The discovery of audiovisual mirror neurons in monkeys gave rise to the hypothesis that premotor areas are inherently involved not only when observing actions but also when listening to action-related sound. However, the whole-brain functional formation underlying such "action-listening" is not fully understood. In addition, previous studies in humans have focused mostly on relatively simple and overexperienced everyday actions, such as hand clapping or door knocking. Here we used functional magnetic resonance imaging to ask whether the human action-recognition system responds to sounds found in a more complex sequence of newly acquired actions. To address this, we chose a piece of music as a model set of acoustically presentable actions and trained non-musicians to play it by ear. We then monitored brain activity in subjects while they listened to the newly acquired piece. Although subjects listened to the music without performing any movements, activation was found bilaterally in the frontoparietal motor-related network (including Broca's area, the premotor region, the intraparietal sulcus, and the inferior parietal region), consistent with neural circuits that have been associated with action observations, and may constitute the human mirror neuron system. Presentation of the practiced notes in a different order activated the network to a much lesser degree, whereas listening to an equally familiar but motorically unknown music did not activate this network. These findings support the hypothesis of a "hearing-doing" system that is highly dependent on the individual's motor repertoire, gets established rapidly, and consists of Broca's area as its hub.

A task-dynamic approach to skilled movements of multi-degree-of-freedom effector systems is developed in which task-specific, relatively autonomous action units are specified within a functionally defined dynamical framework. Qualitative distinctions among tasks (e.g., the body maintaining a steady vertical posture or the hand reaching to a single spatial target versus cyclic vertical hopping or repetitive hand motion between two spatial targets) are captured by corresponding distinctions among dynamical topologies (e.g., point attractor versus limit cycle dynamics) defined at an abstract task space (or work space) level of description. The approach provides a unified account for several signature properties of skilled actions: trajectory shaping (e.g., hands move along approximately straight lines during unperturbed reaches) and immediate compensation (e.g., spontaneous adjustments occur over an entire effector system if a given part is disturbed en route to a goal). Both of these properties are viewed as implicit consequences of a task's underlying dynamics and, importantly, do not require explicit trajectory plans or replanning procedures. Two versions of task dynamics are derived (control law, network coupling) as possible methods of control and coordination in artificial (robotic, prosthetic) systems, and the network coupling version is explored as a biologically relevant control scheme.

How do space and time relate m rhythmical tasks that reqmre the hmbs to move singly or together m various modes of coordination ? And what kind of minimal theoretical model could account for the observed data9 Ead~er findings for human cychcal movements were consistent w~th a nonhnear, limit cycle oscdlator model (Kelso, Holt, Rubm, & Kugler, 198 l) although no detailed modehng was performed at that Ume In the present study, lonemauc data were sampled at 200 samples/second, and a detmled analysis of movement amphtude, frequency, peak velooty, and relative phase (for the blmanual modes, m phase and anuphase) was performed As frequency was scaled from l to 6 Hz (m steps of l Hz) using a pacing metronome, amphtude dropped reversely and peak veiooty m-creased WRhm a frequency condmon, the movement's amphtude scaled &rectly with lls peak veloc-Ry These &verse lonematlc behaviors were modeled exphotly m terms oflow-&menslonal (nonhn-ear) dlsslpaUve dynamics, wRh hnear stiffness as the only control parameter Data and model are shown to compare favorably The abstract, dynamical model offers a umfied treatment of a number of fundamental aspects of movement coordination and control How do space and time relate m rhythmical tasks that require the hands to move singly or together in various modes of coordi-nation9 And what kind of minimal theoretical model could account for the observed data? The present article addresses these fundamental questions that are of longstanding interest to experimental psychology and movement science (e g, von Hoist, 1937/1973; Scripture, 1899; Stetson & Bouman, 1935) It is well known, for example, that discrete and repetitive movements of different amplitude vary systematically in movement duration (provided accuracy requirements are held constant, e g, Cralk, 1947a, 1947b) This and related facts were later for-mahzed into F~tts's Law (1954), a relation among movement time, movement amplitude, and target accuracy, whose under-pmnmgs have been extensively studied (and debated upon) quite recently (e g.

No abstract