Tobacco smoking is associated with impaired error monitoring

Tobacco smoking is associated with impaired error monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tobacco smoking is associated with impaired error monitoring C. Henrico Stam, Frederik M. van der Veen, Vaughn R. Steele, Ingmar H.A. Franken This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4191422/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rationale: Addiction is associated with neurophysiological deficits in error monitoring (EM).EM refers to the continuous assessment of ongoing actions and comparing the outcomes of these actions with internal goals and standards, measured by, e.g., event-related potentials (ERPs). Yet, for tobacco smoking, despite being the largest and most lethal addictive substance globally, there is no firm conclusion on the relation with EM due to a paucity of studies. Objectives and methods: A large gender-balanced sample (N=94, of which 46 were people who smoke tobacco) was established. The Eriksen-flanker task, a widely used speeded response task known to result in error commission, was administered while recording the electroencephalogram (EEG). The error-related negativity (ERN) and the error positivity (Pe) were measured, as well as event-related oscillations (EROs) in the theta and delta frequency bands that are known to be actively involved in error monitoring. Results: The results showed a clear and consistently blunted ERN and Pe in smoking participants compared to non-smoking participants, providing important evidence for attenuated EM at multiple levels. Reduced power in event-related theta and delta oscillations corroborated these findings. Both errors and correct responses contributed to the findings, demonstrating their joint importance in EM. Conclusions: Deficient error monitoring was found for people who smoke tobacco, manifested as lower ERN and Pe, which appear to be driven by reduced theta and delta power, respectively. This shows that tobacco smoking is associated with a neurophysiological deficit in EM that has been found in other substance use disorders. Cognitive Neuroscience Psychology tobacco smoking nicotine addiction ERN Pe oscillations theta delta error monitoring Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Tobacco smoking accounts for an estimated risk increase of 2–4 times of coronary heart disease and stroke and a 25 times risk increase of developing lung cancer (Centers for Disease Control and Prevention 2021 ). The nicotine and other substances contained in tobacco are highly addictive (Mcgeoch et al. 1996 ; Rose 2006 ; Nutt et al. 2007 ), and tobacco smoking is the substance addiction with the highest substance-attributable mortality rate in the world (Peacock et al. 2018 ), costing circa 8 million lives each year (World Health Organization 2023 ). While the prevalence of tobacco smoking is declining, still 22.3% of the population smokes. The rapid growth of electronic nicotine delivery systems in the younger population is concerning: in the UK, regular use in youth (11–17) tripled between 2021 and 2023 (ASH (Action on Smoking and Health) 2023 ). For these reasons, advancing knowledge about the neurophysiology of smoking addiction remains critical. Addiction is associated with neurophysiological deficits in error monitoring (Luijten et al. 2014 ; Volkow et al. 2016 ; Liu et al. 2023 ), which is the continuous assessment of ongoing actions and comparing the outcomes of these actions with internal goals and standards to implement remedial actions (Jocham and Ullsperger 2009 ). Unfavorable outcomes can either be errors or conflicts, and the neurophysiology of these outcomes can be studied effectively with Event-Related Potentials (ERPs) and Event-Related Oscillations (EROs). EM is a constituent of cognitive control (Ridderinkhof et al. 2004 ), which refers to a behavioral regulation process that optimizes goal-directed behavior and counteracts automaticity (Friedman and Robbins 2022 ). The two prominent ERP components in EM are error-related negativity (ERN) (Gehring et al. 1993 ) and error positivity (Pe) (Falkenstein et al. 1991 ). The ERN is a negative deflection that peaks about 50 ms after error commission, which reflects a post-response error monitoring system (Yeung et al. 2004 ). The error positivity (Pe), a positive deflection that peaks about 300 ms after error commission, has been associated with error awareness and importance (Overbeek et al. 2005 ). It is common to consider both errors and correct responses. The ERP’s following correct responses are referred to as Correct Response Negativity (CRN) (Vidal et al. 2003 ; Bartholow et al. 2005 ) and Pe-correct (Pc). The CRN is understood to represent an active component of EM, with enhanced activation during and after correctly processing unexpected stimuli (Bartholow et al. 2005 ). Research has shown that this neurophysiological error monitoring mechanism is attenuated in many people with substance use disorders (Pasion and Barbosa 2019 ; Lutz et al. 2021a , b ; Liu et al. 2023 ). Although these meta-analyses confirm the relation between substance use and diminished ERN and/or Pe, specifically for tobacco smoking (the most prominent global substance addiction), EM findings are scarce and inconclusive. One study found a lower Pe in People Who Smoke Tobacco (PWST; n = 23) but no group ERN-differences (Franken et al. 2010 ). A later study (Rass et al. 2014 ) confirmed the null-finding on ERN and a lower Pe in PWST daily ( n = 22) but also found that intermittent smoking ( n = 31 ) had a larger Pe than PWST daily and non-smokers ( n = 30). In summary, the findings are inconsistent. It is crucial to bridge this gap as it raises uncertainties about the applicability of addiction neurophysiology, particularly concerning EM, to approximately 1.3 billion tobacco users worldwide. There are some possible explanations for these inconsistent findings. These studies' relatively small sample sizes may explain these inconsistencies (Lutz et al. 2021a ). Another possibility is the influence of confounders (i.e., internalizing psychopathology and gender), as larger ERN amplitudes were found in internalizing psychopathology, such as obsessive-compulsive disorder (Pasion and Barbosa 2019 ). Furthermore, women may have reduced ERN/Pe compared to men, but research is inconclusive (Lutz et al. 2021a ). The present study addresses these possible causes for inconsistent results between EM and smoking through a larger, gender-balanced sample size and by controlling for personality. It is argued that despite many years of research, there is still a substantial hiatus in understanding the contents of an EEG signal, defined as the cognitive computations that underly and implement perception, cognition, and action (Siegel et al. 2012 ; Cohen 2017 ). Neural oscillations have been proposed as an excellent link to neurophysiology for advancing knowledge about these computations. Oscillations are segmented into frequency bands that correspond with the duration window required for information processing (Canolty and Knight 2010 ); e.g., low-frequency bands (such as delta; 0–3 Hz) correspond with relatively longer processing across larger spatial brain regions, and faster frequencies (such as beta; 13–30 Hz) that are more local and related to shorter processing. Researchers found that event-related oscillations (EROs) generate ERPs through an evoked effect or phase resetting (Sauseng et al. 2007 ). Of specific interest to the present study are the theta (3–9 Hz) and delta (0–3 Hz) frequency bands that have a role in EM. Research has shown that theta-band activity plays a general role in conflict processing and, more specifically, links a neural network in response to conflict (Nigbur et al. 2011 , 2012 ; Cohen and Donner 2013 ). There is a robust relationship between theta power and ERN (Luu et al. 2004 ; Trujillo and Allen 2007 ). However, it has been concluded that theta-band activity may be pivotal in a broader cognitive control mechanism (Cavanagh and Frank 2014 ). Delta-band is involved in error monitoring, which is evidenced by the relationship with ERN (Yordanova et al. 2004 ; Munneke et al. 2015 ), Pe (Luu et al. 2004 ), and P300 (Rawls et al. 2020 ) 1 . However, the role of the delta band is understood to be broader, e.g., in motivation and the sustainment of concentration (Harmony 2013 ). Some studies provide evidence for attenuated theta and/or delta EROs, e.g., with problematic substance use (alcohol, nicotine, or cannabis) (Harper et al. 2019 ), alcohol use disorder (AUD) (Kamarajan et al. 2004 ; Jones et al. 2006 ; Harper et al. 2018 ) and methamphetamine use disorder (Ghaderi et al. 2022 ). There is a general research opportunity to incorporate EROs in EM addiction research, and specifically for tobacco smoking, the present study provides the first cross-sectional results in the field. In summary, evidence is accumulating that diminished EM is associated with addiction. Yet, for tobacco smoking, despite being the most prominent global substance addiction, there is no firm conclusion. A large sample was investigated to close this gap, and advanced analysis methods were used, such as trial-level analysis of ERP data. In addition, time-frequency analysis of the oscillatory brain activity was undertaken to enhance insight into the underlying processes of the ERN and Pe. This study hypothesized that tobacco smoking may be associated with diminished ERN-amplitude (i.e., less negative) and (or) diminished Pe-amplitude (i.e., less positive). Furthermore, we expected to find diminished EROs for PWST following errors for theta and delta frequency compared to non-smokers. Gender and personality (internalizing/externalizing) may be confounding variables (Lutz et al. 2021a ). This study was part of a preregistration (ERPs: https://doi.org/10.17605/OSF.IO/8AQBU ); (EROs: https://doi.org/10.17605/OSF.IO/4FQEA ). This report covers the association between tobacco smoking and error monitoring; other elements of the pre-registration will be reported separately. Any significant deviations from the preregistration protocols are noted throughout the manuscript. METHODS AND MATERIALS Participants The aim was to include at least 90 valid participants, balanced for smoking status and gender. Participants were recruited based on inclusion and exclusion criteria on campus, referral from other studies, social media, and personal networks. Inclusion criteria were age (18–40), literacy in Dutch (speaking and reading), and informed consent. Daily smoking at the time of the study was required to be included as PWST, and non-smoking participation was eligible for people not smoking at the time of the study (i.e., lifetime use allowed). Exclusion criteria were a diagnosis of psychiatric or neuro-physiological disorder or medication (with a known distorting influence on behavior or neurophysiology). Participants were paid 25–30 euros. Psychology students at Erasmus University could choose between payment or 2 hours of course credits. There were 110 participants (55 women, 55 men), of which 94 were included in this study after screening. There were 46 PWST; 63.0% smoked < 10 cigarettes per day, 32.7% smoked 11–20 cigarettes, and 4.3% smoked 21–30 cigarettes per day. Data from 16 participants were excluded (ADHD 2 :4; < 50% correct trials: 3; failed EEG: 2; bad eye vision:1; < 9 artifact fee errors:6). The final study sample included 46 women (47.8% PWST) and 48 men (50.0% PWST). The average age for PWST was 22.5 years; for non-smokers, this was 21.2 years. Variation to pre-registration Payments were increased from 25 to 30 euros over time to stimulate participation. The minimum number of artifact-free error trials for inclusion was set to 9 to replicate prior research (Franken et al. 2010 ). A < 50% correct trial cut-off was applied as a quality threshold. Participation continued beyond the preregistered maximum (100) to compensate for ineligible participation (e.g., reported ADHD). Apparatus/instruments For the Eriksen-flanker task, design and EEG recording followed prior research (Franken et al. 2017 ). In this task, participants were exposed to a series of letters and asked to identify the middle letter in an incongruent and congruent condition. The middle letter may differ from the other letters (e.g., SSHSS/HHSHH) as opposed to the congruent condition (SSSSS/HHHHH). Trials started with a 250 ms cue (^) where the central letter of the letter strings would appear. Letter strings were presented for 50 ms. A feedback symbol (duration = 500 ms) followed 700 ms after the stimulus about the correctness of the response (‘ooo’ or ‘XXX’). When no response was made within 700 ms, participants received a feedback stimulus (‘!