Brain Struct Funct DOI 10.1007/s00429-016-1186-0
ORIGINAL ARTICLE
The system neurophysiological basis of backward inhibition Rui Zhang1 • Ann-Kathrin Stock1 • Rico Fischer2 • Christian Beste1
Received: 6 July 2015 / Accepted: 11 January 2016 Ó Springer-Verlag Berlin Heidelberg 2016
Abstract Task switching is regularly required in our everyday life. To succeed in switching, it is important to inhibit the most recently performed task and instead activate the currently relevant task. The process that inhibits a recently performed task when a new task is to be performed is referred to as ‘backward inhibition’ (BI). While the BI effect has been subject to intense research in cognitive psychology, little is known about the neuronal mechanisms that are related to the BI effect and those that relate to differences in the magnitude of the BI effect. In the current study, we examined the system neurophysiological basis of BI processes using event-related potentials (ERPs) and sLORETA by also taking inter-individual differences in the magnitude of the BI into account. The results suggest that BI processes and inter-individual differences in them strongly depend upon attentional selection mechanisms (reflected by N1-ERP modulations in the current task/trial) mediated via networks consisting of extrastriate occipital areas, the temporo-parietal junction and the inferior frontal gyrus. Other processes and mechanisms related to conflict monitoring, response selection, or the updating, organization and implementation of a new task-set (i.e. N2 and P3 R. Zhang and A.-K. Stock contributed equally.
Electronic supplementary material The online version of this article (doi:10.1007/s00429-016-1186-0) contains supplementary material, which is available to authorized users. & Christian Beste
[email protected] 1
Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstraße 42, 01309 Dresden, Germany
2
Department of General Psychology, Technische Universita¨t Dresden, Dresden, Germany
processes) were not shown to be modulated by BI processes and differences in their magnitude, as evoked with a common BI paradigm. Keywords Backward inhibition Event-related potentials Source localization Cognitive flexibility
Introduction Task switching is a major requirement in the dynamics of our everyday life. During work, for instance, we need to switch between answering the phone, writing emails, arranging appointments, and many other different tasks. Yet, switching between different tasks is associated with considerable performance costs, as we respond slower and make more mistakes when switching than when repeating a task. Knowledge about and preparation for a switch can largely reduce but not eliminate these switch costs, as residual costs remain. Several concepts have been put forward to account for switching effects (for review: Kiesel et al. 2010; Koch et al. 2010; Monsell 2003). One of these proposes that for flexible task switching, the efficient activation of a new task goes along with the inhibition of the no longer relevant task (Mayr and Keele 2000). The process that inhibits the most recently performed task when a new task is to be performed is referred to as ‘backward inhibition’ (BI), and serves the suppression of interferences arising from previous tasks (Allport and Wylie 1999). A stronger BI is thought to relate to a better task-switching performance as it facilitates the activation of a new task set (Mayr and Keele 2000). However, the inhibition of a currently irrelevant task can persist over time, so that it will be difficult to perform this task when it becomes relevant again (Allport et al. 1994; Allport and Wylie 1999). Based
123
Brain Struct Funct
thereon, the BI paradigm was developed to measure task set inhibition. In this paradigm, the BI effect is measured by assessing the time cost of overcoming the inhibition of recently abandoned task set that is relevant again (Mayr and Keele 2000). In particular, performance costs related to BI are observed in task sequences in which Task A is repeated from n - 2 (ABA task triplet) compared to when Task A has no n - 2 sequence history (CBA task triplet). While the BI effect has been subject to intense research in cognitive psychology (e.g. Costa and Friedrich 2012; Druey and Hu¨bner 2007; Katzir et al. 2015; Koch et al. 2004; Regev and Meiran 2015; Scheil and Kleinsorge 2014), little is known about the neuronal mechanisms that are related to the BI effect or to inter-individual differences in it. In an fMRI study investigating the BI effect, Whitmer and Banich (2012) showed that individuals who were better at inhibiting the previous task set (i.e. had a large BI effect) exhibited more activity in the basal ganglia and supplementary motor area/premotor area (BA6) suggesting for altered response selection processes. However, fMRI does not provide a sufficient temporal resolution to investigate distinct cognitive subprocesses and it cannot be ruled out that other cognitive mechanisms in the processing cascade (e.g. attentional selection processes) are altered as well. Furthermore, it has remained unclear whether there are also differences in the timing or magnitude of involved processes that are associated with inter-individual differences in the BI effect. To tackle these questions, EEG and eventrelated potentials (ERPs) provide an excellent approach. There is an EEG study investigating the BI effect (Sinai et al. 2007), showing that compared to baseline condition (where no BI is evident), BI requires increased attentional resources during the reactivation of the recently inhibited set. However, this study did not use source localization techniques and was therefore not able to examine the system neurophysiological basis of BI in terms of functional neuroanatomical networks. Moreover, and unlike the study by Whitmer and Banich (2012), the study by Sinai et al. (2007) did not compare different performance levels in BI, so we still know very little about how BI magnitude (inter-individual difference) modulates the system neurophysiology of BI. As those paradigms primarily operationalize the BI effect via differences in the last of three subsequent trials, we will keep our main focus on the cognitive sub-processes of the last trials in suitable trial triplets. In the current study, we therefore combine EEG with source localization techniques (i.e. sLORETA) to answer the question which neurophysiological processes within the processing cascade from early attentional to response selection mechanisms are changed in timing and intensity by BI and what functional neuroanatomical networks are involved in inter-individual differences in the magnitude of the BI effect.
123
It is likely that attentional selection processes are changed in the current trials (i.e. the last trials of a given trial triplet). First of all, (Sinai et al. 2007) reported that the N1 at parietal sites, which is known to reflect attentional selection processes such as focusing on task-relevant stimuli (Beste et al. 2010a; Getzmann et al. 2013; Herrmann and Knight 2001; Hillyard and Anllo-Vento 1998; Luck et al. 1990; Wascher and Beste 2010), was larger in the BI condition than in the control condition. Moreover, attentional processes have an important gating function (Banich 1998; Vidyasagar 1999). By determining which sensory inputs are processed and can thus be subject to further processing in the brain, they may strongly modulate the efficiency of all subsequent processes because only properly encoded information can easily and fully be processed. Given that top-down attentional effects have been shown to increase activity in sensory cortices concerned with attentional gating and processing task-relevant stimuli (Johnson and Zatorre 2005) as well as the dorsal visual stream (Pammer et al. 2006), we expect to find differences in the N1 component which are related to changes in secondary visual areas and the dorsal visual stream. Another process which could potentially be modulated involved in BI is the N2 component. It has been demonstrated to reflect cognitive control and conflict monitoring processes (e.g. Deng et al. 2015; Donkers and van Boxtel 2004; Huster et al. 2013; Larson et al. 2014; van Veen and Carter 2002). Yet, it is still unclear whether BI actually requires changes in conflict monitoring (as compared to non-BI situations). The main reason for this is that changes in the N2 are usually observed/reported in situations where the active inhibition of a certain process or response is required (e.g. Beste et al. 2010b; Bokura et al. 2001; Huster et al. 2013). Given that the BI effect arises in situations requiring to overcome an inhibition (instead of generating one) and given that Sinai et al. (2007) failed to find relevant N2 effects, we however suspect that neither the N2 nor later components show relevant differences related to the BI effect in the current/last trials. Given our assumption that the BI effect mainly arises from differences in top-down guided attentional processes related to the processing of task-relevant stimuli, we hypothesize that inter-individual differences in the magnitude of the BI effect should also be reflected in the N1 of the last trial of a given triplet.
