|
|
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.
|
|
|
|
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.
|
|
|
|
Frenkel-Toledo, S., Bentin, S., Perry, A., Liebermann, D. G., & Soroker, N. (2013). Dynamics of the EEG Power in the Frequency and Spatial Domains During Observation and Execution of Manual Movements. Brain Res, 1509, 43–57.
Abstract: Mu suppression is the attenuation of EEG power in the alpha frequency range (8-12Hz) while executing or observing a motor action. Whereas typically observed at central scalp sites, there are diverging reports about the extent of the attenuation over the cortical mantle, its exact frequency range and the specificity of this phenomenon. We investigated the modulation of EEG oscillations in frequency-bands from 4 to 12Hz at frontal, central, parietal and occipital sites during the execution of manual movements and during observation of similar actions from allocentric (i.e., facing the actor) and egocentric (i.e., seeing the actor from behind) viewpoints. Suppression was determined relative to observation of a non-biological movement. Action observation elicited greater suppression in the lower (8-10Hz) compared to the higher mu range (10-12Hz), and greater suppression in the entire 4-12Hz range at frontal and central sites compared to parietal and occipital sites. In addition, suppression tended to be greater during observation of a motor action from allocentric compared to egocentric viewpoints. During execution of movement, suppression of the EEG occurred primarily in the higher alpha range and was absent at occipital sites. In the theta range (4-8Hz), the EEG amplitude was suppressed during action observation and execution. The results suggest a functional distinction between modulation of mu and alpha rhythms, and between the higher and lower ranges of the mu rhythms. The activity of the presumed human mirror neuron system seems primarily evident in the lower mu range and in the theta range.
|
|
|
|
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.
|
|
