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Keeping the pace: The effect of slow-paced breathing on error monitoring
International Journal of Psychophysiology 146 (2019) 217–224
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International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho
Keeping the pace: The effect of slow-paced breathing on error monitoring a,∗
b
c
Sven Hoffmann , Lea Teresa Jendreizik , Ulrich Ettinger , Sylvain Laborde
T
a,d
a
Institute of Psychology, German Sport University Cologne, Germany Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Germany Department of Psychology, University of Bonn, Germany d Normandie Université, Caen, France b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Error monitoring Cardiac vagal activity Slow-paced breathing Self-control Anterior cingulate cortex Cognitive control
Detecting errors is crucial for adapting one's own actions. Moreover, behavior is often optimized by adapting to maladaptive actions, i.e. errors. In this regard, recent studies and models of error monitoring point to an involvement of emotional states in error monitoring. A psychophysiological correlate of the latter is the error negativity or error-related negativity (Ne/ERN), reflecting partly the functional implementation of anterior cingulate cortex functions. In the present study, we aimed to test whether neurophysiological aspects of error monitoring can be altered by a relaxation technique, i.e. slow-paced breathing. Slow-paced breathing has been shown to increase cardiac vagal activity. According to the neurovisceral integration model, cardiac vagal activity is thought to be a marker of the effectiveness of executive functions. We tested the effect of slow-paced breathing on error monitoring, i.e. the Ne/ERN and behavioral adaptation in a modified flanker task, a cognitive task during which performance depends on executive control. The Ne was increased following slow-paced breathing compared to a passive control condition. Furthermore, behavioral results indicate that response variability decreased in the slow-paced breathing condition whereas overall performance remained constant. We conclude that slow-paced breathing improves the ability to focus on the task at hand. Thus, the error monitoring system is being supported in keeping the pace, i.e. tracking responses.
1. Introduction Monitoring errors is crucial for behavioral adaptation, since their consequences have a deep impact on action monitoring and thus performance. In order to do so, a common error monitoring system has been suggested. The neurophysiological correlate of such a response monitoring system can be observed almost immediately after errors. The error negativity (Ne, Falkenstein et al., 1990), or error-related negativity (ERN, Gehring et al., 1993) is a sharp negative deflection in the response-related EEG, peaking maximally 100 ms after response onset. It is most prominent at fronto-central electrode positions. The origins of the Ne/ERN signal appear to be the anterior cingulate cortex (ACC) and/or the supplementary motor area (Ullsperger and von Cramon, 2004a,b). There are several theories with respect to the functional significance of the Ne/ERN. One influential theory with respect to the functional implementation of this ACC function is the reinforcement learning theory, which assumes that response monitoring is central to the adaptation of actions (Holroyd and Coles, 2002; Nieuwenhuis et al., 2004). The
theory argues that the ACC indexes events that are worse than expected (Holroyd and Coles, 2002). Dopamine (DA) plays a key role in this process, such that mid-brain DA neurons increase their activity after an error has been committed or negative or unexpected feedback has been processed (Nieuwenhuis et al., 2004). This DA-mediated signaling in the prefrontal cortex (PFC) induces the key correlate of error monitoring, the Ne/ERN. Another theory, the conflict theory assumes that the Ne/ERN is a correlate of ACC activation induced by conflict detection (Carter et al., 1998; Botvinick et al., 2001). This conflict detection system becomes active in the presence of competing, i.e. incongruent, simultaneously active information processing entities. A further theory, the affect evaluation hypothesis (Luu and Tucker, 2004) assumes that the ACC is generally involved in representations of adaptive goals and motivational control, both relevant for self-regulation. In recent years, new models of error monitoring and corresponding empirical findings point to an involvement of these systems in specific behavioral adaptation processes and it has been suggested that changes (via noradrenergic modulation) in phasic autonomic excitability (i.e. arousal) should increase or decrease conscious error processing and
∗ Corresponding author at: German Sport University Cologne, Institute of Psychology, Department Performance Psychology, Am Sportpark Muengersdorf 50933 Cologne, Germany E-mail address: s.hoff[email protected] (S. Hoffmann).
https://doi.org/10.1016/j.ijpsycho.2019.10.001 Received 1 September 2017; Received in revised form 13 September 2019; Accepted 2 October 2019 Available online 24 October 2019 0167-8760/ © 2019 Elsevier B.V. All rights reserved.
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attention and arousal (Yackle et al., 2017). Since the Ne/ERN as error monitoring correlate is modulated by dopamine system states, it might serve as an indirect indicator of DA and ACC activity, and also of processes related to the limbic system and PFC functions (c.f. reinforcement learning theory, conflict theory). Indeed, some studies indicated that the afferent fibers of the vagus nerve modulate NE and thus dopamine uptake in the PFC (Colzato et al., 2017; Steenbergen et al., 2015; Beste et al., 2016). It has been shown that response errors are accompanied by a stronger pupil dilation, a direct indicator of NE release (Wessel et al., 2011; Braem et al., 2015; Murphy et al., 2016). A recent study showed that pupil dilation co-varies with the error negativity depending on the type of the committed error (Maier et al., 2019). If cardiac vagal activity was generally related to executive cognitive functions, a modulation of afferent vagal activity could provoke corresponding effects in specific PFC functions. Indeed, Prinsloo et al. (2010) found that a single slow-paced breathing session reduced the Stroop interference effect. Given that slow-paced breathing can increase cardiac vagal activity (Lehrer, 2013), we assumed that error processing may be altered by this technique as well. More specifically, slow-paced breathing should affect error monitoring processes as reflected in the Ne/ERN. Further, we expected an alteration of performance measures as reflected in response times and accuracy, such that performance should be improved following the slow-paced breathing intervention.
post-error adjustments (Wessel, 2017). However, it has not yet been investigated in detail whether and to which degree error monitoring is involved in self-control. Indeed, it is plausible to assume that self-control involves self-monitoring and adaptive control. On a neurophysiological level this points to an involvement of structures related to those functions: PFC, ACC and the limbic system (Nieuwenhuis et al., 2004; Holroyd and Coles, 2002). A recent model with respect to self-control is the neurovisceral integration model (Thayer et al., 2009b). It defines self-control as a physiological regulation mechanism that can be identified non-invasively through heart rate variability (HRV), an indicator of cardiac vagal activity (the activity of the vagus nerve – the main nerve of the parasympathetic nervous system – regulating cardiac functioning). Cardiac vagal activity reflects the output of the central autonomic network, a network linking the PFC to the heart (for a more detailed description of this network, see Thayer et al. (2009a)). This central autonomic network underlies the effectiveness of neurovisceral selfregulation mechanisms via a common control of cardiac and cognitive regulation. This assumption is supported by meta-analyses on neurophysiological evidence, indicating common brain areas putatively related to both cardiac vagal control and affective or cognitive self-regulation (Beissner et al., 2013; Thayer et al., 2012). The neurovisceral integration model would thus assume that the cerebral control of autonomic function conveys comparable control of executive functions (Thayer et al., 2012). Specifically, the neurovisceral integration model would argue that a higher cardiac vagal activity should be linked to the optimal activation of neural networks underlying the effectiveness of the PFC. Based on previous summary work on self-control, we define it here as the result of cognitive and neurovisceral processes that allow people to resist temptations and override impulses (thoughts and/or emotions) (Inzlicht et al., 2014; Kotabe and Hofmann, 2015). Cardiac vagal activity can be modulated by slow-paced breathing (Lehrer, 2013). Breathing couples the effects of the relationship between heart rate and breathing at a certain pace (6 cycles/min), and between heart rate and blood pressure oscillations at specific frequencies. Both combined to the activity of the baroreflex trigger the resonance properties of the cardiovascular system (Lehrer and Gevirtz, 2014), which results in increased oxygen saturation and increased vagal afferences (Lehrer, 2013; Lehrer and Gevirtz, 2014). The effects of slowpaced breathing on cardiac vagal activity can be strengthened by manipulating the inhalation/exhalation ratio. A prolonged exhalation contributes to larger beat-to-beat heart fluctuations as compared to prolonged inhalation, and hence induces higher cardiac vagal activity (Strauss-Blasche et al., 2000). Indeed, breathing techniques are well known for their therapeutic effects (Brown and Gerbarg, 2009; Nardi et al., 2009) and recent findings indicate that breathing center neurons in the locus coeruleus modulate arousal (Yackle et al., 2017). To the best of our knowledge, so far only one study has investigated the effects of slow-paced breathing on executive cognitive control. Findings showed that a single 10 min session of slow-paced breathing at 6 cycles/ min increased performance, that is, reduced the interference effect in a Stroop task right after the slow-paced breathing session (Prinsloo et al., 2010). But what is the link between vagal activity and cortical function? The vagus nerve likely influences cortical functions via its connection to the locus coeruleus, which constitutes the core structure for norepinephrine (NE) supply (Aston-Jones et al., 1991). NE plays an important role in PFC functions, as NE catabolism via catechol-O-methyltransferase (COMT) is important for dopamine inactivation in the PFC (Bymaster, 2002). Recent research has shown that post-error slowing (Rabbitt, 1966), a strategic component of response and error monitoring, is a direct result of NE modulation (Ullsperger et al., 2010). NE has been hypothesized to be involved in higher order cognition (AstonJones and Cohen, 2005) such that PFC activity is affected by locus coeruleus activity (Joshi et al., 2016; Gilzenrat et al., 2010). Also, breathing center neurons in the locus coeruleus have an impact on