Materials and methods Participants 37 healthy subjects between 18 and 30 years of age (mean age of 24.3 ± 3.5; 19 females) took part in the experiment.
Brain Struct Funct
All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric disorders. Participants were right-handed, having a mean score of 0.87 ± 0.18 in the Edinburgh Handedness Inventory (EHI; Oldfield 1971). The study was approved by the institutional review board of the Medical faculty of the TU Dresden in Germany and was conducted in accordance with the declaration of Helsinki. Experimental setting and task The task applied in this study was a BI paradigm as proposed by Mayr and Keele (2000). We used the version adapted by Koch et al. (2004), aiming at exploring BI processes in task switching and the neural substrates underlying the inter-individual differences in this aspect of task performance. During the experiment, participants were seated in front of a 17-inch-CRT computer monitor with a viewing distance of 57 cm. White cues and stimuli were presented on black background at the screen center. Participants responded with two buttons (left and right Ctrlbuttons) on the keyboard using their left and right index fingers. For stimulus presentation, response recording, and EEG triggers, ‘‘Presentation’’ software (version 14.9. by Neurobehavioral Systems, Inc.) was used. A square, diamond, or triangle frame were used as target cues, indicating task A (odd/even rule), task B (smaller/larger rule), or task D (double-press rule), respectively. Stimuli consisted of digits 1–9 except for 5. Each trial started with the presentation of one of the cues. After a stimulus onset asynchrony (SOA) of 100 ms, a digit occurred centrally inside of the cue frame. Cue and stimulus stayed on the screen until the participants responded. Once the participants responded, there was a fixed time interval of 1500 ms until the next cue was presented. During this response-stimulus interval (RSI), a fixation cross was presented at the center of the screen. In the odd/even and smaller/larger tasks, participants needed to make choices and perform a parity or magnitude judgment: in the odd/even task, the left response key should be pressed when an odd digit was presented and the right response key should be pressed when an even digit appeared. In the smaller/larger task, participants needed to press the left response key when the stimulus was smaller than 5 and the right response key when the stimulus was greater than 5. In contrast to the odd/even and smaller/larger tasks, participants were requested to press both target buttons simultaneously within 1000 ms after the stimulus onset in the double-press task. If they did not comply with this requirement, a speedup sign (Geman Word ‘‘Schneller!’’, translating to ‘‘Faster!’’) appeared above the frame asking participants to respond more quickly. When the participants responded after the speed-up sign appeared, they also received a
feedback with the German words ‘‘zu langsam!’’ (translating to ‘‘too late!’’) at the center of the screen. Both too slow responses and non-simultaneous key-presses (when the two key-presses were separated by more than 50 ms) were counted as errors. An error feedback (German word ‘‘falsch!’’ translating to ‘‘wrong!’’) was shown for 500 ms on the screen when participants did not give the right response in any of the cue conditions. The experiment consisted of 768 trials divided into eight equally sized blocks. After each block, participants received feedback about their mean response time (RT) during the last block (Fig. 1). Within each block, the task (cue) sequence and stimulus sequence were randomized except for n - 1 repetitions, which means that both cues and the stimuli in any two consecutive trials could not be the same (e.g. a trial with a square cue and a stimulus 7 followed by a trial with a square cue and a stimulus 9 could not occur due to the same cue. Likewise, a trial with a diamond cue and a stimulus 3 followed by a trial with a triangle cue and a stimulus 3 could not occur due to the same stimulus). Each cue and stimulus as well as each possible combination of them occurred with the same frequency. It was also ensured the stimulus in the current trial was different from the stimulus used in the last trial with the same cue (e.g. a square cue with any stimulus except 4 was allowed in the current trial when the stimulus in the last square cue trial was 4). Within each block, each trial (except for the first two trials, of course) built a triplet with the last two preceding trials. All twelve possible triplet-combinations (ABA; ADA; BAB; BDB; DAD; DBD; DBA; BDA; DAB; ADB; BAD; ABD) were equally frequent (±1 triplet for two triplet conditions in each block). Triplets where the last trial had the same cue as the n - 2 trial were categorized as back-switching triplets while triplets without that n - 2 cue repetition were categorized as baseline triplets. Tasks and procedure were verbally explained to participants and they also read instructions on the screen. Participants were encouraged to respond as quickly and accurately as possible and they were not asked to keep track of previous trials. To make sure that the participants understood the instructions and kept the rules in mind, they were asked to start with a practice block consisting of 12 trials. The RT and errors for each condition were collected for behavioral analyses. EEG recording and analysis The EEG was recorded from 60 Ag–AgCl electrodes using an equidistant electrode setup with a sampling rate of 500 Hz. The reference electrode was located at Fpz and the ground electrode was located at h = 58, a = 78. Electrode impedances were kept below 5 kX. During off-line data
123
Brain Struct Funct
Fig. 1 Experimental paradigm. Each trial began with the presentation of a cue in the center of the screen. A square cue indicated the odd/even task (left button press for odd numbers, right button press for even numbers). A diamond cue (see bottom left) indicated the smaller/larger rule (left for smaller than 5, right for larger than five). A triangle cue (see bottom left) indicated the double-press rule (simultaneous button press within the first 1000 ms after target onset). After 100 ms, the target stimulus (any number from 1 to 9, except 5)
was presented within the target stimulus until a response was made. In double-press trials only, a speedup sign (‘‘Schneller!’’, translating to ‘‘Faster!’’) appeared above the cue frame in case no response was given within the 1000 ms after target onset. During the inter-trial interval of 2000 ms, there was a 500 ms feedback for incorrect trials (‘‘Falsch!’’, translating to ‘‘Wrong!’’), but no feedback/a fixation cross in correct trials
processing, the recorded data was first down-sampled to 256 Hz. Afterwards, a band-pass filter from 0.5 to 20 Hz with a slope of 48 db/oct each was applied. A raw data inspection was conducted to remove technical artifacts, while periodically occurring artifacts such as pulse artifacts, horizontal and vertical eye movements were subsequently detected and corrected for by means of an independent component analysis (ICA; infomax algorithm). After these corrections, cue-locked segments were formed for trials with correct responses for all conditions separately. Segments started 500 ms prior to the locking point (cue onset was set to time point 0) and ended 2000 ms thereafter, resulting in an overall segment length of 2500 ms. Afterwards, an automated artifact rejection procedure was applied using a maximal value difference above 200 lV in a 200 ms interval as well as an activity below 0.5 lV in a 100 ms period as rejection criteria. Next, a current source density (CSD) transformation was run. This transformation yields a reference-free evaluation of the electrophysiological data which helps to find the electrodes showing the strongest effects (Nunez and Pilgreen 1991). A baseline correction was then set to a time interval from -300 to 0 ms before the segments were averaged for each condition. After that, electrodes P7, P8, PO1, PO2; Cz and CPz were selected on the basis of the scalp topography of the different ERP components. CP5 and CP6 were
selected based on comparison of performance groups. Peak detection was conducted for electrodes P7, P8, CP5, CP6, PO1, PO2 on the single subject data. P1 and N1 were measured at P7 and P8 following the cue (P1: 50–130 ms; N1: 120–220 ms) and following the stimulus, which was added after a SOA of 100 ms (P1: 200–300 ms; N1: 260–400 ms). All these components were quantified in peak amplitude and latency. N1 on the target stimulus was also measured at electrodes CP5 and CP6 (280–370 ms). At electrode Cz, the N2 was quantified by automatically detecting the global minimum in the time window from 400 to 430 ms. The P3 on the cue stimulus was quantified by extracting the mean amplitude of the time interval from 300 to 340 ms at electrodes PO1 and PO2 while the P3 on the target stimulus was quantified by using the mean amplitude of the time interval from 580 to 630 ms at electrodes CPz. All the ERP components were quantified relative to the baseline excluding the potential at electrodes CP5 and CP6, which was quantified relative to the preceding positive peak in the time interval from 220 to 290 ms.
