Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Oct 1.
Published in final edited form as: J Individ Differ. 2022 Dec 21;44(2):97–108. doi: 10.1027/1614-0001/a000383

Five-Factor Model Personality Domains and Facets Associated with Markers of Cognitive Health

Angelina R Sutin 1, Martina Luchetti 1, Damaris Aschwanden 1, Amanda A Sesker 1, Xianghe Zhu 1, Yannick Stephan 2, Antonio Terracciano 1
PMCID: PMC10195061  NIHMSID: NIHMS1829360  PMID: 37214235

Abstract

Using a diverse, age-stratified sample (N=3,478; age range 18–90) this study examines the cross-sectional association between five-factor model personality traits – domains and facets – and three measures of cognitive health – processing speed, visuospatial ability, subjective memory – and whether these associations vary by age, race, and ethnicity. Consistent with the literature on personality and cognitive health, higher openness and conscientiousness were associated with better cognitive performance and subjective memory, whereas higher neuroticism was associated with slower processing speed and worse subjective memory but was unrelated to visuospatial ability. Moderation analyses suggested some associations were stronger in midlife compared to younger and older adulthood but were generally similar across race and ethnicity. The facet-level analyses indicated the components of each domain most strongly associated with cognitive function (e.g., the responsibility facet of conscientiousness) and suggested some differences across facets within the same domain (e.g., depression was associated with worse performance, whereas anxiety was unrelated to performance; sociability was the only facet of extraversion associated with worse performance). The present research is consistent with the larger literature on personality and cognition and extends it by documenting similarities and differences across facets and demographic groups.

Keywords: Personality traits, processing speed, moderation, facets, subjective memory

Five-factor model (FFM; McCrae & John, 1992) personality traits have been associated with numerous aspects of cognitive function (Curtis et al., 2015). Individuals who score higher in neuroticism (the tendency to experience negative emotion and vulnerability to stress) or lower in conscientiousness (the tendency to be organized, disciplined, and responsible), for example, tend to have worse episodic memory, verbal fluency, and visuospatial ability (Sutin, Stephan, Luchetti et al., 2019). These traits are also long-term prospective predictors of significant cognitive outcomes: Higher neuroticism and lower conscientiousness are associated with greater risk of developing Alzheimer’s disease or related dementias in older adulthood (ADRD; Aschwanden et al., 2021). Much of the research on personality and cognitive health has focused on specific age groups and the broad domains of personality. Less work has addressed whether there are demographic differences (e.g., age differences) in the association between personality and cognition or potential differential associations with more specific components of personality (e.g., facets) and cognition.

There are several theoretical reasons why personality may be associated with cognitive function. Neuroticism, for example, is defined in part by feelings of anxiety and depression (McCrae & Costa, 2010), and anxiety and depression are known to interfere with cognitive performance (e.g., through rumination), especially on tasks that require speed (Beaudreau & O’Hara, 2009; Dotson et al., 2008). Individuals higher in extraversion tend to be active (McCrae & Costa, 2010) and are characterized in part by their vigor (Armon & Shirom, 2011). Such activity may support better performance on tasks that require speed. This characteristic may also impair performance on tasks that require greater deliberation, as it may reduce the focus necessary to perform well. Engaging in intellectual and physical activities helps support cognitive health, especially with age (Hertzog et al., 2008), and traits linked to greater investment in such activities may be associated with better cognitive function. And, indeed, openness has been identified as an investment trait (von Stumm & Ackerman, 2013), and it is consistently associated with better cognition (Sharp et al., 2010; Sutin, Stephan, Luchetti et al., 2019). Although agreeableness may be associated with better cognitive function through its prosocial tendencies that support greater social engagement (Buecker et al., 2020) that are protective of cognition (Sutin, Stephan, et al., 2020), findings reported in the literature tend to be mixed for this trait (Curtis et al., 2015; Sutin et al., 2019). Finally, conscientiousness, like openness, may support greater investment (Hill & Jackson, 2016) that contributes to better cognition. In addition, individuals higher in conscientiousness tend to have lifestyles that support cognitive health, including greater engagement in physical activity (Sutin et al., 2016), healthier eating (Mõttus et al., 2013), less substance use (Hakulinen et al., 2015), and a more organized lifestyle (Jackson et al., 2010). And, indeed, there is empirical support for an association between conscientiousness and better cognitive function (Curtis et al., 2015; Sutin et al., 2019), although not all find this association (e.g., Graham & Lachman, 2014).

Research on personality and cognition typically uses samples from specific periods of adulthood. That is, studies tend to focus on particular groups, such as students (Rikoon et al., 2016), midlife adults (Graham & Lachman, 2012), or older adults (Sutin, Stephan, Luchetti et al., 2019), although some studies suggest that the relation between personality and overall cognitive function and memory is similar across age groups in a lifespan sample (Soubelet & Salthouse, 2011). In addition, many studies of personality and cognition either rely on samples that are primarily white or do not test whether associations are similar or different across racial and ethnic groups. Differences across demographic groups, such as age, could be confounded with methodological differences when comparing associations across studies. Ideally, whether the associations vary across the lifespan or across groups should be tested by keeping methodology constant. Samples with a wide age range and that are more diverse are thus needed to determine whether the pattern of associations between personality and cognition generalizes across age, race, and ethnicity.

Most research on personality and cognition has focused on the five broad domains of personality. Each domain, however, is composed of more specific components, referred to as facets (Costa & McCrae, 1992; Roberts et al., 2005). Facets might have greater predictive power for outcomes than the broad domains (Paunonen et al., 2003). In addition, identifying narrow facets associated with cognition can help uncover more specific psychological processes that contribute to associations with cognition. Research on the relation between facets and cognition has focused primarily on conscientiousness. In some cases, the relation between the facets from this domain and cognition has gone in opposite directions, obscuring the relation at the domain level (Moon, 2001; Sutin et al., 2011). In other cases, the association at the domain level has been driven more strongly by some aspects of conscientiousness than others (e.g., responsibility and industriousness more than order and self-control; Sutin, Aschwanden et al., 2021). This more systematic approach is needed for all the traits.

