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. 2024 Dec 3;62(2):e14734. doi: 10.1111/psyp.14734

The interaction of ADHD traits and trait anxiety on inhibitory control

Carolynn Hare 1,✉, Erin J Panda 2, Tyler K Collins 3,4, Sidney J Segalowitz 3, Ayda Tekok‐Kilic 2
PMCID: PMC11871067  PMID: 39627957

Abstract

Attention‐deficit/hyperactivity disorder (ADHD) and anxiety frequently occur together; however, the cognitive outcomes of comorbid anxiety and ADHD are not straightforward. A potential explanation for conflicting results in the literature may be that different core ADHD symptoms show different interactions with anxiety depending on the task‐processing demands. To address this question, we investigated whether different ADHD traits are related to different inhibitory outcomes, contingent upon the level of trait anxiety. The sample consists of 60 non‐clinical university students (X¯ age = 20.5, 53% male). Conners' Adult ADHD Rating Scale and State Trait Anxiety Inventory were used to measure ADHD traits and anxiety, respectively. The participants completed a visual Go/NoGo task with and without distractor conditions while continuous EEG was recorded. Inhibitory control was operationalized as the frontocentral N2 maximum peak amplitude elicited in response inhibition (NoGo/No Distractor), cognitive inhibition (Go/Distractor), dual inhibition (NoGo/Distractor), and control (Go/No Distractor) conditions. We analyzed the moderating effect of trait anxiety on the prediction of inhibitory control by ADHD scores for each Go/NoGo condition with the varying inhibition demands. Results showed that trait anxiety moderated the effects of total ADHD and hyperactivity‐impulsivity scores, but only in the response inhibition condition (NoGo/No Distractor). These findings suggest that depending on the inhibitory demands of the task, unique cognitive outcomes may occur when different ADHD traits coexist with anxiety.

Keywords: ADHD, anxiety, cognitive control, EEG, ERPs, inhibitory control

Short abstract

Attention‐deficit/hyperactivity disorder (ADHD) and anxiety often co‐occur, but their combined effects on inhibitory control remain unclear. We studied how ADHD and anxiety traits influence response and cognitive inhibition in non‐clinical emerging adults using the amplitude of the N2 event‐related potential as an index of inhibitory control. Findings showed that hyperactivity‐impulsivity reduced response inhibition, but only in those with low trait anxiety. Examining these co‐occurring traits may enhance our understanding of psychopathology‐cognition interactions.

1. INTRODUCTION

Clinically, there is a high comorbidity between attention‐deficit/hyperactivity disorder (ADHD) and anxiety, with estimates ranging from 25% (D'Agati et al., 2019) to 40% (Tannock, 2009). ADHD traits and trait anxiety also co‐occur in non‐clinical populations (de Zwaan et al., 2012). This co‐occurrence is consistent with the National Institute of Mental Health (NIMH) multidimensional Research Domain Criteria (RDoC) framework, which regards psychopathologies as continuous, interdependent conditions with symptoms existing as traits along a continuum in the population, rather than discrete diagnostic categories (Hengartner & Lehmann, 2017). Using a RDoC approach can help us to better understand the fundamental processes in affective, cognitive, social, arousal/regulatory, and sensorimotor systems underlying complex psychopathology. Thus, due to high comorbidity rates among ADHD and anxiety, it is important to examine how the interaction between these symptoms affects cognition (Jarrett, 2016; Jensen et al., 2001; Mikami et al., 2010; Reynolds & Lane, 2009). Beyond the comorbidity, the genetic liability for ADHD has been shown to be continuously distributed throughout the population, which allows for the use of quantitative dimensional approaches in general population samples (Chen et al., 2008; Levy et al., 1997; Paloyelis et al., 2010). Inattention and hyperactivity‐impulsivity are thought to be tails of normally distributed traits and high‐extreme ADHD traits have shown significant heritability compared with low ADHD traits (Greven et al., 2016). Further, some research suggests the prevalence of ADHD does not truly decline in adulthood, but rather that it has been underestimated (Faraone et al., 2006; McGough & McCracken, 2006). Moreover, recent literature provides support for integrating developmental psychopathology with the RDoC framework in understanding childhood and youth psychopathology (Conradt et al., 2021), which supports for the use of “trait” approach.

Individually, both anxiety and ADHD are associated with inhibitory control difficulties (Berggren & Derakshan, 2013; Conners et al., 1999; Crosbie et al., 2013; Epstein et al., 2003; Eysenck et al., 2007; Xia et al., 2020), although, as we review below, there are mixed results across studies. There are only a handful of studies examining the interaction of anxiety and ADHD, and so far, these suggest that the co‐occurring anxiety in individuals with ADHD may attenuate the negative impact of ADHD traits on inhibitory control (Klymkiw et al., 2020; Manoli et al., 2020; Ruf et al., 2017). To the best of our knowledge, only one study (Klymkiw et al., 2020) has examined the interaction of ADHD and anxiety using event‐related potentials (ERPs), which is unfortunate as ERPs offer a sensitive measure of the neurophysiological basis of inhibitory control as they unfold in real time. Additionally, to the best of our knowledge, no study has examined the interaction of anxiety on different ADHD traits (inattention, impulsivity/hyperactivity) or during different types of inhibitory control (response inhibition and cognitive inhibition). These are important to consider, as executive functioning differences may exist between the ADHD subtypes (Chhabildas et al., 2001) and so the interaction with anxiety may also differ. In this study, we examined the combined effects of ADHD traits and trait anxiety on the neurophysiological basis of different types of inhibitory control (response inhibition and cognitive inhibition), indexed by the amplitude of the scalp‐recorded ERP, N2. We also explored whether the relationship between ADHD traits and trait anxiety on N2 amplitudes is similar for inattentive and hyperactive‐impulsive subtype traits. The purpose is to better understand the dynamic interaction of ADHD traits and anxiety on inhibitory control in typically developing emerging adults.

1.1. Inhibitory control in ADHD and anxiety

Both anxiety and ADHD have been associated with difficulties in inhibitory control (Berggren & Derakshan, 2013; Conners et al., 1999; Crosbie et al., 2013; Epstein et al., 2003; Eysenck et al., 2007; Xia et al., 2020). Inhibitory control is an executive control mechanism that is necessary to achieve goal‐directed thoughts and actions, especially in the presence of distracting stimuli (Corbetta & Shulman, 2002; Koechlin et al., 2003; Nigg, 2000; Tiego et al., 2018). Inhibitory control can be divided into response inhibition, the suppression of a prepotent motor response, and attentional/cognitive inhibition, the resistance to internal and external distractor stimuli (Tiego et al., 2018). Some tasks used to measure inhibitory control are the continuous performance task (CPT), Go/NoGo Task, and Stop Signal task, all of which require a suppression of a prepotent motor response (i.e., response inhibition). Examining cognitive inhibition can be more difficult as there are fewer experimental designs for this purpose.