123
Source localization (sLORETA) To examine sources relating to amplitude modulations in ERPs in different conditions, source localization was
Brain Struct Funct
conducted using sLORETA (standardized low resolution brain electromagnetic tomography; Pascual-Marqui 2002), which provides a single linear solution to the inverse problem without a localization bias (Marco-Pallare´s et al. 2005; Pascual-Marqui 2002; Sekihara et al. 2005). It has been mathematically proven that sLORETA provides reliable results without localization bias (Sekihara et al. 2005). There is also evidence of EEG/fMRI and EEG/TMS studies underlining the validity of the sources estimated using sLORETA (e.g. Dippel and Beste 2015; Sekihara et al. 2005). For sLORETA, the intracerebral volume is partitioned into 6239 voxels at 5 mm spatial resolution. The standardized current density at each voxel is calculated in a realistic head model (Fuchs et al. 2002) using the MNI152 template. In this study, the voxel-based sLORETA images were compared between the BASE and the BI condition, as well as between groups using the sLORETA-built-in voxelwise randomization tests with 3000 permutations, based on statistical nonparametric mapping (SnPM). Voxels with significant differences (p \ 0.01, corrected for multiple comparisons) between contrasted conditions were located in the MNI-brain http://www.unizh.ch/keyinst/NewLORE TA/sLORETA/sLORETA.htm. Statistics For both the behavioral and neurophysiological data, we excluded the first two trials of each block, all trials with an error as well as the two trials following an error. From the remaining trials, we discarded those with RTs higher than 2500 ms or lower than 100 ms. On average, this time restriction eliminated 0.28 % (±0.92) of all trials. The mean number of the remaining trials for each triplet that entered the RT analysis was above 40 for all triplet conditions (ABA: 42.0 ± 2.1; BAB: 44.1 ± 1.8; DBA: 43.3 ± 1.5; DAB: 43.8 ± 1.8; ADA: 41.9 ± 2.0; BDB: 46.8 ± 1.6; BDA: 44.9 ± 2.0; ADB: 43.4 ± 1.8; DBD: 48.1 ± 1.7; DAD: 43.3 ± 1.8; ABD: 45.9 ± 1.7; BAD: 44.5 ± 1.7). Due to the manual removal of technical and rare motor artifacts and the automated artifact correction run during preprocessing, the number of trials included in the ERP averages was a little bit lower (73.0 ± 4.0 in the BI condition comprising ABA and BAB; 78.2 ± 3.4 in the BASE condition comprising DBA and DAB). As explained in the introduction, we intended to investigate effects of magnitude differences in the BI effect on a system neurophysiological level. For each baseline triplet, we therefore chose the respective back-switching triplet, which only differed in the n - 2 trial cue. In line with the study from Koch et al. (2004), we analyzed the triplets showing the strongest inter-individual differences in BI effects (back-switching triplets ABA and BAB vs. baseline triplets DBA and DAB). We separately averaged the two
back-switching triplets and the two baseline triplets to obtain a measure for the BI condition and the baseline (BASE) condition, respectively. After that, we calculated the RT difference between the BI and BASE conditions as a variable depicting the BI effect [mean (ABA, BAB) – mean (DBA, DAB)]. Using this within-subject BI effect value, the irrelevant inter-individual differences such as general response speed were ruled out so that the relevant inter-individual differences in the magnitude of the BI could be compared. Using this contrast variable, two similarly sized groups (n = 19 with a large BI effect and n = 18 with a small BI effect) were formed by using a median split (median RT difference = 46.45 ms, which denotes a significant RT difference between the groups; refer results section). To investigate the effect of n - 1 trial and response modes on the current trial (Koch et al. 2004; Schuch and Koch 2003), we analyzed the triplets with a double-press in the n - 1 trial (back-switching triplets ADA and BDB vs. baseline triplets BDA; ADB, see supporting information). Based on previous findings that the BI effect is only found when response selection is required in the n - 1 trial, we furthermore analyzed the triplets ending with the doublepress task (back-switching triplets DAD and DBD vs. baseline triplets BAD and ABD, see supporting information). Since the cueing of task D allows for response preparation (as opposed to task A and B) which may reduce the BI effect (Koch et al. 2004) those triplets were however not included in the main analysis described above. Behavioral and neurophysiological data were analyzed using mixed effects ANOVAs comprising the within-subject factors condition (backward inhibition/BI vs. baseline/ BASE) and electrode (wherever applicable). Group (large vs. small BI effect) was used as a between-subjects factor. Separate ANOVAs were calculated for each behavioral and neurophysiological measure. Greenhouse–Geisser correction was applied whenever necessary. Post-hoc tests were Bonferroni-corrected whenever necessary. All included variables were normally distributed as tested with Kolmogorov–Smirnov tests (all z \ 0.9; p [ 0.3).
Results Behavioral data The repeated measures ANOVA on performance accuracy (percentage of hits, see Table 1) revealed a main effect of ‘‘condition’’ showing that accuracy was higher in the BASE condition (70.2 ± 2.7 %) than in the BI condition (67.6 ± 2.8 %), F (1,35) = 5.46; p = 0.025; g2 = 0.135. No other significant effects were found (all F \ 3.3; all p [ 0.082).
