(PDF) Stimulus-Driven Reorienting Impair

Cerebral Cortex, November 2016;26: 4136–4147 doi: 10.1093/cercor/bhw225 Advance Access Publication Date: 22 August 2016 Original Article ORIGINAL ARTICLE Stimulus-Driven Reorienting Impairs Executive Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 Control of Attention: Evidence for a Common Bottleneck in Anterior Insula Fynn-Mathis Trautwein, Tania Singer, and Philipp Kanske Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany Address correspondence to Fynn-Mathis Trautwein, Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany. Email:

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Abstract A classical model of human attention holds that independent neural networks realize stimulus-driven reorienting and executive control of attention. Questioning full independence, the two functions do, however, engage overlapping networks with activations in cingulo-opercular regions such as anterior insula (AI) and a reverse pattern of activation (stimulus- driven reorienting), and deactivation (executive control) in temporoparietal junction (TPJ). To test for independent versus shared neural mechanisms underlying stimulus-driven and executive control of attention, we used fMRI and a task that isolates individual from concurrent demands in both functions. Results revealed super-additive increases of left AI activity and behavioral response costs under concurrent demands, suggesting a common bottleneck for stimulus-driven reorienting and executive control of attention. These increases were mirrored by non-additive decreases of activity in the default mode network (DMN), including posterior TPJ, regions where activity increased with off-task processes. The deactivations in posterior TPJ were spatially separated from stimulus-driven reorienting related activation in anterior TPJ, a differentiation that replicated in task-free resting state. Furthermore, functional connectivity indicated inhibitory coupling between posterior TPJ and AI during concurrent attention demands. These results demonstrate a role of AI in stimulus-driven and executive control of attention that involves down-regulation of internally directed processes in DMN. Key words: fMRI, functional connectivity, ﬂanker task, spatial cueing, temporoparietal junction Introduction Regarding orienting, a dorsal frontoparietal network con- For ﬂexible but coherent action in a complex environment, sisting of intraparietal sulcus (IPS) and frontal eye ﬁelds (FEF) attention must capture important events and single out goal- is involved in spatial allocation of attention towards speciﬁc relevant information. A classical neurocognitive model holds stimuli (Kim et al. 1999; Peelen et al. 2004; Molenberghs et al. that this is accomplished through two functions—orienting and 2007; Slagter et al. 2007); while ventral frontoparietal areas executive control of attention—which rely on distinct brain including temporoparietal junction (TPJ) and inferior frontal networks that can be further decomposed into several sub- gyrus (IFG) are additionally activated whenever a task- networks (Posner and Petersen 1990; Petersen and Posner 2012). relevant stimulus appears outside the current focus of © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

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attention and triggers a reorienting response (Corbetta et al. 2000; Corbetta and Shulman 2002; Kincade et al. 2005; Gillebert et al. 2013; Stoppel et al. 2013; de Haan et al. 2015). Thus, these ventral regions seem to interact with the dorsal frontoparietal network when moving attention from the current focus towards a new source of information in stimulus-driven reor- ienting of attention (Corbetta et al. 2008; Shulman and Corbetta 2012; Wen et al. 2012). Regarding executive control, a network involving anterior insula (AI) and dorsal anterior cingulate stretching into medial frontal cortex (dACC) is considered a core system involved in self-regulation, resolution of conﬂicts between competing information, and focal attention (Botvinick et al. 2004; Rueda et al. 2005; Dosenbach et al. 2008; Craig 2009; Nelson et al. 2010; Kanske and Kotz 2011; van Steenbergen et al. 2015). An import- ant function of this cingulo-opercular network is to allocate resources to external tasks by down-regulating default mode network (DMN) activity and associated internal task-unrelated processes (Wen et al. 2013; Goulden et al. 2014). Furthermore, it also engages a frontoparietal network for ﬁne-grained control adjustments (Dosenbach et al. 2008; Sridharan et al. 2008). Evidence for independence of orienting and executive con- trol comes from several studies that orthogonally manipulated both functions and found no behavioral interference or interin- dividual correlation (Fan et al. 2002, 2005; Fuentes and Campoy 2008), non-overlapping neural networks (Fan et al. 2005) and differential brain oscillations (Fan et al. 2007). Note that the only study which explicitly addressed overlap on a neural level (Fan et al. 2005) relied on a sample size of n = 16, sible that overlap may be detected in larger samples with higher statistical power (Button et al. 2013; Friston 2013; Lindquist et al. 2013). Furthermore, this study did not evaluate interactions on a neural level and assessed orienting and executive control of attention with two distinct, temporally separated events. Speciﬁcally, orienting was assessed by com- paring responses to spatially informative versus spatially non- informative cues. Under these conditions, executive control of attention (assessed by comparing targets with congruent vs. incongruent ﬂanker stimuli) was not affected by the orienting manipulation. In contrast, orienting can also be assessed through targets appearing at the uncued (vs. cued) location thereby inducing stimulus-driven reorienting of attention towards the target (Corbetta et al. 2000; Gillebert et al. 2013; de Haan et al. 2015). Note that the term “stimulus-driven” is some- times used in a narrower context of involuntary or “bottom-up” attention shifts (e.g. Serences et al. 2005). These other mechanisms compared to the reorienting shifts towards a behaviorally relevant target as in the classic reorienting digm, which supposedly also involves top-down processing (Kincade et al., 2005). Importantly, in this paradigm the atten- tion shift is elicited by invalidly cued targets and may induce interference if the target also requires executive control of attention. Interestingly, two recent behavioral studies that tested both functions in this way indicate impaired executive control when reorienting of attention is required (Fan et al. 2009; Spagna et al. 2015). While this suggests that shared pro- cesses supporting both functions represent a general bottle- neck, the neural mechanisms supporting such complex attentional demands are unknown. Previous research suggests some degree of functional overlap and thus at least two possible origins of interference. Firstly, the frontoparietal network involved in executive control overlaps with the dorsal orienting network (Dosenbach et al. 2008). Secondly, cingulo-opercular regions are also activated by 4138 | Cerebral Cortex, 