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Published in final edited form as: Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2022 Jan 20;30(3):336–353. doi: 10.1080/13825585.2022.2027331

Emotion regulation in older adulthood: Roles of executive functioning and social relationships

Marcie L King Johnson 1,*, Anne I Roche 1,3,*, Kristian Markon 1, Natalie L Denburg 1,2
PMCID: PMC9296691  NIHMSID: NIHMS1771233  PMID: 35057711

Abstract

Emotion regulation (ER) is a complex process, shaped by individual and environmental determinants. Although ER is often maintained or even enhanced in older adulthood, the resources that are used to promote successful ER in later life are not well understood. Existing theoretical work hypothesizes that social relationships may act as a buffer for emotion regulation as people age. It is hypothesized that positive social relationships may minimize the effects of age-related declines in some proposed cognitive correlates of ER (e.g., executive functioning, memory, processing speed). The current preliminary study examined how executive functioning and social relationships are related to ER in healthy older adults (N=90; Age: M=74.98, SD=5.41). Results indicated that higher shifting performance (a behavioral index of executive functioning) was associated with higher use of cognitive reappraisal strategies, an aspect of ER. This effect was moderated by quality of social relationships, such that those with lower performance on a shifting task reported higher levels of reappraisal in the presence of positive social relationships. Positive social relationships were also associated with lower use of expressive suppression strategies, another ER strategy. Additional exploratory analyses indicated that no other domains of cognition were associated with ER outcomes. The current findings contribute to the field’s understanding of how an individual’s cognitive and social resources may contribute to ER success in older adulthood and provide important potential future targets for investigation and intervention.

Keywords: emotion-regulation, executive functioning, social relationships, aging, older adult

Emotion regulation (ER) refers to an individual’s attempt to modulate their emotions. Depending on the context, people work to either up- or down- regulate positive or negative emotions in congruence with regulation-related goals (McRae & Gross, 2020). ER is a dynamic process and can take the form of different strategies, including antecedent-focused strategies (e.g., strategies used to reappraise circumstances to alter the emotional impact of the experience) and response-focused strategies (e.g., strategies used to inhibit emotion expression once the emotion is already underway) (Gross, 1998a; Gross, 2015; Gross & John, 2003). One commonly studied approach to ER, cognitive reappraisal, refers to an antecedent-focused strategy by which an individual explores different potential meanings associated with an emotional experience to reframe the situation in accordance with their emotional goals. This strategy has been shown to be successful, frequently resulting in the desired changes in emotion (Gross, 1998a; Ray et al., 2010). In contrast, another commonly explored ER approach, expressive suppression, refers to a response-focused strategy by which an individual attempts to inhibit or reduce ongoing emotion. This strategy is comparatively less successful than cognitive reappraisal and has been shown to result in weak, null, or paradoxical changes in negative emotion (Gross, 1998a; McRae & Gross, 2020).

Evidence suggests that successful ER often requires the use of multiple strategies that are supported by various internal and external resources (Gross, 1998b; McRae & Gross, 2020; Opitz et al., 2012; Urry & Gross, 2010). Internal resources may include cognitive abilities such as executive functioning, memory, and language, which may support one’s ability to direct attention to positive aspects of a situation or to reappraise the meaning of a situation in order to regulate an emotional response (Opitz et al., 2012, 2014; Urry & Gross, 2010). External resources that support ER may involve environmental affordances such as access to supportive people and valued situations, which may support the selection of situations that are likely to lead to positive emotional experiences or help allow for reappraisal of emotionally laden experiences (Opitz et al., 2012; Urry & Gross, 2010).

Given the vast individual and contextual changes associated with aging, it might be unsurprising that the implementation and success of emotion regulation strategies varies with age (Allen & Windsor, 2019; Benson et al., 2019; Gurera & Isaacowitz, 2019; Livingstone & Isaacowitz, 2015, 2018). What may be surprising, however, is that emotional well-being is stable, and possibly enhanced, as individuals get older (Carstensen et al., 2003, 2011; Charles & Carstensen, 2010). Despite facing declines in many domains of functioning (e.g., physical, cognitive), older individuals generally report increases in positive emotion and optimization of positive affect, decreases in negative emotion, and greater emotional control compared to younger adults (Carstensen et al., 2003; Charles & Carstensen, 2010; Gross et al., 1997; Labouvie-Vief & Medler, 2002).