2. Methods 2.1. Participants A sample of 41 healthy subjects (27 female) with a mean age of 23.7 (sd = 3.28; range: 18–30) years participated voluntarily in the study. They gave written informed consent prior to participation. The study was conducted according to the code of the World Medical Association and was approved by the ethics committee of the German Sport University. Data of two participants had to be rejected due to EEG data quality issues, i.e. bad electrodes and too many ocular, movement and muscular artifacts. 2.2. Experimental design 2.2.1. Basic design The experiment consisted of a fully balanced factorial within-subjects design and comprised two phases. In the first phase, after having been seated in an ergonomic chair in front of an LED screen (distance between participants' eyes and screen ≈120 cm), the paced-breathing training phase started (duration = 15 min, cf. Fig. 1). During this phase, participants learned the slow-paced breathing technique. The slowpaced breathing technique was realized with a video showing a little ball moving up and down at the rate of 6 cpm (4.5 s inhalation/5.5 s exhalation), the participants having to inhale continuously through the nose while the ball was going up, and exhale continuously with pursed lips when the ball was going down. The video was realized using the software EZ-AIR PLUS (Thought Technology Ltd., Montreal, Canada), which has been used in previous research (Laborde et al., 2016). The training consisted of asking participants to get used to slowpaced breathing in decreasing progressively their breathing frequency following a pacer. More specifically, they had to breathe for 2 min respectively at 10 cpm, 8 cpm, and 6 cpm (each time with a 45% inhalation/55% exhalation ratio), the experimenter making sure that the participants were inhaling continuously through the nose and exhaling continuously through the mouth with pursed lips, providing feedback if needed. After application of the EEG electrodes, the second phase started: each participant started with a first 5 min baseline, eyes closed, to measure the HRV resting state (Laborde et al., 2017). Then they performed either the control condition (watching a documentary, duration = 15 min) or the experimental condition (slow paced 218
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Fig. 1. Basic study design; HRV = Heart Rate Variability, BR = Breathing Rate.
Reaction times (RTs) were analyzed by means of a repeated measures ANOVA with the within-subject factors flanker condition (neutral, congruent, incongruent), intervention (slow paced breathing, control) and accuracy (error, correct), with RTs faster than 100 and slower than 1000 ms being excluded from the analysis. Additionally, we investigated the RT distribution via fitting of exGaussians, since the shape of response time distributions might be different between interventions. This, in turn, might mask mean differences (Spieler et al., 1996). Briefly, the ex-Gaussian distribution is a convolution of a Gaussian and an exponential distribution, and it has three parameters: μ,σ, and τ. The latter reflects both the mean and standard deviation of the exponential portion, whereas μ and σ represent the mean and standard deviation of the Gaussian portion, respectively. Ex-Gaussian analyses allow differences between conditions to be separated into distributional shifting, reflected in μ and distributional skewing, reflected in τ. This approach is more sensitive to group differences in RT distribution than the classical approach with Gaussian parameters only. In the present study ex-Gaussian parameters were estimated by minimization of the negative log-likelihood function. For calculation of the corresponding parameters, we used the retimes package (Massidda, 2013) in GNU R (R Core Team, 2016). We report mean RTs for correct responses in the figure, mean error rates, and ex-Gaussian parameters and corresponding F-values, p-values (in case of df > 1, i.e. possible violation of sphericity assumption, Greenhouse-Geisser corrected p-values are reported), and partial eta squared (ηp2 ). For all post-hoc t-tests, p-values were FDR-adjusted according to the method of Yekutieli and Benjamini (2001). Cohen's d is reported for effect sizes of t-tests (Cohen, 1988) and we report Bayes factors (BF) for all post-hoc t-tests using JASP (JASP Team, 2018; Marsman and Wagenmakers, 2016). All other RT parameters and statistics were calculated using R (R Core Team, 2016). We report all values rounded to the second decimal place.
breathing, duration = 15 min). The order of conditions was fully counter-balanced across participants such that it was a balanced repeated-measures design. After each experimental condition (slow-paced breathing or control), participants underwent a 5 min HRV resting measurement to be able to check for the effects of the experimental condition on respiration and cardiac vagal activity, and then participants conducted a modified flanker task (Kopp et al., 1996a). Fig. 1 summarizes the basic procedure of the study. 2.2.2. Experiment The flanker task is a measure of cognitive control that has been shown to possess excellent reliability characteristics (Wöstmann et al., 2013). Participants were instructed to respond to an arrowhead occurring in the center of the screen. All stimuli were presented in dark gray on a light gray background. The direction of the target, i.e. arrowhead (left/right, duration = 100 ms) indicated the button that had to be pressed. It was accompanied by two distracting geometric figures (=flankers) below and above the target which appeared 100 ms prior to target-occurrence, which is known to induce maximal distraction (Wascher et al., 1999). In the present experiment, these flankers could be congruent arrow-heads (pointing to the same direction), incongruent arrow-heads (pointing to the opposite direction), or neutral flankers (squares). The probability for congruent flanker was 50% and for incongruent and neutral flankers 25%, respectively. Each flanker task consisted of 320 trials. 2.2.3. Physiological measurements The EEG (amplifier and EEG system: eego by ANT, Hengelo, Netherlands) was recorded monopolarly and continuously during the whole experimental procedure. The EEG was recorded from 64 standard electrode positions according to the 10-20-System. The horizontal and vertical EOG was concomitantly recorded via bipolar channels. The electrocardiogram (ECG), from which heart rate variability parameters were extracted to infer cardiac vagal activity, was also measured concomitantly using bipolar channels (+, −, ground) via the ANT amplifier. The electrodes were placed at the right clavicle lateral to the midclavicular line (−) and the left anterior axillary line (+). EEG, ECG, and EOG were sampled at 1000 Hz. Following the flanker task, participants underwent a 5 min HRV resting measurement to investigate cardiac vagal activity recovery (Laborde et al., 2017).
2.3.2. EEG data With respect to the analysis of EEG data, following import of the raw EEG data in to EEGLAB (Delorme and Makeig, 2004) data were initially filtered with a 0.5 Hz high pass FIR filter, followed by removal of line noise utilizing the CLEANLINE EEGLAB-plugin (Bigdely-Shamlo et al., 2015; Mullen, 2012). Then, data were filtered with a 40 Hz low pass FIR. Subsequently, the data for each participant were segmented into 1000 ms epochs yielding a temporal data set to which an automated artifact rejection procedure (Delorme et al., 2007) was applied, followed by the ICA AMICA algorithm (Palmer et al., 2007). The automated artifact rejection procedure basically calculates the empirical distribution of all data points across all trials and time points and rejects statistical outliers, i.e. trials consisting of data points exceeding a criterion of 3 standard deviations. The amount of maximal rejected trials was fixed to 5%. The derived ICA-weights of these so pruned data were submitted to the previous continuous data set, and independent components representing ocular artifacts were removed by projecting back the mixing matrix with artifact components set to zero (Jung et al., 2001). The cleaned data sets were segmented relative to response onset and linear trends removed. Subsequently, data were submitted to the before
2.3. Data analysis 2.3.1. Behavioral data Error rates were compared by initially transforming the percentage by reflecting and log-transforming the data, since a Shapiro Wilks test indicated that the accuracy data were not normally distributed, skewness = −1.3866, W = 0.866, p = 1.887e−13. The transformation Accuracytransformed = log(100 − rate + 1) is an appropriate transformation for heavily left skewed data (Tabachnick and Fidell, 2013). These transformed accuracy data were analyzed by a repeated-measures ANOVA with the within-subject factors flanker condition (neutral, congruent, incongruent) and intervention (slow paced breathing, control). 219
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Fig. 2. Ex-Gaussians of reaction times for slow paced breathing (SPB) and control condition (TV). Bars reflect within subject confidence intervals according to Cousineau (2005, 2012). Inco = incongruent, Co = congruent trials.
