# The effect of moderate acute psychological stress on working memory-related neural activity is modulated by a genetic variation in catecholaminergic function in humans

# *Shaozheng Qin1,2\*, Helena Cousijn1,3, Mark Rijpkema1, Jing Luo4,5, Barbara Franke1,6, Erno J. Hermans 1,7 and Guillén Fernández 1,7*

*1 Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands*

*2 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA*

*3 Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK*

*4 Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China*

*5 Beijing Key Laboratory of Learning and Cognition, Department of Psychology, Capital Normal University, Beijing, China*

*6 Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands*

*7 Department for Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands*

#### *Edited by:*

*Florin Dolcos, University of Illinois Urbana-Champaign, USA*

#### *Reviewed by:*

*Barry Setlow, University of Florida, USA*

*Antonio Pereira, Federal University of Rio Grande do Norte, Brazil Nicole Oei, Leiden University Medical Center, Netherlands*

#### *\*Correspondence:*

*Shaozheng Qin, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA 94305-5719, USA. e-mail<EMAIL_ADDRESS>
Acute stress has an important impact on higher-order cognitive functions supported by the prefrontal cortex (PFC) such as working memory (WM). In rodents, such effects are mediated by stress-induced alterations in catecholaminergic signaling, but human data in support of this notion is lacking. A common variation in the gene encoding Catechol-O-methyltransferase (COMT) is known to affect basal catecholaminergic availability and PFC functions. Here, we investigated whether this genetic variation (Val158Met) modulates effects of stress on WM-related neural activity in humans. In a counterbalanced crossover design, 41 healthy young men underwent functional magnetic resonance imaging (fMRI) while performing a numerical N-back WM task embedded in a stressful or neutral context. Moderate psychological stress was induced by a well-controlled procedure involving viewing strongly aversive (versus emotionally neutral) movie material in combination with a self-referencing instruction. Acute stress resulted in genotype-dependent effects on WM performance and WM-related activation in the dorsolateral PFC, with a relatively negative impact of stress in *COMT* Met-homozygotes as opposed to a relatively positive effect in Val-carriers. A parallel interaction was found for WM-related deactivation in the anterior medial temporal lobe (MTL). Our findings suggest that individuals with higher baseline catecholaminergic availability (*COMT* Met-homozygotes) appear to reach a supraoptimal state under moderate levels of stress. In contrast, individuals with lower baselines (Val-carriers) may reach an optimal state. Thus, our data show that effects of acute stress on higher-order cognitive functions vary depending on catecholaminergic availability at baseline, and thereby corroborate animal models of catecholaminergic signaling that propose a non-linear relationship between catecholaminergic activity and prefrontal functions.

**Keywords: stress, working memory, Catechol-O-methyltransferase, catecholamine, prefrontal cortex, fMRI**

## **INTRODUCTION**

Acute stress has an important impact on higher-order cognitive function such as working memory (WM). These effects are believed to result from stress-induced alterations of large-scale brain network functioning including the prefrontal cortex (PFC), through stress-sensitive neuromodulatory systems (Goldman-Rakic, 1995; Aston-Jones and Cohen, 2005; Arnsten, 2007, 2009; McEwen, 2007; Sara, 2009; Hermans et al., 2011). In particular, stress-induced changes in catecholaminergic signaling play a central role in modulating prefrontal functions in rodents (Arnsten and Goldman-Rakic, 1998; Arnsten, 2009). Yet, direct human data in support of this notion is lacking. A variation (Val158Met) in the gene encoding Catechol-O-methyltransferase (*COMT*) is associated with individual differences in basal catecholaminergic availability (Lotta et al., 1995; Mannisto and Kaakkola, 1999) and prefrontal functions (Chen et al., 2004). We, therefore, conjecture that effects of acute stress may interact with *COMT* genotype to alter WM-related PFC function in humans.

Exposure to acute stress leads to rapid activation of sympathetic nervous system (SNS) and hypothalamic-pituitary-adrenal (HPA) axis, and is accompanied by a tonical increase of central release of catecholamine levels (de Kloet et al., 2005; Arnsten, 2009; Joels and Baram, 2009). Through ascending projections from brainstem nuclei where the locus coeruleus norepinephrine and midbrain dopaminergic systems locate, these stress-sensitive catecholamines alter neuronal functioning of widely distributed brain regions, particularly including the PFC (Arnsten and Li, 2005; Aston-Jones and Cohen, 2005; Arnsten, 2009; Sara, 2009). Converging evidence from human behavioral and neuroimaging studies have confirmed that acute stress indeed alters WM performance with both detrimental (Elzinga and Roelofs, 2005; Oei et al., 2006; Luethi et al., 2008; Schoofs et al., 2008) and enhancing (Lewis et al., 2008; Weerda et al., 2010; Hidalgo et al., 2011) effects, likely through altered efficiency of WM-related processing in dorsolateral PFC (Porcelli et al., 2008; Qin et al., 2009; Weerda et al., 2010). Interestingly, animal studies suggest that stress-sensitive catecholamines exert an inverted U-shaped influence on prefrontal functions (Aston-Jones and Cohen, 2005; Vijayraghavan et al., 2007), in which prefrontal functioning reaches an optimum at an intermediate level of catecholaminergic activity (Arnsten and Li, 2005; Aston-Jones and Cohen, 2005; Vijayraghavan et al., 2007). These implicate that acute stress may have variable (i.e., either detrimental or enhancing) effects on PFC functions depending on levels of stress-induced catecholaminergic activity.

The intracellular enzyme COMT plays a key role in regulating catecholaminergic availability. A single nucleotide polymorphism (SNP) in the gene coding *COMT*, which causes a Valine-to-Methionine substitution at codon 158 (Val158Met), leads to a 3–4 fold decrease in the ability of this enzyme to catabolize catecholamines (Lotta et al., 1995; Mannisto and Kaakkola, 1999). As a consequence, basal levels of catecholaminergic availability in Met-homozygotes are presumed to be increased throughout the cortex, particularly for the PFC (Chen et al., 2004). Associations have been found in humans between *COMT* genotype and higher-order PFC functions and emotional processing in both health and disease (Drabant et al., 2006; Meyer-Lindenberg et al., 2006; Bishop et al., 2008). Interestingly, recent evidence from human and rodent studies suggests that *COMT* genotype may interact with catecholaminergic manipulations altering PFC function (Mattay et al., 2003; Bertolino et al., 2004; Papaleo et al., 2008). Specifically, one study demonstrated that *COMT* knockout mice (Met-like genotype) is associated with better WM functioning at baseline, but also with a higher sensitivity to stress. In contrast, the *COMT-Val* transgenic mice (Val-like genotype) are associated with inferior WM performance at baseline, but are less sensitive to stress (Papaleo et al., 2008). Based on these findings and the aforementioned inverted U-shaped relationship between catecholaminergic signaling and neurocognitive functioning, one may hypothesize that effects of experimentally induced moderate stress on WM-related prefrontal activity in humans would exhibit a similar genotype-dependency, with stronger detrimental effects of stress in *COMT* Met-homozygotes.

To test this hypothesis, 41 healthy young men underwent functional magnetic resonance imaging (fMRI) while performing a numerical N-back WM task (including 2- and 0-back conditions). In counterbalanced order, one session involved stress induction while the other session served as control. Moderate psychological stress was induced experimentally by a standardized cinematographic procedure in which participants saw strongly aversive (vs. neutral control) movie material combined with a self-referencing instruction, which has been shown previously to elicit physiological and psychological stress responses (Henckens et al., 2009; Qin et al., 2009; Hermans et al., 2011). To validate our stress manipulation, measures of heart rate (HR), α-amylase, salivary cortisol, and subjective negative affect were acquired prior to, during and after fMRI scanning. Given the relatively low frequency of Val-homozygotes in our sample, Val-carriers were treated as a single group in all analyses. We predicted that moderate psychological stress would have an interactive effect on the differential neural activity reflecting WM load (2- vs. 0-back) in Met-homozygotes and Val-carriers, with a stronger stress-induced reduction of differential dorsolateral PFC activity in Met-homozygotes. Because WM-related activation of the dorsolateral PFC is normally accompanied by deactivation in the medial temporal lobe (MTL), and adaptive changes in reciprocal organization of dorsal executive and ventral affective brain areas under arousal and stressful circumstance (Dolcos and McCarthy, 2006; Oei et al., 2011; Cousijn et al., 2012), we also conjectured a corresponding genotype-dependent effect in the MTL.

# **MATERIALS AND METHODS**

# **PARTICIPANTS**

Forty-one young, healthy, right-handed males (Caucasian ethnicity, aged 18–35 years) with normal or corrected-to-normal vision participated in this study. There are 16 Met-homozygotes (Met/Met), 7 Val-homozygotes (Val/Val) and 18 heterozygotes (Val/Met). Participants reported no history of neurological, psychiatric, or endocrine disease, and no current use of psychoactive drugs or corticosteroids. None of them had experienced severe physical or emotional trauma. Only men were included to avoid confounds related to gender differences and menstrual cycle-dependent variance in stress responsiveness (Kirschbaum et al., 1999; Kudielka and Kirschbaum, 2005). All had participated before in MRI experiments to minimize stress responses evoked by unfamiliarity with the environment and procedures. The study was in accordance with the Declaration of Helsinki and institutional guidelines of the local ethics committee (CMO Region Arnhem-Nijmegen, Netherlands). All participants gave written informed consent. Data from two participants (1 Methomozygote and 1 heterozygote) were excluded from further analysis completely, either due to technical failure or excessive head movement during scanning.

Participants were tested in a mixed factorial design with stress induction as within-subject factor and genotype as betweensubject factor. The groups (*COMT* Met-homozygotes and Valcarriers) did not differ significantly regarding age (*p >* 0*.*1) and trait anxiety (*p >* 0*.*4) (see **Table 1**).

# **GENERAL PROCEDURE**

The experiment was carried out in the afternoon (after 1 pm) to ensure relatively stable and low levels of endogenous cortisol. After arrival, 1 h before scanning, participants trained on the WM task extensively and completed various questionnaires. To determine whether baseline cortisol levels differed between COMT

## **Table 1 | Participant demographics.**

*Note: Due to either technical failure or excessive head movement during scanning, data from two participants were excluded from further analyses. SE, standard of error of mean; anxiety, trait anxiety scores.*

coinciding with subjective mood ratings (PANAS). T1 and DTI stand for a high-resolution anatomical scan and a diffusion tensor imaging scan.

genotypes, all participants were asked to take one saliva sample as baseline in the late afternoon on the day before the experiment at home. Other saliva samples and subjective affect positive and negative affect scales (PANAS) were obtained before and after both experimental conditions. The actual fMRI experiment consisted of four short movie clips to ensure that tasks of interest were fully embedded in a continuously stressful (or neutral control) context. Between the first and second movie clips, participants performed the numeric N-back task (**Figure 1**). Before and after the stress and control condition, saliva samples and subjective affect ratings were collected and HR was measured throughout the experiment. The two conditions were separated by approximately 20 min, consisting of a structural MRI scan and a perfusion fMRI scan.

# **STRESS INDUCTION**

In the stress induction condition, moderate acute psychological stress was induced by showing short movie clips within the MRI scanner containing scenes with strongly aversive content (extreme violence) selected from a commercially available movie (*Irreversible*, 2002, by Gaspar Noé). The N-back task was fully embedded in short movie clips (**Figure 1**) containing scenes with extremely aversive content. In the control condition, participants watched equally long movie clips from another movie (*Comment j'ai tué mon père*, 2001, by Anne Fontaine) which were equal in luminance and similar in language but contained only nonarousing scenes. Matching for audiovisual characteristics was performed by the authors by selecting aversive and neutral clips out of a set of candidate clips which best matched on the measures described in Hermans et al., 2011. After short introductory texts, participants were asked to watch the movies attentively and take an eyewitness perspective as to involve them maximally in the action taking place in the movie clips. The WM task was closely surrounded by two movie clips in time in order to boost stress induction and create a continuously stressful context.

We chose to use a stress induction procedure involving exposure to aversive cinematographic material in combination with self-referencing instruction, because of the following considerations (also see Hermans et al., 2011). First, this procedure allows us to create an ecologically valid model for examining stress-induced adaptive alterations (i.e., hypersensitivity at the cost of higher-order cognitive functions) when exposing to acutely stressful experiences in the real world that may ultimately trigger emotional trauma with detrimental effects on various higher-order prefrontal cognitive functions. Second, exposure to highly aversive films in combination with selfreferencing instruction meets the requirements for the physiological stress response in humans, particularly with activation of the sympatho-adrenomedullary system (SAM) and substantial elevation of epinephrine and norepinephrine levels, as described by Mason (Mason, 1968). Finally, unlike stressors based on public speaking, and cognitive performance in combination with negative social evaluation (Kirschbaum et al., 1993; Wang et al., 2005; Pruessner et al., 2008), our stress induction is likely to yield emotion focused rather than social or problem-focused coping strategies and plausibly triggers a state of fearful arousal. From a more general and broader standpoint of view, such a state of fearful arousal has been long believed to be an important feature of psychological stress in both animals and humans (Mason, 1968; Arnsten and Goldman-Rakic, 1998; Mobbs et al., 2007; Arnsten, 2009; Shackman et al., 2011). Moreover, previous studies have shown that this method elicits a measurable psychological, physiological, and neuroendocrine stress response (Henckens et al., 2009; Qin et al., 2009; van Marle et al., 2009), although it has relatively lower cortisol elevations than other social and/or cognitive challenging stressors. Notably, our stress induction procedure has been validated by one recent pharmacological study in humans, demonstrating that stress-sensitive noradrenergic (probably dopaminergic as well) activity plays a central role in regulating acute psychological stress (Hermans et al., 2011). This nicely fits the primary goal of our present study addressing how a genetic variation in catecholaminergic function modulates the effect of acute psychological stress on WM processing in humans.