The Selection, Optimization, and Compensation with Emotion Regulation (SOC-ER) framework may help to explain how an older adult could maintain effective ER to maintain high levels of well-being (Urry & Gross, 2010). The SOC-ER framework posits that individuals select ER strategies based on presently available internal and external resources (Urry & Gross, 2010). According to SOC-ER theory, successful ER for an older adult may rely less heavily on resources that may decline with age (e.g., cognitive capabilities) and more heavily on resources that are resilient to age-related decline (e.g., close social networks) (Opitz et al., 2012; Urry & Gross, 2010). This optimization of available resources may allow older adults to maintain the ability to regulate emotions, even in the face of age-related changes. At present, more empirical data is needed to examine this hypothesis set forward by the SOC-ER theory.

The current work aimed to investigate how internal and external resources relate to ER in older adults. It is well established that various aspects of cognitive functioning (an internal resource), including executive functioning, memory, and speed, largely decline with age (Crawford et al., 2000; Rhodes, 2004; Salthouse, 2004, 2019; Wecker et al., 2000). Interestingly, however, recent evidence suggests that a more nuanced examination of the relationship between age and cognitive abilities may reveal increased variability in age-related cognitive changes, particularly in domains of cognition (e.g., attention, executive functioning) that are composed of multiple components (Verissimo et al., 2021).

How differences in cognitive abilities amongst older adults affects ER in this population remains an open area of investigation. One cognitive function that may be particularly important to ER is executive functioning (EF). A preeminent conceptualization of EF delineates the construct as consisting of at least three related yet distinct components: shifting between tasks or mental sets, updating working memory representations, and inhibiting dominant, automatic responses (Miyake et al., 2000). While EF is a complex construct that may be comprised of additional components, this three-factor model of executive functioning is well-supported in the literature and has been widely examined in various populations, including older adults (Fisk & Sharp, 2004; Hull et al., 2008; Latzman & Markon, 2010). Research shows that working memory, shifting, and inhibition are associated with ER and promote coping strategies including cognitive reappraisal and acceptance (Andreotti et al., 2013; Joorman & Gotlib, 2010; Schmeichel & Tang, 2014). Thus, older adults with lower EF abilities may have less success regulating emotion. More research is needed to investigate the precise nature of the relationship between the various components of EF and ER.

The availability of important external resources, however, may mitigate the effects of cognitive decline on ER in older adulthood. Previous work shows maintenance of close social relationships (an external resource) over the lifespan (Lang & Carstensen, 1994; Lang, 2001; Wrzus et al. 2013), which may benefit ER as people age. Specifically, older adults tend to invest more in close social relationships that provide the opportunity for greater connection and more positive and meaningful emotional experiences (Carstensen et al., 2003; Fung et al., 2001; Lang & Carstensen, 1994). The literature indicates that interpersonal relationships and interactions may be critically important to successful ER (Reeck et al., 2016; Ryan et al., 2005; Zaki & Williams, 2013). For example, attachment theory posits that secure attachment may promote ER through increased problem-solving, reappraisal attempts, and self-reflective capacity (Shaver & Mikulincer, 2007), and previous empirical work has shown different attachment styles are associated with different coping styles (Pascuzzo et al., 2013; Stevens, 2014). In addition, social relationships may aid in ER through social sharing (e.g., capitalizing on positive experiences to promote positive affect and providing relief (Gable et al., 2004; Zech & Rimé, 2005)). Thus, older adults with positive social relationships may experience more successful ER.

Current Study

In this work, we conducted an exploratory study to investigate how particular components of EF (e.g., shifting, updating, inhibition), an internal resource that may decline with age, and quality of social relationships, an external resource that may be maintained, were associated with the use of two ER strategies, cognitive reappraisal and expressive suppression, in a sample of healthy older adults. Current research suggests that higher levels of cognitive reappraisal and lower levels of expressive suppression typically represent successful ER strategy use (McRae & Gross, 2020). Available data from a larger parent study were used to test the following hypotheses: 1. EF (shifting, updating, inhibition) and positive relationships with others, respectively, would be positively related to cognitive reappraisal and inversely related to expressive suppression. 2. The relationship between EF and ER would be moderated by positive relationships, such that high-quality relationships would buffer the effects of low levels of EF on ER. In addition, it remains an open question as to how other internal cognitive resources (e.g., memory, language) some of which have been shown to change with age, may relate to ER in older adults and whether this relationship differs from the relationship between EF and ER in older adults. Therefore, we also examined whether other internal cognitive resources, including memory, language, processing speed, and visuospatial abilities, were associated with the same ER strategies and investigated if these relationships may be moderated by positive relations with others.

