The identification of different iterative rates associated with the control of voluntary upper-limb reaches
Bibliographic record
Abstract
The current study sought to investigate online feedback utilization by evaluating acceleration profiles of voluntary reaches using analyses performed in the frequency domain. This approach afforded the evaluation of specific rates of discontinuities rather than their mere presence/ absence. Eighteen participants performed medial-to-lateral reaching movements in either Full-Vision, No-Vision or Memory-Guided conditions. Blocks of 100 reaches were completed in each condition to two targets (i.e., 50 trials to each of a 10 and a 30 cm target amplitude). Participants were instructed to move quickly, but to prioritize movement accuracy. Movements were measured using a tri-axis accelerometer at 250 Hz. In order to isolate relative contributions of corrective discontinuities, the acceleration profiles were detrended with a 5th order polynomial and converted to a proportional power spectra via a Fast Fourier Transform. Performance was evaluated across frequencies ranging from 2 to 30 Hz. A peak in the 6 to 10 Hz range was observed in all conditions but was of a greater magnitude in the Full-Vision Condition compared to the other two conditions (i.e., the No-Vision and Memory-Guided conditions). In addition, a secondary peak at 20 Hz was more prominent in the conditions without vision and could represent non-corrective mechanical oscillations of the finger. Thus, visual control was exemplified by iterations of approximately 100 to 167 ms (i.e., 6-10 Hz), whereas discontinuities not associated with visual feedback occurred at an iterative rate of approximately 50 ms (i.e., 20 Hz). Although the quantification of feedback utilization through the assessment of trajectory discontinuities in temporal domain has proven difficult, their evaluation in the frequency domain is a viable alternative. Acknowledgments: Natural Sciences and Engineering Research Council of Canad; Canada Foundation for Innovation; Ontario Research Fund
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".