Neural correlates of short-term perceptual learning in orientation discrimination indexed by event-related potentials

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1 Chinese Science Bulletin 2007 Science in China Press Springer-Verlag Neural correlates of short-term perceptual learning in orientation discrimination indexed by event-related potentials SONG Yan 1, PENG DanLing 1, LI XiaoLan 1, ZHANG Yi 1, KANG Jing 1, QU Zhe 2 & DING YuLong 2 1 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing , China; 2 Department of Psychology, Sun Yat-Sen University, Guangzhou , China The current work investigated the neural correlates of visual perceptual learning in grating orientation discrimination by recording event-related potentials (ERPs) from human adults. Subjects were trained with a discrimination task of grating orientation in three consecutive training sessions within 2 h. While reaction times (RTs) were shortened gradually across training sessions, the N1 was decreased and the P2 was increased over the parietal and occipital areas. A broadly distributed P3 was increased along with more practices. In addition, the time course of learning reflected in the P2 and P3 amplitudes was in line with the changes of reaction times and exhibited a stable level during later training. The implications of these results to the neural mechanisms subserving perceptual learning were discussed. event-related potentials (ERPs), vision, perceptual learning, grating orientation, human adults Perceptual learning is defined as a change of performance, usually an improvement, as a result of training. Psychological studies of human adults have found perceptual learning in all sensory submodalities such as olfaction, taste, hearing, and vision. In the visual domain, improvement through training occurs for several classical tasks such as motion perception [1], orientation discrimination [2], discrimination of spatial frequencies [3] and texture [4], as well as in seeing form from motion [5]. Among these visual tasks, orientation discrimination is one of the most intensively studied and best known. Much behavioral research has found that improvement in orientation discrimination task is often restricted to a particular location in the visual field or a particular orientation that has been trained [2]. Such specificity for elemental stimuli features might reflect learning-induced changes taking place at the early processing stages in the visual system, at the level where retinotopic organization of the visual inputs are still retained, and where different orientations are processed separately [6]. However, such learning-induced neural changes may also involve a larger network of brain regions, including feed back influences coming from higher-order cortical areas [7]. For example, perceptual learning seems to be specific both to the task used during the training and to the visual context [8], suggesting that learning is also influenced by attentional mechanisms. In humans, the effects of learning on the early visual system have been mainly investigated at the behavioral level to date, whereas neural studies of perceptual learning are very lacking. No consistent conclusions have been reached yet as to whether perceptual judgments improve with practice and how this occurs. For learning in grating orientation discrimination as well, there are only a few neural studies in humans such as one PET study by Schiltz et al. [9]. They reported that orientation discrimination learning involves both striate Received May 17, 2006; accepted November 27, 2006 doi: /s Corresponding authors ( songyan@bnu.edu.cn and edsdyl@mail.sysu.edu.cn) Supported by the National Natural Science Foundation of China (Grant Nos , and ) and the Beijing Natural Science Foundation (Grant No ). Chinese Science Bulletin February 2007 vol. 52 no

2 and extrastriate visual areas. In another relevant study, Ding et al. [10] recorded event-related potentials (ERPs) of human adults when they were trained with a line orientation discrimination task. The results showed that the N1 amplitudes decreased over the left parietal area and the P2 amplitudes increased over the left parietal/occipital areas. However, the stimuli and paradigm used in Ding et al. s study were rather different than those used in many classical behavioral studies and the PET study. It is very difficult to compare Ding et al. s ERP results with other researchers findings. Here, we designed an event-related potential experiment to determine the neural substrates that mediate visual perceptual learning in grating orientation discrimination. We used a classical paradigm that has been intensively studied and is well known to most researchers. In contrast to other neuroimaging methods (such as PET, fmri), the temporal resolution of ERPs is much higher, making it possible to quantify the latency of any observed learning effects. By observing the training-related changes of ERPs, including their scalp distribution and time course, we investigated the neural correlates of short-term perceptual learning in grating orientation discrimination. 