2016, Vol. 26, No. 11 A B Figure 1. Behavioral task and results (A) Example trial a short ﬁxation period, a central cue indicates the depicted) or incorrect location (invalidly cued target) stimulus. The direction of the middle arrow indicates button press) and is ﬂanked by arrows pointing in ent (inc.) direction. (B) Behavioral results. Upper for reaction time and error rate in invalidly cued and tions as well as an interaction effect of both conditions. time, individual differences in executive control of cued incongruent targets – validly cued congruent reorienting (invalidly cued congruent targets – validly were not correlated; but this correlation was signiﬁcant the trials). Subjects were instructed to press one of two buttons depending on the direction of the middle arrow (index ﬁnger of the right hand for upward arrows and middle ﬁnger of the right hand for downward arrows). In half of the trials the middle arrow was ﬂanked by congruent arrows pointing in the same direction (congruent target condition) and in the other half by arrows pointing in the opposite direction (incongruent target condition). Thus the task gives rise to a 2 × 2 factorial design (validly vs. invalidly cued target; congruent vs. incongruent ﬂankers). Overall, 240 trials were presented in a pseudo- randomized order. After each third of the task, two successive questions asked participants to rate task focus (“were you focused on the task?”) and task-unrelated thoughts (“did you think of something else?”) by moving a marker on a visual analog scale. Rating scales were structured visually by marks ranging from zero to six and were anchored to “not at all” and “entirely”. Each rating remained on the screen for eight seconds. Prior to the 16 min. scanning session, participants were familiarized with the task in a short training session (30 trials). Acquisition of resting state data was done on the same day in a separate scanning session. During the 6 min. run, functional images to MNI space. The normalized images (3 mm isotropic voxel) were spatially smoothed with a Gaussian ker- nel of full-width half-maximum at 8 mm. A high-pass temporal ﬁlter with cutoff of 128 s was applied to remove low-frequency drifts from the data. Assessing Activations of Attention Networks After preprocessing, statistical analysis was carried out using the general linear model (Friston et al. 1995). Regressors of interest included onsets of the four target conditions (validly and invalidly cued targets with congruent or incongruent ﬂan- kers) convolved with a canonical hemodynamic response func- tion (HRF). Targets with no or incorrect button presses were omitted. As regressors of no interest, HRF convolved onsets of the cue stimuli and the six motion parameters were included in the design matrix. To further reduce inﬂuence of potential noise-artifacts, we used the RobustWLS Toolbox (Diedrichsen and Shadmehr 2005), which down-weights images with higher noise variance through a weighted-least-squares approach. To assess effects of stimulus-driven reorienting, executive control, and their interaction, contrast images for the experimental design were calculated for each subject. For random effects analysis (Friston et al. 1999), these were entered into a factorial design with two factors (“cue validity” “ﬂanker congruence”), with two dependent measurement levels in each factor and unequal variances. Subsequently, the follow- ing t-contrasts were deﬁned: Reorienting related activity was assessed by contrasting invalidly cued targets against validly cued targets only within the congruent ﬂanker condition. versa, executive control, that is, incongruently versus congru- ently ﬂanked targets, was assessed only within the valid cue condition. Common activations within both of these contrasts were evaluated through conjunction analysis by intersecting both individually thresholded contrasts (i.e. testing junction null hypothesis; cf. Nichols et al. 2005). Furthermore, contrasts were deﬁned to test for positive interactions (super- additive effects of invalid cueing and incongruent ﬂankers) and negative interactions (sub-additive effects of both conditions). To qualify the interactions, percent signal change values for each subject were extracted from spheres (radius = 10 mm) around cluster peak voxels using the rfxplot toolbox (Gläscher 2009). All second-level statistical parametric maps were corrected for multiple comparisons with an extent familywise error (FWE) corrected threshold at p < 0.05 (voxel selection threshold at p < 0.001). For clusters spanning several anatomical regions, results were assessed at the voxel level with an FWE threshold at p < 0.05 (following Woo et al. 2014). tion, statistical maps were mapped onto a rendering of the cor- tical surface using Caret software (Van Essen et al. 2001). Assessing Task Unrelated Processes Furthermore, activations due to task unrelated processes were assessed by estimating a separate GLM that included, in addition to the task regressors described above, regressors for on- and off-task periods. To obtain these regressors, the three 10 s intervals prior to the ratings of task focus and task unre- lated thoughts were classiﬁed based on the composite of both ratings (task focus minus task unrelated thinking) as on- or off-task and convolved with an HRF. The rating scales were structured visually by marks ranging from zero to six. In order to use only probes that could be classiﬁed clearly as on- or off- task relative to the subject’s own ﬂuctuations, each probe that 4140 | Cerebral Cortex, 2016, Vol. 26, No. 11 Behavioral Data In order to assess RT and error-rate effects (Fig. 1) driven reorienting and executive control independently from each other, we tested effects of cue validity in the ﬂanker condition and effects of ﬂanker congruency cue condition. Furthermore, to test for interactive both attention processes, we performed a 2 (cue validity: vs. invalid cue) × 2 (ﬂanker congruence: incongruent gruent ﬂanker) repeated-measures ANOVA. Invalidly compared to validly cued targets led (t(1,281) = 27.53, p < 0.001). Similarly, RTs were incongruent compared to the congruent ﬂanker condition (t(1,281) = 29.55, p < 0.001). Furthermore, a signiﬁcant effect (F(1,281) = 47.47; p < 0.001) demonstrated increases in response costs for the combination of invalid and incongruent ﬂankers (consequently, also main effects of the ANOVA were signiﬁcant for cue validity, F(1,281) p < 0.001, and ﬂanker congruence, F(1,281) = 1079.37; Thus, response costs of simultaneous stimulus-driven ing and executive control demands were larger than what would be expected if both processes independently added costs or occurred in parallel. This suggests that both functions depend on a common mechanism that constitutes a processing bottleneck under concurrent demands. Error-rates showed the same pattern, with higher error-rates for invalidly compared to validly cued targets (t(1,281) = 9.45, p < 0.001) and more errors in incongruent compared to congru- ent ﬂanker trials (t(1,281) = 11.47, p < 0.001). increases in error-rates were indicated by a signiﬁcant inter- action (F(1,281) = 286.55; p < 0.001) of cue validity (main effect: F(1,281) = 