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An instrument designed to separate 2 midlevel traits within each of the Big Five (the Big Five Aspect Scales [BFAS]) was used to clarify the relation of personality to cognitive ability. The BFAS measures Openness to Experience and Intellect as separate (although related) traits, and refers to the broader Big Five trait as Openness/Intellect. In 2 samples (N = 125 and 189), Intellect was independently associated with general intelligence (g) and with verbal and nonverbal intelligence about equally. Openness was independently associated only with verbal intelligence. Implications of these findings are discussed for the empirical and conceptual relations of intelligence to personality and for the mechanisms potentially underlying both Openness/Intellect and cognitive ability.
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Openness/Intellect and Cognitive Ability 1
RUNNING HEAD: Openness/Intellect and Cognitive Ability
Openness to Experience, Intellect, and Cognitive Ability
Colin G. DeYoung*
University of Minnesota
Lena C. Quilty
Centre for Addiction and Mental Health
Jordan B. Peterson
University of Toronto
Jeremy R. Gray
Yale University
*Corresponding author: C. DeYoung, Dept. of Psychology, 75 East River Rd.,
Minneapolis, MN 55455; email:
Openness/Intellect and Cognitive Ability 2
An instrument designed to separate two mid-level traits within each of the Big Five (the
Big Five Aspect Scales; BFAS) was used to clarify the relation of personality to cognitive
ability. The BFAS measures Openness to Experience and Intellect as separate (though
related) traits, and refers to the broader Big Five trait as Openness/Intellect. In two
samples (N = 125 and 189), Intellect was independently associated with general
intelligence (g) and with verbal and nonverbal intelligence about equally. Openness was
independently associated only with verbal intelligence. Implications of these findings are
discussed for the empirical and conceptual relations of intelligence to personality and for
the mechanisms potentially underlying both Openness/Intellect and cognitive ability.
Key Words: Openness to Experience, Intellect, Intelligence, Verbal Intelligence,
Nonverbal Intelligence
Openness/Intellect and Cognitive Ability 3
Openness to Experience, Intellect, and Cognitive Ability
The five factor model or Big Fivethe most widely used taxonomy of personality
traits in psychologywas developed empirically rather than theoretically, by examining
patterns of correlation among personality trait descriptors (John, Naumann, & Soto,
2008). After the statistical identification of five factors in the pool of personality traits, it
remained to interpret and label the factors, sometimes a contentious process. The most
extensive debate has surrounded the interpretation of the fifth factor, which has been
described variously as Culture, Intellect, Openness to Experience, and Imagination.
Currently, the most widely used label for this factor is ―Openness to Experience,‖ but the
compound label ―Openness/Intellect‖ is increasingly in use, reflecting research indicating
that Openness to Experience and Intellect represent two equally central aspects of the
broader factor, which are correlated but separable (Johnson, 1994; Saucier, 1992, 1994).
Personality traits are hierarchically organized, and Openness and Intellect can be
considered distinct traits at a level of personality organization below the Big Five
(DeYoung, Quilty, & Peterson, 2007). The Big Five trait Openness/Intellect reflects the
shared variance of the two lower-level traits.
Throughout this article, we refer to the Big Five factor by the compound label
―Openness/Intellect.‖ Whenever we refer to ―Openness‖ or ―Intellect‖ alone, we are
referring to a subtrait that constitutes one aspect of this domain. Trait constructs
stemming from factor analysis are capitalized as a reminder that trait labels denote
scientific constructs that may not be identical to colloquial understandings of words like
―intellect‖ or ―openness.‖ Factors need labels, but no label is perfect, and it is important
Openness/Intellect and Cognitive Ability 4
to remember that the constructs in question exist as dimensions of personality variation
independently of their labels.
Intellect encompasses perceived intelligence and intellectual engagement and is
reflected in lexical studies by adjectives like, ―intellectual,‖ ―intelligent,‖ ―clever,‖ and
―philosophical.‖ Openness encompasses engagement with perceptual and aesthetic
domains and is reflected in lexical studies by adjectives like, ―artistic,‖ ―perceptive,‖
―poetic,‖ and ―fantasy-prone.‖ The lexicon additionally includes adjectives that are
associated with both Intellect and Openness, such as ―imaginative,‖ ―original,‖ ―curious,‖
and ―innovative.‖ The latter observation led Saucier (1994) to propose ―Imagination‖ as
an alternative label for the Openness/Intellect factor as a whole, because imagination can
be manifest both intellectually and aesthetically.
The psychological function that appears to be common to all of the traits
encompassed by the Openness/Intellect factor is cognitive exploration. ―Cognition‖ here
is conceived broadly in terms of mental processes involved in learning about the world
and one’s experience, including both reasoning and perceptual processes. Cognitive
exploration involves exploration of information and is in contrast to behavioral
exploration, in which motor activity is used to explore and emphasis is given to acquiring
reward rather than information. (Behavioral exploration appears to be primarily
associated with Extraversion in the Big Five; Depue & Collins, 1999; DeYoung, 2010.)
