Observational Learning Figure 3. Localization of the Effects in Alpha and Mu Power. The alpha and mu effects (pause – observation) were calculated using minimum- norm analysis in BESA 5.1.6. (Brain Electrical Source Analysis, MEGIS Software, Germany). The minimal norm was computed with spatiotemporal weighting according to [49] fitted to an individual Talairach transformed brain surface. Effects are scaled with respect to the maximum difference between pause and observation intervals (100%, in blue). The left sources represent the alpha power, reflecting the difference between pause and observation intervals in the detection task, and the right sources represent the mu power, reflecting the difference between pause and observation in the imitation task. doi:10.1371/journal.pone.0014482.g003 right). In addition, the minimum norm displays an effect in the right occipital gyrus reflecting the difference in alpha power between observation and pause intervals of the imitation task that was found concurrently over posterior areas. Coherence analysis Figure 4 presents the network found to be activated more strongly during the observation of movements than during pause intervals (see Method section below for details on the procedure). This network basically consists of two individual networks each connecting parietal, central and frontal areas in each respective hemisphere. An ANCOVA with Period (observation, pause) and Repetition (S1, S2, S3, S4) as within-subject factors and imitation accuracy as a covariate tested if coherence within the pair of fronto-parietal Figure 4. Fronto-parietal Coherence Network. Pairs of electrodes showing higher mu-frequency coherence during the observation of movement sequences than during the pause intervals. Only electrode pairs with the largest normalized coherence effects (1.z.2, z.2) are shown. doi:10.1371/journal.pone.0014482.g004 networks during observation for imitation was related to individual differences in imitation performance. Within the networks, coherence varied as a function of imitation accuracy, F(1, 12) = 4.8, p = .049, showing larger coherence for subjects with higher imitation accuracy. Furthermore, imitation accuracy significantly interacted with Period and Repetition, F(3, 36) = 4.0, p = .026. This interaction effect is reflected in Figure 5B where the coherence data is split between subjects with high and low imitation accuracy (Figure 5A). To further specify how coherence in the fronto-parietal networks is supporting imitation accuracy over time, a series of correlation analyses were performed between imitation accuracy and each consecutive observation and pause. One subject with exceptionally high coherence values (.0.6) was removed from the correlation analysis to avoid spurious correlations. This did not affect the overall pattern of correlation results. The results of this correlation analysis are represented in Figure 5C. Interestingly, a positive correlation was found between imitation accuracy and fronto- parietal coherence during the first observation of the sequence, r = 0.48, p = .05. No significant correlation was found in the subsequent pause, r = 0.20, p = .25. Interestingly, a reversed pattern of results was found during the third repetition, where imitation accuracy correlated positively with fronto-parietal coherence during the pause, r = 0.54, p = .027, but not during observation of movement sequences. These findings suggest that the critical difference between individuals who do well in imitation learning and those who do not, is that the former will i) coherently activate fronto-parietal systems during the initial observations of the movement sequence, and ii) are able to reproduce fronto- parietal activation during pause intervals after having observed several repetitions of the same movement sequence. Discussion In the present study we investigated the neural mechanisms supporting the acquisition of new action sequences focusing on brain oscillations in the mu/alpha frequency range. To determine PLoS ONE | www.plosone.org 5 December 2010 | Volume 5 | Issue 12 | e14482