Skip to main content
NCBI home page
Archives of Clinical Neuropsychology logoLink to Archives of Clinical Neuropsychology
. 2022 Sep 3;38(2):213–223. doi: 10.1093/arclin/acac070

Testing the Reserve Capacity Model: Does Race Moderate the Relationship Between Negative Emotions and Neurocognition?

Rachel F Bloom 1, Karen L Siedlecki 2,✉
PMCID: PMC9940113  PMID: 36062422

Abstract

Objective

To test whether race moderates the relationship between negative emotions and neurocognition by applying the reserve capacity model within a large sample that spans adulthood.

Method

The study sample (N = 1,020) consisted of community-dwelling adults between 18 and 84 years of age who were drawn from the Virginia Cognitive Aging Project. Demographic variables were used to match a sample of Black participants to a sample of White participants. Race was examined as a moderator of the relationship between negative emotions (i.e., depressive symptoms, trait anxiety, and the negative affect subscale from the Positive and Negative Affect Schedule) and neurocognitive variables (episodic memory, reasoning, spatial visualization, and processing speed) with multiple-group structural equation modeling.

Results

After accounting for sociodemographic variables, depressive symptoms were negatively associated with processing speed in both groups, and with worse reasoning in the White subsample. Negative affect was associated with lower reasoning performance in both groups and with lower spatial visualization in the White subsample. Trait anxiety was not significantly associated with the neurocognitive constructs in either group. Multigroup structural equation models showed that the magnitudes of the associations were not different between the Black and White subsamples. Thus, race did not moderate the relationships between depressive symptoms, trait anxiety, and negative affect with neurocognition.

Conclusions

Negative emotions are associated with lower performance on different neurocognitive tasks, but race does not moderate these relationships. Future research should examine perceived discrimination or other psychosocial variables when examining the relationships among negative emotions and neurocognition.

Keywords: Neurocognition, Depression, Anxiety, Negative affect

Introduction

Reserve capacity refers to the tangible, interpersonal, and intrapersonal psychosocial resources individuals can draw upon during times of stress to regulate their emotions and cognitions such that the possibility of developing negative health outcomes is attenuated (Gallo, de Los Monteros, & Shivpuri, 2009). The reserve capacity model (RCM) posits that those with lower socioeconomic status are vulnerable to the increased negative health impacts of various psychopathologies because of their diminished reserve capacity (Gallo & Matthews, 2003) and provides a general framework under which to consider psychosocial variables in health disparities which affect people who are marginalized by thier socioeconomic position (Crystal et al., 2017; Gallo, 2009). Greater economic disadvantage is associated with greater risk for interpersonal conflict, major life disruptions, and hardships (Gallo, 2009) which, in turn, are associated with higher levels of depression and anxiety (Evans & Erickson, 2019). The RCM provides an explanation for this relationship wherein the loss of interpersonal and tangible resources (through conflict or through loss of income and agency resulting from mental health difficulties and challenging life experiences) can further deplete the resources people have to cope with everyday stresses and tasks, creating a positive feedback loop of poorer mental and physical health outcomes (Gallo & Matthews, 2003). This underscores the importance of integrating socioeconomic status as a meaningful predictor in psychosocial models of health behavior (Matthews & Gallo, 2011) as well as other social disparities in the context of aging (Brewster et al., 2019). The RCM also offers an explanation for how lower socioeconomic standing can exacerbate the relationship between negative affect, a component of subjective well-being commonly measured by the negative affect subscale of the Positive and Negative Affect Schedule (PANAS; Watson et al., 1998), and declines in physical and cognitive health (Moriarty & Finn, 2014), which has been found to have a stronger predictive effect at lower levels of education (Schöllgen, Huxhold, & Schmiedek, 2012).

In past research, the RCM has primarily been utilized in drawing associations between socioeconomic status and health outcomes. Some preliminary work has extended this model in conjunction with the Minority Stress Theory, which asserts that exposure to racial discrimination and acculturative stress among Black, Indigenous, and People of Color (BIPOC) constitutes a hostile psychological environment leading to negative cognitive, mental, and other health consequences (Calabrese, Meyer, Overstreet, Haile, & Hansen, 2015; Forrester, Gallo, Whitfield, & Thorpe, 2019; Meyer, 2003; Nkwata, Zhang, Song, Giordani, & Ezeamama, 2021). Exposure to racism in both its explicit and ambiguous forms can reinforce the salience of exclusionary behavior in subsequent routine social interactions, interfering with cognitive processing (Broudy et al., 2007). In particular, subtle discrimination (also labeled as micro-aggressions; Williams, 2020) has been shown to affect BIPOC individuals as much or more than blatant discrimination, as BIPOC individuals face an additional cognitive burden of attempting to categorize or assess the true intent of ambiguous comments regarding their group membership compared to White individuals (Salvatore & Shelton, 2007). In other words, subtle discrimination may be more difficult to interpret and thus may deplete cognitive resources, especially those related to executive functioning (Bair & Steele, 2010; Holoien & Shelton, 2012).

Zahodne, Nowinski, Gershon, and Manly (2014) applied the theoretical frame of the RCM to an assessment of the relationship between depressive symptoms and neurocognition. They hypothesized that greater exposure to stressors, such as racial discrimination, coupled with lower levels of self-efficacy, may result in Black Americans having reduced psychological resources to buffer the deleterious effects of depressive symptoms on neurocognition. Zahodne et al. were the first to extend the RCM to test cognitive outcomes, and they found that higher levels of depressive symptoms were more strongly associated with lower executive functioning among Black participants as compared to non-Hispanic White participants, specifically in task-switching, inhibition, and episodic memory tasks. The researchers suggested that these findings may be explained by lower levels of reserve capacity to buffer the negative impacts of depressive symptoms and that these are consistent with previous work showing that depressive disorders may be more disruptive to health status among Black Americans (Williams et al., 2007), specifically in relation to cognitive functioning. It is noteworthy that compared to older White adults, older Black adults (>65 years) are twice as likely to be diagnosed with Alzheimer’s disease; Hispanic individuals are about one and a half times as likely (Alzheimer’s Association, 2021). In the context of this research, the increased prevalence of Alzheimer’s disease and related dementias could represent one correlate of reduced reserve capacity among Black individuals which leaves them more vulnerable to cognitive insult (Zahodne et al., 2014).