In terms of response inhibition, individuals with ADHD have been shown to make more commission errors (where inhibition of a prepotent response is required) and have slower or more variable reaction times during tasks that require sustained attention and inhibition, such as CPT compared with controls (Corkum & Siegel, 1993; Epstein et al., 2003; Overtoom et al., 1998). In community samples, higher ADHD traits are related to poorer response inhibition, slower reaction times, and greater response variability in inhibitory tasks (Crosbie et al., 2013). Further, greater response variability has been found in different cognitive domains, such as error monitoring, in individuals with ADHD (Shiels & Hawk, 2010). Clinically, differences in processing speed and inhibitory control are related to different ADHD subtypes (ADHD‐Inattentive, ADHD‐hyperactive/impulsive and ADHD‐Combined) (Solanto et al., 2009). In children with ADHD‐Combined, the literature is mixed regarding differences in inhibitory control, with some studies showing increased CPT commission errors (Hinshaw et al., 2002) or longer stop signal reaction times (SSRT; Geurts et al., 2005; Nigg et al., 2002) and others unable to find subtype differences on one or more of these measures (Bauermeister et al., 2005; Chhabildas et al., 2001). On the other hand, individuals with ADHD‐Inattentive subtype have been shown to have slower reaction times (Chhabildas et al., 2001; Nigg et al., 2002; Solanto et al., 2007; Wu et al., 2022). This is consistent with reports of “sluggish cognitive tempo,” which is characterized by inconsistent alertness and orientation in individuals with ADHD‐Inattentive type (McBurnett et al., 2001). In terms of anxiety, Grillon et al. (2017) found improved response inhibition (i.e., reduced commission errors) in anxious adults compared with controls using a standard Go/NoGo task. Overall, both ADHD and anxiety have been linked to abnormal response inhibition; however, behavioral measures may be limited because they tell us about the outcome of the inhibition rather than measuring internal activation in real‐time during the inhibition process.

1.2. Event‐related potentials and inhibitory control in ADHD and anxiety

An ERP approach measures cognitive processing as it occurs. The N2 ERP component is a negative‐going neurophysiological response elicited around 200–250 milliseconds after stimulus presentation, when inhibition of a prepotent response is required (such as in NoGo conditions). Some studies have shown an association between ADHD diagnosis and an altered N2 ERP component. Specifically, those with ADHD have demonstrated smaller N2 amplitudes (Dimoska et al., 2003; Johnstone et al., 2009; Liotti et al., 2010) and longer N2 latencies in NoGo conditions compared with controls (Woltering et al., 2013) suggesting reduced and/or delayed processing. To the best of our knowledge, little research has compared N2 amplitudes during response inhibition as a function of ADHD subtype traits, which would help elucidate how the neurophysiological basis of inhibitory control relates to specific ADHD behaviors. In terms of anxiety, ERP results have yielded controversial findings. For example, although some studies showed larger NoGo N2 amplitudes (Hum et al., 2013; Righi et al., 2009), other studies revealed either no difference (Yang & Li, 2014) or even reported smaller N2 amplitudes (Xia et al., 2020) during tasks requiring inhibition in clinically anxious or high trait anxious participants compared with non‐anxious controls. Hum et al. (2013) found larger NoGo N2 amplitudes in anxious children, but their results also suggested that these children showed greater medial‐frontal activity to all task conditions, as well as greater response accuracy. This activity may be the basis for the hypervigilant regulatory style in anxious individuals. On the other hand, the finding of smaller NoGo N2 amplitudes in anxious participants have been interpreted as response inhibition deficits due to abnormal premotor inhibitory control and inefficient evaluation and monitoring (Xia et al., 2020). Overall, the results of behavioral studies (e.g., Grillon et al., 2017) suggest that anxiety is related to augmented response inhibition, although its neurophysiological correlates of this heightened response are not clear (Xia et al., 2020; Yang & Li, 2014). One possibility is that due to the high comorbidity between ADHD and anxiety, inconsistencies across studies may reflect an interaction of anxiety and ADHD traits. Klymkiw et al. (2020) examined inhibitory control in youth with comorbid ADHD+anxiety and found the ADHD+anxiety group demonstrated larger NoGo N2 amplitudes compared with the ADHD‐only group. Thus, examining the interaction of ADHD and anxiety further may help elucidate these inconsistent results.

Most ERP studies have focussed on response inhibition, whereas the current study includes cognitive and response inhibition (separately and combined). Cognitive inhibition, the ability to resist internal and external distractor stimuli, is generally impaired in anxious individuals (Hum et al., 2013), especially when the distractors are related to perceived threat (Cisler & Koster, 2010; Eysenck et al., 2007). This is typically studied in tasks using peripheral and sometimes threatening distractors, in which the participant must ignore the distractors to complete the task. Previous literature suggests that peripherally presented threatening stimuli can capture attention, indexed by faster reaction times in individuals with high trait anxiety (Mogg et al., 2000) and in those diagnosed with clinical anxiety (Chen et al., 2002). On the other hand, in those with ADHD, cognitive inhibition does not seem to be affected (Barkley, 1997; Carr et al., 2006; Engelhardt et al., 2008; Gaultney et al., 1999). Cognitive inhibition can be more difficult to measure as it is harder to isolate compared with response inhibition. There is limited research looking at ADHD+anxiety comorbidity and limited research using ERP methodologies for cognitive inhibition. In the present study, we used four different task conditions to measure different aspects of inhibitory control to allow us to isolate cognitive inhibition within CPT: (1) Go/No Distractor (no inhibition), (2) Go/Distractor (cognitive inhibition), (3) NoGo/No Distractor (response inhibition), (4) NoGo/Distractor (both cognitive and response inhibition).

1.3. Current study

Due to the frequent co‐occurrence of ADHD+anxiety, both at the trait and clinical levels, in the present study, we investigated the interaction of ADHD traits and trait anxiety on inhibitory control using ERP methodology. First, we examine how ADHD traits impact inhibitory control and how trait anxiety affects this relationship. We hypothesize significant interactions involving ADHD traits and trait anxiety during conditions that require response inhibition (NoGo/No Distractor), cognitive inhibition (Go/Distractor), and dual inhibition (NoGo/Distractor). Specifically, we expected smaller N2 amplitudes in those with high ADHD and low anxiety traits compared with those with high ADHD and high anxiety traits. Second, we examined whether these relationships differ based on inattentive or hyperactive‐impulsive symptomology. There is limited research on subtype traits, so no specific hypotheses are made.

2. METHODS

2.1. Participants

Seventy‐seven university students (37 males, 39 females, and 1 other gender) aged 18–26 were recruited through an online recruitment system and posters around the University Campus. Seventeen participants were excluded from the data analysis due to technical problems, failure to complete the task, unusable EEG data, or incomplete questionnaires. The final sample consisted of 60 participants with a mean age of 20.5 (SD = 2.23, Min = 18, Max = 26) and 53% of participants were male. Most participants (76.7%) were native English speakers. This study received clearance from the university's research ethics board. All participants gave informed consent and received 2 h of research credit upon completion.

