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Friedman, J., SKM, V., Zatsiorsky, V. M., & Latash, M. L. (2009). The sources of two components of variance: an example of multifinger cyclic force production tasks at different frequencies. Exp Brain Res, 196(2), 263–277.
Abstract: In a multifinger cyclic force production task, the finger force variance measured across trials can be decomposed into two components, one that affects the combined force output (“bad variance”) and one that does not (“good variance”). Previous studies have found similar time patterns of “bad variance” and force rate leading to an approximately linear relationship between them. Based on this finding and a recently developed model of multifinger force production, we expected the “bad variance” during cyclic force production to increase monotonically with the rate of force change, both within a cycle and across trials at different frequencies. Alternatively, “bad variance” could show a dependence on task frequency, not on actual force derivative values. Healthy subjects were required to produce cyclic force patterns to prescribed targets by pressing on unidimensional force sensors, at a frequency set by a metronome. The task was performed with only the index finger, and with all four fingers. In the task with all four fingers, the “good variance” increased approximately linearly with an increase in the force magnitude. The “bad variance” showed within-a-cycle modulation similar to that of the force rate. However, an increase in the frequency did not lead to an increase in the “bad variance” that could be expected based on the natural relationships between action frequency and the rate of force change modulation. The results have been interpreted in the framework of an earlier model of multifinger force production where “bad variance” is a result of variance of the timing parameter. The unexpected lack of modulation of the “bad variance” with frequency suggests a drop in variance of the timing parameter with increased frequency. This mechanism may serve to maintain a constant acceptable level of variance under different conditions.
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Liebermann, D. G., & Franks, I. M. (2004). The use of feedback-based technologies in skill acquisition. In M. Hughes, & I.M. Franks (Eds.), Notational analysis of Sport and Coaching Science. E & FN Spon Pub.
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Goodman, D., & Liebermann, D. G. (1992). Time-to-contact as a determiner of action: vision and motor control. In D. Elliott, & J. Proteau (Eds.), Vision and Motor Control (pp. 335–349). Amsterdam, Holland: Elsevier Pub. Co.
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Liebermann, D. G., & Hoffman, J. R. (2005). Timing of preparatory landing responses as a function of availability of optic flow information. J Electromyogr Kinesiol, 15(1), 120–130.
Abstract: This study investigated temporal patterns of EMG activity during self-initiated falls with different optic flow information ('gaze directions'). Onsets of EMG during the flight phase were monitored from five experienced volunteers that completed 72 landings in three gaze directions (downward, mid-range and horizontal) and six heights of fall (10-130 cm). EMG recordings were obtained from the right gastrocnemius, tibialis anterior, biceps femoris and rectus femoris muscles, and used to determine the latency of onset (L(o)) and the perceived time to contact (T(c)). Impacts at touchdown were also monitored using as estimates the major peak of the vertical ground reaction forces (F(max)) normalized to body mass, time to peak (T(max)), peak impulse (I(norm)) normalized to momentum, and rate of change of force (dF(max)/dt). Results showed that L(o) was longer as heights of fall increased, but remained within a narrow time-window at >50 cm landings. No significant differences in L(o) were observed when gaze direction was changed. The relationship between T(c) and flight time followed a linear trend regardless of gaze direction. Gaze direction did not significantly affect the landing impacts. In conclusion, availability of optic flow during landing does not play a major role in triggering the preparatory muscle actions in self-initiated falls. Once a structured landing plan has been acquired, the relevant muscles respond relative to the start of the fall.
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Friedman, J., & Flash, T. (2009). Trajectory of the index finger during grasping. Exp Brain Res, 196(4), 497–509.
Abstract: The trajectory of the index finger during grasping movements was compared to the trajectories predicted by three optimization-based models. The three models consisted of minimizing the integral of the weighted squared joint derivatives along the path (inertia-like cost), minimizing torque change, and minimizing angular jerk. Of the three models, it was observed that the path of the fingertip and the joint trajectories, were best described by the minimum angular jerk model. This model, which does not take into account the dynamics of the finger, performed equally well when the inertia of the finger was altered by adding a 20 g weight to the medial phalange. Thus, for the finger, it appears that trajectories are planned based primarily on kinematic considerations at a joint level.
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