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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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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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Prushansky, T., Kaplan-Gadasi, L., & Friedman, J. (2023). The relationship between thoracic posture and ultrasound echo intensity of muscles spanning this region in healthy men and women. Physiother Theory Pract, 39(6), 1257–1265.
Abstract: PURPOSE: Skeletal muscle echogenicity intensity (EI) is considered a measure of muscle quality, being associated with old age and pathologies. Whether EI variations can be identified in healthy adults, due to habitual shortened or elongated muscle position is unknown. Thus, this study aimed to assess the relationship between thoracic kyphosis angulation and EI scores of muscles spanning this region ((Lower Trapezius (LT), Rhomboid Major (RM), Erector Spine (ES)) in healthy young people and in addition to examine the relationship between the change in thoracic kyphosis angle from relaxed to upright position (� degrees ) and the EI of these muscles. METHODS: Thoracic kyphosis in relaxed and erect standing was measured using a digital inclinometer in 29 healthy adults (16 women, 13 men), aged 25-35 years. The thoracic kyphosis angles including the difference between relaxed and erect postures (� degrees ) were correlated to the EI scores of right and left LT, RM and ES. RESULTS: No significant differences in EI were found between the 3 muscles EI or between sides, hence they were pooled together to a total thoracic EI score (TTEI). Although the TTEI did not correlate with relaxed or erect thoracic kyphosis, it was significantly but negatively correlated with � degrees in the entire group: Pearson's correlation coefficient of r = -0.544; p = .01 and in men; r = -0.732; p = .01, failing to reach significance in women; r = -0.457. CONCLUSION: The negative association between the EI of the explored muscles and � degrees could imply a possible relationship between these muscles range of movement excursions and their composition.
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Zopf, R., Friedman, J., & Williams, M. A. (2015). The plausibility of visual information for hand ownership modulates multisensory synchrony perception. Experimental Brain Research, 233(8), 2311–2321.
Abstract: We are frequently changing the position of our bodies and body parts within complex environments. How does the brain keep track of one’s own body? Current models of body ownership state that visual body ownership cues such as viewed object form and orientation are combined with multisensory information to correctly identify one’s own body, estimate its current location and evoke an experience of body ownership. Within this framework, it may be possible that the brain relies on a separate perceptual analysis of body ownership cues (e.g. form, orientation, multisensory synchrony). Alternatively, these cues may interact in earlier stages of perceptual processing—visually derived body form and orientation cues may, for example, directly modulate temporal synchrony perception. The aim of the present study was to distinguish between these two alternatives. We employed a virtual hand set-up and psychophysical methods. In a two-interval force-choice task, participants were asked to detect temporal delays between executed index finger movements and observed movements. We found that body-specifying cues interact in perceptual processing. Specifically, we show that plausible visual information (both form and orientation) for one’s own body led to significantly better detection performance for small multisensory asynchronies compared to implausible visual information. We suggest that this perceptual modulation when visual information plausible for one’s own body is present is a consequence of body-specific sensory predictions.
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Friedman, J., Amiaz, A., & Korman, M. (2022). The online and offline effects of changing movement timing variability during training on a finger-opposition task. Sci Rep, 12(1), 13319.
Abstract: In motor learning tasks, there is mixed evidence for whether increased task-relevant variability in early learning stages leads to improved outcomes. One problem is that there may be a connection between skill level and motor variability, such that participants who initially have more variability may also perform worse on the task, so will have more room to improve. To avoid this confound, we experimentally manipulated the amount of movement timing variability (MTV) during training to test whether it improves performance. Based on previous studies showing that most of the improvement in finger-opposition tasks comes from optimizing the relative onset time of the finger movements, we used auditory cues (beeps) to guide the onset times of sequential movements during a training session, and then assessed motor performance after the intervention. Participants were assigned to three groups that either: (a) followed a prescribed random rhythm for their finger touches (Variable MTV), (b) followed a fixed rhythm (Fixed control MTV), or (c) produced the entire sequence following a single beep (Unsupervised control MTV). While the intervention was successful in increasing MTV during training for the Variable group, it did not lead to improved outcomes post-training compared to either control group, and the use of fixed timing led to significantly worse performance compared to the Unsupervised control group. These results suggest that manipulating MTV through auditory cues does not produce greater learning than unconstrained training in motor sequence tasks.
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