The present research examines the association between FFM personality traits and three aspects of cognition (processing speed, visuospatial ability, subjective memory) in a relatively large and diverse sample that ranged in age from 18 to 90 and included facets, as well as domains, of personality. We hypothesize that neuroticism will be associated with worse cognitive function, both because the processes associated with this trait are likely to inhibit performance (Beaudreau & O’Hara, 2009; Dotson et al., 2008) and because of the literature that shows a negative association between this trait and cognition (Curtis et al., 2015; Sutin et al., 2019). We also hypothesize that extraversion will be associated with better performance on the processing speed task because of the quick tempo associated with this trait (Armon & Shirom, 2011) and previous empirical findings (Sutin, Stephan, Luchetti et al., 2019). We further hypothesize that openness and conscientiousness will both be associated with better cognition because the processes associated with these traits lead to greater investment and the greater intellectual and physical activity that is protective of cognitive health (Hill & Jackson, 2016; von Stumm & Ackerman, 2013). Previous research has also found empirical support for a positive association between these traits and cognitive function (Sharp et al., 2010; Sutin, Stephan, Luchetti et al., 2019). Given the ambiguity in the literature for agreeableness, we do not make a specific hypothesis for this trait. We further address whether these associations are moderated by age, race, and ethnicity to determine whether the associations generalize across age and racial and ethnic groups. We construe the moderation analyses as exploratory and do not have specific theoretical reasons to expect differences by age, race, or ethnicity. Still, we include the moderation analysis because it is important to test for differences across demographic groups to be able to uncover potential differences rather than rely on assumptions. Finally, this work expands personality-cognition associations beyond domains to identify facet-level associations. We broadly expect the facets to follow their domain-level associations. We do expect, however, that this analysis may reveal that some facets are more strongly related to the outcomes than other facets within the same domain.

Method

Participants and Procedure

Participants were from the first wave of the Behavioral, Psychological, and Social Response (BPSR) to the coronavirus pandemic study (Aschwanden, Strickhouser, et al., 2021; Sutin, Luchetti, et al., 2020). Participants were recruited through Dynata and directed to a Qualtrics survey. Specifically, participants received an email from Dynata inviting them to complete an online survey. If they clicked on the link, it took them to a webpage that described the study and then the informed consent. If participants consented to participate, the webpage advanced to a survey. Participants reported on their sociodemographic characteristics and completed measures of psychological function and cognitive tasks. The order of measures was the same for all participants. Data were collected in January-February 2020.

The sample was stratified to have a roughly equal number of men and women and to be about 20% Black. Participants who identified as Asian and Hispanic or Latino were also represented in the sample. The sample was stratified to have similar numbers of participants across seven age strata: 18–19, 20–29, 30–39, 40–49, 50–59, 60–69, and 70 and older. Participants were included in the analytic sample if they provided valid survey data, defined as taking at least 5 minutes to complete the survey with no straight-lining responses (giving the same response for every item). A total of 3,478 participants had valid personality data and completed at least one of the three cognitive measures.

Measures

Personality.

FFM personality traits were measured with the Big Five Inventory-2 (BFI-2), a reliable and valid personality measure (Soto & John, 2016). Participants rated 60 items on a scale from 1 (strongly disagree) to 5 (strongly agree) that measured neuroticism (alpha=.87), extraversion (alpha=.77), openness (alpha=.78), agreeableness (alpha=.80), and conscientiousness (alpha=.86). In addition to the broad domains, the BFI-2 assessed three narrow facets for each broad domain: anxiety (alpha=.68), depression (alpha=.73), and emotional volatility (alpha=.72) for neuroticism, sociability (alpha=.70), assertiveness (alpha=.58), and energy level (alpha=.55) for extraversion, curiosity (alpha=.54), aesthetic sensitivity (alpha=.58), and creative imagination (alpha=.62) for openness, compassion (alpha=.58), respectfulness (alpha=.68), and trust (alpha=.55) for agreeableness, and organization (alpha=.71), productiveness (alpha=.70), and responsibility (alpha=.67) for conscientiousness. In addition to the BFI-2, participants completed two scales that measured a facet of conscientiousness, specifically the Roberts and colleagues’ responsibility scale (Roberts et al., 2005) (alpha=.56) and the dutifulness scale from the NEO-PI-3 First Half (McCrae & Costa, 2010) (alpha=.64). Note that these two facets were analyzed separately and were not included in the overall conscientiousness domain. Following scoring instructions from the authors of these measures (McCrae & Costa, 2010; Roberts et al., 2005; Soto & John, 2016), items were reversed-scored when needed and the mean of the items taken in the direction of the trait label (e.g., higher scores for neuroticism indicated higher neuroticism).

Cognition.

Two cognitive tasks were completed online as part of the Qualtrics survey. First, participants completed a matching task similar to the Symbol Digit Substitution Task (Lezak, 2004). Nine symbols were each matched with a number. Participants were given a list of symbols and had two minutes to match as many symbols to numbers as possible out of a total of 50 items. The score was the correct number of symbols matched in two minutes. Second, participants completed a visuospatial ability task. A figure with a missing piece was presented to the participant. The participant had to identify the correct missing piece from several options. The score was the number of correct items out of eight. Both tests have good reliability and validity (Feenstra et al., 2018; Scott et al., 2019). Participants completed these measures on a laptop computer (43.5%), desktop computer (37.6%), mobile device (9.5%), or tablet (9.4%). In addition to the cognitive tasks, participants rated their subjective memory (i.e., “How would you rate your memory at the present time?”) on a scale from 1 (poor) to 5 (excellent). This subjective memory item is a standard measure that has been well validated and incorporated in national cohort studies of aging (e.g., Brailean et al., 2019; Hülür et al., 2015). On all measures, higher scores indicated better cognitive function.

Covariates.