Materials and Methods

Participants

Healthy adults were recruited from the community to participate in a large study examining cognitive aging. Participants lived independently, and the health of each participant was confirmed using a semi-structured interview assessing neurological status, medical history, current medications, alcohol/drug use, and mood (after Tranel et al., 1997). The final dataset included in the current work consisted of 90 participants (mean age = 74.98, SD = 5.41, median age = 75.00, range = 60–88 years; 55% female; 100% Caucasian). On average, participants had 16.34 years of education (SD = 2.77, min = 12 years, max = 20 years). Participants were financially compensated for their participation. Participants provided informed consent and all procedures were approved by the University’s Institutional Review Board.

Measures

Participants completed a variety of neuropsychological and self-report measures as part of a larger study. Not all participants completed every measure due to logistic or administrative barriers. Details regarding how missing data were handled can be found below. Available performance-based neuropsychological measures were used to assess EF and other cognitive processes including memory, language, processing speed, and visuospatial abilities. Available self-report measures were used to assess ER and social relationships. See Table 1 for detailed information regarding the measures examined in the current work.

Table 1.

Included measures, primary outcome scores, and descriptive statistics

Measures Scores used for analyses n mean(sd)
Emotion regulation
ERQ Cognitive Reappraisal Total raw score 87 29.02(5.67)
ERQ Expressive Suppression Total raw score 87 14.39(4.45)
Positive relations with others
PWB Scales Positive Relations with Others Total raw score 89 68.48(11.43)
Executive functioning
D-KEFS Sorting Test: Free Sorting Total correct sorts 89 10.53(2.39)
D-KEFS Sorting Test: Sort Recognition Sort recognition description score 89 34.09(11.15)
D-KEFS Verbal Fluency Test: Letter Fluency Total correct responses 89 38.03(12.03)
D-KEFS Verbal Fluency Test: Category Fluency Total correct responses 89 39.78(8.53)
D-KEFS Verbal Fluency Test: Category Switching Total correct responses 89 13.66(3.2)
Total switching accuracy 89 12.38(3.55)
D-KEFS Design Fluency Total correct 89 25.53(6.39)
D-KEFS Trail Making Test: Number-Letter Switching Total time to complete 89 95.8(37.73)
D-KEFS Color-Word Interference Test: Inhibition Total time to complete 87 65.62(17.82)
D-KEFS Color-Word Interference Test: Inhibition/Switching Total time to complete 87 72.44(21.21)
D-KEFS Tower Test Total achievement score 89 17.61(4.24)
WAIS-IV Forward Digit Span Total raw score 80 9.86(2.2)
WAIS-IV Backward Digit Span Total raw score 80 9.01(2.29)
Memory
CFT 30-minute delay raw score 72 14.76(5.59)
AVLT 30-minute delay raw score 80 9.84(2.71)
Language
WASI-II Vocabulary Total raw score 80 45.75(5.17)
WASI-II Similarities Total raw score 80 35.2(4.73)
BNT Total raw score 75 57.61(3.02)
WRAT-4 Reading Total raw score 41 63.2(5.3)
Processing speed
WAIS-IV Coding Total raw score 79 58.8(13.67)
D-KEFS Trail Making Test: Number Sequencing Total time to complete 89 39.72(14.69)
Visuospatial abilities
WASI-II Block Design Total raw score 81 34.6(9.31)
WASI-II Matrix Reasoning Total raw score 80 19.98(3.72)
CFT Copy raw score 72 29(4.2)
JOLO Total raw score 79 25.23(3.46)
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Note. ERQ= Emotion Regulation Questionnaire; PWB= Psychological Well-Being; D-KEFS= Delis-Kaplan Executive Function System; WAIS-IV= Wechsler Adult Intelligence Scale – Fourth Edition; CFT= Rey-Osterrieth Complex Figure Test; AVLT= Rey Auditory-Verbal Learning Test; WASI-II= Wechsler Abbreviated Scale of Intelligence- Second Edition; BNT= Boston Naming Test; WRAT-4= Wide Range Achievement Test-4; JOLO= Benton Judgment of Line Orientation.