## **N-BACK TASK**

Using a blocked-design, participants completed six cycles of alternating 0- and 2-back conditions interleaved by a jittered resting-fixation baseline ranging from 8 to 12 s. Within each block, a random digit sequence consisting of 15 single digits was shown to participants. Each digit was presented for 400 ms, followed by an inter-stimulus interval of 1400 ms. Each block lasted 27 s. During the 0-back condition, participants were asked to detect whether the current item on the screen was a "1" or not. During the 2-back condition, participants were asked to detect whether the current item had appeared two positions back in the sequence. Participants were instructed to make a button press with their index finger when detecting a target. Before fMRI scanning, they were extensively trained in performing the task to minimize interindividual variability and reduce practice effects. During this pretraining session, we trained every participant to perform 10 cycles of alternating 0- and 2-back conditions as used in our previous study (Qin et al., 2009). We did not set up specific performance criteria during pretraining, because we intended to minimize potential anticipatory and psychological stress induction when performing challenging WM task conjoint with performance evaluation (Dickerson and Kemeny, 2004; Pruessner et al., 2008).

# **PHYSIOLOGICAL AND PSYCHOLOGICAL MEASUREMENTS OF STRESS**

To assess the SNS and HPA axis responses to the stress manipulation, saliva was sampled with salivette collection devices to determine the levels of α-amylase and cortisol. Samples were taken on the day before the experiment and before and after both conditions (five in total) and were stored at −20◦C until analysis. The analysis (Cousijn et al., 2010; Strahler et al., 2010) was carried out at the Biopsychology Department in Dresden, where samples were prepared for biochemical analysis by centrifuging at 1500 × *g* for 5 min, which resulted in a clear supernatant of low viscosity. Salivary-free cortisol concentrations were determined with a chemiluminescence assay with high sensitivity of 0.16 ng/mL (IBL). Concentration of α-amylase in saliva was measured by an enzyme kinetic method: saliva was processed on a Genesis RSP8/150 liquid handling system (Tecan). First, saliva was diluted 1:625 with double-distilled water by the liquid handling system. Twenty microliters of diluted saliva and standard were then transferred into standard transparent 96-well microplates (Roth). Standard was prepared from "Calibrator f.a.s." solution (Roche Diagnostics) with concentrations of 326, 163, 81.5, 40.75, 20.38, 10.19, and 5.01 U/L α-amylase, respectively, and double-distilled water as zero standard. After that, 80 mL of substrate reagent (α-amylase EPS Sys; Roche Diagnostics) were pipetted into each well using a multichannel pipette. The microplate containing sample and substrate was then warmed to 37◦C by incubation in a water bath for 90 s. Immediately afterwards a first interference measurement was obtained at a wavelength of 405 nm with a standard ELISA reader (Anthos Labtech Instruments HT2). The plate was then incubated for another 5 min at 37◦C in the water bath, before a second measurement at 405 nm was taken. Increases in absorbance were calculated for unknowns and standards. Increases of absorbance of diluted samples were transformed to α-amylase concentrations using a linear regression calculated for each microplate (Graphpad Prism 4.0 c for MacOSX; Graphpad Software). For one subject no data were acquired and for one subject the analysis did not succeed, whereas data of a third subject were not taken into account because he consumed caffeine shortly before the experiment. Behavioral and fMRI data quality of these three subjects were good, thus they were included in further analysis.

To assess autonomic activity throughout the experiment, we continuously recorded HR with an infrared pulse oximeter (MRcompatible) placed on the index finger of the left hand. Offline artifact correction and analysis of the HR frequency was done with in-house software. The HR frequency was averaged for the duration of each movie clip and the task. HR data from one participant were excluded due to excessive artifacts, while corresponding behavioral and fMRI data were included into further analysis.

Mood state was assessed using the positive and negative affect scale (PANAS) questionnaire (Watson et al., 1988) at four time points coinciding with the collection of salivary samples.

# **GENOTYPING**

Genetic analyses were performed at the Department of Human Genetics of the Radboud University Nijmegen Medical Centre, in a laboratory which has a quality certification according to CCKL criteria. High molecular weight DNA was isolated from saliva using Oragene containers (DNA Genotek, Ottawa, ON, Canada) according to the protocol supplied by the manufacturer. All participants were genotyped for the *COMT* SNP rs4680 (G *>* A; Val1 58 Met) using Taqman® analysis (Applied Biosystems, Nieuwerkerk a/d IJssel, Netherlands). Genotyping was carried out in a volume of 10μl containing 10 ng of genomic DNA, 5 μl of Taqman Mastermix (2×; Applied Biosytems), 0.375 μl of the Taqman assay, and 3.625μl of Milli-Q. The amplification protocol consisted of an initial denaturation step at 95◦C for 10 min followed by 40 cycles of denaturation at 92◦C for 15 s and annealing and extension at 60◦C for 60 s. Allele-specific fluorescence was subsequently measured on an ABI 7500 FAST (Applied Biosystems). Taqman genotyping assays were validated before use and 5% duplicates and blanks were taken along as quality controls during genotyping. Genotyping results were only considered valid if duplicates and blanks were called correctly and genotypes could be called for at least 95% of the sample tested. All genotype frequencies were tested for Hardy-Weinberg Equilibrium.

# **fMRI DATA ACQUISITION**

During MRI scanning, whole brain T2∗-weighted echo planar imaging based on blood oxygenation level-dependent contrast (EPI-BOLD) fMRI data were acquired with a Siemens Trio 3.0 T MR-scanner (Erlangen, Germany) using an ascending slice acquisition sequence. Parameters of the sequence are following: 37 axial slices; volume repetition time, TR 2.18 s; echo time, TE 25 ms; 80◦ flip angle; slice matrix size, 64 × 64; slice-thickness, 3.0 mm; slice gap, 0.3 mm, field of view, FOV 212 × 212 mm. Two hundred six volumes were acquired during the N-back task. High-resolution structural images (1 × 1 × 1 mm) were acquired using a T1-weighted 3D magnetization-prepared rapid gradientecho (MPRAGE) sequence (TR, 2.3 s, TE, 3.03 ms, 8◦ flip angle, 192 contiguous sagittal slices, slice matrix size, 256 × 256, FOV, 256 × 256 mm).

# **Table 2 | Physiological and psychological measurements of stress (***n* **= 39).**

*Note: HR, heart rate; Pre-, saliva sampling prior to stressor or control session; Post-, saliva sampling posterior to stressor or control session; M1 and M2, movie clips prior and posterior to N-back task; Negative affect, negative affect scale; SE, standard error of mean.*

*Note: RT, reaction times; SE, standard error of mean.*

## **fMRI DATA ANALYSIS**

Image preprocessing and statistical analysis was performed using SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). The first five EPI volumes were discarded to allow for T1-equilibration. Remaining functional images were rigid-body motion corrected and the mean image was coregistered to each participant's T1-weighted MR-image. Subsequently, images were transformed into a common stereotactic space, and resampled into 2 mm isotropic voxels. Finally, images were spatially smoothed by convolving with an isotropic 3D-Gaussian kernel (8 mm full width at half maximum). The data were statistically analyzed using general linear models and statistical parametric mapping (Friston et al., 1995).

To assess neural activity associated with 0- and 2-back conditions, the two conditions were modeled separately as boxcar regressors and convolved with the canonical hemodynamic response function in SPM5. Additionally, realignment parameters were included to account for movement-related variability. The analysis furthermore included high-pass filtering using a cutoff of 1/128 Hz, global intensity normalization, and serial correlations correction using a first-order autoregressive [or AR(1)] model.

The contrast parameter images for the 2- vs. the 0-back condition, generated at the single-subject level, were submitted to a second-level analysis within a stress-by-genotype group mixed factorial analysis of variance (ANOVA). The analysis of WM-related deactivations was conducted based on the opposite contrast of 0- vs. 2-back condition. We used an alpha of 0.05 corrected for multiple comparisons based on suprathreshold cluster size statistics (Worsley et al., 1996). The initial threshold for this analysis was set at *p <* 0*.*001, uncorrected. Given our clear hypotheses regarding the PFC, this region was additionally investigated with a reduced search region consisting of

an unbiased, anatomically defined, gray matter mask of the PFC covering the entire prefrontal areas, by using non-stationary suprathreshold cluster-size approach based on Monte-Carlo simulations (Forman et al., 1995; Ward, 2000; Nichols and Hayasaka, 2003; Hayasaka et al., 2004). This approach allows us to determine the minimum cluster size that controls for false positive rate. The PFC mask was anatomically defined by combination of a gray matter mask and the anatomical automatic labeling (AAL) template of the PFC. Monte-Carlo simulations were implemented in Matlab using methods similar to the AlphaSim procedure. Ten thousand iterations of random 3D images, with the same resolution and dimensions as the fMRI data, were generated. The resulting images with the gray matter mask of interest were smoothed with the same 8 mm FWHM Gaussian kernel used to smooth our fMRI data. The maximum cluster size was then computed for each iteration and the probability distribution was estimated across the 10,000 iterations. The cluster threshold corresponding to a voxel-wise threshold of *p <* 0*.*001 for height, and a significance level of *p <* 0*.*05 corrected for multiple spatial comparisons was determined to be 28 voxels. Parameter estimates were extracted from clusters of activation associated with interaction effects to characterize the neural activation patterns of the stress and neutral conditions in the two groups using MarsBar (Brett et al., 2002).

# **RESULTS**

# **EFFECTIVENESS OF STRESS INDUCTION**

Measurements of salivary cortisol, α-amylase, and subjective negative affect were submitted to separate 2 × 2 × 2 repeated measures ANOVAs with stress (stress induction vs. control) and time (pre vs. post) as within-subject factors, and genotype (*COMT* Met-homozygotes vs. Val-carriers) as between-subject factor, and with the order effect of stress manipulation as a covariate of no interest (similarly hereinafter for other ANOVAs including functional imaging data). Time-by-stress interactions were found for salivary cortisol [*F(*1*,* 34*)* = 4*.*46, *p <* 0*.*05], α-amylase [*F(*1*,* 34*)* = 8*.*59, *p* = 0*.*006] (**Table 2**), and negative affect [*F(*1*,* 37*)* = 20*.*49, *p <* 0*.*001]. For cortisol, a decrease was observed from preto post-stressor levels in the neutral condition [*t(*35*)* = 5*.*12, *p <* 0*.*001], but no significant change in the stress condition [*t(*35*) <* 1]. For α-amylase, we observed an increase in the stress condition [*t(*35*)* = 2*.*33, *p <* 0*.*03] and a decrease in the control condition [*t(*35*)* = 2*.*07, *p <* 0*.*05]. Negative affect increased in the stress condition only [*F(*1*,* 37*)* = 8*.*23, *p <* 0*.*01]. In addition, a two independent samples *T* test revealed *t* difference [*t(*34*) <* 1] in cortisol between Met-homozygotes (7*.*89 ± 7*.*92) and Val-carriers (8*.*04 ± 2*.*17) at baseline which were collected one day prior to the experiment.

HR data were analyzed with a 2 × 3 × 2 repeated measures ANOVA across averaged measures during the N-back task and the two surrounding movie clips. We found a main effect of stress [*F(*1*,* 74*)* = 46*.*62, *p <* 0*.*001], and further *t*-tests confirmed that HR was consistently higher during all three phases of the experiment in the stress condition [all *t(*38*) >* 2*.*29, *p <* 0*.*03].

None of the statistical tests described above revealed significant interaction effects of genotype and order-related effects.

 Stress, stress group; Control, control group; PFC, prefrontal cortex; L, left; R, right; BA, Brodmann Area; MNI, MNI coordinates (SPM5).*

Together, these results consistently confirm that the N-back task was indeed embedded in a stressful context in the stress condition.

## **WM PERFORMANCE**

The mean accuracy and reaction times (RTs) are included in **Table 3**. WM load effects on accuracy and RTs (i.e., differences between 2- vs. 0-back) were submitted to two separate 2 (stress) by 2 (genotype) ANOVAs. For accuracy, we found a main effect of genotype [*F(*1*,* 36*)* = 4*.*95, *p <* 0*.*03] with worse for Met-homozygotes in general, and an interaction between genotype and stress [*F(*1*,* 36*)* = 4*.*47, *p <* 0*.*04] (see **Figure 2**). Further analyses revealed that *COMT* Met-homozygotes performed worse than Val-carriers in the stress condition [*t(*37*)* = 2*.*85, *p <* 0*.*01], while no genotype effect was found in the control condition [*t(*37*)* = 1*.*31, *p >* 0*.*20]. For RTs, neither a main effect of genotype nor an interaction effect was found [all *F* values *<*1]. To explore potential differences between Val-homozygotes and heterozygotes, we also conducted separately an additional 2-by-3 ANOVA by splitting Val-carriers into heterozygotes and Valhomozygotes, two separate, genotype groups for accuracy and RT data. We found a similar (but weaker) main effect of genotype and an interaction between the factors of genotype and stress. We did not find a significantly larger effect for Val-homozygotes than heterozygotes, likely due to the small sample size. Altogether, these results indicate that there was a larger detrimental effect on accuracy in COMT Met-homozygotes as compared to Val-carriers in the stress condition.