2.2. Questionnaires

Conners' Adult ADHD Rating Scale‐Long Version (CAARS; Conners et al., 1999). The scale consists of 66 items with a 4‐point Likert scale, ranging from “not at all, never” to “very much, very frequently.” There are three DSM‐IV clinical indices, Inattentive Symptoms, Hyperactive‐Impulsive Symptoms, and Total ADHD Symptoms, which are analyzed here. The scale has high internal reliability with alpha coefficients between .86 and .92 (Erhardt et al., 1999), and high test–retest reliability ranges from .88 to .91 (Erhardt et al., 1999).

State Trait Anxiety Inventory (STAI; Spielberger et al., 1983). The STAI includes 40 items: 20 items for the trait anxiety subscale and 20 items for the state anxiety subscale, each rated on a 4‐point Likert scale from “almost never” to “almost always.” Trait anxiety, analyzed here, is defined as a more stable temperamental characteristic than state (or situational) anxiety. High scale scores indicate higher anxiety levels. The scale has high internal reliability with alpha coefficients between .86 and .96, and moderate test–retest reliability with a range of .65 to .75 (Spielberger et al., 1983).

2.3. Go‐NoGo task with and without distractors

The task was adapted from Tekok‐Kilic et al. (2001). E‐Prime software Version 2 was used to display the stimuli, which were black on a white background. The stimuli consisted of 11 letters of the alphabet (A, B, C, D, E, F, G, H, J, L, X) and 100 distractor stimuli (simple line drawings). A total of 1270 letters were presented in a quasi‐random sequence in the middle of the computer screen, one letter at a time. Each letter was presented for 200 ms, with an interstimulus interval (from stimulus offset to stimulus onset) of 800 ms (see Figure 1). The participants were required to press two buttons with right and left thumbs on a response pad quickly and accurately to the letter X, but only when it is preceded by the letter A and not to respond to any other letter sequences. In half of the trials, a distractor was presented right after the offset of letter A for a duration of 200 ms. Distractors appeared in one of 8 locations equally: all 4 corners and either directly above, below or beside the letter being presented. The distractors were simple line drawings depicting pleasant or neutral (e.g., basketball), and unpleasant (e.g., sewer rat) objects and animals; all distractor types are combined in the present study.1 The goal of adding distractors to the task was (1) to increase the cognitive load, and (2) include task‐irrelevant information that needed to be ignored. The four main conditions of this study were (1) A‐X letter sequences (Go/No Distractor), (2) A–X letter sequences (Go/Distractor), (3) A‐not‐X letter sequences (NoGo/No Distractor), and (4) A‐not‐X letter sequences (NoGo/Distractor), all of which had 50 trials each. Overall, NoGo trials indexed response inhibition and the distractors indexed cognitive inhibition. The total duration of the task was 22 min, which included a practice and two blocks of testing separated by a short break (Block A = 13.5 min, Block B = 8.5 min).

FIGURE 1.

FIGURE 1

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Distractor AX‐continuous performance task conditions. (a) Go/No Distractor Trials (Control), (b) NoGo/No Distractor Trials (Response Inhibition), (c) Go/Distractor Trials (Cognitive Inhibition), (d) NoGo/Distractor Trials (Dual Inhibition).

2.3.1. Electrophysiological recording

Continuous EEG was acquired in a sound‐attenuated electrically shielded chamber using Netstation (version 4.5.1, EGI, Inc.), a 128‐channel HydroCel Geodesic Sensor Net, and NetAmps 300 amplifier (Electical Geodesics, Inc., Eugene, Oregon). The EEG signal was online referenced to the vertex electrode (Cz) and sampled at a rate of 500 Hz with a band‐pass filter from 0.01 to 100 Hz. Impedances were kept below 100 kΩ. After the EEG recordings, participants completed self‐report questionnaires. They then received a debriefing letter and any questions they had were answered.

2.3.2. Data processing

Data were processed offline with Brain Vision Analyzer (BVA) 2.0 (Brain Products 2.0). Data were band‐pass filtered from 1 to 30 Hz, and consistently noisy electrodes (6 and 113) were removed. Since electrode 6 (FCz) was of interest, it was interpolated by pooling the surrounding electrodes (5, 7, 12, 13, 106, and 112) using the linear derivation tool and equal weights for all electrodes for all participants, although clearly, this is less preferable than having a clean raw signal. Data were re‐referenced to the average of all electrodes. Epochs of 1200 ms were extracted from the data, time‐locked to the stimulus following A, either X (Go trials) or not‐X (NoGo trials), with the 200 ms before stimulus onset used for baseline correction. This was done for the four conditions: Go/Distractor, Go/No Distractor, NoGo/Distractor, and NoGo/No Distractor. There were 50 trials in each condition. Only trials with a correct response were included in analyses. Eyeblink artifacts were corrected using the Gratton et al. (1983) method and then an automatic artifact threshold of ±100 μv was used to reject trials containing artifacts. Participants had to keep at least half of the trials (25) to be included in the final sample. Data were then exported to MNE‐Python (MNE version 1.7 and Python 3.11) for peak picking analysis (Gramfort et al., 2013).

A time window of 200–300 ms was used to determine NoGo N2 peak amplitudes for both conditions based on visual inspection and from previous literature (Weissflog et al., 2013). The same latency window was used to pick the maximum negative‐going peak as the N2 in the Go conditions. Frontal central electrodes are the prominent sites for NoGo N2 (Coch & Gullick, 2011; van Noordt et al., 2015, 2016), and so the electrodes depicted in Figure 2 were used to determine N2 peak amplitudes. The baseline‐to‐peak values were calculated for the electrodes of interest highlighted in Figure 2. For each participant, data from the electrode with the maximum peak (highest amplitude between 200 and 300 ms) were selected for analyses (Weissflog et al., 2013). This resulted in a peak N2 that was specific to each participant and condition. We then averaged the ERPs for the 30 ms surrounding the peak N2 (i.e., seven data points before and after the peak). We chose the averaging window of 30 milliseconds because the N2 component normally rises and falls within about 100 ms (from the positivity of the P2 at about 200 ms to the next positivity of the P3b at about 300 ms). Averaging over 30 ms surrounding the peak negative deflection in the N2 time region captures a value that represents the degree of negativity demonstrated in the ERP and simultaneously avoids high frequency irregularities produced by the latency jitter of trial‐to‐trial variation and residual muscle artifacts from entering into the averaged ERP. This value represents approximately the central third of the N2 component while avoiding portions of the ERP heavily influenced by the non‐N2 amplitude segments (generated by the positivities preceding and following it). For slower components, such as the P3b, larger averaging is of course possible.

FIGURE 2.

FIGURE 2

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Electrodes of interest for N2 component. The 128 channel EGI HydroCel Geodesic Sensor Net. The highlighted area shows the electrodes used for analysis.