Diminished reserve capacity may play a role in moderating the relationship of negative emotions to cognitive functioning as well. Moderation occurs when the size, sign, or strength of the effect of X on variable Y is dependent, or is predicted by, a third variable (i.e., the moderating variable; Hayes, 2017). The RCM theorizes that negative emotions, including depression, anxiety, and negative affect, can deplete cognitive resources and thwart efforts to restore them, as they “may increase the likelihood that ambiguous stimuli are viewed as threatening or harmful, thereby resulting in further degradations of the reserve capacity,” (Gallo & Matthews, 2003, p. 36).

One of the few studies that have examined the moderating influence of minority stress on the relationship between psychological health and cognitive outcomes recommended that future studies should consider differences in emotion-health couplings based on race (Consedine, 2008). The current research aims to extend previous work which assessed race as a moderator for the relationship between depressive symptoms and neurocognition under the RCM (Zahodne et al., 2014) by examining whether race also moderates the relationships between trait anxiety and negative affect on cognition. While previous work has demonstrated that trait anxiety (Howarter & Bennett, 2013) and negative affect (Schöllgen et al., 2012) may be associated with the health and quality of life outcomes in the context of the RCM, little research has examined their role when neurocognition is the outcome of interest.

The RCM and its connection to minority status was chosen as a theoretical model for the current study in order to replicate and extend the limited previous work introduced by Zahodne and colleagues (2014). In the current study, we use multiple-group structural equation modeling to examine race as a moderator of the relationship between negative emotions and neurocognition. This study examined three central hypotheses tied to the moderation of three different predictors by racial category, which serves as a proxy for (but is not equivalent with) minority stress (Stinchcombe & Hammond, 2021). We hypothesize that race will moderate the relationship between depressive symptoms and cognitive functioning, between trait anxiety and cognitive functioning, and between negative affect and cognitive functioning such that the relationships among these variables will be stronger for Black participants compared to White participants.

Materials and Methods

Participants

Participants were drawn from the Virginia Cognitive Aging Project (VCAP; Salthouse, 2014), a longitudinal study of cognition in community-dwelling adults. These data are archival and the current project comprises secondary data analysis. The total sample comprises 5,429 participants between the ages of 18 and 99 of whom 79% identify as White (n = 4291), and 11.9% (n = 644) identify as Black. Of those who identify as Black, only three (0.5%) identify as Hispanic or Latinx, so participants who identify as Hispanic or Latinx (n = 99; 1.8%) were filtered out to create comparable subgroups of non-Hispanic White and non-Hispanic Black participants. To create equivalent sample sizes between the two groups, participants were matched on age, gender, and years of education using the case control method in SPSS 25.0 (IBM Corp). Selecting participants based on exact matches yielded groups of 510 White participants and 510 Black participants (see Table 1) ranging in age from 18 to 84 (Mage = 43.06, SD = 14.60). Participants without exact matches were excluded.

Table 1.

Sample characteristics

Total (N = 1,020) White (n = 510) Black (n = 510) t or χ2 df p Cohen’s d
M SD % Missing data M SD M SD
Age 43.06 14.6 0 43.06 14.61 43.06 14.61 .00 1018 1 .00
Gender (% women) 73.13 0 73.13 73.13 .00 1 1 .00
Education 13.91 2.10 0 13.91 2.11 13.91 2.11 .00 1018 1 .00
Self-rated health 2.31 .88 0 2.18 .91 2.45 0.82 -4.89 1018 <.01 -.31
Depressive symptoms 13.61 9.42 4.71 13.35 9.59 13.87 9.24 -.85 970 .39 -.06
Negative affect 14.5 5.83 30.2 13.61 4.94 15.11 6.31 -3.42 719 <.01 -.26
Anxiety 38.27 10.86 29.31 38.14 11.25 38.36 10.6 -.27 710 .79 -.02
Matrix reasoning 6.67 3.41 2.55 7.74 3.38 5.62 3.1 10.31 992 <.01 .65
Shipley abstraction 11.56 4.05 26.76 13.17 3.53 10.42 4.00 9.70 745 <.01 .72
Letter sets 9.95 3.11 12.84 11.03 2.70 8.92 8.0092 10.74 887 <.01 .72
Spatial relations 6.88 4.71 2.16 8.77 5.13 5.05 3.37 13.58 996 <.01 .86
Paper folding 5.39 2.87 2.75 6.48 2.79 4.32 2.52 12.83 990 <.01 .82
Form boards 6.03 4.09 10.10 7.45 4.24 4.69 3.45 10.85 915 <.01 .72
Recall 33.57 6.58 .05 35.60 6.04 31.56 6.47 10.29 1013 <.01 .65
Paired associates 2.47 1.68 16.86 3.14 1.68 1.96 1.49 10.82 846 <.01 .75
Logical memory 40.97 10.73 1.08 44.8 9.49 37.15 10.55 8.72 1007 <.01 .76
Digit symbol 71.43 17.67 .10 75.74 17.12 67.13 17.17 8.02 1017 <.01 .50
Pattern comparison 16.24 3.52 1.76 17.03 3.38 15.46 3.50 7.20 1000 <.01 .46
Letter comparison 10.44 2.49 1.67 15.46 2.35 10.16 2.58 3.66 1001 <.01 .23
Open in a new tab

The VCAP participants were recruited through flyers, newspaper advertisements, and referrals from other participants. All participants were within driving distance to the Cognitive Aging Lab at the University of Virginia located in Charlottesville, VA. Inclusion criteria included being over the age of 18, having a high school level (or equivalent) of education, and having no vision or hearing impairments that would prohibit completion of the tasks. Within the matched samples, the mean education level was equivalent to about 2 years of college (Meducation = 13.91, SD = 2.10). Occupation was assessed with the question, “What is your current occupation, or the occupation you held for most of your working life?” and the GT Occupation Scale (Ganzeboom & Treiman, 1996) was used to categorize the open-ended responses. The most common category was “technicians and associate professionals” comprising 20.9% (n = 213) of the total sample. The full distribution of occupations for the matched samples are provided in the supplementary material.