# **STRESS-BY-COMT GENOTYPE INTERACTION IN WM-RELATED ACTIVATION IN THE DORSOLATERAL PFC**

First, by contrasting 2- with 0-back conditions (collapsing across groups), we confirmed activation of a WM-related frontalparietal network (**Table 4**), indicating robust main effects of WM load. We also found main effect of genotype on the dorsolateral PFC when collapsing across the stress and control conditions, as well as in the control condition alone (**Table 4**). More important for the question at issue, we found a suprathreshold cluster in the right dorsolateral PFC (local maxima at [30, 30, 42], *t* value = 4.03, cluster *p <* 0*.*05 small volume correction using non-stationary suprathreshold cluster-size approach) for the interactive effect of stress (control vs. stress) and genotype (*COMT* Met-homozygotes vs. Val-Carriers) on this contrast. There was no reliable activation outside of the PFC in this contrast of stress-by-*COMT* interaction. As shown in **Figure 3**, stress induction had opposite effects on WM-related activity in this region depending on genotype. We also analyzed the data by splitting Val-carriers into heterozygotes and Val-homozygotes, two separate groups. This analysis revealed almost an identical main effect of genotype and interaction between stress and genotype in the dorsolateral PFC. Val-homozygotes showed numerically lower activation in the DLPFC in the control condition, and larger stress-induced positive effect as relative to heterozygotes. These effects, however, do not reach the level of significance applied to the analysis otherwise. This lower reliability is likely due to the small sample size of Val-homozygotes in our present study.

**FIGURE 3 | Stress-by-COMT genotype interaction effect on WM-related activity in the dorsolateral PFC. (A)** Transversal (left panel) and coronal (right panel) view of activation in the right dorsolateral PFC showing significant stress-by-COMT genotype interaction effect. Statistical parametric maps are superimposed onto spatially normalized and averaged (*n* = 39) high-resolution T1-weighted images (thresholded at *p <* 0*.*001 uncorrected for visualization purposes). **(B)** Bar graphs representing parameter estimates of WM-related activation under control and stress conditions in Met-homozygotes (Met/Met) and Val-carriers. The data for these bar graphs were only extracted to illustrate the interaction effect. Note: Control, control condition; PFC, prefrontal cortex; Stress, stress condition; error bars in the graph represent standard error of mean; T, color coded *t* values obtained from the whole brain analysis.

# **STRESS-BY-COMT GENOTYPE INTERACTION IN WM-RELATED DEACTIVATION IN THE MTL**

By contrasting 0- with 2-back conditions (collapsing across groups), we replicated previous findings showing deactivation in regions comprising the "default mode" network (**Table 5**). For the interactive effect of stress (control vs. stress) and genotype (*COMT* Met-homozygotes vs. Val-carriers) on this contrast, we found significant clusters in the bilateral anterior MTL extending into amygdala (local maxima at [−46, 2, −20] and [30, 0, −22], both *t* values *>* 4.17, both cluster *p <* 0*.*05 whole-brain corrected). As shown in **Figure 4**, this interaction exhibits a pattern that is opposite to the one in the dorsolateral PFC—that is, a clear pattern of stress-induced positive and negative effect for Met-homozygotes and Val-carriers, respectively.

# **DISCUSSION**

The present study investigated whether effects of psychological stress on WM-related neural activity are modulated by a genetic variation in catecholaminergic function (determined by *COMT* genotype) in humans. As expected, stress induction resulted in

## **Table 5 | Brain deactivations related to WM, and modulations of stress and COMT genotype.**

*Note: Only clusters significant at p < 0.05 corrected on cluster level were reported.* ∗∗∗*p < 0.05 FWE whole brain corrected;* ∗∗*cluster p < 0.05 whole brain corrected. Stress, stress group; Control, control group; MTL, medial temporal lobe; PFC, prefrontal cortex; L, left; R, right; BA, Brodmann Area; MNI, MNI coordinates (SPM5).*

*COMT* genotype-dependent effects on WM performance and WM-related neural activity in the dorsolateral PFC, with a relatively negative impact of stress in *COMT* Met-homozygotes as opposed to a relatively positive effect in Val-carriers. A similar interaction was found for WM-related deactivation in the anterior MTL, with a stress-induced positive and negative effect for Met-homozygotes and Val-carriers, respectively. As evidenced by elevation of both HR and α-amylase, our stress induction procedure succeeded in triggering an increase of sympathetic and noradrenergic activity, and thus resulted in increased central release of stress-sensitive catecholamines (de Kloet et al., 2005; Joels and Baram, 2009; Ulrich-Lai and Herman, 2009), which has been confirmed by our recent pharmacological fMRI study in humans (Hermans et al., 2011). The effectiveness of the stress induction did not differ between genotype groups. We, therefore, discuss how stress-induced elevation of catecholamines, together with genetic variation in catecholaminergic function, accounts for our observed *COMT* genotype-dependent effects of stress on WM processing.

Our findings of an opposite effect of stress induction in *COMT* Met-homozygotes as compared to Val-carriers are in line with animal models of the role of catecholamines in the neuromodulation of cognition. Such models assume that catecholaminergic activity exhibits an inverted U-shaped relationship with higherorder prefrontal cognitive function, and that optimal performance is reached at moderate levels of catecholamines (Arnsten and Li, 2005; Aston-Jones and Cohen, 2005; Arnsten, 2007, 2009). Thus, these models predict that the effect of stress-induced elevation of catecholaminergic activity may vary depending on the baseline. In humans, homozygous carriers of the Met allele have lower *COMT* availability, presumably resulting in higher catecholaminergic activity at baseline. Our findings show that a moderate level of stress has a relatively negative impact on neurocognitive functioning in these individuals, possibly because an elevation of catecholaminergic activity shifts the PFC functioning toward a supraoptimal state at the right side of the alleged inverted-U shaped curve (see **Figure 5A**). Interestingly, we found an opposite effect in Val-carriers, where a similar amount of stress had a relatively positive effect. To interpret this pattern of results, it is important to consider that the stressor used in this study was relatively mild. For instance, we observed a decrease of cortisol from pre- to post-stressor levels in the control condition but no significant change in the stress condition. Exposure to more stressful situations (i.e., higher levels of cortisol induced) has been found to result generally in detrimental effects on WM processing (Oei et al., 2006; Schoofs et al., 2008, 2009), particularly when high levels of cortisol and noradrenergic activity occurred concurrently (Elzinga and Roelofs, 2005; Schoofs et al., 2008). Moreover, we previously found detrimental effects of acute stress on activity in the dorsolateral PFC across an unselected female sample (Qin et al., 2009), but that study did not implement a crossover design (i.e., repeated testing) and may, therefore, have been slightly more stressful for the participants in the stress condition. It is well possible that higher levels of acute stress would also have a negative impact in COMT Val-carriers. Thus, various (i.e., decreased or improved) effects of acute stress on PFC functions observed in previous studies (Wang et al., 2005; Porcelli et al., 2008; Pruessner et al., 2008; Weerda et al., 2010) may be partly explained by the non-linear relationship between levels of stress-induced catecholamines and prefrontal functioning, and potential interactions with individual differences in basal catecholaminergic availability. In sum, our findings suggest that individuals with high catecholaminergic availability at baseline (COMT Met-homozygotes) are more susceptible to the

detrimental effects of stress, whereas Val-carriers appear more resilient.

Animal studies have shown that at a cellular level, the prefrontal WM network is strengthened through actions of α2A-adrenoceptors and increased neural firing via inhibition of cAMP-HCN (cyclic adenosine monophosphate-hyperpolarization-activated cyclic nucleotide-gated cation channel) signaling (Wang et al., 2007), while optimal levels of dopamine decrease task-irrelevant neuronal firing by increasing cAMP-HCN signaling (Arnsten, 2007, 2009; Vijayraghavan et al., 2007). Interestingly, recent studies in behaving primates suggest that elevated levels of catecholamines under stress impair prefrontal functioning by excessive cAMP-HCN and phosphatidylinositol-protein kinase C (PKC) intracellular signaling pathways (Arnsten, 2007, 2009). Activation of these two stress-sensitive signaling pathways is thought to be regulated by molecular inhibitors provided by enzymes such as COMT (Arnsten, 2009). The valine-tomethionine substitution at codon 158 in the COMT protein may

reduce the ability of COMT to inhibit the activation of these stress-sensitive pathways. By showing a genotype-dependent difference in stress resilience, with relatively stronger stress sensitivity in human carriers of the *COMT* Met allele, our data provide the first evidence for these notions in humans.

state. Note: The bars represent the magnitude of neural activation in the dorsolateral PFC and deactivation in the MTL, respectively. PFC, prefrontal

cortex; MTL, medial temporal lobe.

Human neuroimaging studies consistently show that WMrelated activation of the dorsal frontoparietal "executive" network is accompanied by a reciprocal deactivation in regions comprising the "default mode" network (DMN), including the MTL and amygdala (Selemon and Goldman-Rakic, 1988; Dolcos and McCarthy, 2006; Esposito et al., 2006; Arnsten, 2009). Our data robustly replicate these findings, but moreover reveal an interaction between stress and *COMT* genotype in the bilateral anterior MTL, extending into the amygdala. This interaction shows an opposite pattern to the one observed in the dorsolateral PFC (see **Figures 4** and **5A**,**B**), with less deactivation after stress in *COMT* Met-homozygotes as compared with Val-carriers. This finding may be a consequence of the concomitant effect on frontal regions, resulting in altered efficacy of these regions to suppress the DMN. An alternative, but partly complementary, explanation of this finding is that stress-sensitive catecholaminergic changes may exert regionally specific effects on PFC functioning (Aston-Jones and Cohen, 2005; Shin et al., 2005; Dolcos and McCarthy, 2006; Etkin and Wager, 2007), but at the same time have their opposite effects on neural processes in limbic regions, particularly the MTL and the amygdala (Coull et al., 2001; de Kloet et al., 2005; Chamberlain et al., 2006; Admon et al., 2009; van Marle et al., 2009). The function of such a large-scale reallocation of neural resources (Bouret and Sara, 2005; Sara, 2009; Oei et al., 2011) may be to effectuate a hypervigilant brain state promoting rapid behavioral adaptation to adverse conditions at the cost of elaborative executive functions.

It is worth noting that our stress induction procedure refers to an emotion focused, moderate acute psychological stressor rather than socially and cognitively oriented stressors such as the Trier social stress test (TSST) and arithmetic calculation with negative evaluation (Kirschbaum et al., 1993; Dickerson and Kemeny, 2004; Wang et al., 2005; Pruessner et al., 2008). The reason why we did not observe a reliable increase (but nevertheless a significant difference between stress and neutral conditions) in cortisol lies probably in the specifics of the experimental design. The genetic analysis required a within-subject design reducing the contextual novelty effects substantially. Furthermore, we opted for a short succession of the two conditions for reasons of practicality. Nevertheless, converging evidence from multiple elegant readout measurements in the present study as well as our previous studies, including increase in hear rate and cortisol, changes in pupil dilation responses (Henckens et al., 2009; Qin et al., 2012) and increase in α-amylase and amygdala perfusion (Cousijn et al., 2010, 2012), firmly promote the conclusion that our emotion focused stress induction procedure was effective. That is, our procedure indeed successfully induced a state characterized by hyperactivation of catecholaminergic systems and negative mood (Arnsten and Goldman-Rakic, 1998; Arnsten, 2009; Hermans et al., 2011; Shackman et al., 2011; Weymar et al., 2011).

Behaviorally, we found COMT genotype-dependent effects of moderate acute psychological stress on WM performance, but no main effect of genotype at baseline in the control condition. The lack of a genotype effect on baseline WM performance may be attributed to a lack of sensitivity at a behavioral level (Meyer-Lindenberg and Weinberger, 2006). A pretraining procedure prior to fMRI scanning may also play a role in reducing the potential genotype effect. Nonetheless, we found a robust main effect of genotype on WM-related activity in the dorsolateral PFC, indicating that brain imaging genetics may have higher sensitivity for subtle genetic effects on putative neural activity related to cognition and behavior, and thus serving as so-called intermediate phenotype (Meyer-Lindenberg and Weinberger, 2006).

In conclusion, the present study demonstrates *COMT* genotype-dependent effects of moderate stress on WM performance, WM-related activation of the dorsolateral PFC, and WM-related deactivation in the anterior MTL, with a more negative impact of stress in the genotype that is associated with higher baseline levels of catecholamines. These findings are in line with animal models of catecholaminergic function that assume an inverted U-shaped relationship between catecholaminergic activity and PFC-dependent higher-order cognitive functions.

# **ACKNOWLEDGMENTS**

This work was supported by the Netherlands Organization for Scientific Research (NWO) (918.66.613, 451.07.019, and 446.10.010).