The rationale for using each individual's maximum peak as the center of this 30 ms time window, rather than averaging over a set (and often longer) time window, is twofold. First, when blind smoothing is used by selecting a priori a time window, individual differences in ERP latency peaks can cause a dramatic misrepresentation of the amplitude that individuals actually produce when their peak latency varies from the group average. This is especially the case when the ERP peak of interest has a relatively short latency, as is the case with the N2, although this is less of an issue with very slow components like the P3b or CNV. We estimated that 30 ms would be a safe period for smoothing that would capture the rise and fall of the amplitude around the peak with a cleaner representation would maintain temporal specificity and reduce the chances of including noise.

Second, such a priori time windows must be set wide enough to be sure to capture the peak component of interest for all participants, but not too wide that it includes periods outside the specific response of the component, contaminating the measure. (Note we also scored the ERPs using only the maximum peak value with no smoothing over 30 ms, obtaining largely similar results as analysis of the smoothed N2 amplitudes, which are presented in Table S1).

Finally, the rationale for choosing the site with the maximum peak for each individual (rather than selecting a site to use with all participants a priori) is that the traditional 10–20 sites are standard across participants with respect to the skull, but not with respect to cortical morphology because of individual differences in gyral patterns (Rubens et al., 1976). These differences lead to slightly different projections of the scalp (Luck, 2014), and with the high‐density montage, we can respect these individual differences in scalp patterns. We have found in the past that removing this noise produced by these individual differences has yielded greater sensitivity to experimental conditions compared with scoring the same location with respect to skull morphology (e.g., Jetha et al., 2012, in a study of early visual ERP components). Thus, by selecting the time and electrode location that is specific to each individual's N2, we are better suited to relate individual differences in electrophysiological responses with individual differences in self‐reported behavior.

2.4. Data analyses

2.4.1. Behavioral performance

Hit rate (HR) and response time (RT) were compared for Go/Distractor versus Go/No Distractor conditions using paired t‐tests (the NoGo condition did not require a response). HR was calculated by counting how many times participants responded on Go trials and dividing this by the total number of Go trials. False alarms (FA) commission error rates were calculated by the number of responses on NoGo trials, divided by the total number of NoGo trials. There were 100 NoGo trials, 50 of which had distractors. RT was calculated as an average response time for correctly answered Go trials.

2.4.2. Moderation analyses

First ERPs were examined by comparing N2 amplitudes across all participants as a function of Condition (Go vs. NoGo) and Trial type (Distractor vs. No Distractor) using a 2‐way ANOVA. Bivariate correlations then examined how N2 amplitudes in the four conditions related to self‐report ADHD traits and trait anxiety.

The main hypotheses about the interaction of ADHD traits and trait anxiety were tested with 12 separate moderation models using PROCESS macro Model 1 (Hayes, 2013) via SPSS Version 26 Software (see Figure 3). ADHD traits were entered as focal predictors (X), with 3 separate models run for Total ADHD, inattentive, hyperactive‐impulsive subtype scores. The outcome variable (Y) was the N2 amplitude measured separately in the three inhibition conditions (Go/Distractor, NoGo/Distractor, NoGo/No Distractor) and one control condition (Go/NoDistractor). In each model, the primary moderator (W) was trait anxiety.

FIGURE 3.

FIGURE 3

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Moderation model.

In each moderation model, the significant two‐way interactions examining ADHD traits and trait anxiety were probed by using a series of post hoc regression equations, referred to as simple slope analysis. For example, to test the relationship between impulsivity and Go/Distractor N2 amplitude as a function of trait anxiety, simple slope analyses were conducted to see whether these slopes differed significantly from zero when the level of trait anxiety was high (1 standard deviation above the mean), moderate (the mean) and low (1 standard deviation below the mean). It is important to note that as the N2 amplitude is a negative value, a positive relationship between impulsivity and N2 amplitude means that a higher impulsivity score is associated with a smaller N2 amplitude Likewise, a negative relationship between impulsivity and N2 amplitude means that higher impulsivity scores are associated with larger N2 amplitudes.

3. RESULTS

3.1. Behavioral performance

There was no significant difference between Hit Rates for Go/Distractor (M = 0.98, SD = 0.05) and Go/No Distractor (M = 0.98, SD = 0.03) conditions, t(59) = 0.708, p = .482. On average, participants made few FAs (M = 0.67, SD = 1.07, 0–4); 62% of the participants did not make any FAs, and the maximum number of FAs was 4. Participants made significantly more FAs for NoGo/Distractor (M = 0.52, SD = 0.91) compared with NoGo/No Distractor conditions (M = 0.15, SD = 0.36), t(59) = 3.22, p < .01. Participants showed a faster RT for Go/Distractor (M = 314.2, SD = 56.5) compared with Go/No Distractor (M = 360.1, SD = 79.5) trials, t(59) = −6.01, p < .001. In short, the distractor condition affected performance in a simple comparison – shorter RTs and more FA.

3.2. Correlations among self‐report measures

Mean self‐report ADHD scores (ADHD total, inattentive, hyperactive‐impulsive), and trait anxiety scores, and their correlations are presented in Table 1. The relationship among the ADHD scales were positive and significant. Trait anxiety was significantly positively correlated with ADHD total and hyperactive‐impulsive scores, but not inattentive scores.

TABLE 1.

Correlations between self‐report ADHD traits and trait anxiety scores.

M (SD) 1. 2. 3.
1. ADHD Total 19.54 (7.56) ‐
2. Inattentive 9.67 (4.18) .88** ‐
3. Hyperactive‐Impulsive 10.05 (4.34) .86** .56** ‐
4. Trait Anxiety 41.47 (10.25) .38** .15 .51**
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Note: Scores on the DSM‐IV total ADHD symptoms ranged from 6 to 38 (the maximum possible score is 54). A score of 26 or above in males and 32 or above in females gives a T‐score of above 70, suggesting symptoms that are very much above average. Scores on the DSM‐IV inattentive scale ranged from 1 to 21 and from 2 to 19 on the DSM‐IV hyperactive‐impulsive scale (the maximum possible scores are 27). Scores on trait anxiety ranged from 23 to 66 (the minimum score is 20 and the maximum score is 80).

**

p < .01.

3.3. Correlations among self‐report and behavioral measures

Trait anxiety was related to RTs for Go/Distractor and Go/No Distractor conditions, such that those with higher trait anxiety were slower at responding to the Go trials than those with lower trait anxiety (See Table 2 and Figure 4). FA in the distractor condition were significantly positively related to all ADHD traits, such that those with higher ADHD traits made more false alarms (although please note that number of FAs are not normally distributed, skew = 2.38 and kurtosis = 6.26, and should be interpreted with caution). Moderation models were run for RT measures, with ADHD traits as the predictor, trait anxiety as the moderator and RT as the outcome, similar to the N2 models. Consistent with the correlation results, only trait anxiety was significant in the models, although the models and interactions were not significant. Moderations were not run for the HRs and FAs as these variables were not normally distributed.