On each occasion, participants attended three 2-hr sessions across a 2-week period. In Session 1, participants completed original versions of the tasks described below. In the second and third sessions of Occasion 1, participants either completed the alternate/parallel versions of the neurocognitive tasks which are highly correlated with the original versions (Salthouse, 2012), or different tasks entirely (Salthouse, 2017). Participants also completed a packet of questionnaires between the first and third sessions. Data for the current study are from the first session of the baseline assessment, which comprises only the original version of the neurocognitive tasks, and were collected between 2001 and 2018. This study was approved by the local Institutional Review Board, and informed consent was obtained from each participant.

Measures

Negative emotions

Depressive symptoms were measured with the 20-item Center for Epidemiologic Studies-Depression scale (CES-D; Radloff, 1977). The CES-D has demonstrated high reliability and validity. For example, in a “nationally representative cohort study of noninstitutionalized, English-speaking adults aged 24–74 years,” Cosco, Prina, Stubbs, and Wu (2017, p. 476) found that the CES-D was reliable and valid and suggested that continued use of the CES-D in non-institutionalized samples was warranted. In the current analytic sample, the Cronbach alpha for the CES-D was .90.

Trait anxiety was measured with the State Trait Anxiety Inventory (STAI; Spielberger et al., 1983). The Trait subscale was used to test hypotheses, rather than the State subscale, in reflection of the impact consistent (vs. transient) anxiety may exert on cognitive performance under the theoretical model of the RCM. The reliability of the STAI-T is well documented (e.g., Barnes, Harp, & Jung, 2002), and in this sample, alpha = .93. While recent research suggests that rather than a unidimensional measure of trait anxiety, the STAI-T may be better conceptualized as a measure of both anxiety and depression or of general negative emotion (Balsamo et al., 2013), the STAI-T has a demonstrated utility in discriminating between healthy control participants and those with psychiatric illnesses (Spielberger et al., 2009).

The 10-item negative affect scale from the PANAS (Watson et al., 1988) was used to assess negative affect. The following 10 items are included in the negative affect subscale: distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, and afraid. Participants indicated the extent they felt each adjective at the “present moment” on a scale from 1 (“very slightly or not at all”) to 5 (“extremely”). Research has demonstrated the reliability and construct validity of the PANAS in a large sample of non-clinical adults broadly representative of the population of adults in the United Kingdom (Crawford & Henry, 2004). In the current sample, alpha was .89.

Higher values are indicative of higher levels of depressive symptoms, trait anxiety, and negative affect, respectively.

Neurocognitive domains

Four neurocognitive domains were examined in the current study. Each of the domains was modeled as a latent construct with three indicator variables. The variables that loaded on to each neurocognitive domain are listed below and descriptions can be found in other articles (Salthouse, 2010; Siedlecki, Falzarano, & Salthouse, 2019). In these analyses, raw scores were used for each respective variable. The factor structure of the variables has been established in several previous papers (e.g., Salthouse, 2004, 2005; 2007).

Episodic memory

Episodic memory was assessed with a paired associate task (Salthouse, Fristoe, & Rhee, 1996), the Wechsler Memory Scale-III (WMS) word recall subtest (Wechsler, 1997a), and the WMS logical memory subtest (Wechsler, 1997a).

Reasoning

The reasoning construct comprised a computerized version of a progressive matrix reasoning task (Raven, 1962), Shipley abstraction (Zachary, 1986), and Letter sets (Ekstrom, French, Harman, & Dermen, 1976) tasks.

Spatial visualization

Form boards (Ekstrom et al., 1976), paper folding (Ekstrom et al., 1976), and spatial relations (Bennett, Seashore, & Wesman, 1997) were used to measure the spatial visualization construct.

Processing speed

Letter and pattern comparison tasks (Salthouse & Babcock, 1991) and the Wechsler Adult Intelligence Scale-III digit symbol subtest (Wechsler, 1997b) were indicators on the processing speed construct.

Statistical Analysis

Group differences between the White and Black groups were examined with t-tests (or χ2 test for categorical variables, i.e., gender). Multiple-group structural equation modeling was performed with Amos 25.0 (Arbuckle, 2017) to examine moderation. The four neurocognitive domains (episodic memory, reasoning, spatial visualization, and processing speed) were represented in each model as latent constructs with three observed variables (see Fig. 1 for the depiction of the four-factor model). These latent constructs were regressed on the antecedent variable (either depressive symptoms, negative affect, or trait anxiety, respectively) along with the covariates of gender, age, years of education, and self-rated health. Self-rated health was assessed with a single item, “How would you rate your health at the current time?” with options ranging from 1 = excellent to 5 = poor. Because income was not measured, years of education served as a measure of SES among this sample. Following the procedure described by Zahodne et al. (2014), we first forced the magnitude of the structural relations (covariances and unstandardized loadings) to be the same across the two groups. This model was labeled the fixed model. Then, for each model, the regression coefficients from the psychosocial predictor to the neurocognitive constructs were allowed to vary between the two groups; this was labeled as the free model. The fit of the free model was compared to that of the fixed model, and if the change in χ2 (per df) was significantly different across the models, then it was concluded that the magnitudes of the regression coefficients were significantly different across the two groups. Moderation can be examined in several ways; in the current paper, moderation was ascertained by examining the fit of the fixed models to the fit of the free models (e.g., Zahodne et al., 2014). Worse model fit in the fixed model as compared to the free model suggests that the magnitude of the relevant relationships between the antecedent and dependent variables are different across the groups, thus providing evidence that the relationship is moderated, or dependent, on race. Additional fit statistics were evaluated to support this conclusion, including the root mean square error of approximation (RMSEA) and Akaike information criterion (AIC), where lower values indicate better fit, and the comparative fit index (CFI), where higher values are indicative of better fit. Full information maximum likelihood (FIML) estimation was used to deal with missing data. FIML minimizes the bias in parameter estimates when data cannot be assumed to be missing completely at random, while also allowing for the retention of the full analytic sample to maintain statistical power (Enders, 2001).

