When things look wrong: Theta activity in rule violation

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1 Neuropsychologia 45 (2007) Note When things look wrong: Theta activity in rule violation Gabriel Tzur a,b, Andrea Berger a,b, a Department of Behavioral Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel b Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel Received 17 July 2006; received in revised form 12 April 2007; accepted 3 May 2007 Available online 17 May 2007 Abstract A violation of a rule or expectation is known to evoke a phasic negative potential over the medial frontal cortex. This electrophysiological effect has been shown for incorrect mathematical equations and incongruent words at the end of sentences. The cognitive processes elicited in rule violation seem to involve violation of expectation, error detection, and conflict between competing cognitions. Consistent with the conceptual relation between rule violation and error/conflict detection, rule violation conditions should involve a power increase in the theta frequency band involving the anterior cingulate cortex (ACC). The present study verifies the connection between rule violation and theta activity using a wavelet analysis. Moreover, low resolution brain electromagnetic tomography (LORETA) source localization connects this theta activity to the ACC. Furthermore, the results show that theta activity is sensitive to the salience of the violation, that is, the degree of deviation of the conflicting/erroneous stimulus from the correct (expected) one Elsevier Ltd. All rights reserved. Keywords: Anterior cingulate cortex; Conflict detection; Error related negativity; N400; Error detection 1. Introduction Rule violation task refers to an experimental situation in which participants discriminate incorrect from correct information. The detection of incorrect information is known to evoke a phasic negative potential over the medial frontal cortex. This wave is usually named the N400, and has been shown for incorrect mathematical equations (e.g., 4+3=9) and incongruent words within sentences (Jost, Hennighausen, & Rosler, 2004; Niedeggen & Rosler, 1999; Niedeggen, Rosler, & Jost, 1999; Nunez-Pena & Honrubia-Serrano, 2004; Szucs & Csepe, 2005; Wang, Kong, Tang, Zhuang, & Li, 2000). The medial frontal negativity seems not to be exclusively related to arithmetic or to language violations. This has been demonstrated by Nigam, Hoffman, and Simons (1992). In this study, similar brain activity was observed when participants read a sentence ending either in a word or picture unrelated to the content of the sentence, suggesting that the critical aspect eliciting Corresponding author at: Department of Behavioral Sciences, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 84105, Israel. Tel.: ; fax: address: andrea@bgu.ac.il (A. Berger). the electrophysiolgical effect of negative medial frontal activity is the inconsistency and violation of prior expectations. The cognitive processes elicited in rule violation seem to involve violation of expectation, error detection, and conflict between competing cognitions. These mentioned functions have been related to frontal structures and especially to the anterior cingulate cortex (ACC) (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Bush, Luu, & Posner, 2000; Carter et al., 1998; Dehaene, Posner, & Tucker, 1994; Gehring, Goss, Coles, Meyer, & Donchin, 1993; Luu, Tucker, Derryberry, Reed, & Poulsen, 2003). Moreover, the ACC activity in error/conflict detection has been found to be expressed in the theta frequency (Luu & Tucker, 2001; Luu et al., 2003; Luu, Tucker, & Makeig, 2004). Consistent with a conceptual relation between rule violation and error/conflict detection, Hald, Bastiaansen, and Hagoort (2006) recently reported increased power in the theta frequency band over medial frontal areas for semantic violations, although they did not examine the source of this activity. The first aim of the present study was to verify the connection between rule violation and theta activity. Second, this study examined whether the source of this theta activity could be localize to the ACC. In other words, the study was designed to verify whether violations of arithmetic rules (i.e., mathematical /$ see front matter 2007 Elsevier Ltd. All rights reserved. doi: /j.neuropsychologia

2 G. Tzur, A. Berger / Neuropsychologia 45 (2007) equations) would be reflected mostly in theta activity (4 8 Hz) involving the ACC. Moreover, since several prior studies have found that in arithmetic violations, larger discrepancies between correct and incorrect solutions result in larger event related potential (ERP) amplitudes (Niedeggen & Rosler, 1999; Szucs & Csepe, 2005), we hypothesized that more salient violations would be reflected in stronger theta activity. Therefore, a third aim of the study was to examine the sensitivity of the theta power to the salience of the violation, that is, to the degree of deviation of the incorrect solution from the correct (expected) one. Hence, in this study, the incorrect condition was manipulated for three levels of deviation from the correct solution (e.g., for the equation 1+2=, the correct solution was 3, and the incorrect solutions were either 4, 6 or, 8 ). A wavelet time frequency analysis (Samar, Bopardikar, Rao, & Swartz, 1999) was conducted on the stimulus locked ERPs, to verify the existence of dominant theta activity. We hypothesized that an incorrect solution would be reflected by an increase in power in the theta frequency band, and that this theta effect would be stronger for the more deviating incorrect equations. Source localization was done by using the LORETA method (Pascual-Marqui, Michel, & Lehmann, 1994) to search for ACC involvement. 