TABLE 2.

Correlations between self‐report and behavioral measures.

ADHD total Inattentive Hyperactive‐impulsive Trait anxiety
Hit rate ND −.06 −.15 .04 −.09
Hit rate D −.04 −.09 .00 −.19
False alarm ND −.15 −.11 −.05 −.06
False alarm D .36** .33* 34** .02
RT ND .08 .14 −.02 .34**, †
RT D .07 −.15 .01 .31**, †
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Note: False alarms show a skewed distribution, as 62% of participants had no false alarms. Correlations that did not survive multiple corrections are labeled with †.

*

p < .05.

**

p < .01.

FIGURE 4.

FIGURE 4

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Scatterplots of reaction time for Go Distractor and Go No Distractor and trait anxiety.

3.4. N2 amplitude differences across conditions, trial types and self‐report measures

A two‐way repeated measures ANOVA on N2 amplitude was performed for Condition (Go vs. NoGo) and Trial type (Distractor vs. No Distractor). There was a significant main effect of Condition (F(1, 59) = 18.40, p < .001, η p 2 = 0.238). As seen in Figure 5, NoGo N2 amplitudes (blue and green lines) were significantly larger than Go N2 amplitudes (black and red lines). There was no main effect of Trial type (Distractor vs. No Distractor; F(1, 59) = 0.01, p = .94, η p 2 = 0.00) and no significant Condition X Trial type interaction [F(1, 59) = 1.81, p = .18 η p 2 = 0.03]. See Table 3 for means and standard deviations.

FIGURE 5.

FIGURE 5

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N2 difference waveforms (NoGo minus Go) for No Distractor (a) and distractor (b) trials. The baseline‐to‐peak values were calculated for the electrodes of interest highlighted in Figure 2. For each participant, data from the electrode with the maximum peak (highest amplitude between 200 and 300 ms) was entered into analyses. N2 amplitudes were larger for the NoGo compared with the Go conditions.

TABLE 3.

Means and standard deviations of N2 amplitudes (μV) and latencies (ms).

Condition/trial Amplitude (μV) Latency (ms)
M SD M SD
Go Distractor −2.09 2.56 271.20 33.93
Go No Distractor −1.85 1.95 271.93 33.96
NoGo Distractor −2.94 2.53 258.37 23.29
NoGo No Distractor −3.14 2.33 256.00 20.09
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Correlations were run between N2 amplitudes in the four conditions and self‐report ADHD and anxiety measures. Only inattentive scores were significantly and positively correlated with Go/Distractor N2 amplitude, as shown in Table 4 (r = .28, p < .05) but this correlation did not survive a Benjamini‐Hochberg multiple comparison correction (See Figure 6). Specifically, people who reported higher inattentiveness tended to show smaller N2 amplitudes in the Go/Distractor condition, which indexed cognitive inhibition. Total ADHD, hyperactive‐impulsive, and trait anxiety scores were not significantly correlated with N2 amplitudes for any of the four conditions (Go/Distractor, Go/No Distractor, NoGo/Distractor, NoGo/No Distractor).

TABLE 4.

Correlations between self‐report and N2 amplitude conditions.

ADHD total Inattentive Hyperactive‐impulsive Trait anxiety
Go No Distractor −.14 −.05 −.16 .02
Go Distractor .20 .27* .07 .10
NoGo No Distractor .11 .08 .16 .08
NoGo Distractor .13 .09 .12 .04
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*

p < .05, did not survive correction for multiple comparisons.

FIGURE 6.

FIGURE 6

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Correlation between self‐report inattentive traits and Go/Distractor N2 amplitude (cognitive inhibition).

3.5. Moderation analyses

As presented in Table 5, among the 12 moderation models involving ADHD‐trait (total scores, inattentive, hyperactive‐impulsive), trait anxiety, and N2 amplitudes (Go/Distractor, NoGo/No Distractor, NoGo/Distractor), two models were significant: the models involving total ADHD scores and hyperactive‐impulsive scores as focal predictors (X), trait anxiety as a moderator (W) and N2 amplitude in the NoGo/No Distractor condition (Response Inhibition) as the outcome variable (Y). The models for the other task conditions (Go/No Distractor, Go/Distractor, NoGo/Distractor) and when inattentive scores were the focal predictor were not significant and are not discussed in detail (see Table 5).

TABLE 5.

Moderation results with N2 mean amplitudes in the four inhibition conditions as the dependent variables, ADHD traits as the predictor, and trait anxiety as the moderator.

Dependent variables
Response inhibition NoGo/No distractor amplitude Dual inhibition NoGo/distractor amplitude Cognitive inhibition Go/distractor amplitude No inhibition Go/No distractor amplitude
b SE CI b SE CI b SE CI b SE CI
Total ADHD models
ADHD (X) .03 0.04 [−0.05, 0.12] .05 0.05 [−0.05, 0.14] .06 0.05 [−0.03, 0.16] −.05 0.04 [−0.12, 0.03]
Trait anxiety (Y) −.02 0.03 [−0.08, 0.05] −.02 0.04 [−0.09, 0.05] .01 0.04 [−0.06, 0.08] .01 0.03 [−0.04, 0.07]
ADHD*TA −.01 ** 0.01 [−0.02, −0.00] −.01† 0.01 [−0.02, 0.00] .003 0.01 [−0.01, 0.01] .002 0.00 [−0.01, 0.01]
R 2 .13 * .07 .05 .03
Hyperactive‐Impulsive (H‐I) models
H‐I (X) .10 0.07 [−0.04, 0.23] .07 0.08 [−0.08, 0.23] .03 0.08 [−0.13,0.19] −.07 0.06 [−0.20, 0.05]
Trait anxiety (Y) −.02 0.03 [−0.08, 0.04] −.02 0.04 [−0.09, 0.05] .02 0.04 [−0.04, 0.10] .01 0.03 [−0.05, 0.06]
H‐I*TA −.02 ** 0.01 [−0.04, 0.01] −.01 0.01 [−0.03, 0.00] .006 0.01 [−0.01, 0.02] −.00 0.01 [−0.01, .01]
R 2 .15 * .05 .02 .03
Inattentive models
Inattentive (X) .03 0.08 [−0.14, 0.19] .06 0.09 [−0.13, 0.24] .18 † 0.09 [−0.00, 0.37] −.04 0.07 [−0.19, 0.10]
Trait anxiety (Y) .01 0.04 [−0.06, 0.08] −.01 0.04 [−0.08, 0.07] −.01 0.04 [−0.09, 0.06] .01 0.03 [−0.05, 0.07]
In*TA −.02† 0.01 [−0.03, −0.00] −.01 0.01 [−0.03, 0.01] −.003 0.01 [−.01, 0.02] .01 0.01 [−0.02, 0.01]
R 2 .07 .04 .08 .02
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Note: N = 60.

†

p < .08.

*

p < .05.

**

p < .01.