437.93; p < 0.001) and ﬂanker congruence (main effect: F(1,281) = 368.50; p < 0.001). Thus, error-rates conﬁrm the results of the RT analysis. To test independence of interindividual differences in reor- ienting (i.e. the size of the validity effect in congruent trials) and executive control (i.e. the size of the ﬂanker effect in valid trials) capacities we ﬁrst checked the intercorrelation (Spearman correlations) of RT scores and error rates. This yielded a signiﬁcant correlation for executive control (rs = 0.26, p < 0.001), while this was not the case for reorienting (rs = 0.05, p = 0.43), indicating that reorienting RT scores and error rates are inﬂuenced by different sub-processes. For RT scores, correl- ation analysis of interindividual differences of both capacities (Fig. 1) revealed that executive control and stimulus-driven reorienting were not correlated (rs = −0.04; p > 0.5). For error- rates, a positive correlation between both scores was observed (rs = 0.17; p < 0.01). This correlation remained practically unchanged (rs = 0.16; p < 0.01) when controlling for the com- mon reference condition (i.e. the valid cue congruent ﬂanker condition that is used as a subtraction baseline for both scores) through partial correlation analysis. fMRI Results Common and Speciﬁc Activations of Stimulus-driven Reorienting and Executive Control To assess areas involved in stimulus-driven reorienting inde- pendently of executive control, we contrasted invalidly with validly cued targets in the congruent condition. Vice versa, executive control of attention was assessed by contrasting incongruent with congruent ﬂanker trials in the valid cue con- dition. Furthermore, the conjunction of both of these contrasts A C Figure 2. Activation, overlap and interaction of attention trasting invalidly versus validly cued congruent targets control). (B) A positive interaction of cue (invalid observed in posterior portions of bilateral TPJ and in interaction peak regions (depicted in B). Left AI and left TPJ show a reverse pattern of super-additive gruent targets. (D) Psychophysiological interaction control demands. The graph shows task induced changes TUT = task unrelated thoughts, con. = congruent, inc. invalidly cued incongruent targets. Thus, this area still showed responses to miscued targets, which were reduced under executive control demands. These interaction clusters in left and right TPJ were located posterior to the reorienting clusters (Fig. 3), stretching from angular gyrus into lateral occipital cor- tex; and the right cluster overlapped with the posterior portion of a connectivity based parcellation of right TPJ (Bzdok et al. 2013). This pattern is consistent with the view that posterior TPJ is part of the DMN, being deactivated during externally directed attention, while anterior TPJ is coupled with the ven- tral orienting network (Kubit and Jack 2013). To conﬁrm this dif- ferentiation, we directly contrasted activity patterns in anterior and posterior TPJ of the left and right hemisphere. To this end we extracted bold responses for the four peaks and computed the size of the reorienting effect (invalid_congruent – valid_congruent) as well as the size of the interaction effect [(invalid_congruent – valid_congruent) – (invalid_incongruent – valid_incongruent)]. Paired t-tests indicated signiﬁcantly stronger interactions for the posterior peaks (Fig. 3; right hemisphere: t(1,281) = 2.49, p < 0.02; left hemisphere: t(1,281) 4142 | Cerebral Cortex, 2016, Vol. 26, No. 11 Table 1. Activation peaks for stimulus-driven reorienting, executive control, and conjunction. Hemisphere MNI coordinates Stimulus-driven reorienting (invalid congruent > valid congruent) Frontal Pole L −45, 42, −9 5.29 dACC / MFC L −6, 15, 51 11.9 3757 Anterior Insula L −27, 24, −3 11.74 Anterior Insula R 33, 24, −6 11.06 Middle Frontal Gyrus L −39, 6, 36 11.01 Middle Frontal Gyrus R 42, 9, 30 10.76 dACC / MFC R 9, 18, 48 10.74 Superior Frontal Gyrus R 15, 12, 60 8.84 Inferior Frontal Gyrus R 51, 21, 6 8.11 Anterior TPJ R 54, −45, 24 12.66 3400 Anterior TPJ L −54, −48, 36 10.00 Precuneus R 9, −54, 48 8.99 Intraparietal Sulcus L −33, −48, 42 8.96 Middle Temporal Gyrus R 57, −36, −3 8.48 Intraparietal Sulcus R 33, −48, 42 8.37 Posterior TPJ R 42, −63, 18 8.33 Precuneus L −9, −57, 51 8.17 Posterior TPJ L −42, −69, 15 7.55 Occipital Cortex R 24, −60, 6 5.52 22 Occipital Cortex R 12, −87, 3 6.79 191.00 Occipital Cortex L −9, −90, 0 6.07 Executive control (valid incongruent > valid congruent) Anterior Insula R 30, 24, 0 8.99 135 Anterior Insula L −27, 24, 0 8.79 108 dACC / MFC R 9, 18, 48 8.09 270 Precentral Gyrus R 48, 9, 30 8.62 185 Middle Fontal Gyrus R 27, 6, 54 8.24 215 Middle Fontal Gyrus L −27, −3, 51 8.51 345 Precentral Gyrus L −45, 3, 33 8.17 Inferior Temporal Gyrus R 54, −57, −9 9.38 157 Intraparietal Sulcus R 24, −60, 48 10.85 Occipital Cortex R 33, −69, 27 9.19 Inferior Temporal Gyrus L −42, −66, −6 8.89 Occipital Cortex L −27, −72, 27 9.28 Intraparietal Sulcus L −18, −63, 48 8.85 Conjunction: Executive control ∩ Stimulus-driven reorienting Anterior Insula R 30, 24, 0 8.99 133 Anterior Insula L −27, 24, 0 8.79 108 dACC / MFC R 9, 18, 48 8.09 268 Precentral Gyrus R 48, 9, 30 8.62 166 Middle Fontal Gyrus R 27, 6, 54 8.11 163 Middle Fontal Gyrus L −27, 0, 51 8.49 270 Precentral Gyrus L −45, 3, 33 8.17 Intraparietal Sulcus L −36, −45, 45 8.06 Intraparietal Sulcus R 33, −48, 45 8.18 391 Occipital Cortex R 39, −72, 27 7.46 70 Notes: Activations were assessed at the voxel level threshold at p < 0.05. All clusters exceeding an reported. Clusters are ordered from anterior to posterior. count refer to sub-peaks within larger clusters; the of these clusters is always provided as a ﬁrst row, rows. attentional demand might be related to inhibition of such processes, we probed for task unrelated thoughts during the task and used these ratings as parametric modulators of the pre-rating epochs. This analysis yielded regions typically cated in the DMN (Fig. 2). Arguing for inhibition of task unre- lated processes during combined reorienting and executive A B Figure 3. Functional differentiation of subregions in trast (red) and negative interaction contrast (green). ity analysis (shown in C, only done for right TPJ). posterior compared to anterior TPJ, while stimulus-driven works of anterior TPJ (red) and posterior TPJ (green) neuroscience. Previous research has described two main atten- tion systems that supposedly rely on several independent neural networks: one system spatially orienting attention towards relevant stimuli and another system exerting execu- tive control (Posner and Petersen 1990; Petersen and Posner 2012). Here, we put the assumption of independence of the two systems to test by assessing behavioral responses and neural activations in a task that concurrently demands both functions through spatial cueing and ﬂanker-target conﬂict. Results repli- cate cingulo-opercular and frontoparietal networks for execu- tive control as well as dorsal and ventral orienting networks. However, in contrast to the assumption of independence, we provide evidence for a shared mechanism in AI, leading to interferences (i.e. lower processing efﬁciency) in situations