Individuals high in Openness/Intellect display the ability and tendency to seek, detect,
comprehend, and utilize more information than those low in Openness/Intellect.
Measuring the Two Aspects of Openness/Intellect
Beyond what is common to both Openness and Intellect, an important question
for research is what distinguishes these two traits. Conceptually, the distinction between
Openness/Intellect and Cognitive Ability 5
reasoning and perceptual processes appears crucial. Intellect reflects the ability and
tendency to explore abstract information through reasoning, whereas Openness reflects
the ability and tendency to explore sensory and aesthetic information through perception,
fantasy, and artistic endeavor. Emprically, research on the question of what distinguishes
Openness and Intellect was hindered until recently by the lack of a measurement
instrument designed specifically to assess Openness and Intellect as distinct traits.
Although some measures of the broader Openness/Intellect factor were formally labeled
―Openness to Experience‖ and others ―Intellect,‖ they typically included content
reflecting both Openness and Intellect, regardless of their label, and exhibited similar
external correlates (DeYoung, Peterson, & Higgins, 2005).
DeYoung et al. (2007) created specific Openness and Intellect scales using the
International Personality Item Pool (IPIP; Goldberg et al., 2006), following the
identification of distinct but correlated Openness and Intellect factors in 15 scales
designed to measure facets (subtraits) of Openness/Intellect. Velicer’s MAP test
(O’Connor, 2000) indicated exactly two factors in the 15 facets, and both Openness and
Intellect factors were clearly marked by 6 facets, suggesting their equal importance to the
broader Openness/Intellect domain. Scales to measure these factors were developed by
examining correlations of factor scores with over 2000 IPIP items, then selecting items
that were among the most strongly correlated with each facet. Items were excluded from
the final scales if their loading on the other subfactor of Openness/Intellect was within
.10 of their primary loading (or within .10 of their loading on any other factor within the
other four of the Big Five). This procedure was repeated for the factors identified within
the other Big Five domains. The resulting instrument, the Big Five Aspect Scales (BFAS;
Openness/Intellect and Cognitive Ability 6
DeYoung et al., 2007), measures two correlated subfactors, or ―aspects,‖ within each of
the Big Five.
Openness/Intellect and Intelligence
The goal of the present study was to utilize the BFAS to clarify the relations of
Openness and Intellect to cognitive ability. Openness/Intellect is the only one of the Big
Five that is consistently positively correlated with intelligence tests (r = .30; Ackerman &
Heggestad, 1997; DeYoung, 2011) with an effect size larger than two-thirds of all
significant effects reported in psychology for variables that do not share method variance
(Hemphill, 2003). Neuroticism consistently shows a weak negative correlation with
intelligence tests (r = -.15; Ackerman & Heggestad, 1997), but this correlation appears to
be due to test anxiety (Moutafi, Furnham, & Tsaousis, 2006).
The fact that Openness/Intellect shows by far the largest correlation with
intelligence tests of any of the Big Five is consistent with the fact that descriptors of
intelligence fall within this personality dimension in factor analysis. Indeed, given that
the average intercorrelation among facets of Openness/Intellect in the most widely used
Big Five inventory is only .28 (Costa & McCrae, 1992), one might argue that intelligence
should be considered a facet of the Openness/Intellect domain. Some have argued that
personality traits are distinct from abilities, with the latter reflecting maximal ability and
the former typical behavior, but this distinction has been challenged (DeYoung, 2011).
The lexical studies that produced the Big Five model have almost always included
descriptors of abilities, and personality is a broad enough concept to cover both.
The association of Openness/Intellect with intelligence is well established, but
questions remain about the associations of intelligence with Openness and Intellect
separately. Simply based on descriptive content, one would hypothesize that intelligence
Openness/Intellect and Cognitive Ability 7
tests should be associated more strongly with Intellect than with Openness. Although
previous research has lacked dedicated measures of Intellect and Openness, this
hypothesis can be provisionally tested by examining research that has utilized the NEO
PI-R facet scales (Costa & McCrae, 1992).
The Ideas facet of the NEO PI-R is a good
marker of Intellect, and the following four facets are good markers of Openness (listed
from largest to smallest loading): Aesthetics, Fantasy, Feelings, and Actions (DeYoung et
al., 2007). In studies that consider these facets individually, Ideas typically predicts
intelligence more strongly than do the four facets that mark Openness (DeYoung et al.,
2005; 2009; Furnham, Dissou, Sloan, & Chamorro-Premuzic, 2007; Holland et al., 1995;
McCrae, 1993; Moutafi, Furnham, & Crump, 2003, 2006). This pattern suggests that
Intellect is indeed more strongly associated with intelligence than is Openness.
