Visual-haptic disparity of target will modulate action-guidance strategy
Bibliographic record
Abstract
The contribution of vision to action guidance is flexible. For example, Wolpert (2007) has demonstrated that humans integrate sensory information as a function of sensory reliability. Modalities that are more reliable are used more extensively to make a perceptual judgment than those that are less reliable (e.g. Ernst & Banks, 2002). The present study was performed to test how different visual-haptic mappings influenced grasping actions. We presented a Müller-Lyer figure (MLF) on a half-silvered mirror, such that the image mapped onto a physical object located below the mirror. This set-up gave participants the feeling that they were physically grasping the shaft of the MLF. The physical object did not always match the size of the image, which created a visual-haptic disparity. Participants performed two counterbalanced blocks of trials. In block A, the physical object was adjusted opposite to the size of the illusory percept (constant disparity block) and in block B, the visual-haptic mapping was random (variable disparity block). Participants were instructed to reach out and grasp the shaft of the MLF (kinematic variables were derived using an Optotrak Certus). It was hypothesized that grip scaling would be proportional to the size of the physical object in the constant disparity block, but would reflect the size of the illusory percept in the variable disparity block. Contrary to our hypothesis, participants' grasping behaviors were influenced by the illusion regardless of the mapping condition. That is, peak grip aperture (pGA) was smaller for the fins-in compared to the fins-out MLF configuration. Additionally, pGA decreased over the first several trials. This adaptation in pGA required fewer trials in the constant disparity condition. Thus, haptic feedback uncertainty in the variable block delayed pGA adaptations. These results suggest that visual guidance strategies are altered by inter-modal mapping certainty.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".