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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: J Affect Disord. 2019 Jun 22;256:550–559. doi: 10.1016/j.jad.2019.06.026

Rumination and executive functions: Understanding cognitive vulnerability for psychopathology

Alta du Pont 1,2, Soo Hyun Rhee 1,2, Robin P Corley 1, John K Hewitt 1,2, Naomi P Friedman 1,2
PMCID: PMC6751021  NIHMSID: NIHMS1037648  PMID: 31280080

Abstract

Introduction:

Both rumination, a pattern of repetitive, self-focused thought in response to distress, and deficits in executive functions (EFs), a set of cognitive abilities that facilitate higher-order thinking, have transdiagnostic associations with psychopathology. Although empirical studies suggest associations between EFs and rumination, this literature has not examined subtypes of rumination and different components of EFs. It also has not examined whether rumination and EFs explain overlapping variance in psychopathology, which is relevant to theoretical models suggesting that rumination might mediate the EF–psychopathology association.

Methods:

We used structural equation modeling to examine the association between latent factors for two types of rumination (anger and depressive) and three components of EF (a Common EF factor, and factors specific to updating working memory and shifting mental sets) and whether they independently relate to internalizing and externalizing psychopathology in a population sample of 764 young adults (mean age 23 years) from the Colorado Longitudinal Twin Study.

Results:

Depressive and Anger Rumination showed small correlations with a Common EF factor (rs= –.09 to –.11). Anger Rumination and Common EF ability were associated with independent variance in externalizing psychopathology, whereas Depressive Rumination, but not Common EF, was associated with internalizing psychopathology.

Limitations:

Examination of cross-sectional relations in a population sample led to low symptom endorsement for psychopathology and necessitated examination of lifetime, rather than past-year, psychopathology.

Conclusions:

Inconsistent with mediation hypotheses, Common EF abilities and rumination are correlated yet largely independent constructs that both predict psychopathology.

Keywords: Brooding, reflection, executive control, cognitive control, inhibition, task switching

Research examining factors that augment one’s vulnerability to ruminate, or repetitively think about the causes of one’s distress, has implicated executive functions (EFs). EFs are cognitive abilities that facilitate goal-related behavior by regulating thoughts and actions (Friedman & Miyake, 2017; Yang, Cao, Shields, Teng, & Liu, 2016). Some suggest that rumination disrupts EF processes, leading to deficits in autobiographical recall and other cognitive processes (e.g., Ramponi, Barnard, & Nimmo-Smith, 2004), whereas others hypothesize that EF deficits underlie rumination, leading to an inability to inhibit and disengage from negative, self-focused information (Koster, De Lissnyder, Derakshan, & De Raedt, 2011; van Vugt, van der Velde, & ESM-MERGE Investigators, 2018). Prior studies have focused exclusively on one form of rumination—depressive rumination—and have relied heavily on single-task measurement of EFs (e.g., Go/no-go, Stoop; Yang et al., 2016). In this study, we build upon the existing literature by examining the associations between two types of rumination and three EF components at the latent variable level, and investigating whether they independently relate to internalizing and externalizing psychopathology.

One complication in examining the association between rumination and EFs is that there are multiple separable EFs (Miyake et al., 2000). Thus, rumination may be related to some EFs but not others. Indeed, a recent meta-analysis concluded that rumination is associated with deficits in tasks tapping inhibition and set-shifting, but not working memory (Yang et al., 2016). Separable EFs are also correlated; thus, correlations with different EFs could reflect truly distinct associations, but could also reflect the same association (i.e., with common variance). Friedman et al. (2008) re-parameterized a model with three correlated EF latent variables (based on nine tasks tapping inhibition, updating, or set-shifting) into a bifactor model with three orthogonal factors: a Common EF factor predicting all nine tasks, and Shifting-Specific and Updating-Specific factors predicting remaining covariances among the shifting and updating tasks.1 This Unity/Diversity model of EFs acknowledges correlations among EFs (unity) and their separability (diversity), and suggests that performance on an EF task will reflect Common EF abilities, such as goal maintenance, as well as EF-specific processes, depending on the task. The latter include speed of clearing no-longer-relevant goals for Shifting-Specific, and working memory gating and retrieval for Updating-Specific. As studies examining rumination have implicated tasks tapping multiple EFs (Yang et al., 2016), it is likely that rumination is associated with Common EF.

Examination of the rumination–EF association is further complicated by the fact that rumination is a multifaceted construct with subtypes focused on different emotional content. As rumination has primarily been examined as a risk and maintenance factor for depression, many use the terms “rumination” and “depressive rumination” interchangeably, and the majority of research on rumination and EFs has focused on depression rumination (Yang et al., 2016).