‘) informing them that their answer was not fast enough. Feedback was provided to support task focus and is known not to influence ERN/Pe amplitude, accuracy, or reaction time (Schroder et al. 2020 ). The experiment started with a practice phase of 8 trials and was followed by 5 blocks of 80 trials. Congruent ( n = 200) and incongruent stimuli ( n = 200) were random but balanced at participant level. The device recording responses was a Serial Response Box (SR BOX) (Psychology Software Tools) with 5 buttons. Participants were to press [1] when the middle letter was ‘S’ and [5] when the middle letter was ‘H.’ Nicotine dependence was measured with the Fagerstrom Test of Nicotine Dependence (FTND) (Heatherton et al. 1991 ). The score indicates the level of addiction to nicotine on a scale of 1–10 and includes questions about, e.g., the number of cigarettes smoked and abstaining during illness. Smoking was validated with a Breathalyzer pre-test. The Dutch version of the BIS/BAS scales (Carver and White 1994 ; Franken et al. 2005 ) was administered. Total BIS and BAS scores were z-scaled for the entire sample. The experiment order was: time estimation task/ Eriksen-flanker task, delay discounting task, demographics, Fagerstrom test of Nicotine dependence questionnaire, BIS/BAS questionnaire, and alcohol use questionnaire (Lemmens et al. 1992 ). The total experiment lasted circa 1.5 hours per participation. The order of the flanker task and time estimation task was counterbalanced to control for carry-over effects between the tasks. Stimuli for the Eriksen-Flanker task were presented electronically using the E-Prime 3.0 software (Psychology Software Tools, Pittsburgh, PA). The questionnaires were administered in Qualtrics, version 2022 (Qualtrics, Provo, UT). EEG Recording and signal processing The EEG was recorded using a Biosemi Active-Two amplifier system from 32 scalp sites (10–20 system) with Ag/AgCl (active) electrodes mounted in an elastic cap. Six additional electrodes were attached to the left and right mastoids, two outer canthi of both eyes (HEOG), and infraorbital and supraorbital channels of the eye (VEOG). Signals were recorded with a low-pass filter of 134 Hz and were digitized with a sample rate of 512 Hz and 24-bit analog/digital conversion. BioSemi uses the common mode sense (CMS) and driven right-leg (DRL) electrodes to create a feedback loop that replaces the conventional ground electrode. The CMS was used as an online reference. Data were off-line re-referenced to computed linked mastoids and filtered with a bandpass of .1–30 Hz (phase shift-free Butterworth filters; notch filter 50 Hz). For ERP analysis, channels Fz, Cz, and Pz were selected. After ocular correction (Gratton et al., 1983) with VEOG as a reference, trials exceeding ± 75 µV, voltage step > 50 µV/ms, or activity < 0.5 µV were excluded from the analysis. Data was segmented in epochs of 1000 ms, 200 ms before, and 800 ms after response. The mean pre-response period of 200–50 ms served as a baseline. While the baseline was proximate to response, this is not an issue for obtaining good internal consistency of ERP measures (Sandre et al. 2020 ; Klawohn et al. 2020 ). The ERN was defined as the mean value in the 25–75 ms time segment after the onset of the response. The Pe was defined as the mean value in the 200–400 ms time segment after the onset of the response. For ERO analysis, all 32 channels were included. After ocular (Gratton & Coles) correction with common reference, trials exceeding ± 100 µV voltage step > 50 µV/ms or minimum activity < 0.5 µV were excluded for channels of interest (all electrodes except rim electrodes F7, F8, Fp1, Fp2, O1, O2, Oz, P7, P8, T7, and T8). EEGLAB functions were used to automatically reject channels (criterium: >=3 z-scores based on probability) and subsequently to interpolate missing channels (spherical method). A mean of 1.6 channels ( SD = .9) was interpolated per participant. Experiments were conducted by 4 trained master students in Clinical Psychology as part of their master thesis assignment. Variance to pre-registration The baseline for ERN/Pe was increased from 100 ms to 200 ms to 50 ms pre-stimulus to reduce proximity to response and to have a slightly more extended baseline period. Artifact rejection applied for ERO was optimized by adding 19 extra channels to F3/F4. This step was necessary to clean the data adequately for TF-PCA analysis. ERP Analysis EEG data was inspected using BrainVision Analyzer (Brain products GmbH 2014 ). ERP data was extracted from Vision Analyzer for analysis in R (R Core Team, 2022 ), and ERO data was extracted for Time-Frequency analysis. The dependability of ERP measures (Table 2 ) was measured using the ERP Reliability Analysis (ERA) Toolbox v 0.5.1 (Clayson and Miller 2017 ). The ERA Toolbox used CmdStan version 2.24.1 (Stan Development Team, 2020), and Markov chain Monte Carlo estimation procedures used 3 chains and 10,000 iterations each to estimate variance components. Time-frequency analysis For this part of the study, the Time-Frequency Principal Component Analysis (TF-PCA) method was used to measure oscillatory power in the theta (3–9 Hz) and delta (< 3 Hz) bands (Bernat et al. 2005 ; Buzzell et al. 2022 ). The time-frequency surface of the EEG signal was subjected to a PCA that provides both the most important scalp regions in terms of activity and the most important time scales for when there is neurophysiological activity. Power is understood as marginal power, not absolute power, because of the TF-transformation process (Janssen and Claasen 1985 ). TF-PCA was conducted based on an extracted epoch from − 1000 ms before response to + 2000 ms after response. TF analysis typically requires longer epochs than ERP analysis. As a rule of thumb, 3 cycles are needed for the lowest analysis frequency (Cohen 2014 ); e.g., a 3-second epoch will capture 3 cycles of 1 Hz. The chosen epoch size was consistent with other TF-PCA studies (Bernat et al. 2011 ; Morales et al. 2022 ) on delta frequency. Subsequently, the time windows for decomposition were set from − 100 ms before response to + 500 ms post response to focus on the events of interest (ERN and Pe). The analysis was conducted on phase-locked data (average power). The toolbox operates at the participant level, so a trial-level statistical analysis could not be undertaken. The software used was the Psychophysiology toolbox (Curtin, 2011), EEGLAB (version 2023.1, Delorme & Makeig, 2004 ), and the TF-PCA toolbox (Bernat et al. 2005 ; Buzzell et al. 2022 ). EEGLAB, the Psychophysiology toolbox, and the TF-PCA toolbox were run in MATLAB (Version: 9.11.0; R2021b Update 6) (The MathWorks Inc. 2023). Analysis plan The first step in the analysis was to investigate the association between ERPs (i.e., trial-level ERN/Pe) as independent variables and tobacco smoking as a between-subject factor by linear mixed model regression for correct responses and errors. Linear mixed model regression has the advantage of allowing the use of trial-level granular data while accounting for both individual differences (i.e., random effects) and hypothesis testing (fixed effects) simultaneously (Pinheiro and Bates 2006 ; Gueorguieva 2011 ). Gender and personality (z-scaled BIS/BAS) were included in this analysis. Models with a 5-way interaction between smoking, BIS, BAS, response, and gender suffered from substantial multicollinearity (VIF > 10). The following model with a separate interaction term for smoking was applied (VIFs < 5) in Wilkinson notation: ERN/Pe amplitude ~ condition * group + response * scaled(BIS) * scaled(BAS) * gender + (1 + condition|subject). The second step was to investigate time-frequency differences in theta and delta bands by principal component analysis. To identify the main components, the participants’ mean ERN (Fz) and Pe (Pz) were regressed on theta and delta power on the same channels (in Supplemental Information). Finally, regressions were run with EROs as dependent variables in models identical to the ERP analysis adapted only to repeated measures ANOVA. For the linear mixed model analyses, R-packages lme4 version 1.1–31 (Bates et al. 2015 ), and lmerTest version 3.1-3 (Kuznetsova et al. 2017 ) and emmeans version 1.8.7 (Lenth 2023 ) were used. JASP (JASP Team 2023 ) (version 0.17.2.1) was used for other statistical analysis. Variation to pre-registration BIS/BAS (z-scaled) were covariates in the analysis instead of dichotomizing these into a single factor variable (internalizing/externalizing). This was deemed more informative. Neurophysiological measures were not scaled to ease interpretation; this did not influence the results. The regression model for EROs focused only on the main channels of interest (Fz, Pz), as identified in the ERP analysis. Regression models were validated by inspecting multicollinearity (VIF); models with VIF > 5 were excluded. This was not noted in pre-registration but is an important test to avoid biased results (Schielzeth et al. 2020 ). RESULTS Participant characteristics and behavioral performance Descriptive variables for the included participants are provided in Table 1 . The internal consistency (McDonald’s ω) of total BAS and BIS scores was .75 and .75, respectively. The main difference between PWST and non-smokers was BAS Fun-seeking ( t (92) = -2.33, p = .02). The level of nicotine dependence for PWST was modest ( M = 2.2, SD = 2.00), given that the maximum obtainable score for the FTND is 10 points. The internal consistency of FTND (ω) was .77. Between PWST and non-smokers there was no significant difference in age ( t (92) = -1.93, p = .06), gender ( χ 2 (1, N = 94) = .04, p = .83) or education ( χ 2 (5, N = 94) = 6.54, p = .26), indicating good comparability of the two groups. Table 1 Descriptive statistics for study participants Non-smokers PWST (n = 48) (n = 46) t-test Effect size Continuous variables M SD M SD t (92) p d Age 21.23 2.09 22.50 4.05 -1.93 .06 − .40 BAS 40.44 4.58 42.15 4.12 -1.90 .06 − .39 BIS 20.81 3.32 19.85 4.09 1.26 .21 .26 Smoking - FTND - - 2.24 2.00 - Percentage errors on task 11.0% 7.3% 11.1% 7.1% − .07 .94 − .02 Contingencies n % n % χ 2 ( N = 94) p Gender .04 .83 - Women 24 50.0 22 47.8 - Men 24 50.0 24 52.2 Education (types) 6.54 .26 - Master/bachelor level (2) 45 93.8 38 82.6 - Other levels (4) 3 6.2 8 17.4 Note : PWST: people who smoke tobacco; BAS: behavioral activation system; BIS: behavioral inhibition system; FTND: Fagerstrom Test of Nicotine Dependence; * p < .05, ** p < .01, *** p < .001 For personality characteristics, neither BAS ( t (92) = -1.90, p = .06) nor BIS was associated with being a smoker ( t (92) = 1.26, p = .21). Concerning task performance, PWST committed the same level of errors ( t (92) = − .07, p = .94) on the task (11.1% of trials) as non-smokers (11.0% of trials). ERP analysis As expected, the ERN/CRN was most pronounced at Fz and least at Pz. Pe/Pc was strongest at Pz and weakest at Fz (Figure S1). Summary information for ERP components (before regression) by group is provided in Table 2 . The overall dependability of the ERN was .82 (CI [.73, .89] for PWST and .83 (CI [.75, .89] for non-smokers, respectively. For the Pe this was .84 (CI [.77, .90]) for PWST and .87(CI [.81, .92]) for non-smokers respectively. The dependability of ERP components was acceptable to good. Table 2 Summary data for ERP components, amplitude (before regression), and trials Group Component Mean SD Overall dependability Trials M ± SD Trial Range PWST ( n = 46) CRN .7 1.9 .97 CI [.96 .98] 301 ± 77 61–374 ERN -4.7 2.0 .82 CI [.73 .89] 36 ± 20 9-115 Pc .3 2.2 .98 CI [.98 .99] 301 ± 77 61–374 Pe 7.7 2.2 .84 CI [.77 .90] 36 ± 20 9-115 Non-smokers ( n = 48) CRN 2.3 2.0 .98 CI [.97 .99] 318 ± 61 130–382 ERN -5.3 2.0 .83 CI [.75 .89] 38 ± 25 9-109 Pc -1.7 2.2 .98 CI [.97 .99] 318 ± 61 130–382 Pe 9.2 2.2 .87 CI [.81 .92] 38 ± 25 9-109 Note : PWST: people who smoke tobacco. Overall dependability: reliability coefficient estimates and their 95% credible intervals after applying minimum trial cutoff (9). Figure 1 presents grand averaged ERPs (A, C) for Fz and Pz and estimated marginal means (B, D). Table S1 provides the ANOVA results of the linear mixed models (LMMs). Condition (error or correct) was strongly associated with both ERN amplitude ( F (1,85.0) = 142.17, p < .001, η 2 p = .63) and Pe amplitude ( F (1,81.8) = 199.39, p < .001, η 2 p = .71). Group (smoking or non-smoking) interacted with condition for both ERN, ( F (1,87.6) = 5.81, p = .02, η 2 p = .06) and Pe, ( F (1,84.6) = 9.62, p < .01, η 2 p = .10). There was no fixed effect of group on ERN and Pe. As shown in Fig. 1 (A, B), PWST had a smaller ERN ( M =-4.2, SE = .70) compared to non-smokers ( M =-5.4, SE = .70). Furthermore, PWST had a larger (on a negative scale) CRN ( M = 1.0, SE = .62) than non-smokers ( M = 2.4, SE = .61). Contrast comparisons by condition were not significant, for both incorrect responses (Δ M = 1.4, p = .22) and correct