123
Brain Struct Funct Table 1 Accuracy (percentage of hits) as a function of condition (BI vs. BASE) and group (small vs large BI effect) BI (%)
BASE
Small BI effect group
62.0 ± 3.9
66.3 ± 3.8
Large BI effect group
73.2 ± 3.8
74.1 ± 3.7
Please note that only triplets with correct responses in all three trials were included in the analyses, thus increasing error rates. Of note, the chance level would be at 12.5 % when assuming a 50 % chance level for each individual trial of the analyzed triplets
Table 2 RT (in ms) as a function of condition (BI vs. BASE) and group (small vs large BI effect) BI (ms)
BASE (ms)
Small BI effect group
788 ± 39
766 ± 38
Large BI effect group
788 ± 23
707 ± 21
For the RTs (see Table 2), the repeated measures ANOVA revealed a main effect of ‘‘condition’’ showing that RTs were larger (slower) in the BI condition (788 ± 22 ms) than in the BASE condition (736 ± 21 ms) [F(1,35) = 136.71; p \ 0.001; g2 = 0.796]. Also, an interaction effect of ‘‘condition 9 group’’ was found [F(1,35) = 44.95; p \ 0.001; g2 = 0.562]. Naturally, the BI effect was larger in the group with large BI effects (81 ± 7 ms) compared to the BI effect of the group with small BI effects (22 ± 5 ms) [t(35) = -6.70; p \ 0.001]. Post-hoc tests revealed that these RT differences between BI and BASE conditions were significant in both groups, i.e. in the group with large BI effect [BI: 788 ± 23 ms; BASE: 707 ± 21 ms; t(18) = 11.43; p \ 0.001] as well as in the group with small BI effect [BI: 788 ± 39 ms; BASE: 766 ± 38 ms; t(17) = 4.32; p \ 0.001], respectively. Given the non-significant interaction of ‘‘condition 9 group’’ [F(1,35) = 2.369; p = 0.133; g2 = 0.063] and the lack of group differences [F(1,35) = 3.206; p = 0.082; g2 = 0.084; group with large BI effect: 73.6 ± 3.7 %; group with small BI effect: 64.1 ± 3.8 %] for accuracy as well as non-significant correlations between accuracy and RTs in both conditions which were separately calculated for both groups (all p [ 0.194), a speed-accuracy tradeoff can be excluded. Together, the behavioral data showed that all participants responded slower and made more errors in the BI condition than the BASE condition and that there were considerable differences in the BI effect between groups built by means of a median split (*60 ms). The further analyses of response modes (see supplementary material) showed that group differences regarding the BI effect could only be found when the n - 1 trial
123
required a Go response (task A or task B, where the response selection was required) and triplets ended with a Go response. As we focused on the inter-individual differences referring to the BI effect, we decided to limit our analyses to the triplets showing the inter-individual differences in BI effects (back-switching triplets ABA and BAB vs. baseline triplets DBA and DAB). Neurophysiological data To determine which neurophysiological mechanisms underlie differences in the magnitude of the BI effect, the P1, N1 and N2 as well as P3 ERPs were examined. P1 and N1 on cue stimulus The P1 and N1 ERPs are shown in Fig. 2a. For the P1 following the cue at electrodes P7/P8, the mixed effects ANOVA revealed a difference in amplitudes between electrodes P7 and P8 [F(1,35) = 9.75; p = 0.004; g2 = 0.218]. The P1 was larger at electrode P8 (30.12 ± 3.64 lV/m2) than at electrode P7 (20.65 ± 2.41 lV/m2). No other significant effects were present for P1 amplitudes or latencies (all F \ 3.9; all p [ 0.055). For the N1 following the cue at electrodes P7/P8, the amplitudes revealed a main effect of group [F(1,35) = 4.90; p = 0.033; g2 = 0.123]. N1 amplitudes were more negative in the group with a large BI effect (-38.41 ± 4.13 lV/m2) than in the group with a small BI effect (-25.29 ± 4.25 lV/m2). No other significant effects were found in amplitudes or latencies (all F \ 2.74; all p [ 0.107). To examine the source of this group-depended N1 amplitude difference between conditions, a sLORETA analysis was performed: for the entire cohort (i.e. main effect of ‘‘condition’’), N1 modulations between the BI and the BASE condition were due to activity changes in the middle occipital gyrus, cuneus and lingual gyrus (BA18) as well as the insular cortex (BA13) and inferior frontal gyrus (IFG) (BA44 and BA45). P1 and N1 on target stimulus Analyzing the P1 following the target stimulus at electrodes P7/P8, no effects in amplitudes or latencies was significant (all F \ 2.48; all p [ 0.125). For the N1 amplitudes following the stimulus at electrodes P7/P8, a main effect of condition [F(1,35) = 7.19; p = 0.011; g2 = 0.170] showed that the N1 was more negative in the BI (-15.80 ± 2.98 lV/ m2) than in the BASE condition (-13.07 ± 3.24 lV/m2). No other significant effects existed for amplitudes or latencies (all F \ 3.9; all p [ 0.05). To examine whether the P1 and N1 on the target also show group dependent differences, we calculated the difference wave between the BASE and the BI condition and plotted a scalp topography map (see
Brain Struct Funct Fig. 2 P1 and N1 ERPs evoked by the cue and target stimuli. Time point zero denotes the onset on the cue; the target stimulus was added to the visual array 100 ms later. Hence, the first two peaks show the P1 and N1 elicited by the cue while the following two peaks shown the P1 and N1 elicited by the target. a Depiction of P1 and N1 ERPs at electrodes P7 and P8 (mean value). As shown in the picture, the N1 elicited by the cue showed a significant group difference which was related to activity changes within BA18, BA 13, BA44, and BA 45. b Depiction of P1 and N1 ERPs at electrodes CP5 and CP6 (mean value). As shown in the picture, the N1 elicited by the target showed a significant group difference (difference curve depicted below in red) which was related to activity changes within BA40, BA189 and BA30
Fig. 3b). This map showed a difference at electrode CP5. Therefore, we analyzed this electrode for potential group differences. Hence, we repeated the above analysis (ANOVA) on these electrodes. Of notice, we also took the corresponding contralateral electrode (i.e. CP6) into account. The mixed effect ANOVA on the target N1 at electrodes CP5/CP6, again revealed a main effect of ‘‘condition’’ [F(1,35) = 6.32; p = 0.017; g2 = 0.153] showing a larger N1 amplitude (peak to peak difference) in the BI (24.16 ± 2.22 lV/m2) compared to the BASE condition (22.36 ± 1.98 lV/m2). There was also an interaction of ‘‘condition 9 electrodes’’ [F(1,35) = 4.20; p = 0.048; g2 = 0.107], which was accounted for by a significant
condition difference at electrode CP5 [BI: 25.11 ± 2.64 lV/ m2; BASE: 22.27 ± 2.36 lV/m2; t(36) = 3.11; p = 0.004] but not at electrode CP6 [BI: 23.44 ± 2.35 lV/m2; BASE: 22.60 ± 2.05 lV/m2; t(36) = 0.96; p = 0.344]. No significant effects for latencies were found (all F \ 2.1; all p [ 0.2). Importantly, the interaction of ‘‘condition 9 group’’ [F(1,35) = 4.68; p = 0.037; g2 = 0.118] was also significant. The condition difference for N1 amplitudes was significant in the group with large BI effect [BI: 28.33 ± 3.69 lV/m2; BASE: 24.99 ± 3.19 lV/m2; t(18) = 2.9; p = 0.009], but not in the group with small BI effect [BI: 19.99 ± 2.35 lV/m2; BASE: 19.74 ± 2.39 lV/
123
Brain Struct Funct
Fig. 3 a The N2 ERP at electrode Cz showed no significant group or condition differences. b Neither the cue P3 nor the target P3 at electrode PO1/PO2 showed any significant group or condition effect
m2; t(17) = 0.30; p = 0.766]. Within the group showing a large BI effect, the sLORETA results show that the N1 amplitude differences between the BI and the BASE condition were due to activity changes in the right inferior parietal lobe (BA40) including the temporo-parietal junction (TPJ), the left cuneus and lingual gyrus (BA18), as well as the parahippocampal gyrus (BA30).
Discussion In the current study, we investigated the BI effect on a systems neuroscience level with a focus on the neurophysiological mechanisms and functional neuroanatomical networks modulated by the strength of BI. BI effect and BI magnitude differences in behavior
N2 and P3 The N2 ERP data is shown in Fig. 3a. For N2 amplitudes and latencies at electrode Cz, no significant group effects (all F B 0.887; all p [ 0.353) or other effects were revealed (all F B 2.513; all p C 0.122). To evaluate this lack of N2 effects further, Bayesian analysis of the data was performed. Using Bayesian analyses, it is possible to provide a quantification of the degree to which the data supports the null hypothesis (Masson 2011; Wagenmakers 2007) (i.e. the assumption that there is no difference between BI and BASE conditions or an interaction with the factor group). This analysis (cf. Wagenmakers 2007) revealed that the probability of the null hypothesis being true (i.e. no interaction of group and condition), given the obtained data (p(H0|D), was 80.7 % for the amplitudes and 85.9 % for latencies, thus providing strong evidence for the null hypothesis according to the criteria provided by Raftery (1995). The P3 is shown in Fig. 3b. For the P3 on the cue at electrodes PO1 and PO2 as well as P3 at electrode CPz, no significant effects were found (all F \ 2.80; all p [ 0.103). For the P3, the bayesian analysis revealed that the probability of the null hypothesis being true, given the obtained data (p(H0|D), was 83 % thus providing positive evidence for the null hypothesis according to the criteria provided by Raftery (1995).