## **REFERENCES**

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(2009). Stressed memories: how acute stress affects memory formation in humans. *J. Neurosci.* 29, 10111–10119.

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(2005). Perfusion functional MRI reveals cerebral blood flow pattern under psychological stress. *Proc. Natl. Acad. Sci. U.S.A.* 102, 17804–17809.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 30 November 2011; accepted: 07 April 2012; published online: 11 May 2012.*

*Citation: Qin S, Cousijn H, Rijpkema M, Luo J, Franke B, Hermans EJ and Fernández G (2012) The effect of moderate acute psychological stress on working memory-related neural activity is modulated by a genetic variation in catecholaminergic function in humans. Front. Integr. Neurosci. 6:16. doi: 10.3389/fnint.2012.00016*

*Copyright © 2012 Qin, Cousijn, Rijpkema, Luo, Franke, Hermans and Fernández. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

# On the influence of emotion on conflict processing

# *Philipp Kanske\**

*Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany \*Correspondence<EMAIL_ADDRESS>
#### *Edited by:*

*Mara Mather, University of Southern California, USA*

#### *Reviewed by:*

*Kristin Prehn, Freie Universität Berlin, Germany Noga Cohen, Ben-Gurion University of the Negev, Israel*

# **Introduction**

The ability to show coherent goal-directed behavior, even in the presence of distraction, requires the detection and resolution of conflict, for example between opposing action tendencies. This defining feature of cognitive control has been extensively studied with experimental tasks like the Stroop, Simon, or Flanker paradigm (Stroop, 1935; Simon and Rudell, 1967; Eriksen and Eriksen, 1974), which also yielded the description of an underlying neural network for conflict detection and resolution (Fan et al., 2003; Ridderinkhof et al., 2004). Understanding the influence of emotion on this system is particularly important because they are intrinsically tied to one another in real life (Pessoa, 2008). Recent evidence seems to be contradictory with reports of facilitation and impairment effects of emotion on the processing of conflict. On a conceptual basis both accounts can be supported. It may be argued that keeping up goal-directed behavior is especially difficult in emotional situations because of their greater distracting potential. On the other hand, emotional stimuli are also signals of relevance for a situation that might require particularly efficient cognitive control (Scherer, 1994). In line with this latter view, Norman and Shallice (1986) suggested that emotion, along with other instances like novelty or error commitment, might trigger cognitive control processes. The present opinion article aims at elucidating these diverging views by specifying two critical factors – task relevance and individual differences – that result in an enhancing or hindering influence of emotion on the processing of conflict.

# **Task relevance**

To study conflict processing, participants are typically given a certain goal related to a target stimulus and need to ignore other irrelevant information. The irrelevant information could be related to peripheral distractor stimuli (e.g., in the Flanker task) or to stimulus-response-associations (e.g., in the Simon task). If this information is incongruent with the given goal the resulting conflict needs to be detected and solved in order to show coherent behavior. A series of recent studies demonstrated that the speed, with which conflict is resolved in such tasks is enhanced, if the presented conflict stimuli are emotional, i.e., emotion speeds up cognitive conflict processing (Kanske and Kotz, 2010, 2011b). This effect is independent of emotional valence and observable for positive and negative stimuli alike (Kanske and Kotz, 2011a). It is also present in the visual and auditory modality (Kanske and Kotz, 2011d) and in Flanker, as well as Simon-type conflict tasks (Kanske and Kotz, 2011b,c). The latter is especially interesting as the mechanisms underlying Flanker and Simon tasks are very different. Conflict in the Simon task is not elicited through peripheral distractor stimuli, but through incompatible stimulus presentation and response side mapping. If, for example participants perform a voice gender discrimination with female voices requiring a right hand response, but the stimulus is presented on the left side, the elicited action tendencies will conflict and responses are prolonged. Even though this task does not require participants to focus on emotions, responses in the incompatible condition are accelerated if the spoken word is emotional (Kanske and Kotz, 2011b). The presence of the emotional modulation of conflict processing across these different conflict tasks and different sensory modalities strongly suggests that it is a general mechanism acting on the supra-modal processing steps of conflict detection and resolution rather than on visual or auditory spatial attention. Critically, however, in each of these studies the emotionally valent stimuli were task-relevant, i.e., the stimuli that participants needed to process and react to in order to solve the task. This contrasts other studies that modulated emotion through the presentation of additional stimuli that were unrelated to the task. For example, when emotional stimuli are presented before the conflict stimuli, conflict resolution is not facilitated, but impaired (Hart et al., 2010). Furthermore, conflict adaptation, i.e., the enhanced processing of conflict after presentation of an incongruent trial, is also compromised if emotional stimuli are presented between conflict trials (Padmala et al., 2011). This pattern of hindering and enhancing effects of emotion on conflict processing suggests that the exact role that emotion plays in a task is crucial for determining its impact. Emotion will only enhance cognitive control if the behaviorally relevant stimuli that participants react to during a task are emotional. Transiently induced emotion by task-unrelated stimuli, in contrast, yields hindering effects (for potentially conflicting results see Birk et al., 2011; Melcher et al., 2012, but see also Cohen and Henik, 2012, in this Research Topic for a discussion). Corroborating evidence for this conclusion also comes from two recent studies on the effects of motivation on conflict processing. Similarly to emotional stimuli that signal importance of a situation, if relevance is increased through reward for correct task performance, cognitive control is triggered resulting in accelerated conflict processing (Padmala and Pessoa, 2011). If, however, the motivational salience of the distractor stimuli is increased, conflict resolution is impaired (Krebs et al., 2010). It may thus be relevance in general that speeds up conflict processing, rather than emotion specifically.

# **Neural underpinnings**

A crucial node in the neural network underlying cognitive control is the anterior cingulate cortex (ACC), in particular its dorsal portion (Ridderinkhof et al., 2004). The ventral ACC has been shown to be more sensitive to emotion (Bush et al., 2000) and also to conflict between emotional stimuli of different valence (Etkin et al., 2006). This ventral portion is also heavily connected to the amygdala, which is critically involved in emotion detection and generation (Phelps and LeDoux, 2005). In the context of emotional influences on conflict processing, interactions between these three regions seem important. Conflicting stimuli that are emotionally negative activate the ventral ACC in addition to the dorsal portion, which is active for incongruent stimuli irrespective of their emotional status (Kanske and Kotz, 2011b). Furthermore, functional connectivity between the ventral and dorsal ACC, as well as between ventral ACC and the amygdala is increased in incongruent emotional compared to neutral trials (Kanske and Kotz, 2011c). This suggests that the ACC is integrating the need for increased cognitive control in conflicting situations with task-relevant emotional stimuli. The emotional saliency information is signaled by the amygdala and the ACC prioritizes these situations resulting in enhanced conflict processing. The relative speed of this process is apparent in an emotional modulation of the first conflict-related component of the event-related potential of the EEG (Kanske and Kotz, 2011a). Already 200 ms after stimulus onset, emotional incongruent stimuli elicit an enhanced negativity over central electrodes, which has been localized to the dorsal ACC in non-emotional conflict processing (N200; van Veen and Carter, 2002). The effects of task-irrelevant emotional stimuli on reducing conflict processing efficiency are also mediated by the ACC. Activation reduction in this region during emotional distraction correlates with increased response times in the conflict task (Hart et al., 2010). The data on the neural underpinnings of reward-related influences on conflict processing show some similarities with the nucleus accumbens involved in behavioral facilitation of conflict resolution by reward associated stimuli, and a medial frontal region just dorsal to the ACC integrating information on the reward value of distractor stimuli (Krebs et al., 2011).

# **Individual differences**

As individuals differ in sensitivity to emotional stimuli and cognitive control capabilities, there are also variations in the modulation of conflict processing through emotion. A recent correlational study examined this across six experiments on the influence of task-relevant emotional stimuli on conflict processing (Kanske and Kotz, 2012). Individuals high in subclinical depression and anxiety, both emotional states with deficient emotion processing (Kalia, 2005; Li et al., 2008), show a reduced increase in cognitive control for negative emotional stimuli. In contrast, the temperament trait effortful control, which correlates positively with conflict processing and describes the ability for self-regulation (Gerardi-Caulton, 2000) is associated with an enhanced emotion induced facilitation of conflict processing. These relations also translate to the neural level. High depression and anxiety are characterized by a smaller conflict-related increase in ventral ACC activity and N200 amplitude in taskrelevant emotional stimuli, while effortful control has the opposite effects. Anxiety might also be related to the impairing effect of task-irrelevant emotional stimuli on conflict processing, but the evidence regarding the direction of this effect is rather mixed (Dennis and Chen, 2007; Dennis et al., 2008). The presence of these individual differences and their systematic relation to emotional states and temperament across different experimental designs (Kanske and Kotz, 2012) may therefore represent an important aspect when explaining the enhancing and hindering effects of emotion on cognitive control.

# **Integration**

The discussed results suggest a tight integration of emotion and cognitive control in situations that require the resolution of conflict. **Figure 1** illustrates this for conflict processing in neutral stimuli (**Figure 1A**), and for an emotional task-irrelevant (**Figure1B**) or task-relevant (**Figure1C**) stimulus. In the well-described situation of conflict in neutral stimuli, cognitive control resources will be recruited and bias information processing in line with current task-demands through amplification of the cortical representation of the task-relevant stimulus (Egner and Hirsch, 2005). If a task-unrelated stimulus is emotional, relevance detectors like the amygdala or nucleus accumbens will divert resources toward the processing of that stimulus, thereby impairing the processing of task-related conflict (Pessoa, 2009). The ACC closely follows this pattern by showing emotion-related activation increase that predicts impaired task performance (Lim et al., 2008). ACC activity related to conflict, however, is decreased under emotional distraction, which also correlates with impaired behavior (Hart et al., 2010). In contrast, when the task-relevant stimulus is emotional, the current task-goal and emotional information signaled through the relevance detector coincide. Therefore, increased cognitive control resources will be recruited yielding an enhanced bias in information processing toward task-relevance. Here, the ACC also follows this pattern with the additional activation of the ventral portion for conflict in emotional stimuli, where the resolution of conflict is accelerated compared to neutral stimuli (Kanske and Kotz, 2011b,c). These interactions are variable, for example with individual differences in emotion-related amygdala reactivity and limbic-prefrontal connectivity in depression and anxiety (Johnstone et al., 2007; Kanske and Kotz, 2012). Here, the altered relevance detection seems to result in the dysfunctional integration of emotion and current task-goals apparent in reduced ventral ACC activation. This consequently leads to impaired performance.

# **Challenges and questions**

The evident pattern might show some valence specificity. Even though negative and positive emotional stimuli yield comparable behavior and EEG results (Kanske and Kotz, 2010, 2011a), there is less data yet on the neural mechanisms underlying the influence of positive emotion on conflict processing. As depression and anxiety selectively affect the influence of negative, but not positive emotional stimuli on conflict processing (Kanske and Kotz, 2012), the mechanisms that mediate the influence on conflict processing might be different (see also Prehn et al., 2011). For positive emotion they might resemble more the pattern observed for reward-related stimuli with nucleus accumbens involvement (Krebs et al., 2011). The mechanisms might also differ for highly salient emotional stimuli, as suggested by Pessoa (2009). This notion is supported by the observed

**cognitive control in conflict processing.** The situation for neutral **(A)** and emotional task-irrelevant **(B)** or task-relevant stimuli **(C)** is displayed. Circles

stimulus; CC, cognitive control; RD, relevance detector.

effects for anxiety and depression, which are characterized by increased sensitivity to negative emotional stimuli and increased related amygdala responding (Kanske and Kotz, 2012). The similarity of the results for motivation and emotion suggests that it may be relevance in general that yields enhanced conflict processing. This question should be followed up by direct comparisons of differently salient stimuli. Lastly, the relation of emotion and cognitive control is not uni-directional, rather, different levels of control may also affect emotion. There is some indication that individuals with high emotion regulation capacity attenuate emotional processing when cognitive control is recruited for conflict processing (Cohen et al., in press). The challenge for future investigations will be to concurrently study these reciprocal relations and their underlying neural mechanisms.

# **Conclusion**

Conflict processing is directly influenced by emotion, with individual differences in temperament and emotional state, as well as the task-relevance of the emotionally valent stimuli critically determining if this influence is an enhancing or a hindering one. The resulting pattern is highly evolutionary adaptive as the time that conflict yields an organism incapable of responding to potentially dangerous negative or rewardsignaling positive stimuli is reduced. In contrast, if stimuli are present that are not relevant to current task-goals, but may still be relevant to higher order goals because of their danger- or reward-value, their processing is prioritized over task-performance. In consequence, the flexibility of behavior is greatly enhanced with slower and faster processing routes depending on current task-related and overarching goals.

# **References**

prefrontal-subcortical circuitry in major depression. *J. Neurosci.* 27, 8877–8884.

Prehn, K., Heekeren, H. R., and van der Meer, E. (2011). Influence of affective significance on different levels of processing using pupil dilation in an analogical reasoning task. *Int. J. Psychophysiol.* 79, 236–243.

Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., and Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. *Science* 306, 443–447.

Stroop, J. (1935). Studies of interference in serial verbal reactions. *J. Exp. Psychol.* 18, 643–662.

van Veen, V., and Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. *Physiol. Behav.* 77, 477–482.