2. Method 2.1. Participants Seventeen right-handed participants (fourteen females and three males), all Ben-Gurion University of the Negev students, were tested. Participants gave informed consent and participated in the study as partial fulfillment of course requirements. The mean age of the group was 23.8 years (S.D. = 1.3). They were all healthy with no history of neurological illnesses and had normal or corrected-to-normal vision Procedure Participants were presented with 360 trials (plus 30 practice trials) of simple mathematical equations (addition or subtraction), which were followed by either correct solutions (180 trials) or incorrect solutions (180 trials). Within the incorrect solution condition, there were three possible levels of deviation, appearing with equal probability. For example, for the equation =, the incorrect solution could be either 4 (L1), 6 (L3) or 8 (L5). The number of positive and negative deviations (of incorrect solutions from correct ones) was equal. Equations including identical operands (e.g., 3 + 3, 4 + 4) were excluded. The 360 trials were presented in a random order in four blocks (45 correct and incorrect trials in each block). Each trial began with a fixation point (500 ms), followed by an equation (1500 ms), then a black screen (600 ms for baseline calculation), and ended with a solution (1500 ms). Random inter-trial intervals (ITIs; 200/400/600 ms) were inserted in order to reduce a monotonous task rhythm. Participants were seated 60 cm in front of a computer monitor and asked to be as relaxed as possible in order to reduce muscle tension. They were told at the beginning of the experiment that they were participating in a cognitive research in the field of numerical processing, and that they would be presented with simple mathematical equations followed by either correct or incorrect solutions. They were asked to distinguish between correct and incorrect solutions by pressing a button (i.e., 1 for correct and 4 for incorrect, the side of the buttons was counterbalanced between participants). The addition of the motor response helped to engage the participants in the task. This manipulation also ensured that only trials in which participants succeeded in distinguishing a correct solution from an incorrect one were included in the analyses. Therefore, participants were asked to respond only when they were sure of their decisions and not as quickly as possible Electroencephalogram (EEG) recording The EEG was recorded from 128 scalp sites using the EGI s Geodesic Sensor net and system (Tucker, 1993). Electrode impedances were kept below 40 k, an acceptable level for this system (Ferree, Luu, Russell, & Tucker, 2001). All channels were referenced to the Cz channel and data were collected using a Hz bandpass filter. Signals were collected at 250 samples per second and digitized with a 16-bit A/D converter Time frequency analysis Time frequency analysis of the data was conducted using wavelet-based analysis (Samar et al., 1999). Before the wavelet analysis, each participant s raw unfiltered (0 100 Hz) EEG data were segmented into trials, time-locked to the presentation of the solution. The segmented data were then inspected for artifacts (e.g., bad-channels resulting from channel-saturation, muscle movement, etc.) while excluding channels within each segment that exceeded the fast average amplitude of 200 V or the differential average amplitude of 100 V. Segments having 10 or more bad channels were excluded, and segments with fewer than 10 bad channels were included after replacing the bad-channel data with spherical interpolation of the neighboring channel values. Prior to wavelet analysis, the data of each trial were re-referenced to the average of all of the sensors at each time point, and then averaged (a phase lock analysis) into correct and three incorrect (i.e., L1, L3, L5) conditions (stimuluslocked to the solution presentation) (Tallon-Baudry, Bertrand, Delpuech, & Pernier, 1996). Following this, a family of Morlet wavelets was constructed at intervals of 0.5 Hz frequency, ranging from 1 to 30 Hz. Our wavelet family was computed using a f 0 /σ f ratio of 7 (Muthukumaraswamy & Johnson, 2004; Samar et al., 1999). The average power values (i.e., squared amplitude) were obtained relative to a 200 ms baseline pre-solution interval. 