1 Materials and methods 1.1 Subjects Fifteen college and graduate students (9 females) participated in this experiment as paid volunteers. Subjects were years old with normal or corrected-tonormal vision and were naive to the task. All were right-handed. 1.2 Stimuli All the stimuli (six types) were white on a uniform black background and were presented in the center of the monitor. The grating stimuli ( ) were square wave, 1.2 cycle/ gratings of the same mean luminance (9.0 cd/m 2 ). In order to eliminate cues other than orientation, noise was superimposed on the edges of the bars (Figure 1). The subjects were trained to discriminate small orientation difference around an oblique orientation of 45. Stimuli were presented clockwise or counter-clockwise with respect to this reference. The reference grating was only presented before the beginning of each block to remind the subjects of the reference orientation. No visible indication for the reference Figure 1 Stimuli used in the present experiment. The orientation difference between the grating and the reference orientation ranged from 9 to 11. The reference grating was only presented before the beginning of each block to remind the subjects of the reference orientation. No visible indication for the reference orientation of 45 was provided during the EEG recording. orientation of 45 was provided during the EEG recording. The orientation difference between the grating stimuli and the reference orientation ranged from 9 to 11 (δ = 9, 10 or 11 ). Therefore, there were totally six types of stimuli, whose orientation were 34, 35, 36, 54, 55 and 56, respectively. Stimulus duration was 200 ms and interstimulus intervals (ISI) were randomized between 1800 and 2200 ms. At the center of the display, a green cross ( ) was present during the interstimulus intervals as the fixation. 1.3 Procedure The subjects were then required to press the right key for gratings tilted clockwise from the reference orientation and the left key for grating tilted counter-clockwise. Both accuracy and speed were emphasized. The six types of stimulus were randomized and with equal probability (16.67%) in a block. Each subject was given three consecutive training sessions during which both reaction times and ERPs were recorded. Each session contained nine blocks of 40 trials and lasted about 30 min. Subjects were given breaks of min between two sessions to maintain high concentration and to prevent fatigue. At the very beginning, the subjects practiced the operation for one or two blocks to ensure that they understood the task. The whole experiment lasted about 2 h. 1.4 ERP recording and data analysis Electroencephalogram (EEG) was recorded using the Scan4.2 package (NeuroScan, Inc.). A Quick-cap with 62 tin scalp electrodes was used. The horizontal electro-oculogram (EOG) was recorded from two electrodes positioned at the outer canthus of each eye, and the vertical EOG was recorded from an electrode located below ARTICLES NEUROBIOLOGY SONG Yan et al. Chinese Science Bulletin February 2007 vol. 52 no

3 the left eye. EEG was physically referenced to the left mastoid and then was off-line re-referenced to the average of the left and right mastoid. Electrode impedance was kept below 5 kω. EEG was amplified with a band pass of Hz, digitized on-line at a sampling rate of 500 Hz. Each epoch of EEG was 200 ms of prestimulus to 800 ms of post-stimulus. Trials contaminated by eye blinks, eye movement, or muscle potentials exceeding ±60 μv at any electrode, as well as incorrect behavioral responses were excluded from the ERP averages, resulting in exclusion of about 10% of the trials from the average. The baseline for ERP measurements was the mean voltage of a 200 ms prestimulus interval. Behavioral data were analyzed with repeated measures analysis of variances (ANOVAs) with the factor being training (session 1, 2 or 3). For ERP analysis, Multiple ANOVAs were firstly performed using a sliding time window of 20 ms from ms (i.e , , , ms, etc.) at each electrode with the factor being training (session 1, 2 or 3) to provide a finer temporal analysis of the learning effect. Significant learning effects were only obtained at electrodes over the central, parietal and occipital cortical areas. Further statistical analysis was then restricted to a small set of electrodes (C3 C4, P3 P4, and O1 O2) where learning effects were most significant. ANOVAs were analyzed for the mean amplitude of the N1 component (at O1, O2, P3 and P4 sites), P2 component (at O1, O2, P3 and P4 sites) and P3 component (at C3, C4, P3, P4, O1 and O2 sites). The factors were training (session 1, 2 or 3), area (parietal or occipital for N1 and P2; central, parietal or occipital for P3) and hemisphere (left or right hemisphere). To clarify the source of the significant Session effect in different time courses, further pairwise comparisons were performed to compare the difference between each pair of training sessions. 