that concurrently demand stimulus-driven reorienting and execu- tive control of attention. This was evidenced by super-additive increases in response costs and left AI activity for the combin- ation of both attentional processes. Deactivations due to dual attentional demands in posterior TPJ and precuneus—regions also activated by task unrelated thought—further indicated reliance on common mechanisms. Task-dependent functional connectivity analysis revealed negative coupling between regions showing non-additive deactivation (posterior TPJ) and activation (left AI), suggesting that task unrelated processes might be increasingly suppressed with higher and concurrent attentional demands. This ﬁnding also revealed a spatial differ- entiation of processes within TPJ, with suppression of posterior TPJ by attentional demands, and activation of anterior TPJ dur- ing reorienting—a task-based differentiation which we con- ﬁrmed with resting-state functional connectivity. Resource Competition of Attention Systems Previous research has mainly characterized executive control and stimulus-driven reorienting of attention as two independ- ent systems (Fan et al. 2005; Petersen and Posner 2012). However, more recent behavioral studies tailored to reveal interactions between both systems found evidence for interfer- ence (Fan et al. 2009; Spagna et al. 2015). Consistent with these studies, we found that response costs resulting from incongru- ent ﬂankers surrounding the target stimulus were enhanced when targets were preceded by invalid cues. This condition induces reorienting—attention is moved from the previously 4144 | Cerebral Cortex, 2016, Vol. 26, No. 11 were no interactions in any of the dorsal frontoparietal areas. In line with these results, recent accounts have attributed cen- tral functions to cingulo-opercular regions within the domain of attention. These include focal attention (Nelson et al. 2010), a uniﬁed bottleneck of perception and response related atten- tion (Tombu et al. 2011), stimulus- and task-driven alertness (Sterzer and Kleinschmidt 2010), and salience-based regulation of executive regions and the DMN in order to allocate process- ing resources to relevant stimuli (Sridharan et al. 2008; Uddin 2015; Cai et al. 2016). Furthermore, a recent study (Han and Marois 2014) indicated that AI might in fact be more central to the attentional reorienting process then temporoparietal areas, which could rather relate to subsequent stimulus evaluation processes (Geng and Vossel 2013). Several sub-processes such as salience or error processing, contingent capture (Folk 1992), target detection (Kubit and Jack 2013), and disengagement and shifting of attention (Posner et al. 1984) might contribute to the reorienting process as implemented in the current study—which limits conclusions about the precise mechanism that interfered with executive control of attention. It has been shown that the ventral atten- tion network is not activated during attentional capture driven purely by perceptual saliency (Kincade et al. 2005), but during reorienting to non-target stimuli with task relevant tures (Serences et al. 2005) and targets with low salience (Indovina and Macaluso 2007). Thus it can be assumed that the involved mechanism is not a pure bottom-up process, but also involves top-down modulations that ﬁlter for task relevant fea- tures (Corbetta et al. 2008). Furthermore, post-perceptual pro- cesses such as contextual updating and adjustment of expectations might also be involved (Geng and Vossel 2013). This also implies that the present results do not rule out that attentional capture purely driven by perceptual features might work independently from executive control of attention. The fact that previous studies assessing orienting with spatially informative versus non-informative cues did not ﬁnd an inter- action with executive control (Fan et al. 2002, 2005; Fuentes and Campoy 2008)—but the present and other studies inducing reorienting through invalid cues did (Fan et al. 2009; Spagna et al. 2015)—might further clarify the mechanism involved in the interaction. One process classically thought to be present only in reorienting (induced by invalid cueing) is disengage- ment of attentional focus from the previous target location (Posner et al. 1984). Thus a potential explanation of these dis- crepancies is that inhibitory mechanisms involved in disen- gaging attention during reorienting create interference. This is plausible because inhibitory mechanisms are also involved in conﬂict resolution (Nee et al. 2007) and agrees with the locus of neural interactions in cingulo-opercular, but not in dorsal frontoparietal regions. Furthermore, the low temporal reso- lution of fMRI data limits conclusions about the exact time course dynamics of processes involved in stimulus-driven reorienting and executive control of attention. While both are elicited by the appearance of the same stimulus (in the case of invalidly cued incongruent targets), it is possible that the conﬂict processing is only engaged when attention has at least partially reoriented. Future studies using methods with higher temporal resolution could reveal to which extent overlapping dynamics of the two processes result in the behavioral interaction. Nevertheless, the present results are particularly supportive of the view that the mechanism implemented in AI is respon- sible for suppression of DMN activity, which is considered a mechanism to reduce interference from internally focused view is supported by the fact that the cue mainly caused deac- tivations in posterior TPJ. Furthermore, if suppression is main- tained for invalidly cued incongruent targets due to executive control demands, this would explain why there is a response (i.e., release of suppression) only for invalidly cued congruent targets. A second factor that might contribute to the interaction pattern in right posterior TPJ is spatial “blurring” of reorienting and suppression effects due to methodological noise (e.g., limited spatial resolution), or true overlap of related neural populations. Furthermore, the fact that left posterior TPJ mainly showed deac- tivations for invalid incongruent targets is consistent with previ- ously shown right hemispheric dominance in stimulus-driven reorienting (Corbetta et al. 2008; Kubit and Jack 2013). In sum, the current results provide evidence that stimulus- driven reorienting and executive control of attention rely on a shared mechanism in left AI and potentially other cingulo- opercular regions, leading to behavioral interference and inhib- ition of task unrelated processes in the DMN. This argues for a central role of AI within both types of attention, leading to add- itional down-regulation of internally directed processes when capacity limits are reached. Furthermore, results support a functional segregation of TPJ into an anterior part responding to unexpected, task-relevant stimuli, and a posterior part that is suppressed during high attentional task demands. This characterization of the overlap, differentiation, and interaction of attentional functions bears relevance for our understanding of a range of clinical conditions (Broyd et al. 2009), and can also contribute to the future development of interventions that support self-regulation of attention in clin- ical and healthy populations (Lutz et al. 2008; Malinowski 2013; Tang and Posner 2014). Funding European Research Council (ERC) under the European Community’s Seventh Framework Programme (205557 to T.S.). Notes We are grateful to the Department of Social Neurosciences for their support with the ReSource project, to Manuela Hofmann, Sylvia Neubert and Nicole Pampus for their help with data acquisition, and to Shameem Wagner for proofreading the manuscript. Conﬂict of Interest: None declared. References Anticevic A, Cole MW, Murray JD, Corlett PR, Wang X-J, Krystal JH. 2012. The role of default network deactivation in cogni- tion and disease. Trends Cogn Sci. 16:584–592. 