3. Results The statistical analyses were done on the mean of a group of nine channels, located between (and including) Cz and Fz of the system. This localization is comparable to the N400 component in rule-violation studies (Jost et al., 2004; Niedeggen & Rosler, 1999; Niedeggen et al., 1999). The wavelet analysis results showed a relative power increase in the theta frequency band (4 8 Hz) for the incorrect (averaged L1, L3, L5) compared to the correct condition. This power increase began approximately 100 ms after the presentation of the solution and ended about 400 ms later (see Fig. 1(a and b)). The analysis of this effect was conducted in the following way: For each condition (i.e., correct, L1, L3, L5), the maximum amplitude of the mean power of each frequency band (i.e., delta: 1 4 Hz, theta: 4 8 Hz, alpha: 8 12 Hz and beta: Hz) was extracted from a ms time-window, for each participant. The extracted power data were then analyzed using repeated measures ANOVAs (Kiebel, Tallon-Baudry, & Friston, 2005), with the solution conditions (correct, L1, L3, L5) and the frequency bands (delta, theta, alpha and beta) as within-subject variables (significance level was set to.05). Both the frequency bands main effect and solution frequency bands interaction effect reached statistical significance, F(3, 48) = 8.33, p < and F(9, 144) = 3.51, p < 0.001, respectively. The solution main effect reached a marginal statistical significance, F(3, 48) = 2.58, p =

3 3124 G. Tzur, A. Berger / Neuropsychologia 45 (2007) solution from the correct one (i.e., L3 and L5) was related to a greater power increase than for smaller deviations (i.e., L1). The first planned comparison (correct versus incorrect) reached statistical significance only in the theta frequency band (4 8 Hz), F(1, 16) = 4.99, p < 0.042, while it was far from reaching significance in the delta, alpha and beta frequency bands, F(1, 16) = 0.87, p = 0.363, F(1, 16) = 1.2, p = 0.288, and F(1, 16) = 0.88, p = 0.361, respectively, indicating greater power for the incorrect condition compared to the correct one only in the theta frequency band (Fig. 1(c)). Therefore, the additional planned comparisons were done only in the theta band. The comparison between L1 versus (L3 and L5) reached statistical significance, F(1, 16) = 5.92, p < 0.028, indicating that the theta power effect depended on the degree of deviation from the correct solution. However, the L3 versus L5 comparison was not significant, F(1, 16) = 1.62, p = 0.222, which might suggest some nonlinearity of this effect (see Fig. 1(c)) Source localization Data analyses The ERP data of each participant (i.e., segmented, averaged, etc.) were filtered to 4 12 Hz (focusing on theta and alpha rhythms (following Luu & Tucker, 2001; Luu et al., 2003, 2004) and were analyzed using the LORETA method (Pascual-Marqui et al., 1994). This was done in order to determine the brain sources related to the theta power increase between the correct and incorrect (L5) conditions. Fig. 1. Grand average time frequency plot from the Fz channel for the correct (a) and incorrect (b) conditions. Dark areas indicate low power values whereas light areas show high power. Only in the theta frequency band (4 8 Hz) is a greater power increase seen between conditions. (c) Relative power in the different frequency bands for the different conditions. Greater power activity is found in the theta band for the most salient incorrect solutions (L3 and L5) Statistical analyses Two-tailed t-tests for dependent samples were used to compare the neurophysiological parameters between the correct and incorrect (L5) conditions. In LORETA, the difference analysis between the conditions corresponds to the statistical nonparametric mapping (Holmes, Blair, Watson, & Ford, 1996). The LORETA analysis relies on a bootstrap method with 5000 randomized samples (Pascual-Marqui, 1999) and gives exact significance thresholds (corrected for multiple comparisons), regardless of non-normality. The localization of differences between conditions were computed by voxel-by-voxel t-tests for dependent measures of the average LORETA images over a ms time window (a time window consistent with the theta power effect, see Fig. 1(a and b)), based on the log transformed power of the estimated electric current density. Within the interaction effect, a planned comparison was conducted comparing correct versus incorrect conditions for each frequency band (i.e., delta, theta, alpha and beta) separately. This was done in order to evaluate which of the four frequency bands expressed power differences between the correct and incorrect conditions. Whenever a difference in power between the correct and incorrect conditions reached statistical significance, two additional planned orthogonal comparisons were conducted within the three levels of the incorrect condition: (1) L1 versus (L3 and L5) and 2) L3 versus L5. This was done in order to find out whether greater deviation of the incorrect 4. Results The statistical comparison of the cortical sources between the conditions revealed a significantly stronger activation after incorrect solutions in several areas of the brain, including the ACC and the cingulate gyrus (BA: 24, 32, see Fig. 2), the precentral and postcentral gyrus, the superior, medial and inferior frontal gyrus (BA: 2, 3, 5, 6, 7, 9, 10, 32, 43, 45), the angular gyrus, the precuneus, the superior and inferior parietal lobule (BA: 7, 39, 40), the inferior and transverse temporal gyrus (BA: 20, 42) and the middle occipital gyrus (BA: 19).