Several theoretical models suggest that depressive rumination and EFs may have a causal association that contributes to and maintains psychopathological symptoms (e.g., Demeyer et al., 2012). One model, the impaired disengagement hypothesis, suggests that low levels of attentional control lead to prolonged and habitual rumination (e.g., De Raedt and Koster, 2010; Koster et al., 2011). Koster and colleagues (2011) suggest that most people experience negative and critical self-focused thoughts as incongruent with their positive self-image, which leads to disengagement of attention from the negative thoughts. However, disruptions of conflict signaling processes (e.g., with reduced attentional control) can lead to sustained focus on negative thoughts and habitual engagement in rumination. Alternatively, the dual-process model of cognitive vulnerability and resource allocation hypothesis suggests that rumination taxes limited cognitive resources and requires effortful redirection of attention (Kofta & Sedek, 1998; Levens, Muhtadie, & Gotlib, 2009). These models hypothesize that rumination impairs appropriate resource allocation and leads to EF deficits. Regardless of the direction of causality, causal hypotheses imply that rumination and EFs are associated and explain overlapping variance in psychopathology.

Few studies have examined the associations between EFs and anger rumination, a less studied subtype of rumination that shares the repetitive, self-focused process of depressive rumination, but is focused on a personally relevant anger-inducing event. One theoretical model proposed by Denson (2013), the Multiple Systems Model, hypothesizes that difficulties with inhibitory control, task-switching, and attentional disengagement have a bidirectional relation with anger rumination. Specifically, EF deficits contribute to the initiation of anger rumination after an anger-inducing event and anger rumination diminishes one’s EF capacity, which then maintains engagement in anger rumination.

Some of the few empirical studies examining anger rumination and EFs have been conducted by Whitmer and Banich (2007, 2010, 2012). Their work has included both anger and depressive rumination, and has provided mixed evidence as to whether anger and depressive rumination show similar relations to EFs. Whitmer and Banich (2007) found that anger rumination was associated with slower set-shifting, but that depressive rumination was associated with decreased inhibition. Later studies using different tasks (2012, 2010) did not find differential associations between anger rumination, depressive rumination, and EFs. Thus, it is unclear whether the shared ruminative process or the content of ruminative subtypes is associated with EF deficits, and the answer may depend on which EFs are examined. Examining the association between ruminative subtypes and EFs as defined by the Unity/Diversity model, in a less task-dependent way, may indicate that the ruminative process, rather than the content, is associated with Common EF deficits. However, given Whitmer and Banich’s (2007) findings, we may also find a relation between anger rumination and Shifting-Specific abilities.

Understanding rumination–EF associations is important because rumination and EF deficits are common across psychopathological disorders (e.g., Gould, 2010; Johnson et al., 2016), and the mechanisms by which rumination and EF deficits increase vulnerability to psychopathology remains unclear (e.g., Everasert, Koster, & Derakshan, 2012). Cognitive symptoms of psychopathology lead to significant functional impairment, contributing to difficulty attending school, living independently, and maintaining employment (e.g., Green, 2016). If rumination and EFs explain overlapping variance in psychopathology, then additional studies parsing the temporal relations between rumination and EFs may provide important insights into how vulnerability factors work together to augment vulnerability for psychopathology (Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001; Nolen-Hoeksema & Watkins, 2011). However, if rumination and EFs predict independent variance in psychopathology, then interventions focused on ameliorating the effects of EF deficits directly may be useful for addressing the impairing cognitive symptoms of psychopathology (e.g., Baune & Renger, 2014; Porter et al., 2017).

Here, we build upon our prior work investigating how anger and depressive rumination relate to psychopathology in this sample (du Pont et al., 2018) by investigating the associations between rumination and EFs, and whether the associations between rumination and psychopathology remain after accounting for individual differences in EFs. Specifically, we ask, 1) Are anger and depressive rumination differentially associated with EFs? And 2) Do rumination and EFs predict independent variance in psychopathology? In addressing these questions, we use the latent variable Unity/Diversity Model of EFs. This approach reduces problems associated with the use of individual EF tasks, which measure non-EF variance in addition to the EFs of interest (i.e., the task impurity problem) and can be unreliable (Miyake et al., 2000). Latent variables capture common variance across tasks that differ in non-EF requirements, thus enabling purer measures that are free from random error.

Method

Participants

Participants were 764 individuals (362 males, 402 females; 409 from monozygotic and 355 from dizygotic twin pairs) from the ongoing Colorado Longitudinal Twin Study (LTS). The present study included those who participated in a study by the Center for Antisocial Drug Dependence (CADD; see Rhea et al., 2006, 2013) and completed rumination and EF measures as a part of a concurrent study. Participants completed the psychopathology measures before coming to the lab to complete the EFs/rumination measures. Participants usually completed the measures within a month of each other, at a mean age of 22.8 (SD=1.3, range 21.0–28.0). The sample was 92.1% Caucasian, 5.2% multiracial, 0% Black/African-American, 1.3% other, and 1.2% not reported. Research protocols were approved by the University of Colorado’s Institutional Review Board and informed consent was obtained from all participants.

Measures

Rumination.

The measures of anger and depressive rumination were completed on a computer in the laboratory along with other questionnaires. Subscale scores for depressive and anger rumination were computed for participants who completed 80% of the items in that subscale.

Depressive rumination.

