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.
|
Friedman, J., & Flash, T. (2007). Task-dependent selection of grasp kinematics and stiffness in human object manipulation. Cortex, 43(3), 444–460.
Abstract: Object manipulation with the hand is a complex task. The task has redundancies at many levels, allowing many possibilities for the selection of grasp points, the orientation and posture of the hand, the forces to be applied at each fingertip and the impedance properties of the hand. Despite this inherent complexity, humans perform object manipulation nearly effortlessly. This article presents experimental findings of how humans grasp and manipulate objects, and examines the compatibility of grasps selected for specific tasks. This is accomplished by looking at the velocity transmission and force transmission ellipsoids, which represent the transmission ratios of the corresponding quantity from the joints to the object, as well as the stiffness ellipsoid which represents the directional stiffness of the grasp. These ellipsoids allow visualization of the grasp Jacobian and grasp stiffness matrices. The results show that the orientation of the ellipsoids can be related to salient task requirements.
|
Nahab, F., Kundu, P., Gallea, C., Kakareka, J., Pursley, R., Pohida, T., et al. (2011). The neural processes underlying self-agency. Cerebral Cortex, 21(1), 48–55.
Abstract: Self-agency (SA) is the individual’s perception that an action is the consequence of his/her own intention. The neural networks underlying SA are not well understood. We carried out a novel, ecologically valid, virtual-reality experiment using BOLD-fMRI where SA could be modulated in real-time while subjects performed voluntary finger movements. Behavioral testing was also performed to assess the explicit judgment of SA. Twenty healthy volunteers completed the experiment. Results of the behavioral testing demonstrated paradigm validity along with the identification of a bias that led subjects to over- or underestimate the amount of control they had. The fMRI experiment identified two discrete networks. These leading and lagging networks likely represent a spatial and temporal flow of information, with the leading network serving the role of mismatch detection and the lagging network receiving this information and
mediating its elevation to conscious awareness, giving rise to SA.
|
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.
|
Friedman, J., Brown, S., & Finkbeiner, M. (2013). Linking cognitive and reaching trajectories via intermittent movement control. Journal of Mathematical Psychology, 57(3-4), 140–151.
Abstract: Theories of decision-making have traditionally been constrained by reaction time data. A limitation of reaction time data, particularly for studying the temporal dynamics of cognitive processing, is that they index only the endpoint of the decision making process. Recently, physical reaching trajectories have been used as proxies for underlying mental trajectories through decision space. We suggest that this approach has been oversimplified: while it is possible for the motor control system to access the current state of the evidence accumulation process, this access is intermittent. Instead, we demonstrate how a model of arm movements that assumes intermittent, not continuous, access to the decision process is sufficient to describe the effects of stimulus quality and viewing time in curved reaching movements.
|