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A critical role for the right fronto-insular cortex in switching between central- executive and default-mode networks. Proc Natl Acad Sci USA. 105:12569–12574. Sterzer P, Kleinschmidt A. 2010. Anterior insula activations in perceptual paradigms: often observed but barely under- stood. Brain Struct Funct. 214:611–622. Interaction of attention systems Trautwein et al. | 4137 stimulus-driven attentional capture (Corbetta and Shulman 2002; Asplund et al. 2010; Nelson et al. 2010) and have been char- acterized as salience network, which detects relevant stimuli (Sridharan et al. 2008; Sterzer and Kleinschmidt 2010; Uddin 2015). Thus, frontoparietal and cingulo-opercular networks have both been implicated in executive control and in stimulus- driven reorienting of attention, potentially constituting process- ing bottlenecks under concurrent attentional demands. Besides activation overlap, both functions have opposing effects in TPJ, with activation increase for reorienting (Corbetta et al. 2008), but deactivation during controlled attentional pro- cessing (Shulman et al. 1997, 2003; Todd et al. 2005; Kubit and Jack 2013). It is debated, whether this pattern reﬂects a unitary mechanism (Shulman et al. 2007) or separate, but spatially Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 neighboring functions (Kubit and Jack 2013). Supporting a seg- mentation view, resting state connectivity studies parcellated the TPJ into anterior portions linked with the ventral attention network and posterior portions coupled with default mode and social cognition related areas (Mars et al. 2012; Bzdok et al. 2013; Kanske et al. 2015). Critically, probing whether reorienting related activation and executive control related deactivation can be attributed to such different subregions of TPJ or are spa- tially non-separable in tension requires simultaneous assess- ment of the two functions. Using fMRI, we addressed these questions by combining two standard approaches to induce stimulus-driven reorienting (spatial cueing) and executive control of attention (ﬂanker-tar- get conﬂict) in a large sample of healthy participants (n = 282). and it is pos- Materials and Methods 2013; Ingre Participants Data were acquired within a large-scale longitudinal study, the ReSource project (for details about recruitment procedure and testing see Singer et al. in press) of which only the baseline measurement time point before intervention is used here. Data was available for 307 out of 332 study participants (missingness due to study dropout: n = 5; missingness due to medical or tech- nical issues n = 20). Of these, 25 were excluded due to incorrect or poor task performance (error-rate in one of the experimental blocks above 50%; percentage of misses above 12.5%) leaving a sample of 282 healthy participants (mean age = 40 years, SD = 9; 163 female; 258 right-handed). All participants gave written informed consent and the Ethics Committee of the University of Leipzig, Germany approved the study. might rely on Stimuli and Task para- To assess executive control and stimulus-driven reorienting of attention, we employed a task that combines a ﬂanker-target conﬂict (Eriksen and Eriksen 1974) with spatial cueing of the target location (Posner 1980). Similar combination tasks have been employed previously (Greene et al. 2008; Fan et al. 2009; Spagna et al. 2015), however, these involved exogenous cueing (a star appearing at the location of the target) whereas we employed endogenous cues (a central arrow indicating the tar- get location). This is consistent with previous research on the ventral attention network (Corbetta et al. 2000). Details of the task are provided in Figure 1. After a random ﬁxation period (1500, 1700, 2100, 2900, 4500, or 7700 ms), a central arrow cue appeared for 200 ms indicating the position of the target stimu- lus. After a random interval (200, 500, or 800 ms), ﬁve arrows appeared at the cued location (valid cue condition, 80% of the trials) or at the uncued location (invalid cue condition, 20% of participants were instructed to focus on a ﬁxation cross in the center of the screen. MRI Data Acquisition Brain images were acquired on a 3 T Siemens Verio scan- ner (Siemens Medical Systems, Erlangen), equipped with a 32-channel head coil. Structural images were acquired using an MPRAGE T1-weighted sequence (TR = 2300 ms; TE = 2.98 ms; TI = 900; ﬂip angle = 9°; 176 sagittal slices; matrix size = 256 × 256; FOV = 256 mm; slice thickness = 1 mm), yielding a ﬁnal voxel size of 1 × 1 × 1 mm3. For functional imaging of task and resting state data, a T2*-weighted echo-planar imaging (EPI) sequence was used (TR = 2000 ms; TE = 27 ms, ﬂip angle = 90°). Thirty- Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 seven axial slices were acquired covering the whole brain with a slice thickness of 3 mm, in-plane resolution 3 × 3 mm2, 1 mm interslice gap, FOV = 210 mm; matrix size 70 × 70. Each run began with three dummy volumes that were discarded, and 490 volumes were acquired during task execution and 190 volumes during rest. Behavioral Data Analysis Behavioral data was analyzed using the R software for statis- tical computing (http://www.R-project.org/). Trials without a response within 200–1700 ms following target onset were dis- carded. For each participant, mean reaction times (RTs) of cor- rect trials and error rates were calculated for the four conditions of the experimental design. To test for response costs elicited purely by miscued target location and by ﬂanker- of the behavioral task. After target conﬂict, irrespective of interaction effects, we used correct (validly cued target, paired t-tests to compare invalidly with validly cued trials in of the forthcoming target the congruent ﬂanker condition and incongruent with congru- the response (left or right ent ﬂanker trials in the valid cue condition. Furthermore, to a congruent (con.) or incongru- test for an interaction of cue validity and ﬂanker-target conﬂict, panel: increases in response costs the condition means were inserted into a repeated measures incongruent target condi- Lower panel: For reaction ANOVA with the factors validity (invalid vs. valid cue) and con- ﬂanker-target conﬂict (validly gruence (incongruent vs. congruent ﬂankers). For correlation targets) and stimulus-driven analyses, we calculated difference scores for reorienting (inval- cued congruent targets) idly cued congruent targets minus validly cued congruent for error rates. targets) and conﬂict resolution (validly cued incongruent minus validly cued congruent targets) for both error rate and reaction time. The invalidly cued incongruent target condition was not used to avoid correlations being driven by the interaction term. Furthermore, we controlled for common variance of both scores potentially induced by the same reference condition (i.e. validly cued congruent target condition, which was used as a control variable) by using partial correlation analysis. All reported associations are based on Spearman’s rank correlation coefﬁcient. fMRI Data Analysis Preprocessing of Task Data Images were analyzed using SPM8 (http://www.