Behavioural Brain Research 274 (2014) 53–61

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Expectancy effects during response selection modulate attentional selection and inhibitory control networks Witold X. Chmielewski ∗ , Moritz Mückschel, Veit Roessner, Christian Beste Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstrasse 42, D-01309 Dresden, Germany

a r t i c l e

i n f o

Article history: Received 11 June 2014 Received in revised form 29 July 2014 Accepted 3 August 2014 Available online 10 August 2014 Keywords: Conflict monitoring Attention Response inhibition Expectancy EEG Source localization

a b s t r a c t Choosing the correct response from a subset of alternatives is a fundamental problem and particularly demanding where conflicting response tendencies are evident. One phenomenon in this context is the congruency sequence-(Gratton effect), for which different theoretical explanations have been put forward. A critical aspect that differs between these explanations is the expectancy of what will happen in forthcoming trials. In the current study we examine the relevance of expectancy for sequence congruency effects and related neurophysiological processes using a flanker task in which we manipulate the probability that the n + 1 trial presents the same stimulus–response mapping than the n trial. We ask what cognitive subprocesses involved in response selection may be modulated by expectancy effects. To distinguish different subprocesses probably modulated by expectancy effects we use event-related potentials (ERPs) in combination with source localization techniques. The data show that cognitive subprocesses modulated by expectancy depend on the nature of expected transitions between succeeding trials. Expectancy effects only affected trial transitions within the same category (i.e., ‘compatible-compatible’ and ‘incompatible-incompatible’), but not between compatibility categories (i.e., ‘compatible-incompatible’ and ‘incompatible-compatible’). On compatible trial transitions attentional selection processes operating via the precuneus mediated expectancy effects, while on incompatible trial transitions inhibitory processes were modulated that were mediated via the medial and middle frontal gyrus, the orbitofrontal cortex, the insular and the parahippocampal gyrus. Conflict monitoring processes per se were not modulated by expectancy effects. The data shows that there are different subprocesses underlying the influence of expectancy on sequence effects during response selection. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Response selection is one fundamental problem every creature is faced with. Response selection is particular demanding when one has to overcome strong but wrong response tendencies, as it is for example the case in conflict paradigms like the Stroop, Simon, or Flanker paradigm [1]. In these paradigms cognitive control processes are recruited to assist conflict resolution [1,2]. An interesting and well-replicated phenomenon in conflict paradigms is the congruency sequence effect (or Gratton effect) [3], which is a lower interference effect after a trial in which also an incompatible (i) stimulus–response mapping was evident, compared to the effect

∗ Corresponding author. Tel.: +49 351 458 7184; fax: +49 351 458 7318. E-mail addresses: [email protected], [email protected] (W.X. Chmielewski). http://dx.doi.org/10.1016/j.bbr.2014.08.006 0166-4328/© 2014 Elsevier B.V. All rights reserved.

after a compatible trial (c) [3]. However, different theories have been put forward to explain this effect. In their original conception it is assumed that the sequential differences in the size of the congruency effects reflect strategic cognitive control adjustments that rely on the participants expectancies regarding the nature of the forthcoming trial [4,5]. This means that participants expect to encounter the same stimulus-response mapping in the forthcoming trial, as in the current trial. In other words, when the current trial composes a compatible stimulus–response mapping, participants expect the same to occur in the next trial, but they do not expect to be faced with an incompatible stimulus–response contingency. Opposed to this proactive control account in the expectancy theory [4], the conflict monitoring theory [6] provides a slightly different way how to account for conflict effects. This theory assumes that after high-conflict trials (i.e., where incompatible stimulusresponse mappings are evident), cognitive control enhances the processing of task-relevant information irrespective of the subjects

