We have also ruled out the possibility that there could be an oculomotor explanation for our GBA results (see supplementary Text S1 and supplementary Figure S5), as it has been recently suggested to be the case of most scalp EEG studies [50]. Indeed, we believe that GBA as the one measured here is a relevant neurophysiological phenomena related to local and/or large-scale neuronal synchrony and cell assembly conformation [7,8]. If brain oscillations are related to neural assemblies formation by affecting the probability of temporal coincidence of unitary spikes relative to the phase of each oscillation cycle [9], then it is possible to link training-dependent GBA amplitude modifications to changes in the conformation of functional cell ensembles. This has long been considered as one of the most probable neural mechanisms for learning [51]. In the specific case of our paradigm, the progressive increment of GBA from the first to the third training session could reflect an increase in the strength and/or the number of synaptic connections, promoting a better signal-to-noise ratio and therefore improving perceptual performance. The following progressive decrease in GBA, on the other hand, is compatible with a second form of boosting target detection by means of an overall synaptic downscalling leading to the establishment of a sparse code. Here, only neurons with the most strong and/or selective response would remain as part of the responsive cell assembly, thus increasing coding efficiency. In this context, it is important to note that, although this is the first report on the role of oscillatory neural activity in perceptual learning, the finding of an increase-followed- by-a-decrease pattern of amplitude in neural activity has been previously found in fMRI studies that showed similar training Neurophysiology of Human Perceptual Learning dependent modifications in V1 [52] and regions of the ventral visual pathway [53]. Given the strong coupling between GBA and BOLD signal [54,55], we believe the convergence between our results regarding brain oscillatory activity and these previous fMRI studies is quite significant. Compared to GBA, ABA followed a mirror profile of amplitude changes across training, dropping initially but then increasing after the third session. Despite being the most prominent EEG rhythm, the functional role of alpha is still debated among the alternatives of cortical idling, inhibition or active top-down control hypotheses [56,57]. For example, regarding attentional orienting, a decrease in ABA has been found over contralateral-to-shift parietal cortex [58], but also an increase over parietal areas ipsilateral-to-shift when using a behaviorally relevant no-shift condition as control [59]. A clearer picture, however, may be drawn in the case of visual discrimination, where it has been shown that higher levels of pre-stimulus ABA correlates with lower performance in the task [60]. Given the complexity of this rhythm, it is certainly possible that ABA plays different roles depending on the task at hand [56]. In our study, subjects were trained to perform a visual search that involved both attention and discrimination and we observed an inverse correlation in the amplitude of ABA and GBA throughout the process (supplementary Figure S3). Taking into account the previous considerations regarding the putative roles of GBA and ABA, this leads us to propose that the present results may be revealing complementary properties of high neural excitability-GBA and low neural excitability-ABA during learning Figure 8. Neurophysiological model for visual perceptual learning. A) Schematic representation of the psychophysical results. B) Schematic representation of ERPs results. C) Schematic representation of oscillatory neural activity results. D) Simplified cartoon representing neural activity reorganizations proposed in the model as the neurophysiological basis of perceptual learning. Each panel represents putative neural networks placed on different brain regions. Empty circles represent inactive neurons or neural populations, while filled circles represent active neurons or neural populations. Arrows represent activity modulations among the different neural networks. For A, B and C, the blue lines mark the moment around which perceptual performance becomes saturated. doi:10.1371/journal.pone.0019221.g008 PLoS ONE | www.plosone.org 11 April 2011 | Volume 6 | Issue 4 | e19221