(training session) on dependent variables: d’, RT, c, ERP-wave amplitude and oscillatory activity band amplitude. Subject-level, Wilcoxon signed-rank test was performed to identify significant increases of energy in the above-mentioned frequency bands of oscillatory activity, and false discovery rate (FDR) was used to control for multiple comparisons [31]. Group level, paired samples t-student test was used: 1) To test the significance of the difference-wave for obtaining N2pc; 2) To decide whether to use absolute or relative values for P3 amplitude; 3) As planned pair-wise comparisons between training sessions where we expected gradual change in performance indexes; and 4) To examine if changing target orientation during the control session had an effect on psychophysical performance and brain activity. Finally, Pearson correlation coefficient was obtained in order to test the level of dependency between psychophysical and electrophysiological data. Due to the binary nature of the task (target detected or missed) it is not possible to extract a trial-by- trial correlation leading to a block-by-block design for ERPs- psychophysics and a session-by-session design for oscillatory activity-psychophysics. All of these procedures were implemented using the statistical computational package SPSS Statistics (IBMH). Results Psychophysical data Psychophysical results confirmed that the improvement of subject’s performance in the task was the result of perceptual Neurophysiology of Human Perceptual Learning learning. We observed a significant effect of training on sensitivity index d’ (F4,45 = 50.861, P,0.001; Figure 2A) with planned pair- wise comparisons, revealing a steady increase of sensitivity, particularly between first-second (t9 = 25.224, P,0.01), second- third (t9 = 25.544, P,0.001) and third-fourth (t9 = 26.586, P,0.001) training session. This trend did not continue between the fourth and last training session (t9 = 1.413, P = 0.191) suggesting that subjects reached a performance plateau. Impor- tantly, we found that performance improvement was specific for the trained stimuli as shown by the significant decrease in sensitivity when subjects were tested using a different target orientation in a subsequent control session (t9 = 6.403, P,0.001). Sensitivity improvement did not occur at the expense of the speed of response: there was a significant effect of training over mean RT (F4,45 = 95.495, P,0.001; Figure 2B), with responses getting faster along sessions. Planned pair-wise comparisons indicated a significant steady decrease in mean RT, between first-second (t9 = 4.944, P,0.01), second-third (t9 = 7.864, P,0.001), and third- fourth (t9 = 5.722, P,0.001), but not between the fourth and last training session (t9 = 20.39, P = 0.706). As with sensitivity, mean RT changes were specific for the trained orientation and did not transfer to other targets as shown by the slower responses obtained during the control session (t9 = 27.375, P,0.001). Sensitivity (d’) and response speed (RT) measures showed a negative correlation (R = 20.93, P,0.001; Figure 2C), allowing us to use both measures jointly as an indication of perceptual learning. Figure 2. Psychophysical assessment of perceptual learning. A) Increase of mean sensitivity along training. B) Decrease of mean RT along training. C) Block-level correlation showing a dependence between sensitivity and RT. D) Response criterion along training. All significant changes between sessions are indicated by asterisks. Control sessions are indicated by a red marker. doi:10.1371/journal.pone.0019221.g002 PLoS ONE | www.plosone.org 4 April 2011 | Volume 6 | Issue 4 | e19221