3.5.1. Total ADHD score predicting N2 in NoGo/No distractor condition (response inhibition)

The overall model testing the effects of total ADHD scores on N2 amplitude as a function of trait anxiety was statistically significant, accounting for 13.0% of the variability in N2 amplitude (F(3, 56) = 2.78, p = .05). There was a statistically significant two‐way interaction of total ADHD × trait anxiety (b = −.01, t(52) = −2.72, p = .009). This interaction was probed by a simple slope analysis to understand how the relationship between total ADHD score and N2 amplitude differs as the level of trait anxiety changes. The results showed that in participants with low trait anxiety, total ADHD was positively related to N2 amplitude (b = .16, t(56) = 2.54, p = .01); that is, smaller (more positive N2 amplitudes in those with high total ADHD scores). In participants with high trait anxiety, the relationship between ADHD and N2 amplitude was not significant (b = −.10, t(56) = −1.54, p = .13).

3.5.2. Total ADHD score predicting N2 amplitude in NoGo/distractor condition (both cognitive and response inhibition)

The overall model testing the effects of total ADHD scores on N2 amplitude as a factor of trait anxiety was not statistically significant, accounting for 7.0% of the variability in NoGo/Distractor N2 amplitude (F(3, 56) = 1.41, p = .25). The interaction showed a non‐significant trend (b = −.01, t(52) = −1.80, p = 0.08), but was not probed further, as the model was not significant.

3.5.3. Total ADHD score predicting N2 amplitude in go/distractor condition (cognitive inhibition)

The overall model of total ADHD scores on N2 amplitude as a factor of trait anxiety was not statistically significant, accounting for 4.5% of the variability in Go/Distractor N2 amplitude (F(3, 56) = 0.88, p = .46). Further, the interaction was not significant.

3.5.4. Hyperactive‐impulsive scores predicting N2 amplitude in NoGo /No distractor condition (response inhibition)

The overall model testing the effects of hyperactive‐impulsive scores on N2 amplitude as a factor of trait anxiety was statistically significant, accounting for 15.0% of the variability in NoGo/No Distractor N2 amplitude when response inhibition is required (F(3, 56) = 3.28, p = .03). There was a statistically significant two‐way interaction of hyperactive‐impulsivity × trait anxiety (b = −.02, t(52) = −2.83, p = .007). This interaction was probed by a simple slopes analysis, to understand how the relationship between hyperactivity‐impulsivity and N2 amplitude changes as the level of trait anxiety changed (see Figure 7). The results show that in participants with high trait anxiety, hyperactivity‐impulsivity was positively related to N2 amplitude (b = .32, t(56) = 2.96, p = .005; i.e. smaller N2 amplitudes in those with high hyperactivity‐impulsivity). In participants with high trait anxiety, the relationship between hyperactivity‐impulsivity and N2 amplitude was not significant (b = −.13, t(56) = −1.32, p = .19).

FIGURE 7.

FIGURE 7

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Hyperactive‐impulsive scores and NoGo/No Distractor N2 amplitude by trait anxiety. The graphs show the relationship between N2 (NoGo/No Distractor Amplitude) and ADHD‐hyperactive‐impulsive scores in individuals with low anxiety (STAI score > 38) and high anxiety (STAI score < 42). These figures show that in participants with low trait anxiety, higher hyperactivity‐impulsivity scores are related to smaller, more positive N2 amplitudes when response inhibition is required. Conversely, in participants with high trait anxiety, there is a trend toward higher hyperactivity‐impulsivity scores being related to larger, more negative N2 amplitudes when response inhibition is required.

3.5.5. Hyperactive‐impulsivity scores predicting N2 in NoGo/distractor and go distractor conditions

The models testing the effects of hyperactive‐impulsive scores on N2 amplitude as a factor of trait anxiety on N2 amplitudes for NoGo/Distractor (F(3, 56) = 1.07, p = .37) and Go/Distractor trials (F(3, 56) = 0.38, p = .77) were not statistically significant.

3.5.6. Inattentive scores predicting N2 amplitude in each inhibitory condition

The moderation models with inattentive scores as a focal predictor and trait anxiety as a moderator were not significant in any of the inhibition conditions (see Table 5). However, in line with the non‐significant trend in the bivariate correlation results, there was a main effect of inattention on N2 amplitude in the Go/Distractor condition (b = .18, t(52) = 2.00, p = .050). Additionally, there was a non‐significant interaction of inattentive scores and trait anxiety on N2 amplitude in the NoGo/No Distractor condition (b = −.02, t(52) = −1.90, p = .06).

In sum, the results showed that trait anxiety moderated the relationship between total ADHD scores and inhibitory control, as well as the relationship between hyperactive‐impulsive scores and the inhibitory control. The moderating role of anxiety was only found to be significant when the task required response inhibition. In addition, in the Go/Distractor condition, bivariate correlation between inattentive scores and N2 amplitudes was significant at the p < .05 level when the task required cognitive inhibition (although this effect did not survive correction for multiple comparisons, and so should be interpreted as marginal).

4. DISCUSSION

The literature on how ADHD and anxiety affect the electrophysiological basis of inhibitory control has been mixed, possibly due to these studies not taking both factors into account simultaneously (i.e., N2 effects are canceled out due to an interaction with trait anxiety or inattentiveness). Additionally, few studies have separated the effect of different ADHD traits (inattentive vs. hyperactive‐impulsive) on different types of inhibitory control (cognitive, response, or dual inhibition). First, the findings of this study showed that anxiety moderated the relationship between ADHD traits and response inhibition, as evidenced by smaller N2 amplitudes during the NoGo/No Distractor condition in those who reported high ADHD and low anxiety traits. This moderating relationship depended on the specific ADHD traits and type of inhibitory control, as it was found for hyperactive‐impulsive, but not inattentive traits and during tasks that require response, but not cognitive inhibition. Interestingly, cognitive inhibition does not seem to be as affected by ADHD subtype traits or anxiety as response inhibition, as our NoGo model with distractors followed the same patterns as the model without distractors but do not reach significance, suggesting the addition of distractors impacts this relationship. Additionally, inattentiveness was associated with reduced cognitive inhibition, as evidenced by a trend toward smaller N2 amplitudes during the Go/Distractor condition in those with higher self‐report inattentive traits. Importantly, our findings are based on a non‐clinical sample of university participants, suggesting that the interplay between both externalizing and internalizing behaviors on inhibitory control may occur on a continuum even in the general population.

The neurophysiological results provide us information into examining subtype differences and understanding comorbidities. In the behavioral results, we found that higher trait anxiety was related to slower RTs in the Go condition, consistent with prior literature suggesting excessive engagement of the behavioral inhibition system in anxiety (Epstein et al., 2003; Gray & McNaughton, 2000; Grillon et al., 2017; Quay, 1997; Sylwan, 2004). Additionally, in the distractor condition, all ADHD measures were related to false alarm performance; participants that reported higher ADHD scores (either inattentiveness, hyperactivity‐impulsivity, or both) made more FA, although this result should be taken with caution, given the overall low number of FA. Conversely, in the neurophysiological results, total ADHD and hyperactive‐impulsive traits interacted with trait anxiety, while inattentive traits did not – an effect that was seen for the response inhibition condition only. These findings suggest that the neurophysiological measures may offer greater specificity for understanding the relationship between ADHD subtype and comorbid traits and different types of inhibitory control than do behavioral results.

