apertures (PGAs) scaled to target size and were comparable for "before" and "after" perceptual judgment trials-a result indicating that haptic feedback from physically grasping the target did not "boost" perceptual accuracy. Most notably, pantomime-grasp PGAs were insensitive to target size; that is, responses elicited a visual resolution power less than natural grasps. These results provide convergent evidence that pantomime-grasps are mediated by the same visual information as obligatory perceptions and do not provide a proxy for natural grasps. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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) | 0.000 | 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 | 0.001 |
| 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".