Fig. 1.

Fig. 1

Open in a new tab

Four-factor structural equation model with standardized coefficients. Note: Rectangles depict observed variables, and ovals depict latent variables; latent variables labeled “e” represent the error and unique variance associated with each observed variable. *p < .01.

Due to the relatively large sample size and multiple analyses, a p level of .01 was used for all analyses.

Results

Group differences

The two groups were matched on age, gender, and years of education, so there were no differences between the groups on these characteristics. Group characteristics are presented in Table 1. White participants rated their health as significantly better than Black participants (t(1,018) = −4.89, p < .01; Cohen’s d = −.31) and reported lower levels of negative affect (t(719) = −3.42, p < .01; Cohen’s d = −.26). The White group performed better on the neurocognitive measures (see Table 1). Inspection of effect sizes presented in Table 1 indicate that the magnitude of the effect of race on neurocognition was the smallest for measures of processing speed (Cohen’s ds = .23, .46, and .50 for letter comparison, pattern comparison, and digit symbol, respectively) and was the greatest for the spatial visualization measures (Cohen’s ds = .86, .82, and .72 for spatial relations, letter sets, and form boards, respectively).

Multiple-group structural equation models

Model fit for each predictor is presented in Table 2. There were no significant differences between the fixed models (i.e., magnitude of the structural relations set to be equivalent across the two groups) and free models (i.e., regression coefficients from the antecedent to the neurocognitive constructs were allowed to vary between the two groups) when depressive symptoms or trait anxiety were predictors of neurocognition; the changes in χ2 per change in df were not significant. These models generally fit well (RMSEAs = .044 and .045; CFIs = .931). In the model with negative affect as the antecedent, the change in χ2 (Δdf(4)) was 11.16 between the free and fixed models, but this difference did not reach significance at the a priori p level of .01 (p = .025). In addition, the RMSEA was the same in the fixed and free models (.045), and while the CFI indicated a slightly better fit in the free model (see Table 2), the difference was nominal (CFI change = −.001). Thus, race did not moderate the relationship between negative emotions and neurocognition.

Table 2.

Fit statistics for the multiple-group structural equation models

Χ2 df Χ2/df CFI RMSEA AIC Δ Χ2 Δ df p
Depressive symptoms
 aFixed model 658.857 220 2.99 .931 .044 898.86
 bFree model 656.508 216 3.04 .931 .045 904.51 2.35 4 .672
Trait anxiety
 aFixed model 659.407 220 3.00 .931 .044 899.41
 bFree model 654.107 216 3.03 .931 .045 902.11 5.30 4 .258
Negative affect
 aFixed model 670.28 220 3.05 .929 .045 910.28
 bFree model 659.12 216 3.05 .930 .045 907.12 11.16 4 .025
Open in a new tab

Note: CFI = comparative fit index; RMSEA = root mean square error of approximation; AIC = Akaike information criterion.

aCovariances and regression are constrained to equivalence.

bPaths from predictor to cognitive constructs freely estimated.

The standardized path coefficients from the antecedent (either depressive symptoms, trait anxiety, or negative affect) to each neurocognitive construct (episodic memory, reasoning, spatial visualization, and processing speed) from the free models are presented in Table 3. As can be seen, the standardized coefficients between the two groups are similar in magnitude for each model. After accounting for age, gender, years of education, and self-rated health, the depressive symptoms variable was negatively associated with processing speed in both groups (βs = −.14 and −.10, ps < .01) and was also associated with worse reasoning in the White subsample (β = −.14, p < .01). Trait anxiety was not associated with any of the neurocognitive constructs in either group. Negative affect was significantly negatively associated with reasoning across both groups (βs = −.24 and −.16, ps < .01) and with spatial visualization in the White subsample (β = −.21, p < .01).

Table 3.

Standardized coefficients in the free models

White subsample (n = 510) Black subsample (n = 510)
Depressive symptoms
 Reasoning −.14* -.07
 Spatial visualization −.09 -.00
 Episodic memory −.09 -.05
 Speed −.14* -.10*
Trait anxiety
 Reasoning −.12 -.02
 Spatial visualization −.03 .03
 Episodic memory −.05 .03
 Speed −.02 -.05
Negative affect
 Reasoning −.24* -.16*
 Spatial visualization −.21* -.11
 Episodic memory −.15 -.12
 Speed −.02 -.04
Open in a new tab

* p < .01.

Exploratory analyses

Since previous work demonstrating that race moderates the relationship between depressive symptoms and cognition comprised only older adults (Zahodne et al., 2014), exploratory analyses were conducted in a subsample of participants over the age of 50 years (N = 384). As can be seen in Table 4, there were no significant differences in fit between the fixed and free models in the multiple-group structural equation models between the White and Black groups for any of the negative emotion predictors within the subsample of participants over the age of 50.

Table 4.

Fit statistics for the multiple-group structural equation models in the subsample of participants over age 50 (N = 384)

Χ2 df Χ2/df CFI RMSEA AIC ΔΧ2 Δdf p
Depressive symptoms
 aFixed model 415.495 220 1.89 .891 .048 655.60
 bFree model 412.714 216 1.91 .890 .049 660.71 2.781 4 .578
Trait anxiety
 aFixed model 414.447 220 1.88 .891 .048 654.45
 bFree model 406.657 216 1.88 .893 .048 654.66 7.790 4 .100
Negative affect
 aFixed model 409.591 220 1.86 .893 .047 649.59
 bFree model 403.497 216 1.87 .895 .048 651.50 6.094 4 .192
Open in a new tab

Note: CFI = comparative fit index; RMSEA = root mean square error of approximation; AIC = Akaike information criterion.

aCovariances and regression are constrained to equivalence.

bPaths from predictor to cognitive constructs freely estimated.

