Revealing multiple online visuomotor processes via spectral analysis of upper-limb acceleration profiles
Bibliographic record
Abstract
The presence of visual information notably improves endpoint accuracy and precision, presumably through feedback-related processes. The relatively stable corrective reaction times to visual perturbations (Oostwoud Wijdenes et al., 2013; Saunders & Knill, 2003; Veyrat-Masson et al., 2010) may permit the use of spectral analysis in the quantification of online sensorimotor processes. The current study employed spectral analyses of acceleration traces to assess the contribution of visuomotor processes. Ten participants completed lateral-to-medial reaching movements with and without online vision (i.e., vision occluded at movement onset). Kinematic data was recorded at 200 Hz from both an Optotrak Certus and a triple-axis accelerometer. Movement start and end were determined from the position data. Then, the movement acceleration data were pre-processed to account for the pre-planned acceleration-deceleration phases (van Donkelaar & Franks, 2001; Warner, 1998). Next, a fast-fourier transform was applied to the acceleration data, and the proportional power spectra were calculated (bin-width approx. 3 Hz). Overall, the spectra of both vision and no-vision reaches exhibited a peak at 6.25 Hz. In addition, a 2 Vision-Condition by 4 Bin (3, 6, 9 and 12 Hz) repeated-measures ANOVA revealed significantly greater contributions of both the 3.13 and 6.25 Hz oscillations (wavelengths of 320 and 160 ms, respectively) to the trajectories of reaches made with online vision, compared to those performed without vision. Although it is not clear if the observed differences stem from one or multiple processes, the latencies of 3.13 and 6.25 Hz oscillations correspond to those associated with “slow and deliberate” vs. “fast automatic” corrective processes, respectively (Pisella et al., 2000). Therefore, spectral analyses appear to be sensitive to online feedback utilization and may be useful to identify different types of sensorimotor processes. Acknowledgments: NSERC (Natural Sciences and Engineering Research Council of Canada),;CFI (Canada Foundation for Innovation) ; ORF (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 | 0.001 |
| 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".