Although Intellect appears to be more strongly associated with intelligence, the
four NEO PI-R facets that mark Openness often do show significant associations with
intelligence. However, any association between Openness and intelligence might be due
only to the variance that Openess shares with Intellect. In other words, Openness might
not be associated with intelligence after controlling for Intellect. The present study
provides the first test of this possibility, by using the BFAS Openness and Intellect scales
as simultaneous predictors in regression. Although such a test could be conducted using
facets of the NEO PI-R in previously reported data (e.g., DeYoung et al., 2005), the
present study has the advantage of using an instrument specifically designed to measure
Openness and Intellect as distinct factors at a level of personality structure below the Big
Five but above the facets.
Openness/Intellect and Cognitive Ability 8
Verbal and Nonverbal Intelligence
The present study examined the relations of Openness and Intellect not only to
general intelligence (g) but also to different subcomponents of inteligence. Intelligence is
hierarchically organized, with g located at the apex of the hierarchy. Below g in the
hierarchy are a few abilities that are more specific than g but still fairly general, and
below these are a great many specific abilities (Carroll, 1993; Johnson & Bouchard,
2005a, 2005b). The most widely used distinction, at the level of the hierarchy
immediately below g, is between fluid and crystallized intelligence (Horn & Cattell,
1966). Fluid intelligence refers to abilities that are innate and independent of prior
education or experience, whereas crystallized intelligence refers to abilities that require
knowledge or skill acquired from education or experience. However, recent evidence
from factor analysis suggests that individual differences in cognitive abilities do not, in
fact, covary according to whether they are fluid or crystallized, but rather according to
whether they are verbal or nonverbal (Johnson & Bouchard, 2005a, 2005b).
Of course, some components of ability may be experience-independent (fluid)
whereas others may be experience-dependent (crystallized), but mostperhaps alltests
of cognitive ability involve both fluid and crystallized components, such that tests
traditionally considered to measure fluid versus crystallized intelligence do not, in fact,
measure those two constructs distinctly. Instead, most putatively ―fluid‖ tests measure
nonverbal intelligence, and most putatively ―crystallized‖ tests measure verbal
intelligence. The verbal tests cannot be considered purely crystallized because verbal
ability is just as heritable (genetically influenced) as nonverbal ability, even when
controlling for g (Johnson & Bouchard, 2007; Johnson et al., 2007). And the nonverbal
tests cannot be considered purely fluid, (1) because nonverbal ability is influenced by
Openness/Intellect and Cognitive Ability 9
environmental factors in studies of heritability (Johnson & Bouchard, 2007; Johnson et
al., 2007) and (2) because it may be improved by schooling (Ceci, 1991) and by training
on video games (Feng, Spence, & Pratt, 2007), working memory tasks (Jaeggi,
Buschkuehl, Jonides, & Perrig, 2008; but see Moody, 2009), and other mentally
stimulating activities (Tranter & Koutstaal, 2008). For these reasons, we refer to verbal
and nonverbal intelligence, rather than to fluid and crystallized intelligence.
Total Openness/Intellect is more strongly associated with verbal than nonverbal
intelligence (Ackerman & Heggestad, 1997; Ashton et al., 2000; Austin, Deary, &
Gibson, 1997; Baker & Bichsel, 2006; Bates & Shieles, 2003; Beauducel1, Liepmann,
Felfe, Nettelnstroth, 2007; DeYoung et al., 2005; Holland et al., 1995). The present study
examined the hypothesis that this reflects different patterns of association of verbal and
nonverbal intelligence with Intellect versus Openness. This conjecture was based in part
on three studies that have reported associations of the NEO PI-R facets with separate tests
of verbal and nonverbal intelligence (DeYoung et al., 2005; McCrae, 1993; Moutafi et
al., 2006). In these studies, the four facets that mark Openness appeared more likely to be
associated with tests of verbal intelligence than with tests of nonverbal intelligence,
whereas Ideas was often associated with both forms of intelligence about equally (but see
Holland et al., 1995). Our specific hypothesis, therefore, was that, in multiple regression,
Intellect would predict both verbal and nonverbal intelligence, whereas Openness would
predict only verbal intelligence.
Note that this hypothesis implies that Openness will be associated with verbal
intelligence even after controlling for Intellect. Why would Openness, which seems
primarily to reflect engagement with sensory information, be associated with verbal
intelligence independently of Intellect? One possible reason is that Openness is
Openness/Intellect and Cognitive Ability 10
associated with implicit learning, the ability to unconsciously detect patterns in the
environment (Kaufman et al., 2010). Implicit learning appears to contribute to verbal
ability specifically, but not to g, perhaps because it facilitates language learning
(Kaufman et al., 2010). Our hypothesis in the present study was based in part on the
recent finding that Openness and Intellect show a double dissociation in predicting
individual differences in implicit learning and working memory (Kaufman et al., 2010).