Discussion

Consistent with previous work on the effect of depression on neurocognition (e.g., Ahern & Semkovska, 2017; Brevik, Eikeland, & Lundervold, 2013; Rock, Roiser, Riedel, & Blackwell, 2014), we found that higher levels of depressive symptoms were associated with worse neurocognitive performance even after statistically controlling for age, gender, years of education, and self-rated health. Specifically, increased depressive symptoms were significantly associated with slower processing speed in both groups and with significantly worse reasoning in the White subsample. It is worth noting that although the depressive symptoms variable was associated with reasoning in only the White subsample, the difference in the magnitude of the coefficients between the two groups was not significant based on the assessment of fit between the free and fixed models.

We also found that higher levels of negative affect were associated with lower neurocognition in both groups, consistent with past research linking negative affect to increased susceptibility to cognitive interference during behavioral tasks (Danhauer et al., 2013), and with negative thinking with a reduced ability to discard information from working memory when it is no longer relevant to the task at hand (Zetsche, Bürkner, & Schulze, 2018). In this sample, higher levels of negative affect were significantly associated with worse reasoning in both groups and with significantly worse spatial visualization in the White subsample only (although, again, differences in coefficients between the two groups were not significant based on model fit comparisons).

In the current study, trait anxiety was not significantly related to neurocognition. Challenges to the flexible adaptation of goal-directed behavior have been implicated in individuals with high trait anxiety (Wilson, Nusbaum, Whitney, & Hinson, 2018), and recent behavioral neuroimaging research has linked trait anxiety to reduced processing efficiency in tasks that require significant attentional regulation (Modi, Kumar, Nara, Kumar, & Khushu, 2018). This discrepancy between these past and the present results may be attributed to the differences in tasks or samples (e.g., Wilson et al.’s, 2018 sample comprised college students, whereas the current sample spans adulthood).

Performance disparities in the Black subsample on the measures of neurocognition likely reflect both well-documented disparities in quality of education (e.g., Manly, Jacobs, Touradji, Small, & Stern, 2002) and the chronic impact of systemic racism and perceived discrimination on neurocognitive functioning (Forrester et al., 2019; Stinchcombe & Hammond, 2022). For example, Barnes and colleagues (2012) found that higher levels of perceived discrimination among a sample of older Black participants was associated with poorer performance on measures of episodic memory and perceptual speed even after statistically controlling for demographic variables and vascular risk factors. Additionally, Thames and colleagues (2013) found that higher levels of perceived discrimination were associated with worse memory performance among Black participants when tested by an examiner of a different race compared to Black participants tested by an examiner of the same race. Secondary data analysis was used in the current study and no information on the demographics of the study examiners was collected; therefore, we were unable to examine how experimenter variables may have differentially impacted neurocognition in our sample. Future research should consider collecting comprehensive data on laboratory factors during cognitive testing, especially when variations in perceived discrimination may impact participant results.

Contrary to our hypotheses, we found no evidence that race moderated the relationships between depressive symptoms, anxiety, and negative affect to neurocognition. While previous work with neurocognition as a dependent variable examined the RCM in samples of older adults (e.g., Zaheed et al., 2021; Zahodne et al., 2014), the current analyses assessed these relationships in a sample that spans adulthood and thus may display different patterns of emotional-cognitive associations due to broader developmental variability. However, follow-up exploratory analyses in the current study using a subsample of participants over the age of 50 also failed to find evidence that race moderated the relationships; in all sets of analyses, there were no significant differences between the fixed and free models.

The previous work that provided evidence in support of the RCM, with neurocognition as the dependent variable across groups of non-Hispanic White and Black participants, examined a small sample of Black participants (N = 37) and found evidence that the relationship between depressive symptoms and executive function and episodic memory was stronger in a sample of Black participants (Zahodne et al., 2014). By contrast, they also found that depressive symptoms were more strongly related to the slower processing speed in White participants only. Evaluating Zahodne et al.’s (2014) mixed findings in the context of the current results suggests that the role of race in moderating the relationship between negative emotions and cognition may not be robust, or may be influenced by other factors. For example, in the current study, race is used as a proxy for perceived discrimination and perceived stress associated with systematic racism; these experiences, while pervasive in psychological, cognitive, and biomedical research (Seng, Lopez, Sperlich, Hamama, & Reed Meldrum, 2012) and aging research (Forrester et al., 2019; Krause, 1988), are not homogenous across individuals. Using race as the proxy for perceived discrimination likely fails to capture the substantial variance within this construct and its specific relationship to neurocognition.

A possible alternate explanation for the moderating effect found by Zahodne and colleagues (2014) could be that separate psychosocial variables account for differences in cognition. Recent studies have examined the relationship between subjective social status and cognitive performance. Results show that a low subjective social status is associated with a poorer baseline memory performance (Zahodne, Kraal, Zaheed, & Sol, 2018) and a higher subjective social status linearly predicts better executive functioning (Stinchcombe & Hammond, 2022). Assessing perceived discrimination in future research can provide additional insight to variations in cognitive aging based on subjective social status (Brewster et al., 2019).

Strengths and Limitations

Our study extends previous work by using a large community-based sample that spans adulthood and uses latent constructs to represent the neurocognitive domains. The use of latent constructs minimizes the measurement error and influences that are specific to the tasks because the latent constructs represent the variance shared among a set of variables. Theoretically, this allows for a better operationalization of the neurocognitive constructs. In addition, we extend previous work by examining other negative emotion correlates of neurocognition, including trait anxiety and negative affect. We examined race as a moderator in the context of the RCM, which is consistent with previous work (e.g., Stinchcombe & Hammond, 2022; Zahodne et al., 2014). However, the current study is limited in its operationalization of race as a moderator rather than racial stress and/or perceived discrimination. Also, because this research was conducted using secondary data, there is limited insight into other variables that may differentially impact psychological state or cognitive performance, including experimenter demographics. That said, this negative finding can be used to inform the future variable selection for data collection testing the RCM, adding evidence that race is not an adequate proxy measure for this area of inquiry. An additional limitation inherent in the study is the use of neuropsychological assessments which may have limited ecological validity in predicting everyday functioning (e.g., Spooner & Pachana, 2006).