Emotion Regulation

Emotion regulation was assessed using the 10-item Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003). The ERQ uses a 7-point Likert scale to measure two ER strategies: cognitive reappraisal and expressive suppression. Scores can range from 6–42 for the cognitive reappraisal subscale and 4–28 for the expressive suppression subscale, with higher scores in each domain reflecting higher usage of each strategy. The scale has been validated in a sample of community-dwelling older adults (Brady et al., 2019). For participants who completed the scale, missing data were handled using person mean imputation if ≤20% of items were missing on a given subscale. This resulted in imputing one item on the cognitive reappraisal subscale for two participants. No items were imputed using this method on the expressive suppression scale.

Social Relationships

Social relationships were measured using the “Positive Relations with Others” subscale from the Ryff Psychological Well-Being Scales (Ryff, 1989). This 14-item self-report scale provides a subjective measure of the quality of an individual’s relationships with others. Participants provide responses using a 6-point Likert-type scale, and scores can range from 14–84. A high score indicates that a person views him or herself as having warm, satisfying, trusting relationships. The Ryff Psychological Well-Being Scales have withstood extensive psychometric scrutiny and have been translated into more than 30 different languages (Ryff, 2014). Many of the initial construction and validation studies of this measure included samples of older adults (e.g., Ryff, 1989; see Ryff, 2014 for review). In the initial validation study of the scales (Ryff, 1989), each dimension of well-being was operationalized with 20-items, showing high internal consistency and test-retest reliability as well as convergent and discriminant validity with other measures of positive functioning. Internal consistency and correlation with the parent 20-item scale for positive relations with others was as follows: internal consistency (coefficient alpha) = .88, correlation with 20-item parent scale = .98.

Sample Characterization Measures

The Mini-Mental State Examination (MMSE; Folstein et al., 1975) was used as a mental status screening tool to ensure that participants were cognitively healthy (n = 80). In addition, the Beck Depression Inventory-Second Edition (BDI-II; Beck et al., 1996) was used to evaluate current depressive mood symptoms (n = 81).

Executive Functioning

Several subtests from the Delis-Kaplan Executive Function System (D-KEFS; Delis et al., 2001) were used to assess EF. Sorting Test Condition 1 (Free Sorting) and Condition 2 (Sort Recognition) were used to measure concept formation and conceptual flexibility. Verbal Fluency Test Condition 1 (Letter Fluency), Condition 2 (Category Fluency), and Condition 3 (Category Switching) were used to assess phonemic and semantic verbal fluency in the context of specified rules and to measure the ability to rapidly switch between two lexical categories. Design Fluency was used as a nonverbal analogue to the verbal fluency task. Trail Making Test Condition 4 (Number-Letter Switching) was used to measure repeated cognitive set-shifting and mental tracking on a task requiring visual scanning and psychomotor responding. Color-Word Interference Test Condition 3 (Inhibition) and Condition 4 (Inhibition/Switching) were used to measure selective attention, flexible shifting from one perceptual set to another as tasks requirements change, and suppression of inappropriate, prepotent responses. Tower Test was used to assess planning, sequencing, and goal-directed behavior. In addition to the D-KEFS measures, forward and backward Digit Span from the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV; Wechsler, 2008) were used to assess auditory attention and working memory, respectively.

Other Cognitive Variables

Memory.

The Rey-Osterrieth Complex Figure Test (CFT; Rey, 1941; Lezak, 1995) and the Rey Auditory-Verbal Learning Test (AVLT; Rey, 1964) 30-minute delay conditions were used to assess non-verbal and verbal encoding, consolidation, storing, and retrieval, respectively.

Language.

Participants completed the Vocabulary and Similarities subtests from the WASI-II (Wechsler et al., 2011) as measures of verbal comprehension. In addition, the Boston Naming Test was used to assess confrontation naming of common objects (BNT; Kaplan et al., 2001). The Wide Range Achievement Test-4 (WRAT-4; Wilkinson & Robinson, 2006) reading subtest was used to assess participants’ reading level.

Processing Speed.

The Coding subtest from the WAIS-IV (Wechsler, 2008) and the Trail Making Test Condition 2 (Number Sequencing) subtest from the D-KEFS were used to assess processing speed under focused attention.

Visuospatial Abilities.

Participants completed the Block Design and Matrix Reasoning subtests from the WASI-II (Wechsler & Hsiao-pin, 2011) as measures of perceptual reasoning. In addition, the CFT copy condition was used as a measure of visuospatial construction and the Benton Judgment of Line Orientation (JOLO; Benton et al., 1994) was used to assess visual perception (i.e., judgments regarding directional orientation of lines).