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expectancy for the forthcoming trial. The critical aspect that hence differs between these approaches to account for sequence effects in conflict paradigms is thus the expectancy of what will happen in the forthcoming trial and how task-relevant information is processed. Goal of the current study is to examine the relevance of expectancy for sequence congruency effects and related neurophysiological processes using a flanker task in which we manipulate the probability that the n + 1 trial presents the same stimulus–response mapping than the n trial. The manipulation of the probability is critical for expectancy effects, since a low probability that the n + 1 trial presents the same stimulus-response mapping as the n trial challenges expectancy effects. If expectancy effects play a role this manipulation will lead to decrease in performance (e.g. increases in reaction times). Goal of this study is further to examine what cognitive subprocesses may be modulated by this manipulation of expectancy effects. To distinguish different subprocesses probably modulated by expectancy effects we use event-related potentials (ERPs). Source localization techniques supplement these analyses to examine which functional neuroanatomical network is involved. Since it is still a matter of debate which cognitive subprocesses are involved in conflict monitoring functions several distinguishable processes may show expectancy modulations: Conflict monitoring processes, for example, have been suggested to be reflected by the N2 [7–11], which is assumed to be modulated by processes occurring in the anterior cingulate cortex (ACC). If processes related to expectancy modulate sequence congruency effects by means of altering conflict monitoring processes we would expect a modulation of the N2. Conflict may be higher in a block where similar trial sequences are not highly predictable, compared to a block where trial sequences are highly predictable. The N2 may thus be higher in formerly mentioned task block, compared to the latter mentioned (i.e., highly predictive task block). However, according to the conflict monitoring theory the N2 and hence conflict monitoring functions should not change depending on sequence predictability, because cognitive control enhances the processing of task-relevant information irrespective of the subjects expectancy for the forthcoming trial [6]. Yet, in relation to conflict monitoring processes attentional selection processes play an important role [4,12]. It has been suggested that strategic cognitive control adaptations are mediated via attentional strategies [4] and already Gratton et al. [3] pointed out that changes in attentional processing strategy may be evident. This is also stressed in the conflict monitoring account where conflict is considered to trigger top-down attentional control adjustments, i.e., adjusting attentional weights on task-relevant and task-irrelevant stimuli [6]. In this regard it is possible that neurophysiological correlates of flanker and target processing are modulated. If attentional selection processes are modulated by expectancy effects, then differences in the neurofunctional correlates of these attentional processes between the highly predictable (repetition) and not highly predictable (non-repetition) condition would be probable. A neurofunctional measure to access these processes is the P1 and N1 components of event-related potentials, which are known to be modulated by the predictability of stimuli [13]. The N1 decreases in amplitude, whenever the predictability of a stimulus is high [14,15], or attentional focus is reduced [16–19]. This would however only have an impact on the target P1–N1 complex, since the target bears important information, whether the current trial is compatible or incompatible and can thus be influenced by expectations raised by the previous trial. In contrast, the flanker stimuli once perceived do not already bear such information, since it is the information of the succeeding target that determines whether or not a compatible or incompatible stimulus–response mapping is required. Attentional processes on the flanker stimuli are therefore unlikely to be

modulated by expectancy effects. Yet, compatible and incompatible stimulus sequences in the repetition condition would be expected to evoke smaller target P1–N1 complex amplitudes in comparison to compatible and incompatible trials in sequences that are not highly predictable. Furthermore, since the occurrence of conflict in incompatible trials puts additional strain on attentional selection processes, higher amplitudes in the P1–N1 complex of incompatible trials in comparison to compatible trials would be expected for target stimuli. However, besides attentional selection processes, several lines of evidence suggest that conflict resolution relies not only on the adjustment of attentional strategies but also on the strengthening of inhibitory influences at the unwanted (incompatible) response representation [20–23], which has received support by several human [24,25] and monkeys studies [26]. Not only attentional selection processes may therefore be modulated, but the process related to the inhibition of a pre-potent response tendency as induced by incompatible flanker information may be modulated, too. It is therefore possible that correlates, for example an P300 like component [27,28] and networks mediating inhibitory control, for example the medial frontal gyrus (BA 9) [21,29,30] and the insular cortex (BA 32) [31,32] are modulated. Obviously, there is no necessity for inhibitory control processes when compatible flanker-target combinations are to be expected. To sum it up, to examine the Gratton-/congruency sequence effect thoroughly in this experiment two conditions with either a high or no stimulus predictability were designed. We assume current trial processing to be modulated by previous trial processing due to three potential mechanisms. If conflict monitoring is the foundation of the congruency sequence effect, trials after incompatible trials should be processed faster and be less error-prone, irrespective of trial predictability. In this experiment this would imply a systematic variation of N2 amplitude, reaction times and error numbers according to previous trial type, which should not differentiate between the highly and the not predictable block. Alternatively, or coincidentally, we assume the following two mechanisms to contribute to the Gratton-/congruency sequence effect. If expectancy is the foundation of the congruency sequence effect, in cases of high predictability, expectancy driven attentional processes, as reflected in the P1–N1 complex, should be more active in the highly predictable block, which should be reflected in shortened reaction time, less error numbers and a reduced N1 amplitude. Moreover, in the incompatible trial transitions of the not predictable block an increased activity of inhibitory processes, as reflected in increased the P3 amplitude, should be observable, thus leading to faster reaction times and reduced error numbers. 2. Materials and methods 2.1. Participants A sample of N = 22 healthy young participants between 19 and 32 years of age (mean age 23.7 ± 3.1) took part in the study. One participant had to be excluded because of poor data quality. All participants reported no neurological or psychiatric disorders, were free of any medication and had normal or corrected-to-normal vision. The study was approved by the institutional review board of the medical faculty of the TU Dresden. Written informed consent was obtained by all participants before the test protocol was conducted. The study was conducted in accordance with the Declaration of Helsinki. 2.2. Task A standard flanker task was used. It was structured as follows: vertically arranged visual stimuli were presented. The