Participants self-reported their sociodemographic characteristics, including age in years, gender (two dummy-coded variables as 1=woman and 1=other gender identity, both compared to 0=man), ethnicity (1=Hispanic/Latino, 0=non-Hispanic/Latino), race (two dummy-coded variables as 1=Black and 1=Asian, both compared to 0=white), and education on a 7-point scale that ranged from 1 (less than high school) to 7 (PhD/equivalent). Device type was also included as a covariate. Two dummy-coded variables contrasted mobile phone (=1) and tablet (=1) against desktop/laptop (=0) because there was generally no difference in performance when the tasks were completed on a desktop or laptop, but there were differences for mobile phone and tablet (Sutin, Luchetti et al., 2021).

Statistical Approach

Linear regression was used to examine the association between personality and the three cognitive measures. Each measure was regressed on a personality trait, controlling for the covariates. Each domain was tested separately. We then tested whether the domain-level associations varied by sociodemographic characteristics. Specifically, we created an interaction term between age, race, and ethnicity and each trait and included it in the regression analysis, along with the main effects and covariates. Further, we tested an additional interaction with age squared to explore whether there were non-linear associations between age and personality on cognition. Finally, we examined the association between each facet and the cognitive outcomes, controlling for covariates. Significance was set to p<.01 because of the large sample and number of tests. We report the 95% confidence interval and exact p-value to let readers make their own judgements about balancing concerns over type 1 and type 2 errors.

Results

Descriptive statistics for all study variables are in Table 1. Table 2 shows the association between each domain and the cognitive measures. Similar to results from a sample of older adults (Sutin, Stephan, Luchetti et al., 2019), personality was associated with performance on processing speed and visuospatial ability. Specifically, participants higher in neuroticism had slower processing speed, whereas participants higher in openness, agreeableness, and conscientiousness had faster processing speed; extraversion was unrelated to processing speed. Higher openness, agreeableness, and conscientiousness were likewise associated with better visuospatial ability. In contrast to processing speed, however, higher extraversion was associated with worse visuospatial ability and neuroticism was unrelated to it. All traits were associated with subjective memory: Higher neuroticism was associated with worse perceived memory, whereas higher extraversion, openness, agreeableness, and conscientiousness were associated with better perceived memory.

Table 1.

Descriptive Statistics for All Study Variables

Variable Mean (SD) % (n)
Age (years) 44.55 (18.54)
 18–19 13.5% (468)
 20–29 13.4% (467)
 30–39 16.3% (567)
 40–49 15.2% (530)
 50–59 17.1% (593)
 60–69 13.1% (455)
 70+ 11.4% (398)
Gender (man) 46.0% (1601)
Gender (woman) 52% (1809)
Gender (other identity) 2.0% (68)
Ethnicity (Latinx) 15.2% (527)
Race (Black) 19.8% (687)
Race (Asian) 7.6% (263)
Race (white) 72.6% (2528)
Education 3.95 (1.58)
 Less than high school 3.1% (109)
 High school diploma or equivalent 20.8% (723)
 Some college 21.0% (729)
 Associate degree 10.6% (368)
 Bachelor’s degree 25.9% (900)
 Master’s degree 15.3% (531)
 PhD or equivalent 3.4% (118)
Personality domain
 Neuroticism 2.74 (.78)
 Extraversion 3.11 (.63)
 Openness 3.48 (.63)
 Agreeableness 3.63 (.64)
 Conscientiousness 3.72 (.72)
Personality facet
 Anxiety 3.04 (.88)
 Depression 2.57 (.92)
 Emotional Volatility 2.62 (.90)
 Sociability 2.92 (.88)
 Assertiveness 3.16 (.77)
 Energy Level 3.26 (.76)
 Curiosity 3.57 (.73)
 Aesthetic Sensitivity 3.33 (.82)
 Creative Imagination 3.53 (.78)
 Compassion 3.70 (.79)
 Respectfulness 3.90 (.79)
 Trust 3.28 (.73)
 Organization 3.76 (.86)
 Productiveness 3.66 (.84)
 Responsibility 3.75 (.79)
 Responsibility (Roberts) 3.86 (.78)
 Dutifulness (NEO) 3.79 (.64)
Cognition
 Processing speed 16.99 (8.38)
 Visuospatial ability 3.60 (2.14)
 Subjective memory 3.71 (.98)
Open in a new tab

Note. N=3,478. SD=standard deviation. Education was reported on a scale from 1 (<high school) to 7 (PhD/equivalent). Possible scores ranged from 1–5 for personality and facets, 0–50 for processing speed, 0–8 for visuospatial ability, and 1–5 for subjective memory.

Table 2.

Association between personality domains and cognitive performance and subjective memory

Personality Domain B SE 95% CI p ΔR2
Processing Speed
Neuroticism −.06 .02 −.09, −.02 .003 .002
Extraversion −.01 .02 −.05, .02 .390 .000
Openness .12 .02 .09, .016 <.001 .015
Agreeableness .11 .02 .08, .15 <.001 .010
Conscientiousness .15 .02 .11, .18 <.001 .016
Visuospatial Ability
Neuroticism −.01 .02 −.05, .03 .590 .000
Extraversion −.07 .02 −.10, −.03 <.001 .004
Openness .20 .02 .16, .23 <.001 .038
Agreeableness .11 .02 .08, .15 <.001 .010
Conscientiousness .09 .02 .05, .12 <.001 .005
Subjective Memory
Neuroticism −.32 .02 −.35, −.28 <.001 .082
Extraversion .25 .02 .22, .28 <.001 .061
Openness .11 .02 .08, .14 <.001 .011
Agreeableness .14 .02 .11, .18 <.001 .018
Conscientiousness .25 .02 .22, .29 <.001 .050
Open in a new tab

Note. Ns range from 3,442 for processing speed to 3,476 for subjective memory due to missing data. Coefficients are standardized beta coefficients controlling for age, gender, race, ethnicity, and education. SE=standard error. CI=confidence interval.

*

p<.01.