Participants completed the 24-item Rumination-Reflection Questionnaire (RRQ; Trapnell and Campbell, 1999), and the 10-item revised version of the Ruminative Responses Scale (RRS; Treynor et al., 2003). The RRQ consists of rumination (RRQ-Ru) and reflection (RRQ-Re) subscales. The RRQ-Ru measures one’s tendency to engage in negative self-focused thought (“I tend to ‘ruminate’ or dwell over things that happen to me for a really long time afterward”); the RRQ-Re was excluded from the study because it measures a general tendency to think introspectively (“I’m very self-inquisitive by nature”), a construct separate from depressive rumination (Johnson et al., 2016; Siegle, Moore, & Thase, 2004).

The revised RRS is a version of Nolen-Hoeksema and Marrow’s (1991) RRS that excludes items that overlap with depression inventories (Treynor et al., 2003). The scale asks participants how they generally respond when they are feeling “down, sad, or depressed,” and consists of two subscales, brooding (RRS-B) and reflection (RRS-R). The RRS-B subscale measures negative, self-focused, perseverative thoughts (“[I] think, why do I have problems that other people don’t have?”) and the RRS-R subscale measures the tendency to reflect on sadness (“Analyze recent events to try to understand why you are depressed”). In contrast to RRQ-Re subscale, the RRS-R subscale assesses one’s tendency to reflect on sadness and thus was included in the present study.

Anger rumination.

Participants also completed the 19-item Anger Rumination Scale (ARS; Sukhodolsky et al., 2001), which has four subscales. The angry afterthoughts (AA) subscale measures cognitive rehearsal of the anger episode (“I re-enact the anger episode in my mind after it has happened”); the angry memories (AM) subscale assesses thoughts of past anger episodes (“I think about certain events from a long time ago and they still make me angry”); the thoughts of revenge subscale captures desired retribution (“I have long living fantasies of revenge after the conflict is over”); and the understanding of causes subscale refers to thoughts about the causes of the anger episode (“I think about the reasons people treat me badly”).

Executive Functions.

The nine EF laboratory tasks are described briefly below. Friedman et al. (2016) describe the tasks in depth, as well as fits of alternative models.

Inhibition.

The three response inhibition tasks required avoiding dominant responses. In the antisaccade task, participants avoided reflexively saccading to a cue and instead looked in the opposite direction to see a briefly appearing target (dependent measure [DM]=% correct targets). In the stop-signal task, participants withheld a well-practiced categorization of arrow direction when the arrow turned red during 25% of the trials (DM=stop-signal reaction time [RT]). In the Stroop task, participants named font colors of color words or asterisks, avoiding the automatic tendency to read the word (DM=incongruent–asterisks RT).

Updating.

The three updating tasks required adding and deleting information in working memory. In the keep-track task, participants saw a series of 15 words from 6 categories and recalled the last words belonging to 2–4 target categories (DM=% targets recalled). In the letter memory task, participants continuously rehearsed aloud the last four letters in a series of 9, 11, or 13 letters (DM=% sets correctly rehearsed). In the spatial n-back task, participants saw 12 squares that flashed one at a time and indicated whether each was the same as that n(2 or 3)-trials before (DM=% correct).

Shifting.

The three set-shifting tasks required participants to switch quickly between subtasks according to a cue that appeared with the stimulus (DM for each task=switch–repeat RT). In the number–letter task, participants saw a number-letter or letter-number pair and categorized the number as even or odd if the pair appeared in one of the top two quadrants of a box but categorized the letter as consonant or vowel if the pair appeared in one of the bottom two quadrants. In the color–shape task, participants categorized either the shape or color of a stimulus depending on the letter (C or S) above it. In the category-switch task, participants categorized a word as describing something smaller or bigger than a soccer ball, or living or nonliving, depending on the symbol (heart or cross) above it.

Psychopathology.

Psychopathology was assessed with two structured clinical interviews: the Diagnostic Interview Schedule–IV (DIS-IV; Robins et al., 2000) and the Composite International Diagnostic Interview–Substance Abuse Module (CIDI-SAM; Cottler, Robins, & Helzer, 1989). For internalizing psychopathology, we used lifetime symptom endorsement from the Major Depressive Disorder (MDD) and generalized anxiety disorder (GAD) modules for the DIS-IV. For externalizing psychopathology, we used lifetime endorsement of the DIS-IV Antisocial Personality Disorder (ASPD) module and substance dependence/abuse symptoms for tobacco, alcohol, cannabis, and seven classes of illicit drugs from the CIDI-SAM. We used lifetime symptoms given the low prevalence of current diagnoses in this sample. We created ordinal psychopathology symptom count variables for each disorder to estimate the underlying liability based on the frequencies within each category and minimize the risk of biased parameter estimates that is typical of skewed symptom count variables (Derks, Dolan, & Boomsma, 2004). Frequencies of the ordinal variables are presented in Table 1.

Table 1.

