remained untested [11]. Several fMRI studies in the last few years suggest that activity in core areas of the MNS (inferior frontal gyrus and inferior parietal lobe) and in cortical and subcortical regions supporting motor learning and visual perception, increase as a function of the complexity of the movements that are to be acquired [6,12–14]. The results of these experiments are inspiring as they reveal some of the underlying neural circuitry supporting observational learning. However, still little is known about the neural mechanisms and functional processes that operate within the extent of spatial activation patterns as indicated by functional imaging studies. For instance, not much is known about the functional contribution of areas and the integration of information between them in supporting functional aspects of observational learning over time. Two interesting proposals concerning the possible functional integration of information between areas supporting observational learning have recently been formulated. Iacoboni [5] proposes that observational learning is supported by internal (inverse and forward) models that dynamically link visual and motor processes to support the observation and imitation of new behavior. Internal models were first conceived to serve the execution of actions by allowing sensory structures to anticipate the perceptual conse- quences of initiated actions (via forward internal models), and use the perceptual error – i.e. the difference between the perceived and anticipated perceptual result – to generate corrective motor commands (using inverse internal models). Iacoboni proposes that similar to action execution, internal models may also be engaged during the observation of others’ actions, providing an internal simulation of the observed behavior. In his model, motor representations in parietal and frontal parts of the MNS are dynamically linked with perceptual representations in the posterior superior temporal sulcus. Similarly, Pineda [15] proposes that action observation (e.g. for observational learning) may cause alpha and mu rhythms in respective visual and sensorimotor structures to become transiently linked supporting the translation of ‘‘seeing’’ and/or ‘‘hearing’’ into ‘‘doing’’. According to this global entrainment hypothesis the coupling between perception and action is realized via global alpha/mu entrainment, resulting in widespread coherence between local alpha and mu generators in visual, auditory and motor structures when tasks demand integrated cognitive processing. However, according to the direct matching hypothesis of Rizzolatti, Fogassi, and Gallese [1] imitation learning is primarily associated with resonance in the motor system and has to be distinguished from activation in the visual system. According to this hypothesis observational learning depends on the core areas of the MNS, the inferior frontal gyrus and the inferior parietal lobe that are supporting motor simulation of observed movements. Different from the global entrainment hypothesis, the direct matching hypothesis does not predict alpha and mu source activations to become synchronized during observational learning. Instead, the direct matching hypothesis would predict coupling between frontal and parietal motor regions that make up the core MNS. Electroencephalograpic (EEG) measurements provide an excel- lent opportunity to investigate the contribution of visual and motor processes and their hypothesized integration during observational learning. Both EEG power and EEG coherence measures may provide relevant information to uncover the possible relationship between perceptual and action processes in observational learning. Variations in alpha and mu power over time, i.e. during different stages of observational learning, may help to determine a possible relationship between these two components. Also, EEG coherence [16], which provides a measure of the functional connectivity as expressed by the relative phase stability between oscillations Observational Learning recorded from different sites, may be used to uncover relations between different cortical areas supporting observational learning. Previous EEG studies in the domain of action observation have mostly focused on EEG power and found that observation of a movement is accompanied by a transient drop in the mu and beta power. Mutukumaraswamy and others, for instance [17,18] have shown the amplitude of the mu rhythm to attenuate when subjects observe a goal-directed grasping movement in much the same way as it is attenuated when subjects performed this same movement themselves. Surprisingly, whereas previous studies have consis- tently focused on desynchronization of the mu rhythm, modula- tions in alpha power during action observation have been largely neglected. Similar to the mu rhythm, alpha, which is strongest over visual cortices, is found to desynchronize to the presentation of stimuli (e.g. actions) and during tasks that require visual attention, e.g. for selecting, anticipating or remembering visual stimuli [15,19–22]. Consequently, in previous studies alpha and mu may have been confounded and the possible individual and joint effects of either component to observational learning remain to be determined. Note that although beta power (,16–30Hz), similar to mu, is found to desynchronize during action observation [9,23,33], its sources are mainly centrally distributed and thus not likely to be confounded with a posterior source. The present study focuses on the learning of new action sequences through observation, analogue to the observations of etiologists and psychologists [1,24–26] that new actions may be learned by recognizing the elementary motor acts and the specific order or sequence between them. For this type of observational learning to work, it is necessary that all movement elements are already within the motor repertoire of the observer. In the present study we used button presses that are well established in the motor repertoire of adult subjects and known for their ability to generate motor activation in an observer as shown by neuroimaging [27– 29] and electro- and magnetoencephalographic studies [30–33]. Sequences of movements were repeated four times to allow subjects to effectively memorize the correct order of movements for subsequent reproduction (see Figure 1). Furthermore, each action sequence repetition was intervened by a short pause to study the neural processes supporting retention and rehearsal of these movements. That is, whereas much attention has been directed at action observation, an important requirement for successful imitation appears to lie in the ability of the observer to retain or rehearse the order of successive motor acts for subsequent imitation. Most likely, efficient rehearsal involves a process of mental imagery in which subjects mentally simulate the motor and perceptual effects that would accompany the actual execution of such actions [11,34]. Consistent with this view, Buccino and colleagues [6] found that subjects activated large parts of the MNS, dorsal motor regions and the dorsolateral prefrontal cortex during a pause interval that intervened between the observation and execution phase in an experiment. To capture the specific neural systems supporting imitation learning, a control task was included in which subjects viewed the same action sequences, without a requirement for subsequent reproduction, but had to subsequently report deviant movements. Results The data of one of the participants contained too many artifacts to allow a reliable estimate of coherence. Data of this participant was excluded from both power and coherence analyses. Analysis of performance showed that the imitation task was challenging for most subjects. No participant succeeded in flawless performance. The amount of errors varied between 2 and 33 of the 40 trials PLoS ONE | www.plosone.org 2 December 2010 | Volume 5 | Issue 12 | e14482