We next tested whether these associations were moderated by age, race, or ethnicity. Age moderated the association between openness, agreeableness, and conscientiousness and processing speed, such that these associations were stronger among relatively younger than older participants (Supplemental Table S2). Simple slopes analysis indicated that the association between openness and processing speed was βopenness=.27 at 1SD below the mean of age (~26.00 years old), βopenness=.13 at the mean of age (~44.55 years old), and βopenness =−.01 at 1SD above the mean of age (~63.10 years old). A similar pattern was apparent for agreeableness: βagreeableness=.23 at 1SD below the mean of age, βagreeableness=.11 at the mean of age, and βagreeableness=−.01 at 1SD above the mean of age and conscientiousness: βconscientiousness=.22 at 1SD below the mean of age, βconscientiousness=.14 at the mean of age, and βconscientiousness=.06 at 1SD above the mean of age. Similar moderation was found for openness and agreeableness and visuospatial ability: The associations were stronger among relatively younger than older participants (βopenness=.30 at 1 SD below the mean of age, βopenness=.20 at the mean of age, βopenness=.10 at 1SD above the mean of age; βagreeableness=.22 at 1 SD below the mean of age, βagreeableness=.11 at the mean of age, βagreeableness=.01 at 1SD above the mean of age). In addition, age moderated the association between neuroticism and visuospatial ability such that the association was positive at relatively younger ages (βneuroticism=.07 at 1SD below the mean of age), null at the mean of age (βneuroticism=−.01), and negative at relatively older ages (βneuroticism=−.09 at 1SD above the mean of age). The interaction with age for neuroticism and conscientiousness was also qualified by a significant interaction with age squared, indicated non-linear associations across age for processing speed and visuospatial ability. A similar pattern was apparent for both traits and both cognitive tasks: The association with each task was stronger in middle age than in younger or older adulthood for both neuroticism (β=.10, p<.001 for processing speed and β=.12, p<.001 for visuospatial ability) and conscientiousness (β=−.08, p=.004 for processing speed and β=−.10, p<.001 for visuospatial ability). Figure 1 and Figure 2 show the standardized beta coefficients for each decade represented in the sample for neuroticism and conscientiousness, respectively. None of the other interactions between personality and age squared was significant. Finally, the opposite pattern was apparent for openness and agreeableness and subjective memory: The association with better perceived memory was stronger at relatively older than younger ages (βopenness=.06 at 1 SD below the mean of age, βopenness=.11 at the mean of age, βopenness=.16 at 1SD above the mean of age; βagreeableness=.09 at 1 SD below the mean of age, βagreeableness=.14 at the mean of age, βagreeableness=.19 at 1SD above the mean of age). None of the other interactions with age or age squared on subjective memory was significant.

Figure 1.

Figure 1.

Open in a new tab

Standardized beta coefficients for the association between neuroticism and processing speed (A) and visuospatial ability (B) by decade of age.

Figure 2.

Figure 2.

Open in a new tab

Standardized beta coefficients for the association between conscientiousness and processing speed (A) and visuospatial ability (B) by decade of age.

Compared with age, there were few significant interactions with race (Supplemental Table S3) or ethnicity (Supplemental Table S4). There was a negative association between extraversion and processing speed for Asian participants (βextraversion=−.24) that was not apparent among white (βextraversion=−.01) or Black (βextraversion=.03) participants and, although the negative association between extraversion and visuospatial ability was apparent across race, the association was stronger among Asian participants (βextraversion=−.25) than white (βextraversion=−.05) or Black (βextraversion=−.04) participants. In addition, there was a positive association between neuroticism and visuospatial ability among Hispanic/Latino participants (βneuroticism=.07) that was not apparent among non-Hispanic/Latino participants (βneuroticism=−.02). There were no other significant interactions with either race or ethnicity for the cognitive tasks. There were also no significant interactions for subjective memory: The association between personality and subjective memory was similar across race and ethnicity.

Finally, we examined facet-level associations between personality and cognition (Table 3). For neuroticism, depression and emotional volatility were both associated with worse processing speed but were unrelated to visual reasoning; anxiety was unrelated to either task. For extraversion, sociability had a negative association with both processing speed and visual reasoning; assertiveness and energy level were unrelated to either task. For openness, all three facets – curiosity, aesthetic sensitivity, and creative imagination – were associated with both tasks, particularly visual reasoning. For agreeableness, compassion and respectfulness were associated with better processing speed and visual reasoning; trust was unrelated to either task. All facets of conscientiousness – trust and organization and especially responsibility (both measures) and dutifulness – were associated with both cognitive tasks. Finally, all facets of personality were associated with subjective memory in the same direction as their broad traits.

Table 3.

Association between personality facets and cognitive performance and subjective memory