3. Results
mentioned automated artifact procedure and transformed to current source density (CSD) since data can be evaluated reference free (Nunez et al., 1997; Perrin et al., 1989) and the CSD transformation is comparable to a spatial filter that removes the effect of remote sources on local recordings (Vidal et al., 2003). For estimation the order of the splines was set to 4, the maximum degree of Legendre polynomials was set to n = 10 and the precision was set to 2.72E−7. Following a final automatic artifact correction procedure (removal of segments containing artifacts exceeding 200 μ V in each EEG channel), the data were averaged for both response types (errors, correct). The Ne/ERN was quantified as a mean voltage (−40 to 100 ms relative to button press) at Fz (cf. Fig. 3). The statistical analysis of experimental condition (slow-paced breathing, SPB vs. documentary watching, TV) effects on this mean Ne/ ERN amplitude consisted of t-tests. For t-tests, p-values were FDR-adjusted according to the method of Yekutieli and Benjamini (2001). We report t-, p-values as well as Cohen's d (Cohen, 1988) and we report Bayes factors (BF) for all post-hoc t-tests using JASP (JASP Team, 2018; Marsman and Wagenmakers, 2016). All other statistical analyses of EEG data were conducted using GNU R (R Core Team, 2016). We report all values rounded to the second decimal place.
3.1. Behavioral data With respect to reaction times (in ms) of correct responses, there was a significant effect of condition (congruent, incongruent, neutral), F (2,76) = 157.90, p = 5.785e−17, ηp2 = 0.81, indicating that responses were slower with respect to incongruent stimuli (m = 326.15, sd = 75.31) compared to neutral (m = 316.95, sd = 45.07) and congruent stimuli (m = 320.33, sd = 33.53). Since the neutral and congruent stimuli provoked too few errors, we compared only the incongruent condition with respect to the intervention effect. Erroneous responses were given faster than correct ones, F(1,38) = 611.23, p = 5.034e−25, ηp2 = 0.94 . With respect to accuracy (in %), there was a significant effect of condition (congruent, incongruent, neutral) as well, F(2,76) = 149.50, p = 9.823e−26, ηp2 = 0.80 , indicating that accuracy was lower for the incongruent condition (m = 66.65, sd = 11.56) compared to the congruent (m = 86.32, sd = 6.99) and neutral condition (m = 91.04, sd = 6.73). Regarding the ex-Gaussian parameters, there was a significant effect of congruency on the μ-parameter, F(2,76) = 122.27, p < .001, ηp2 = 0.76. Further, there was a significant congruency effect on the σ-parameter, F(2,76) = 4.57, p = .01, ηp2 = 0.11. Finally, there was a significant congruency effect on the τ-parameter, F(2,76) = 3.66, p = .03, ηp2 = 0.09, and an effect nearby significance threshold of intervention,
2.3.3. Cardiac vagal activity For analysis i.e. estimation of cardiac vagal activity, data were analyzed with the Kubios software (Tarvainen et al., 2014). The ECG signal was visually inspected for artifacts, which were manually corrected. We estimated the high-frequency HRV (HF-HRV) as an indicator to infer cardiac vagal activity (Malik et al., 1996; Laborde et al., 2017) and the breathing rate with the ECG derived respiration algorithm (Tarvainen et al., 2014). Given HF-HRV is supposed to reflect cardiac vagal activity only when the breathing rate is between 9 and 24 cycles per minute, which corresponds to the interval between 0.15 and 0.40 Hz (Malik et al., 1996; Laborde et al., 2017), we could not infer cardiac vagal activity during the flanker task, given 21 participants out of 38 had a breathing rate higher than 24 cycles per minute. We logtransformed (log10) the HF-HRV and respiration data, given they were not normally distributed (Laborde et al., 2017). We report all values rounded to the second decimal place.
F(2,76) = 4.01, p = .05, ηp2 = 0.10 , and interaction of intervention and congruency, F(2,76) = 2.69, p = .07, ηp2 = 0.07. Post-hoc t-tests(FDRadjusted for multiple comparisons) revealed that this interaction was driven by a significant decrease in the τ-parameter for slow paced breathing compared to control in the incongruent condition (cf. Fig. 2), t(38) = 3.15, p = .016, d = 0.51, BF10 = 11.19. 3.2. EEG data In the SPB condition, the Ne/ERN was more pronounced for erroneous responses (−36.21 μ V2/cm) compared to correct responses (−21.06 μ V2/cm), t(38) = 7.73, p = 1.091 e−08, d = 1.24, BF10 = 9.124e +6. Also, in the TV condition it was more pronounced in erroneous (−32.05 μ V2/cm) compared to correct responses (−19.86 μ V2/cm), t(38) = 7.86, p = 1.091 e−08, d = 1.26, BF10 = 1.355e +7. The difference between correct responses of the TV and SPB condition was not significant, t(38) = 1.66, p = .22, d = 0.26, BF10 = 0.07. 220
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Table 2 Descriptives of breathing rate (cycles per minute) during the flanker task if subjects started with control, i.e. TV (=A) or started with SPB/slow-paced breathing (=B). Intervention
Order
Mean
SD
N
SPB
A B A B
24.02 26.28 26.11 22.67
8.013 16.179 8.157 11.219
20 19 20 19
TV
interaction effect regarding behavioral data and error negativity. However, there was a significant interaction of order and intervention during the flanker task with respect to breathing rate, F(1,37) = 8.70, p = .005, ηp2 = .19 indicating that the breathing rate increased more in the second block if subjects have started with slow-paced breathing (cf. Table 2).
Fig. 3. The error negativity and correct response negativity for slow-paced breathing (PB) and control condition (TV). The topographic maps refer to the Ne/ERN and CRN peak. Note that the EEG data were CSD transformed, thus, the scaling of the y-axis refers to μV/cm2. x-axis = time, time point zero = button press; y-axis = CSD-transformed activity.
4. Summary and discussion The aim of this study was to investigate the effect of a relaxation technique, slow-paced breathing, on performance in a flanker task, a task involving core executive functions. In the present study we replicated the well known interference effect in the flanker task, that is, an increase of RTs in incongruent compared to neutral and congruent flanker-target combinations (Hoffmann and Falkenstein, 2010; Willemssen et al., 2004; Kopp et al., 1996b; Eriksen and Eriksen, 1974). Further, this increase was accompanied by an increase of error rates in the incongruent condition. This flanker effect was also reflected in the ex-Gaussian parameters. With respect to the hypothesized influence of slow-paced breathing on behavioral parameters, not completely in line with the findings of Prinsloo et al. (2010) who used a modified Stroop task, we did not find any statistically significant effect besides a decreased τ-parameter in the incongruent condition after slow paced breathing. With respect to the hypothesized increase of the Ne/ERN, we found an increase of the error negativity after the slow-paced breathing intervention. The results of the present study are at odds with a recent study that showed decreased error evaluation/awareness as reflected in a decreased Pe during meditation (Larson et al., 2013). Also, we could not replicate their behavioral results. In their study, Larson et al. (2013) did not find any significant behavioral effects besides the effect on the Pe. One reason might be that in their study they focused on mean values and not on measures of variability. Also, it has to be considered, that although paced breathing and meditation share some aspects, a guided meditation like in the Larson et al. study is not the same as paced breathing. Meditation is rather an exercise in awareness and attention than respiration like in paced breathing. Thus, it might be that the current study manipulated different aspects of error processing. Given that those kinds of interventions do have an effect on heart rate variability, this implicates that one should take into account measures of variability, since means can mask group differences. Another reason might be that although meditation might have already some subtle effect on error monitoring, it might be that the intervention effect varied strongly across participants. The latter could be
Finally, the Ne/ERN was more pronounced in the slow-paced breathing (−36.21 μ V2/cm) compared to the control condition (−32.05 μ V2/ cm), t(38) = 2.26, p = .08, d = 0.36, BF10 = 4.48, cf. Fig. 3.