Lifetime Psychopathology Symptom Frequencies by Sex

Symptom Count Bin
Measure 0 1 2 3 4
Males
 MDD 285 43 33
 GAD 330 26 5
 ASPD 161 104 96
 Alcohol Use Disorder 134 60 92 49 26
 Cannabis Use Disorder 242 50 45 14 10
 Tobacco Use Disorder 205 66 90
 Illicit Drug Use Disorder 316 45
Females
 MDD 289 47 66
 GAD 346 26 30
 ASPD 256 95 51
 Alcohol Use Disorder 193 78 72 29 29
 Cannabis Use Disorder 346 26 13 10 6
 Tobacco Use Disorder 304 46 51
 Illicit Drug Use Disorder 376 25
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Note. For tobacco use disorder, GAD, MDD, and ASPD: 0 = no symptoms; 1 = symptoms but no diagnosis; 2 = diagnosis according to DSV-IV criteria. For alcohol and cannabis use: 0 = no symptoms, 1 = one symptom, 2 = 2–3 symptoms, 3 = 4–5 symptoms, and 4 = six symptoms or more. For substance use involving illicit drugs: 0 = no symptoms; 1 = one or more symptoms. GAD = generalized anxiety disorder; MDD = major depressive disorder; ASPD = antisocial personality disorder.

Statistical Analysis

Structural equation models (SEM) were estimated with Mplus 8.1 (Muthén & Muthén, 1998–2017). For models including ordinal variables, we used the means and variance adjusted weighted least squares (WLSMV) estimator, which uses pairwise deletion. For all other models, we used maximum likelihood (ML) estimation, which uses full-information maximum likelihood (FIML). We used Mplus’ TYPE = COMPLEX option to correct for the nonindependence within our sample because our participants were twin pairs (Rebollo, de Moor, Dolan, & Boomsma, 2019). This option uses a sandwich estimator to compute standard errors and a scaled chi-square (χ2) to adjusted for non-independence. To conduct nested model comparisons, we used chi-square difference (Δχ2) tests incorporating the scaling factors (Satorra & Bentler, 2001).

Model fit was assessed with the χ2 statistic, supplemented with the comparative fit index (CFI) and the root mean square error of approximation (RMSEA), with CFI>.95 and RMSEA<.06 indicating good fit (Hu & Bentler, 1998). The significance of specific parameters was determined by p-values for the z-statistic (the ratio of the parameter estimate to its standard error) using an alpha level of .05.

We log-transformed the ARS subscales and implemented the trimming and transformations for EF tasks described in Friedman et al. (2016). In brief, we removed scores that were not significantly above chance accuracy, arcsine-transformed the n-back task, and used within-subject trimming procedures outlined by Wilcox and Keselman (2003) for all measures based on mean RTs. RTs were taken from correct trials (average accuracy was greater than 92% across RT tasks). Scores >3 SDs from the mean were replaced with values 3 SD from the mean (fewer than 2% of the observations for any measure). These procedures led to all variables showing acceptable skewness and kurtosis (Table 2).

Table 2.

Descriptive Statistics of Rumination and Executive Function Measures by Sex

Measure n Mean SD Min Max Skewnessa Kurtosisa Reliability
Males
 RRS–B 350 1.88 0.57 1.00 4.00 0.53 0.13 .78b
 RRS–R 350 1.95 0.67 1.00 4.00 0.28 −0.82 .81b
 RRQ–Ru 349 2.69 0.71 1.18 4.92 0.26 −0.17 .83b
 ARS–AM 350 1.58 0.52 1.00 4.00 0.32 −0.76 .83b
 ARS–TR 350 1.44 0.47 1.00 3.50 0.78 −0.02 .71b
 ARS–AA 350 1.53 0.53 1.00 3.67 0.47 −0.81 .86b
 ARS–UC 349 1.78 0.53 1.00 3.50 −0.07 −0.75 .68b
 Anti 348 66.91 % 16.00 24.07 96.30 −0.38 −0.60 .91b
 Stop 344 213 ms 31 116 286 0.41 0.29 .55b
 Stroop 341 157 ms 77 −73 387 −0.58 0.47 .96c
 Keep 349 71.61 % 9.87 44.12 96.43 −0.39 −0.04 .73b
 Letter 349 69.87 % 14.10 37.88 100.00 0.28 −0.77 .93b
 Sback 349 0.07 0.94 −2.52 2.70 −0.35 0.12 .77c
 Num 348 246 ms 166 −73 735 −1.02 0.83 .92c
 Col 343 238 ms 189 −212 792 −1.02 0.87 .92c
 Cat 348 183 ms 154 −81 735 −1.18 1.61 .95c
Females
 RRS–B 400 2.05 0.64 1.00 4.00 0.52 −0.24 .82b
 RRS–R 401 2.13 0.70 1.00 4.00 0.33 −0.53 .80b
 RRQ–Ru 400 2.93 0.76 1.00 5.00 0.33 −0.11 .85b
 ARS–AM 401 1.59 0.57 1.00 4.00 0.51 −0.47 .85b
 ARS–TR 400 1.32 0.38 1.00 3.50 1.26 1.88 .65b
 ARS–AA 400 1.54 0.56 1.00 3.83 0.65 −0.33 .87b
 ARS–UC 399 1.80 0.58 1.00 4.00 0.08 −0.52 .77b
 Anti 400 58.31 % 15.07 20.37 90.74 –0.01 −0.51 .87b
 Stop 391 217 ms 29 127 315 0.02 0.03 .68b
 Stroop 396 155 ms 71 –1 387 –0.84 0.93 .96c
 Keep 400 71.82 % 8.42 44.12 94.64 –0.28 0.12 .58b
 Letter 400 69.79 % 12.49 37.88 100.00 0.13 −0.54 .91b
 Sback 400 −0.08 0.87 −2.74 1.92 –0.31 −0.21 .76c
 Num 400 246 ms 148 −241 735 –0.76 0.88 .89c
 Col 400 206 ms 174 −239 792 –1.05 1.44 .88c
 Cat 399 210 ms 165 −37 735 –1.10 1.00 .94c
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Note. RRS-B=Ruminative Responses Scale-Brooding, RRS-R=Ruminative Responses Scale-Reflection, RRQ-Ru=Rumination-Reflection Questionnaire-Rumination, ARS=Anger Rumination Scale; ARS-AM= Angry memories, ARS-TR= Thoughts of Revenge, ARS-AA=Angry Afterthoughts, ARS-UC=Understanding Causes, Anti=antisaccade, Stop=stop-signal, keep=keep track, Letter=letter memory, Sback=average of z-scores for spatial 2-back and 3-back tasks, Num=number–letter, Col=color–shape, Cat=category-switch, SD=standard deviation.