Personality Facet B SE 95% CI p ΔR2
Processing Speed
Anxiety .001 .02 −.03, .04 .706 .000
Depression −.07 .02 −.11, −.04 <.001 .004
Emotional Volatility −.07 .02 −.10, −.03 <.001 .004
Sociability −.07 .02 −.10, −.04 <.001 .005
Assertiveness .02 .02 −.01, .05 .233 .000
Energy Level .02 .02 −.01, .06 .168 .001
Curiosity .15 .02 .11, .18 <.001 .021
Aesthetic Sensitivity .06 .02 .03, .10 <.001 .004
Creative Imagination .09 .02 .06, .13 <.001 .008
Compassion .12 .02 .09, .16 <.001 .013
Respectfulness .16 .02 .12, .19 <.001 .021
Trust −.02 .02 −.05, .02 .303 .000
Organization .12 .02 .08, .15 <.001 .012
Productiveness .08 .02 .05, .12 <.001 .006
Responsibility .17 .02 .13, .21 <.001 .023
Responsibility (Roberts) .22 .02 .18, .26 <.001 .039
Dutifulness (NEO) .19 .02 .16, .23 <.001 .030
Visuospatial Ability
Anxiety .03 .02 −.01, .06 .109 .001
Depression −.02 .02 −.06, .02 .265 .000
Emotional Volatility −.03 .02 −.07, .00 .088 .001
Sociability −.11 .02 −.14, −.08 <.001 .012
Assertiveness −.01 .02 −.05, .02 .438 .000
Energy Level −.02 .02 −.05, .01 .213 .000
Curiosity .22 .02 .19, .25 <.001 .047
Aesthetic Sensitivity .11 .02 .08, .15 <.001 .012
Creative Imagination .16 .02 .12, .19 <.001 .023
Compassion .12 .02 .08, .15 <.001 .012
Respectfulness .17 .02 .13, .20 <.001 .023
Trust −.02 .02 −.05, .02 .279 .000
Organization .05 .02 .02, .08 .004 .002
Productiveness .05 .02 .02, .08 .006 .002
Responsibility .12 .02 .09, .16 <.001 .012
Responsibility (Roberts) .22 .02 .18, .25 <.001 .038
Dutifulness (NEO) .18 .02 .14, .21 <.001 .026
Subjective Memory
Anxiety −.25 .02 −.28, −.22 <.001 .054
Depression −.29 .02 −.33, −.26 <.001 .073
Emotional Volatility −.25 .02 −.28, −.22 <.001 .053
Sociability −.16 .02 .13, .19 <.001 .025
Assertiveness .15 .02 .12, .18 <.001 .022
Energy Level .28 .02 .25, .31 <.001 .078
Curiosity .06 .02 .03, .09 <.001 .004
Aesthetic Sensitivity .06 .02 .03, .10 <.001 .004
Creative Imagination .14 .02 .10, .17 <.001 .018
Compassion .08 .02 .05, .12 <.001 .006
Respectfulness .10 .02 .06, .14 <.001 .008
Trust .16 .02 .13, .19 <.001 .024
Organization .19 .02 .16, .22 <.001 .032
Productiveness .23 .02 .20, .27 <.001 .046
Responsibility .20 .02 .16, .23 <.001 .030
Responsibility (Roberts) .13 .02 .10, .17 <.001 .014
Dutifulness (NEO) .20 .02 .16, .23 <.001 .032
Open in a new tab

Note. Ns range from 3,442 for processing speed to 3,476 for subjective memory due to missing data. Coefficients are standardized beta coefficients controlling for age, gender, race, ethnicity, and education. SE=standard error. CI=confidence interval.

*

p<.01.

Discussion

Using a large sample that ranged in age from 18 to 90, we examined the association between FFM domains and facets and three aspects of cognitive function: processing speed, visuospatial ability, and subjective memory. Consistent with the literature on personality and cognition (Curtis et al., 2015; Sutin, Stephan, Luchetti et al., 2019), openness and conscientiousness were associated with better performance on the cognitive tasks and with better perceptions of memory, whereas neuroticism was associated with worse processing speed and subjective memory but was surprisingly unrelated to visuospatial ability. Although not hypothesized, agreeableness was associated with all three measures of cognitive health. The moderation analyses further suggested differences by age but not race or ethnicity.

The overall pattern of association between personality and performance on the cognitive tasks was consistent with the literature on personality and cognition. Individuals who score higher in neuroticism, for example, perform worse on cognitive tasks, whereas individuals who are more conscientious perform better (Chapman et al., 2017; Sutin, Stephan, Luchetti et al., 2019). Theoretical models of personality specify feelings of anxiety and depression as expressions of neuroticism (McCrae & Costa, 2010), and such feelings may interfere with cognitive performance (Beaudreau & O’Hara, 2009; Dotson et al., 2008). In contrast, theoretical accounts of conscientiousness suggest that this domain is an investment trait (Hill & Jackson, 2016), and such traits are known to support better cognitive function (von Stumm & Ackerman, 2013). Further, there are several mechanisms that may contribute to these associations. Higher neuroticism and lower conscientiousness, for example, are associated with health-risk behaviors, such as smoking (Hakulinen et al., 2015) and physical inactivity (Sutin et al., 2016), that are risk factors for poor cognitive health (Norton et al., 2014). These traits are also associated with worse clinical profiles (Goodwin & Friedman, 2006) that are harmful for cognitive function (Song et al., 2020). Higher neuroticism and lower conscientiousness are further associated with poor social health, including greater loneliness and social isolation (Buecker et al., 2020), which can be detrimental to cognitive health (Luchetti et al., 2020; Sutin, Stephan, et al., 2020).

The results of this study are also consistent with literature that tends to find that openness is associated with better cognitive function (Sharp et al., 2010). Individuals higher in openness tend to be more intellectually curious and have broad interests (Silvia & Christensen, 2020) that help promote better cognitive health. These associations fit with the theoretical characterization of openness as an investment trait (von Stumm & Ackerman, 2013) that may encourage engagement in cognitively-stimulating activities that support healthier cognitive outcomes. The pattern for extraversion and agreeableness was more surprising. Extraversion, for example, has been associated with faster processing speed and unrelated to visuospatial ability in some studies (Sutin, Stephan, Luchetti et al., 2019) but was unrelated to processing speed and had a negative relation with visuospatial ability in the current research. This latter association, however, may fit with other findings that suggest that extraversion is associated with worse reasoning abilities (Sutin et al., 2021) as visuospatial ability is a measure of visual reasoning. Further, the literature on agreeableness and cognitive function is mixed (Curtis et al., 2015), but the social integration characteristic of agreeableness (Buecker et al., 2020) may help support better cognitive function. For both extraversion and agreeableness, the pattern of associations found in the current study need to be replicated to ensure their robustness. Finally, consistent with the literature on personality and subjective cognition (Aschwanden, Sutin, et al., 2020), the traits were associated with subjective memory in the expected directions.

Our large sample that covered the adult lifespan and was diverse in terms of race and ethnicity allowed us to test for differences across these demographic characteristics. The age differences that emerged were in the opposite direction of the idea that maladaptive aspects of personality have detrimental associations with cognition that accumulate over the lifespan. Previous research, for example, has found that personality is associated with cognitive decline over time (Hock et al., 2014; Luchetti et al., 2016). This pattern suggests that there should be stronger associations between personality and cognition at older ages. The age interactions in the present study, however, indicated the opposite: The associations were generally stronger in younger than older adulthood. It is possible that more measurement error in older adults may attenuate the associations and account for the observed pattern (Terracciano et al., 2018). The associations for neuroticism and conscientiousness were further qualified by an interaction with age squared that indicated that the association between these traits and processing speed and visuospatial ability were stronger in midlife than younger or older adulthood. Further, much of the literature on personality and cognition has focused on primarily white samples or does not consider whether associations vary by race and ethnicity. The present study found few interactions by race or ethnicity, especially considering the number of interactions tested. One pattern did emerge that may be worth considering in future research: There was a negative association between extraversion and both processing speed and visuospatial ability among Asian participants that was either not apparent among white and Black participants (processing speed) or that was stronger than in these other populations (visuospatial ability). Most studies also do not include samples with enough participants of Asian descent to test for an association in this population or to test for an interaction. This pattern should be examined in future research to evaluate whether it replicates.

