Comparing statistical methods for analyzing human limb trajectories of goal-directed movements
Bibliographic record
Abstract
Recently, there has been increased interest in comparing the trajectories of movements made under different conditions to infer information about cognitive processes relating to aspects of motor control such as action planning. One of the more recent analysis methods involves computing the area between two trajectories to targets on opposite sides of the participant's midline for each experimental condition and then submitting those areas to a repeated measures ANOVA. Unfortunately, this method necessarily collapses the nuanced trajectory information into a single score. Therefore, we propose a new method - Bayesian Hierarchical Gaussian Process Regression (BHGPR) - which can be used to compare the entire trajectory among experimental conditions. The experimental data that was used to compare these analysis methods were taken from a study in which participants made reaching movements to targets, appearing on either side, preceded by either high (78.5%) or low predictive cues. The authors from this past study had predicted that movements to non-valid targets preceded by predictive cues would contour a lesser area than those from any other condition. The results from the comparison between traditional methods of analyzing trajectories and BHGPR indicate that BHGPR can be used to compare entire trajectories using credible intervals to demonstrate specific regions where the two trajectories differ.
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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 | 0.119 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".