Comparing statistical methods for inferring contributions of visual online control from human limb trajectories
Bibliographic record
Abstract
Visual online motor control involves using visual information about the limb and the target to adjust the trajectory of the limb towards the target in real-time to improve movement accuracy. The primary objective of the thesis was to demonstrate that improvements to the standard methods of statistical analysis of such trajectory data can substantially improve the quality of the inferences made about those data. A Bayesian hierarchical gaussian process regression (GPR) model was compared to traditional analysis techniques in its ability to accurately estimate experimental effects. Analyses were run on experimental data collected from a basic vision/no-vision goal-directed reaching task, and simulated data from theoretically plausible generative model. Broadly, the expected experimental effects of vision were generated. The Bayesian hierarchical GPR method was successfully implemented and conferred some substantial benefits in contrast to many of the traditional methods. However, given several usability limitations, the Bayesian hierarchical GPR method may be best used as a specialty tool for statistically savvy researchers seeking to maximize the inferential capacity of their analysis of movement trajectories.Acknowledgments: Committee Members: Dave Westwood and Joanna Mills Flemming
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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 | 0.069 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".