AbstractThe 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)