*Received: 30 April 2012; accepted: 20 June 2012; published online: 09 July 2012.*

*Citation: Kanske P (2012). On the influence of emotion on conflict processing. Front. Integr. Neurosci. 6:42. doi: 10.3389/fnint.2012.00042*

*Copyright © 2012 Kanske. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# Do irrelevant emotional stimuli impair or improve executive control?

# *Noga Cohen\* and Avishai Henik*

*Department of Psychology, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel \*Correspondence<EMAIL_ADDRESS>
Our behavior is constantly influenced by emotional stimuli. These stimuli can enhance (i.e., improve) or impair performance, depending on their specific interaction with situational demands (Dolcos et al., 2011). This paper examines factors that mediate the influence of task-irrelevant negative stimuli on executive control (EC). We demonstrate how similar results of emotion-cognition interactions might be interpreted according to opposing theories, following the use of different analysis methods.

Executive control is responsible for monitoring, controlling, and regulating irrelevant information, in order to enable goal-directed behavior (Norman and Shallice, 1986). Recently, there is growing debate regarding the influence of negative stimuli on EC (Hu et al., 2012). Specifically, compared to neutral stimuli, negative stimuli were found to impair (i.e., elongated reaction times – RT), improve (i.e., facilitated RT), or have no influence (i.e., similar RT) on EC. Herein we will first suggest that descriptions of *improved* or *impaired* EC may be misleading, and then discuss three factors that modulate links between EC and emotion: available resources, attentional breadth, and top-down modulation.

# **Tackling the terms** *impaired* **and**  *improved* **EC**

Two tasks widely used to examine EC are the Stroop (1935) and flanker (Eriksen and Eriksen, 1974). In the Stroop task, participants are presented with colored words written in colored ink and need to name the ink color and ignore the word. This task usually contains congruent, incongruent, and neutral targets (e.g., BLUE, RED, XXXX, respectively where the response should be "blue"). In the flanker task, participants need to respond to a target and ignore distracting stimuli. For example, in the arrow-flanker task, participants respond to the direction of the middle arrow and ignore the two flanker arrows. This task also contains congruent (→→→→→), incongruent (→→←→→), and neutral (--→--) targets. Although the Stroop and flanker tasks differ in several aspects (Magen and Cohen, 2002), both create conflict situations during incongruent targets (although, see Goldfarb and Henik, 2007, for discussion of conflict also during congruent trials). This conflict situation activates EC mechanisms designed to detect and solve the conflict.

Studies presenting neutral and negative stimuli prior to Stroop or flanker tasks (Dennis and Chen, 2007a,b; Dennis et al., 2008; Hart et al., 2010; Cohen et al., 2011, 2012; O'Toole et al., 2011; Hu et al., 2012; Melcher et al., 2012) usually analyze the influence of these stimuli on the congruity effect (i.e., RT incongruent minus RT congruent).  However, this analysis does not distinguish between different effects of negative stimuli on congruent and incongruent targets separately. This distinction is important due to the presence of conflict (and hence, activation of EC) mainly during incongruent targets. Without showing a significant difference between negative and neutral stimuli influence on incongruent targets, it might be problematic to claim negative stimuli impair or improve EC. Importantly, this difference should not appear in congruent or neutral targets of the EC task (this would imply a main emotional effect on RT).

We recently showed that compared to neutral stimuli, negative stimuli delayed RTs for congruent flanker targets, but had no influence on incongruent targets (Cohen et al., 2011, 2012). Although these findings indicate a smaller congruity effect following negative stimuli, we did not interpret them as improved EC, but as attenuation of the emotional effect during conflict situations. Nevertheless, our paper was cited as evidence for improved EC (Birk et al., 2011). It is possible that this pattern (i.e., incongruent trials not affected by negative stimuli) is caused by improved EC. However, it is also possible that other mechanisms are responsible for this null effect (see Cohen et al., 2011; Hu et al., 2012, for debate regarding interpretation of their results). Similarly, interpretations of impaired EC may be misleading when data does not indicate delayed RTs for incongruent targets following negative compared to neutral stimuli.

**Figure 1** illustrates typical results (not based on empirical data) that can be found in experiments presenting negative and neutral stimuli prior to an executive task. **Figures 1A,B** demonstrate results suggesting impaired (i.e., larger congruity effect) EC, while **Figures 1C,D** demonstrate results suggesting improved (i.e., smaller congruity effect) EC following presentation of negative stimuli. A similar congruity effect is found in **Figures 1A,B**, and a similar congruity effect is found in **Figures 1C,D**. However, these similar effects are produced in different ways (e.g., the impaired congruity effect in **Figure 1A** is due to elongated RT in negative incongruent trials, whereas the impaired congruity effect in **Figure 1B** is due to facilitated RT in negative congruent trials, with no change in the incongruent trials).

The following sections present three factors that might account for these different interaction patterns. In addition, we will demonstrate how similar results might be interpreted differently, depending on the analysis used.

# **Available resources**

According to the dual competition model (DCM), highly threatening stimuli impair EC since they consume resources required for resolving conflict (Pessoa, 2009). In accordance with this model, several studies demonstrated negative stimuli impair performance in a Stroop-like task (Hart et al.,

2010; Melcher et al., 2011; Padmala et al., 2011) and in the flanker task (Dennis et al., 2008). Although these studies reported impaired EC, close examination of the data of several of them may imply an alternative interpretation for the results.

Support for the DCM is found in studies reporting increased RTs for incongruent targets following negative compared to neutral stimuli (Hart et al., 2010; Melcher et al., 2011; see **Figure 1A**), thus demonstrating negative stimuli impair EC. In contrast, other studies interpreted results as being a consequence of impaired EC although their data did not show increased RTs for incongruent targets following negative stimuli. For example, examining RTs in the study of Dennis et al. (2008) may imply the increased congruity effect found after presentation of negative stimuli (and interpreted as impaired EC), resulted from facilitated RTs for negative compared to neutral stimuli in congruent trials, while RTs for incongruent trials following negative and neutral stimuli were very similar (Dennis et al., 2008; see **Figure 1B**). Findings of facilitation or lack of emotional interference in congruent trials (Dennis et al., 2008; Hart et al., 2010) are inconsistent with data showing delayed RTs in discrimination tasks following irrelevant emotional stimuli (Hartikainen et al., 2000; Buodo et al., 2002). Moreover, the DCM (Pessoa, 2009) would have predicted that resources devoted for processing emotional stimuli would cause delay both in congruent and in incongruent targets (although to a greater extent in incongruent targets; see **Figure 1A**, and Blair et al., 2007, for debate regarding this notion).

The study of Dennis et al. (2008) is an example of our claim that analyzing difference scores (i.e., congruity effect) might lead to incomplete or inaccurate interpretation of results. We suggest that under some circumstances emotional stimuli impair EC, thus supporting the DCM. However, there is also evidence emotional stimuli improve or have no influence on EC (see below). Hence, more data is needed to uncover the specific situations in which this impairment occurs.

# **Attentional breadth**

The DCM discussed above emphasizes the importance of available resources for solving a cognitive conflict. In contrast, attentional breadth theories (e.g., Easterbrook, 1959) emphasize the impact of negative information on attentional allocation. These theories claim negative stimuli narrow attention and hence, reduce interference of distracting or irrelevant information (Derryberry and Tucker, 1994; Chajut and Algom, 2003; van Steenbergen et al., 2011). Indeed, a smaller congruity effect following negative compared to neutral stimuli is often driven by facilitation of incongruent targets (see example in **Figure 1C**), as a result of reduced attention to distractors (in the flanker task) or the irrelevant dimension (in the Stroop task).

Evidence regarding improved EC following presentation of negative stimuli is found in Birk et al.'s (2011) study. The authors found reduced RTs for incongruent flanker targets following presentation of fearful compared to neutral faces (see **Figure 1C**). Several other studies also suggested emotion improves EC (Dennis and Chen, 2007b; Hu et al., 2012; Melcher et al., 2012). However, in contrast to Birk et al.'s study, the reduced congruity effect in the latter studies did not result from reduced RTs in incongruent targets. For example, in a Stroop-like task, Hu et al. (2012) found that compared to neutral situations, negative situations (shock anticipation) delayed RTs in congruent trials, but no difference between neutral and negative situations was found in incongruent trials (see **Figure 1D**). The authors suggested the overall slowdown following shock anticipation was contrasted by the facilitation effect caused by attentional narrowing. Importantly, they based their claim on results showing delayed RTs during neutral Stroop trials (e.g., XXX where the response should be "red"). As discussed in their paper, adding neutral Stroop trials may help unravel different effects of attentional narrowing and available resources on EC.

Hence, it seems that under specific circumstances, negative stimuli improve EC. In addition, it seems that findings of a null emotional effect in incongruent targets are sometimes interpreted as improved EC and sometimes as impaired EC (as described earlier). We suggest this null emotional effect in incongruent targets may result from a top-down regulation mechanism (see supporting explanation below).

# **Top-down modulation**

Decreased emotional response during or following executive activation led to the suggestion EC can trigger top-down mechanisms that inhibit the effect of emotion (Etkin et al., 2006; Blair et al., 2007; Cohen et al., 2011, 2012). Etkin et al. (2006) presented a modified Stroop task where participants had to respond to the emotionality of a face (happy or fearful) and ignore a superimposed word (that was congruent or incongruent with the face emotion). They demonstrated a conflict adaptation effect in which the response for an incongruent target attenuated the response of the following incongruent target. This decrease in emotional conflict was related to a connection between brain areas known to be activated during conflict monitoring (e.g., anterior cingulate cortex) and emotional areas (e.g., amygdala). Similarly, we recently showed that when an executive task *precedes* an emotional stimulus, incongruent trials can attenuate emotional response both behaviorally (Cohen et al., 2011, 2012; see **Figure 1D**) and physiologically (Cohen and Henik, Submitted). In addition, there is evidence that this inhibitory connection characterizes healthy individuals and is deficient in people suffering from depression (Johnstone et al., 2007; for review, see Rogers et al., 2004) or anxiety (Bishop et al., 2004; Etkin et al., 2010). Taken together, there is strong evidence that activation of EC might result in decreased emotional response and hence, in a diminished emotional effect during incongruent targets.

# **Conclusion**

We presented several factors that might account for the various relationships between emotion and EC. In addition, we suggested that the use of difference scores (i.e., the congruity effect) might lead to different interpretations, even in face of similar data sets. We offer two important conclusions:

1. The effect of emotion on EC is modulated by several factors. Negative stimuli impair EC if they consume resources available for resolving the conflict, improve EC if attention is narrowed, or have no influence on EC if top-down processes are activated. Additional factors, such as relevance of the negative stimuli to current goals (Kanske and Kotz, 2011a,b; see also Kanske, in press, in this Research Topic), task demands (Shafer et al., 2012), and individual differences, may also modulate the effects of emotion on EC (for review, see Okon-Singer et al., 2012). More research is required to unravel how all these factors are orchestrated to shape the emotionexecutive relationship.

2. Researchers tend to analyze the influence of emotion on the congruity effect and by doing so, miss important information. A good example for inconsistent interpretation occurs when researchers find a null emotional effect for incongruent targets. This null effect is sometimes interpreted as impaired, and sometimes as improved EC, depending on the effect of emotion on congruent trials (compare **Figures 1B,D**, respectively). Hu et al. (2012) suggested that adding neutral trials to the EC task might help distinguish between impairing and enhancing effects. However, as discussed above this null effect may also result from top-down regulation of the emotional system. This top-down effect should be directly examined by conducting a sequential analysis (Cohen et al., 2011) or by presenting the EC task before the emotional stimuli (Cohen et al., 2012).

Future studies should strive to uncover the specific factors that modulate emotional effect on conflict vs. non-conflict situations. Defining the interactions between these factors could uncover how emotion and EC are mutually linked in order to generate adaptive behavior. On a broader perspective, the use of difference scores, such as the congruity effect, in various cognitive domains (e.g., spatial attention, implicit attitudes, affective priming) may lead to an incomplete view of emotion-cognition interactions, thus hindering the research in this field.

# **References**

Davidson, G. E. Schwartz, and D. Shapiro (New York: Plenum Press) 1–18.