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target-stimulus (arrowhead) was presented in the centre with the arrowhead pointing to the left or right. The central stimuli were flanked by two vertically adjacent arrowheads which both pointed in the same (compatible) or opposite (incompatible) direction as the target. As a reaction to the target stimuli (arrowheads pointing to the left or right) participants were required to press a response button with their left or right index finger to indicate the direction the arrow was pointing to. The flankers preceded the target by 200 ms; i.e., the stimulus-onset asynchrony (SOA) was 200 ms. The target (arrowheads) was displayed for 300 ms. Flankers and target were switched off simultaneously. The response–stimulus interval was 1600 ms. Time pressure was administered by asking the subjects to respond within 600 ms. In trials with reaction times exceeding this deadline a feedback stimulus (1000 Hz, 60 dB SPL) was given 1200 ms after the response; this stimulus had to be avoided by the subjects. This setup of stimuli was used in two different experimental blocks consisting of 120 trials (80 compatible and 40 incompatible), that were presented twice in counterbalanced order across subjects. In the repetition block, trials were presented in a repetitive design to maximize expectancy effects. Trials in the repetition block were displayed in the following order: 30 compatible trials, 20 alternating compatible-incompatible trials, 20 incompatible trials, 20 alternating incompatible-compatible trials and at last 30 compatible trials. By means of this order 60 (2 × 30) compatible–compatible (cC), 19 compatible–incompatible (cI), 19 incompatible–compatible (iC) and 21 incompatible–incompatible trial-transitions (iI) were established in each block, summing up to 120 cC, 38 cI, 38 iC and 42 iI trials in the repetition condition for the whole experiment. In the non-repetition block a pseudo-randomized sequence consisting of 24 trials was utilized and repeated 5 times, again summing up to a total 120 trials. To ensure the non-occurrence of expectancy effects within the 24 trial sequence, the sequence was randomly compiled in MATLAB with a special focus on avoiding the recurrent appearance of any further unwanted short sequences within the 24 trial sequence (see below). Thus a sequence containing 16 compatible and 8 incompatible trials, in which target arrow direction was equally distributed to the left and right was compiled in MATLAB in a pseudo-randomized order. The obtained sequence was subsequently tested in MATLAB for recurrent appearance of any further unwanted short sequences of 3 or more trials in the same succession in terms of Trial Type and 5 or more trials in terms of arrow direction laterality. To obtain the later utilized sequence, the compilation of the 24 trial sequence and the subsequent testing were repeated until the occurrence of additional sequences and thus expectancy or learning effects within the 24 trial sequence could be unequivocally excluded. In this block 59 cC, 20 iI, 20 cI and 20 iC transitions were established, summing up to 118 cC, 40 cI, 40 iC and 40 iI trials in the non-repetition condition in the whole experiment. 2.3. EEG recording and analysis The EEG war recorded from 64 Ag/AgCl electrodes arranged in equidistant positions. Electrode positions were: AFz, AF3, AF4, AF7, AF8, Fpz, Fp1, Fp2, Fz, F1, F2, F5, F6, FCz, FC1, FC2, FC3, FC4, FC5, FC6, Cz, C3, C4, C5, C6, FT7, FT8, FT9, FT10, CP1, CPz, CP2, CP3, CP4, CP5, CP6, TP7, TP8, TP9, TP10, T7, T8, Pz P1, P2, P3 P4, P7, P8, P9, P10, P11, P12, PO1, PO2, Oz, O1, O2, O9, O10 and Iz. The ground and reference electrode were placed at coordinates  = 58,  = 78 and  = 90,  = 90. The sampling rate was 1000 Hz. All electrode impedances were kept below 5 k. Data processing involved a manual inspection of the data to remove technical artifacts. After manual inspection, a band-pass filter ranging from .5 to 20 Hz (48 db/oct) was applied and data was down-sampled to 256 Hz. After filtering, the raw data were inspected a second time. To correct for periodically recurring artifacts (horizontal and vertical eye movements and blinks)