responses (Δ M =-1.2, p = .11). A pairwise contrast test clarified that the interaction between condition and group was caused by the difference between ERN and CRN (ΔERN; M = 2.57, p = .02). As Fig. 1 (C, D) shows, Pe for PWST ( M = 7.2, SE = .78) was smaller compared to non-smokers ( M = 9.4, SE = .77). However, PWST had a larger Pc ( M = .3, SE = .73) than non-smokers ( M =-1.4, SE = .72). Contrast comparison by condition was significant for errors (Δ M = 2.2, p = .05) but not for correct responses (Δ M =-1.6, p = .11). A pairwise contrast test showed that the interaction between condition and group was driven mainly by the difference between Pe and Pc (ΔPe; M =-3.77, p < .01). These results show that the difference between errors and correct responses drove the interactions between ERP-components and group. For ERN, there were no interactions with gender or personality (BIS/BAS). For Pe, fixed effects were found for both gender ( F (1, 84.6) = 6.45, p = < .01, η 2 p = .07) and BIS ( F (1, 85.2) = 7.79, p = < .01, η 2 p = .08). There was a negative association between BIS (β=-1.78, p = .03) and Pe/Pc, and males had a lower Pe/Pc than females (β=-1.88, p = .08). A follow-up analysis within group revealed that these differences came from the non-smoking group for both BIS ( F (1, 39.43) = 5.98, p = .02, η 2 p = .13) and gender ( F (1, 39.66) = 9.82, p < .01, = .20). In the smoking group, neither gender nor BIS was significantly associated with Pe. ERO analysis Supplemental Information (Figure S2, Table S2) provides details on the solution and regression analysis between ERPs and EROs. In summary, one theta frequency component at Fz was strongly negatively associated with the ERN and the CRN. Furthermore, for the CRN, a delta component at Fz was strongly positively associated. For Pe, a positively associated delta component was found at Pz. Theta-frequency Theta differences by group are shown in Fig. 2 and Table S3. Theta was strongly associated with condition ( F (1, 85) = 57.37, p < .001, η 2 p = .40). Furthermore, there was a modest fixed effect of group (C; D; F (1, 85) = 3.97, p = .05, η 2 p = .05), showing lower theta-power for PWST ( M = .02, SE = .00) compared to non-smokers ( M = .03, SE = .00). There was a stronger association between group and condition, (A, B, D; F (1, 85) = 5.39, p = .02, η 2 p = .06). A post hoc Tukey test confirmed that there was a difference for errors ( t = 3.03, p = .02) but not for correct responses ( t = − .07, p = 1.00). Theta power difference by condition (A, B) was stronger for non-smokers, as the color intensity shows, and also spanned a broader frequency range. Topography plot 1C shows that the group difference was mainly in fronto-central and parietal regions. Gender, BIS, and BAS did not influence these, nor did they interact. Delta-frequency Figure 3 and Tables S4 and S5 show Delta power group differences. Starting with the Pe-related component (PC2), a strong association, yet less pronounced than theta, was found with condition ( F (1, 85) = 27.25, p < .001, η 2 p = .24). There was a fixed effect of group (C; D); F (1, 85) = 8.89, p < .01, η 2 p = .10); PWST had less delta-power ( M = .17, SE = .03) compared to non-smokers ( M = .32, SE = .03). The topography shows that this difference was primarily located in the parietal region (white area). There was no interaction between condition and group (A; B; F (1,85) = 1.54, p = .22, η 2 p = .02), so the power difference by group was similar between conditions. There was a fixed effect for gender ( F (1, 85) = 6.08, p = .02, η 2 p = .07) with higher power for women ( M = .31, SE = .04) compared to men ( M = .18, SE = .04). There was further interaction between gender and condition ( F (1, 85) = 7.02, p = .01). A Tukey test highlighted higher power for females ( M = .27, p < .01) on errors, but no difference for correct responses ( M =-.01, p = .88). There was also a positive association between condition and BIS ( F (1, 85) = 3.98, p = .05), which indicated that delta power on correct responses covaried modestly with BIS. Then for the CRN-related delta component (PC4; Table S5), there was an association with condition ( F (1, 85) = 6.91, p = .01, η 2 p = .08). However, there was no fixed effect of group ( F (1, 85) = 2.80, p = .10, η 2 p = .03), nor was there any interaction between condition and group ( F (1, 85) = 1.89, p = .17, η 2 p = .02). There were also no relations with BAS or BIS, nor with gender or any interactions between these. This component of the delta band was not related to smoking. Table 3 provides summary information about the correlations between ERPs and EROs. These demonstrate that ERPs were positively associated with each other, e.g. CRN and ERN ( r (94) = .34, p < .001) and Pe with Pc ( r (94) = .29, p < .01). Furthermore, there was a weak negative association between ERP-components; e.g., the ERN and the Pe ( r (94)=-.26, p < .05), which is an indication of an error complex. Lastly, there was a moderate negative association between ERN and theta-error ( r (94)=-.40, p < .001), and strong associations existed between Pc and delta-correct ( r (94)=-.74, p < .001) and Pe and delta-error ( r (94) = .79, p < .001). Internal consistency (ω) of ERN and theta measures was .51; for Pe and delta measures, this was .48. Therefore, EROs and ERPs individually capture different aspects of error monitoring neurophysiology while associated. Table 3 Pearson correlations between neurophysiological indices of performance monitoring Variable CRN ERN Pc Pe Theta correct Theta error Delta correct Delta error CRN – ERN .34 *** – Pc .16 −.11 – Pe .07 −.26 * .29 ** – Theta correct −.24 * −.07 .10 .16 – Theta error .05 −.40 *** −.05 .28 ** .29 ** – Delta correct .11 .19 −.74 *** −.12 −.11 .01 – Delta error .18 −.28 ** .23 * .79 *** .04 .29 ** .01 – Note: * p < .05, ** p < .01, *** p < .001 DISCUSSION This study aimed to clarify whether tobacco smoking is associated with attenuated error monitoring (EM), manifest in event-related potentials (ERPs) and event-related oscillations (EROs), by investigating a relatively large sample while controlling for potential confounders. Consistent with prior research, a smaller error positivity (Pe) was seen for People Who Smoke Tobacco (PWST) compared to non-smokers (Franken et al. 2010 ). In addition, a blunted error-related negativity (ERN) was found, broadly consistent with findings from a developmental study on smoking initiation (Anokhin and Golosheykin 2015 ). Although it has been shown that ERN and Pe represent distinct aspects of error monitoring (Overbeek et al. 2005 ), they often occur jointly. This has been termed the ERN-Pe error complex (Hajcak et al. 2003 ). Therefore, identifying a blunted ERN for PWST and the same finding on Pe provides robust evidence for broadly dysfunctional error monitoring in PWST at two levels. The blunted ERN suggests hypoactive error detection, and the attenuated Pe indicates a lack of conscious error recognition (Overbeek et al. 2005 ) and/or motivational salience (Ridderinkhof et al. 2009 ) of errors. It may seem bold to refer to dysfunctional error monitoring when PWST committed the same percentage of errors as non-smokers. Yet, this is a classic debate, as findings are inconsistent (Gehring et al. 2018 ). Some studies found a relationship between increased accuracy and larger ERNs, whereas others did not (for an overview, see (Luck and Kappenman 2011 )). The ERP components could be further understood by investigating theta and delta power. Theta power was more strongly associated with condition than delta power, which is consistent with the specific role of the theta band in conflict (Nigbur et al. 2012 ; Cohen and Donner 2013 ; Cavanagh and Frank 2014 ), whereas the delta band is more broadly involved in motivation and attention regulation (Harmony 2013 ). Also congruent with past research, ERN was strongly associated with theta band power (Luu et al. 2004 ; Trujillo and Allen 2007 ), and Pe was driven by delta band power (Luu et al. 2004 ). Correlations between neurophysiological measures were apparent, but the low level of internal consistency demonstrated that EROs and ERPs capture related yet different aspects of error monitoring. Turning to group differences, a weaker theta burst was seen for PWST after errors similar to the ERN. This adds to the evidence that the neurophysiological impact of errors was less for PWST than for non-smokers and implies impaired error monitoring (Cavanagh and Frank 2014 ). This is consistent with findings concerning impaired theta power in alcohol use disorder (AUD) (Kamarajan et al. 2004 ; Jones et al. 2006 ; Harper et al. 2018 ) and problematic substance use of alcohol, nicotine, or cannabis (Harper et al. 2019 ). Furthermore, PWST demonstrated lower delta power for both errors and correct responses. Attenuated delta power was previously found for AUD (Jones et al. 2006 ), but another study did not find this in problematic use (Harper et al. 2019 ). Drawing on broader psychopathology literature, a study found greater theta and delta power in obsessive-compulsive disorder (OCD) patients compared to healthy controls. This is relevant, as OCD patients are known to exhibit hyperactive EM. Taken together, the attenuated theta and delta power found in PWST corroborate the results of ERPs, suggesting an apparent deficit in EM. This study cannot answer the fundamental question of whether impaired error monitoring in tobacco smoking addiction is a cause or consequence. The status quo in the literature suggests a bi-directional relationship (Goschke 2014 ; Friedman and Robbins 2022 ) between error monitoring and addiction. Importantly, this study provides further evidence for impaired EM as a biomarker (Lutz et al. 2021a ) of addiction in the case of tobacco smoking. This study confirms that the inclusion of gender and Personality in ERP/ERO studies is relevant. For non-smokers, a larger Pe-amplitude was found for women, but the ERN did not differ. This finding contradicts prior research(Larson et al. 2011 ) that found higher ERN- and Pe-amplitude for men. Unexpectedly, for non-smokers, there was a negative association between BIS and Pe. It’s difficult to interpret this, as results of studies into internalizing disorders with Pe are inconsistent; for an overview, see (Macedo et al. 2021 ). The lack of relevance of gender and BIS/BAS in PWST suggests uniformity in the sample, which, in principle, supports the generalizability of findings (Lucas 2003 ; Jager et al. 2017 ). As nicotine dependence in the smoker sample was relatively low, it can be argued that the findings relate to tobacco use rather than nicotine addiction. The authors view this differently for two reasons. Firstly, nicotine dependence (tobacco use disorder in DSM-5 terminology) was found (Oliver and Foulds 2021 ) in ca. 65%-80% of people who smoke daily, already at usage rates of 1–10 cigarettes. Another study found a high risk of progressing from daily smoking to dependence (Breslau et al. 2001 ). Secondly, the findings in this study are consistent with and add further evidence of dysfunctional EM found in a broader spectrum of substance addictions (Liu et al. 2023 ). This congruence suggests the presence of addiction problems in the investigated sample of PWST. The results showed a clear and consistently blunted ERN and Pe in PWST compared to non-smokers, providing substantial evidence for attenuated EM at multiple levels. These findings were consistent with reduced power in event-related theta- and delta oscillations. Both errors and correct responses contributed to the findings, demonstrating their joint importance in EM. This study provides evidence for deficient EM in PWST, manifested as lower ERN and Pe, which appears to be driven by reduced theta and delta power. Declarations Compliance with ethical standards Ethical approval was granted under number ETH2122-0373 by the DPECS Research Ethics Review Committee on January 19, 2022. Funding No funding was applicable. Conflict of Interest No conflict declared. CRediT authorship contribution statement Henrico Stam : conceptualization, methodology, software, validation, formal analysis, investigation (supervision; experiments were conducted by 4 trained master students), data curation, writing-original draft, writing-review & editing, visualization. Freddy van der Veen: conceptualization, resources (analysis tools), software, writing-review & editing. Vaughn Steele: conceptualization, resources (analysis tools), software, writing-review & editing. Ingmar Franken : conceptualization, resources, writing-review & editing, supervision. Acknowledgements We thank Erasmus Behavioral Lab staff for their support. We also thank the 4 master students for their work in conducting the experiments. Finally, we thank Dr. E.M. Bernat for providing support in interpreting time-frequency analysis results. 