*Received: 20 March 2012; accepted: 28 May 2012; published online: 18 June 2012.*

*Citation: Cohen N and Henik A (2012) Do irrelevant emotional stimuli impair or improve executive control? Front. Integr. Neurosci. 6:33. doi: 10.3389/fnint.2012.00033*

*Copyright © 2012 Cohen and Henik. This is an openaccess article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

# Neurocognitive correlates of the effects of yoga meditation practice on emotion and cognition: a pilot study

# *Brett E. Froeliger 1,2\*, Eric L. Garland3,4, Leslie A. Modlin1 and F. Joseph McClernon1,2*

*1 Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA*

*2 Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA*

*3 College of Social Work, Florida State University, Tallahassee, FL, USA*

*4 Trinity Institute for the Addictions, Florida State University, Tallahassee, FL, USA*

#### *Edited by:*

*Mara Mather, University of Southern California, USA*

#### *Reviewed by:*

*Ullrich Wagner, Charité - University Medicine Berlin, Germany Mara Mather, University of Southern California, USA*

#### *\*Correspondence:*

*Brett E. Froeliger, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Box 2701, Durham, NC 27708, USA. e-mail<EMAIL_ADDRESS>
Mindfulness meditation involves attending to emotions without cognitive fixation of emotional experience. Over time, this practice is held to promote alterations in trait affectivity and attentional control with resultant effects on well-being and cognition. However, relatively little is known regarding the neural substrates of meditation effects on emotion and cognition. The present study investigated the neurocognitive correlates of emotion interference on cognition in Yoga practitioners and a matched control group (CG) underwent fMRI while performing an event-related affective Stroop task. The task includes image viewing trials and Stroop trials bracketed by neutral or negative emotional distractors. During image viewing trials, Yoga practitioners exhibited less reactivity in right dorsolateral prefrontal cortex (dlPFC) to negative as compared to neutral images; whereas the CG had the opposite pattern. A main effect of valence (negative *>* neutral) was observed in limbic regions (e.g., amygdala), of which the magnitude was inversely related to dlPFC activation. Exploratory analyses revealed that the magnitude of amygdala activation predicted decreased self-reported positive affect in the CG, but not among Yoga practitioners. During Stroop trials, Yoga practitioners had greater activation in ventrolateral prefrontal cortex (vlPFC) during Stroop trials when negative, compared to neutral, emotional distractor were presented; the CG exhibited the opposite pattern. Taken together, these data suggest that though Yoga practitioners exhibit limbic reactivity to negative emotional stimuli, such reactivity does not have downstream effects on later mood state. This uncoupling of viewing negative emotional images and affect among Yoga practitioners may be occasioned by their selective implementation of frontal executive-dependent strategies to reduce emotional interference during competing cognitive demands and not during emotional processing *per se*.

**Keywords: mindfulness, fMRI, emotion-cognition**

# **INTRODUCTION**

Hatha Yoga is a 600 year old practice that integrates physical poses (i.e., asana), meditation, breath work (i.e., pranayama), study of tantric philosophy and community outreach. The practice of yoga is shown to improve cognition in healthy (Manjunath and Telles, 2004) and clinical populations including multiple sclerosis (Velikonja et al., 2010) and major depressive disorder patients (Sharma et al., 2006). Yoga has also proven effective for improving emotional function in healthy (Hartfiel et al., 2011) and in clinical populations including reducing negative affect (Vadiraja et al., 2009), anxiety (Vadiraja et al., 2009; Streeter et al., 2010), depression (Banerjee et al., 2007), and improving emotional wellbeing (Moadel et al., 2007). These observed beneficial effects on cognitive and emotional health are thought to result in part from increased mindfulness arising from various yoga practices (Salmon et al., 2009).

Hatha yoga involves mindfulness *practice*, that is, repeated placement of attention onto an object while alternately acknowledging and letting go of distracting thoughts and emotions. Within the Hatha yoga tradition, mindfulness practice occurs both during the physical postures as well as during formal mindfulness meditation on one's breathing, where the object of mindfulness practice might include proprioceptive or interoceptive sensations stemming from physical posture or respiration, respectively. In turn, mindful yoga practices may generate the *state* of mindfulness, which, when evoked recurrently through repeated practice, may accrue into *trait* or *dispositional* mindfulness (Chambers et al., 2009; Garland et al., 2010). The state of mindfulness is characterized by a nonjudgmental and metacognitive monitoring of moment-by-moment cognition, emotion, perception, and sensation without fixation on thoughts of past and future (Kabat-Zinn, 1982; Garland, 2007; Lutz et al., 2008). Correspondingly, trait mindfulness is characterized as the tendency to adopt a nonjudgmental awareness of one's thoughts, emotions, experiences, and actions in everyday life (Baer et al., 2006). Trait mindfulness can be promoted by recurrent mindfulness practice. For example, individuals participating in an eight-week Mindfulness-Based Stress Reduction course evidenced increases in trait mindfulness which mediate the effects of training on clinical outcomes (Carmody and Baer, 2008; Greeson et al., 2011). Moreover, participants in a yoga intervention exhibited significant increases in trait mindfulness after eight weeks of training (Shelov et al., 2009).

Insofar as the practice of mindfulness generates state mindfulness via intentional attending to emotions without cognitive fixation or elaborative processing of emotional experience, this practice may produce positive effects on emotion-cognition interactions. For example, mindfulness practice has been shown to result in improved ability to regulate negative emotions (Chiesa and Serretti, 2010), enhanced attentional orienting (Jha et al., 2007; Lutz et al., 2008), and increased cognitive flexibility (Wenk-Sormaz, 2005; Zeidan et al., 2011). In light of these short-term benefits, long-term mindfulness practice has the potential to promote durable alterations in trait affectivity and attentional control with resultant effects on well-being and cognitive function. These salutary, trait-level effects may be observed in identified positive associations between trait mindfulness and enhanced affect regulation (Chambers et al., 2009), attentional control (Moore and Malinowski, 2009), and autonomic recovery from emotional provocations (Garland, 2011). Plausibly, such lasting functional improvements may derive from mindfulness-induced neuroplasticity in brain regions instantiate cognition and emotion (Hölzel et al., 2011).

# **NEUROCOGNITIVE MODEL OF EMOTION-COGNITION INTERACTIONS**

Outside of the context of yoga, meditation, or mindfulness, a prevailing neurobiological model posits that affective and cognitive processes are coordinated via an interaction between a dorsofrontal executive network and a ventral-affective circuit (Mayberg, 1997; Drevets and Raichle, 1998). Task-relevant targets activate the dorsolateral prefrontal cortex (dlPFC), whereas emotional distractors activate the amygdala (Yamasaki et al., 2002). Exerting cognitive control over emotional processes leads to increased activation in the dlPFC, with corresponding reciprocal deactivation in the amygdala (Ochsner et al., 2002; Ochsner and Gross, 2008).

A nascent database has emerged on the neurocognitive correlates of yoga and meditation practice. Neuroimaging research has demonstrated differences in task-related brain function between experienced meditation practitioners and meditation naïve controls. For example, fMRI analyses indicate that meditation practitioners exhibit greater meditation-related neural activation in brain regions involved in attentional control (e.g., prefrontal cortex), conflict resolution (e.g., dorsal anterior cingulate cortex) and emotional processing (e.g., medial/orbitofrontal cortices) (Hölzel et al., 2007; Baron Short et al., 2010). Moreover, compared to non-meditators, mindfulness practitioners evidence attenuated electrophysiological activation in frontal scalp regions to negative emotional stimuli during passive picture viewing (Sobolewski et al., 2011), and yoga meditation practitioners (YMP) exhibit sustained reductions in the late positive brain potential during cognitive reappraisal of negative emotional stimuli (Gootjes et al., 2011) and reduced power in high-frequency EEG spectrum during negative emotional information processing (Aftanas and Golosheykin, 2005). In addition, on an auditory oddball task, mindfulness meditation reduced N1, P2, and P3a amplitude to distractor stimuli (Cahn and Polich, 2006). Taken together, these findings suggest that meditation practitioners evidence significantly different neural responses in cognitive and affective brain circuitry than non-meditators which may mediate the identified salutary effects of mindfulness practice on attentional and emotional processes. Though these data provide important neural clues for the effects of Yoga meditation (YM) practice on emotional and cognitive processes, the precise neural mechanisms underlying the effects of YM on emotion-cognition interactions (e.g., emotional information processing; emotional interference on cognition) remain largely unknown.

# **CURRENT STUDY**

The present study investigated the neurocognitive correlates of emotional interference on a cognitively demanding task within a sample of meditation practitioners and matched controls. In the present study, we sought to investigate the effects of YM on emotion-cognition interactions. YMP and a matched control group (CG) of yoga and meditation naïve subjects underwent fMRI scanning while performing an Affective Stroop Task (Blair et al., 2007; Vythilingam et al., 2007; Hasler et al., 2009; Mueller-Pfeiffer et al., 2010; Froeliger et al., 2011, 2012), a modified version of the Number Stroop task (Pansky and Algom, 2002). The Affective Stoop task was designed to evaluate emotional information processing and its effects on cognitive conflict resolution. We hypothesized that YMP, as compared to the CG, would exhibit less brain activation [i.e., Blood-oxygenation-level-dependent (BOLD) response] during negative emotional information processing and greater brain activation during Stroop trials in the executive control system (e.g., PFC).

# **MATERIALS AND METHODS PARTICIPANTS**

Fourteen [7 Hatha YMP, 7 Hatha yoga and meditation-naïve control (CG)] participants between the ages of 18 and 55 years were enrolled. MP participants reported engaging in mindfulness meditation on average 7 days per week [0] over the course of the previous 5.7 yrs [3.8]. In addition, participants in the YMP group were also involved in an active and ongoing hatha yoga practice (*>*45-min a day, three-four times per week, *>*3 years). The matched CG reported no current or past dedicated meditation or yoga practice. In addition, all participants were right-handed, free of any psychiatric condition or any major medical condition that would make participation unsafe or uncomfortable. Additional exclusionary criteria included current alcohol or drug abuse, use of tobacco or nicotine products and positive urine drug screen. Female participants were required to have a negative urine pregnancy test at screening and within 12 h prior to the fMRI scan. The protocol was approved by the institutional review board at Duke University Medical Center, and all participants provided written informed consent before participating in study-related activities.

# **PROCEDURES**

After screening, eligible participants completed one training session during which they practiced the experimental task and were placed in a mock scanner in order to habituate to the scanning environment. Following training, participants completed one fMRI session.

# **ASSESSMENT OF TRAIT AND STATE AFFECT**

Baseline measures included assessment of depressive symptoms with the Center for Epidemiological Studies-Depression (CES-D) scale (Radloff, 1977) and anxiety symptoms with the Beck Anxiety Inventory (BAI). State-dependent mood was measured using the 20-item positive and negative affect schedule (PANAS) (Watson et al., 1988). This measure results in two orthogonal scales—Positive Affect (attentive, proud) and Negative Affect (distressed, angry).

# **AFFECTIVE STROOP TASK**

The Affective Stroop Task used in the present study was similar to that used in other studies evaluating emotion-cognition interactions (Blair et al., 2007; Vythilingam et al., 2007; Hasler et al., 2009; Mueller-Pfeiffer et al., 2010; Froeliger et al., 2011). During each imaging session, participants performed two runs of the task (**Figure 1**). Stimuli consisted of number grids and distractor images. The number grids consisted of numerals (1's through 6's) randomly presented within a 9-point grid-field. Distractor

Froeliger et al. Yoga meditation fMRI

stimuli were negative and neutral valence images selected from the International Affective Picture Series (IAPS) (Lang et al., 1997) on the basis of 9-point arousal (1-lowest, 9-highest) and valence (1-negative, 5-neutral, 9-positive) scales. Valence and arousal ratings for chosen images did not overlap across categories (Negative, Valence *<*3, Arousal *>*6; Neutral, Valence 4 to 6, Arousal *<*3) and were matched on mean luminance, chromatic features, and scene complexity. Within the aST, two primary types of trials are randomly presented, Stroop trials that contain numerical grids and distractor images, and viewing trials—requiring the participant to only view images (**Figure 1**). The Stroop trials began with a fixation cross, followed by a number grid, a negative or neutral distractor image, a unique number grid, and concluded with the re-presentation of the distractor image each for 800 ms. Participants were instructed to report by button press which number grid presented (1st or 2nd) contained greater numerosity (the quantity of numbers presented) as quickly and accurately as possible. Stroop trials were further broken down into two subcategories: congruent and incongruent trials. During congruent trials, the number grids presented numerals with a face value congruent with the numerosity (e.g., two 2's; five 5's). During the incongruent trials, the face value of the numerals did not match the numerosity (e.g., four 3's; three 4's). Response accuracy and reaction times (RT's) were recorded for each trial. During the viewing trials the numerical grids were replaced with a crosshair and no responses were recorded. Finally, numerical trials began with a brief (800 ms) instruction "Study"; whereas emotional information processing trials began with a brief (800 ms) instruction "View". During each 8 1/2 min run, six event types (negative congruent, negative incongruent, neutral congruent, neutral incongruent, negative view, and neutral view) were each randomly presented equally (15 events), resulting in a total of 30 events per type during each scanning session.

# **ANALYSIS OF BEHAVIORAL DATA**

Analyses of the effects of group on overall task response RT and accuracy during the Stroop trials were conducted using a 2 (Group: YMP, control) × 2 (Task Condition: congruent, incongruent) × 2 (Distractor Valence: negative, neutral) ANOVA. Behavioral analysis of the effects of group and emotional distractor valence on Stroop [incongruent-congruent] accuracy and RT were evaluated in a 2 (Group: YMP, control) × 2 (Valence: negative, neutral) ANOVA.

## **fMRI METHODS**

A 3T General Electric Signa EXCITE HD scanner (Milwaukee, WI) equipped with 40 mT/m gradients was used for image acquisition. At the start of each fMRI session, a high-resolution threedimensional fast spoiled gradient recalled echo (3D-FSPGR) anatomical sequence was collected (FOV = 25.6 cm, matrix = 2562, flip angle = 12◦, 166 slices, slice thickness = 1 mm). BOLD functional images were collected for 34 contiguous slices parallel to the horizontal plane connecting the anterior and posterior commissures. A gradient-recalled inward spiral pulse imaging sequence was used (34 slices, TR = 1500 ms, TE = 30 ms, FOV = 25.6 cm, matrix = 64 × 64, flip angle = 60◦, slice thickness = 3.8 mm, resulting in 4 × 4 × 3*.*8 mm voxels).