Data Analytic Strategy

Data Reduction of Neuropsychological Variables

Because we included multiple measures of neuropsychological functioning, we conducted a confirmatory factor analysis (CFA) on the neuropsychological variables for the purpose of reducing the number of variables entered in the omnibus ER models. We focused on three factors of EF, shifting, updating, and inhibition (Miyake et al., 2000), and included additional exploratory analyses examining other cognitive domains, including memory, language, processing speed, and visuospatial abilities. Given the exploratory nature of the current work, corrections for multiple comparisons were not applied and results should be interpreted in this context.

The CFA was conducted using the ‘lavaan’ R (R Core Team, 2018) package, with full maximum likelihood treatment of missing data (Rossel, 2012). We specified a three-factor model of EF, as illustrated in Figure 1 (RMSEA = 0.058, SRMR = 0.058, χ2 = 70.605, df = 54). This model included residual covariances between indicators of the same measure, for the Verbal Fluency Test, Color-Word Test, and Digit Span task to account for common method bias and improve model fit. Standardized factor scores were extracted and used in the omnibus ER analyses.

Figure 1. Executive functioning factors.

Figure 1

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Note: CFA results for executive functioning. Residual covariances as described in the text are not shown for clarity. CW: Inhibition= Delis-Kaplan Executive Function System (D-KEFS) Color-Word Interference Test: Inhibition; CW: Inhibition/Switch= D-KEFS Color-Word Interference Test: Inhibition/Switching; DF= D-KEFS Design Fluency; DS: Forward= Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV) Forward Digit Span; DS: Backward= WAIS-IV Backward Digit Span; TMT: Number-Letter= D-KEFS Trail Making Test: Number-Letter Switching; Tower= D-KEFS Tower Test; ST: Free Sort= D-KEFS Sorting Test: Free Sorting; ST: Recognition = D-KEFS Sorting Test: Sort Recognition; VF: Cat Fluency= D-KEFS Verbal Fluency Test: Category Fluency; VF: Cat Switch Correct= D-KEFS Verbal Fluency Test: Category Switching (correct response); VF: Cat Switch Acc= D-KEFS Verbal Fluency Test: Category Switching (accuracy).

We conducted a second CFA to model the other cognitive variables included in the current work. We specified a four-factor model comprised of memory, language, processing speed, and visuospatial abilities factors, as illustrated in Figure 2 (RMSEA = 0.086, SRMR = 0.088, χ2 = 77.972, df = 47). This model included a residual covariance term between indicators of the CFT to account for common method bias and improve model fit. Standardized factor scores were extracted and used in the omnibus ER analyses.

Figure 2. Other cognitive factors.

Figure 2

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Note: CFA results for memory, language, processing speed, and visuospatial processing. Residual covariances as described in the text are not shown for clarity. AVLT= Rey Auditory-Verbal Learning Test; Block Design= Wechsler Abbreviated Scale of Intelligence- Second Edition (WASI-II) Block Design; BNT= Boston Naming Test; CFT Copy= Rey-Osterrieth Complex Figure Test Copy Trial; CFT Delay= Rey-Osterrieth Complex Figure Test Delay Trial; Coding= Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV) Coding; JOLO= Benton Judgment of Line Orientation; Matrix Reasoning= WASI-II Matrix Reasoning; Reading= Wide Range Achievement Test-4 (WRAT-4) Reading; Similarities= WASI-II Similarities; TMT: Number Sequence= Delis-Kaplan Executive Function System (D-KEFS) Trail Making Test: Number Sequencing; Vocabulary=WASI-II Vocabulary.

Multiple Regression

Correlations between all model variables can be found in Table 2. Multiple linear regression was conducted using the ‘sem’ function (with the missing= ‘fiml’ option) in the ‘lavaan’ package (Rossel, 2012) to examine the relationship between ER (cognitive reappraisal and expressive suppression), social relationships, and cognitive functioning. Specifically, models were built to investigate the relationship between three aspects of EF (shifting, updating, inhibition), quality of social relationships, and emotion regulation. Additional analyses examined how other cognitive factors (memory, language, processing speed, and visuospatial abilities) and quality of social relationships are related to the use of cognitive reappraisal and expressive suppression as well.

Table 2.