3.3. Cardiac vagal activity Regarding HF-HRV, a repeated-measures ANOVA with the factors condition (slow paced breathing vs. control) and time (before intervention, after intervention, and after flanker task) and HF-HRV as dependent variable was ran. No main effect of condition was found F (1,38) = 1.19, p = .28, ηp2 = .03, nor a significant main effect of time, F (2,37) = 2.17, p = .128, ηp2 = .10 . No significant interaction effect of condition and time was found, F(2,37) = 0.51, p = .60, ηp2 = .03. Regarding breathing rate, a repeated-measures ANOVA with the factors condition (slow-paced breathing vs. control) and time (before the intervention, after the intervention, during flanker task, after flanker task) and breathing rate as the dependent variable was run. No main effect of condition was found F(1,38) = 0.26, p = .61, ηp 2 = .01. A significant main effect of time effect was found, F(3,36) = 44.29, p < .001, ηp2 = .79. Given our focus was on the interaction of condition and time, we do not further elaborate on the main effect of time. A significant interaction effect of condition and time was found, F (3,36) = 6.83, p = .001, ηp2 = .36. Further, post-hoc tests revealed a significant difference in respiration rate following the intervention, t (38) = 3.28, p = .002, BF = 15.25, with a more slowly breathing rate following the intervention compared to the breathing rate following the TV documentary. The other time points revealed no significant differences in breathing rates (all p > .05). Tables 1 and 2 summarize these results. To test for order effects, we ran the same analyses again with order (starting with control vs. starting with slow-paced breathing) as a between-subjects factor. We found no significant main effects of order, nor
Table 1 Descriptive statistics (means, standard deviation in brackets) for breathing rate (BR) and high-frequency heart rate variability (HF-HRV). During the Flanker task, for slow-paced breathing (SPB) and control condition (TV), breathing rate was higher than 24 cpm, and therefore high-frequency heart rate variability did not reflect cardiac vagal activity, which is why we don’t report it in this table. T1 = baseline, T2 = after intervention, DF = during flanker task, AF = after flanker task. Dependent variable
Condition
T1
T2
DF
AF
BR
SPB TV SPB TV
13.95 (3) 13.78 (2.93) 1996.84 (2457.44) 1667.21 (1705.31)
12.21 (2.52) 13.84 (2.84) 1669.96 (2417.19) 1621.85 (1524.91)
25.12 (12.38) 24.43 (9.67)
14.17 (2.71) 13.38 (232) 1810.04 (1821.03) 1885.69 (1939.94)
HF-HRV
221
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due to the fact that “meditation performance” can hardly be controlled. Thus, a more controllable intervention might be more adequate. Slowpaced breathing fits this standardization requirement, given the breathing pace can be set and controlled across participants. Thus, it is possible to check post-hoc whether participants did follow the 6 cycles/ min breathing pacer, which they did in the current experiment. There are essentially three interpretations of the observed pattern of an increased Ne/ERN and a decreased τ-parameter. First, it could be that since the Ne is related to the response onset and more slowly responses provoke a smaller Ne, the Ne became larger in the slow-paced breathing condition since there were fewer extreme values, i.e. the distribution of RTs was not as strongly right-skewed as in the control condition. However, against this speaks the finding that it is rather uncommon that the Ne/ERN increases during slower response times, i.e. if participants conduct a more difficult task (Hoffmann and Falkenstein, 2011; Maier and Steinhauser, 2016), especially, if accuracy is not stipulated in the instruction and thus os constant across conditions (Hoffmann and Falkenstein, 2011). Second, it could be that the Ne/ERN was in general increased. However, irrespective of whether this amplitude effect was due to the first or second reason, the decreased τparameter indicates a selective and higher precision in the given responses of incongruent trials indicating an effect of slow-paced breathing on conflict processing (see Yeung et al., 2004). A closer look at the τ-parameter reveals that for the control conditions the results show the typical pattern: an increased τ-parameter in the incongruent condition (Spieler et al., 2000). Striking is the strong decrease in this parameter after slow-paced breathing. One could interpret this finding in the light of resolved conflict. Typically, a higher precision (i.e. accuracy) in such tasks is accompanied by an increased Ne/ERN (Gehring et al., 1993). Finally, it might be that the breathing technique induced a more cautious strategy with respect to incongruent trials. This might have led to an increase of the Ne/ERN. Indeed other studies have shown that the Ne/ERN is modulated by motivational state, i.e. it might reflect “ individuals' concern with the outcome of events” (Santesso and Segalowitz, 2009). Though it remains unclear how exactly the effect emerges, slow-paced breathing seems to support a more even response pace. The decrease in respiration rate after the slow-paced breathing intervention in comparison to the control condition showed that the manipulation was effective in reducing respiration rate, however, no differences emerged in terms of cardiac vagal activity. Regarding respiratory rate, our findings are in line with Prinsloo et al. (2010), who also found a reduction in breathing rate after the slow-paced breathing intervention, and no differences between treatments during the cognitive task (i.e., modified Stroop). Regarding cardiac vagal activity, our results are not directly comparable to those of Prinsloo et al. (2010), given they only mention HFHRV results during the intervention, where cardiac vagal activity cannot be properly inferred with HF-HRV given the breathing rate is lower than 9 cycles per minute. Wells et al. (2012) did show an increase of HF-HRV directly after a slow-paced breathing intervention, but the intervention in their study lasted 30 min, whereas in our study it lasted only 15 min. Thus, there appear to be two competing explanations as to why we did not observe any changes in cardiac vagal activity: either our intervention was too short, in comparison to the one realized by Wells et al. (2012); or it induced stress in our participants. Actually, even if learning the slow-paced breathing is relatively easy, and can be implemented in an acute basis, we have to keep in mind that decreasing our breathing rate to 6 cpm(instead of 12 to 20 usually in healthy adults (Sherwood, 2006) may represent a stress for the organism, and some subjects may adapt faster than others, while some would need more than one session to feel comfortable with the method (Lehrer et al., 2000). Thus, in further research breathing rate should be tested e.g. with gas exchange analysis. Another reason might be that a resonance frequency specific to each individual may exist and needs to be determined before starting the study and participants need to train to get
used to slow-paced breathing more often (Lehrer et al., 2000). Additionally, the effect of vagal activity on cognitive control might be very specific. In a recent study by Steenbergen et al. (2015) the authors showed a tVNS mediated effect on performance in a stopchange paradigm, i.e. on response selection during action cascading. Thus, this specific effect might not generalize to the used flanker herein: one could argue that this effect is rather unspecific, i.e. modulation in arousal (Yackle et al., 2017), such that a higher arousal leads to a more focused attention in the flanker task. In sum, it may be concluded that because paced breathing does not show a direct effect on the analyzed HRV parameters, but on the Ne/ ERN, that the EEG is more sensitive to the experimental manipulation. This modulation on the functional level is not directly modulated by HRV but by respiration rate: There was a relationship between respiratory rate after the intervention (and not during the task itself) and Ne/ERN during the task. This could be linked to the fact that “respiration, via multiple sensory pathways, provides a subtle but continuous rhythmic modulation of cortical neuronal activity that modulates sensory, motor, emotional and cognitive processes” (Heck et al., 2017). This quotes makes clear, that the underlying neurophysiological mechanisms are not yet specified in detail. In this regard, a recent study showed that volitional breathing recruits frontal networks (Herrero et al., 2018). If respiration influences Ne/ERN regardless of any impact on cardiac vagal activity, this may suggest the operation of other mechanisms. For example, it was found that inspiratory load was more taxing in terms of cortical resources and provoked a decrease in cognitive performance (Nierat et al., 2016). Therefore, it may be speculated that a higher respiratory rate is also more taxing for cortical resources. Finally, slow paced breathing induces oscillatory activity in the brain, and those high amplitude oscillations are suggested to enhance functional connectivity in brain networks associated with emotional regulation, especially in medial pre-frontal regulatory regions (Mather and Thayer, 2018; Thayer et al., 2012). However, future research should thus consider the influence of respiratory rate on cortical resources, i.e. investigate the specific circumstances under which heartbeat and respiratory rhythm synchronize (Vaschillo et al., 2004). In this regard, gas exchanges should be measured in order to asses the adequacy of the breathing frequency with respect to the participant capabilities and characteristics (Giardino et al., 2003; Yasuma and Hayano, 2004). Acknowledgments We thank the Performance Psychology group for providing invaluable feedback, support and an inspiring environment. In particular, we would like to thank Alice Heinrich for technical support in running the experiments. References
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Keeping the pace: The effect of slow-paced breathing on error monitoring a,∗
b
c
Sven Hoffmann , Lea Teresa Jendreizik , Ulrich Ettinger , Sylvain Laborde
T
a,d
a
Institute of Psychology, German Sport University Cologne, Germany Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Germany Department of Psychology, University of Bonn, Germany d Normandie Université, Caen, France b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Error monitoring Cardiac vagal activity Slow-paced breathing Self-control Anterior cingulate cortex Cognitive control
Detecting errors is crucial for adapting one's own actions. Moreover, behavior is often optimized by adapting to maladaptive actions, i.e. errors. In this regard, recent studies and models of error monitoring point to an involvement of emotional states in error monitoring. A psychophysiological correlate of the latter is the error negativity or error-related negativity (Ne/ERN), reflecting partly the functional implementation of anterior cingulate cortex functions. In the present study, we aimed to test whether neurophysiological aspects of error monitoring can be altered by a relaxation technique, i.e. slow-paced breathing. Slow-paced breathing has been shown to increase cardiac vagal activity. According to the neurovisceral integration model, cardiac vagal activity is thought to be a marker of the effectiveness of executive functions. We tested the effect of slow-paced breathing on error monitoring, i.e. the Ne/ERN and behavioral adaptation in a modified flanker task, a cognitive task during which performance depends on executive control. The Ne was increased following slow-paced breathing compared to a passive control condition. Furthermore, behavioral results indicate that response variability decreased in the slow-paced breathing condition whereas overall performance remained constant. We conclude that slow-paced breathing improves the ability to focus on the task at hand. Thus, the error monitoring system is being supported in keeping the pace, i.e. tracking responses.