Conclusion

While depressive symptoms and negative affect were significantly negatively associated with neurocognitive functioning, race did not moderate these relationships. This null finding is noteworthy as a replication of existing protocol (Zahodne et al., 2014) with differing results from that original study and for the challenge it presents to simplified conceptualizations of race and racial stress. Under the RCM, reserve capacity may also be influenced by factors that were not included or measured in the current study (e.g., perceived discrimination or subjective social status). Scant research has been published testing the RCM in relation to neurocognitive outcomes (Zaheed et al., 2021; Zahodne et al., 2014) or cognitive health (Estrella et al., 2021), and the current study thus provides a contribution to this nascent literature.

Supplementary Material

Supplementary_Material_acac070
Click here for additional data file. (14.6KB, docx)

Acknowledgements

We are grateful to Tim Salthouse for providing us with these data.

Contributor Information

Rachel F Bloom, Department of Psychology, Fordham University, Bronx, NY10458, USA.

Karen L Siedlecki, Department of Psychology, Fordham University, Bronx, NY10458, USA.

Funding

The collection of the data in this project was supported by a National Institute on Aging Grant (R01AG024270) to Timothy A. Salthouse.

Conflict of Interest

None declared.

References

  1. Ahern, E., & Semkovska, M. (2017). Cognitive functioning in the first-episode of major depressive disorder: A systematic review and meta-analysis. Neuropsychology, 31(1), 52–72. 10.1037/neu0000319. [DOI] [PubMed] [Google Scholar]
  2. Alzheimer’s Association (2021). 2021 Alzheimer's disease facts and figures. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 17(3), 327–406. 10.1002/alz.12328. [DOI] [PubMed] [Google Scholar]
  3. Arbuckle, J. L. (2017). Amos (version 25.0). SPSS.
  4. Bair, A. N., & Steele, J. R. (2010). Examining the consequences of exposure to racism for the executive functioning of Black students. Journal of Experimental Social Psychology, 46(1), 127–132. 10.1016/j.jesp.2009.08.016. [DOI] [Google Scholar]
  5. Balsamo, M., Romanelli, R., Innamorati, M., Ciccarese, G., Carlucci, L., & Saggino, A. (2013). The state-trait anxiety inventory: Shadows and lights on its construct validity. Journal of Psychopathological Behavioral Assessment, 35, 475–486. 10.1007/s10862-013-9354-5. [DOI] [Google Scholar]
  6. Barnes, L. L., Harp, D., & Jung, W. S. (2002). Reliability generalization of scores on the Spielberger state-trait anxiety inventory. Educational and Psychological Measurement, 62(4), 603–618. [Google Scholar]
  7. Barnes, L. L., Lewis, T. T., Begeny, C. T., Yu, L., Bennett, D. A., & Wilson, R. S. (2012). Perceived discrimination and cognition in older African Americans. Journal of the International Neuropsychological Society, 18, 856–865. https://doi.org 10.1017/S1355617712000628 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bennett, G. K., Seashore, H. G., & Wesman, A. G. (1997). Differential aptitude test. San Antonio, TX: Psychological Corporation. [Google Scholar]
  9. Brevik, E. J., Eikeland, R. A., & Lundervold, A. J. (2013). Subthreshold depressive symptoms have a negative impact on cognitive functioning in middle-aged and older males. Frontiers in Psychology, 4, 1–8. 10.3389/fpsyg.2013.00309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brewster, P., Barnes, L., Haan, M., Johnson, J. K., Manly, J. J., Nápoles, A. M., et al. (2019). Progress and future challenges in aging and diversity research in the United States. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 15(7), 995–1003. 10.1016/j.jalz.2018.07.221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Broudy, R., Brondolo, E., Coakley, V., Brady, N., Cassells, A., Tobin, J. N., et al. (2007). Perceived ethnic discrimination in relation to daily moods and negative social interactions. Journal of Behavioral Medicine, 30(1), 31–43. 10.1007/s10865-006-9081-4. [DOI] [PubMed] [Google Scholar]
  12. Calabrese, S. K., Meyer, I. H., Overstreet, N. M., Haile, R., & Hansen, N. B. (2015). Exploring discrimination and mental health disparities faced by black sexual minority women using a minority stress framework. Psychology of Women Quarterly, 39(3), 287–304. 10.1177/0361684314560730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cosco, T. D., Prina, M., Stubbs, B., & Wu, Y. T. (2017). Reliability and validity of the Center for Epidemiologic Studies Depression Scale in a population-based cohort of middle-aged U.S. adults. Journal of Nursing Measurement, 25(3), 476–485. 10.1891/1061-3749.25.3.476. [DOI] [PubMed] [Google Scholar]
  14. Consedine, N. S. (2008). Health-promoting and health-damaging effects of emotions - the view from developmental functionalism. In M. Lewis, J. M. Haviland–Jones, & L. F. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 676–690). New York, NY: Guilford Press. [Google Scholar]
  15. Crawford, J. R., & Henry, J. D. (2004). The Positive And Negative Affect Schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43, 245–265. 10.1348/0144665031752934. [DOI] [PubMed] [Google Scholar]
  16. Crystal, S., Shea, D. G., & Reyes, A. M. (2017). Cumulative advantage, cumulative disadvantage, and evolving patterns of late-life inequality. The Gerontologist, 57(5), 910–920. 10.1093/geront/gnw056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Danhauer, S. C., Legault, C., Bandos, H., Kidwell, K., Costantino, J., Vaughan, L., et al. (2013). Positive and negative affect, depression, and cognitive processes in the Cognition in the Study of Tamoxifen and Raloxifene (Co-STAR) Trial. Aging, Neuropsychology and Cognition, 20(5), 532–552. 10.1080/13825585.2012.747671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Manual for kit of factor-referenced cognitive tests. Princeton, NJ: Educational Testing Service. [Google Scholar]