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Henrico Stam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFACHgjFD8TMUCHGA4S1JDAwSDYgtDAQp8XgALFa+Bt4DzD+/GEXbXy8/fHnAgabxO1AEbxaJA7wJTDzJCTnbjtzxkx6BkNa4hygCF4tBgw8BswMCcy5227ksDHzMBxOnAEUIaiF8UdCfe7m+c8ff+Zh+E+cFgaehMO5GyQYDKR5GA4Q1iJxmMfgME/a8dwZZ3KAfjFINp7BTEALf3uP4cMfNtW5/e3HgSFWYSc7g73H8AE+LaC4QDLTgAEpdkbBKBgFo2AUkA0Ae79DkyIi8dAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0967-1041","institution":"Erasmus University Rotterdam","correspondingAuthor":true,"prefix":"","firstName":"C.","middleName":"Henrico","lastName":"Stam","suffix":""},{"id":285602043,"identity":"19773c81-2376-40db-9883-2077752644ba","order_by":1,"name":"Frederik M. van der Veen","email":"","orcid":"https://orcid.org/0000-0002-2466-6988","institution":"Erasmus University Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"Frederik","middleName":"M. van der","lastName":"Veen","suffix":""},{"id":285602044,"identity":"5b1bbd77-025e-4354-abde-aeb640f0c299","order_by":2,"name":"Vaughn R. Steele","email":"","orcid":"https://orcid.org/0000-0002-7903-2114","institution":"Yale Department of Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Vaughn","middleName":"R.","lastName":"Steele","suffix":""},{"id":285602127,"identity":"63cdc65d-e248-49ee-9402-0df43f9a3cbe","order_by":3,"name":"Ingmar H.A. Franken","email":"","orcid":"https://orcid.org/0000-0002-7853-2694","institution":"Erasmus University Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"Ingmar","middleName":"H.A.","lastName":"Franken","suffix":""}],"badges":[],"createdAt":"2024-03-30 09:06:23","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4191422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4191422/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53890202,"identity":"34397e4f-1254-460f-8937-6875c8e246df","added_by":"auto","created_at":"2024-04-01 20:56:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":295627,"visible":true,"origin":"","legend":"\u003cp\u003eERP components and marginal means for amplitude\u003c/p\u003e\n\u003cp\u003eNote: This figure shows the grand averaged ERP components for non-smokers (blue) and PWST (red) in panels [A, C] and estimated marginal means in panels [B, D]. Topography plots show 25-75 ms (E) and 200-400 ms (F). PWST: people who smoke tobacco; ERN: error-related negativity; CRN: correct response negativity, Pe: error positivity; Pc: correct positivity; [B, D]: error bars and upper/lower numbers (grey) represent 95% CI for the marginal mean (\u003cstrong\u003ebold\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4191422/v1/465c28be39ca14500909dcc9.jpeg"},{"id":53890198,"identity":"56299b0e-1ad1-4ddc-a673-fe47e23b13ac","added_by":"auto","created_at":"2024-04-01 20:56:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198756,"visible":true,"origin":"","legend":"\u003cp\u003eTheta power difference between people who smoke tobacco and non-smokers\u003c/p\u003e\n\u003cp\u003eNote: Figure shows group differences in Theta-power (Fz). [A, B] show the within-subject difference. [C] shows the between subjects difference. Topography plot inserts show power differences (t-test within-subject, ANOVA between subject); color range from black (\u003cem\u003ep \u003c/em\u003e\u0026gt; .10) to white \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01). [D] shows repeated measures ANOVA plot. PWST: people who smoke tobacco.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4191422/v1/811abe0baa8b2cb5a471fdd8.jpeg"},{"id":53890153,"identity":"a91d0dde-544e-4ea2-9942-01a2ca496053","added_by":"auto","created_at":"2024-04-01 20:56:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195551,"visible":true,"origin":"","legend":"\u003cp\u003eDelta power differences between people who smoke tobacco and non-smokers\u003c/p\u003e\n\u003cp\u003eNote: Figure shows group differences in Delta-power (PC2). \u0026nbsp;[A, B] show the within-subject differences. [C] shows the between subjects’ differences. Topography plot inserts show power differences (t-test within-subject, ANOVA between subject); color range from black (\u003cem\u003ep \u003c/em\u003e\u0026gt; .10) to white \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01). [D] shows repeated measures ANOVA plot. PWST: people who smoke tobacco.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4191422/v1/c01d11d1fef7330195767ac6.jpeg"},{"id":53890247,"identity":"070b87da-8487-4247-b82d-3ec0c5392c00","added_by":"auto","created_at":"2024-04-01 20:56:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4191422/v1/8c0039b2-3cda-49a0-af42-1b8ab344c294.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTobacco smoking is associated with impaired error monitoring\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTobacco smoking accounts for an estimated risk increase of 2\u0026ndash;4 times of coronary heart disease and stroke and a 25 times risk increase of developing lung cancer (Centers for Disease Control and Prevention \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The nicotine and other substances contained in tobacco are highly addictive (Mcgeoch et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Rose \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nutt et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and tobacco smoking is the substance addiction with the highest substance-attributable mortality rate in the world (Peacock et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), costing circa 8\u0026nbsp;million lives each year (World Health Organization \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the prevalence of tobacco smoking is declining, still 22.3% of the population smokes. The rapid growth of electronic nicotine delivery systems in the younger population is concerning: in the UK, regular use in youth (11\u0026ndash;17) tripled between 2021 and 2023 (ASH (Action on Smoking and Health) \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For these reasons, advancing knowledge about the neurophysiology of smoking addiction remains critical.\u003c/p\u003e \u003cp\u003eAddiction is associated with neurophysiological deficits in error monitoring (Luijten et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Volkow et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is the continuous assessment of ongoing actions and comparing the outcomes of these actions with internal goals and standards to implement remedial actions (Jocham and Ullsperger \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Unfavorable outcomes can either be errors or conflicts, and the neurophysiology of these outcomes can be studied effectively with Event-Related Potentials (ERPs) and Event-Related Oscillations (EROs). EM is a constituent of cognitive control (Ridderinkhof et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which refers to a behavioral regulation process that optimizes goal-directed behavior and counteracts automaticity (Friedman and Robbins \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe two prominent ERP components in EM are error-related negativity (ERN) (Gehring et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and error positivity (Pe) (Falkenstein et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The ERN is a negative deflection that peaks about 50 ms after error commission, which reflects a post-response error monitoring system (Yeung et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The error positivity (Pe), a positive deflection that peaks about 300 ms after error commission, has been associated with error awareness and importance (Overbeek et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is common to consider both errors and correct responses. The ERP\u0026rsquo;s following correct responses are referred to as Correct Response Negativity (CRN) (Vidal et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bartholow et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Pe-correct (Pc). The CRN is understood to represent an active component of EM, with enhanced activation during and after correctly processing unexpected stimuli (Bartholow et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Research has shown that this neurophysiological error monitoring mechanism is attenuated in many people with substance use disorders (Pasion and Barbosa \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lutz et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough these meta-analyses confirm the relation between substance use and diminished ERN and/or Pe, specifically for tobacco smoking (the most prominent global substance addiction), EM findings are scarce and inconclusive. One study found a lower Pe in People Who Smoke Tobacco (PWST; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23) but no group ERN-differences (Franken et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A later study (Rass et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) confirmed the null-finding on ERN and a lower Pe in PWST daily (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22) but also found that intermittent smoking (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31\u003cem\u003e)\u003c/em\u003e had a larger Pe than PWST daily and non-smokers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). In summary, the findings are inconsistent. It is crucial to bridge this gap as it raises uncertainties about the applicability of addiction neurophysiology, particularly concerning EM, to approximately 1.3\u0026nbsp;billion tobacco users worldwide.\u003c/p\u003e \u003cp\u003eThere are some possible explanations for these inconsistent findings. These studies' relatively small sample sizes may explain these inconsistencies (Lutz et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Another possibility is the influence of confounders (i.e., internalizing psychopathology and gender), as larger ERN amplitudes were found in internalizing psychopathology, such as obsessive-compulsive disorder (Pasion and Barbosa \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, women may have reduced ERN/Pe compared to men, but research is inconclusive (Lutz et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The present study addresses these possible causes for inconsistent results between EM and smoking through a larger, gender-balanced sample size and by controlling for personality.\u003c/p\u003e \u003cp\u003eIt is argued that despite many years of research, there is still a substantial hiatus in understanding the \u003cem\u003econtents\u003c/em\u003e of an EEG signal, defined as the cognitive computations that underly and implement perception, cognition, and action (Siegel et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cohen \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Neural oscillations have been proposed as an excellent link to neurophysiology for advancing knowledge about these computations. Oscillations are segmented into frequency bands that correspond with the duration window required for information processing (Canolty and Knight \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); e.g., low-frequency bands (such as delta; 0\u0026ndash;3 Hz) correspond with relatively longer processing across larger spatial brain regions, and faster frequencies (such as beta; 13\u0026ndash;30 Hz) that are more local and related to shorter processing. Researchers found that event-related oscillations (EROs) generate ERPs through an evoked effect or phase resetting (Sauseng et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf specific interest to the present study are the theta (3\u0026ndash;9 Hz) and delta (0\u0026ndash;3 Hz) frequency bands that have a role in EM. Research has shown that theta-band activity plays a general role in conflict processing and, more specifically, links a neural network in response to conflict (Nigbur et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cohen and Donner \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). There is a robust relationship between theta power and ERN (Luu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Trujillo and Allen \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, it has been concluded that theta-band activity may be pivotal in a broader cognitive control mechanism (Cavanagh and Frank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Delta-band is involved in error monitoring, which is evidenced by the relationship with ERN (Yordanova et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Munneke et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Pe (Luu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and P300 (Rawls et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003csup\u003e1\u003c/sup\u003e. However, the role of the delta band is understood to be broader, e.g., in motivation and the sustainment of concentration (Harmony \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome studies provide evidence for attenuated theta and/or delta EROs, e.g., with problematic substance use (alcohol, nicotine, or cannabis) (Harper et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), alcohol use disorder (AUD) (Kamarajan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Harper et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and methamphetamine use disorder (Ghaderi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is a general research opportunity to incorporate EROs in EM addiction research, and specifically for tobacco smoking, the present study provides the first cross-sectional results in the field.