Correlations between all model variables

CR ES PRO Shifting Updating Inhibition Memory Language Processing speed
ES −0.140 -- -- -- -- -- -- -- --
PRO 0.160 −0.370 ** -- -- -- -- -- -- --
Shifting 0.130 −0.010 −0.090 -- -- -- -- -- --
Updating 0.030 −0.050 0.050 0.620 ** -- -- -- -- --
Inhibition −0.020 0.000 0.000 −0.660 ** −0.860 ** -- -- -- --
Memory 0.000 −0.040 −0.150 0.610 ** 0.560 ** −0.650 ** -- -- --
Language 0.200 −0.120 −0.170 0.490 ** 0.300 * −0.370 ** 0.530 ** -- --
Processing speed −0.070 −0.030 −0.090 0.520 ** 0.590 ** −0.680 ** 0.920 ** 0.290 * --
Visuospatial abilities 0.040 −0.030 −0.160 0.590 ** 0.500 ** −0.560 ** 0.900 ** 0.560 ** 0.700 **
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Note. CR= Cognitive Reappraisal; ES = Expressive Suppression; PRO = Positive Relations with Others.

*

p=<0.01

**

p=<0.001

Results

Characterization of Sample

Scores on the MMSE were near ceiling level (M = 29.21, SD = 1.07) and mood fell in the minimally depressed, non-clinical range (M =4.86, SD = 4.65). Overall, participants’ performance on the neuropsychological instruments reflected normal, age-appropriate functioning (Lezak et al., 2012).

Cognitive Reappraisal

Model estimates for cognitive reappraisal are presented in Table 3. There was a main effect of shifting (Estimate = 10.468, Z = 2.752, p = 0.006, 95% CI [3.013, 17.923]), such that individuals with higher shifting scores had higher cognitive reappraisal scores. In addition, as illustrated in Figure 3, there was a significant interaction between shifting and positive relations with others (Estimate = −0.143, Z = −2.543, p = 0.011, 95% CI [−0.254, −0.033]). We conducted a chi-square test comparing model fits with and without the interaction term and found that the model constraining the interaction term to zero performed significantly worse than the model including the interaction term (see Table 4). Overall, results show that for individuals with lower shifting scores, higher levels of positive relations with others were associated with higher cognitive reappraisal scores. No other cognitive variables were significantly associated with cognitive reappraisal.

Table 3.

Cognitive reappraisal: Parameter estimates

Models Independent Variables Estimates SE Z-value p-value
Model 1: CR, PRO, Shifting PRO 0.055 0.051 1.08 0.28
Shifting 10.468 3.804 2.752 0.006*
Shifting × PRO −0.143 0.056 −2.543 0.011*
Model 2: CR, PRO, Updating PRO 0.076 0.052 1.461 0.144
Updating 2.902 3.612 0.804 0.422
Updating × PRO −0.042 0.055 −0.778 0.436
Model 3: CR, PRO, Inhibition PRO 0.078 0.052 1.505 0.132
Inhibition −0.031 3.477 −0.009 0.993
Inhibition × PRO −0.002 0.052 −0.03 0.976
Model 4: CR, PRO, Memory PRO 0.079 0.052 1.497 0.134
Memory 2.677 3.398 0.788 0.431
Memory × PRO −0.04 0.052 −0.765 0.444
Model 5: CR, PRO, Language PRO 0.09 0.051 1.764 0.078
Language 7.889 4.067 1.94 0.052
Language × PRO −0.096 0.06 −1.6 0.11
Model 6: CR, PRO, Processing speed PRO 0.077 0.052 1.484 0.138
Processing speed 0.913 3.22 0.284 0.777
Processing speed × PRO −0.02 0.048 −0.414 0.679
Model 7: CR, PRO, Visuospatial abilities PRO 0.077 0.053 1.461 0.144
Visuospatial abilities 4.303 3.907 1.102 0.271
Visuospatial abilities × PRO −0.061 0.06 −1.009 0.313
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Note. CR = Cognitive Reappraisal; PRO = Positive Relations with Others.

Figure 3.

Figure 3

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Interaction between shifting and positive relations with others

Table 4.

Chi-Squared difference test: Model comparison

Models AIC BIC χ2 χ2 diff p-value
Main effects with interaction 2460.4 2487.9 317.78
Main effects without interation 2465 2490 324.41 6.6276 0.010*
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Expressive Suppression

Model estimates for expressive suppression are presented in Table 4. There was a main effect of positive relations with others, showing an inverse relationship between positive relations with others and expressive suppression. Specifically, higher levels of positive relations with others were associated with reduced expressive suppression, with model estimates ranging from −0.141 to −0.163 and with p-values all <0.001. No cognitive variables were significantly related to expressive suppression.