2 Results 2.1 Behavioral measures The results of reaction times (RTs) are shown in Figure 2. The mean reaction times decreased significantly across training sessions (586, 512, 491 ms in sessions 1 3, respectively; F(2,28)=19.948, P<0.001), suggesting that the performance of discrimination was improved by training. Further pairwise comparisons revealed that the Figure 2 Changes in RTs (upper), N1 amplitudes at O1 site (middle) and P2 amplitudes at O1 site (lower) in the course of learning. Error bars indicate the standard error of the mean (SEM). improvement reached significance only between the first two sessions (session 1 vs. session 2: P<0.005; session 2 vs. session 3: P>0.05). Response accuracy was high (averaged 95%) and stable throughout the experiment. 2.2 ERP measures ERPs of the three training sessions were all characterized by P1 ( ms), N1 ( ms), P2 ( ms) and N2 ( ms, with two small peaks at 240 and 350 ms) over the occipital-temporal and parietal areas; P160 ( ms), N300 ( ms) over the central/frontal areas (with the maximum at the frontal area); and a broadly distributed P3 ( ms). It is obvious that peak latencies of each component were similar for all training sessions. There were, however, significant differences of ERP amplitude on three important components, i.e. N1, P2 and P3, over posterior and central areas (Figure 3). Their amplitudes were measured as the mean voltages within the intervals 354 SONG Yan et al. Chinese Science Bulletin February 2007 vol. 52 no

4 Figure 3 The grand averaged ERPs elicited by grating stimuli during the three training sessions. The thin continuous, dotted, and thick continuous lines refer to sessions 1 3, respectively. Note that the N1 decreased and the P2 increased across training sessions at the parietal/occipital electrodes, while the P3 increased across training sessions at the central, parietal and occipital electrodes , , and ms, respectively. For N1, the main effect of training was significant (F(2,28)=4.235, P<0.05), suggesting the N1 amplitudes were decreased by training (Figures 2 and 3). In addition, the marked decrease across training sessions distributed over both the occipital and parietal areas (training area: F(2,28)=1.627, P>0.05; Figure 4). Further pairwise comparisons showed that the decrement of N1 amplitude reached significance both between sessions 1 and 2 (parietal and occipital areas: two Ps<0.05), and sessions 2 and 3 (two Ps<0.05). These results suggested that the N1 amplitudes decreased gradually over the three training sessions. As opposed to the decrease in N1, training elicited a reliable increase in P2 (F(2,28)=7.152, P<0.005; Figures 2 4). (F(2,28)=0.739, P>0.05). The interaction between training and area was not significant (F(2,28)=1.627, P>0.05), suggesting that P2 increased over both the parietal and occipital areas (Figure 4). Further pairwise comparisons showed that the training-induced P2 increment mainly occurred during the first two sessions (parietal and occipital areas: session 1 vs. session 2, two Ps<0.05; session 2 vs. session 3: two Ps>0.05). Similar to P2, the P3 amplitude was also enhanced during the training (F(2,28)=8.447, P<0.005). The interaction of training area was not significant (F(4,56)=0.924, P>0.05), indicating that the training effect did not differ across central, parietal and occipital areas (Figure 4). The training effect on P3 mainly occurred during the first two sessions (central, parietal and occipital areas: session 1 vs. session 2, all Ps<0.05; session 2 vs. session 3: all Ps>0.05). There were not significant hemisphere main effects or interactions (Fs<1) for any of the three components, suggesting that the training effects were not different at electrodes over the left and right hemispheres (Figure 4). 3 Discussion In the present study, behavioral performance showed that the participants reaction times were significantly shortened by training within brief time epochs. Our behavioral results suggest that the ERP effects observed here might not reflect the effect of fatigue, which always increases reaction times. Moreover, since habituation in visual tasks usually elicits smaller P3 [11], our enhanced ARTICLES NEUROBIOLOGY Figure 4 Maps of the difference wave (ERPs in the third training session minus those in the first training session). Note that the difference wave in the N1 ( ms) and P2 ( ms) time windows focused over the parietal/occipital areas. The distribution of scalp electrodes used for EEG recording is shown on the right side. SONG Yan et al. Chinese Science Bulletin February 2007 vol. 52 no