The facet-level analyses offered a window into the more specific components of the traits associated with cognitive performance. For neuroticism, the depression and emotional volatility facets were associated with slower processing speed. These associations are consistent with the literature on acute feelings of depression and slower speed of processing (Dotson et al., 2014). The anxiety facet, in contrast, was unrelated to cognitive performance. Anxiety is generally associated with worse cognitive performance, especially when performance is perceived as evaluative (Coy et al., 2011). In the present research, however, test anxiety might have been at a minimum because participants completed the tasks alone (i.e., not in front of a tester) and were completely anonymous to the researchers. It may be the case that anxiety is unrelated to cognition when the interpersonal evaluative aspect of the test is absent. The sociability aspect of extraversion was associated with worse performance on both tasks, whereas the other two facets of extraversion (assertiveness, energy level) were unrelated. Individuals high in sociability may have difficulty focusing their attention on solitary tasks (Claypoole et al., 2018). It was also surprising that energy level was unrelated to processing speed since energy level is associated with vigor (Armon & Shirom, 2011), which can be beneficial on tests of speed, although personality facets that assess energy level tend to be unrelated to long-term cognitive outcomes (Terracciano et al., 2014). All three facets of openness were associated with better processing speed and visuospatial ability, especially curiosity. Individuals high in curiosity have broad interests and tend to be interested in learning new things (Silvia & Christensen, 2020). Their search for knowledge may lead to processing information more quickly and to a better ability to manipulate abstract information. Again, the associations for agreeableness were surprising and indicate at the facet level, compassion and respectfulness are associated with better cognitive function whereas trust is unrelated to it.

The associations between the facets of conscientiousness and processing speed and visuospatial ability were in the expected direction: Similar to domain-level conscientiousness, all facets of this trait were associated with better performance. It is particularly noteworthy, however, that the facets that measured the interpersonal aspects of conscientiousness (two measures of responsibility, one measure of dutifulness) had the strongest associations of the conscientiousness facets with the cognitive outcomes. Conscientiousness is typically thought of as an agentic trait, with its characteristic organization and industriousness important for cognitive function (McCrae & Costa, 2010). There is a growing literature, however, that there is an interpersonal component to this trait that is relevant to cognition (Sutin, Aschwanden et al., 2021). Specifically, part of the definition of conscientiousness includes a tendency to be responsible to others that is inherently interpersonal. A growing literature indicates that interpersonal facets, specifically responsibility and dutifulness, are associated with better cognitive health, from performance on cognitive tasks (Sutin, Stephan, Aschwanden et al., 2020) to risk of Alzheimer’s disease (Terracciano et al., 2014) and dementia (Sutin et al., 2018; Terracciano et al., 2022). The results of the current sample are consistent with this growing literature and highlight the importance of this interpersonal facet to cognitive health.

The present research had several strengths, including a large sample that covered the adult lifespan and that was relatively diverse in terms of race and ethnicity, measurement of three aspects of cognitive health, and a personality inventory that included facets as well as broad domains (as opposed to briefer measures included in many studies that are useful when time is limited but provide minimal measurement of the traits). There are also limitations that could be addressed in future research. The sample, for example, was limited to Americans with internet access. The results may thus not generalize to other countries or populations with fewer economic resources. Likewise, our sample was more educated than the general population, which may also limit generalizability. The cognitive tasks only assessed two domains – processing speed and visuospatial ability – and there are other domains, such as memory and executive function, that also need to be considered. The cognitive tasks also did not measure crystallized cognitive ability, which would be important to measure as a component of cognitive function. Future research would thus benefit from more generalizable samples and a larger battery of cognitive tests. In addition, the data were cross-sectional and observational, which do not support claims of causality and directionality (Ziegler et al., 2015). Further, we could not disentangle potential cohort effects from age effects because cross-sectional data confound cohort and age (Clouston et al., 2021; Sutin et al., 2013). Despite these limitations, the current research adds to the literature on personality and cognition by replicating the basic pattern of associations from the literature, along with some unexpected findings, in a sample that covered most of the adult lifespan, evaluating moderators of these associations, and the more specific facets that give insight into the relation between domain-level personality and cognitive health.

Supplementary Material

Supplemental Material

Funding acknowledgement:

Preparation of this manuscript was supported by grants R01AG053297 and R01AG068093 from the National Institute on Aging of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