  19. Enders, C. K. (2001). The performance of the full information maximum likelihood estimator in multiple regression models with missing data. Educational and Psychological Measurement, 61(5), 713–740. 10.1177/00131640121971482. [DOI] [Google Scholar]
  20. Estrella, M. L., Durazo-Arvizu, R. A., Gallo, L. C., Tarraf, W., Isasi, C. R., Perreira, K. M., et al. (2021). Psychosocial factors associated with cognitive function among middle-aged and older Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos and its sociocultural ancillary study. Journal of Alzheimer's Disease, 79(1), 433–449. 10.3233/JAD-200612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Evans, C. R., & Erickson, N. (2019). Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995-2008. Social Science & Medicine, 220, 1–11. 10.1016/j.socscimed.2018.10.019. [DOI] [PubMed] [Google Scholar]
  22. Forrester, S. N., Gallo, J. J., Whitfield, K. E., & Thorpe, R. J. (2019). A framework of minority stress: From physiological manifestations to cognitive outcomes. The Gerontologist, 59(6), 1017–1023. 10.1093/geront/gny104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gallo, L. C. (2009). The reserve capacity model as a framework for understanding psychosocial factors in health disparities. Applied Psychology: Health and Well-Being, 1(1), 62–72. 10.1111/j.1758-0854.2008.01000.x. [DOI] [Google Scholar]
  24. Gallo, L. C., de  Los Monteros, K. E., & Shivpuri, S. (2009). Socioeconomic status and health: What is the role of reserve capacity?  Current Directions in Psychological Science, 18(5), 269–274. 10.1111/j.1467-8721.2009.01650.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gallo, L. C., & Matthews, K. A. (2003). Understanding the association between socioeconomic status and physical health: Do negative emotions play a role?  Psychological Bulletin, 129(1), 10–51. 10.1037/0033-2909.129.1.10. [DOI] [PubMed] [Google Scholar]
  26. Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally comparable measures of occupational status for the 1988 International Standard Classification Of Occupations. Social Science Research, 25, 201–239. 10.1006/ssre.1996.0010. [DOI] [Google Scholar]
  27. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press. [Google Scholar]
  28. Holoien, D. S., & Shelton, J. N. (2012). You deplete me: The cognitive costs of colorblindness on ethnic minorities. Journal of Experimental Social Psychology, 48(2), 562–565. 10.1016/j.jesp.2011.09.010. [DOI] [Google Scholar]
  29. Howarter, A. D., & Bennett, K. K. (2013). Perceived discrimination and health-related quality of life: Testing the reserve capacity model in Hispanic Americans. The Journal of Social Psychology, 153(1), 62–79. 10.1080/00224545.2012.703973. [DOI] [PubMed] [Google Scholar]
  30. Krause, N. (1988). Gender and ethnicity differences in psychological well-being. Annual Review of Gerontology & Geriatrics, 8, 156–186. 10.1007/978-3-662-38443-5_6. [DOI] [PubMed] [Google Scholar]
  31. Manly, J. J., Jacobs, D. M., Touradji, P., Small, S. A., & Stern, Y. (2002). Reading level attenuates differences in neuropsychological test performance between African American and white elders. Journal of the International Neuropsychological Society, 8, 341–348. 10.1017/S1355617702813157. [DOI] [PubMed] [Google Scholar]
  32. Matthews, K. A., & Gallo, L. C. (2011). Psychological perspectives on pathways linking socioeconomic status and physical health. Annual Review of Psychology, 62, 501–530. 10.1146/annurev.psych.031809.130711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Meyer, I. H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5), 674–697. 10.1037/0033-2909.129.5.674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Modi, S., Kumar, M., Nara, S., Kumar, P., & Khushu, S. (2018). Trait anxiety and neural efficiency of abstract reasoning: An fMRI investigation. Journal of Biosciences, 43(5), 877–886. 10.1007/s12038-018-9800-3. [DOI] [PubMed] [Google Scholar]
  35. Moriarty, O., & Finn, D. P. (2014). Cognition and pain. Current Opinion in Supportive and Palliative Care, 8(2), 130–136. 10.1097/SPC.0000000000000054. [DOI] [PubMed] [Google Scholar]
  36. Nkwata, A. K., Zhang, M., Song, X., Giordani, B., & Ezeamama, A. E. (2021). The relationship of race, psychosocial stress and resiliency indicators to neurocognitive impairment among older Americans enrolled in the health and retirement survey: A cross-sectional study. International Journal of Environmental Research and Public Health, 18(3), 1358. 10.3390/ijerph18031358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. 10.1177/014662167700100306. [DOI] [Google Scholar]
  38. Raven, J. (1962). Advanced progressive matrices, set II. London: Lewis. [Google Scholar]
  39. Rock, P. L., Roiser, J. P., Riedel, W. J., & Blackwell, A. D. (2014). Cognitive impairment in depression: A systematic review and meta-analysis. Psychological Medicine, 44(10), 2029–2040. 10.1017/S0033291713002535. [DOI] [PubMed] [Google Scholar]
  40. Salthouse, T. A. (2004). Localizing age-related individual differences in a hierarchical structure. Intelligence, 32, 541–561. 10.1016/j.intell.2004.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Salthouse, T. A. (2005). Relations between cognitive abilities and measures of executive functioning. Neuropsychology, 19, 532–545. 10.1037/0894-4105.19.4.532. [DOI] [PubMed] [Google Scholar]
  42. Salthouse, T. A. (2007). Implications of within-person variability in cognitive and neuropsychological functioning on the interpretation of change. Neuropsychology, 21, 401–411. 10.1037/0894-4105.21.4.401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Salthouse, T. A. (2010). Influence of age on practice effects in longitudinal neurocognitive change. Neuropsychology, 24, 563–572. 10.1037/a0019026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Salthouse, T. A. (2012). Does the direction and magnitude of cognitive change depend on initial level of ability?  Intelligence, 40, 352–361. 10.1016/j.intell.2012.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Salthouse, T. A. (2014). Correlates of cognitive change. Journal of Experimental Psychology: General, 143(3), 1026–1048. 10.1037/a0034847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Salthouse, T. A. (2017). Shared and unique influences on age-related cognitive change. Neuropsychology, 31(1), 11–19. 10.1037/neu0000330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Salthouse, T. A., & Babcock, R. L. (1991). Decomposing adult age differences in working memory. Developmental Psychology, 27, 763–776. 10.1037/0012-1649.27.5.763. [DOI] [Google Scholar]