123
A BI effect was found for triplets with a choice Go response in the n - 1 trial (ABA, BAB vs DBA, DAB as well as DBD, DAD vs. ABD, BAD) while no BI effect was found for triplets with a simple double-press response in the n - 1 trial (ADA, BDB vs. BDA, ADB). This is in line with previous studies on the BI effect (Koch et al. 2004; Schuch and Koch 2003). Base on the study of Koch et al. (2004), the behavioral data was used to distinguish groups showing high and low BI by means of a median split of RT differences between BI (ABA, BAB) and BASE (DBA, DAB) conditions. This procedure revealed a robust group difference for BI performance, but only when both the last and previous trials required choice Go responses. Taken together, these findings stress that the inhibition in the previous (n - 1) trial is key to the phenomenon of the BI effect. Whenever inhibition is not required in the n - 1 trial (i.e. when it requires a DP response), no BI effect will be found. However, the process of overcoming an inhibition in the current trial (i.e. in the last trial of the triplets) also plays an important role as it accounts for inter-individual differences in the magnitude of the BI effect. Only when an inhibition has to be overcome (i.e. in case of a choice GO response), significant inter-individual differences seem to emerge (Koch et al. 2004). While investigating the neurophysiological effects associated with task
Brain Struct Funct
variations trials n and n - 1 would sure contribute to our understanding of the general mechanisms underlying the BI effect, it does not promote our understanding of interindividual BI effect differences, as no group effects were found for triplets with task D in either trial n or n - 1. Hence, only ERPs associated with triplets requiring choice Go responses in both of the last two trials were neurophysiologically investigated. In line with our assumption that the higher cognitive requirements in those triplets account for the BI effect and its inter-individual variation (as operationalized via differences in the last trial of a given triplet), the ERP data of the last trial of each of these triplets was further analyzed using these median split groups. System electrophysiology of the BI effect The neurophysiological data shows that BI (i.e. the main effect of ‘‘condition’’) modulates the N1 amplitude on target stimuli at parietal and centro-parietal sites, with a larger N1 in the BI than in the BASE condition. In this regard, the current results replicate the data by Sinai et al. (2007), who suggested that increased attentional resources are allocated during the reactivation of a recently inhibited task. The N1 has been suggested to reflect attentional control processes such as focusing of attention towards task-relevant stimuli (Herrmann and Knight 2001; Hillyard and Anllo-Vento 1998; Luck et al. 1990). The larger N1 in the BI condition may be attributed to increased processing requirements in the BI condition and suggests that more attentional control is required to re-activate the recently abandoned task in the BI condition (as compared to the BASE condition, where this is not necessary). Sources of activity differences in the cuneus, lingual gyrus and middle occipital gyrus (BA18) reflecting these modulations in the sLORETA analysis have previously been demonstrated to be associated with attentional modulation and selective attention (Luck et al. 1997; Munneke et al. 2011; Reynolds and Desimone 1999). The insular cortex, also shown in the sLORETA analysis, has also been claimed to play a role in the modulation of attentional selection, especially in the conjunction of bottom-up processing with top-down attentional sets that may be required to re-activate a recently abandoned task (Hilti et al. 2013; Lopez-Larson et al. 2012; Uddin 2015). Similar functional roles have been attributed to the IFG (BA44 and BA45) (Micheli et al. 2015; Mincic 2010; Pessoa et al. 2009; Rossi et al. 2009). The neurophysiological data therefore suggest that the BI effect heavily depends on attentional selection mechanisms. Corroborating the specificity of results obtained for the N1, the data obtained for the N2 and P3 ERPs dissociated from the pattern observed for the N1. No effects between
the BI and BASE condition were obtained for the N2 and P3. An analysis of the N2 and P3 data using bayesian statistics revealed that there is strong evidence in favor for the null hypothesis; i.e. that that there is indeed no effect of condition variation on the N2 and P3. This suggests that processes like conflict monitoring or response selection (reflected by the N2) (Beste et al. 2008; Deng et al. 2015; Donkers and van Boxtel 2004; Huster et al. 2013; Larson et al. 2014; van Veen and Carter 2002) are unaffected by BI processes. It is possible that overcoming the inhibition of a previous rule, which constitutes the BI effect, is finished at the upstream attentional processing stage (i.e. at the N1 level). Therefore, no conflicts may be evident in later processing stages so that no modulation in the N2 is observed. This may also explain why no modulation of the P3 has been observed. In choice reaction time tasks, the P3 is likely to reflect processes between stimulus evaluation and responding; i.e. the response selection process per se (Falkenstein et al. 1994a, b; Verleger et al. 2005). In the context of task switching, the P3(b) has been suggested to reflect updating, organization, and implementation of the new task-set (Barcelo´ et al. 2000; Beste et al. 2011; Gajewski et al. 2010; 2011; Gehring et al. 2003; Goffaux et al. 2006; Karayanidis et al. 2003; Kieffaber and Hetrick 2005; Lorist et al. 2000; Nicholson et al. 2005, 2006; Rushworth et al. 2002; Stock et al. 2015). The current results suggest that these processes are unaffected by the BI effect, possibly because the processes underlying the BI effect are confined to the level of attentional selection. It is however unclear und should be subject to further studies, whether this would also be the case in other BI paradigms that require switching between response modalities (e.g. limbs) instead of task rules (like described by Philipp and Koch 2005). Importantly, the N2 and the P3 have frequently been shown to be modulated by inhibition processes. Because there are no direct repetitions (i.e. there is a switch of cues/task rules in every single trial), the inhibition of the previous task set is required in each trial (both BI and BASE) of the experiment. Hence, groups should differ in inhibition measures if the BI effect was merely due to differences in the magnitude of inhibition processes. Because we found no significant main effects of group for the N2 and P3, it can be rather safely assumed that differences in the BI effect arise from differences in how inhibition is overcome, but not from differences in the magnitude of inhibition processes in the current trial n. For further studies, it would also be interesting to investigate the neurophysiological processes in trial n - 1 as our behavioral analyses (see supporting information) show that the BI effect is only found when inhibition is required by a Go choice response in the n - 1 trial. While this does not contribute to the inter-individual variations
123
Brain Struct Funct
which were the focus of this study, ‘‘later’’ processes in trial n - 1 might be associated with the triggering of inhibition, whereas ‘‘earlier’’ processes (attentional selection) in the current trial might reflect the overcoming of inhibition. Unfortunately, we were not able to include the n - 1 trial information in the ERP analyses due to a lack of power as more trials per condition would have been required to additionally account for this factor while keeping the signal-to-noise ratio at a reasonable level. Hence, this issue remains to be investigated in future studies. System electrophysiology of BI magnitude differences As regards differences in the strength of the BI effect, the results show that the target N1 was differentially modulated across performance groups between the BI and the BASE condition. The interaction obtained for the target N1 parallels the interaction effect obtained for the RT data: participants showing large BI effects on the behavioral level also exhibited a larger condition difference in terms of N1 amplitudes. Participants, who were good at inhibiting a recently performed and currently not relevant task, might utilize the attentional control, which helps to adapt to the new environmental requirements. In the BI condition, participants with large BI (i.e. who were better at suppressing the previous task and/or worse at overcoming the previous inhibition of the n - 2 task), inhibited the recently performed task more intensely and therefore experienced stronger carry-over inhibitions from n - 2 trials than participants with a small BI effect. Most likely, refocusing to the ‘‘re-relevant’’ task is more effortful for subjects showing a strong BI effect, thus requiring more attentional control which results in larger N1 amplitudes. In contrast, less carry-over inhibition from n - 2 trials can be present in the BASE condition. Consequently, there were no differences in terms of attentional requirements (N1 amplitudes) between participants with large and small BI effects. The source localization analysis revealed that inter-individual differences resulted from activation differences in the right inferior parietal lobe (BA40) including the TPJ, the left middle occipital gyrus cuneus and lingual gyrus (BA18), as well as the parahippocampal gyrus (BA30). These regions largely overlap with regions obtained for the main effect of ‘‘condition’’ (refer text section above). Yet, the TPJ and parahippocampal region also come into play. Similar to extrastriate areas, also the TPJ has been suggested to be involved in the control of attentional processes (Pessoa et al. 2009) and re-focusing the attention to the currently relevant task (Cabeza et al. 2012; Mu¨ckschel et al. 2014). This re-focusing is what is required during BI.