a

Skewness and kurtosis were calculated on log-transformed scores for the ARS scales.

b

Cronbach’s alpha.

c

Split-half correlations adjusted with the Spearman-Brown prophecy formula.

Results

Preliminary analyses: Factor structure of rumination, EFs, and psychopathology

Zero-order correlations for all variables are presented in supplemental Tables S1 and S2. We used the two-factor Anger and Depressive Rumination model and two-factor Internalizing and Externalizing Psychopathology model that we developed in prior work (du Pont et al., 2018). The former includes a residual correlation between the ARS understanding causes and the RRS-R, because both involve thinking about the causes of an emotional state. The RRQ-Ru was predicted by both the rumination factors because the scale did not prompt participants to focus on a sad emotion state. For EFs, we also used a model developed in prior work (Friedman et al., 2016). This Unity/Diversity model includes a Common EF factor as well as Shifting-Specific and Updating-Specific factors (Figure 1b; Friedman et al., 2016)2.

Figure 1.

Figure 1

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Measurement models of (A) rumination and (B) executive functions (EFs) and (C) psychopathology. Panel A and C adapted from du Pont et al. (2018). Ellipses represent latent variables; rectangles represent manifest variables or individual scales. Standardized parameters for men are depicted above those for women, standard errors are in brackets. Note that standardized loadings differ slightly across sex even when unstandardized parameters are sex-invariant, because variances differ. For model A, the factor variance of Anger Rumination and the covariance between Anger and Depressive Rumination were constrained across sex but all other parameters not used to identify the model varied across sex. For model B, all unstandardized loadings and the factor variances for Common EF and Shifting-Specific were constrained to equality across sex, but the factor variance for Updating-Specific, as well as task intercepts and residual variances, varied across sex. For model C, the factor variance of Internalizing and the Internalizing-Externalizing covariance were constrained across sex, and all other parameters not used to identify the model varied across sex. AA= angry afterthoughts; TR= thoughts of revenge; AM=angry memories; UC=understanding causes; RRQ-RU=rumination; RRS-B=brooding; RRS-R= reflection. Anti=antisaccade; Stop=stop-signal; keep=keep track; Letter=letter memory; Sback=spatial n-back; Num=number-letter; Col=color-shape; Cat=category-switch. GAD=generalized anxiety disorder; MDD=major depressive disorder; ASPD=antisocial personality disorder; ALC=alcohol use disorder; CAN=cannabis use disorder; TOB=tobacco use disorder; ILL=substance use disorder involving illicit drug use. *p<.05.

We tested sex invariance for each measurement model, and used the most invariant model we could for the analyses. For rumination, χ2(26)=59.06, p<.001, RMSEA =.058, CFI=.987 (Figure 1a; du Pont et al., 2018), we used a configurally invariant model, in which the parameters used to identify the factors (the first factor loading for each latent variable) were invariant across sex, as was the Anger Rumination factor variance and Anger–Depressive Rumination covariance. We used a metrically invariant EF model in which all unstandardized factor loadings were invariant, as well as the factor variances for the Common EF and Shifting-Specific factors; the Updating-Specific factor variance differed across sex, χ2(54)=71.36, p<.001, RMSEA =.029, CFI=.985. Lastly, we used a configurally invariant psychopathology model, χ2(28)=29.04, p=.187, RMSEA=.025, CFI=.996 (Figure 1c), in which all parameters varied across sex except those used to identify the model (i.e., the factor means and scale factors), with a sex-invariant Externalizing Psychopathology factor variance and Internalizing–Externalizing Psychopathology covariance.

Are anger and depressive rumination differentially associated with EFs?