  48. Salthouse, T. A., Fristoe, N., & Rhee, S. H. (1996). How localized are age-related effects on neuropsychological measures?  Neuropsychology, 10, 272–285. 10.1037/0894-4105.10.2.272. [DOI] [Google Scholar]
  49. Salvatore, J., & Shelton, J. N. (2007). Cognitive costs of exposure to racial prejudice. Psychological Science, 18(9), 810–815. 10.1111/j.1467-9280.2007.01984.x. [DOI] [PubMed] [Google Scholar]
  50. Schöllgen, I., Huxhold, O., & Schmiedek, F. (2012). Emotions and physical health in the second half of life: Interindividual differences in age-related trajectories and dynamic associations according to socioeconomic status. Psychology and Aging, 27(2), 338–352. 10.1037/a0026115. [DOI] [PubMed] [Google Scholar]
  51. Seng, J. S., Lopez, W. D., Sperlich, M., Hamama, L., & Reed Meldrum, C. D. (2012). Marginalized identities, discrimination burden, and mental health: Empirical exploration of an interpersonal-level approach to modeling intersectionality. Social Science & Medicine (1982), 75(12), 2437–2445. 10.1016/j.socscimed.2012.09.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Siedlecki, K. L., Falzarano, F., & Salthouse, T. A. (2019). Examining sex differences in neurocognitive functioning across adulthood. Journal of the International Neuropsychological Society, 25, 1051–1060. 10.1017/S1355617719000821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Spielberger, C. D., Goruch, R. L., Lushene, R. E., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the state-trait inventory STAI (form Y). Mind Garden, Palo Alto, CA, USA. [Google Scholar]
  54. Spielberger, C.D. & Reheiser, E.C. (2009). Assessment of emotions: Anxiety, anger, depression, and curiosity. Applied Psychology: Health and Well-Being, 1, 271–302. 10.1111/j.1758-0854.2009.01017.x. [DOI] [Google Scholar]
  55. Spooner, D. M., & Pachana, N. A. (2006). Ecological validity in neuropsychological assessment: A case for greater consideration in research with neurologically intact populations. Archives of Clinical Neuropsychology, 21(4), 327–337. [DOI] [PubMed] [Google Scholar]
  56. Stinchcombe, A., & Hammond, N. G. (2022). Correlates of memory and executive function in mid-aged and older adults in the CLSA: A minority stress approach. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 77, 1105–1117. 10.1093/geronb/gbab084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Thames, A. D., Hinkin, C. H., Byrd, D. A., Bilder, R. M., Duff, K. J., Mindt, M. R., et al. (2013). Effects of stereotype threat, perceived discrimination, and examiner race on neuropsychological performance: Simple as black and white?  Journal of the International Neuropsychological Society, 19, 583–593. 10.1017/S1355617713000076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. 10.1037//0022-3514.54.6.1063. [DOI] [PubMed] [Google Scholar]
  59. Wechsler, D. (1997a). Wechsler memory scale (3rd ed.). San Antonio, TX: The Psychological Corporation. [Google Scholar]
  60. Wechsler, D. (1997b). Wechsler adult intelligence scale (3rd ed.). San Antonio, TX: The Psychological Corporation. [Google Scholar]
  61. Williams, D. R., González, H. M., Neighbors, H., Nesse, R., Abelson, J. M., Sweetman, J., et al. (2007). Prevalence and distribution of major depressive disorder in African Americans, Caribbean Blacks, and non-Hispanic Whites: Results from the National Survey of American Life. Archives of General Psychiatry, 64, 305–315. 10.1001/archpsyc.64.3.305. [DOI] [PubMed] [Google Scholar]
  62. Williams, M. T. (2020). Microaggressions: Clarification, evidence, and impact. Perspectives on Psychological Science, 15(1), 3–26. 10.1177/1745691619827499. [DOI] [PubMed] [Google Scholar]
  63. Wilson, C. G., Nusbaum, A. T., Whitney, P., & Hinson, J. M. (2018). Trait anxiety impairs cognitive flexibility when overcoming a task acquired response and a preexisting bias. PloS One, 13(9), e0204694. 10.1371/journal.pone.0204694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zachary, R. A. (1986). Shipley Institute of Living Scale: Revised Manual. Los Angeles: Western Psychological Services. [Google Scholar]
  65. Zaheed, A. B., Sharifian, N., Kraal, A. Z., Sol, K., Manly, J. J., Schupf, N., et al. (2021). Mediators and moderators of the association between perceived stress and episodic memory in diverse older adults. Journal of the International Neuropsychological Society, 27, 883–895. 10.1017/S1355617720001253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zahodne, L. B., Kraal, A. Z., Zaheed, A., & Sol, K. (2018). Subjective social status predicts late-life memory trajectories through both mental and physical health pathways. Gerontology, 64(5), 466–474. 10.1159/000487304. [DOI] [PubMed] [Google Scholar]
  67. Zahodne, L. B., Nowinski, C. J., Gershon, R. C., & Manly, J. J. (2014). Depressive symptoms are more strongly related to executive functioning and episodic memory among African American compared with non-Hispanic White older adults. Archives of Clinical Neuropsychology, 29(7), 663–669. 10.1093/arclin/acu045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zetsche, U., Bürkner, P. C., & Schulze, L. (2018). Shedding light on the association between repetitive negative thinking and deficits in cognitive control—a meta-analysis. Clinical Psychology Review, 63, 56–65. 10.1016/j.cpr.2018.06.001. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary_Material_acac070
Click here for additional data file. (14.6KB, docx)

Articles from Archives of Clinical Neuropsychology are provided here courtesy of Oxford University Press

RESOURCES