In multiple regression, Intellect was associated with working memory (a key contributor
to g; Conway, Kane, & Engle, 2003; Gray & Thompson, 2004) but not implicit learning,
whereas Openness was associated with implicit learning but not working memory
(Kaufman et al., 2010). This pattern of association with basic cognitive mechanisms is in
keeping with the hypothesis that Intellect is associated with all aspects of intelligence,
whereas Openness is independently associated only with verbal intelligence. We tested
this hypothesis in two samples.
Sample 1 consisted of 125 undergraduates (92 female, 33 male) at the University
of Toronto, who completed the study for course credit. By race/ethnicity, they were 46%
East Asian, 26.5% White, 13.5% South Asian, 6.5% Black, 5% Middle Eastern, and 2.5%
Hispanic. They ranged in age from 17 to 38 (M = 19.47; SD = 3.03). This sample is a
subset of the sample described by DeYoung et al. (2007; Study 2) in relation to the
construction of the BFAS. This subset came into the laboratory for a session that included
cognitive testing, whereas the rest of that sample simply completed questionnaires via the
Openness/Intellect and Cognitive Ability 11
Sample 2 consisted of 191 White men recruited from the area around New Haven,
Connecticut, including from several colleges. Flyers and Internet advertisements were
used to recruit for ―psychology studies involving genetics and brain imaging,‖ and
participation was restricted by race and sex to avoid heterogeneity in genetic data not
discussed here. Two participants were not included in analyses because BFAS data were
unavailable due to computer error. The remaining 189 ranged in age from 18 to 40 (M =
24.23; SD = 5.18). Seventy-four participants were students.
The rest of the sample had a
wide range of mostly lower- and middle-class occupations, with 20 indicating that they
were currently unemployed. All participants completed assessments in the laboratory and
were given monetary compensation for their participation.
Sample 1. The Big Five and their 10 aspects were assessed using the BFAS
(DeYoung et al., 2007), with responses given on a 5-point Likert scale. Each of the 10
scales comprised 10 items, and scores for the Big Five were computed by averaging
scores for the two aspect scales in each domain. Descriptive statistics for the BFAS are
presented in Table 1. Intelligence was assessed by the Matrix Reasoning and Vocabulary
subtests of the Wechsler Adult Intelligence ScaleIII (WAIS-III; Wechsler, 1997). The
Matrix Reasoning subtest is an indicator of nonverbal intelligence, requiring participants
to identify a patterned rectangle that logically completes an abstract visual pattern. The
Vocabulary subtest is an indicator of verbal intelligence. Raw scores for both subtests
were scaled to yield age appropriate scores. Matrix Reasoning had a mean of 11.74 (SD =
2.68), and Vocabulary had a mean of 11.58 (SD = 3.07). The two subtests were
correlated, r = .24 (p < .01), and the average of the two subtests was used as an estimate
of g.
Openness/Intellect and Cognitive Ability 12
[Insert Tables 1 and 2 about here.]
Sample 2. The Big Five and their 10 aspects were assessed with the BFAS as in
Sample 1. Intelligence was assessed with four subtests of the WAIS-III (Wechsler, 1997).
Matrix Reasoning and Block Design were used as indicators of nonverbal intelligence.
Vocabulary and Similarities were used as indicators of verbal intelligence. Matrix
Reasoning and Vocabulary were as described for Study 1. Block Design requires
participants to recreate designs as fast as possible, using cubic blocks that are red on two
sides, white on two sides, and half-red, half-white on the other sides. Similarities requires
participants to explain analogies (e.g., ―How are an enemy and a friend alike?‖). Raw
scores for all subtests were scaled to yield age appropriate scores (Wechsler, 1997).
Descriptive statistics and correlations among the four subtests are presented in Table 2.
The average of Vocabulary and Similarities was used as an indicator of verbal
intelligence, and the average of Matrix Reasoning and Block Design was used as an
index of nonverbal intelligence. Verbal and nonverbal intelligence were correlated, r =
.38 (p < .01), and their average was used as an estimate of g (factor scores were not used,
to avoid capitalizing on sampling variability).
Table 3 shows correlations between the BFAS and the cognitive variables, g
nonverbal intelligence, and verbal intelligence. As expected, Openness/Intellect and its
two aspects showed the strongest and most consistent correlations with cognitive ability.