1. Introduction Monitoring errors is crucial for behavioral adaptation, since their consequences have a deep impact on action monitoring and thus performance. In order to do so, a common error monitoring system has been suggested. The neurophysiological correlate of such a response monitoring system can be observed almost immediately after errors. The error negativity (Ne, Falkenstein et al., 1990), or error-related negativity (ERN, Gehring et al., 1993) is a sharp negative deflection in the response-related EEG, peaking maximally 100 ms after response onset. It is most prominent at fronto-central electrode positions. The origins of the Ne/ERN signal appear to be the anterior cingulate cortex (ACC) and/or the supplementary motor area (Ullsperger and von Cramon, 2004a,b). There are several theories with respect to the functional significance of the Ne/ERN. One influential theory with respect to the functional implementation of this ACC function is the reinforcement learning theory, which assumes that response monitoring is central to the adaptation of actions (Holroyd and Coles, 2002; Nieuwenhuis et al., 2004). The
theory argues that the ACC indexes events that are worse than expected (Holroyd and Coles, 2002). Dopamine (DA) plays a key role in this process, such that mid-brain DA neurons increase their activity after an error has been committed or negative or unexpected feedback has been processed (Nieuwenhuis et al., 2004). This DA-mediated signaling in the prefrontal cortex (PFC) induces the key correlate of error monitoring, the Ne/ERN. Another theory, the conflict theory assumes that the Ne/ERN is a correlate of ACC activation induced by conflict detection (Carter et al., 1998; Botvinick et al., 2001). This conflict detection system becomes active in the presence of competing, i.e. incongruent, simultaneously active information processing entities. A further theory, the affect evaluation hypothesis (Luu and Tucker, 2004) assumes that the ACC is generally involved in representations of adaptive goals and motivational control, both relevant for self-regulation. In recent years, new models of error monitoring and corresponding empirical findings point to an involvement of these systems in specific behavioral adaptation processes and it has been suggested that changes (via noradrenergic modulation) in phasic autonomic excitability (i.e. arousal) should increase or decrease conscious error processing and
∗ Corresponding author at: German Sport University Cologne, Institute of Psychology, Department Performance Psychology, Am Sportpark Muengersdorf 50933 Cologne, Germany E-mail address: s.hoff[email protected] (S. Hoffmann).
https://doi.org/10.1016/j.ijpsycho.2019.10.001 Received 1 September 2017; Received in revised form 13 September 2019; Accepted 2 October 2019 Available online 24 October 2019 0167-8760/ © 2019 Elsevier B.V. All rights reserved.
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attention and arousal (Yackle et al., 2017). Since the Ne/ERN as error monitoring correlate is modulated by dopamine system states, it might serve as an indirect indicator of DA and ACC activity, and also of processes related to the limbic system and PFC functions (c.f. reinforcement learning theory, conflict theory). Indeed, some studies indicated that the afferent fibers of the vagus nerve modulate NE and thus dopamine uptake in the PFC (Colzato et al., 2017; Steenbergen et al., 2015; Beste et al., 2016). It has been shown that response errors are accompanied by a stronger pupil dilation, a direct indicator of NE release (Wessel et al., 2011; Braem et al., 2015; Murphy et al., 2016). A recent study showed that pupil dilation co-varies with the error negativity depending on the type of the committed error (Maier et al., 2019). If cardiac vagal activity was generally related to executive cognitive functions, a modulation of afferent vagal activity could provoke corresponding effects in specific PFC functions. Indeed, Prinsloo et al. (2010) found that a single slow-paced breathing session reduced the Stroop interference effect. Given that slow-paced breathing can increase cardiac vagal activity (Lehrer, 2013), we assumed that error processing may be altered by this technique as well. More specifically, slow-paced breathing should affect error monitoring processes as reflected in the Ne/ERN. Further, we expected an alteration of performance measures as reflected in response times and accuracy, such that performance should be improved following the slow-paced breathing intervention.
post-error adjustments (Wessel, 2017). However, it has not yet been investigated in detail whether and to which degree error monitoring is involved in self-control. Indeed, it is plausible to assume that self-control involves self-monitoring and adaptive control. On a neurophysiological level this points to an involvement of structures related to those functions: PFC, ACC and the limbic system (Nieuwenhuis et al., 2004; Holroyd and Coles, 2002). A recent model with respect to self-control is the neurovisceral integration model (Thayer et al., 2009b). It defines self-control as a physiological regulation mechanism that can be identified non-invasively through heart rate variability (HRV), an indicator of cardiac vagal activity (the activity of the vagus nerve – the main nerve of the parasympathetic nervous system – regulating cardiac functioning). Cardiac vagal activity reflects the output of the central autonomic network, a network linking the PFC to the heart (for a more detailed description of this network, see Thayer et al. (2009a)). This central autonomic network underlies the effectiveness of neurovisceral selfregulation mechanisms via a common control of cardiac and cognitive regulation. This assumption is supported by meta-analyses on neurophysiological evidence, indicating common brain areas putatively related to both cardiac vagal control and affective or cognitive self-regulation (Beissner et al., 2013; Thayer et al., 2012). The neurovisceral integration model would thus assume that the cerebral control of autonomic function conveys comparable control of executive functions (Thayer et al., 2012). Specifically, the neurovisceral integration model would argue that a higher cardiac vagal activity should be linked to the optimal activation of neural networks underlying the effectiveness of the PFC. Based on previous summary work on self-control, we define it here as the result of cognitive and neurovisceral processes that allow people to resist temptations and override impulses (thoughts and/or emotions) (Inzlicht et al., 2014; Kotabe and Hofmann, 2015). Cardiac vagal activity can be modulated by slow-paced breathing (Lehrer, 2013). Breathing couples the effects of the relationship between heart rate and breathing at a certain pace (6 cycles/min), and between heart rate and blood pressure oscillations at specific frequencies. Both combined to the activity of the baroreflex trigger the resonance properties of the cardiovascular system (Lehrer and Gevirtz, 2014), which results in increased oxygen saturation and increased vagal afferences (Lehrer, 2013; Lehrer and Gevirtz, 2014). The effects of slowpaced breathing on cardiac vagal activity can be strengthened by manipulating the inhalation/exhalation ratio. A prolonged exhalation contributes to larger beat-to-beat heart fluctuations as compared to prolonged inhalation, and hence induces higher cardiac vagal activity (Strauss-Blasche et al., 2000). Indeed, breathing techniques are well known for their therapeutic effects (Brown and Gerbarg, 2009; Nardi et al., 2009) and recent findings indicate that breathing center neurons in the locus coeruleus modulate arousal (Yackle et al., 2017). To the best of our knowledge, so far only one study has investigated the effects of slow-paced breathing on executive cognitive control. Findings showed that a single 10 min session of slow-paced breathing at 6 cycles/ min increased performance, that is, reduced the interference effect in a Stroop task right after the slow-paced breathing session (Prinsloo et al., 2010). But what is the link between vagal activity and cortical function? The vagus nerve likely influences cortical functions via its connection to the locus coeruleus, which constitutes the core structure for norepinephrine (NE) supply (Aston-Jones et al., 1991). NE plays an important role in PFC functions, as NE catabolism via catechol-O-methyltransferase (COMT) is important for dopamine inactivation in the PFC (Bymaster, 2002). Recent research has shown that post-error slowing (Rabbitt, 1966), a strategic component of response and error monitoring, is a direct result of NE modulation (Ullsperger et al., 2010). NE has been hypothesized to be involved in higher order cognition (AstonJones and Cohen, 2005) such that PFC activity is affected by locus coeruleus activity (Joshi et al., 2016; Gilzenrat et al., 2010). Also, breathing center neurons in the locus coeruleus have an impact on