4.1. Trait anxiety moderates the impact of total ADHD and hyperactivity/impulsivity on response inhibition

In the NoGo/No Distractor condition, participants were presented with a letter that was not “X” but followed an “A” and were to withhold a response. In this condition, anxiety moderated the relationship between total ADHD traits and N2 amplitude and between hyperactive‐impulsive traits and N2 amplitude. Specifically, in those with low anxiety, participants who reported higher total ADHD traits showed a smaller N2 than those who reported lower total ADHD traits, whereas this relationship was not seen in those with high anxiety. The same pattern of an interaction between anxiety and ADHD traits was seen when examining hyperactive‐impulsive traits separately; it was not found when examining inattentiveness alone, or in the other conditions when cognitive inhibition was required. Most ERP studies of inhibitory control in ADHD suggest that the NoGo N2 is smaller in those with ADHD compared with controls (Albrecht et al., 2005; Dimoska et al., 2003; Johnstone et al., 2009; Liotti et al., 2007, 2010), although few studies have examined ADHD symptom types separately or examined the role of co‐occurring anxiety. Our results are consistent with the results of those previous studies and further add that this pattern of reduced NoGo N2 amplitudes may be particularly evident in individuals who tend to have difficulty with hyperactivity‐impulsivity (rather than inattentiveness) and who do not struggle concurrently with anxiety.

In terms of the interaction with anxiety, the literature on how anxiety affects the N2 amplitude has been mixed, possibly due to these studies not taking ADHD and anxiety into account simultaneously. We know that anxiety is often related to excessive engagement of the behavioral inhibition system (Epstein et al., 2003; Gray & McNaughton, 2000; Grillon et al., 2017; Quay, 1997; Sylwan, 2004). However, the effect of anxiety on N2 amplitude in Go/NoGo tasks has been mixed, as some results suggest a larger N2 (Hum et al., 2013; Righi et al., 2009), some suggest no difference in N2 amplitude (Yang & Li, 2014) and yet others suggest a smaller amplitude (Xia et al., 2020). In addition, one study looking at comorbid ADHD+anxiety suggests that NoGo N2 amplitude was larger in ADHD+anxious youth than ADHD‐only youth, which is similar to the findings of the present study (Klymkiw et al., 2020). The authors suggested that the addition of anxiety in youth with ADHD may have led to increased allocation of attentional resources due to a desire to perform well on the task. This rationale is drawn from processing efficiency theory (Eysenck & Calvo, 1992). As our study involves a non‐clinical sample of university students, we extend these results to an older and less affected group.

Our finding of anxiety moderating the relationship between NoGo N2 amplitudes and hyperactive‐impulsive, but not inattentive traits, is noteworthy. These were participants who tended to respond with “pretty much, often” or “very much, frequently” to statements such as “I'm always on the go, as if driven by a motor” or “I blurt out things,” and so it makes sense that these participants may show a reduced response in a NoGo task that requires withholding a response. Previous research has indicated a unique relationship between impulsivity and anxiety. For example, clinical studies have shown that higher rates of impulsivity have been found in patients with a comorbid anxiety disorder compared with patients without an anxiety disorder or controls (Del Carlo et al., 2012; Taylor et al., 2008). In an ERP study using N1 as a neurophysiological response to measure selective attention and attention processing for goal‐directed actions during a delay discounting task, it was found that the high trait anxious individuals were impulsive decision makers and they had larger N1 amplitude in immediate‐option trials compared the low trait anxious individuals (Xia et al., 2017). Thus, our finding of trait anxiety interacting with hyperactivity‐impulsivity but not inattentiveness to impact response inhibition is consistent with these previous studies. Most ADHD studies examining the electrophysiological basis of inhibitory control consider ADHD subtypes together and do not compare subtypes or ADHD traits. The present study underscores the need for more research on differences in subtypes or ADHD traits and how different presentations may impact behavior. Understanding subtypes could help to explain what aspects of behavior, such as cognitive or response inhibition, are more impacted. Overall, these findings emphasize that the dynamic interaction between multiple psychopathologies (i.e., comorbid ADHD and anxiety), leading to different outcomes, therefore, suggest the importance of examining the impact of comorbid patterns. In addition, our results also support the unique relationship between impulsivity and anxiety especially when response inhibition is required (Del Carlo et al., 2012; Taylor et al., 2008).

4.2. Inattentiveness is associated with reduced cognitive inhibition

In the Go/Distractor condition, participants were presented with task‐irrelevant simple line drawings before they were to respond to the target letter X. The results showed a trend toward the amplitude of the N2 component in response to these Go/Distractor trials being smaller in participants that reported more inattentive traits. These were participants who tended to respond with “pretty much, often” or “very much, frequently” to statements such as “things I hear or see distract me from what I am doing,” “I have trouble keeping my attention focused when working” or “it is hard for me to keep track of several things at once.” This pattern suggests that individuals who experience difficulty maintaining attention and distractions in daily life also show reduced N2 responses during a continuous performance task involving distractors. Previous research has inconsistently found differences in ADHD subtypes in relation to cognitive processing (Bauermeister et al., 2005; Chhabildas et al., 2001; Geurts et al., 2005; Nigg et al., 2002; Solanto et al., 2007, 2009; Wu et al., 2022), but research looking at ADHD subtypes and cognitive inhibition is particularly limited. Generally, cognitive inhibition as related to ADHD is less explored than response inhibition. In addition, ADHD subtypes have not been extensively examined in this context—perhaps due to the challenge of isolating and measuring cognitive inhibition. Future research should further explore how cognitive inhibition interacts with ADHD subtypes. It may be that clinically significant levels of inattention are required for cognitive inhibition to be measurably impacted, which could account for the limited effects observed in our non‐clinical university sample.

Interestingly, we found no main effect or interaction involving anxiety in this condition. Anxiety may not have had an impact on cognitive inhibition because our distractors were simple line drawings, which participants rated after the EEG session was complete as neutral overall or pleasant and not threatening. Previous studies have found that anxiety relates to a decreased tendency to resist internal and external distractions, particularly distractions that are or are perceived to be threatening (Mogg et al., 2000). Thus, the relationship between anxiety and cognitive inhibition may be specific to more threatening contexts in which the prefrontal cortex has difficulty supressing a heightened fear response from the amygdala (Gold et al., 2015). In contrast, the results of our study suggest that cognitive inhibition in more neutral contexts may relate to inattentive but not anxious tendencies, at least in our sample of non‐clinical participants. The results are consistent with Sluggish Cognitive Tempo, which is often discussed in the relationship between inattention and anxiety. Sluggish Cognitive Tempo is characterized by inconsistent alertness and orientation (e.g., drowsiness, apparent daydreaming) (McBurnett et al., 2001) and is associated with inattention, but only when hyperactivity‐impulsivity is not present. The distractors may have been more distracting for those with higher inattentiveness scores thus reducing the N2 amplitude to Go trials in the cognitive inhibition condition.