Of interest, however, traits from two other domains, Neuroticism and Agreeableness, also
showed correlations with cognitive ability in both samples. Correlations with
Neuroticism were inconsistent across the two samples (associated with Withdrawal in
Sample 1, but with Volatility in Sample 2). In contrast, correlations with Agreeableness
Openness/Intellect and Cognitive Ability 13
were more consistent: both samples showed positive correlations of Compassion with g
and verbal intelligence.
[Insert Table 3 about here.]
The pattern of correlations of cognitive ability with the two aspects of
Openness/Intellect was as predicted, in both samples. Intellect showed correlations of
similar magnitude with both verbal and nonverbal intelligence, whereas Openness was
correlated only with verbal intelligence. Correlations with g were systematically related
to the correlations with its subcomponents: Intellect was correlated more strongly with g
than with either verbal or nonverbal intelligence alone, whereas Openness was correlated
more weakly with g than with verbal intelligence because of the lack of association
between Openness and nonverbal intelligence.
[Insert Table 4 about here.]
Regressions were performed to test the independent contributions of Intellect and
Openness to the three cognitive ability variables (Table 4). As predicted, only Intellect
was significantly associated with g and nonverbal intelligence, but both Intellect and
Openness predicted verbal intelligence independently. Both Intellect and Openness
contributed incrementally to the prediction of verbal intelligence, whereas only Intellect
contributed incrementally to the prediction of g and nonverbal intelligence.
Correlations of the BFAS Openness and Intellect scales confirmed several
hypotheses regarding the associations of personality with cognitive ability. In keeping
with its inclusion of descriptors of intelligence and intellectual engagement, Intellect was
associated with g and with both verbal and nonverbal intelligence. Openness, which
describes engagement with sensory information and aesthetics, was associated with g in
Openness/Intellect and Cognitive Ability 14
zero-order correlations, but was not associated with g after controlling for Intellect. As
predicted, the only association of Openness with cognitive ability after controlling for
Intellect was with verbal intelligence. In multiple regression, Openness and Intellect
contributed about equally to verbal intelligence. Notably, results were very similar in two
samples that were very different demographically, suggesting that our findings are likely
to be robust.
The pattern of results in this study clarifies two puzzles that have been noted
regarding the relation of Openness/Intellect to intelligence (DeYoung, 2011). First, a
number of studies have found that Openness/Intellect is more strongly related to verbal
than nonverbal intelligence. Explanations for this phenomenon have often invoked the
problematic description of verbal intelligence as ―crystallized‖ and suggested that
Openness/Intellect reflects greater motivation to learn, leading to greater crystallized
intelligence. The current findings suggest another, simpler explanation:
Openness/Intellect is more strongly related to verbal than nonverbal intelligence because
both aspects of the domain are associated with verbal intelligence, whereas only Intellect
is associated with nonverbal intelligence. Although Intellect does reflect, in part,
intellectual engagement, this does not lead differentially to ability in the verbal domain;
the correlations of Intellect with verbal and nonverbal intelligence were very similar in
Second, several previous studies of NEO PI-R facet scales have suggested that
Openness is probably less strongly related to intelligence and particularly nonverbal
intelligence than is Intellect, but it was not clear whether any association of Openness
with intelligence was simply due to variance shared with Intellect. The present study
indicates that any zero-order association of Openness with g or nonverbal intelligence is
Openness/Intellect and Cognitive Ability 15
indeed probably due to variance shared with Intellect. However, Openness did predict
verbal intelligence even after controlling for Intellect, indicating that the association of
Openness with verbal intelligence cannot be explained by its association with Intellect.
An important question, therefore, is why Openness should be independently
associated with verbal intelligence. One possibility is that part of the specific cognitive
substrate of Openness contributes to verbal intelligence. Openness is associated with
implicit learning, the ability to learn patterns unconsciously, whereas Intellect is not
(Kaufman et al., 2010). Additionally, implicit learning is related to verbal (but not
nonverbal) intelligence, independently of g. Individuals high in Openness may have
greater verbal skill in part because they have more capacity for implicit learning of the
patterns of language.
The present results contribute to a program of research seeking to understand the
mechanisms underlying Intellect and Openness. In part, the demonstration of the
association between Intellect and intelligence simply serves as validation of Intellect as a
construct, given that Intellect encompasses descriptors of intelligence. Although ability
tests are more accurate than self-reports of intelligence (Paulhus, Lysy, & Yik, 1998), the
latter nonetheless reflect intelligence to a meaningful extent. Cognitive and brain
mechanisms that support intelligence, such as those associated with working memory, are
likely to be a crucial substrate of Intellect (DeYoung et al., 2009). In contrast,
mechanisms associated with perception and detection of patterns may important
components of the substrate of Openness (Kaufman et al., 2010). In addition to
mechanisms specific to Intellect or Openness, there must also be mechanisms that the two
traits share, one of which is likely to be a drive to explore information of all kinds. This
Openness/Intellect and Cognitive Ability 16
drive has been linked to the neurotransmitter dopamine (DeYoung et al., 2005; DeYoung
et al., 2011).