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an independent component analysis (ICA; Infomax algorithm) was applied to the un-epoched data set. Components that reveal horizontal and vertical eye movements and blinks were visually identified by means of recurrent (and potentially periodically appearing) similar waveform and by means of scalp topography. Independent components reflecting blinks as well as horizontal eye-movements have very specific scalp topographies and can be identified on the basis of these topographies. ICA components reflecting the above mentioned artifacts were then discarded. Afterwards, the EEG data was segmented as follows. For EEG-segmentation the repetition and non-repetition blocks were pooled to a repetition and a non-repetition condition in which cC, iI, cI and iI transitions were epoched, based on the 4 possible current- and previous-trial combinations. This was conducted under the constraint that both, the current- and the previous-trial were responded to correctly. Trial segmentation was target-locked and began 1000 ms before the target presentation of the respective trial and ended 1000 ms after its presentation, thus accumulating to a segment of 2000 ms. Afterwards an automated artifact rejection procedure was applied, using a maximal value difference of 200 ␮V in a 100 ms interval as well as an activity below .5 ␮V in a 200 ms period as rejection criteria. Afterwards, a current source density (CSD) transformation [33] was utilized to re-reference the data, after which the resulting CSD values were stated in ␮V/m2 . The CSD transformation was used in order to eliminate the reference potential from the data. A second advantage of the CSD-transformation is that it serves as a spatial filter [33], which makes it possible to identify electrodes that best reflect activity related to cognitive processes. For baseline correction a 200 ms time interval from 600 ms till 400 ms before the target presentation (i.e., also before flanker stimulus presentation) was utilized and averages were calculated for the single subject data.1 Peak detection was conducted for electrodes FCz, Fz, CP5 and CP6 on the single subject data. Electrodes were selected on the basis of the scalp topography of the different ERP components. This procedure was validated as follows (see also: [34]): For each ERP component a search interval was defined (noted below), in which the component is expected to be maximal. After this, we extracted the mean amplitude within each of these search intervals at each of the 65 electrode positions. This was done after CSD-transformation of the data, because the CSD-transformation has the effect of a spatial filter that accentuates scalp topography. Subsequently, we compared each electrode against an average of all other electrodes using Bonferroni-correction for multiple comparisons (critical threshold p = .0007). Only electrodes that showed significantly larger mean amplitudes (i.e., negative for N-potentials and positive for the P-potentials) than the remaining electrodes were chosen. These were essentially the electrodes found in the visual inspection of the data. CP5 and CP6 electrodes were used to examine the visual P1 and the subsequent N1 on the flanker and the target stimuli. As a prerequisite P1 always had to precede N1 in the data. The flanker P1 was determined as the most positive peak 200–0 ms before target presentation and the flanker N1 as the most negative peak 200 ms before until 0 ms (i.e., target presentation). The target P1 was determined as the most positive peak in the time slot 0–250 ms after target presentation, while target N1 was determined as the most negative peak 50–300 ms after target presentation. At electrodes FCz and Fz the N2 was defined as the most negative peak between 200 and 400 ms after target presentation. The scalp topography suggests that the N2 was maximal at these electrode sites (cf. results section). The N2 was quantified relative to the positive peak preceding the N2 (i.e., the peak-to-peak amplitude was calculated). This positive peak

1 Using a different baseline (i.e. the 100 ms interval before flanker presentation) the results reported in the methods section remain unchanged.

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preceding the N2 was defined as the most positive peak in the time interval between 100 and 300 ms after target presentation. Furthermore, a P3 like component was defined as the most positive peak in a search interval of 300–600 ms after target presentation at FCz and Fz. This peak was quantified relative to the N2. Peak picking was conducted semi-automatically. Since all of these search intervals were rather extended, peaks were manually relocated, if necessary. 2.4. Source localization analysis Source localization was carried out using standardized low resolution brain electromagnetic tomography (sLORETA: [35]). sLORETA gives a single linear solution to the inverse problem based on extra-cranial measurements without a localization bias [35–37]. For sLORETA, the intracerebral volume is partitioned in 6239 voxels at 5 mm spatial resolution and the standardized current density at each voxel is calculated in a realistic head model [38] using the MNI152 template [39]. In the present study the voxelbased sLORETA-images were compared between test sessions and trials using the sLORETA-built-in voxel-wise randomization tests with 2000 permutations, based on statistical nonparametric mapping. Basis for the sLORETA analysis is the single subject ERP data. Voxels with significant differences (p < .01, corrected for multiple comparisons) between test sessions were located in the MNI-brain and Brodmann areas (BAs) as well as coordinates in the MNI-brain were determined using the sLORETA software www.unizh.ch/keyinst/NewLORETA/sLORETA/sLORETA.htm 2.5. Statistics The behavioural data (RTs and error rates) were analyzed in repeated-measures ANOVA. This ANOVA included the factor Sequence (cC, cI, iC and iI) as well as the factor Block Type (high predictive vs. low predictive). Greenhouse-Geisser correction was applied and conducted post-hoc tests were Bonferroni-corrected where appropriate. All variables included in the analyses were normal distributed, as indicated by Kolmogorov–Smirnov tests (all z < .9; p > .3). For the neurophysiological data an additional factor electrode (electrode sites at which the ERP effects were quantified) was also fed into the model as an additional within-subject factor. 3. Results 3.1. Behavioural data For the descriptive data, the mean and standard error of the mean (SEM) are given. For the reaction time (RT) data, the repeated measures ANOVA revealed a main effect Sequence (F(3,60) = 163.01; p < .001; 2 = .891). Bonferroni-corrected pairwise comparisons showed that RTs were fastest in the cC sequence (298 ± 6.6) and increased to the iC (342 ± 7.5), iI (379 ± 8.4) and cI sequence (390 ± 8.1). All sequences differed from each other (p < .003). The pattern of results (for both blocks combined, not for the control condition alone) well replicates the classical pattern of the Gratton effect, i.e. that RTs are highest when an incompatible trials follows a compatible trial. The main effect Block Type (F(1,20) = 80.33; p < .001; 2 = .801) revealed that RTs were faster in the repetition block (343 ± 7.8), compared to the non-repetition block (362 ± 7.3). Importantly, there was an interaction Sequence × Block Type (F(3,60) = 3.36; p = .024; 2 = .164). This interaction is shown in Fig. 1. Bonferroni-corrected dependent samples t-tests revealed that RTs were faster in the repetition block, compared to the nonrepetition block in iI and cC trial transitions (all t20 < − 2.50; p < .011). The calculation of the effect sizes revealed that the Block Type effect was large in the cC and iI condition, where Cohens d