\u003c/p\u003e \u003cp\u003eIn summary, evidence is accumulating that diminished EM is associated with addiction. Yet, for tobacco smoking, despite being the most prominent global substance addiction, there is no firm conclusion. A large sample was investigated to close this gap, and advanced analysis methods were used, such as trial-level analysis of ERP data. In addition, time-frequency analysis of the oscillatory brain activity was undertaken to enhance insight into the underlying processes of the ERN and Pe. This study hypothesized that tobacco smoking may be associated with diminished ERN-amplitude (i.e., less negative) and (or) diminished Pe-amplitude (i.e., less positive). Furthermore, we expected to find diminished EROs for PWST following errors for theta and delta frequency compared to non-smokers. Gender and personality (internalizing/externalizing) may be confounding variables (Lutz et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study was part of a preregistration (ERPs: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/8AQBU\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/8AQBU\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); (EROs: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/4FQEA\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/4FQEA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This report covers the association between tobacco smoking and error monitoring; other elements of the pre-registration will be reported separately. Any significant deviations from the preregistration protocols are noted throughout the manuscript.\u003c/p\u003e"},{"header":"METHODS AND MATERIALS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eThe aim was to include at least 90 valid participants, balanced for smoking status and gender. Participants were recruited based on inclusion and exclusion criteria on campus, referral from other studies, social media, and personal networks. Inclusion criteria were age (18\u0026ndash;40), literacy in Dutch (speaking and reading), and informed consent. Daily smoking at the time of the study was required to be included as PWST, and non-smoking participation was eligible for people not smoking at the time of the study (i.e., lifetime use allowed).\u003c/p\u003e\n\u003cp\u003eExclusion criteria were a diagnosis of psychiatric or neuro-physiological disorder or medication (with a known distorting influence on behavior or neurophysiology). Participants were paid 25\u0026ndash;30 euros. Psychology students at Erasmus University could choose between payment or 2 hours of course credits.\u003c/p\u003e\n\u003cp\u003eThere were 110 participants (55 women, 55 men), of which 94 were included in this study after screening. There were 46 PWST; 63.0% smoked\u0026thinsp;\u0026lt;\u0026thinsp;10 cigarettes per day, 32.7% smoked 11\u0026ndash;20 cigarettes, and 4.3% smoked 21\u0026ndash;30 cigarettes per day. Data from 16 participants were excluded (ADHD\u003csup\u003e2\u003c/sup\u003e:4; \u0026lt; 50% correct trials: 3; failed EEG: 2; bad eye vision:1; \u0026lt; 9 artifact fee errors:6). The final study sample included 46 women (47.8% PWST) and 48 men (50.0% PWST). The average age for PWST was 22.5 years; for non-smokers, this was 21.2 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariation to pre-registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePayments were increased from 25 to 30 euros over time to stimulate participation. The minimum number of artifact-free error trials for inclusion was set to 9 to replicate prior research (Franken et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). A\u0026thinsp;\u0026lt;\u0026thinsp;50% correct trial cut-off was applied as a quality threshold. Participation continued beyond the preregistered maximum (100) to compensate for ineligible participation (e.g., reported ADHD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eApparatus/instruments\u003c/h2\u003e\n\u003cp\u003eFor the Eriksen-flanker task, design and EEG recording followed prior research (Franken et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this task, participants were exposed to a series of letters and asked to identify the middle letter in an incongruent and congruent condition. The middle letter may differ from the other letters (e.g., SSHSS/HHSHH) as opposed to the congruent condition (SSSSS/HHHHH). Trials started with a 250 ms cue (^) where the central letter of the letter strings would appear. Letter strings were presented for 50 ms. A feedback symbol (duration\u0026thinsp;=\u0026thinsp;500 ms) followed 700 ms after the stimulus about the correctness of the response (\u0026lsquo;ooo\u0026rsquo; or \u0026lsquo;XXX\u0026rsquo;). When no response was made within 700 ms, participants received a feedback stimulus (\u0026lsquo;!\u0026lsquo;) informing them that their answer was not fast enough. Feedback was provided to support task focus and is known not to influence ERN/Pe amplitude, accuracy, or reaction time (Schroder et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The experiment started with a practice phase of 8 trials and was followed by 5 blocks of 80 trials. Congruent (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200) and incongruent stimuli (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200) were random but balanced at participant level. The device recording responses was a Serial Response Box (SR BOX) (Psychology Software Tools) with 5 buttons. Participants were to press [1] when the middle letter was \u0026lsquo;S\u0026rsquo; and [5] when the middle letter was \u0026lsquo;H.\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eNicotine dependence was measured with the Fagerstrom Test of Nicotine Dependence (FTND) (Heatherton et al. \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). The score indicates the level of addiction to nicotine on a scale of 1\u0026ndash;10 and includes questions about, e.g., the number of cigarettes smoked and abstaining during illness. Smoking was validated with a Breathalyzer pre-test. The Dutch version of the BIS/BAS scales (Carver and White \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e; Franken et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) was administered. Total BIS and BAS scores were z-scaled for the entire sample.\u003c/p\u003e\n\u003cp\u003eThe experiment order was: time estimation task/ Eriksen-flanker task, delay discounting task, demographics, Fagerstrom test of Nicotine dependence questionnaire, BIS/BAS questionnaire, and alcohol use questionnaire (Lemmens et al. \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). The total experiment lasted circa 1.5 hours per participation. The order of the flanker task and time estimation task was counterbalanced to control for carry-over effects between the tasks.\u003c/p\u003e\n\u003cp\u003eStimuli for the Eriksen-Flanker task were presented electronically using the E-Prime 3.0 software (Psychology Software Tools, Pittsburgh, PA). The questionnaires were administered in Qualtrics, version 2022 (Qualtrics, Provo, UT).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eEEG Recording and signal processing\u003c/h2\u003e\n\u003cp\u003eThe EEG was recorded using a Biosemi Active-Two amplifier system from 32 scalp sites (10\u0026ndash;20 system) with Ag/AgCl (active) electrodes mounted in an elastic cap. Six additional electrodes were attached to the left and right mastoids, two outer canthi of both eyes (HEOG), and infraorbital and supraorbital channels of the eye (VEOG). Signals were recorded with a low-pass filter of 134 Hz and were digitized with a sample rate of 512 Hz and 24-bit analog/digital conversion. BioSemi uses the common mode sense (CMS) and driven right-leg (DRL) electrodes to create a feedback loop that replaces the conventional ground electrode. The CMS was used as an online reference. Data were off-line re-referenced to computed linked mastoids and filtered with a bandpass of .1\u0026ndash;30 Hz (phase shift-free Butterworth filters; notch filter 50 Hz).\u003c/p\u003e\n\u003cp\u003eFor ERP analysis, channels Fz, Cz, and Pz were selected. After ocular correction (Gratton et al., 1983) with VEOG as a reference, trials exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;75 \u0026micro;V, voltage step\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026micro;V/ms, or activity\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026micro;V were excluded from the analysis. Data was segmented in epochs of 1000 ms, 200 ms before, and 800 ms after response. The mean pre-response period of 200\u0026ndash;50 ms served as a baseline. While the baseline was proximate to response, this is not an issue for obtaining good internal consistency of ERP measures (Sandre et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Klawohn et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ERN was defined as the mean value in the 25\u0026ndash;75 ms time segment after the onset of the response. The Pe was defined as the mean value in the 200\u0026ndash;400 ms time segment after the onset of the response.\u003c/p\u003e\n\u003cp\u003eFor ERO analysis, all 32 channels were included. After ocular (Gratton \u0026amp; Coles) correction with common reference, trials exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;100 \u0026micro;V voltage step\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026micro;V/ms or minimum activity\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026micro;V were excluded for channels of interest (all electrodes except rim electrodes F7, F8, Fp1, Fp2, O1, O2, Oz, P7, P8, T7, and T8). EEGLAB functions were used to automatically reject channels (criterium: \u0026gt;=3 z-scores based on probability) and subsequently to interpolate missing channels (spherical method). A mean of 1.6 channels (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.9) was interpolated per participant.\u003c/p\u003e\n\u003cp\u003eExperiments were conducted by 4 trained master students in Clinical Psychology as part of their master thesis assignment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariance to pre-registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline for ERN/Pe was increased from 100 ms to 200 ms to 50 ms pre-stimulus to reduce proximity to response and to have a slightly more extended baseline period. Artifact rejection applied for ERO was optimized by adding 19 extra channels to F3/F4. This step was necessary to clean the data adequately for TF-PCA analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eERP Analysis\u003c/h2\u003e\n\u003cp\u003eEEG data was inspected using BrainVision Analyzer (Brain products GmbH \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). ERP data was extracted from Vision Analyzer for analysis in R (R Core Team, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), and ERO data was extracted for Time-Frequency analysis.\u003c/p\u003e\n\u003cp\u003eThe dependability of ERP measures (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) was measured using the ERP Reliability Analysis (ERA) Toolbox v 0.5.1 (Clayson and Miller \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The ERA Toolbox used CmdStan version 2.24.1 (Stan Development Team, 2020), and Markov chain Monte Carlo estimation procedures used 3 chains and 10,000 iterations each to estimate variance components.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eTime-frequency analysis\u003c/h2\u003e\n\u003cp\u003eFor this part of the study, the Time-Frequency Principal Component Analysis (TF-PCA) method was used to measure oscillatory power in the theta (3\u0026ndash;9 Hz) and delta (\u0026lt;\u0026thinsp;3 Hz) bands (Bernat et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Buzzell et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The time-frequency surface of the EEG signal was subjected to a PCA that provides both the most important scalp regions in terms of activity and the most important time scales for when there is neurophysiological activity. Power is understood as marginal power, not absolute power, because of the TF-transformation process (Janssen and Claasen \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTF-PCA was conducted based on an extracted epoch from \u0026minus;\u0026thinsp;1000 ms before response to +\u0026thinsp;2000 ms after response. TF analysis typically requires longer epochs than ERP analysis. As a rule of thumb, 3 cycles are needed for the lowest analysis frequency (Cohen \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e); e.g., a 3-second epoch will capture 3 cycles of 1 Hz. The chosen epoch size was consistent with other TF-PCA studies (Bernat et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Morales et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) on delta frequency.