Discussion

The current study investigated the association between three aspects of EF (shifting, updating, and inhibition), social relationships, and ER in older adults. Additional analyses also examined the relationship between other domains of cognitive functioning, including memory, language, processing speed, visuospatial abilities, and ER. Results indicated both a main effect of shifting and an interaction between shifting and positive relations with others, highlighting the overall associations of shifting with cognitive reappraisal as well as the moderating effects of positive relations for those low in shifting. Specifically, results indicated a main effect of shifting on cognitive reappraisal, such that higher levels of shifting ability were associated with higher cognitive reappraisal, while updating and inhibition were not. In addition, we found a significant interaction between shifting and positive relations with others, such that for people with lower shifting scores, higher levels of positive relations with others were associated with higher cognitive reappraisal scores. The current results also revealed a main effect of positive relations with others on expressive suppression, such that higher levels of positive relations with others predicted lower levels of expressive suppression. No other cognitive variables were significantly related to cognitive reappraisal or expressive suppression.

The current work partially supports the hypothesis that EF (shifting, updating, inhibition) and positive relationships with others would be positively related to cognitive reappraisal and inversely related to expressive suppression and that the relationship between EF and ER would be moderated by positive relationships. EF is a complex construct, and the current findings suggest that the various components of EF may be differentially related to cognitive reappraisal. Interestingly, research shows that the shifting component of EF may be more sensitive to age-related declines specifically (as opposed to declines in processing speed which can be confounded with age), than updating or inhibition (Fisk & Sharp, 2004). Similarly, recent work suggests that while some aspects of attention/executive functioning decline with age others do not (and may improve, e.g., inhibitory efficiency), providing additional evidence that multifaceted cognitive constructs may be differentially affected by age (Verissimo et al., 2019).

Moreover, the card sorting measures that comprised the shifting factor of EF in this study arguably tap into higher-level executive functions. It is certainly plausible that the ability to cognitively reappraise an emotion-laden situation requires multiple higher-order cognitive abilities that are best captured by a complex task such as card sorting. Overall, results support the hypothesis that differences in EF are related to differences in cognitive reappraisal in older adults and illustrates the utility in examining the various components of EF independently given that different individuals may experience deficits in different EF domains. This finding provides further insight and specificity into the current understanding of the relationship between the internal resource of executive functioning and cognitive reappraisal in older adults.

Not only do the current findings demonstrate an effect of shifting abilities on cognitive reappraisal, but they also provide some preliminary evidence that this effect is moderated by positive relations with others. In the current sample, individuals with low levels of shifting abilities reported high levels of cognitive reappraisal in the presence of high positive relations with others. This buffering effect indicates that different resources may account for emotion regulation success in different individuals and aligns with the hypotheses proposed by the SOC-ER framework, which posits that individuals select and optimize ER strategies based on available resources (Urry & Gross, 2010). Thus, older adults who have lower shifting abilities may compensate for the impact of these deficits on ER success by optimizing social resources.

The inverse relationship between positive relations with others and expressive suppression aligns with previous work showing an association between suppression and worse social functioning (Butler et al., 2003; Gross & John, 2003; Sasaki et al., 2021; Srivastava et al., 2009). Previous work has suggested that those who use suppression strategies to regulate emotion experience a sense of inauthenticity (Gross & John, 2003), which may impact one’s ability to engage in close relationships. The relationship between expressive suppression and social relationships may also be bidirectional such that individuals who have less close social relationships do not have as many outlets through which to express their emotions and are thus more likely to engage in suppression strategies (d’Arbeloff et al., 2018; Dryman & Heimberg, 2018).

No significant associations between memory, language, processing speed, or visuospatial abilities and either cognitive reappraisal or expressive suppression emerged in the current work. This finding is partially in line with previous work suggesting that fluid but not crystallized cognitive abilities are associated with greater success using cognitive reappraisal (Opitz et al., 2014). Additional work has also reported a lack of support for an association between verbal aptitude and cognitive reappraisal (McRae et al., 2012). Together, current and past work supports the notion that some but not all cognitive abilities are related to ER.

Implications

As hypothesized in the extant literature, current findings provide empirical support that EF and social support are important resources for ER in older adulthood. Although many older adults fare well in terms of ER, not all do. Our findings support the idea that poor ER may reflect a failure to draw on ER strategies that rely on available resources. The current work suggests that interventions targeting social support in older adulthood may be particularly useful to help maintain or enhance ER and well-being in later life. Despite age-related changes in cognitive abilities which may negatively impact the use of cognitive reappraisal, it appears that there are external resources that can be capitalized on to promote successful ER. On an individual level, therapies focused on interpersonal relationships or interpersonal effectiveness may be useful to address ER concerns in older adults. On a societal level, building an infrastructure that promotes community support among older adults would likely have a positive impact on ER and well-being. Future work should investigate the effects of interventions or programs designed to promote social support in older adults on ER.