Before examining the correlations between rumination and EFs, we constrained the covariances between latent variables to be invariant across sex. These constraints did not significantly hurt model fit, Δχ2(7)=3.09, p=.877; thus, we interpreted the parameters from this model, shown in Table 3, χ2(442)=525.59, p=.004, CFI=.969, RMSEA=.022. Although the unstandardized covariances were equated across sex, the standardized correlations could differ because the Depressive Rumination and Updating-Specific factor variances varied across sex.

Table 3.

Latent Variable Correlations between Rumination, EFs, and Psychopathology for Men/Women

Latent Variable 1. Internalizing 2. Externalizing 3. Anger Rumination 4. Depressive Rumination
1. Internalizing
2. Externalizing .69*[.12]/.51*[.06]
3. Anger Rumination .43*[.10]/.32*[.06] .31*[.04]/.31*[.04]
4. Depressive Rumination .84*[.15]/.57*[.06] .29*[.05]/.27*[.05] .73*[.04]/.67*[.04]
5. Common EF −.08[.10]/−.06 [.08] −.21*[.06]/−.21*[.06] −.09+[.05]/−.09+[.05] −.11*[.06]/−.11*[.05]
6. Shifting-Specific −.01[.10]/.01 [.08] −.04 [.06]/−.04 [.06] .03 [.05]/.03 [.05] .01 [.05]/.01 [.05]
7. Updating-Specific −.15[.10]/−.17+[.10] −.09+[.05]/−.14+[.08] −.06 [.05]/−.09 [.07] −.02 [.05]/−.02 [.07]
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Note. Standard errors are in brackets. The correlations between the Common EF, Shifting-Specific, and Updating-Specific latent variables are fixed to zero in the Unity/Diversity model.

*

p<.05

+

p<.10

There was a small association between Depressive Rumination and Common EF (rmen= –.11, p=.041; rwomen=–.11, p=.037), but no other EF components. Similarly, there was a marginally significant negative association between Anger Rumination and Common EF (r= –.09, p=.071), but no relations with Updating-Specific or Shifting-Specific factors. To test whether Anger and Depressive Rumination were differentially associated with EFs, we constrained the covariances between Anger rumination and each EF component (Table 3, row 5–7, column 3) to be equal to the covariances between Depressive Rumination and each EF component (Table 3; row 5–7, column 4). Anger and Depressive Rumination were equally associated with Common EF, Δχ2(1)=2.30, p=.129, Updating-Specific, Δχ2(1)=1.37, p=.241, and Shifting-Specific factors, Δχ2(1)=1.41, p=.235, at the unstandardized level. Together, the results from the correlational model indicate that Anger and Depressive Rumination are not differentially associated with EFs: Both subtypes of rumination were associated with Common EF, and showed no statistically significant associations with Updating-and Shifting-Specific factors.

Do rumination and EFs predict independent variance in psychopathology?

First, we examined the correlations between rumination, EFs, and psychopathology (Table 3). Anger and Depressive Rumination correlated positively with Internalizing (rs=.32–.43) and Externalizing Psychopathology (rs=.27–.31), and Common EF correlated negatively with Externalizing (r= –.21, p=.001).

Next, we regressed psychopathology on rumination and EFs to examine whether these associations remained significant when controlling for their covariances (Figure 2, χ2(442)=526.94, p=.003, CFI=.969, RMSEA=.022). We constrained the unstandardized covariances and regression paths between the latent variables to be sex invariant, Δχ2(8)=8.11, p=.423, but standardized parameters could differ when factor variances differed. Anger Rumination and Common EF were independently associated with Externalizing, but Depressive Rumination no longer predicted Externalizing Psychopathology after controlling for EFs and Anger Rumination. Depressive Rumination remained positively and strongly associated with Internalizing Psychopathology, whereas the Anger Rumination–Internalizing association became negative and marginally significant (suggesting a suppression effect as discussed by du Pont et al., 2018). Common EF was still not associated with Internalizing Psychopathology, consistent with the correlational model. In combination, rumination and EFs explained 40% and 59% of the variance in Internalizing Psychopathology in women and men, respectively, and 15% of the variance in Externalizing Psychopathology in both women and men.

Figure 2.

Figure 2

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Regression model of rumination, executive functions (EFs), and psychopathology. Ellipses indicate latent variables. For simplicity, the manifest variables that load on the latent variables are not depicted. Standardized parameters are depicted for men above the standardized parameters for women. Standard errors are in brackets. All unstandardized covariances and regression paths were constrained to be equal across sex. For clarity, bold lines were used to depict significant associations, solid lines depict marginally significant associations, and dotted lines were used to depict nonsignificant associations. Internalizing Psychopathology r2 = .59 and .40 in men and women, respectively, and Externalizing Psychopathology r2 = .15 in both men and women. *p<.05, +p<.10.

Discussion

We found that both Anger and Depressive Rumination were weakly associated with the Common EF factor, but not the Updating- and Shifting-Specific factors. However, rumination and EFs were associated with independent variance in psychopathology, particularly Externalizing Psychopathology. These results support the hypothesis that ruminative tendencies are somewhat related to EFs, but suggest that rumination and EFs may actually be independent risk factors for psychopathology. We expand upon these results and their implications in the following sections.