Fig. 1. Reaction time (RT) data (y-axis) on the target stimulus for the different trial transition (x-axis). The non-repetition condition is given in black squares; the repetition condition is given in white diamonds. The mean and standard error of the mean are given (*** p < .001).

effect size was d = .83 and d = .94, respectively. However, when analyzing the iC and cI condition, there was no significant modulatory effect of Block Type and reaction times did not differ (p > .12); also the effect sizes were much smaller d < .12. As to the error rates the ANOVA revealed a main effect Sequence (F(3,60) = 57.86; p < .001; 2 = .743) showing that error rates were lowest in the cC sequence (2.1 ± .5) and increased to the iC (6.2 ± 1), iI (12.6 ± 2) and cI sequence (25.1 ± 2.7). All sequences differed from each other (p < .003). The main effect Block Type (F(1,20) = 19.41; p < .001; 2 = .493) showed that error rates were lower in the repetition block (9.6 ± 1.3), compared to the non-repetition block (13.4 ± 1.7). There was again an interaction Sequence × block (F(3,60) = 21.12; p < .001; 2 = .514). Bonferronicorrected dependent samples t-tests revealed that there was no difference in error rates between blocks in cC trials (t20 = −.9; p > .2). In all other trials, error rates were lower in the repetition, compared to the non-repetition block (t20 = −3.67; p < .001). The means and standard deviations for all trial transitions in both experimental blocks are shown in Table 1. However, it may be argued that the results are biased, since there are more cC trials than other trial transitions (cf. methods section). To control for this, we repeated the analysis using only the first 40 cC trials. The results remained the same.

3.2. Neurophysiological data The behavioural data did not show differences between the experimental blocks manipulating expectancy effects for cI and iC trials. The analysis of electrophysiological correlates in these trials did not reveal significant effects (all F < .8; p > .3). Below we detail the analyses performed for cC and iI trials for correlates of attentional processing as well as conflict and inhibition.

Table 1 Error rates (means and standard deviations) on the different trial transitions in the repetition block and the non-repetition block.

cC iC cI II

Repetition block

Non-repetition block

1.5 (1.8) 8.3 (7.1) 14.5 (11.1) 9.8 (7.8)

2.5 (3.1) 4.1 (6.4) 19.1 (14.5) 17.4 (12.4)

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Fig. 2. (A) Event-related current source densities denoting the P1 and N1 on flanker and target stimuli at electrodes CP5 and CP6 for the different experimental conditions. The y-axis denotes ␮V/m2 and the x-axis denotes the time in ms. The upper panel shows current source densities on cC and iI trials in the repetition block, the lower panel in the non-repetition block. Green lines denote the potentials in cC trials of the repetition blocks, black lines denote the potentials in cC trials of the non-repetition blocks, orange lines denote the potentials in iI trials of the repetition blocks, blue lines denote the potentials in iI trials in the non-repetition blocks. (B) Comparison of the P1/N1 potentials on flanker and target stimuli on cC trials in the repetition and the non-repetition block. The purple line denotes the difference wave cCrep–cC non-rep. The scalp topography plot denotes the potential distribution of this difference wave showing strong differences in an electrode cluster around electrode CP5. The sLORETA plots (bottom) denote the source of the difference on cC trials between the repetition and the non-repetition block for the target N1 (non-repetition > repetition). The source is located in the precuneus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3. Correlates of attentional processing Perceptual and attentional selection processes are reflected in the P1 and N1 ERPs [40]. The P1 and N1 on the flanker and the target stimuli are shown in Fig. 2A and B. For the flanker stimuli, the repeated measures ANOVA shows that there are no differences in P1 amplitude between cC and iI trials (F(1,20) = .23; p > .6), electrode positions (F(1,20) = .15; p > .9) and Block Type (F(1,20) = .65; p > .8) nor any interaction between

these factors (all F < .14; p > .7). On flanker stimuli an effect of Block Type cannot be expected, since the flanker information is not the critical aspect modulated by this manipulation. The same is evident for the N1; there were no main effects of the factors in the model (all F < .33; p > .5) and no interaction effects (all F < .75; p > .3). For the target stimuli the picture was different, but only for the N1. For the P1 on the target stimuli, the repeated measures ANOVA also revealed no significant main or interaction effect (all F < .55; p > .5). However, for the N1 on the target the