123
In his context, Geng and Vossel (2013) proposed that the TPJ is involved in contextual updating in attentional control. The TPJ may mediate the updating of an internal model which initiates context and task-appropriate actions based on the environmental context/incoming sensory information (Geng and Vossel 2013). This is well in line with our results showing a modulation of the N1 amplitude. The observed inter-individual difference seems to be affected by the attentional resources necessary to update the environmental context when switching back to a previously inhibited task is required (as compared to switching to a non-inhibited task). The current results on an involvement of the TPJ also fit to other studies on task switching showing that switches between stimulus categories were related to activity changes in the TPJ (Philipp et al. 2013). Furthermore, the observed lateralization matches findings of other studies on the BI effect which suggest that the underlying processes are mainly located in the right hemisphere (Dreher and Berman 2002; Mayr et al. 2006; Aron et al. 2004). Similar to the TPJ, the parahippocampal region has been shown to be involved in processes related to attentional re-focusing (e.g. Thiel and Fink 2008; Mattler et al. 2006; Vossel et al. 2006). When comparing these findings to those from the fMRI study by Whitmer and Banich (2012), it becomes apparent that we did not demonstrate the inter-individual activation differences that they observed within the striatum. As sLORETA only allows to locate the source of neocortical activity and cannot properly represent subcortical activity differences, it was however not possible to reliably identify the striatum as a source of the observed neurophysiological differences. Hence, it is still possible that on top of the regions reported in this study, the striatum also contributes to inter-individual differences in the BI effect. As already mentioned above, there was also a main effect for the cue N1 amplitudes. Participants with large BI effect had a larger N1 than the participants with small BI effect, which was due to activation differences in the cuneus, lingual gyrus and middle occipital gyrus (BA18), the insular cortex, and the IFG (BA44 and BA45), all of which are involved in top-down guided attentional selection processes. Given that task switching is based on cue switching in the employed task, it has been suggested that the observed effects may refer to the processing of the cues and that a change in the cue is associated with less facilitation at perceptual encoding of the cue itself (Logan and Bundesen 2003). However, participants were never presented with a direct (n - 1) cue repetition and the issue of cue priming is less critical with the n - 2 repetition found in BI trials. Matching this, a BI effect can even be found when n - 2 cue repetitions are excluded (Gade and Koch 2008). Also, even though there may well be some degree of temporal overlap when it comes to cue and target
Brain Struct Funct
processing, both stimuli produced temporally distinct attentional ERPs (i.e. P1 and N1) and we furthermore found differential effects on those components. Based thereon, the hypothesis that BI is mainly based on priming effects should be dismissed as an oversimplification—even more so as the participants could not select a response before the presentation of the target. The larger cue N1 amplitudes hence suggest that individuals with a larger BI effect seem to allocate more attentional resources when the cue announces a task switch. Together with the results on the target N1, the results therefore suggest that the participants with large BI effect generally had intensified attentional control processes as compared to the participants with small BI effect. The enhanced attentional control in cue and target processing may have led to the more effective suppression of just-performed task and eased the current task performance and thus to the larger BI effect. Given that the BI effect may also be observed when response modalities (but not task rules) are switched (Philipp and Koch 2005), the modulation of the N1 ERP might however also reflect a more basic allocation of attentional resources required for any kind of cued switching behavior. Against this background, it would be interesting to conduct future studies using different switching cues (task switching indicated by color, spatial information or auditory stimuli) and response-modalities (manual, verbal) to examine whether attentional selection is a fundamental principle to all kinds of BI effects. As with the main effect of ‘‘condition’’ (i.e. the general BI effect), there was no modulation of the N2 and P3 regarding inter-individual differences, which is also confirmed by the bayesian analysis. This suggests that processes related to response selection or to the updating, organization, and implementation of the new task-set do not contribute to inter-individual differences in the magnitude of the BI effect.
Conclusions In summary, our study examined the system neurophysiological basis of BI processes using EEG and sLORETA, taking differences in the magnitude of the BI effect into account. The neurophysiological results suggest that BI processes and inter-individual differences in BI magnitude strongly depend upon attentional selection mechanisms mediated via networks consisting of extrastriate occipital areas and the TPJ and the IFG. In our study, other processes related to conflict monitoring, response selection or the updating, or the organization and implementation of a new task-set (i.e. N2 and P3 processes) were not shown to be modulated by BI processes and differences in the magnitude of the BI effect. This further strengthens the
assumption that BI as well as inter-individual differences in its magnitude strongly rely on attentional selection processes. As evidenced in the supplementary material, processing of the previous tasks and response preparation times also play a role in the magnitude of the BI effect, but they do not seem to significantly contribute to inter-individual variation in a comparable manner. Acknowledgments This work was supported by a Grant from the German Research Foundation (DFG) awarded to C. B. (BE4045/10-2) and was partially supported by a Grant of the DFG awarded to R. F. (CRC 940, Project A3).