It is important to note that the relationship between inattentiveness and reduced N2 in the Go/Distractor condition may be due to true differences in the N2 magnitude, a different response to the distractor stimulus during the baseline interval, or both. Thus, it is difficult to tease apart whether people who are more inattentive showed reduced processing of the Go stimuli in conditions when the distractors were present, or if individual differences in the processing of the distractor continued into the baseline interval of the Go stimuli, spilling into the analysis of the N2. However, the fact that inattentiveness showed a relationship with N2 amplitudes in the Go/Distractor but not the NoGo/Distractor condition, despite both involving the same distractors, suggests this effect cannot simply reflect extended distractor processing. Instead, this trend suggests reduced processing of Go stimuli, when presented after a visual distractor in people who report more inattentiveness. Future studies may attempt to tease these possibilities apart by modifying the timing of the distractor and target stimuli, the baseline interval, or isolating the underlying cognitive processes using methods such as source or independent component analysis.

Finally, we did not see a relationship with or between ADHD and/or anxiety traits and N2 amplitudes in NoGo/Distractor condition when both cognitive and response inhibition was required (dual inhibition). This may be because while hyperactive‐impulsive and anxious traits impact response inhibition, inattentive traits tended to impact cognitive inhibition, but these effects were not powerful enough to be seen in the dual inhibition condition. In the future, making the distractors more emotionally evocative or threat‐related may lead to a relationship with anxiety. Although the stimuli seemed to be more distracting for individuals with high versus low inattentive traits, they did not appear to capture the attention of individuals with anxiety. This may be because participants tended to rate the stimuli, as indicated earlier, as pleasant or neutral, rather than threatening. Changing the distractors to be more emotionally evocative, may help us understand how increasing the demands on the inhibitory system can impact the processing in ADHD, anxiety, or both.

4.3. Limitations and future directions

This study offers new insights into the interacting role of trait anxiety and ADHD traits on inhibitory processes; however, it does have some limitations. For example, the outcome variable, N2 amplitude, may not have captured the full range of variance in each predictor trait, due to the observed ceiling effect (i.e., little variation in HRs and FA) in the paradigm, which all together could have reduced the power of hypothesized predictions. Future studies using a more difficult task with more emotionally charged distractors may extenuate the combined effect of anxiety and hyperactive‐impulsive traits.

Additionally, unlike many studies that used the grand‐average response across all participants to select electrodes and time window for analysis (e.g., Hum et al., 2013), we instead quantified peak N2 amplitudes for each participant on an individual basis (see also Klymkiw et al., 2020). This method is more sensitive to individual differences in where and when the N2 occurs. Indeed, our behavioral analysis showed slower reaction time in those with higher anxiety, suggesting that the peak N2 latency may also be later. Averaging over a large time window and several electrodes may either wash out or completely miss effects for some individuals. However, peak amplitude analysis runs the risk of incorporating more noise, which we mitigated against by applying a 30 Hz low pass filter and analyzing a 30 ms time window surrounding each individual's peak. Future studies may consider using independent component analysis (ICA) to further remove muscle artifacts or use bootstrapping for a more robust analysis of latency effects (Hafer et al., 2022; van Noordt et al., 2015).

In the future, this study should be replicated with adolescents or youth, using either a larger community and/or well‐defined clinical samples. Individuals at university may be a unique sample who have learned to mask their symptoms or compensate for executive functioning deficits if they do have diagnoses or even undiagnosed symptoms of these disorders (ADHD and/or anxiety). Further, university students with ADHD are resilient, they overcome risk factors to persist with studies and therefore are an important group to study and understand (Woltering et al., 2013). Finding an interaction between ADHD and anxiety in a non‐clinical sample suggests that even stronger results may be obtained in future studies including clinical samples who are at extreme ends of the continuum. In addition to clinical samples, sex differences should be considered. Research suggests that females are more likely to have high trait anxiety than males (Franklin et al., 2018), whereas more males are likely to rate higher in attentional difficulties and have more hyperactive‐impulsive symptoms compared with inattentive (Panevska et al., 2014). Although the present study with 60 participants resulted in medium to large effects, more participants would be needed to explore the additional role of gender.

5. CONCLUSION

The present study is one of the first to examine the impact of ADHD traits and trait anxiety together on an ERP index of different facets of inhibitory control, such as cognitive inhibition and response inhibition. The results provide insight into understanding how the combination of these traits, which have high comorbidity, impacts cognitive processing, especially as these traits individually did not impact cognitive processing. Overall, we found that trait anxiety moderated the effects of hyperactive‐impulsive, but not inattentive ADHD traits, on N2 amplitude in the NoGo/No Distractor (response inhibition) condition. These results emphasize the importance of using a dimensional approach and comorbid conditions when considering how psychopathology affects cognition.

AUTHOR CONTRIBUTIONS

Carolynn Hare: Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing – original draft; writing – review and editing. Erin J. Panda: Conceptualization; methodology; writing – review and editing. Tyler K. Collins: Data curation; formal analysis; software; writing – review and editing. Sidney J. Segalowitz: Conceptualization; methodology; writing – review and editing. Ayda Tekok‐Kilic: Conceptualization; funding acquisition; project administration; resources; software; supervision; writing – review and editing.

FUNDING INFORMATION

This article was funded by Brock University's Internal Grant, Council for Research in the Social Sciences (CRISS), Social Sciences and Humanities Research Council of Canada (SSHRC), and SSHRC CGS Master's Award.

CONFLICT OF INTEREST STATEMENT

The authors have no financial or other conflicts of interest to disclose.

ETHICS STATEMENT

The study received clearance by Brock University's ethics board (REB #17‐105).

Supporting information

Table S1.

PSYP-62-e14734-s001.docx (18.6KB, docx)

ACKNOWLEDGMENTS

We would like to extend our thanks to those who took the time to participate in the study, to Abraham Omorogieva for his help with data collection, and to two anonymous reviewers for their thoughtful suggestions.

Hare, C. , Panda, E. J. , Collins, T. K. , Segalowitz, S. J. , & Tekok‐Kilic, A. (2025). The interaction of ADHD traits and trait anxiety on inhibitory control. Psychophysiology, 62, e14734. 10.1111/psyp.14734

Footnotes

1

Participants rated how pleasant or unpleasant the distractors were after the EEG session was complete. Most stimuli were rated as pleasant or neutral.

DATA AVAILABILITY STATEMENT

The anonymized raw EEG data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

PSYP-62-e14734-s001.docx (18.6KB, docx)

Data Availability Statement

The anonymized raw EEG data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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