Neuroticism, Agreeableness, and Intelligence
We formed no hypotheses regarding the associations of cognitive ability with
traits other than Openness and Intellect. It is worth noting, however, that traits in the
Neuroticism and Agreeableness domains showed significant correlations with cognitive
ability. With regard to Neuroticism, this was not unexpected because meta-analysis has
indicated a weak negative association with intelligence, probably due to test anxiety
(Ackerman & Heggestad, 1997).
The finding of an association of intelligence with the Compassion aspect of
Agreeableness is more novel and may begin to address one of the remaining puzzles in
the relation of intelligence to personality (DeYoung, 2011). Agreeableness is not
typically correlated with intelligence; however, measures of certain traits that would
usually be categorized within Agreeableness are correlated with intelligence. For
example, questionnaire measures of aggression are typically negatively correlated with
intelligence (Ackerman & Heggestad, 1997). Of more relevance to the present finding,
assessments of the ability to empathize are often correlated with intelligence. The largest
body of findings on this phenomenon is probably from the Mayer-Salovey-Caruso
Emotional Intelligence Test (MSCEIT), which comprises a battery of tasks like
identifying emotions in facial expressions or judging how best to manage others’
emotions in social situations, and which shows a correlation of about .3 with intelligence
(Mayer, Salovey, & Caruso, 2004; Roberts, Schulze, & MacCann, 2008). This
association is typically stronger for verbal ability, just as it was for Compassion in the
present samples. Compassion includes items that describe empathy (e.g., ―Feel others’
Openness/Intellect and Cognitive Ability 17
emotions‖; ―Sympathize with others’ feelings‖), so the present findings using the BFAS
are consistent with previous findings using the MSCEIT. (Notably, associations of
Compassion with intelligence cannot be estimated from studies using the NEO PI-R
because that instrument does not include any facets that are good indicators of
Compassion separately from Politeness [DeYoung et al., 2007].)
It may be that the ability to empathize is facilitated by intelligence. However,
another possibility is that the ability to empathize is facilitated by Openness, and this
association may explain the correlation between Compassion and intelligence (especially
verbal intelligence). This hypothesis is likely because Compassion and Openness are
substantially correlated, r = .40 and .36 in the present samples. Consistent with this
hypothesis, post hoc analyses in both samples showed that Compassion remained
significantly related to verbal intelligence after controlling for Intellect, but not after
controlling for Openness. These findings suggest an important role for Openness in
empathy, which could be explored in future research.
The BFAS, an instrument which breaks down each of the Big Five personality
traits into two correlated aspects, successfully clarified the relations of personality to
cognitive ability. Although it has long been known that Openness/Intellect is the only Big
Five trait that is positively associated with intelligence, the meaning of this association
has remained unclear, both because of the possibility that it might be due specifically to
verbal (often presumed to be ―crystallized‖) intelligence, and because of lingering debate
about the conceptual relation of Openness/Intellect to intelligence (DeYoung, 2011).
Although descriptors of intelligence fall within Openness/Intellect, its most common
Openness/Intellect and Cognitive Ability 18
label, ―Openness to Experience,‖ does not seem to be closely related to intelligence
Parsing the Big Five at the aspect level allows recognition that intelligence has a
place in the Big Five, subsumed under Intellect, while acknowledging that Openness and
Intellect are indeed distinct (though related), and that Openness does not subsume
standard descriptors of intelligence. In keeping with this pattern, we found that Intellect
was independently associated with g and with both verbal and nonverbal intelligence
about equally. Additionally, however, Openness was independently associated with
verbal intelligence, a finding that suggests a potentially fruitful avenue for further
research. The association of Openness and verbal intelligence may be a function of
Openness’ association with implicit learning (Kaufman et al., 2010). The present study
demonstrates the utility of measuring Openness and Intellect as separate but related
constructs. Additionally, the demonstration of differential patterns of association for the
BFAS Openness and Intellect scales provides new evidence of their validity.
Openness/Intellect and Cognitive Ability 19
Ackerman, P. L. & Heggestad, E. D. (1997). Intelligence, personality, and interests:
Evidence for overlapping traits. Psychological Bulletin, 121, 219-245.
Ashton, M. C., Lee. K., Vernon, P. A., & Jang, K. L. (2000). Fluid intelligence,
crystallized intelligence, and the Openness/Intellect factor. Journal of Research in
Personality, 34, 197-207.
Austin, E. J., Deary, I. J., & Gibson, G. J. (1997). Relationship between ability and
personality: Three hypotheses tested. Intelligence, 25, 4970.