2. Methods 2.1. Participants A sample of 41 healthy subjects (27 female) with a mean age of 23.7 (sd = 3.28; range: 18–30) years participated voluntarily in the study. They gave written informed consent prior to participation. The study was conducted according to the code of the World Medical Association and was approved by the ethics committee of the German Sport University. Data of two participants had to be rejected due to EEG data quality issues, i.e. bad electrodes and too many ocular, movement and muscular artifacts. 2.2. Experimental design 2.2.1. Basic design The experiment consisted of a fully balanced factorial within-subjects design and comprised two phases. In the first phase, after having been seated in an ergonomic chair in front of an LED screen (distance between participants' eyes and screen ≈120 cm), the paced-breathing training phase started (duration = 15 min, cf. Fig. 1). During this phase, participants learned the slow-paced breathing technique. The slowpaced breathing technique was realized with a video showing a little ball moving up and down at the rate of 6 cpm (4.5 s inhalation/5.5 s exhalation), the participants having to inhale continuously through the nose while the ball was going up, and exhale continuously with pursed lips when the ball was going down. The video was realized using the software EZ-AIR PLUS (Thought Technology Ltd., Montreal, Canada), which has been used in previous research (Laborde et al., 2016). The training consisted of asking participants to get used to slowpaced breathing in decreasing progressively their breathing frequency following a pacer. More specifically, they had to breathe for 2 min respectively at 10 cpm, 8 cpm, and 6 cpm (each time with a 45% inhalation/55% exhalation ratio), the experimenter making sure that the participants were inhaling continuously through the nose and exhaling continuously through the mouth with pursed lips, providing feedback if needed. After application of the EEG electrodes, the second phase started: each participant started with a first 5 min baseline, eyes closed, to measure the HRV resting state (Laborde et al., 2017). Then they performed either the control condition (watching a documentary, duration = 15 min) or the experimental condition (slow paced 218
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Fig. 1. Basic study design; HRV = Heart Rate Variability, BR = Breathing Rate.
Reaction times (RTs) were analyzed by means of a repeated measures ANOVA with the within-subject factors flanker condition (neutral, congruent, incongruent), intervention (slow paced breathing, control) and accuracy (error, correct), with RTs faster than 100 and slower than 1000 ms being excluded from the analysis. Additionally, we investigated the RT distribution via fitting of exGaussians, since the shape of response time distributions might be different between interventions. This, in turn, might mask mean differences (Spieler et al., 1996). Briefly, the ex-Gaussian distribution is a convolution of a Gaussian and an exponential distribution, and it has three parameters: μ,σ, and τ. The latter reflects both the mean and standard deviation of the exponential portion, whereas μ and σ represent the mean and standard deviation of the Gaussian portion, respectively. Ex-Gaussian analyses allow differences between conditions to be separated into distributional shifting, reflected in μ and distributional skewing, reflected in τ. This approach is more sensitive to group differences in RT distribution than the classical approach with Gaussian parameters only. In the present study ex-Gaussian parameters were estimated by minimization of the negative log-likelihood function. For calculation of the corresponding parameters, we used the retimes package (Massidda, 2013) in GNU R (R Core Team, 2016). We report mean RTs for correct responses in the figure, mean error rates, and ex-Gaussian parameters and corresponding F-values, p-values (in case of df > 1, i.e. possible violation of sphericity assumption, Greenhouse-Geisser corrected p-values are reported), and partial eta squared (ηp2 ). For all post-hoc t-tests, p-values were FDR-adjusted according to the method of Yekutieli and Benjamini (2001). Cohen's d is reported for effect sizes of t-tests (Cohen, 1988) and we report Bayes factors (BF) for all post-hoc t-tests using JASP (JASP Team, 2018; Marsman and Wagenmakers, 2016). All other RT parameters and statistics were calculated using R (R Core Team, 2016). We report all values rounded to the second decimal place.
breathing, duration = 15 min). The order of conditions was fully counter-balanced across participants such that it was a balanced repeated-measures design. After each experimental condition (slow-paced breathing or control), participants underwent a 5 min HRV resting measurement to be able to check for the effects of the experimental condition on respiration and cardiac vagal activity, and then participants conducted a modified flanker task (Kopp et al., 1996a). Fig. 1 summarizes the basic procedure of the study. 2.2.2. Experiment The flanker task is a measure of cognitive control that has been shown to possess excellent reliability characteristics (Wöstmann et al., 2013). Participants were instructed to respond to an arrowhead occurring in the center of the screen. All stimuli were presented in dark gray on a light gray background. The direction of the target, i.e. arrowhead (left/right, duration = 100 ms) indicated the button that had to be pressed. It was accompanied by two distracting geometric figures (=flankers) below and above the target which appeared 100 ms prior to target-occurrence, which is known to induce maximal distraction (Wascher et al., 1999). In the present experiment, these flankers could be congruent arrow-heads (pointing to the same direction), incongruent arrow-heads (pointing to the opposite direction), or neutral flankers (squares). The probability for congruent flanker was 50% and for incongruent and neutral flankers 25%, respectively. Each flanker task consisted of 320 trials. 2.2.3. Physiological measurements The EEG (amplifier and EEG system: eego by ANT, Hengelo, Netherlands) was recorded monopolarly and continuously during the whole experimental procedure. The EEG was recorded from 64 standard electrode positions according to the 10-20-System. The horizontal and vertical EOG was concomitantly recorded via bipolar channels. The electrocardiogram (ECG), from which heart rate variability parameters were extracted to infer cardiac vagal activity, was also measured concomitantly using bipolar channels (+, −, ground) via the ANT amplifier. The electrodes were placed at the right clavicle lateral to the midclavicular line (−) and the left anterior axillary line (+). EEG, ECG, and EOG were sampled at 1000 Hz. Following the flanker task, participants underwent a 5 min HRV resting measurement to investigate cardiac vagal activity recovery (Laborde et al., 2017).
2.3.2. EEG data With respect to the analysis of EEG data, following import of the raw EEG data in to EEGLAB (Delorme and Makeig, 2004) data were initially filtered with a 0.5 Hz high pass FIR filter, followed by removal of line noise utilizing the CLEANLINE EEGLAB-plugin (Bigdely-Shamlo et al., 2015; Mullen, 2012). Then, data were filtered with a 40 Hz low pass FIR. Subsequently, the data for each participant were segmented into 1000 ms epochs yielding a temporal data set to which an automated artifact rejection procedure (Delorme et al., 2007) was applied, followed by the ICA AMICA algorithm (Palmer et al., 2007). The automated artifact rejection procedure basically calculates the empirical distribution of all data points across all trials and time points and rejects statistical outliers, i.e. trials consisting of data points exceeding a criterion of 3 standard deviations. The amount of maximal rejected trials was fixed to 5%. The derived ICA-weights of these so pruned data were submitted to the previous continuous data set, and independent components representing ocular artifacts were removed by projecting back the mixing matrix with artifact components set to zero (Jung et al., 2001). The cleaned data sets were segmented relative to response onset and linear trends removed. Subsequently, data were submitted to the before
2.3. Data analysis 2.3.1. Behavioral data Error rates were compared by initially transforming the percentage by reflecting and log-transforming the data, since a Shapiro Wilks test indicated that the accuracy data were not normally distributed, skewness = −1.3866, W = 0.866, p = 1.887e−13. The transformation Accuracytransformed = log(100 − rate + 1) is an appropriate transformation for heavily left skewed data (Tabachnick and Fidell, 2013). These transformed accuracy data were analyzed by a repeated-measures ANOVA with the within-subject factors flanker condition (neutral, congruent, incongruent) and intervention (slow paced breathing, control). 219
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Fig. 2. Ex-Gaussians of reaction times for slow paced breathing (SPB) and control condition (TV). Bars reflect within subject confidence intervals according to Cousineau (2005, 2012). Inco = incongruent, Co = congruent trials.