Preprocessing was conducted using statistical parametric mapping software (SPM8; Wellcome Department of Imaging Neuroscience, London) to attenuate noise and artifacts. The first four volumes of each run were discarded to allow for T1 stabilization. All functional images underwent correction for acquisition timing and for head motion using rigid-body rotation and translation (Friston et al., 1994). Each participant's data was then subsequently warped into a standard stereotaxic space (Montreal Neurological Institute) with an isotropic 2 mm voxel size and smoothed with an 8 mm FWHM Gaussian filter.

## **fMRI DATA ANALYSIS**

Participant's data from each session was entered into a first-level, whole-brain analysis using the General Linear Model (Friston et al., 1994) to examine BOLD response to each of the six trial types; negative view, neutral view, negative congruent, neutral congruent, negative incongruent, neutral incongruent. For the first-level model, each event of each trial was modeled as a delta regressor at the onset of the event and convolved with a canonical hemodynamic response function. Motion was removed through rigid body rotation and translation and included as covariates, and a high-pass filter (128 s; 0.008 Hz) was applied to remove slow signal drift. Statistical images were thresholded with a mask containing regions of interest (ROI) that have been previously found to play a role in emotion-cognition interactions (Drevets, 2000; Ochsner et al., 2002, 2004; Yamasaki et al., 2002; Dolcos and McCarthy, 2006; Wang et al., 2008). These included bilateral posterior, dorsal and paracingulate cortices; inferior, middle and superior frontal gyri; inferior parietal lobule (IPL); insula and amygdala. These ROI's were obtained from automated anatomical labeling (AAL) (Tzourio-Mazoyer et al., 2002) in Marina (Walter et al., 2003). The goal of the analyses were to identify effects of YM experience (YMP, controls) on (1) emotional information processing [e.g., viewing emotional images (negative, neutral)] and (2) emotional distraction on Stroop-BOLD response.

## *Analysis of group, emotional distraction and stroop effect*

To examine the effects of group and emotional distraction on the neurocorrelates of the Stroop effect, a Stroop contrast image (incongruent-congruent) of the second numerical grid in the trial (decision making event) was created separately for (1) negative and (2) neutral emotional distractors trials at the first level—resulting in two contrast images; negative emotional and neutral emotional distractor Stroop contrast maps. Regressors for each event were entered into a 2 (Group: YMP, control) × 2 (Valence: negative, neutral) random effects ANOVA. Main effects of Group and Valence; and Group × Valence interactions were evaluated.

## *Analyses of group and emotion reactivity*

To examine between group differences in brain activity while viewing negative emotional images, regressors for each event of interest (1st presentation of an image during a trial; negative, neutral) were entered into a 2 (Group: YMP, control) × 2 (Valence: negative, neutral) random effects ANOVA. Main effects of Group and Valence; and Group × Valence interactions were evaluated.

Results were thresholded using the total number of voxels from the complete set of ROI's (i.e., one ROI mask containing all regions indicated in the Materials and Methods). In all analyses, voxels were considered significant if they passed a statistical threshold of *p <* 0*.*05 cluster-corrected. Cluster size for the comparisons was determined using AlphaSim and running 1000 Monte Carlo simulations (Ward, 2000) (*p <* 0*.*005, uncorrected; 432-µL cluster of contiguous significant voxels).

# *Exploratory analysis of affect and BOLD response*

To examine the relationship between change in affect during performance of the Affective Stroop task and BOLD response during negative emotional viewing trials, a zero-order correlation was computed between % BOLD signal change in the amygdala cluster identified in the main effects model and the change score in positive affect (pre-post task self-report). We further explored this association using a multiple regression model to test whether the relationship between change in affect and BOLD response during negative emotional viewing was moderated by meditation experience. We regressed change in positive affect on the following set of variables: % signal change (BOLD response) to negative emotional viewing (the independent variable), a dichotomous variable coded 1 for controls and 2 for YMP (the moderator), and a group membership X BOLD response to negative emotional viewing interaction term. The significance of the interaction term indicated the presence of a moderation effect which was then explored graphically by plotting the regression lines (Baron and Kenny, 1986).

# **RESULTS**

## **STUDY PARTICIPANTS**

YMP and CG participants did not significantly differ with regard to demographics or measures of trait and state affect (see **Table 1**). Among the YMP group, age was not significantly correlated with years of meditation practice (*r* = −0*.*01, *p* = 0*.*98) or yoga (*r* = −0*.*27, *p* = 0*.*55).

# **BEHAVIORAL DATA**

No significant differences in Stroop RT were observed: there was no main effect of group [YMP (11.65 ms), and controls (14.3 ms)] or valence [negative distractor (15.7 ms) and neutral distractor (10.3 ms)], nor was there a significant group X valence interaction on RT (all *p*-values *>* 0.1). Similarly, no significant differences in Stroop Accuracy were observed: there was no main effect of group [YMP (0.002), and controls (0.04)] or valence [negative distractor (0.02) and neutral distractor (0.02)], nor was there a significant or group X valence interaction on accuracy (all *p*-values *>* 0.2) (**Table 2**).

# **ANALYSIS OF BOLD RESPONSE DURING VIEWING TRIALS** *Interaction results*

BOLD response to distractor images was modulated by a group X distractor valence interaction in right dlPFC [i.e., middle frontal gyrus] (effect size: *d* = 2*.*09) [see **Table 3**; **Figure 2**]: the CG were found to have greater activation to negative as compared to neutral emotional images, whereas the YMP group exhibited comparatively decreased activation to both negative and neutral emotional images.

# *Main effects*

A significant main effect of distractor valence was identified in left hippocampus and amygdala and right insula, characterized by greater activation to negative as compared to neutral images [see **Table 3**; **Figure A1**]. No main effect of group was found.

# *Exploratory correlational analyses*

Exploratory analyses were performed to evaluate correlations between % signal change in dlPFC and amygdala clusters identified in the prior analyses during viewing trials. Across YMP and CG participants, a significant negative correlation was found between % signal change in the dlPFC and amygdala during negative (*r* = −0*.*575, *p.03 2-tailed*) but not neutral (*r* = −0*.*05, *p*.8) distractor images.

# **ANALYSIS OF THE EFFECTS OF EMOTIONAL DISTRACTION ON STROOP-BOLD RESPONSE**

## *Interaction results*

Stroop-BOLD response was modulated by a group X distractor valence interaction in left ventrolateral prefrontal cortex (vlPFC) [i.e., inferior frontal gyrus] (effect size: *d* = 2.4) [see **Table 4**; **Figure 3**]: YMP exhibited greater Stroop-BOLD when negative, as compared to neutral emotional distractor images were presented, whereas the CG had greater Stroop-BOLD response during trials that presented neutral as compared to negative emotional distractors.

# *Main effects*

A significant main effect of group was identified in left superior frontal gyrus (SFG), such that the CG had greater Stroop-BOLD response as compared to the YMP group [see **Table 4**]. A significant main effect of distractor valence on Stroop-BOLD response was identified in left vlPFC and bilateral anterior cingulate cortex (ACC), such that negative distractor Stroop trials elicited greater activation than neutral distractor Stroop trials [see **Table 4**].

#### **Table 2 | Affective Stroop task: behavioral stroop performance (incongruent-congruent).**

#### **Table 3 | BOLD response to viewing trials.**

## **EXPLORATORY ANALYSIS OF SELF-REPORTED AFFECT AND EMOTIONAL VIEW TRIAL BOLD RESPONSE**

No significant main effects of time or group X time interaction effects were observed for change in negative affect from baseline through completion of the Affective Stroop task. In contrast, a significant main effect of time on positive affect was observed; across YMP and controls, positive affect decreased significantly from baseline through completion of the Affective Stroop task, [*F(*1*,* 12*)* = 5*.*54, *p* = 0.04]. The group X time interaction was nonsignificant, [*F(*1*,* 12*)* = 0*.*78, *p* = 0.40]. Across the entire sample, change in positive affect from baseline through completion of the Affective Stroop task was significantly correlated with BOLD response to negative distractors in the left amygdala, *r* = 0. 58, *p* = 0.03 (**Figure 4**). Yet, in exploratory moderation analyses, a significant group X emotional view-BOLD interaction on change in positive affect was observed, *B* = −3*.*31, *SE* = 1*.*17, *p* = 0.018 (see **Figure 3**). Graphical inspection of the plot of the interaction effect and *post-hoc* probing of the simple slopes (Aiken and West, 1991) indicated that among controls, higher BOLD response to viewing negative emotional images was significantly related to larger decreases in positive affect during participation in the Affective Stroop task (*t* = 3.75, *p* = 0.004). Simple slopes analysis revealed no such significant relationship between BOLD response to viewing negative emotional images and affective reactivity among YMP (*t* = 1.13, *p* = 0.29).

#### **Table 4 | Stroop-BOLD response.**

**FIGURE 3 | fMRI contrast of the Group × Distractor Valence interaction on Stroop-BOLD response.** Among Yoga Meditation Practitioners (YMP), Stroop-BOLD was greater during negative, as compared to neutral

## **DISCUSSION**

The present study represents one of the first attempts to discriminate YMP from meditation-naïve subjects on the basis of the neural substrates of negative emotional reactivity and emotion-cognition interactions. Though the study failed to identify any significant task-related behavioral findings, it did identify a number of significant task-related neural differences between groups—suggesting within the context of this study that the groups differed from one another, not on *how well* they performed the task, but rather on *how* they performed the task. The study yielded three main findings. First, YMP were less reactive in right dlPFC (i.e., MFG) during viewing negative emotional images. Secondly, during a cognitively demanding task, the presence of emotionally irrelevant distractor images resulted in greater vlPFC (i.e., IFG) activation in YMP. Thirdly, among meditators amygdala activation to negative emotional distracters was uncoupled with task-related changes in affect, unlike nonmeditators whose decreases in positive affect were correlated with increased amygdala activation. Taken together, these data suggest that YMP may selectively recruit dissociable frontal executivedependent strategies in response to emotionally salient information as a function of cognitive demands and not during emotional processing *per se*. Furthermore, bottom-up driven emotional responding among YMP does not have downstream effects on later mood state, dissimilar to that observed among controls.

Stroop-BOLD was greater during negative, as compared to neutral distractor trials in left Inferior Frontal Gyrus (IFG) [*x* = −38, *y* = 40, *z* = −2], whereas the opposite pattern was observed among the Control Group (CG).

**FIGURE 4 | Plot of interaction between meditation experience and BOLD response in the amygdala to viewing negative emotional images on change in positive affect during participation in the Affective Stroop taskab.**

## **SELECTIVE RECRUITMENT OF FRONTAL EXECUTIVE MECHANISMS** *Emotional information processing*

In the present study, YMP exhibited less activation in right dlPFC (i.e., MFG) in response to all distractors images, whereas controls had heightened activation to negative emotional distractors. The MFG is involved in attention (Cabeza and Nyberg, 2000), cognitive control (Kerns et al., 2004), goal directed processes (Blair et al., 2007) and exerting cognitive control over emotional processes (e.g., emotion regulation) (Ochsner et al., 2002; Ochsner and Gross, 2008). With regard to emotion regulation, the mediation hypothesis posits that executive circuitry (e.g., dlPFC) reduces negative affective response by top-down modulation of affective circuitry (Wager et al., 2008), marked by activation in dlPFC that is inversely related to activation in the amygdala. The current study finding that % signal change in dlPFC was negatively correlated with % signal change in the amygdala during negative, but not neutral, image trials is consistent with the meditation hypothesis model. Moreover, though controls exhibit a reciprocal pattern of executive-limbic BOLD response during viewing negative emotional images, YMP do not. Though the experimental task was not designed to probe *how* subjects processed the emotional images, this pattern of brain activation is consistent with the notion that mindfulness, whether generated via yoga postures or sitting meditation, increases attention toward emotion without active attempts to cognitively restructure affective experience (Hölzel et al., 2011). Recent neuroimaging evidence suggests that mindfulness practitioners evidence decreased fronto-executive activation during processing of emotionally aversive experiences (e.g., pain) (Gard et al., 2011). Such attention to emotional information without cognitive control may reflect the attitude of acceptance and nonjudgment that is held to be an essential component of yoga and mindfulness.

## *Emotion-cognition interactions*

With regard to the effects of negative emotion processing on cognition, YMP had greater Stroop-Bold response in left vlPFC (i.e., IFG) when negative, as compared to neutral, emotional distractors were presented; whereas controls exhibited the opposite pattern. The vlPFC (i.e., IFG) is part of a network involved in inhibitory control (Aron and Poldrack, 2006; Aron et al., 2007), social emotional processes (Carr et al., 2003), and cognitive control over emotional distraction (Dolcos and McCarthy, 2006). Plausibly, the observed pattern of brain activation may indicate that YMP selectively recruited neurocognitive resources to disengage from negative emotional information processing and engage the cognitive demands presented by the Stroop task. Increased activity in the vlPFC may prevent working memory functions from becoming disturbed by incoming sensory input stemming from negative emotional stimuli by deactivating emotional information processing signals ascending from subcortical routes to the amygdala (Austin, 2009). In contrast, CG participants marshaled comparatively fewer frontal-executive resources to resolve emotional interference in the face of this demanding task.