ﬁl.ion.ucl.ac.uk/ spm). All volumes were coregistered to the SPM single-subject canonical EPI image, slice-time corrected and realigned to the mean image volume in order to correct for head motion. Note that no reslicing was applied after initial coregistration, thus the latter did not affect slice-time correction. A high resolution anatomical image of each subject was ﬁrst coregistered to the SPM single-subject canonical T1 image and then to the average functional image. The transformation matrix obtained by nor- malizing the anatomical image was then used to normalize Interaction of attention systems Trautwein et al. | 4139 deviated at least one mark from the individual subject’s mean was classiﬁed as on- or off-task. This analysis was constrained to those subjects where at least one on-task and one off-task probe was available (n = 96). Parameter estimates for on-task versus off-task episodes were contrasted and entered into a one-sample t-test for random effects analysis. Task-dependent Functional Connectivity Analysis Condition speciﬁc changes in functional connectivity were ana- lyzed using a generalized form of psychophysiological inter- action analysis (gPPI), which allows for ﬂexible modeling of multiple experimental conditions (McLaren et al. 2012). The gPPI model included the same task onset and motion parameter regressors as described above. In addition, regressors for a seed Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 region time course extracted from a sphere (radius = 10 mm), and the interaction of each task regressor with the seed region time course were included. For each subject, contrasts for the four target condition by time course interactions were calcu- lated. For the second-level analysis, these were entered into the same full-factorial design as described above. each cell of Analysis of Resting State Data Resting state data was analyzed with SPM8 and DPARSF (Chao- and Gan and Yu-Feng 2010). The ﬁrst 10 volumes were discarded. The remaining functional scans were slice-time corrected and realigned. T1 images were coregistered to the functional scans and a DARTEL template was created using the averaged T1 images from all subjects. The following nuisance covariates Vice were included: six head motion parameters, the head motion scrubbing regressor, white matter signal, and the CSF signal. Time courses were then band-pass ﬁltered (0.01–0.08 Hz) to reduce the very low-frequency drift, high-frequency respira- tory, and cardiac noise. the con- For functional connectivity calculation, spheres (radius = 10 mm) around task-related peak regions (for details see results section) were deﬁned as seed regions. The averaged time courses were then obtained from the sphere ROIs and voxel- wise correlations computed to generate the functional connect- ivity maps. The correlation coefﬁcient map was then converted into z maps by Fisher’s r-to-z transform to improve normality. These maps, calculated in original space, were normalized into MNI space and re-sampled to 3-mm isotropic voxels as well as smoothed with a 4 mm FWHM kernel. Using random effect ana- lysis, connectivity of different seeds was compared using paired t-tests thresholded at the voxel level at p < 0.05 (FWE corrected). corrected For visualiza- Results The current study aimed at delineating common and distinct neural contributions as well as the interactive effects of two core attentional functions: stimulus-driven reorienting and executive control of attention. Both functions were independ- ently manipulated in a cued ﬂanker task, allowing to address the following questions: (1) Are there common and distinct neural correlates? (2) Are there non-additive effects on behavior and neural activations, indicating interference in a common bottleneck for both attentional demands? (3) Are both functions independent on the level of interindividual differences, or is there evidence of a common capacity? (4) How are different subregions of the TPJ affected by the tension between stimulus-driven reorienting (activation of TPJ) and executive control demands (deactivation of TPJ)? was evaluated to test for commonalities of both networks (Fig. 2, see Table 1 for a complete list of activated clusters). of stimulus- Invalidly cued targets induced robust activations in areas previously associated with the ventral orienting network congruent (Corbetta et al. 2008), including left and right TPJ and middle in the valid and IFG. Furthermore, parts of the dorsal attention network effects of were activated, such as bilateral intraparietal sulcus and super- valid ior frontal gyrus. Further activations involved cingulo-opercular vs. con- task-control regions with peaks in bilateral anterior insula and dACC (paracingulate gyrus). to slower RTs Executive control of attention activated areas typically slower in the involved in conﬂict resolution (Nee et al. 2007), such as the cingulo-opercular task-control network as well as in dorsal par- interaction ietal (IPS) and frontal areas (precentral and middle frontal gyri, super-additive Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 near locations often labeled FEF (e.g. Han and Marois 2014). cues Conjunction analysis revealed activations in almost all clus- ters of the main executive control contrast, including AI, dACC, = 973.84; and dorsal frontoparietal areas. Speciﬁc activation for executive p < 0.001). control of attention was found in inferior temporal gyrus, while reorient- stimulus-driven reorienting was associated with speciﬁc activa- tions in the TPJ and IFG. Interactions of Stimulus-driven Reorienting and Executive Control Having replicated both attention networks as reported in previ- ous research as well as having characterized their considerable overlap, we then tested for positive and negative interactions of cue validity and ﬂanker congruence. Positive interaction Again super-additive could arise from interference of stimulus-driven reorienting and executive control related processes leading to a dispropor- tionate increase of activation. Moreover, negative interactions could arise in regions that are deactivated when task-demands are especially high (Fig. 2, see Table 2 for a complete list of interaction clusters). A positive interaction was found in left anterior insula, which overlapped with activations in the conjunction analysis. To qualify this interaction effect, we extracted the mean per- cent signal change within a 10 