A new multivariate analysis method suggests timing is key factor in visually-guided reach-to-grasp movements
Bibliographic record
Abstract
INTRODUCTION: Visually guided reach-to-grasp actions have been proposed to consist of distinct transport and grip components, which may result from the different visual properties required to guide them (location and object size and shape, respectively). However, kinematic studies of hand actions have typically investigated the effect of different conditions on dependent measures one at a time. We examined the interrelationships between various kinematic variables using a multivariate approach. In addition, we developed new measures of grip accuracy and tested how these related to standard reach-to-grasp variables. METHODS: Participants (n=24) performed reach-to-grasp or reach-to-touch movements upon different-sized rectangular objects. Our analysis involved correlating standard kinematic variables (e.g., maximum grip aperture, MGA, and peak velocity of reach, PV, and time at which they occurred, tMGA and tPV) and new grip accuracy variables (shift and orientation of initial grip) with one another across all trials for each subject. We applied a multivariate analysis [using similar logic as representational similarity analysis (RSA) used in fMRI] to test various models of reach-to-grasp components, including a transport-grip model which predicts strong correlations within (but not between) traditional transport and grip and a timing-based model which predicts strong correlations between variables associated with the timing of movement. RESULTS: Our timing-based model of reach-to-grasp movements, but not the transport-grip model, showed strong correlation with the data. Furthermore, as opposed to our hypothesis, participants did not display larger grips on trials where their grip accuracy was low. CONCLUSION: Though standard kinematic measures used in studying reach-to-grasp movements (e.g., PV, MGA) have been theoretically divided into transport and grip variables, these variables do not explain the relationships between variables. The development of new measures of grip accuracy and new applications of multivariate analyses open up new questions for kinematic studies of reach-to-grasp movements in clinical populations and healthy controls. Meeting abstract presented at VSS 2017
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".