Neurophysiology of Human Perceptual Learning Figure 3. Effect of training over mean P3 amplitude. Grand average ERPs obtained from session 1, 3 and 5 (sessions 2 and 4 are omitted for clarity) and from electrodes: A) LP2, left parietal; B) RP2, right parietal; C) ZP3, central mid-line. D) Average topographic distribution of scalp potential peak in the P3 time window for the corresponding sessions. doi:10.1371/journal.pone.0019221.g003 Pearson correlation coefficient between both sensitivity and mean RT, versus neural activity measures (ERP wave amplitude and oscillatory activity). We found a significant positive correlation between P3 amplitude and sensitivity (Figure 7A) over left (R = 0.69, P,0.001), central (R = 0.68, P,0.001) and right (R = 0.5, P,0.001) electrodes. Furthermore, there was a significant negative correlation between P3 amplitude and mean RT (Figure 7B) over left (R = 20.75, P,0.001), central (R = 20.69, P,0.001) and right (R = 20.56, P,0.001) electrodes. In the case of N2pc amplitude, there was an even stronger positive correlation with sensitivity (Figure 7C) over left (R = 0.807, P,0.001) and right (R = 0.828, P,0.001) electrodes, and also a clear negative correlation with mean RT (Figure 7D) over left (R = 20.822, P,0.001) and right (R = 20.828, P,0.001) electrodes. Summarizing, as sensitivity increased and mean RT decreased throughout the training process we observed a clear trend of increase in P3 and N2pc amplitudes. On the other hand, and as was expected from the amplitude profiles of oscillatory neural activity throughout training, we found no significant correlations between either GBA or ABA amplitude and sensitivity or mean RT. Discussion Taken together, the present results show that training in a visual search task produces specific and measurable changes in neural activity associated with the process of perceptual learning. It seems, however, that the relation between perceptual learning and changes in brain activity is not straightforward. Indeed, while amplitude changes found in late components of the visual ERP point to a reorganization of neural networks involved in target detection and other context-related processes, modifications in alpha and gamma band oscillatory activity indicate the presence of two phases in learning acquisition. In other words, the observed relationship between psychophysical and electrophysiological results is inconsistent with a single neurophysiological process that could account for perceptual learning, but rather suggests a more complex scenario of dynamically interacting neural reorganiza- tions. In the following we discuss the main results and propose a neurophysiological model to account for our findings. Our behavioral results confirm the occurrence of perceptual learning in the visual search task as evidenced by an improvement in sensitivity and a reduction in RT throughout training (see Figure 2A,B). This improvement was found to be significant from first to fourth, but not between fourth and fifth sessions, suggesting that perceptual performance reached an asymptotic level towards the end of training. When analyzing the subject’s response criterion, on the other hand, we did not find a significant effect of training as revealed by the ANOVA design, suggesting that the training process did not modify the tendency of the subjects to indicate the presence or absence of the target. However, an apparent drop was observable in the overall pre-post comparison. A possible explanation for this would be that, albeit minimally, subjects increased FAs as they progressed through the task due to increased confidence. However, as shown in the supplementary Figure S1b, FAs maintained a constant level, thus ruling out that subjects became more prone to signal the presence of the target. Because c is calculated as the negative average of the z-scores of HR and FA (see Eq. 4), this could explain the progressive but non- significant drop of c seen in Figure 2a, given that HR systematically increases with training (see supplementary Figure S1a), while FAs stay unchanged. Finally, the behavioral improve- ments we observed were specific for the trained target orientation as can be seen from the drop in sensitivity and increase in RT when testing with a non-trained orientation in the control session. This is in agreement with results from the original version of this visual search paradigm, whereby practice-related enhancements in performance were shown to be specific for the trained object and orientation [23]. Our ERP results show training-dependent changes in the amplitude of ERP waves P3 and N2pc, but not for P1/N1. Such lack of modulation in early components may seem in contradiction with previous studies suggesting a link between low degree of learning generalization of learned stimuli and changes in early stages of the visual path. However, such studies have used fine- grained stimuli, presented always at the same location and with the relevant dimension consisting of a single basic feature, such as line orientation or position. In contrast, we trained subjects to find a simple shape in an array of distractors, a task that is unlikely to produce modifications in early visual areas because of the small receptive fields of its neurons [32]. Additionally, because targets could appear at different locations within the array it is possible that the resulting potentials cancelled out in the averages. Finally, and in agreement with fMRI studies of perceptual learning [33], triangles orientation may have been perceptually easy to determine, therefore leading to little or no reorganization in early stages of visual processing yet inducing more pronounced changes in later ones [5]. Although practice had a consistent effect on P3 amplitude, it turned out to be non-specific for the trained target, as revealed by the results of the control session (see Figure 3). This change cannot be attributed to habituation [34] or attention [35], since, on one hand, repetitive stimuli presentation leads to a decrease rather than an increase of P3 amplitude, while, on the other, an increase in RTs as the one observed during the control session (Figure 2b) would be incompatible with a general target-independent attentional facilitation. Previous studies have found training- dependent increments in the amplitude of the P3 wave, but they did not evaluate the level of specificity by testing subjects’ performance with untrained stimuli [3]. If the amplitude increment of P3 is correlated with performance, but not specifically related to the trained stimulus, the underlying changes in neural activity could be responsible for some other relevant aspect of task execution. We propose that modulation in P3 could reflect an unspecific task- or process-based learning [36] that is boosting performance through diffuse reinforcement signals [37]. This kind of task-based learning interpretation for P3 amplitude modulation is consistent with early (and most widely accepted) theories for the functional role of the P3 wave, according to which the rather heterogeneous cognitive conditions that affect P3 amplitude can be grouped under the concept of context update [38]. This would imply a process-based learning that is enhancing performance through the optimization of activity related to context update (i.e. updating of information regarding stimulus and general environmental conditions), but not to the specific identity of the target. Optimization of unspecific task-based processes is also consistent with previous behavioral evidence PLoS ONE | www.plosone.org 7 April 2011 | Volume 6 | Issue 4 | e19221