Pantomime-grasping demonstrates a shape-dependent visuoperceptual resolution
Bibliographic record
Abstract
Grasping requires that an individual process absolute visual information for optimal hand/target interactions. For example, the visuomotor system demonstrates peak grip aperture (PGA) scaling for targets that differ by as little as 0.5 mm – a resolution far greater than the visuoperceptual system. In the present work, participants grasped adjacent to a target (i.e., pantomime-grasp) to determine whether actions requiring decoupled stimulus-response spatial relations rely on the same visual information as their naturalistic counterparts. For each trial a target and an adjacent non-target was presented, and participants grasped or pantomime-grasped the target. Importantly, target and non-targets differed in size by 0.5 mm, and prior to or after the grasp, or pantomime-grasp, participants' reported whether the target was larger than the non-target (i.e., perceptual judgment). Experiment 1 employed rectangular bars as target stimuli, whereas Experiment 2 employed circular annuli. Experiment 1 showed that PGAs for grasps and pantomime-grasps scaled to target size and surprisingly participants provided accurate perceptual judgments of target size. Experiment 2 PGAs for grasps – but not pantomime-grasps – scaled to target size and in both tasks participants did not provide accurate perceptual judgements. Accordingly, results demonstrate that the perceptual system's resolution is shape-dependent (Experiment 1), and that grasps, and pantomime-grasps, are mediated via distinct visual information (Experiment 2).Acknowledgments: Supported by NSERC.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".