Distinct visual coding strategies support grasping and pantomimed actions for 3D objects
Bibliographic record
Abstract
Previous research by our group has shown that grasping a 3D object elicits a time-dependent adherence to Weber's law whereas grasping a 2D object produces a time-independent adherence to the law's psychophysical properties (Holmes et al., 2012: J Vis). Such results indicate that unitary relative information mediates 2D grasping whereas relative and absolute visual cues mediate the early and late stages, respectively, of 3D grasping. Interestingly, some work focusing on a late occurring kinematic marker (i.e., peak grip aperture) has shown that decoupling the spatial relations between stimulus and response (i.e., pantomiming) renders aperture specification via relative visual information (Goodale et al., 1994: Neuropsychologia; Westwood et al., 2000: Exp Brain Res). As such, the present study sought to determine whether pantomimed actions elicit a time-independent or time-dependent use of relative visual information. Participants grasped and pantomimed grasping differently sized (20, 30, 40 and 50 mm) 2- and 3D objects. Importantly, we computed just-noticeable-difference (JND) scores as within-participants standard deviations in grip aperture at decile increments of grasping time and interpreted linear scaling of JNDs to object size as extant adherence to Weber's law. As expected, JNDs for 3D grasping elicited a time-dependent scaling to object size consist with our group's earlier work. In turn, JNDs for 2D grasping as well as 2- and 3D pantomiming scaled to target size throughout the response. Thus, results support the position that decoupling stimulus and response for 2- and 3D objects renders aperture shaping via unitary and relative visual information.Acknowledgments: Supported by 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".