Neurophysiology of Human Perceptual Learning obtained as the difference between posterior electrodes ipsi and contralateral to target position in the search array [29,30]. Oscillatory brain activity was analyzed by means of a time- frequency (TF) representation using a Morlet wavelet transform of the signal obtained from each single trial: 1 ð Þ 2 ð Þ 3 ð Þ in front of the subject’s eyes. Each trial consisted in the presentation of a 300 ms search array including (S1) or not including (S2) a target, followed by a variable inter-trial interval (ITI) of 900, 1200 or 1500 ms (Figure 1A). Subjects were required to press the left or right button of a mouse in trials where they did or did not find the target respectively (two-alternative forced choice). The array subtended 4.2u64.2u and consisted of 24 equilateral triangles and a central fixation cross. Triangles were 279 long and had a separation of 549 between their centers. The target triangle was defined that which presented one of the four possible orthogonal positions of their basis, and held constant across all sessions and subjects. In contrast, distractor triangles could have any of the remaining three orientations. The target was randomly presented in 50% of the trials at any of the 24 possible locations. However, since the stimuli array can be divided into two main visual eccentricities (inner and outer), 25% of the trials the target was forced to appear at one of the two possible eccentricities. Target-absent search arrays were presented in the remaining 50% of the trials. We used black triangles on a gray background in order to minimize the formation of afterimages due to the offset of a high-contrast image. Stimuli presentation and behavioral response acquisition were controlled by Presentation Package (Neurobehavioral System, Inc.). After an explanation of the task, subjects were presented with an example of both types of arrays, namely S1 and S2 and the target orientation was indicated. All subjects were instructed to respond as fast as possible, to reduce blinking and avoid eye movements during execution of the task. In order to cancel lateralized motor-related neural activity and the development of a specific neuro-motor training of one hand over the other, the response hand was alternated between successive blocks. Electrophysiological data The EEG was recorded from 80 non-polarizable Ag/AgCl electrodes mounted on an elastic cap (Quick-Cap, Compumedics Neuroscan Inc.) spanning bilateral frontal, central, temporal, parietal and occipital positions as shown in Figure 1B. All sites were recorded respect to a CPZ reference electrode (here denoted as ZP2) and re-referenced off-line either to the algebraic average of the left and right mastoids (M1, M2) for ERP acquisition, or to an average of all electrodes for time-frequency analysis. Blinking, vertical and horizontal eye movements were monitored with three electrooculogram electrodes, two bellow and above the left eye and one on the external canthus of the right eye. Electrode impedance was kept bellow 10 KOhm and all the recordings were performed in a Faraday cage to reduce electromagnetic contamination. The EEG signal was acquired at a sampling rate of 1000 Hz, amplified and band-pass filtered between 1–200 Hz (NuAmps, Compumedics Neuroscan Inc.). Trials containing excessive blinking or eye movements were rejected, while other sources of signal contamination, such as myographic activity were corrected using ICA decomposition [28] and visual inspection of the data. ERPs were obtained for each condition and type of behavioral response (see bellow) by averaging over trials for each session and low-pass filtered at 30 Hz using MATLAB (Mathworks, Natick, MA, USA) toolbox EEGLAB [28]. ERP waves or components were identified on the basis of their polarity, latency and distinctive topographical properties. All ERPs were corrected respect to a 2300 to 250 ms pre-stimulus baseline and the amplitude of each component or wave was calculated as the mean potential in the following time windows (ms): P1, 75-135; N1, 80-180; N2pc, 200-350; P3, 320-550. The N2pc wave, which has been consistently shown to be involved in visual search tasks, was PLoS ONE | www.plosone.org 3 April 2011 | Volume 6 | Issue 4 | e19221 s(t) : E(t, f 0)~w(t, f 0) s(t^2)v, where time-varying energy E(t, f 0) of signal s(t) in a frequency around f 0 band is the result of convolving its square norm with a complex wavelet w (t, f 0). The wavelet family was defined with a ratio of f0/sf = 7 ranging from 2 to 90 Hz. TF-representations acquired in this way were averaged across trials according to each condition and divided into four frequency bands, namely: theta (4– 8 Hz), alpha (8–14 Hz), beta (20–30 Hz) and gamma (30–60 Hz). The mean power for each frequency band was normalized as amplitude z-scores relative to the baseline (2300 to 250 ms): in order to present brain oscillatory activity as power variations (activations or deactivations) related to stimulus presentation. z score (Pj)~(Pj mj)=sj, with Pj, m j and s j, representing power, mean and standard deviation of electrode J, respectively. TF-analysis was performed with the software package for electrophysiological analysis Elan- Pack, developed at INSERM U821 (http://u821.lyon.inserm.fr/). Psychophysical assessment We used signal detection theory indexes applied to psycho- physics to measure the perceptual learning process. All behavioral data analysis was performed using custom designed routines in MATLAB. Responses were classified in four types: yes to S1 (hit), no to S1 (miss), yes to S2 (false alarm) and no to S2 (correct rejection). Hit and false alarm rates were calculated as: H = P(‘‘yes’’/S1) and FA = P(‘‘yes’’/S2). In order to assess perceptual improvement in visual search performance, the sensitivity index to the target was measured as: d0~z(H){z(FA), where z(H) and z(FA) correspond to the inverse of the normal distribution transform of H and FA respectively. Values of H = 1 and FA = 0 were corrected to H = 0.99 and FA = 0.01 to avoid infinite values of d’. With the purpose of measuring bias as the tendency to respond ‘‘yes’’, a criterion index was obtained as: c~ 0:5 ½ z(H)zz(FA) : 4 ð Þ In this manner, if FA is equal to the miss rate, c = 0, if FA is higher or lower than the miss rate, c has negative or positive values respectively. Reaction times (RT) were computed as the time between search array onset and response execution. Statistical analysis Because we were interested in analyzing changes related to the actual learning-acquisition process, a repeated-measures ANOVA was used to analyze the effect of training as a five-level factor