Limitations

Though the current findings make an important contribution to the literature, limitations should be acknowledged. Data were collected from a convenience sample of individuals who participated in a larger study. Given the number of variables examined here, this work should be considered exploratory as the sample size is small and may be underpowered to interpret non-significant effects. However, power analyses using extant literature examining emotion regulation (using the ERQ scale) in adult and older populations showed effect sizes of reappraisal and expressive suppression, respectively, estimated to range between r=0.2 and r=0.3. In order to have 80% power to detect a significant result given this effect size a sample of between 85 and 194 was needed. Therefore, the current sample of 90, while on the low end of what might be needed to detect a significant effect, was within an appropriate range. Future work with a larger sample is needed to build on current findings. Additionally, the current data were cross-sectional in nature, preventing claims about the influence of age-related changes in cognition on ER. Given that there was no young adult comparison group, it is only possible to say that the current effects are evident in, but may not be specific to, older adults. Lastly, the demographic homogeneity (Caucasian, well-educated) and high cognitive functioning status of the sample limits claims of generalizability to other groups. Specifically, older adults with more significant cognitive impairment may be even more susceptible to the potential negative effects of lower EF on ER, and current results may not be generalizable to these individuals. Future research exploring the influence of EF and social relationships on ER in older adults with poorer cognitive functioning will be important in terms of potential clinical implications for this group.

Concluding Remarks

The current study makes an important contribution to the literature exploring the mechanisms that promote successful ER during older adulthood. Results provide initial support that certain aspects of EF, namely shifting, and social relationships may be particularly important for ER in older adults. The findings also lend preliminary support to the hypothesis that resources that are resilient to age-related change may help to buffer the effect of lower cognitive abilities on ER success in older adults.

Table 5.

Expressive suppression: Parameter estimates

Models Independent Variables Estimates SE Z-value p-value 95% CI
Model 1: ES, PRO, Shifting PRO −0.146 0.04 −3.674 <0.001* [−0.223, −0.068]
Shifting 0.101 2.713 0.037 0.97
Shifting × PRO −0.005 0.04 −0.127 0.899
Model 2: ES, PRO, Updating PRO −0.141 0.039 −3.668 <0.001* [−0.217, −0.066]
Updating −1.036 2.824 −0.367 0.714
Updating × PRO 0.013 0.043 0.301 0.764
Model 3: ES, PRO, Inhibition PRO −0.143 0.039 −3.702 <0.001* [−0.218, −0.067]
Inhibition 0.241 2.868 0.084 0.933
Inhibition × PRO −0.003 0.043 −0.075 0.94
Model 4: ES, PRO, Memory PRO −0.15 0.038 −3.935 <0.001* [−0.224, −0.075]
Memory 3.84 2.672 1.437 0.151
Memory × PRO −0.068 0.041 −1.649 0.099
Model 5: ES, PRO, Language PRO −0.163 0.038 −4.247 <0.001* [−0.239, −0.088]
Language 2.132 2.73 0.781 0.435
Language × PRO −0.046 0.04 −1.151 0.25
Model 6: ES, PRO, Processing speed PRO −0.141 0.038 −3.699 <0.001* [−0.216, −0.066]
Processing speed 2.927 2.615 1.119 0.263
Processing speed × PRO −0.05 0.039 −1.278 0.201
Model 7: ES, PRO, Visuospatial abilities PRO −0.155 0.039 −4.026 <0.001* [−0.231, −0.080]
Visuospatial abilities 3.9 2.848 1.37 0.171
Visuospatial abilities × PRO −0.069 0.044 −1.558 0.119
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Note. ES = Expressive Suppression; PRO = Positive Relations with Others.

Acknowledgements

This work was supported by the National Institutes of Health Predoctoral Training Grant (T32-GM108540) to A.I.R. and M.K.J.; a National Science Foundation Graduate Research Fellowship Program grant (award number: 1546595) to M.K.J.; and by a grant from NIH/NIA (AG 046539) to N.L.D.

Footnotes

Declaration of interest statement

We declare no conflicts of interest.

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