\u003c/p\u003e\n\u003cp\u003eSubsequently, the time windows for decomposition were set from \u0026minus;\u0026thinsp;100 ms before response to +\u0026thinsp;500 ms post response to focus on the events of interest (ERN and Pe). The analysis was conducted on phase-locked data (average power). The toolbox operates at the participant level, so a trial-level statistical analysis could not be undertaken.\u003c/p\u003e\n\u003cp\u003eThe software used was the Psychophysiology toolbox (Curtin, 2011), EEGLAB (version 2023.1, Delorme \u0026amp; Makeig, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), and the TF-PCA toolbox (Bernat et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Buzzell et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). EEGLAB, the Psychophysiology toolbox, and the TF-PCA toolbox were run in MATLAB (Version: 9.11.0; R2021b Update 6) (The MathWorks Inc. 2023).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eAnalysis plan\u003c/h2\u003e\n\u003cp\u003eThe first step in the analysis was to investigate the association between ERPs (i.e., trial-level ERN/Pe) as independent variables and tobacco smoking as a between-subject factor by linear mixed model regression for correct responses and errors. Linear mixed model regression has the advantage of allowing the use of trial-level granular data while accounting for both individual differences (i.e., random effects) and hypothesis testing (fixed effects) simultaneously (Pinheiro and Bates \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gueorguieva \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Gender and personality (z-scaled BIS/BAS) were included in this analysis. Models with a 5-way interaction between smoking, BIS, BAS, response, and gender suffered from substantial multicollinearity (VIF\u0026thinsp;\u0026gt;\u0026thinsp;10). The following model with a separate interaction term for smoking was applied (VIFs\u0026thinsp;\u0026lt;\u0026thinsp;5) in Wilkinson notation:\u003c/p\u003e\n\u003cp\u003eERN/Pe amplitude\u0026thinsp;~\u0026thinsp;condition * group\u0026thinsp;+\u0026thinsp;response * scaled(BIS) * scaled(BAS) * gender + (1\u0026thinsp;+\u0026thinsp;condition|subject).\u003c/p\u003e\n\u003cp\u003eThe second step was to investigate time-frequency differences in theta and delta bands by principal component analysis. To identify the main components, the participants\u0026rsquo; mean ERN (Fz) and Pe (Pz) were regressed on theta and delta power on the same channels (in Supplemental Information). Finally, regressions were run with EROs as dependent variables in models identical to the ERP analysis adapted only to repeated measures ANOVA.\u003c/p\u003e\n\u003cp\u003eFor the linear mixed model analyses, R-packages \u003cem\u003elme4\u003c/em\u003e version 1.1\u0026ndash;31 (Bates et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and \u003cem\u003elmerTest\u003c/em\u003e version 3.1-3 (Kuznetsova et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and \u003cem\u003eemmeans\u003c/em\u003e version 1.8.7 (Lenth \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) were used. JASP (JASP Team \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) (version 0.17.2.1) was used for other statistical analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eVariation to pre-registration\u003c/h2\u003e\n\u003cp\u003eBIS/BAS (z-scaled) were covariates in the analysis instead of dichotomizing these into a single factor variable (internalizing/externalizing). This was deemed more informative. Neurophysiological measures were not scaled to ease interpretation; this did not influence the results. The regression model for EROs focused only on the main channels of interest (Fz, Pz), as identified in the ERP analysis. Regression models were validated by inspecting multicollinearity (VIF); models with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were excluded. This was not noted in pre-registration but is an important test to avoid biased results (Schielzeth et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipant characteristics and behavioral performance\u003c/h2\u003e\n\u003cp\u003eDescriptive variables for the included participants are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The internal consistency (McDonald\u0026rsquo;s \u0026omega;) of total BAS and BIS scores was .75 and .75, respectively. The main difference between PWST and non-smokers was BAS Fun-seeking (\u003cem\u003et\u003c/em\u003e(92) = -2.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02). The level of nicotine dependence for PWST was modest (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.2, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.00), given that the maximum obtainable score for the FTND is 10 points. The internal consistency of FTND (\u0026omega;) was .77. Between PWST and non-smokers there was no significant difference in age (\u003cem\u003et\u003c/em\u003e(92) = -1.93, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.06), gender (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94)\u0026thinsp;=\u0026thinsp;.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.83) or education (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (5, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94)\u0026thinsp;=\u0026thinsp;6.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26), indicating good comparability of the two groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive statistics for study participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNon-smokers\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePWST\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;48)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003et-test\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eEffect size\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eContinuous variables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et (92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking - FTND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage errors on task\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eContingencies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e2 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e- Women\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e50.0\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e47.8\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e- Men\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e50.0\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e52.2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation (types)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e- Master/bachelor level (2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e93.8\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e82.6\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e- Other levels (4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e6.2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e17.4\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e: PWST: people who smoke tobacco; BAS: behavioral activation system; BIS: behavioral inhibition system; FTND: Fagerstrom Test of Nicotine Dependence; * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor personality characteristics, neither BAS (\u003cem\u003et\u003c/em\u003e(92) = -1.90, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.06) nor BIS was associated with being a smoker (\u003cem\u003et\u003c/em\u003e(92)\u0026thinsp;=\u0026thinsp;1.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.21). Concerning task performance, PWST committed the same level of errors (\u003cem\u003et\u003c/em\u003e(92)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.94) on the task (11.1% of trials) as non-smokers (11.0% of trials).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eERP analysis\u003c/h2\u003e\n\u003cp\u003eAs expected, the ERN/CRN was most pronounced at Fz and least at Pz. Pe/Pc was strongest at Pz and weakest at Fz (Figure S1). Summary information for ERP components (before regression) by group is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall dependability of the ERN was .82 (CI [.73, .89] for PWST and .83 (CI [.75, .89] for non-smokers, respectively. For the Pe this was .84 (CI [.77, .90]) for PWST and .87(CI [.81, .92]) for non-smokers respectively. The dependability of ERP components was acceptable to good.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary data for ERP components, amplitude (before regression), and trials\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComponent\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall dependability\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTrials \u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTrial Range\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePWST (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.97 CI [.96 .98]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e301\u0026thinsp;\u0026plusmn;\u0026thinsp;77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61\u0026ndash;374\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eERN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.82 CI [.73 .89]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e36\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.98 CI [.98 .99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e301\u0026thinsp;\u0026plusmn;\u0026thinsp;77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61\u0026ndash;374\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.84 CI [.77 .90]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e36\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-smokers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.98 CI [.97 .99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e318\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130\u0026ndash;382\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eERN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.83 CI [.75 .89]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.98 CI [.97 .99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e318\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130\u0026ndash;382\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.87 CI [.81 .92]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: PWST: people who smoke tobacco. Overall dependability: reliability coefficient estimates and their 95% credible intervals after applying minimum trial cutoff (9).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents grand averaged ERPs (A, C) for Fz and Pz and estimated marginal means (B, D). Table S1 provides the ANOVA results of the linear mixed models (LMMs).\u003c/p\u003e\n\u003cp\u003eCondition (error or correct) was strongly associated with both ERN amplitude (\u003cem\u003eF\u003c/em\u003e(1,85.0)\u0026thinsp;=\u0026thinsp;142.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.63) and Pe amplitude (\u003cem\u003eF\u003c/em\u003e(1,81.8)\u0026thinsp;=\u0026thinsp;199.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.71). Group (smoking or non-smoking) interacted with condition for both ERN, (\u003cem\u003eF\u003c/em\u003e(1,87.6)\u0026thinsp;=\u0026thinsp;5.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.06) and Pe, (\u003cem\u003eF\u003c/em\u003e(1,84.6)\u0026thinsp;=\u0026thinsp;9.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.10). There was no fixed effect of group on ERN and Pe.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(A, B), PWST had a smaller ERN (\u003cem\u003eM\u003c/em\u003e=-4.2, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.70) compared to non-smokers (\u003cem\u003eM\u003c/em\u003e=-5.4, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.70). Furthermore, PWST had a larger (on a negative scale) CRN (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.62) than non-smokers (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.61). Contrast comparisons by condition were not significant, for both incorrect responses (\u0026Delta;\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.22) and correct responses (\u0026Delta;\u003cem\u003eM\u003c/em\u003e=-1.