Anger and Depressive Rumination are similarly associated with EFs

The finding that depressive rumination is associated with Common EF is consistent with theoretical models of rumination and EFs, as well as prior empirical work showing associations between depressive rumination and deficits in multiple forms of correlated EFs (e.g., inhibition, updating, and set-shifting; Altamirano et al., 2010; Wagner et al., 2014). Because the Unity/Diversity model partitions EFs into common and specific variance, it provides additional information about the nature of these prior associations. Our results suggest that Depressive Rumination is associated with multiple correlated EFs because it is related to the variance they share, rather than being independently related to each EF. Given that Common EF explains all the variance in response inhibition (i.e., in a hierarchical model, the Inhibition factor loads at 1 on the Common EF factor; Friedman & Miyake, 2017), the association between Depressive Rumination and Common EF is also consistent with prior studies documenting associations between depressive rumination and inhibiting abilities (e.g., Joormann & Gotlib, 2010; Vălenaş & Szentágotai-Tătar, 2017; Yang et al., 2016).

This study is one of the few studies examining anger rumination and multiple EFs. Unlike Whitmer and Banich (2007), we did not find evidence that anger and depressive rumination are differentially associated with EFs. We did not have a measure comparable to their backward inhibition measure, and because their study focused on a single shifting task, we do not know if their shifting effect was due to Shifting-Specific versus Common EF variance. Additionally, Whitmer and Banich used a sample selected for extreme RRS scores, which may also contribute to the inconsistency between our findings and theirs (as noted by Kowalczyk & Grange, 2017). In other work, however, Whitmer and Banich (2010, 2012) found that both anger and depressive rumination were associated similarly with EF measures, although those studies also used different EF tasks. Nevertheless, our finding that Anger and Depressive Rumination are equally associated with Common EF suggests that the repetitive process of rumination, regardless of the emotional content (anger versus sadness) is inversely related to Common EF.

This study contributes to existing research that separates disorder-specific content from the process of repetitive negative thinking (Ehring & Watkins, 2008; Watkins, 2008). Our results and effect sizes are consistent with meta-analytic work by Zetsche, Bürkner, and Schulze (2018), who found associations between repetitive negative thinking and deficits across measures of cognitive control (r= –.11). Additionally, given that we did not find differential relations between ruminative subtypes and EFs, our work suggests that understanding the process of rumination, rather than refining existing theories to create hypotheses based on content-driven ruminative subtypes, should be the focus of future work.

Existing theoretical models suggests that EF deficits may serve as a major precipitating factor for rumination (e.g., the impaired disengagement hypothesis; Koster et al., 2011) or that misallocation of limited cognitive resources leads to costly, ruminative thought (e.g., the resource allocation hypothesis; Levens et al., 2009). However, the small relations between EFs and rumination suggest that rumination and EFs are largely independent processes. In contrast to results expected by theory-driven mediational hypotheses, the independent variance in rumination and Common EF predicted psychopathology. Our findings are not inconsistent with causal hypotheses of rumination and EFs, but suggest that potentially causal relations explain little variance in rumination/EFs, and may only partly contribute to psychopathology.

Zetsche and colleagues (2018) report that the association between rumination and resistance to proactive interference, or one’s ability to resist intrusions from previously relevant information, was significantly larger than other cognitive control abilities (after controlling for depressive symptoms). Friedman and Miyake (2004) also found that this inhibition-related function related to unwanted intrusive thoughts, controlling for response inhibition and distractor interference. However, the present study did not include any measures that tap into this function specifically. Future work examining repetitive negative thinking and EFs should incorporate additional content-independent measures (e.g., the Perseverative Thinking Questionnaire; Ehring et al., 2011) and examine additional EF processes to determine if the small associations between rumination and EF may be driven by the specific forms of rumination or EF processes examined here.

Anger Rumination and Common EF are independently associated with Externalizing Psychopathology

Our regression results indicate that Anger Rumination and Common EF have independent associations with Externalizing Psychopathology. As mentioned earlier, this pattern is not consistent with causal hypotheses about the rumination–EFs association (e.g., De Raedt & Koster, 2010; Levens, Muhtadie, & Gotlib, 2009), although it does not rule out this possibility. Rather, it supports the predictive value of both Anger Rumination and Common EF, and the importance of including these constructs in future work examining psychopathology.

The independent association between Anger Rumination and Externalizing Psychopathology adds to prior studies indicating that anger rumination is associated with weekly intake of alcohol by college students, as well as aggression in adolescents and adults after controlling for depressive rumination (Peled & Moretti, 2007, 2010). Interestingly, Peled and Moretti (2010, 2007) found that anger does not mediate the association between anger rumination and aggression, which suggests that the cognitive process of anger rumination, and not just the increase in anger that rumination may generate, is important. The importance of the repetitive, self-focused process of anger rumination is further underscored by our finding that the overlapping variance between Anger Rumination and Depressive Rumination explains more variance in Externalizing Psychopathology than the unique variance in Anger Rumination (du Pont et al., 2018).

