Does behavioral dissociation of real vs. pantomime movements only apply to visually guided action?
Bibliographic record
Abstract
Pantomime reaching movements to imagined objects differ kinematically from real reaching movements to visual objects. It has been proposed that these differences may be related to the availability of online visual input. Specifically, real reaches are proposed to rely on online visual information localized largely in the left dorsal stream whereas pantomime reaches are proposed to rely on perceptual information processed in more widespread areas including the ventral stream and the right dorsal stream. Withdrawing the hand to place an object in the mouth is a natural and common movement; yet, it differs from other reaching movements in that it relies almost entirely on somatosensory guidance and may be mediated by a ‘hard-wired’ cortical motor representation that controls the arm, hand, and mouth. Given these differences, it could be hypothesized that hand-to-mouth movements might not be subject to the same real vs. pantomime dissociation as visually guided reaching. The present study used frame-by-frame video analysis and linear kinematics to analyze hand and mouth movements as participants withdrew the arm and hand to place either real or imagined food items into the mouth for eating. Pantomime hand-to-mouth movements were characterized by longer movement durations, lower peak velocities, and smaller openings of the mouth aperture to receive the food item than real hand-to-mouth movements. Nonetheless, mouth opening scaled to object size regardless of whether the food item was real or imagined. The results are discussed in relation to the idea that neurobehavioral dissociation of real vs. pantomime actions is a general feature of movement control that applies to both visually and nonvisually guided actions. Meeting abstract presented at VSS 2015
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".