Baker, T. J., & Bichsel, J. (2006). Personality predictors of intelligence: Differences
between young and cognitively healthy older adults. Personality and Individual
Differences, 41, 861-871.
Bates, T. C., & Shieles, A. (2003). Crystallized intelligence as a product of speed and
drive for experience: The relationship of inspection time and openness to g and
Gc. Intelligence, 31, 275287.
Beauducel, A., Liepmann, D., Felfe, J., & Nettelnstroth, W. (2007). The impact of
different measurement models for fluid and crystallized intelligence on the
correlation with personality traits. European Journal of Psychological
Assessment, 23, 71-78.
Carroll, J. B. (1993). Human cognitive abilities. New York: Cambridge University Press.
Ceci, S. J. (1991). How much does school influence general intelligence and its cognitive
components: A reassessment of the evidence. Developmental Psychology, 27,
Conway, A. R., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its
relation to general intelligence. Trends in Cognitive Sciences, 7, 547-552.
Openness/Intellect and Cognitive Ability 20
Costa, P. T., Jr., & McCrae, R. R. (1992). NEO PI-R Professional Manual. Odessa, FL:
Psychological Assessment Resources.
Depue, R. A. & Collins, P. F. (1999). Neurobiology of the structure of personality:
Dopamine, facilitation of incentive motivation, and extraversion. Behavioral and
Brain Sciences, 22, 491-569.
DeYoung, C. G. (2010). Personality neuroscience and the biology of traits. Social and
Personality Psychology Compass, 4, 11651180.
DeYoung, C. G. (2011). Intelligence and personality. In R. J. Sternberg & S. B. Kaufman
(Eds.), The Cambridge handbook of intelligence (pp. 711737). New York:
Cambridge University Press.
DeYoung, C. G., Cicchetti, D., Rogosch, F. A., Gray, J. R., Eastman, M., & Grigorenko,
E. L. (2011). Sources of cognitive exploration: Genetic variation in the prefrontal
dopamine system predicts Openness/Intellect. Journal of Research in Personality,
45, 364-371.
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2005). Sources of Openness/Intellect:
Cognitive and neuropsychological correlates of the fifth factor of personality.
Journal of Personality, 73, 825-858.
DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10
aspects of the Big Five. Journal of Personality and Social Psychology, 93, 880-
DeYoung, C. G., Shamosh, N. A., Green, A. E., Braver, T. S., & Gray, J. R. (2009).
Intellect as distinct from Openness: Differences revealed by fMRI of working
memory. Journal of Personality and Social Psychology, 97, 883-892.
Openness/Intellect and Cognitive Ability 21
Feng, J., Spence, I., & Pratt, J. (2007). Playing an action video games reduces gender
differences in spatial cognition. Psychological Science, 18, 850855.
Furnham, A., Dissou, G., Sloan, P., Chamorro-Premuzic, T. (2007). Personality and
intelligence in business people: A study of two personality and two intelligence
measures. Journal of Business and Psychology, 22, 99-109.
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R.,
& Gough, H. C. (2006). The International Personality Item Pool and the future of
public-domain personality measures. Journal of Research in Personality, 40, 84-
Gray, J. R., & Thompson, P. M. (2004). Neurobiology of intelligence: Science and ethics.
Nature Reviews Neuroscience, 5, 471-482.
Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. American
Psychologist, 58, 78-80.
Holland, D. C., Dollinger, S. J., Holland, C. J., & MacDonald, D. A. (1995). The
relationship between psychometric intelligence and the five-factor model of
personality in a rehabilitation sample. Journal of Clinical Psychology, 51, 7988.
Horn, J. L . & Cattell. R. B, (1966). Refinement and test of the theory of fluid and
crystallized general intelligences. Joumal of Educational Psychology, 57, 253-
Jaeggi, S.M., Buschkuehl, M., Jonides, J., & Perrig,W. J. (2008). Improving fluid
intelligence with training on working memory. Proceedings of the National
Academy of Sciences of the United States of America, 105, 6829−6833.
John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the integrative Big
Five trait taxonomy: History: measurement, and conceptual issue. In O. P. John,
R. W. Robins, & L. A. Pervin (Eds). Handbook of personality: Theory and
research (pp. 114-158). New York: Guilford Press.
Openness/Intellect and Cognitive Ability 22
Johnson, J. A. (1994). Clarification of factor five with the help of the AB5C model.
European Journal of Personality, 8, 311-334.
Johnson, W., & Bouchard, T. J., Jr. (2005a). The structure of human intelligence: It's
verbal, perceptual, and image rotation (VPR), not fluid crystallized. Intelligence,
33, 393-416.
Johnson, W., & Bouchard, T. J., Jr. (2005b). Constructive replication of the visual
perceptual-image rotation model in Thurstone’s (1941) battery of 60 tests of
mental ability, Intelligence, 33, 417-430.