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repeated measures ANOVA revealed a main effect Trial Type (F(1,20) = 9.27; p = .006; 2 = .317) showing that the N1 was larger on iI (−25.6 ± 1.1) than on cC trials (−21.1 ± 1.2). The N1 was larger at electrode CP5 (−25.1 ± 1.4) than at electrode CP6 (−25.1 ± .6) (F(1,20) = 6.77; p = .017; 2 = .253). There was no main effect Block Type (F(1,20) = 1.65; p > .3), but an interaction Block Type × Trial Type (F(1,20) = 6.35; p = .020; 2 = .241). No other interaction effects were significant (all F < 1.1; p > .4). Bonferroni-corrected post-hoc tests revealed that there was no difference in the N1 amplitude for iI trials between the repetition (−25.6 ± 1.2) and the non-repetition block (−25.7 ± 1.3) (p > .9). Yet, for the cC trials, the N1 was smaller in the repetition (−19.2 ± 1.2), compared to the non-repetition block (−22.95 ± 1.3) (t20 = 4.66; p < .001). To examine which brain areas underlie this differential modulation of the N1 on cC trials across the different task blocks we conducted a sLORETA analysis contrasting the cC trials in the repetition block, with the cC trials in the non-repetition block (cCrep < cCnon-rep ). The results show that the precuneus (BA7) exhibited stronger activation in the non-repetition condition than in the repetition condition (Fig. 2B bottom). There were generally no latency effects, neither for the flanker P1 and N1, nor for the target P1 and N1 (all F < .6; p > .7). As done with the behavioural data we re-ran the analyses using only the first 40 cC trials. As with the behavioural data the results remained the same. The results presented above are therefore unbiased with potentially different signal-to-noise ratios between the transition types compared. 3.4. Correlates of conflict and inhibition The repeated measures ANOVA on the N2 amplitudes only revealed a main effect Sequence (F(1,20) = 15.05; p = .001; 2 = .429) showing that the N2 was larger (i.e., more negative) in the iI sequence (−36.5 ± 4.2), compared to the cC sequence (−26.4 ± 3.4). This reflects the usual effect that the N2 is larger (i.e., more negative) on incompatible than on compatible trials [9]. There was no main effect electrode (F(1,20) = .69; p > .4) and interestingly, there was no main effect of Block Type F(1,20) = 1.53; p > .2) or any interaction with the factor Block Type (all F < 1.03; p > .3). The N2 is shown in Fig. 3A as a mean of electrode Fz and FCz, because the ANOVA revealed no differences between electrodes. As to the latencies, there were generally no effects (all F < .5; p > .6). To summarize, the N2 amplitude shows that it does not parallel the observed modulation on RTs observed for cC and iI trials across the different experimental blocks. Yet, as can be seen in Fig. 3A and B potentials differ in positivity after the N2 in the time range at approximately 350 ms for iI trial transitions, which falls in the time range of the reaction times. This P3-like component was therefore quantified relative to the peak of the N2. The analyses of this frontal P3-like ERP revealed the following: There was no main effect electrode (F(1,20) = .87; p > .3), but a main effect Sequence (F(1,20) = 45.00; p < .001; 2 = .692). This effect shows that the P3-like potential was higher in the iI (39.9 ± 4.1), than in the cC condition (21.4 ± 2.1). The Block Type effect was also significant (F(1,20) = 21.11; p < .001; 2 = .514) showing that the P3-like potential was larger in the non-repetition block (35.2 ± 3.4), compared to the repetition block (26.2 ± 2.8). Interestingly, there was an interaction Sequence × Block Type (F(1,20) = 35.55; p < .001; 2 = .640). This interaction is shown in Fig. 3. Bonferroni-corrected dependent samples t-tests revealed that on cC trials, there was no difference between the repetition block (20.9 ± 2.3) and the non-repetition block (21.9 ± 2.2) (t20 = −.71; p > .3). However, for the iI trials, the amplitude was larger in the non-repetition (48.3 ± 4.9) block than in the repetition block (31.4 ± 3.7) (t20 = −5.56; p < .001). The results hence dissociate from the N1 effects, where Block Type effects were restricted to

the cC sequence. To examine which brain regions mediate the effect observed on iI trials we run a sLORETA analysis. In this analysis we contrasted the iI sequence in the repetition condition against the iI sequence in the non-repetition condition. The results are shown in Fig. 3B. In the repetition block there was lower activity in a network encompassing the left medial frontal gyrus (BA10) and the left orbital gyrus (BA11). Moreover, activity was lower in the left middle frontal gyrus (BA10) and the insular (BA13), as well as the parahippocampal gyrus (BA19). We did not analyze the cC sequence across the different experimental blocks, because there was no difference in the amplitude of this positivity in the post-hoc tests. As with the data on neurophysiological correlates of attentional processing the results remained the same when only the first 40 cC trial transitions were used.