  1. Armon G, & Shirom A (2011). The across-time associations of the five-factor model of personality with vigor and its facets using the bifactor model. Journal of Personality Assessment, 93, 618–627. [DOI] [PubMed] [Google Scholar]
  2. Aschwanden D, Strickhouser JE, Luchetti M, Stephan Y, Sutin AR, & Terracciano A (2021). Is personality associated with dementia risk? A meta-analytic investigation. Ageing Research Review, 67, 101269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aschwanden D, Strickhouser JE, Sesker AA, Lee JH, Luchetti M, Stephan Y, … Terracciano A (2021). Psychological and behavioural responses to coronavirus disease 2019: The role of personality. European Journal of Personality, 35, 51–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aschwanden D, Sutin AR, Luchetti M, Allemand M, Stephan Y, & Terracciano A (2020). A systematic review and meta-analysis of the association between personality and cognitive failures/complaints. Soc Personal Psychol Compass, 14(11). [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Beaudreau SA, & O’Hara R (2009). The association of anxiety and depressive symptoms with cognitive performance in community-dwelling older adults. Psychology and Aging, 24, 507–512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brailean A, Steptoe A, Batty GD, Zaninotto P, & Llewellyn DJ (2019). Are subjective memory complaints indicative of objective cognitive decline or depressive symptoms? Findings from the English Longitudinal Study of Ageing. Journal of Psychiatric Research, 110, 143–151. [DOI] [PubMed] [Google Scholar]
  7. Buecker S, Maes M, Denissen JJA, & Luhmann M (2020). Loneliness and the big five personality traits: A meta-analysis. European Journal of Personality, 34, 8–28. [Google Scholar]
  8. Chapman BP, Benedict RH, Lin F, Roy S, Federoff HJ, & Mapstone M (2017). Personality and performance in specific neurocognitive domains among older persons. American Journal of Geriatric Psychiatry, 25, 900–908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Claypoole VL, Waldfogle GE, Neigel AR, & Szalma JL (2018). Exploring the effects of extraversion on social facilitation and vigilance task performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, 1216–1220. [Google Scholar]
  10. Clouston SAP, Terrera GM, Rodgers JL, O’Keefe P, Mann FD, Lewis NA, … Hofer SM (2021). Cohort and period effects as explanations for declining dementia trends and cognitive aging. Population and Development Review, 47, 611–637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Costa PT Jr., & McCrae RR (1992). Revised NEO Personality Inventory (NEO-PI-R) and the NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources. [Google Scholar]
  12. Coy B, O’Brien WH, Tabaczynski T, Northern J, & Carels R (2011). Associations between evaluation anxiety, cognitive interference and performance on working memory tasks Applied Cognitive Psychology, 25, 823–832. [Google Scholar]
  13. Curtis RG, Windsor TD, & Soubelet A (2015). The relationship between Big-5 personality traits and cognitive ability in older adults - a review. Neuropsychology Development and Cognition B Aging Neuropsychology and Cognition, 22, 42–71. [DOI] [PubMed] [Google Scholar]
  14. Dotson VM, Resnick SM, & Zonderman AB (2008). Differential association of concurrent, baseline, and average depressive symptoms with cognitive decline in older adults. American Journal of Geriatric Psychiatry, 16, 318–330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dotson VM, Szymkowicz SM, Kirton JW, McLaren ME, Green ML, & Rohani JY (2014). Unique and interactive effect of anxiety and depressive symptoms on cognitive and brain function in young and older adults. Journal of Depression and Anxiety, Suppl 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Feenstra HE, Vermeulen IE, Murre JM, & Schagen SB (2018). Online self-administered cognitive testing using the Amsterdam cognition scan: Establishing psychometric properties and normative data. Journal of Medical Internet Research, 20, e192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Goodwin RD, & Friedman HS (2006). Health status and the five-factor personality traits in a nationally representative sample. Journal of Health Psychology, 11, 643–654. [DOI] [PubMed] [Google Scholar]
  18. Graham EK, & Lachman ME (2012). Personality stability is associated with better cognitive performance in adulthood: are the stable more able? Journal of Gerontology B Psychological Sciences and Social Sciences, 67, 545–554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Graham EK, & Lachman ME (2014). Personality traits, facets and cognitive performance: Age differences in their relations. Personality and Individual Differences, 59, 89–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hakulinen C, Hintsanen M, Munafò MR, Virtanen M, Kivimäki M, Batty GD, & Jokela M (2015). Personality and smoking: Individual-participant meta-analysis of nine cohort studies. Addiction, 110, 1844–1852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hertzog C, Kramer AF, Wilson RS, & Lindenberger U (2008). Enrichment effects on adult cognitive development: can the functional capacity of older adults be preserved and enhanced? Psychological Science in the Public Interest, 9, 1–65. [DOI] [PubMed] [Google Scholar]
  22. Hill PL, & Jackson JJ (2016). The invest-and-accrue model of conscientiousness. Review of General Psychology, 20, 141–154. [Google Scholar]
  23. Hock RS, Lee HB, Bienvenu OJ, Nestadt G, Samuels JF, Parisi JM, … Spira AP (2014). Personality and cognitive decline in the Baltimore Epidemiologic Catchment Area follow-up study. American Journal of Geriatric Psychiatry, 22, 917–925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hülür G, Hertzog C, Pearman AM, & Gerstorf D (2015). Correlates and moderators of change in subjective memory and memory performance: Findings from the Health and Retirement Study. Gerontology, 61, 232–240. [DOI] [PubMed] [Google Scholar]
  25. Jackson JJ, Wood D, Bogg T, Walton KE, Harms PD, & Roberts BW (2010). What do conscientious people do? Development and validation of the Behavioral Indicators of Conscientiousness (BIC). Journal of Research in Personality, 44, 501–511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lezak MD (2004). Neuropsychological assessment (4th ed.). Oxford University Press. [Google Scholar]
  27. Luchetti M, Terracciano A, Aschwanden D, Lee JH, Stephan Y, & Sutin AR (2020). Loneliness is associated with risk of cognitive impairment in the Survey of Health, Ageing and Retirement in Europe. International Journal of Geriatric Psychiatry, 35, 794–801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Luchetti M, Terracciano A, Stephan Y, & Sutin AR (2016). Personality and cognitive decline in older adults: Data from a longitudinal sample and meta-analysis. Journal of Gerontology B Psychological Sciences and Social Sciences, 71, 591–601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McCrae RR, & Costa PT (2010). NEO Inventories for the NEO Personality Inventory-3, NEO Five-Factor Inventory-3, and NEO Personality Inventory-Revised Professional Manual. PAR. [Google Scholar]
