Pantomime-grasping: Tactile processing is altered from relative to absolute only when haptic calibration is allowed
Bibliographic record
Abstract
Tactile- or mechanoreceptive-guided pantomime-grasping is described as a non-visual grasp performed to the location of a target previously presented on an individuals' limb (i.e., palm). Our group's previous work has shown that relative tactile information mediate this type of pantomime action and is a result our group has taken to evince a perception-based mode of control. Notably, however, receiving haptic feedback (i.e., proprioceptive-based feedback from thumb and forefinger position) about target size through physically grasping an object following a tactile-guided pantomime-grasp alters the mediation of motor output from relative to absolute size information. Thus, our group has proposed that the provision of haptic feedback engenders the computation of an error signal related to "expected" and "actual" haptic information and hence supports an absolute haptic calibration. The goal of the present study was to determine whether the aforementioned absolute calibration is specific to the provision of haptic feedback as opposed to being modality-independent. To that end, participants' performed tactile-guided pantomime-grasps in conditions wherein haptic or visual feedback about target size was provided at the end of the response. In particular, for the haptic feedback condition participants were able to grasp a veridical target, whereas in the visual feedback condition a virtual visual rendering of the veridical target was provided. Results showed that haptic – but not visual – feedback supports an absolute calibration process. Accordingly, we propose that terminal haptic feedback is a specific sensory consequence necessary to support an absolute haptic calibration.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".