4. Discussion In the current study we examined the role of expectancies for congruency sequence effects in response selection. This was done in a flanker paradigm in which we varied the probability that congruency sequences changed, or remained constant over a number of trials and were hence more or less expectable. This was done in separate blocks counterbalanced across subjects. The reaction time data shows that the Block Type (i.e., probability of congruency sequences) had an effect on cC and iI trial transitions, but not on the other conditions. A possible explanation for this might be, that even though a repetitive presentation of successive cI or iC transitions is given, which should provoke the expectation that every second trial is similar and thus lead to an adjustment of attentional selection strategies, the expectancy effect only seems to directly affect the following trial. The analysis of the neurophysiological data was constrained on this behavioural finding, i.e., only effects of Block Type on cC and iI trials were analyzed, because possible differences in cI and iC trials are not interpretable due to lack of modulations at the behavioural level. In the neurophysiological data, differential effects of Block Type (sequence probabilities) on cC and iI trial transitions were found for the N1 on target stimuli and for a positive deflection of the ERP at the time of approximately 350 ms, which falls in the time interval of the reaction time effects. Interestingly, the analysis on the N1 and the positive deflection at 350 ms revealed a dissociated pattern of results: While for the N1, effects of Block Type (probability of congruency sequences) were restricted to the cC sequence, the effects of Block Type for the positive deflection at 350 ms were restricted to the iI sequence. This shows that the probability of sequence effects affects different cognitive subprocesses depending on the nature of trial transition. Effects of sequence probability (expectancy effects) on the frontal positivity were restricted to the iI sequence. This frontal positivity was larger for the block where sequence repetitions were less likely. Previous results suggest that conflict resolution, as evident in incompatible trials, can be accomplished by inhibitory control processes on the unwanted (incompatible) response representation [20,22–26]. It is therefore possible that this positivity reflects inhibitory control processes. These inhibitory control processes may reflect the suppression of unwanted (incompatible) response representation [20–23]. The high predictability of trial transitions may ease this ‘strategy’ to apply inhibitory control. In this regard it seems plausible that in the repetition block this potential likely reflecting inhibitory control processes is less pronounced. On the contrary, in the task block where similar trial sequences in succession are less likely it is not possible to prepare for the upcoming trial. As a consequence inhibitory control processes have to be stronger in order to control conflicting information. That may be the reason why the potential was stronger in this condition. Corroborating

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Fig. 3. (A) Event-related current source densities denoting the N2 at fronto-central electrode sites. The current source densities were averaged across electrode sites Fz and FCz. The y-axis denotes ␮V/m2 and the x-axis denotes the time in ms. The scalp topography plots (CSD maps) denote a centrally accentuated negativity reflecting a typical N2 scalp topography. The topography is shown for the peak of the current source density. Green lines denote the potentials in cC trials of the repetition blocks, black lines denote the potentials in cC trials of the non-repetition blocks, orange lines denote the potentials in iI trials of the repetition blocks, blue lines denote the potentials in iI trials in the non-repetition blocks. (B) Comparison of cC trials in the repetition and non-repetition block (top) and the iI trials in the repetition and the non-repetition block (bottom). As can be seen, the positivity after the N2 is larger for the non-repetition block than for the repetition block. The purple line denotes the difference wave for iI trials between the repetition and the non-repetition block. The map denotes the scalp topography of this difference wave at its peak. The sLORETA plots (right) denote the network (brain regions) differentially activated in the time range of the positivity. A network consisting of the middle and medial frontal gyrus, orbitofrontal cortex, insular cortex and parahippocampal gyrus is shown (repetition < non-repetition). The scale represents t-values. Scaling was adjusted in a way that only significant activation differences are shown (t-thresholds) that were higher than the critical t-threshold as obtained using the sLORETA-built-in voxel-wise randomization tests with 2000 permutations, based on statistical nonparametric mapping. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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the interpretation that the frontal positivity reflects some form of response inhibition, the sLORETA analysis suggest that a network comprising the medial and middle frontal gyrus (BA9, BA10 and BA11), the insular (BA13) and the parahippocampal gyrus is likely related to modulations of the frontal positivity across blocks. The medial and middle frontal gyrus [29,30,41,42], the insula [31,43] as well as the parahippocampal gyrus [30,44] have been shown to be involved in inhibitory control functions. Interestingly, attentional selection processes, as reflected by the N1 on the target or the flanker stimuli, were not differentially modulated across blocks for iI trial transitions, though the target N1 was generally larger for iI than for cC trials. While this shows that attentional selection processes are more demanded in conflicting situations, which is in line with assumptions that adjustments in these processes help to resolve conflicts [6], it also shows that the probability of sequences and hence expectancy does not necessarily modulate attentional selection processes during response selection. An important factor that has to be taken into account is the nature of trial transitions. The data shows that in cC trials, a larger N1 was evident in the task with a low probability of sequence repetitions. This effect was mediated via stronger activations in the precuneus (BA7), an area that is known to be involved in top-down attentional control [45–47]. This shows that depending on the type of transition (cC vs. iI) different cognitive subprocesses are involved. Furthermore, and as expected, the effects of attentional modulation were restricted to the processing of the target information, while the processing of flanker information was not affected. This is exactly in line with the assumption, that the perception of the target, due to its inherent information about the congruency of the task can be manipulated by expectancy effects, while the perception of the flanker, due to its bearing of no information about the congruency of the following target, is not manipulated by expectations. Together, the results suggest that expectancy effects modulate top-down attentional processing only in conditions where no conflict monitoring processes are necessary. Once conflict processing is evident due to incompatible flanker-target constellations, expectancy effects do not further impact attentional processing of the target, but impact inhibitory control processes. Opposed to attentional selection and inhibitory control processes, the neurophysiological data showed that the N2 as a correlate for conflict monitoring processes [9,10] did not reveal differential effects depending on sequence probability. There was only a main effect Compatibility showing that the N2 was generally larger for incompatible trials than for compatible trials and larger for the iI trial transition than for the cC trial transition. While this underlines that the N2 reflects conflict monitoring functions [9,10], since response conflict is higher in the incompatible condition than in the compatible condition, it also shows that the intensity of conflict monitoring processes per se is robust against the expectancy of certain trial transitions. This is in line with the conflict monitoring theory of [6], stating that cognitive control enhances the processing of task-relevant information irrespective of the subjects expectancy for the forthcoming trial. As a limitation of the study it should be noted that the constraint of the expectancy effect to be only affecting the subsequent trial, might however be due to methodological limitations, since a mere presentation of 10 subsequent cI or Ic repetitions might not be enough to cause expectancy effects. This may explain the lack of effects in these conditions and future studies should definitely address this aspect. It should also be noted that due to the ‘inverse problem’ source localization techniques have to be interpreted with caution. While the aim of the study was to examine the plausibility of different theoretical accounts of the Gratton effect, the experimental manipulation induced has also influenced the magnitude of the effect. Fig. 1 suggests that there was a significant Gratton-effect

