Introducing haptic feedback through object touch changes visual information supporting 2D object grasp from relative to absolute
Bibliographic record
Abstract
Grasping a 2D object requires the processing of relative target features and functions distinct from the absolute visual information supporting the grasp of a 3D object (Holmes & Heath, 2013: Brain Cogn). Interestingly, the distinct visual cues mediating 2D and 3D grasping may – in part – reflect the fact that the former task does not entail the provision of terminal haptic feedback (Davarpanah Jazi et al. 2015: Neuropsychologia). As such, in the present study we investigated whether the provision of terminal haptic feedback influences the nature of the information supporting 2D grasping. In particular, participants grasped differently sized 3D objects and their 2D counterparts in conditions wherein terminal haptic feedback was present (i.e., 2DH+) or absent (i.e., 2DH-). More specifically, the 2DH+ condition provided terminal haptic feedback comparable to that associated with the grasping of a 3D object. Just noticeable difference scores (JNDs) computed at peak grip aperture in the 2DH- condition scaled to target size, whereas values for 3D and 2DH+ trials elicited a null scaling. In other words, grasping a 2D object adhered to the relative psychophysical principle of Weber's law, whereas the provision of terminal haptic feedback resulted in grasps that violated the law. Accordingly, we propose that terminal haptic feedback provides absolute size cues that supports veridical aperture shaping.Acknowledgments: National 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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".