Do you feel my vibe? Quantifying iterative visuo-motor feedback-loops in the jerk profiles of manual aiming movements using frequency analyses
Bibliographic record
Abstract
Contemporary models of visuo-motor control posit that the use of visual information during voluntary movement is likely an iterative process. Stemming from this assumption, it was hypothesized that iterative changes in the kinematics of movements should be present, and visual-motor feedback processing times should be measurable using frequency analysis (Fast Fourier Transform, FFT). To test this hypothesis, we evaluated reaching movements of 10, 20, and 30 cm amplitudes performed with and without visual feedback (V & NV, respectively). Trajectories in both the primary and secondary movement axes were differentiated to attain jerk profiles using a 2-point central difference algorithm and filtered with a 20 Hz low-pass filter at each stage. The power spectral density (PSD) functions were estimated for each trajectory using FFT. Differences between V and NV were quantified in decibels (DB: NV as reference signal) and analyzed across the 7.8–13.7 Hz range (iteration times: 73-128 ms). No significant DB differences were found for the primary movement axis. Conversely, the secondary movement axis showed an increase in the DB of the 11.71 Hz bin relative to the 7.8 and 9.7 Hz bins. This indicated that in the presence of vision, there is greater power in the 11.71 Hz oscillation bin. Therefore, although frequency analysis failed to distinguish between visual conditions in the primary axis, visual control in the secondary axis may occur at a rate of approximately 11.71 Hz (i.e., 85 ms iteration times).Acknowledgments: Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".