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.11). A pairwise contrast test clarified that the interaction between condition and group was caused by the difference between ERN and CRN (\u0026Delta;ERN; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02).\u003c/p\u003e\n\u003cp\u003eAs Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(C, D) shows, Pe for PWST (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.2, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78) was smaller compared to non-smokers (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.4, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.77). However, PWST had a larger Pc (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.3, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.73) than non-smokers (\u003cem\u003eM\u003c/em\u003e=-1.4, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.72). Contrast comparison by condition was significant for errors (\u0026Delta;\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05) but not for correct responses (\u0026Delta;\u003cem\u003eM\u003c/em\u003e=-1.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.11). A pairwise contrast test showed that the interaction between condition and group was driven mainly by the difference between Pe and Pc (\u0026Delta;Pe; \u003cem\u003eM\u003c/em\u003e=-3.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). These results show that the difference between errors and correct responses drove the interactions between ERP-components and group.\u003c/p\u003e\n\u003cp\u003eFor ERN, there were no interactions with gender or personality (BIS/BAS). For Pe, fixed effects were found for both gender (\u003cem\u003eF\u003c/em\u003e(1, 84.6)\u0026thinsp;=\u0026thinsp;6.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.07) and BIS (\u003cem\u003eF\u003c/em\u003e(1, 85.2)\u0026thinsp;=\u0026thinsp;7.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.08). There was a negative association between BIS (\u0026beta;=-1.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03) and Pe/Pc, and males had a lower Pe/Pc than females (\u0026beta;=-1.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08). A follow-up analysis within group revealed that these differences came from the non-smoking group for both BIS (\u003cem\u003eF\u003c/em\u003e(1, 39.43)\u0026thinsp;=\u0026thinsp;5.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.13) and gender (\u003cem\u003eF\u003c/em\u003e(1, 39.66)\u0026thinsp;=\u0026thinsp;9.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, =\u0026thinsp;.20). In the smoking group, neither gender nor BIS was significantly associated with Pe.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eERO analysis\u003c/h2\u003e\n\u003cp\u003eSupplemental Information (Figure S2, Table S2) provides details on the solution and regression analysis between ERPs and EROs. In summary, one theta frequency component at Fz was strongly negatively associated with the ERN and the CRN. Furthermore, for the CRN, a delta component at Fz was strongly positively associated. For Pe, a positively associated delta component was found at Pz.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eTheta-frequency\u003c/h2\u003e\n\u003cp\u003eTheta differences by group are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S3. Theta was strongly associated with condition (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;57.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.40). Furthermore, there was a modest fixed effect of group (C; D; \u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;3.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.05), showing lower theta-power for PWST (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00) compared to non-smokers (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00). There was a stronger association between group and condition, (A, B, D; \u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;5.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.06). A post hoc Tukey test confirmed that there was a difference for errors (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02) but not for correct responses (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00).\u003c/p\u003e\n\u003cp\u003eTheta power difference by condition (A, B) was stronger for non-smokers, as the color intensity shows, and also spanned a broader frequency range. Topography plot 1C shows that the group difference was mainly in fronto-central and parietal regions. Gender, BIS, and BAS did not influence these, nor did they interact.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eDelta-frequency\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Tables S4 and S5 show Delta power group differences. Starting with the Pe-related component (PC2), a strong association, yet less pronounced than theta, was found with condition (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;27.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.24). There was a fixed effect of group (C; D); \u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;8.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.10); PWST had less delta-power (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03) compared to non-smokers (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.32, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03). The topography shows that this difference was primarily located in the parietal region (white area).\u003c/p\u003e\n\u003cp\u003eThere was no interaction between condition and group (A; B; \u003cem\u003eF\u003c/em\u003e(1,85)\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.22, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.02), so the power difference by group was similar between conditions. There was a fixed effect for gender (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;6.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.07) with higher power for women (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.31, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04) compared to men (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04). There was further interaction between gender and condition (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;7.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01). A Tukey test highlighted higher power for females (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01) on errors, but no difference for correct responses (\u003cem\u003eM\u003c/em\u003e=-.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.88). There was also a positive association between condition and BIS (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;3.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05), which indicated that delta power on correct responses covaried modestly with BIS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003cp\u003eThen for the CRN-related delta component (PC4; Table S5), there was an association with condition (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;6.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.08). However, there was no fixed effect of group (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;2.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.03), nor was there any interaction between condition and group (\u003cem\u003eF\u003c/em\u003e(1, 85)\u0026thinsp;=\u0026thinsp;1.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.02). There were also no relations with BAS or BIS, nor with gender or any interactions between these. This component of the delta band was not related to smoking.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides summary information about the correlations between ERPs and EROs. These demonstrate that ERPs were positively associated with each other, e.g. CRN and ERN (\u003cem\u003er\u003c/em\u003e(94)\u0026thinsp;=\u0026thinsp;.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Pe with Pc (\u003cem\u003er\u003c/em\u003e(94)\u0026thinsp;=\u0026thinsp;.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Furthermore, there was a weak negative association between ERP-components; e.g., the ERN and the Pe (\u003cem\u003er\u003c/em\u003e(94)=-.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), which is an indication of an error complex. Lastly, there was a moderate negative association between ERN and theta-error (\u003cem\u003er\u003c/em\u003e(94)=-.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and strong associations existed between Pc and delta-correct (\u003cem\u003er\u003c/em\u003e(94)=-.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Pe and delta-error (\u003cem\u003er\u003c/em\u003e(94)\u0026thinsp;=\u0026thinsp;.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Internal consistency (\u0026omega;) of ERN and theta measures was .51; for Pe and delta measures, this was .48. Therefore, EROs and ERPs individually capture different aspects of error monitoring neurophysiology while associated.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePearson correlations between neurophysiological indices of performance monitoring\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCRN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eERN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePc\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTheta\u003c/p\u003e\n\u003cp\u003ecorrect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTheta\u003c/p\u003e\n\u003cp\u003eerror\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDelta\u003c/p\u003e\n\u003cp\u003ecorrect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDelta\u003c/p\u003e\n\u003cp\u003eerror\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eERN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTheta correct\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTheta error\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDelta correct\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDelta error\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"16\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to clarify whether tobacco smoking is associated with attenuated error monitoring (EM), manifest in event-related potentials (ERPs) and event-related oscillations (EROs), by investigating a relatively large sample while controlling for potential confounders.\u003c/p\u003e \u003cp\u003eConsistent with prior research, a smaller error positivity (Pe) was seen for People Who Smoke Tobacco (PWST) compared to non-smokers (Franken et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, a blunted error-related negativity (ERN) was found, broadly consistent with findings from a developmental study on smoking initiation (Anokhin and Golosheykin \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although it has been shown that ERN and Pe represent distinct aspects of error monitoring (Overbeek et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), they often occur jointly. This has been termed the ERN-Pe error complex (Hajcak et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, identifying a blunted ERN for PWST and the same finding on Pe provides robust evidence for broadly dysfunctional error monitoring in PWST at two levels. The blunted ERN suggests hypoactive error detection, and the attenuated Pe indicates a lack of conscious error recognition (Overbeek et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and/or motivational salience (Ridderinkhof et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) of errors.\u003c/p\u003e \u003cp\u003eIt may seem bold to refer to dysfunctional error monitoring when PWST committed the same percentage of errors as non-smokers. Yet, this is a classic debate, as findings are inconsistent (Gehring et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some studies found a relationship between increased accuracy and larger ERNs, whereas others did not (for an overview, see (Luck and Kappenman \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe ERP components could be further understood by investigating theta and delta power. Theta power was more strongly associated with condition than delta power, which is consistent with the specific role of the theta band in conflict (Nigbur et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cohen and Donner \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cavanagh and Frank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas the delta band is more broadly involved in motivation and attention regulation (Harmony \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Also congruent with past research, ERN was strongly associated with theta band power (Luu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Trujillo and Allen \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and Pe was driven by delta band power (Luu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Correlations between neurophysiological measures were apparent, but the low level of internal consistency demonstrated that EROs and ERPs capture related yet different aspects of error monitoring.