5 P3 suggested that the ERP effects reported here might not be accounted for by habituation. Therefore, the changes of ERPs in the present study might relate to perceptual learning rather than other possible factors. Several ERP components showed significant changes during perceptual learning within the two hours, indicating that the cortex can dynamically modify the processing of visual information according to immediate behavioral requirements [12]. Compared with the reaction times, the ERP data provided more details about the time course of learning. For example, the change of P2 amplitudes was only significant between sessions 1 and 2, and did not reach significance between sessions 2 and 3. This kind of change was similar to the changes in reaction times. Previous research has reported that P2 was sensitive to stimulus orientation [13]. Therefore, it is not strange to find that the time course of the P2 increment was in line with the behavioral data in our study. We suggest that P2 might be partially related to the electrophysiological substrates of perceptual mechanisms underlying the learning effect observed in RTs. It should be noted that the training effects on two early ERP components (N1 and P2) focused over the posterior areas, which might reflect neuronal plasticity in the low level of visual cortex. We also found similar N1 and P2 effects over posterior areas when subjects did a line orientation discrimination task in visual search paradigm [10]. It is a very important question in perceptual learning as to whether there is similar neural mechanism with different stimuli and different paradigms. Our previous study indicated that the neuronal mechanisms involved in perceptual learning are related to the complexity of the stimuli used in the discrimination task [14]. That is, compared with simple attributes, learning of complex visual attributes may occur at relatively higher levels of information processing. In the present study, our results show that learning in grating orientation discrimination has very similar neural substrates to line orientation discrimination, although the two studies adopted different stimuli and different paradigms. Our results further indicate that the neuronal mechanisms involved in perceptual learning may mainly depend on the essential nature of the stimuli, independent of lines or gratings. On the other hand, the N1 decrement also might reflect a reduction in attentional modulation after training. First, many studies have found that the N1 amplitude can be modulated by attention [15]. Second, previous studies have found that there is strong interaction between perceptual learning and attention: perceptual learning is under top-down control, and attentional effects are subject to learning [8,16,17]. Gilbert et al. [12] even proposed that one of the consequences of learning is to release the dependence of performance from attentional control. Therefore, the possibility that the N1effects might correspond to a reduction of attentional modulation during perceptual learning can not be excluded in the present study. In addition to the early N1 and P2 components, we also found a broadly distributed P3 enhancement, and this ERP effect was paralleled by the effect on RTs. The P3 increment has also been found in auditory perceptual learning when the subjects were rats [18] or humans [19]. In our previous study [20], we found the increment of P3 can be retained from day to day during long-term perceptual learning. We suggested that the P3 might reflect a consequence of perceptual learning such as enhanced confidence after learning [21]. On the other hand, there might be other factors, such as procedural learning or finger movement learning. Accurate explanations for the P3 increment should also be sought in further studies. Whether perceptual learning increases or decreases neural activities is still a key unsolved issue. In the present study, negative waves decreased while positive waves increased across training sessions. In addition, we found that learning changed the amplitudes, but not the latencies, of the ERP components. The change of ERP latencies might need more time (or sleep) to be observed for the present task. For example, Atienza et al. [22] found that mismatch negativity (MMN) latency did not changed after auditory training during wakefulness, but this latency was significantly shortened during succedent REM sleep. In conclusion, the present ERP study shows that training in grating orientation discrimination induced several significant effects in brain activity within two hours, suggesting different stages of visual processing are involved in short-term visual perceptual learning. The ERP changes observed confirm the hypothesis concerning the existence of learning related changes at early levels of visual processing in human adults. 356 SONG Yan et al. Chinese Science Bulletin February 2007 vol. 52 no

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