mm sphere around the peak voxel, which showed a super-additive increase of activation in the condition of joint reorienting and executive control demands (Fig. 2). This pattern mirrors the interaction found in the behavioral analysis, indicating an interference of processes and increased neural resource allocation for invalidly cued incongruent targets in the left anterior insula. Since the con- junction analysis yielded additional areas of functional overlap in frontoparietal and anterior cingulate cortex, areas that have previously been discussed as possible sources of interference (Fan et al. 2009), we also employed a more sensitive ROI approach and extracted the signal from the main peaks of the conjunction analysis. Even though these analyses need to be interpreted carefully as the ROI deﬁnition is not fully independ- ent, results notably showed signiﬁcant super-additive interac- tions in all cingulo-opercular regions (bilateral AI and dACC, all p-values < 0.04), while none of the interactions for dorsal frontal and parietal conjunction peaks were signiﬁcant (all p-values > 0.05). Whole brain analysis of negative interactions yielded clus- ters in precuneus, as well as left and right posterior TPJ (Fig. 2). Interactions in precuneus and left posterior TPJ were character- ized by pronounced decreases of activity for invalidly cued incongruent targets. Thus, these patterns were antagonistic to the super-additive effects in the left anterior insula. Furthermore, right posterior TPJ showed an increase of activity for invalidly cued congruent targets, but a relative decrease for Interaction of attention systems Trautwein et al. | 4141 B Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 D networks. (A) Activations and overlap of attention networks assessed independently from each other by con- (stimulus-driven reorienting, or reorienting) and validly cued incongruent versus congruent targets (executive vs. valid) and target condition (incongruent – congruent) was found in left AI, while negative interactions where in Precuneus, overlapping with activations associated with task unrelated thoughts (TUT). (C) Percent signal change shows super-additive increases of activity during reorienting and executive control demands, whereas Precuneus decreases of activity. Right TPJ shows a response for invalidly cued congruent but not for invalidly cued incon- analysis yielded negative coupling between right posterior TPJ and left AI during dual reorienting and executive in connectivity between posterior TPJ and left AI in the four experimental conditions. Abbreviations: = incongruent. reorienting effects were stronger in anterior TPJ (right hemi- sphere: t(1,281) = 7.30, p < 0.001; left hemisphere: t(1,281) = 8.9, p < 0.001). Furthermore, it has been suggested that posterior TPJ also shows a response to miscued targets (as observed in right TPJ), even though it is not involved in the reorienting process itself. Accordingly, activity is suppressed due to the attentional focus induced by the cue, leading to a release of this suppression when the attentional set is broken by a miscued target (Kubit and Jack 2013). To evaluate this hypothesis, we also tested differences in cue related deactivations between anterior and posterior TPJ, which were signiﬁcant (right hemisphere: t(1,281) = 9.52, p < 0.001; left hemisphere: t(1,281) = 11.49, p < 0.001). Inhibition of Task Unrelated Processes Previous research has shown that spontaneous task unrelated thought is correlated with activity in the DMN (Mason et al. 2007), which competes for processing resources with attention networks in order to sustain such processes (Anticevic et al. = 2.26, p < 0.03), while 2012). To test whether the decreases of activity under dual Table 2. Activation peaks for positive and negative interactions. Hemisphere MNI coordinates t-Value Voxels t-Value Voxels Positive interaction Anterior Insula L −30, 27, 6 4.19 72.00 Negative interaction 10 Precuneus R 6, −57, 33 4.41 308 Posterior TPJ R 45, −69, 27 4.60 283 Posterior TPJ L −42, −69, 33 4.43 309 Notes: Activations were assessed at an extent familywise error (FWE) corrected threshold of p < 0.05 (voxel selection threshold at p < 0.001). Clusters are ordered from anterior to posterior. Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 control of attention, the clusters overlapped with negative interactions in bilateral TPJ and precuneus. Task-dependent Functional Connectivity of Posterior TPJ Having shown a potential explanation of the non-additive deactivations—suppression of task unrelated processes—we were interested whether the source of such suppression would be overshooting attentional demands in left anterior insula. To this end, we assessed task-dependent changes in functional connectivity by means of generalized psychophysiological interaction analysis (gPPI; McLaren et al. 2012). Speciﬁcally, we tested for correlations between the interaction peak in right TPJ and voxels that showed non-additive positive effects of reor- ienting and executive control. To this end, we entered the ﬁrst- level PPI contrasts of the four conditions into a full-factorial design, and tested for negative interactions within a mask deﬁned by the positive interaction contrast. This analysis yielded a signiﬁcant cluster in left anterior insula (x = −30, y = 24, z = −6; t = 3.4; small volume correction, Fig. 2). Thus, connectivity between posterior TPJ and left AI changed for the combination of invalid cues and incongruent ﬂankers. 1033 Extracted values indicated that there was increased positive connectivity for the invalidly cued congruent target, but nega- 182 tive coupling for the invalidly cued incongruent target. 882 Differential Resting State Connectivity of TPJ Subregions Based on resting state connectivity and metanalytic clustering, previous research has differentiated the TPJ into at least two subregions; a posterior part showing connectivity with the DMN, and an anterior part showing connectivity with the ven- tral orienting network (Mars et al. 2012; Bzdok et al. 2013). This raises the question as to whether the current task-based differ- entiation within TPJ conforms to previous resting state con- 479 nectivity based parcellations. We therefore analyzed resting state data from the same individuals as in the task analysis (Fig. 3). Relative to the posterior interaction peak, the anterior reorienting peak showed increased connectivity with anterior with an FWE corrected cingulate and medial frontal cortex, bilateral anterior insula, extent of k < 5 voxels are Rows without voxel inferior and middle frontal gyrus. In contrast, the interaction main peak and voxel count peak was associated with increased connectivity to posterior and sub-peaks in subsequent cingulate and precuneus, hippocampus, and medial frontal cor- tex. Thus, consistent with previous literature, the functional peak activations for reorienting and the interaction effect were part of corresponding ventral attention and DMN resting-state networks. impli- Discussion Describing the architecture of human attentional systems has been one of the main challenges tackled by cognitive Interaction of attention systems Trautwein et al. | 4143 C Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 TPJ. (A) White areas depict spheres in anterior TPJ and posterior TPJ around peak activations of reorienting con- These spheres were used to extract percent signal change (shown in B) and activation time courses for connectiv- (B) Deactivation induced by orienting cues (CUE) and interaction of dual attentional demands (INT) were stronger in reorienting (OR) elicited stronger activations in anterior TPJ. (C) Resting state functional connectivity net- contrasted against each other. indicated ﬁeld to the location of target appearance—while it requires executive control in order to resolve the ﬂanker-target conﬂict. Thus, in a situation with concurrent stimulus-driven reorienting and executive control requirements, both processes interfere with each other, pointing towards a shared mechanism or a common “bottleneck.” Furthermore, we tested whether reorienting and executive control are independent on the level of interindividual differences. In agreement with other studies (Fan et al. 2002, 2005; Fossella et al. 2002; Greene et al. 2008; Spagna et al. 2015), reaction time scores of both capacities were not correlated. However, we observed a signiﬁcant positive cor- relation for error rates. To our knowledge, correlational ana- lyses in error-rates have only been reported by Fan et al. (2007), who also found a positive correlation. Note that the correlation remained signiﬁcant when controlling for variation induced by the common reference condition of both scores. Considering that error rates also showed a stronger interaction effect in the within subject analysis (effect size η2 of interaction term: 0.13 for errors vs. 0.006 for RT) and thus potentially have a higher susceptibility to the performance limiting bottleneck, this might indeed indicate that both functions are limited by a com- mon individual capacity. The lacking correlation between RT scores of both capacities might be further explained by the fact that, for reorienting, there was no correlation between RT scores and error rates. Thus interindividual differences in both of these indices might be inﬂuenced by different sub-processes contributing to stimulus-driven reorienting. Future research might provide a more ﬁne-grained understanding of these dif- ferent sub-processes by modeling cognitive processes that account for the RT and accuracy distributions across trials (e.g. Voss et al., 2013). Previously, two potential origins of interference have been proposed (Fan et al. 2009): (1) the dorsal orienting network, which might not only be recruited by orienting but also to ﬁlter out incongruent ﬂanker stimuli and (2) cingulo-opercular areas which are not only activated during executive control, but also by task relevant events (Uddin 2015). Consistent with both pos- sibilities, overlapping activations were found in frontoparietal and cingulo-opercular regions. Importantly, however, whole brain analysis yielded non-additive increases of activity and hence evidence for interference of processes only in left AI. Moreover, a more sensitive region of interest analysis revealed the same interaction pattern in right AI and dACC, while there processes, such as task unrelated thought, during externally focused attention (Anticevic et al. 2012; Wen et al. 2013). If sim- ultaneous executive control and stimulus-driven reorienting of attention causes overshoot in processing demands, this might also induce increased down-regulation of DMN activity. In agreement with such an antagonistic relationship, we observed non-additive decreases of activity for concurrent reorienting and conﬂict resolution demands in bilateral posterior TPJ and in precuneus, areas linked to the DMN (Gusnard and Raichle 2001). Indicating that these deactivations indeed reﬂect inhib- ition of internally focused cognition, the negative interaction effects overlapped with activations induced by task unrelated thought. Furthermore, to directly characterize the relationship between these non-additive decreases and increases of activity, Downloaded from https://academic.oup.com/cercor/article/26/11/4136/2374067 by guest on 11 June 2022 we investigated task-dependent functional connectivity of the main deactivation peak in posterior right TPJ, which yielded negative coupling between this region and left AI under com- bined reorienting and conﬂict resolution demands. Consistent with its role as a causal hub between inwardly and outwardly directed processing systems (Menon and Uddin 2010; Kanske et al. 2016), this suggests that AI may down-regulate distracting processes in the DMN when capacity limits are reached. fea- Functional Differentiations in TPJ The negative interaction effect indicating down-regulation of posterior TPJ during concurrent attentional demands is also informative in relation to the debated question of segregated functions versus a unitary mechanism within TPJ (Cabeza et al. 2012; Nelson et al. 2012). Speciﬁcally, this interaction was stron- ger in posterior TPJ compared to anterior TPJ. Vice versa, reorienting preferentially activated anterior TPJ. This double dissociation provides direct evidence that anterior TPJ is recruited in the reorienting process, while posterior TPJ is deac- tivated during high attentional task demands. This dissociation is consistent with resting state functional connectivity studies and meta-analytic evidence, which situates posterior TPJ in a network activated during social cognition and mind- wandering, but deactivated by a wide range of attention tasks; while linking anterior TPJ with the ventral attention network (Shulman et al. 1997; Fox et al. 2006; Bzdok et al. 2013; Carter and Huettel 2013; Kanske et al. 2015; Krall et al. 2015). In order to conﬁrm this differentiation and relate it to prior parcellation studies, we analyzed resting-state functional con- nectivity. Consistent with previous research (Yeo and Krienen 2011), results yielded coupling of posterior TPJ with typical DMN regions including medial PFC, precuneus, and hippocam- pus, while anterior TPJ was coupled with regions of cingulo- opercular and ventral attention networks including AI, dACC, and inferior and middle frontal gyri. It is important to note that right posterior TPJ also showed an increase of activity for invalid congruent targets. This pat- tern might be explained by several factors: Firstly, the increase might reﬂect release of suppression, which was suggested by Kubit and Jack (2013) to account for the meta-analytic ﬁnding that reorienting partially overlapped with a “pure” social cogni- tion cluster in posterior TPJ, but also with a “pure” anterior tar- get detection cluster. Accordingly, TPJ activations in the reorienting paradigm are the result of two distinct but spatially neighboring mechanisms: Posterior TPJ is suppressed when attention is focused in response to the cue, and this suppres- sion is released when the attentional set is broken by the mis- cued target. In contrast, anterior TPJ is activated due to target detection. 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(PDF) Stimulus-Driven Reorienting Impairs Executive Control of Attention: Evidence for a Common Bottleneck in Anterior Insula