The timing of early aperture shaping metrics evince feedback- and feedfoward-based corrections
Bibliographic record
Abstract
Jeannerod's (1984: J Mot Behav) seminal work provided an understanding of the timing of several transport and aperture shaping metrics underlying goal-directed grasping. Most notably, Jeannerod's work is recognized for defining the invariant timing of peak grip aperture (75.7% of grasping time). It is, however, important to recognize that Jeannerod's work, as well as much subsequent research, has not focused on the spatiotemporal properties of early aperture shaping. Indeed, the importance of such an examination is recognized by the results of earlier work by our group showing that distinct visual metrics support early and late aperture shaping (e.g., Holmes et al. 2011: Vis Res). To that end, the present investigation examined the spatiotemporal properties of an early aperture shaping metric (i.e. peak aperture velocity: PAV) in a large corpus of participants (N = 123) when grasping differently sized target objects. Results showed that the magnitude and size of grip aperture at PAV increased linearly as a function of increasing object size. Notably, however, the time to PAV exhibited a non-normal distribution that was exemplified by two subgroups. In particular, one group achieved PAV early in (i.e., at 20% of,) the response whereas a second group achieved PAV later in (i.e., at > ~50% of,) the response. Such findings indicate that PAV is a temporally variant metric and may therefore reflect feedback- or feedforward-based modifications to an unfolding aperture trajectory.Acknowledgments: Supported by an NSERC Discovery Grant and an NSERC USRA
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".