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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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Noy, L., Alon, U., & Friedman, J. (2015). Corrective jitter motion shows similar individual frequencies for the arm and the finger. Exp Brain Res, 233(4), 1307–1320.
Abstract: A characteristic of visuomotor tracking of non-regular oscillating stimuli are high-frequency jittery corrective motions, oscillating around the tracked stimuli. However, the properties of these corrective jitter responses are not well understood. For example, does the jitter response show an idiosyncratic signature? What is the relationship between stimuli properties and jitter properties? Is the jitter response similar across effectors with different inertial properties? To answer these questions, we measured participants' jitter frequencies in two tracking tasks in the arm and the finger. Thirty participants tracked the same set of eleven non-regular oscillating stimuli, vertically moving on a screen, once with forward-backward arm movements (holding a tablet stylus) and once with upward-downward index finger movements (with a motion tracker attached). Participants' jitter frequencies and tracking errors varied systematically as a function of stimuli frequency and amplitude. Additionally, there were clear individual differences in average jitter frequencies between participants, ranging from 0.7 to 1.15 Hz, similar to values reported previously. A comparison of individual jitter frequencies in the two tasks showed a strong correlation between participants' jitter frequencies in the finger and the arm, despite the very different inertial properties of the two effectors. This result suggests that the corrective jitter response stems from common neural processes.
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Liebermann, D. G., Biess, A., Friedman, J., Gielen, C. C. A. M., & Flash, T. (2006). Intrinsic joint kinematic planning. I: reassessing the Listing's law constraint in the control of three-dimensional arm movements. Exp Brain Res, 171(2), 139–154.
Abstract: This study tested the validity of the assumption that intrinsic kinematic constraints, such as Listing's law, can account for the geometric features of three-dimensional arm movements. In principle, if the arm joints follow a Listing's constraint, the hand paths may be predicted. Four individuals performed 'extended arm', 'radial', 'frontal plane', and 'random mixed' movements to visual targets to test Listing's law assumption. Three-dimensional rotation vectors of the upper arm and forearm were calculated from three-dimensional marker data. Data fitting techniques were used to test Donders' and Listing's laws. The coefficient values obtained from fitting rotation vectors to the surfaces described by a second-order equation were analyzed. The results showed that the coefficients that represent curvature and twist of the surfaces were often not significantly different from zero, particularly not during randomly mixed and extended arm movements. These coefficients for forearm rotations were larger compared to those for the upper arm segment rotations. The mean thickness of the rotation surfaces ranged between approximately 1.7 degrees and 4.7 degrees for the rotation vectors of the upper arm segment and approximately 2.6 degrees and 7.5 degrees for those of the forearm. During frontal plane movements, forearm rotations showed large twist scores while upper arm segment rotations showed large curvatures, although the thickness of the surfaces remained low. The curvatures, but not the thicknesses of the surfaces, were larger for large versus small amplitude radial movements. In conclusion, when examining the surfaces obtained for the different movement types, the rotation vectors may lie within manifolds that are anywhere between curved or twisted manifolds. However, a two-dimensional thick surface may roughly represent a global arm constraint. Our findings suggest that Listing's law is implemented for some types of arm movement, such as pointing to targets with the extended arm and during radial reaching movements.
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Latash, M. L., Friedman, J., Kim, S.W., Feldman, A.G., Zatsiorsky, V.M. (2010). Prehension Synergies and Control with Referent Hand Configurations. Exp Brain Res, 202(1), 213–229.
Abstract: We used the framework of the equilibrium-point hypothesis (in its updated form based on the notion of referent configuration) to investigate the multi-digit synergies at two levels of a hypothetical hierarchy involved in prehensile actions. Synergies were analyzed at the thumb-virtual finger level (virtual finger is an imaginary digit with the mechanical action equivalent to that of the four actual fingers) and at the individual finger level. The subjects performed very quick vertical movements of a handle into a target. A load could be attached off-center to provide a pronation or supination torque. In a few trials, the handle was unexpectedly fixed to the table and the digits slipped off the sensors. In such trials, the hand stopped at a higher vertical position and rotated into pronation or supination depending on the expected torque. The aperture showed non-monotonic changes with a large, fast decrease and further increase, ending up with a smaller distance between the thumb and the fingers as compared to unperturbed trials. Multi-digit synergies were quantified using indices of co-variation between digit forces and moments of force across unperturbed trials. Prior to the lifting action, high synergy indices were observed at the individual finger level while modest indices were observed at the thumb-virtual finger level. During the lifting action, the synergies at the individual finger level disappeared while the synergy indices became higher at the thumb-virtual finger level. The results support the basic premise that, within a given task, setting a referent configuration may be described with a few referent values of variables that influence the equilibrium state, to which the system is attracted. Moreover, the referent configuration hypothesis can help interpret the data related to the trade-off between synergies at different hierarchical levels.
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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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