3. Results
mentioned automated artifact procedure and transformed to current source density (CSD) since data can be evaluated reference free (Nunez et al., 1997; Perrin et al., 1989) and the CSD transformation is comparable to a spatial filter that removes the effect of remote sources on local recordings (Vidal et al., 2003). For estimation the order of the splines was set to 4, the maximum degree of Legendre polynomials was set to n = 10 and the precision was set to 2.72E−7. Following a final automatic artifact correction procedure (removal of segments containing artifacts exceeding 200 μ V in each EEG channel), the data were averaged for both response types (errors, correct). The Ne/ERN was quantified as a mean voltage (−40 to 100 ms relative to button press) at Fz (cf. Fig. 3). The statistical analysis of experimental condition (slow-paced breathing, SPB vs. documentary watching, TV) effects on this mean Ne/ ERN amplitude consisted of t-tests. For t-tests, p-values were FDR-adjusted according to the method of Yekutieli and Benjamini (2001). We report t-, p-values as well as Cohen's d (Cohen, 1988) and we report Bayes factors (BF) for all post-hoc t-tests using JASP (JASP Team, 2018; Marsman and Wagenmakers, 2016). All other statistical analyses of EEG data were conducted using GNU R (R Core Team, 2016). We report all values rounded to the second decimal place.
3.1. Behavioral data With respect to reaction times (in ms) of correct responses, there was a significant effect of condition (congruent, incongruent, neutral), F (2,76) = 157.90, p = 5.785e−17, ηp2 = 0.81, indicating that responses were slower with respect to incongruent stimuli (m = 326.15, sd = 75.31) compared to neutral (m = 316.95, sd = 45.07) and congruent stimuli (m = 320.33, sd = 33.53). Since the neutral and congruent stimuli provoked too few errors, we compared only the incongruent condition with respect to the intervention effect. Erroneous responses were given faster than correct ones, F(1,38) = 611.23, p = 5.034e−25, ηp2 = 0.94 . With respect to accuracy (in %), there was a significant effect of condition (congruent, incongruent, neutral) as well, F(2,76) = 149.50, p = 9.823e−26, ηp2 = 0.80 , indicating that accuracy was lower for the incongruent condition (m = 66.65, sd = 11.56) compared to the congruent (m = 86.32, sd = 6.99) and neutral condition (m = 91.04, sd = 6.73). Regarding the ex-Gaussian parameters, there was a significant effect of congruency on the μ-parameter, F(2,76) = 122.27, p < .001, ηp2 = 0.76. Further, there was a significant congruency effect on the σ-parameter, F(2,76) = 4.57, p = .01, ηp2 = 0.11. Finally, there was a significant congruency effect on the τ-parameter, F(2,76) = 3.66, p = .03, ηp2 = 0.09, and an effect nearby significance threshold of intervention,
2.3.3. Cardiac vagal activity For analysis i.e. estimation of cardiac vagal activity, data were analyzed with the Kubios software (Tarvainen et al., 2014). The ECG signal was visually inspected for artifacts, which were manually corrected. We estimated the high-frequency HRV (HF-HRV) as an indicator to infer cardiac vagal activity (Malik et al., 1996; Laborde et al., 2017) and the breathing rate with the ECG derived respiration algorithm (Tarvainen et al., 2014). Given HF-HRV is supposed to reflect cardiac vagal activity only when the breathing rate is between 9 and 24 cycles per minute, which corresponds to the interval between 0.15 and 0.40 Hz (Malik et al., 1996; Laborde et al., 2017), we could not infer cardiac vagal activity during the flanker task, given 21 participants out of 38 had a breathing rate higher than 24 cycles per minute. We logtransformed (log10) the HF-HRV and respiration data, given they were not normally distributed (Laborde et al., 2017). We report all values rounded to the second decimal place.
F(2,76) = 4.01, p = .05, ηp2 = 0.10 , and interaction of intervention and congruency, F(2,76) = 2.69, p = .07, ηp2 = 0.07. Post-hoc t-tests(FDRadjusted for multiple comparisons) revealed that this interaction was driven by a significant decrease in the τ-parameter for slow paced breathing compared to control in the incongruent condition (cf. Fig. 2), t(38) = 3.15, p = .016, d = 0.51, BF10 = 11.19. 3.2. EEG data In the SPB condition, the Ne/ERN was more pronounced for erroneous responses (−36.21 μ V2/cm) compared to correct responses (−21.06 μ V2/cm), t(38) = 7.73, p = 1.091 e−08, d = 1.24, BF10 = 9.124e +6. Also, in the TV condition it was more pronounced in erroneous (−32.05 μ V2/cm) compared to correct responses (−19.86 μ V2/cm), t(38) = 7.86, p = 1.091 e−08, d = 1.26, BF10 = 1.355e +7. The difference between correct responses of the TV and SPB condition was not significant, t(38) = 1.66, p = .22, d = 0.26, BF10 = 0.07. 220
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Table 2 Descriptives of breathing rate (cycles per minute) during the flanker task if subjects started with control, i.e. TV (=A) or started with SPB/slow-paced breathing (=B). Intervention
Order
Mean
SD
N
SPB
A B A B
24.02 26.28 26.11 22.67
8.013 16.179 8.157 11.219
20 19 20 19
TV
interaction effect regarding behavioral data and error negativity. However, there was a significant interaction of order and intervention during the flanker task with respect to breathing rate, F(1,37) = 8.70, p = .005, ηp2 = .19 indicating that the breathing rate increased more in the second block if subjects have started with slow-paced breathing (cf. Table 2).
Fig. 3. The error negativity and correct response negativity for slow-paced breathing (PB) and control condition (TV). The topographic maps refer to the Ne/ERN and CRN peak. Note that the EEG data were CSD transformed, thus, the scaling of the y-axis refers to μV/cm2. x-axis = time, time point zero = button press; y-axis = CSD-transformed activity.
4. Summary and discussion The aim of this study was to investigate the effect of a relaxation technique, slow-paced breathing, on performance in a flanker task, a task involving core executive functions. In the present study we replicated the well known interference effect in the flanker task, that is, an increase of RTs in incongruent compared to neutral and congruent flanker-target combinations (Hoffmann and Falkenstein, 2010; Willemssen et al., 2004; Kopp et al., 1996b; Eriksen and Eriksen, 1974). Further, this increase was accompanied by an increase of error rates in the incongruent condition. This flanker effect was also reflected in the ex-Gaussian parameters. With respect to the hypothesized influence of slow-paced breathing on behavioral parameters, not completely in line with the findings of Prinsloo et al. (2010) who used a modified Stroop task, we did not find any statistically significant effect besides a decreased τ-parameter in the incongruent condition after slow paced breathing. With respect to the hypothesized increase of the Ne/ERN, we found an increase of the error negativity after the slow-paced breathing intervention. The results of the present study are at odds with a recent study that showed decreased error evaluation/awareness as reflected in a decreased Pe during meditation (Larson et al., 2013). Also, we could not replicate their behavioral results. In their study, Larson et al. (2013) did not find any significant behavioral effects besides the effect on the Pe. One reason might be that in their study they focused on mean values and not on measures of variability. Also, it has to be considered, that although paced breathing and meditation share some aspects, a guided meditation like in the Larson et al. study is not the same as paced breathing. Meditation is rather an exercise in awareness and attention than respiration like in paced breathing. Thus, it might be that the current study manipulated different aspects of error processing. Given that those kinds of interventions do have an effect on heart rate variability, this implicates that one should take into account measures of variability, since means can mask group differences. Another reason might be that although meditation might have already some subtle effect on error monitoring, it might be that the intervention effect varied strongly across participants. The latter could be
Finally, the Ne/ERN was more pronounced in the slow-paced breathing (−36.21 μ V2/cm) compared to the control condition (−32.05 μ V2/ cm), t(38) = 2.26, p = .08, d = 0.36, BF10 = 4.48, cf. Fig. 3.