In conjunction, these findings suggest a brain model associated with YM practice whereby frontal executive-dependent strategies to reduce emotional processing are selectively implemented as a function of whether competing cognitive demands are presented. In other words, in the absence of concurrent task performance, YMP appear to process emotional information without effortful cognitive control; however, when emotional experience occurs within the context of a demanding task situation, YMP may resolve emotional interference via recruitment of regions of cortex that subserve cognitive control. Plausibly, this strategy would ensure neurocognitive resource efficiency and confer significant behavioral advantages, such as the psychological benefits observed in clinical and non-clinical samples (Chiesa and Serretti, 2010).

## **IMPACT OF LIMBIC RESPONSE ON SUBSEQUENT AFFECT**

Exploratory analyses revealed that Affective Stroop performance was associated with degradation of positive affect over time and non-significant effects on negative affect. This decrease in positive state affect was likely the result of exposure to aversive images coupled with engagement in a cognitively demanding task. These findings are consistent with prior literature on emotioncognition interactions (Holdwick and Wingenfeld, 1999; Calkins et al., 2011). For example, Calkins et al. (2011) found that individuals participating in a cognitive control task experienced a significant decrease in positive affect after a negative mood induction, but reported a trend toward a lower induction of negative mood following mood induction. Hence, cognitive control tasks may operate as a buffer from subsequent negative mood induction, by virtue of the fact that they engage regions of prefrontal cortex involved in downregulation of negative emotion. Such prefrontal control could protect individuals from negative emotional reactivity in the context of a cognitive and affective challenge. Alternatively, lack of significant task-related changes in negative affect may stem from low statistical power driven by the modest sample size in this study.

During viewing negative emotional images, CG participants exhibited a stereotypic limbic-mediated affective response, such that increased activation in amygdala to negative emotional distractors predicted greater decay of positive affect over the task session. In contradistinction, YMP amygdala responses during viewing negative emotional images were uncoupled with changes in positive affect. Conceptually, this finding complements the lack of dlPFC activation observed among YMP during exposure to negative emotional distractors. If YMP can process emotional information without effortful cognitive control through mindful awareness and acceptance of experience, they may avoid the negative consequences of response-focused forms of emotion regulation like suppression (Wenzlaff and Wegner, 2000; Gross, 2002; Campbell-Sills et al., 2006), which has been shown to deplete neurocognitive resources during affective cue-exposure (Garland and Roberts-Lewis, 2012).

## **CONCLUSION AND LIMITATIONS**

The present study included a well-controlled, matched sample of YMP and YM naive subjects and a neuroimaging paradigm that allows for modeling of the interactive effects of emotion on cognition—an area of research currently underrepresented in the literature.

However, limitations included a relatively small sample size and the use of a cognitive paradigm with sufficient task difficulty to subjects which may have attenuated the ability to detect behavioral Stroop effects. The small sample may have limited the reliability of study findings on the observed impact of limbic responses on subsequent affect, and thus this analysis should be replicated in studies with larger sample sizes. Moreover, the cross-sectional design of the current study cannot elucidate whether the observed group differences in neurocognitive function reflect trait-level factors linked with the initiation and maintenance of long-term YM practice, or whether these differences are the result of recurrent yoga practice over time. However, if these findings reflect differences that are a result of recurrent practice over time, they suggest that yoga mediation practice may provide putative therapeutic benefits for individuals with dysregulated affect and/or cognitive control deficits. One such example may be individuals with a substance abuse disorder. For example, the extant research on the neurobiology of substance abuse disorders posits that chronic drug use is associated with dysregulated prefrontal-dependent cognitive control function, which may play a key role in negative affect and inhibitory control (Koob and

# **REFERENCES**

Volkow, 2010). To test these hypotheses, large-scale longitudinal studies are needed to follow individuals as they initiate the discipline of YM practice and cultivate progressively deeper states of mindfulness over time.

# **ACKNOWLEDGMENTS**

We thank Natalie Goutkin and Luke Pool for their assistance with data acquisition and Allen Song and the Brain Imaging and Analysis Center (BIAC) for funding the fMRI data acquisition for the Yoga Meditation practitioners. We also thank F. Joseph McClernon for providing the laboratory space where the study was conducted. This research was supported by NIDA grant DA026536Z awarded to Brett E. Froeliger. Preparation of this manuscript was supported by NIDA grant DA032517 awarded to Eric L. Garland.

neural systems for imitation to limbic areas. *Proc. Natl. Acad. Sci. U.S.A.* 100, 5497–5502.

through mindfulness is associated with decreased cognitive control and increased sensory processing in the brain. *Cereb. Cortex* (in press).

consequences. *Psychophysiology* 39, 281–291.

depression. *Indian J. Physiol. Pharmacol.* 50, 375–383.

creation of MAsks for Region of INterest Analyses [*abstract*]," in *Ninth International Conference on Functional Mapping of the Human Brain* (New York, NY).

**Conflict of Interest Statement:** Drs. Froeliger, Garland and McClernon report having research funding from the National Institute on Drug Abuse. Ms. Modlin reports no conflicts of interest.

*Received: 29 February 2012; accepted: 28 June 2012; published online: 26 July 2012. 6*

*Citation: Froeliger BE, Garland EL, Modlin LA and McClernon FJ (2012) Neurocognitive correlates of the effects of yoga meditation practice on emotion and cognition: a pilot study. Front. Integr. Neurosci. 6:48. doi: 10.3389/fnint. 2012.00048*

*Copyright © 2012 Froeliger, Garland, Modlin and McClernon. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# **APPENDIX**

# Reasoning, cognitive control, and moral intuition

# *Richard Patterson1, Jared Rothstein2 and Aron K. Barbey3,4,5,6,7,8\**

#### *Edited by:*

*Lihong Wang, Duke University, USA*

#### *Reviewed by:*

*Antonio Pereira, Federal University of Rio Grande do Norte, Brazil Lihong Wang, Duke University, USA*

#### *\*Correspondence:*

*Aron K. Barbey, Decision Neuroscience Laboratory, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA. e-mail<EMAIL_ADDRESS>http://www.DecisionNeuroscience Lab.org/*

Recent Social Intuitionist work suggests that moral judgments are intuitive (not based on conscious deliberation or any significant chain of inference), and that the reasons we produce to explain or justify our judgments and actions are for the most part *post hoc* rationalizations rather than the actual source of those judgments. This is consistent with work on judgment and explanation in other domains, and it correctly challenges one-sidedly rationalistic accounts. We suggest that in fact reasoning has a great deal of influence on moral judgments and on intuitive judgments in general. This influence is not apparent from study of judgments simply in their immediate context, but it is crucial for the question of how cognition can help us avoid deleterious effects and enhance potentially beneficial effects of affect on judgment, action, and cognition itself. We begin with established work on several reactive strategies for cognitive control of affect (e.g., suppression, reappraisal), then give special attention to more complex sorts of conflict ("extended deliberation") involving multiple interacting factors, both affective and reflective. These situations are especially difficult to study in a controlled way, but we propose some possible experimental approaches. We then review proactive strategies for control, including avoidance of temptation and mindfulness meditation (Froeliger et al., 2012, this issue). We give special attention to the role of slow or "cool" cognitive processes (e.g., deliberation, planning, and executive control) in the inculcation of long-term dispositions, traits, intuitions, skills, or habits. The latter are critical because they in turn give rise to a great many of our fast, intuitive judgments. The reasoning processes involved here are distinct from *post hoc* rationalizations and have a very real impact on countless intuitive judgments in concrete situations. This calls for a substantial enlargement of research on cognitive control, drawing on work in developmental psychology, automatization, educational theory, and other fields.

#### **Keywords: intuition, reasoning, decision neuroscience, affect, neurosciences**

It seems obvious that on occasion people make impulsive judgments or decisions and live to regret it. What to do about this is less obvious, but traditionally common sense and philosophy have agreed that reason offers some hope: "think it over before you decide," "look before you leap." Most people are inclined to think that reasoning, if given half a chance, can help us recognize some impulses or intuitions as foolish and others as sound. We can then give due weight to sound intuitions and use various means to counter the force of foolish ones. But does reasoning actually have this much influence? Recent work by "Social Intuitionists" on decision-making and moral judgment in particular (Haidt, 2001; Greene, 2007) suggests that almost everybody, almost all the time, exercises moral judgment via quick, intuitive (non-deliberative), "hot" (affect laden) processes. The reasons people produce to explain or justify their moral judgments are for the most part *post hoc* rationalizations, and not

the actual basis or cause of those judgments. There are exceptions, but these are scarce in everyday life and as a rule restricted to people specifically trained to think through moral judgments, conflicts or dilemmas. The same holds in many domains: people often cannot give coherent explanations of their judgments or actions at all, yet cling to those judgments nonetheless [see Mills and Keil (2004) for experimental evidence]. The real explanation for their inability to produce a coherent, defensible account is that their judgments are not based on reasoning, but are intuitive and the result of processes not available to conscious reflection. This is the basic Social Intuitionist picture of moral judgment.

Our view is that although there is much truth in this picture, there is also much left out. Above all it underestimates the causal influence of slower, controlled cognitive processes on affect and emotion on at least three fronts: first, reactive strategies for

*1 Philosophy Department, Emory University, Atlanta, GA, USA*

control of impulse, intuition, or emotion; second, proactive measures to avoid unwanted impulses from arising in the first place, to prepare for coping with them if they do arise, or to actively arouse affect that might in turn have enhanced effects on action or cognition; and third, proactive inculcation, (re)shaping, or elimination of the underlying bases of many of our fast, intuitive or "gut" responses. These bases include character traits, habits of perception, expertise of various sorts, dispositions to intense gut reactions such as disgust, and others. In this third area we also review interesting new work on mindfulness meditation and its influence on the downstream effects of emotional processing.

In the larger picture we see these causal roles for reasoning as part of a general theory of cognition/affect interaction that incorporates rather than contests recent work by Social Intuitionists. Our reservations about Social Intuitionism have to do not with the claim that most of our moral judgments are intuitive, but that it takes too narrow a view of the potential role of reasoning in generating or shaping those intuitive judgments, hence in influencing them for better or worse.

# **REACTIVE REGULATION OF AFFECT AND EMOTION STRATEGIES FOR COGNITIVE CONTROL**

There are several experimentally established reactive strategies for cognitive control, including suppression, distraction, distancing (adopting a third-party view of a conflict), and reappraisal of a conflict situation, especially of the option favored by impulse or intuition (see the reviews by Ochsner et al., 2002; Kim and Hamann, 2007; Goldin et al., 2008). We focus here on reappraisal. The object of an intuitive or impulsive response—for example, my next cigarette—may initially appear to me as a source of pleasure ("Satisfying, and they are mild!"). But if I am trying to quit smoking I can consciously reappraise the cigarette as a "coffin nail," or think of smoking as a "smelly, obnoxious habit." Reappraisal is pervasive in advertising, political campaigns (Lakoff and Johnson, 1980) and elsewhere, and often plays a role in moral decision-making. Social Intuitionists would rightly point out that reappraisals frequently amount to rationalizations. At times these rationalizations can be quite self-serving: "If I don't take over Joe's old job (after Joe has been unjustly fired) someone else will; so I might as well do it." In fact I may already intend to take the job. But in light of my self-serving rationalization, taking the job appears not as acquiescence or participation in an injustice, but as something that is for all practical purposes harmless. And my refusing the job now appears not as a protest against injustice but as a pointless sacrifice on my part. I may well even believe that these rationalizations explain and justify my taking the job, when in fact the real explanation is simply that I am an opportunistic cad.

Reappraisal is not limited to internally generated rationalizations, however. As Social Intuitionists point out, (re)appraisal often works by means of one person triggering a gut or intuitive response in someone else. Thus, an anti-abortionist displays a photo of a 6-months-old fetus to a freedom-of-choice advocate: "Abortion until the last trimester allows the killing of a living creature such as this." This may stimulate a new gut reaction and a different intuitive moral judgment. But we suggest a major addition to this characterization of the new judgment as "intuitive." For the strategy of showing such photos, or of erecting billboards graphically depicting the internal ravages of smoking, may itself have been well-researched, long-planned and executed, by epidemiologists, statisticians, social activists, publicists, legislators, and so on. The end result may well be a fast, non-deliberated, affect laden reaction on someone's part—or millions of such responses if a campaign of public education is widely successful. But these automatic, intuitive reactions are to a significant extent the causal effects of previous slow, controlled processes of thought and planning. Thus for the purpose of discovering how reasoning might influence affect, impulse, or intuitive judgment, it is crucial to go beyond the fact that a given response considered in its immediate context is fast and intuitive rather than deliberated. This holds whether or not people are aware of how their own intuitions were shaped.

Recent work on reappraisal indicates that people who use this strategy more frequently also use it more effectively (Cohen et al., 2012). This result raises further questions for investigation, including that of whether people can through practice improve their ability to use this strategy. Interestingly, Kanske (2012, this issue) finds that "temperament trait effortful control" correlates with "enhanced (task-relevant) emotion-induced facilitation of conflict processing," and that this translates to the neural level. Given this, along with Cohen's et al. (2012) suggestive results, research into the plasticity and educability/trainability of temperament trait effortful control could lead to methods of improving this important ability in "normals" and perhaps methods of addressing individual or group deficiencies.