References Allport A, Wylie G (1999) Task-switching: positive and negative priming of task-set. In: Humphreys GW, Duncan J, Treisman AM (eds) Attention, space and action: studies in cognitive neuroscience. Oxford University Press, Oxford, pp 273–296 Allport A, Styles EA, Hsieh S (1994) Shifting intentional set: exploring the dynamic control of task. In: Umilta` C, Moscovitch M (eds) Attention and performance XV: conscious and nonconscious information processing. MIT Press, Cambridge, pp 421–452 Aron AR, Robbins TW, Poldrack RA (2004) Inhibition and the right inferior frontal cortex. Trends Cogn Sci 8:170–177 Banich MT (1998) The missing link: the role of interhemispheric interaction in attentional processing. Brain Cognit 36(2):128–157. doi:10.1006/brcg.1997.0950 Barcelo´ F, Mun˜oz-Ce´spedes JM, Pozo MA, Rubia FJ (2000) Attentional set shifting modulates the target P3b response in the Wisconsin card sorting test. Neuropsychologia 38(10):1342–1355 Beste C, Saft C, Andrich J, Gold R, Falkenstein M (2008) Stimulusresponse compatibility in Huntington’s disease: a cognitiveneurophysiological analysis. J Neurophysiol 99(3):1213–1223. doi:10.1152/jn.01152.2007 Beste C, Baune BT, Falkenstein M, Konrad C (2010a) Variations in the TNF-a gene (TNF-a-308G?A) affect attention and action selection mechanisms in a dissociated fashion. J Neurophysiol 104(5):2523–2531. doi:10.1152/jn.00561.2010 Beste C, Willemssen R, Saft C, Falkenstein M (2010b) Response inhibition subprocesses and dopaminergic pathways: basal ganglia disease effects. Neuropsychologia 48(2):366–373. doi:10.1016/j.neuropsychologia.2009.09.023 Beste C, Ness V, Falkenstein M, Saft C (2011) On the role of frontostriatal neural synchronization processes for response inhibition—evidence from ERP phase-synchronization analyses in pre-manifest Huntington’s disease gene mutation carriers. Neuropsychologia 49(12):3484–3493. doi:10.1016/j.neuropsycholo gia.2011.08.024 Bokura H, Yamaguchi S, Kobayashi S (2001) Electrophysiological correlates for response inhibition in a Go/NoGo task. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 112(12): 2224–2232 Cabeza R, Ciaramelli E, Moscovitch M (2012) Cognitive contributions of the ventral parietal cortex: an integrative theoretical account. Trends Cognit Sci 16(6):338–352. doi:10.1016/j.tics. 2012.04.008 Costa RE, Friedrich FJ (2012) Inhibition, interference, and conflict in task switching. Psychon Bull Rev 19(6):1193–1201. doi:10. 3758/s13423-012-0311-1
123
Brain Struct Funct Deng Y, Wang Y, Ding X, Tang Y-Y (2015) Conflict monitoring and adjustment in the task-switching paradigm under different memory load conditions: an ERP/sLORETA analysis. Neuroreport. doi:10.1097/WNR.0000000000000310 Dippel G, Beste C (2015) A causal role of the right inferior frontal cortex in the strategies of multi-component behaviour. Nature Commun. doi:10.1038/ncomms7587 Donkers FCL, van Boxtel GJM (2004) The N2 in go/no-go tasks reflects conflict monitoring not response inhibition. Brain Cognit 56(2):165–176. doi:10.1016/j.bandc.2004.04.005 Dreher JC, Berman KF (2002) Fractionating the neural substrate of cognitive control processes. Proc Natl Acad Sci USA 99:14595–14600 Druey MD, Hu¨bner R (2007) The role of temporal cue-target overlap in backward inhibition under task switching. Psychon Bull Rev 14(4):749–754 Falkenstein M, Hohnsbein J, Hoormann J (1994a) Effects of choice complexity on different subcomponents of the late positive complex of the event-related potential. Electroencephalogr Clin Neurophysiol 92(2):148–160 Falkenstein M, Hohnsbein J, Hoormann J (1994b) Time pressure effect on late components of the event-related potential (ERP). J Psychophysiol 8:22–30 Fuchs M, Kastner J, Wagner M, Hawes S, Ebersole JS (2002) A standardized boundary element method volume conductor model. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 113(5):702–712 Gade M, Koch I (2008) Dissociating cue-related and task-related processes in task inhibition: evidence from using a 2:1 cue-totask mapping. Can J Exp Psychol Revue Canadienne de Psychologie Expe´rimentale 62(1):51–55. doi:10.1037/11961961.62.1.51 Gajewski PD, Wild-Wall N, Schapkin SA, Erdmann U, Freude G, Falkenstein M (2010) Effects of aging and job demands on cognitive flexibility assessed by task switching. Biol Psychol 85(2):187–199. doi:10.1016/j.biopsycho.2010.06.009 Gajewski PD, Hengstler JG, Golka K, Falkenstein M, Beste C (2011) The Met-allele of the BDNF Val66Met polymorphism enhances task switching in elderly. Neurobiol Aging 32(12):2327.e7–19. doi:10.1016/j.neurobiolaging.2011.06.010 Gehring WJ, Bryck RL, Jonides J, Albin RL, Badre D (2003) The mind’s eye, looking inward? In search of executive control in internal attention shifting. Psychophysiology 40(4):572–585 Geng JJ, Vossel S (2013) Re-evaluating the role of TPJ in attentional control: contextual updating? Neurosci Biobehav Rev 37(10, Part 2):2608–2620. doi:10.1016/j.neubiorev.2013.08.010 Getzmann S, Gajewski PD, Hengstler JG, Falkenstein M, Beste C (2013) BDNF Val66Met polymorphism and goal-directed behavior in healthy elderly—evidence from auditory distraction. NeuroImage 64:290–298. doi:10.1016/j.neuroimage.2012.08.079 Goffaux P, Phillips NA, Sinai M, Pushkar D (2006) Behavioural and electrophysiological measures of task switching during single and mixed-task conditions. Biol Psychol 72(3):278–290. doi:10. 1016/j.biopsycho.2005.11.009 Herrmann CS, Knight RT (2001) Mechanisms of human attention: event-related potentials and oscillations. Neurosci Biobehav Rev 25(6):465–476 Hillyard SA, Anllo-Vento L (1998) Event-related brain potentials in the study of visual selective attention. Proc Natl Acad Sci USA 95(3):781–787 Hilti CC, Jann K, Heinemann D, Federspiel A, Dierks T, Seifritz E, Cattapan-Ludewig K (2013) Evidence for a cognitive control network for goal-directed attention in simple sustained attention. Brain Cognit 81(2):193–202. doi:10.1016/j.bandc.2012.10.013 Huster RJ, Enriquez-Geppert S, Lavallee CF, Falkenstein M, Herrmann CS (2013) Electroencephalography of response
123
inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol 87(3):217–233. doi:10.1016/j.ijpsycho.2012. 08.001 Johnson JA, Zatorre RJ (2005) Attention to simultaneous unrelated auditory and visual events: behavioral and neural correlates. Cereb Cortex (New York, N.Y.: 1991) 15(10):1609–1620. doi:10.1093/cercor/bhi039 Karayanidis F, Coltheart M, Michie PT, Murphy K (2003) Electrophysiological correlates of anticipatory and poststimulus components of task switching. Psychophysiology 40(3):329–348 Katzir M, Ori B, Hsieh S, Meiran N (2015) Competitor rule priming: evidence for priming of task rules in task switching. Psychol Res 79(3):446–462. doi:10.1007/s00426-014-0583-3 Kieffaber PD, Hetrick WP (2005) Event-related potential correlates of task switching and switch costs. Psychophysiology 42(1):56–71. doi:10.1111/j.1469-8986.2005.00262.x Kiesel A, Steinhauser M, Wendt M, Falkenstein M, Jost K, Philipp AM, Koch I (2010) Control and interference in task switching— a review. Psychol Bull 136(5):849–874. doi:10.1037/a0019842 Koch I, Gade M, Philipp AM (2004) Inhibition of response mode in task switching. Exp Psychol 51:52–58. doi:10.1027/1617-3169.51.1.52 Koch I, Gade M, Schuch S, Philipp AM (2010) The role of inhibition in task switching: a review. Psychon Bull Rev 