\u003c/p\u003e \u003cp\u003eTurning to group differences, a weaker theta burst was seen for PWST after errors similar to the ERN. This adds to the evidence that the neurophysiological impact of errors was less for PWST than for non-smokers and implies impaired error monitoring (Cavanagh and Frank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This is consistent with findings concerning impaired theta power in alcohol use disorder (AUD) (Kamarajan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Harper et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and problematic substance use of alcohol, nicotine, or cannabis (Harper et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, PWST demonstrated lower delta power for both errors and correct responses. Attenuated delta power was previously found for AUD (Jones et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), but another study did not find this in problematic use (Harper et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Drawing on broader psychopathology literature, a study found greater theta and delta power in obsessive-compulsive disorder (OCD) patients compared to healthy controls. This is relevant, as OCD patients are known to exhibit hyperactive EM. Taken together, the attenuated theta and delta power found in PWST corroborate the results of ERPs, suggesting an apparent deficit in EM.\u003c/p\u003e \u003cp\u003eThis study cannot answer the fundamental question of whether impaired error monitoring in tobacco smoking addiction is a cause or consequence. The status quo in the literature suggests a bi-directional relationship (Goschke \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Friedman and Robbins \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) between error monitoring and addiction. Importantly, this study provides further evidence for impaired EM as a biomarker (Lutz et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) of addiction in the case of tobacco smoking.\u003c/p\u003e \u003cp\u003eThis study confirms that the inclusion of gender and Personality in ERP/ERO studies is relevant. For non-smokers, a larger Pe-amplitude was found for women, but the ERN did not differ. This finding contradicts prior research(Larson et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) that found higher ERN- and Pe-amplitude for men. Unexpectedly, for non-smokers, there was a negative association between BIS and Pe. It\u0026rsquo;s difficult to interpret this, as results of studies into internalizing disorders with Pe are inconsistent; for an overview, see (Macedo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The lack of relevance of gender and BIS/BAS in PWST suggests uniformity in the sample, which, in principle, supports the generalizability of findings (Lucas \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Jager et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs nicotine dependence in the smoker sample was relatively low, it can be argued that the findings relate to tobacco use rather than nicotine addiction. The authors view this differently for two reasons. Firstly, nicotine dependence (tobacco use disorder in DSM-5 terminology) was found (Oliver and Foulds \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in ca. 65%-80% of people who smoke daily, already at usage rates of 1\u0026ndash;10 cigarettes. Another study found a high risk of progressing from daily smoking to dependence (Breslau et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Secondly, the findings in this study are consistent with and add further evidence of dysfunctional EM found in a broader spectrum of substance addictions (Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This congruence suggests the presence of addiction problems in the investigated sample of PWST.\u003c/p\u003e \u003cp\u003eThe results showed a clear and consistently blunted ERN and Pe in PWST compared to non-smokers, providing substantial evidence for attenuated EM at multiple levels. These findings were consistent with reduced power in event-related theta- and delta oscillations. Both errors and correct responses contributed to the findings, demonstrating their joint importance in EM. This study provides evidence for deficient EM in PWST, manifested as lower ERN and Pe, which appears to be driven by reduced theta and delta power.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eCompliance with ethical standards\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted under number ETH2122-0373 by the DPECS Research Ethics Review Committee on January 19, 2022.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict declared.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRediT authorship contribution statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHenrico Stam\u003c/strong\u003e: conceptualization, methodology, software, validation, formal analysis, investigation (supervision; experiments were conducted by 4 trained master students), data curation, writing-original draft, writing-review \u0026amp; editing, visualization. \u003cstrong\u003eFreddy van der Veen:\u003c/strong\u003e conceptualization, resources (analysis tools), software, writing-review \u0026amp; editing. \u003cstrong\u003eVaughn Steele:\u003c/strong\u003e conceptualization, resources (analysis tools), software, writing-review \u0026amp; editing. \u003cstrong\u003eIngmar Franken\u003c/strong\u003e: conceptualization, resources, writing-review \u0026amp; editing, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Erasmus Behavioral Lab staff for their support. We also thank the 4 master students for their work in conducting the experiments. Finally, we thank Dr. E.M. Bernat for providing support in interpreting time-frequency analysis results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnokhin AP, Golosheykin S (2015) Neural correlates of error monitoring in adolescents prospectively predict initiation of tobacco use. Dev Cogn Neurosci 16:166\u0026ndash;173. https://doi.org/10.1016/j.dcn.2015.08.001\u003c/li\u003e\n\u003cli\u003eASH (Action on Smoking and Health) (2023) Use of e-cigarettes among young people in Great Britain. https://ash.org.uk/resources/view/use-of-e-cigarettes-among-young-people-in-great-britain. Accessed 13 Jan 2024\u003c/li\u003e\n\u003cli\u003eBartholow BD, Pearson MA, Dickter CL, et al (2005) Strategic control and medial frontal negativity: Beyond errors and response conflict. 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Clinical Neurophysiology 125:1417\u0026ndash;1426. https://doi.org/10.1016/j.clinph.2013.12.001\u003c/li\u003e\n\u003cli\u003eRawls E, Miskovic V, Lamm C (2020) Delta phase reset predicts conflict-related changes in P3 amplitude and behavior. Brain Res 1730:. https://doi.org/10.1016/j.brainres.2020.146662\u003c/li\u003e\n\u003cli\u003eRidderinkhof KR, Ramautar JR, Wijnen JG (2009) To PE or not to PE: A P3-like ERP component reflecting the processing of response errors. Psychophysiology 46:531\u0026ndash;538. https://doi.org/10.1111/j.1469-8986.2009.00790.x\u003c/li\u003e\n\u003cli\u003eRidderinkhof KR, Van Den Wildenberg WPM, Segalowitz SJ, Carter CS (2004) Neurocognitive mechanisms of cognitive control: The role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn 56:129\u0026ndash;140. https://doi.org/10.1016/j.bandc.2004.09.016\u003c/li\u003e\n\u003cli\u003eRose JE (2006) Nicotine and nonnicotine factors in cigarette addiction. In: Psychopharmacology. pp 274\u0026ndash;285\u003c/li\u003e\n\u003cli\u003eSandre A, Banica I, Riesel A, et al (2020) Comparing the effects of different methodological decisions on the error-related negativity and its association with behaviour and gender. International Journal of Psychophysiology 156:18\u0026ndash;39. https://doi.org/10.1016/j.ijpsycho.2020.06.016\u003c/li\u003e\n\u003cli\u003eSauseng P, Klimesch W, Gruber WR, et al (2007) Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion. Neuroscience 146:1435\u0026ndash;1444\u003c/li\u003e\n\u003cli\u003eSchielzeth H, Dingemanse NJ, Nakagawa S, et al (2020) Robustness of linear mixed-effects models to violations of distributional assumptions. 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Biol Psychol 64:265\u0026ndash;282. https://doi.org/10.1016/S0301-0511(03)00097-8\u003c/li\u003e\n\u003cli\u003eVolkow ND, Koob GF, McLellan AT (2016) Neurobiologic Advances from the Brain Disease Model of Addiction. New England Journal of Medicine 374:363\u0026ndash;371. https://doi.org/10.1056/nejmra1511480\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2023) Tobacco fact sheet. https://www.who.int/news-room/fact-sheets/detail/tobacco. Accessed 11 Nov 2023\u003c/li\u003e\n\u003cli\u003eYeung N, Botvinick MM, Cohen JD (2004) The Neural Basis of Error Detection: Conflict Monitoring and the Error-Related Negativity. Psychol Rev 111:931\u0026ndash;959. https://doi.org/10.1037/0033-295X.111.4.931\u003c/li\u003e\n\u003cli\u003eYordanova J, Falkenstein M, Hohnsbein J, Kolev V (2004) Parallel systems of error processing in the brain. Neuroimage 22:590\u0026ndash;602. https://doi.org/10.1016/j.neuroimage.2004.01.040\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The reason to consider P3 is that it has been suggested that the Pe is similar to the P3, in response to the internal (i.e., without external cues) detection of errors (Davies et al. 2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The participants with ADHD and impaired eye vision had not correctly understood the exclusion criteria, as they reported the diagnosis in the demographical questionnaire or afterwards to the experimenter.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplemental Information, Supplementary Figures and Supplementary Tables are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Erasmus University Rotterdam","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tobacco smoking, nicotine, addiction, ERN, Pe, oscillations, theta, delta, error monitoring ","lastPublishedDoi":"10.21203/rs.3.rs-4191422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4191422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRationale: \u003c/strong\u003e\u003c/em\u003eAddiction is associated with neurophysiological deficits in error monitoring (EM).EM refers to the continuous assessment of ongoing actions and comparing the outcomes of these actions with internal goals and standards, measured by, e.g., event-related potentials (ERPs). Yet, for tobacco smoking, despite being the largest and most lethal addictive substance globally, there is no firm conclusion on the relation with EM due to a paucity of studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjectives and methods: \u003c/strong\u003e\u003c/em\u003eA large gender-balanced sample (N=94, of which 46 were people who smoke tobacco) was established. The Eriksen-flanker task, a widely used speeded response task known to result in error commission, was administered while recording the electroencephalogram (EEG). The error-related negativity (ERN) and the error positivity (Pe) were measured, as well as event-related oscillations (EROs) in the theta and delta frequency bands that are known to be actively involved in error monitoring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003c/em\u003eThe results showed a clear and consistently blunted ERN and Pe in smoking participants compared to non-smoking participants, providing important evidence for attenuated EM at multiple levels. Reduced power in event-related theta and delta oscillations corroborated these findings. Both errors and correct responses contributed to the findings, demonstrating their joint importance in EM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003e\u003c/em\u003eDeficient error monitoring was found for people who smoke tobacco, manifested as lower ERN and Pe, which appear to be driven by reduced theta and delta power, respectively. This shows that tobacco smoking is associated with a neurophysiological deficit in EM that has been found in other substance use disorders.\u003c/p\u003e","manuscriptTitle":"Tobacco smoking is associated with impaired error monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 20:53:35","doi":"10.21203/rs.3.rs-4191422/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3de55cb-3052-4ad2-b02a-d99b69cadd6b","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30066471,"name":"Cognitive Neuroscience"},{"id":30066472,"name":"Psychology"}],"tags":[],"updatedAt":"2024-04-01T20:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 20:53:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4191422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4191422","identity":"rs-4191422","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}