3.3. Cardiac vagal activity Regarding HF-HRV, a repeated-measures ANOVA with the factors condition (slow paced breathing vs. control) and time (before intervention, after intervention, and after flanker task) and HF-HRV as dependent variable was ran. No main effect of condition was found F (1,38) = 1.19, p = .28, ηp2 = .03, nor a significant main effect of time, F (2,37) = 2.17, p = .128, ηp2 = .10 . No significant interaction effect of condition and time was found, F(2,37) = 0.51, p = .60, ηp2 = .03. Regarding breathing rate, a repeated-measures ANOVA with the factors condition (slow-paced breathing vs. control) and time (before the intervention, after the intervention, during flanker task, after flanker task) and breathing rate as the dependent variable was run. No main effect of condition was found F(1,38) = 0.26, p = .61, ηp 2 = .01. A significant main effect of time effect was found, F(3,36) = 44.29, p < .001, ηp2 = .79. Given our focus was on the interaction of condition and time, we do not further elaborate on the main effect of time. A significant interaction effect of condition and time was found, F (3,36) = 6.83, p = .001, ηp2 = .36. Further, post-hoc tests revealed a significant difference in respiration rate following the intervention, t (38) = 3.28, p = .002, BF = 15.25, with a more slowly breathing rate following the intervention compared to the breathing rate following the TV documentary. The other time points revealed no significant differences in breathing rates (all p > .05). Tables 1 and 2 summarize these results. To test for order effects, we ran the same analyses again with order (starting with control vs. starting with slow-paced breathing) as a between-subjects factor. We found no significant main effects of order, nor
Table 1 Descriptive statistics (means, standard deviation in brackets) for breathing rate (BR) and high-frequency heart rate variability (HF-HRV). During the Flanker task, for slow-paced breathing (SPB) and control condition (TV), breathing rate was higher than 24 cpm, and therefore high-frequency heart rate variability did not reflect cardiac vagal activity, which is why we don’t report it in this table. T1 = baseline, T2 = after intervention, DF = during flanker task, AF = after flanker task. Dependent variable
Condition
T1
T2
DF
AF
BR
SPB TV SPB TV
13.95 (3) 13.78 (2.93) 1996.84 (2457.44) 1667.21 (1705.31)
12.21 (2.52) 13.84 (2.84) 1669.96 (2417.19) 1621.85 (1524.91)
25.12 (12.38) 24.43 (9.67)
14.17 (2.71) 13.38 (232) 1810.04 (1821.03) 1885.69 (1939.94)
HF-HRV
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due to the fact that “meditation performance” can hardly be controlled. Thus, a more controllable intervention might be more adequate. Slowpaced breathing fits this standardization requirement, given the breathing pace can be set and controlled across participants. Thus, it is possible to check post-hoc whether participants did follow the 6 cycles/ min breathing pacer, which they did in the current experiment. There are essentially three interpretations of the observed pattern of an increased Ne/ERN and a decreased τ-parameter. First, it could be that since the Ne is related to the response onset and more slowly responses provoke a smaller Ne, the Ne became larger in the slow-paced breathing condition since there were fewer extreme values, i.e. the distribution of RTs was not as strongly right-skewed as in the control condition. However, against this speaks the finding that it is rather uncommon that the Ne/ERN increases during slower response times, i.e. if participants conduct a more difficult task (Hoffmann and Falkenstein, 2011; Maier and Steinhauser, 2016), especially, if accuracy is not stipulated in the instruction and thus os constant across conditions (Hoffmann and Falkenstein, 2011). Second, it could be that the Ne/ERN was in general increased. However, irrespective of whether this amplitude effect was due to the first or second reason, the decreased τparameter indicates a selective and higher precision in the given responses of incongruent trials indicating an effect of slow-paced breathing on conflict processing (see Yeung et al., 2004). A closer look at the τ-parameter reveals that for the control conditions the results show the typical pattern: an increased τ-parameter in the incongruent condition (Spieler et al., 2000). Striking is the strong decrease in this parameter after slow-paced breathing. One could interpret this finding in the light of resolved conflict. Typically, a higher precision (i.e. accuracy) in such tasks is accompanied by an increased Ne/ERN (Gehring et al., 1993). Finally, it might be that the breathing technique induced a more cautious strategy with respect to incongruent trials. This might have led to an increase of the Ne/ERN. Indeed other studies have shown that the Ne/ERN is modulated by motivational state, i.e. it might reflect “ individuals' concern with the outcome of events” (Santesso and Segalowitz, 2009). Though it remains unclear how exactly the effect emerges, slow-paced breathing seems to support a more even response pace. The decrease in respiration rate after the slow-paced breathing intervention in comparison to the control condition showed that the manipulation was effective in reducing respiration rate, however, no differences emerged in terms of cardiac vagal activity. Regarding respiratory rate, our findings are in line with Prinsloo et al. (2010), who also found a reduction in breathing rate after the slow-paced breathing intervention, and no differences between treatments during the cognitive task (i.e., modified Stroop). Regarding cardiac vagal activity, our results are not directly comparable to those of Prinsloo et al. (2010), given they only mention HFHRV results during the intervention, where cardiac vagal activity cannot be properly inferred with HF-HRV given the breathing rate is lower than 9 cycles per minute. Wells et al. (2012) did show an increase of HF-HRV directly after a slow-paced breathing intervention, but the intervention in their study lasted 30 min, whereas in our study it lasted only 15 min. Thus, there appear to be two competing explanations as to why we did not observe any changes in cardiac vagal activity: either our intervention was too short, in comparison to the one realized by Wells et al. (2012); or it induced stress in our participants. Actually, even if learning the slow-paced breathing is relatively easy, and can be implemented in an acute basis, we have to keep in mind that decreasing our breathing rate to 6 cpm(instead of 12 to 20 usually in healthy adults (Sherwood, 2006) may represent a stress for the organism, and some subjects may adapt faster than others, while some would need more than one session to feel comfortable with the method (Lehrer et al., 2000). Thus, in further research breathing rate should be tested e.g. with gas exchange analysis. Another reason might be that a resonance frequency specific to each individual may exist and needs to be determined before starting the study and participants need to train to get
used to slow-paced breathing more often (Lehrer et al., 2000). Additionally, the effect of vagal activity on cognitive control might be very specific. In a recent study by Steenbergen et al. (2015) the authors showed a tVNS mediated effect on performance in a stopchange paradigm, i.e. on response selection during action cascading. Thus, this specific effect might not generalize to the used flanker herein: one could argue that this effect is rather unspecific, i.e. modulation in arousal (Yackle et al., 2017), such that a higher arousal leads to a more focused attention in the flanker task. In sum, it may be concluded that because paced breathing does not show a direct effect on the analyzed HRV parameters, but on the Ne/ ERN, that the EEG is more sensitive to the experimental manipulation. This modulation on the functional level is not directly modulated by HRV but by respiration rate: There was a relationship between respiratory rate after the intervention (and not during the task itself) and Ne/ERN during the task. This could be linked to the fact that “respiration, via multiple sensory pathways, provides a subtle but continuous rhythmic modulation of cortical neuronal activity that modulates sensory, motor, emotional and cognitive processes” (Heck et al., 2017). This quotes makes clear, that the underlying neurophysiological mechanisms are not yet specified in detail. In this regard, a recent study showed that volitional breathing recruits frontal networks (Herrero et al., 2018). If respiration influences Ne/ERN regardless of any impact on cardiac vagal activity, this may suggest the operation of other mechanisms. For example, it was found that inspiratory load was more taxing in terms of cortical resources and provoked a decrease in cognitive performance (Nierat et al., 2016). Therefore, it may be speculated that a higher respiratory rate is also more taxing for cortical resources. Finally, slow paced breathing induces oscillatory activity in the brain, and those high amplitude oscillations are suggested to enhance functional connectivity in brain networks associated with emotional regulation, especially in medial pre-frontal regulatory regions (Mather and Thayer, 2018; Thayer et al., 2012). However, future research should thus consider the influence of respiratory rate on cortical resources, i.e. investigate the specific circumstances under which heartbeat and respiratory rhythm synchronize (Vaschillo et al., 2004). In this regard, gas exchanges should be measured in order to asses the adequacy of the breathing frequency with respect to the participant capabilities and characteristics (Giardino et al., 2003; Yasuma and Hayano, 2004). Acknowledgments We thank the Performance Psychology group for providing invaluable feedback, support and an inspiring environment. In particular, we would like to thank Alice Heinrich for technical support in running the experiments. References
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