only in the repetition condition. The reason for this is currently unknown, but may be subject for future research. It should also be noted that the sources provided have to be treated with some caution, since source localization techniques have not the same reliability as functional imaging data. In summary, the study shows that different subprocesses underlie the influence of expectancy on sequence effects during response selection. The subprocesses that are modulated by expectancy depend on the nature of trial transitions. Expectancy effects beforemost affect trial transitions within the same category (i.e., cC and iI), but not between compatibility categories. On compatible trial transitions attentional selection processes mediate expectancy effects, while on incompatible trials transition inhibitory processes are modulated. Conflict monitoring processes per se were not modulated by expectancy effects. As suggested by source localization analyses, these processes were mediated via distinct functional-neuroanatomical networks, which are involved in attentional selection and inhibitory control processes. Acknowledgements This work was supported by a Grant from the Deutsche Forschungsgemeinschaft (DFG) BE4045/10-1. References [1] Keye D, Wilhelm O, Oberauer K, Stürmer B. Individual differences in response conflict adaptations. Front Psychol 2013;4:947. [2] Botvinick MM. Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cogn Affect Behav Neurosci 2007;7:356–66. [3] Gratton G, Coles MG, Donchin E. Optimizing the use of information: strategic control of activation of responses. J Exp Psychol Gen 1992;121:480–506. [4] Duthoo W, Notebaert W. Conflict adaptation: it is not what you expect. Q J Exp Psychol 2012;65:1993–2007. [5] Schmidt JR. Questioning conflict adaptation: proportion congruent and Gratton effects reconsidered. Psychon Bull Rev 2013;20:615–30. [6] Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. Conflict monitoring and cognitive control. Psychol Rev 2001;108:624–52. [7] Beste C, Ness V, Lukas C, Hoffmann R, Stüwe S, Falkenstein M, et al. Mechanisms mediating parallel action monitoring in fronto-striatal circuits. NeuroImage 2012;62:137–46. [8] Beste C, Domschke K, Falkenstein M, Konrad C. Differential modulations of response control processes by 5-HT1A gene variation. NeuroImage 2010;50:764–71. [9] Folstein JR, Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 2008;45:152–70. [10] Van Veen V, Carter CS. The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiol Behav 2002;77:477–82. [11] Willemssen R, Falkenstein M, Schwarz M, Müller T, Beste C. Effects of aging, Parkinson’s disease, and dopaminergic medication on response selection and control. Neurobiol Aging 2011;32:327–35. [12] Verguts T, Notebaert W, Kunde W, Wühr P. Post-conflict slowing: cognitive adaptation after conflict processing. Psychon Bull Rev 2011;18:76–82. [13] Näätänen R, Picton T. The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. Psychophysiology 1987;24:375–425. [14] Bäß P, Jacobsen T, Schröger E. Suppression of the auditory N1 event-related potential component with unpredictable self-initiated tones: evidence for internal forward models with dynamic stimulation. Int J Psychophysiol 2008;70:137–43. [15] Wilkinson RT, Ashby SM. Selective attention, contingent negative variation and the evoked potential. Biol Psychol 1974;1:167–79. [16] Luck SJ, Woodman GF, Vogel EK. Event-related potential studies of attention. Trends Cogn Sci 2000;4:432–40. [17] Schneider D, Beste C, Wascher E. On the time course of bottom-up and topdown processes in selective visual attention: an EEG study. Psychophysiology 2012;49:1660–71. [18] Wascher E, Beste C. Tuning perceptual competition. J Neurophysiol 2010;103:1057–65. [19] Wascher E, Hoffmann S, Sänger J, Grosjean M. Visuo-spatial processing and the N1 component of the ERP. Psychophysiology 2009;46:1270–7. [20] Klein P-A, Petitjean C, Olivier E, Duque J. Top-down suppression of incompatible motor activations during response selection under conflict. NeuroImage 2014;86:138–49. [21] Ocklenburg S, Güntürkün O, Beste C. Lateralized neural mechanisms underlying the modulation of response inhibition processes. NeuroImage 2011;55:1771–8.

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