The independent association between Common EF and Externalizing Psychopathology is also consistent with the existing literature. Prior studies examining the association between EFs and externalizing psychopathology have found associations with behavioral disinhibition, a generalized vulnerability to externalizing psychopathology, in children at age 12 and 17 (Young et al., 2009); antisocial behavior, antisocial personality disorder, and psychopathy in adults (Morgan & Lilienfeld, 2000; Zeier, Baskin-Sommers, Hiatt Racer, & Newman, 2012); and substance use disorder (Dolan, Bechara, & Nathan, 2008). One potential pathway from EFs to externalizing psychopathology is via impulsivity (e.g., Fino et al., 2014). For example, a study by Romer et al. (2009) of EFs, impulsivity, and risk and problem behaviors in preadolescents age 10–12 years found that EFs were associated with behavioral problems and risk behaviors, but only via their associations with impulsivity. However, more recent work suggests that EFs and impulsivity may be largely separate constructs that independently predict achievement and psychopathology (Friedman et al., under review; Malanchini et al., 2018).

Depressive Rumination, but not EFs, are independently associated with Internalizing Psychopathology

Results of multiple regression analyses indicated that Internalizing Psychopathology was strongly related to Depressive Rumination, although it had a marginally significant negative association with Anger Rumination and no association with Common EF. The lack of association between Internalizing and Common EF is inconsistent with prior work suggesting that depression is associated with broad EF impairments (Snyder, 2013). As noted by Snyder (2013), some evidence suggests that individuals with more severe depressive symptoms show greater EF impairments (e.g., McDermott & Ebmeier, 2009). Thus, one explanation for this inconsistency is that we examined a community sample rather than a clinical sample. Given that other studies have not found an association between depression symptom severity and EF deficits (e.g., Harvey et al., 2004; Porter et al., 2007), future work should examine whether deficits in EFs are associated with depression symptom severity across community and clinical samples.

Limitations and future directions

The present study examined the relations between rumination, EFs, and lifetime psychopathology symptoms as distributed throughout a community sample. As reviewed by Whitmer and Gotlib (2013), some effects of trait rumination are independent of depression (e.g., inhibition of previously relevant information). Others effects of rumination vary across populations; for example, rumination is unrelated to one’s ability to ignore distracting negative information in depressed samples, but associated with improved resistance to distracter interference in nondepressed samples. The importance of exploring these questions in samples that span the full range of clinical severity is further underscored by evidence that the factor structure of rumination may vary across nondepressed and clinically depressed samples (Whitmer & Gotlib, 2011). Additionally, the low endorsement of psychopathology symptoms in the present sample necessitated the use of lifetime, rather than past-year, psychopathology. Despite the stability of EFs across time (Friedman et al., 2016), the use of lifetime psychopathology, trait rumination, and current EF abilities may have led to attenuated correlations in the present sample.

Longitudinal studies building upon the cross-sectional results presented here should further examine the independent relations of anger and depressive rumination with internalizing psychopathology and anger rumination and Common EF with externalizing psychopathology. As anger and depressive rumination share a genetic etiology and are predominately differentiated by environmental influences (du Pont, Rhee, Corley, Hewitt, & Friedman, in press), the independent effect of anger rumination on externalizing and marginally significant effect on internalizing are likely environmental in nature. One factor that may contribute to anger rumination’s relations with psychopathology is perceived discrimination. Borders and Liang (2011) found that anger rumination and recent perceived discrimination explained overlapping variance in depression, hostility, and aggression in a sample of ethnic minority Americans. In contrast, individual differences in Common EF are substantially heritable (Friedman et al., 2016), and Common EF and externalizing psychopathology may share genetic liability (e.g., Young et al., 2009).

Conclusion

Mapping the interplay of cognitive processes that augment vulnerability for psychopathology is critical for understanding the etiology of psychopathology, but also for successful prevention and intervention. The present study found small associations between two ruminative subtypes and Common EF, but our results did not support existing causal hypotheses. Examination of Internalizing and Externalizing Psychopathology indicated that Depressive Rumination independently predicts Internalizing, whereas Anger Rumination and Common EF independently predict Externalizing. Together, our results support the importance of both Common EF and rumination as predictors of psychopathology.

Supplementary Material

Supplement

Highlights.

  • Small associations of rumination with Common Executive Function (EF)

  • Anger and Depressive Rumination not differentially associated with EFs

  • Independent associations between Common EF, rumination, and psychopathology

  • Results are inconsistent with rumination as mediator of Common EF-psychopathology association

Acknowledgements:

Preliminary results from this study were presented at the Annual Meeting of the Society for Research in Psychopathology in Denver, CO in September 2017.

Funding: This research was supported by the National Institutes of Health (NIH) grants MH063207, MH016880, AG046938 and DA011015.

Footnotes

1

As noted in previous work, there is no evidence of an inhibition-specific factor after accounting for Common EF (Friedman & Miyake, 2017; Miyake & Friedman, 2012).

2

As in prior work, we included a residual correlation between the antisaccade and spatial n-back tasks (Friedman et al., 2016; Ito et al., 2015). This residual correlation likely reflects method covariance, as these two tasks are the only tasks that require eye movement across the screen.

Declarations of interest: none

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