Absolute haptic cues mediate pantomime-grasping only when egocentric visual cues are delayed
Bibliographic record
Abstract
Pantomime-grasping entails a movement with dissociated stimulus-response relations or responses towards the location previously occupied by a physical target objects. Unlike naturalistic grasps that are mediated via a target's absolute visual properties and specified in egocentric reference frames, relative and allocentric visual cues support pantomime-grasping. Notably, however, our group recently demonstrated that providing haptic feedback (i.e., of a physically removed target) following a pantomime-grasp supports an absolute visuo-haptic calibration (Davarpanah Jazi et al 2015: Exp. Brain Res.). In the current study we examined specific sensory and spatial requirements necessary to support a visuo-haptic calibration during pantomime-grasping. To that end, in a series of experiments participants pantomime-grasped differently-sized target objects while receiving haptic feedback (i.e., through physical touch) following response completion. Notably, the target's spatial location as well as online limb and target vision were manipulated. Results showed that an absolute visuo-haptic calibration process is limited to situations wherein a spatially-overlapping pantomime-grasp is performed following a visually-based memory delay. In accounting for our results we have drawn upon the maximum likelihood estimator model's (MLE) tenet that multisensory cues integrate in an optimal fashion with processing weighted towards the more reliable sense. As such, we propose that the decay of visual cues renders an increased weighting and salience of haptic signals and thus, supports an absolute visuo-haptic calibration.Acknowledgments: Natural Sciences and Engineering Research Council
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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) | 0.003 | 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".