4 G. Tzur, A. Berger / Neuropsychologia 45 (2007) Fig. 2. Graphical representation of the LORETA t-statistics comparing the ERPs for incorrect and correct solutions. The red color indicates local maxima of increased electrical activity for incorrect compared to correct conditions (t-threshold for p < 0.05 is ). Black arrows mark the ACC as the source center of significantly increased activity in the incorrect condition. 5. Discussion The findings of the present study show that violation of arithmetic rules (Niedeggen & Rosler, 1999; Szucs & Csepe, 2005) are related to a relative increase in power of the theta frequency band (4 8 Hz). This finding is consistent with the study of Hald et al. (2006) that reported a power increase in the theta frequency band over medial frontal areas for semantic violations. In the present study, the LORETA (Pascual-Marqui et al., 1994) source localization technique was used to estimate the brain structures related to rule violations. Our findings suggest that the ACC is one of the brain structures that are most likely to be involved. These findings are in line with the vast literature connecting the ACC to cognitive processes related to error detection and conflict between competing cognitions and responses (Botvinick et al., 2001; Bush et al., 2000; Carter et al., 1998; Dehaene et al., 1994; Gehring et al., 1993; Luu et al., 2003, 2004; Van-Veen & Carter, 2002; Van-Veen, Holroyd, Cohen, Stenger, & Carter, 2004; Yeung, Botvinick, & Cohen, 2004). Moreover, they are consistent with Herrmann, Rommler, Ehlis, Heidrich, and Fallgatter (2004), who found similar brain sources, including the ACC, when localizing the brain structures related to the ERN component, using LORETA. Therefore, considering the findings of the present study, in light of Herrmann et al. s work and the well-established involvement of the ACC in error/conflict detection, it is reasonable to relate rule/expectation violation to the ACC. One of the theories regarding ACC function holds that the ACC monitors for the presence of conflict between simultaneously active but incompatible processing streams. This view is known as the conflict detection theory. It suggests that during errors, conflict arises between the executed incorrect response and activation of the correct response, due to ongoing stimulus evaluation. This is reflected in the ERN component (Botvinick et al., 2001; Carter et al., 1998; Van-Veen & Carter, 2002; Van-Veen et al., 2004; Yeung et al., 2004). This theory might also account for rule/expectation violation tasks, if we consider that a conflict arises between the expected rule (e.g., 1+2=3) and the presented violation (e.g., 1+2=8). In fact, when we debriefed our participants, they reported they silently calculated the correct solution of the presented operands, while comparing it to the presented solution. When the presented solution was identical to their silent calculation they responded correct, and when it was different they responded incorrect. It should also be mentioned that additional brain structures, such as the precentral and postcentral gyrus, and the superior, medial and inferior frontal gyrus were also localized to some extent. It could be argued that the activity in these different brain structures might be related either to the rule/expectation violation itself and/or to the additional cognitive processes involved in the specific task used in this study (e.g., numerical and arithmetical calculation processing, working memory, visual processing, etc.). For example, working memory activity might be required to hold in mind the expected solution to the arithmetical equation to be compared to the presented solution, while the ACC activity might reflect the monitoring process, or the reaction to the detection of the conflict/violation. It has been suggested that the ACC serves to detect events or internal states indicating a need to shift the focus of attention or strengthen top-down control (Botvinick et al., 2001). An additional important finding of the present study is that more salient violations elicited brain activity with greater power in the theta frequency band, compared to less salient violations. That is, stronger violations were reflected in a greater increase in power in the theta frequency band. This finding is consistent with the idea that stronger conflicts result in greater activation in the ACC (Botvinick et al., 2001; Yeung et al., 2004). Hence, theta power sensitivity to the salience of the violation provides an additional support to the relation between rule violation and conflict detection.

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