  30. McCrae RR, & John OP (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60, 175–215. [DOI] [PubMed] [Google Scholar]
  31. Moon H (2001). The two faces of conscientiousness: duty and achievement striving in escalation of commitment dilemmas. Journal of Applied Psychology, 86, 533–540. [DOI] [PubMed] [Google Scholar]
  32. Mõttus R, McNeill G, Jia X, Craig LC, Starr JM, & Deary IJ (2013). The associations between personality, diet and body mass index in older people. Health Psychology, 32, 353–360. [DOI] [PubMed] [Google Scholar]
  33. Norton S, Matthews FE, Barnes DE, Yaffe K, & Brayne C (2014). Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurology, 13, 788–794. [DOI] [PubMed] [Google Scholar]
  34. Paunonen SV, Haddock G, Forsterling F, & Keinonen M (2003). Broad versus narrow personality measures and the prediction of behaviour across cultures. European Journal of Personality, 17, 413–433. [Google Scholar]
  35. Rikoon SH, Brenneman M, Kim LE, Khorramdel L, MacCann C, Burrus J, & Roberts RD (2016). Facets of conscientiousness and their differential relationships with cognitive ability factors. Journal of Research in Personality, 61, 22–34. [Google Scholar]
  36. Roberts BW, Chernyshenko OS, Strark S, & Goldberg LR (2005). The structure of Conscientiousness: An empirical investigation based on seven major personality questionnaires. Personnel Psychology, 58, 103–139. [Google Scholar]
  37. Scott EP, Sorrell A, & Benitez A (2019). Psychometric properties of the NIH Toolbox cognition battery in healthy older adults: reliability, validity, and agreement with standard neuropsychological tests. Journal of the International Neuropsychological Society, 25, 857–867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sharp ES, Reynolds CA, Pedersen NL, & Gatz M (2010). Cognitive engagement and cognitive aging: Is openness protective? Psychology and Aging, 25, 60–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Silvia PJ, & Christensen AP (2020). Looking up at the curious personality: Individual differences in curiosity and openness to experience. Current Opinion in Behavioral Sciences, 35, 1–6. [Google Scholar]
  40. Song R, Xu H, Dintica CS, Pan KY, Qi X, Buchman AS, … Xu W (2020). Associations between cardiovascular risk, structural brain changes, and cognitive decline. Journal of the American College of Cardiology, 75, 2525–2534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Soto CJ, & John OP (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113, 117–143. [DOI] [PubMed] [Google Scholar]
  42. Soubelet A, & Salthouse TA (2011). Personality-cognition relations across adulthood. Developmental Psychology, 47, 303–310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sutin AR, Aschwanden D, Stephan Y, & Terracciano A (2021). The association between facets of conscientiousness and performance-based and informant-rated cognition, affect, and activities in older adults. Journal of Personality. 10.1111/jopy.12657 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sutin AR, Luchetti M, Aschwanden D, Lee JH, Sesker AA, Strickhouser JE, … Terracciano A (2020). Change in five-factor model personality traits during the acute phase of the coronavirus pandemic. PLoS One, 15(8), e0237056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Sutin AR, Luchetti M, Aschwanden D, Stephan Y, & Terracciano A (2021). Sense of purpose in life, cognitive function, and the phenomenology of autobiographical memory. Memory, 29, 1126–1135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Sutin AR, Stephan Y, Aschwanden D, Luchetti M, Strickhouser JE, & Terracciano A (2020). Evaluations of a previous day as a pathway between personality and healthy cognitive aging. Journal of Aging and Health, 32, 642–653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sutin AR, Stephan Y, Damian RI, Luchetti M, Strickhouser JE, & Terracciano A (2019). Five-factor model personality traits and verbal fluency in 10 cohorts. Psychology and Aging, 34, 362–373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Sutin AR, Stephan Y, Luchetti M, Artese A, Oshio A, & Terracciano A (2016). The five factor model of personality and physical inactivity: A meta-analysis of 16 samples. Journal of Research in Personality, 63, 22–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sutin AR, Stephan Y, Luchetti M, Strickhouser JE, Aschwanden D, & Terracciano A (2021). The association between five factor model personality traits and verbal and numeric reasoning. Aging, Neuropsychology, and Cognition. 10.1080/13825585.2021.1872481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sutin AR, Stephan Y, Luchetti M, & Terracciano A (2019). Five-factor model personality traits and cognitive function in five domains in older adulthood. BMC Geriatr, 19, 343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sutin AR, Stephan Y, Luchetti M, & Terracciano A (2020). Loneliness and risk of dementia. Journal of Gerontology B Psychological Sciences and Social Sciences, 75, 1414–1422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Sutin AR, Stephan Y, & Terracciano A (2018). Facets of conscientiousness and risk of dementia. Psychological Medicine, 48, 974–982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Sutin AR, Terracciano A, Kitner-Triolo MH, Uda M, Schlessinger D, & Zonderman AB (2011). Personality traits prospectively predict verbal fluency in a lifespan sample. Psychology and Aging, 26, 994–999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sutin AR, Terracciano A, Milaneschi Y, An Y, Ferrucci L, & Zonderman AB (2013). The effect of birth cohort on well-being: the legacy of economic hard times. Psychological Science, 24, 379–385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Terracciano A, Piras MR, Sutin AR, Delitala A, Curreli NC, Balaci L, … Fiorillo E (2022). Facets of personality and risk of cognitive impairment: Longitudinal findings in a rural community from Sardinia. Journal of Alzheimer’s Disease. 10.3233/JAD-220400 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Terracciano A, Stephan Y, Luchetti M, & Sutin AR (2018). Cognitive impairment, dementia, and personality stability among older adults. Assessment, 25, 336–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Terracciano A, Sutin AR, An Y, O’Brien RJ, Ferrucci L, Zonderman AB, & Resnick SM (2014). Personality and risk of Alzheimer’s disease: New data and meta-analysis. Alzheimer’s & Dement, 10, 179–186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. von Stumm S, & Ackerman PL (2013). Investment and intellect: a review and meta-analysis. Psychological Bulletin, 139, 841–869. [DOI] [PubMed] [Google Scholar]
  59. Ziegler M, Cengia A, Mussel P, & Gerstorf D (2015). Openness as a buffer against cognitive decline: The Openness-Fluid-Crystallized-Intelligence (OFCI) model applied to late adulthood. Psychology and Aging, 30, 573–588. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplemental Material
NIHMS1829360-supplement-Supplemental_Material.docx (37.5KB, docx)

RESOURCES