17(1):1–14. doi:10.3758/PBR.17.1.1 Larson MJ, Clayson PE, Clawson A (2014) Making sense of all the conflict: a theoretical review and critique of conflict-related ERPs. Int J Psychophysiol Off J Int Org Psychophysiol 93(3):283–297. doi:10.1016/j.ijpsycho.2014.06.007 Logan GD, Bundesen C (2003) Clever homunculus: is there an endogenous act of control in the explicit task-cuing procedure? J Exp Psychol Hum Percept Perform 29:575–599 Lopez-Larson MP, King JB, Terry J, McGlade EC, Yurgelun-Todd D (2012) Reduced insular volume in attention deficit hyperactivity disorder. Psychiatry Res 204(1):32–39. doi:10.1016/j.pscy chresns.2012.09.009 Lorist MM, Klein M, Nieuwenhuis S, De Jong R, Mulder G, Meijman TF (2000) Mental fatigue and task control: planning and preparation. Psychophysiology 37(5):614–625 Luck SJ, Heinze HJ, Mangun GR, Hillyard SA (1990) Visual eventrelated potentials index focused attention within bilateral stimulus arrays. II. Functional dissociation of P1 and N1 components. Electroencephalogr Clin Neurophysiol 75(6):528–542 Luck SJ, Chelazzi L, Hillyard SA, Desimone R (1997) Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. J Neurophysiol 77(1):24–42 Marco-Pallare´s J, Grau C, Ruffini G (2005) Combined ICA-LORETA analysis of mismatch negativity. NeuroImage 25(2):471–477. doi:10.1016/j.neuroimage.2004.11.028 Masson MEJ (2011) A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav Res Methods 43(3):679–690. doi:10.3758/s13428-010-0049-5 Mattler U, Wu¨stenberg T, Heinze H-J (2006) Common modules for processing invalidly cued events in the human cortex. Brain Res 1109(1):128–141. doi:10.1016/j.brainres.2006.06.051 Mayr U, Keele SW (2000) Changing internal constraints on action: the role of backward inhibition. J Exp Psychol Gen 129(1):4–26 Mayr U, Diedrichsen J, Ivry R, Keele SW (2006) Dissociating taskset selection from task-set inhibition in the prefrontal cortex. J Cogn Neurosci 18:14–21 Micheli C, Kaping D, Westendorff S, Valiante TA, Womelsdorf T (2015) Inferior-frontal cortex phase synchronizes with the temporal-parietal junction prior to successful change detection. NeuroImage. doi:10.1016/j.neuroimage.2015.06.043 Mincic AM (2010) Neural substrate of the cognitive and emotional interference processing in healthy adolescents. Acta Neurobiologiae Experimentalis 70(4):406–422
Brain Struct Funct Monsell S (2003) Task switching. Trends Cognit Sci 7(3):134–140. doi:10.1016/S1364-6613(03)00028-7 Mu¨ckschel M, Stock A-K, Beste C (2014) Psychophysiological mechanisms of interindividual differences in goal activation modes during action cascading. Cereb Cortex (New York, N.Y.: 1991) 24(8):2120–2129. doi:10.1093/cercor/bht066 Munneke J, Heslenfeld DJ, Usrey WM, Theeuwes J, Mangun GR (2011) Preparatory effects of distractor suppression: evidence from visual cortex. PloS One 6(12):e27700. doi:10.1371/journal. pone.0027700 Nicholson R, Karayanidis F, Poboka D, Heathcote A, Michie PT (2005) Electrophysiological correlates of anticipatory taskswitching processes. Psychophysiology 42(5):540–554. doi:10. 1111/j.1469-8986.2005.00350.x Nicholson R, Karayanidis F, Davies A, Michie PT (2006) Components of task-set reconfiguration: differential effects of ‘‘switchto’’ and ‘‘switch-away’’ cues. Brain Res 1121(1):160–176. doi:10.1016/j.brainres.2006.08.101 Nunez PL, Pilgreen KL (1991) The spline-Laplacian in clinical neurophysiology: a method to improve EEG spatial resolution. J Clin Neurophysiol Off Publ Am Electroencephalogr Soc 8(4):397–413 Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1):97–113 Pammer K, Hansen P, Holliday I, Cornelissen P (2006) Attentional shifting and the role of the dorsal pathway in visual word recognition. Neuropsychologia 44(14):2926–2936. doi:10.1016/ j.neuropsychologia.2006.06.028 Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24(Suppl D):5–12 Pessoa L, Rossi A, Japee S, Desimone R, Ungerleider LG (2009) Attentional control during the transient updating of cue information. Brain Res 1247:149–158. doi:10.1016/j.brainres.2008. 10.010 Philipp AM, Koch I (2005) Switching of response modalities. Q J Exp Psychol A 58:1325–1338 Philipp AM, Weidner R, Koch I, Fink GR (2013) Differential roles of inferior frontal and inferior parietal cortex in task switching: evidence from stimulus-categorization switching and responsemodality switching. Human Brain Mapp 34(8):1910–1920. doi:10.1002/hbm.22036 Raftery AE (1995) Bayesian model selection in social research. In: Mardsen P (ed) Sociological methodology. Blackwell, Cambridge, pp 11–196 Regev S, Meiran N (2015) Cue-type manipulation dissociates two types of task set inhibition: backward inhibition and competitor rule suppression. Psychol Res. doi:10.1007/s00426-015-0663-z Reynolds JH, Desimone R (1999) The role of neural mechanisms of attention in solving the binding problem. Neuron 24(1):19–29, 111–125
Rossi AF, Pessoa L, Desimone R, Ungerleider LG (2009) The prefrontal cortex and the executive control of attention. Exp Brain Res 192(3):489–497. doi:10.1007/s00221-008-1642-z Rushworth MFS, Passingham RE, Nobre AC (2002) Components of switching intentional set. J Cognit Neurosci 14(8):1139–1150. doi:10.1162/089892902760807159 Scheil J, Kleinsorge T (2014) N - 2 repetition costs depend on preparation in trials n - 1 and n - 2. J Exp Psychol Learn Mem Cognit 40(3):865–872. doi:10.1037/a0035281 Schuch S, Koch I (2003) The role of response selection for inhibition of task sets in task shifting. J Exp Psychol Human Percept Perform 29(1):92–105. doi:10.1037/0096-1523.29.1.92 Sekihara K, Sahani M, Nagarajan SS (2005) Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. NeuroImage 25(4):1056–1067. doi:10.1016/j.neuroimage.2004.11.051 Sinai M, Goffaux P, Phillips NA (2007) Cue-versus response-locked processes in backward inhibition: evidence from ERPs. Psychophysiology 44(4):596–609. doi:10.1111/j.1469-8986.2007. 00527.x Stock A-K, Gohil K, Beste C (2015) Age-related differences in task goal processing strategies during action cascading. Brain Struct Funct. doi:10.1007/s00429-015-1071-2 Thiel CM, Fink GR (2008) Effects of the cholinergic agonist nicotine on reorienting of visual spatial attention and top-down attentional control. Neuroscience 152(2):381–390. doi:10.1016/j. neuroscience.2007.10.061 Uddin LQ (2015) Salience processing and insular cortical function and dysfunction. Nature Rev Neurosci 16(1):55–61. doi:10.1038/ nrn3857 Van Veen V, Carter CS (2002) The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiol Behav 77(4–5): 477–482 Verleger R, Jas´kowski P, Wascher E (2005) Evidence for an integrative role of P3b in linking reaction to perception. J Psychophysiol 19(3):165–181 Vidyasagar TR (1999) A neuronal model of attentional spotlight: parietal guiding the temporal. Brain Res Brain Res Rev 30(1):66–76 Vossel S, Thiel CM, Fink GR (2006) Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex. NeuroImage 32(3):1257–1264. doi:10.1016/j.neuroimage.2006.05.019 Wagenmakers E-J (2007) A practical solution to the pervasive problems of p values. Psychon Bull Rev 14(5):779–804 Wascher E, Beste C (2010) Tuning perceptual competition. J Neurophysiol 103(2):1057–1065. doi:10.1152/jn.00376.2009 Whitmer AJ, Banich MT (2012) Brain activity related to the ability to inhibit previous task sets: an fMRI study. Cognit Affect Behav Neurosci 12(4):661–670. doi:10.3758/s13415-012-0118-6
123