Johnson, W., & Bouchard, T. J., Jr. (2007). Sex differences in mental abilities: g masks
the dimensions on which they lie. Intelligence, 35, 23-39.
Johnson, W., Bouchard, T. J., Jr., McGue, M., Segal, N. L., Tellegen, A., Keyes, M., &
Gottesman, I. I. (2007). Genetic and environmental influences on the Verbal-
Perceptual-Image Rotation (VPR) model of the structure of mental abilities in the
Minnesota study of twins reared apart. Intelligence, 35, 542-562.
Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jiménez, L., Brown, J., & Mackintosh, N.
J. (2010). Implicit learning as an ability. Cognition, 116, 321340.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2004). Emotional intelligence: Theory,
findings, and implications. Psychological Inquiry, 60, 197-215.
McCrae, R. R. (1993). Openness to Experience as a basic dimension of personality.
Imagination, Cognition, and Personality, 13, 39-55.
Moody, D. E. (2009). Can intelligence be increased by training on a task of working
memory? Intelligence,37, 327-328.
Openness/Intellect and Cognitive Ability 23
Moutafi, J., Furnham, A., & Crump, J. (2003). Demographic and personality predictors of
intelligence: A study using the NEO Personality Inventory and the Myers-Briggs
Type Indicator. European Journal of Personality, 17, 79-94.
Moutafi, J., Furnham, A., & Crump, J. (2006). What facets of openness and
conscientiousness predict fluid intelligence score? Learning and Individual
Differences, 16, 3142.
Moutafi, J., Furnham, A., & Tsaousis, I. (2006). Is the relationship between intelligence
and trait neuroticism mediated by test anxiety? Personality and Individual
Differences, 40, 587597.
O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of
components using parallel analysis and Velicer’s MAP test. Behavior Research
Methods, Instruments, and Computers, 32, 396-402. Program available online:
Paulhus, D. L., Lysy, D. C., & Yik, M. S. M. (1998). Self-report measures of intelligence:
Are they useful as proxy IQ tests? Journal of Personality, 66, 525-554.
Roberts, R. D., Schulze, R., MacCann, C. (2008). The measurement of emotional
intelligence: A decade of progress? In G. Boyle, G. Matthews, D. H. Saklofske
(Eds.), The Sage Handbook of Personality Theory and Assessment, Volume 2. Los
Angeles: Sage.
Saucier, G. (1992). Openness versus intellect: Much ado about nothing? European
Journal of Personality, 6, 381-386.
Saucier, G. (1994). Trapnell versus the lexical factor: More ado about nothing? European
Journal of Personality, 8, 291-298.
Openness/Intellect and Cognitive Ability 24
Tranter, L. J., & Koutstaal, W. (2008). Age and flexible thinking: An experimental
demonstration of the beneficial effects of increased cognitively stimulating
activity on fluid intelligence in healthy older adults. Aging, Neuropsychology, and
Cognition, 15, 184207.
Wechsler, D. (1997). WAIS-III administration and scoring manual. San Antonio, TX:
Harcourt Brace & Company.
Openness/Intellect and Cognitive Ability 25
Author Note
This research was supported by grants from the National Institute of Mental Health (F32
MH077382) to Colin G. DeYoung, from the National Science Foundation (DRL
0644131) to Jeremy R. Gray, and from the Social Sciences and Humanities Research
Council of Canada to Jordan B. Peterson.
Openness/Intellect and Cognitive Ability 26
1. Note that, in the NEO PI-R, the Openness/Intellect domain is labeled ―Openness to
Experience,‖ despite the fact that it contains a facet measuring Intellect rather than
Openness according to a previous factor analysis (DeYoung et al., 2007). In this article,
we distinguish between Openness‖ and Intellect facets of the NEO PI-R based on that
factor analysis, rather than labeling them all as facets of ―Openness to Experience.‖
2. None of these were students at Yale University; Yale students were excluded to avoid
skewing the distribution of intelligence scores in the sample. Importantly, this exclusion
did not lead to a truncated upper range of intelligence; estimated IQ in the sample without
Yale students ranged from 92 to 144. If 48 additional Yale students were included in the
sample, effect sizes were slightly attenuated, but results remained substantively the same.
The most notable difference in results was that, in regression, Openness only marginally
predicted verbal intelligence, β = .12, p = .08. However, if status as a Yale student was
entered as an additional covariate (dummy coded), Openness significantly predicted
verbal intelligence at a comparable level to that reported here, β = .19, p < .01.
Openness/Intellect and Cognitive Ability 27
Table 1
Descriptive Statistics for the Big Five Aspect Scales (BFAS)
Sample 1 (N = 125)
Sample 2 (N = 189)
Note. SD = standard deviation; = Cronbach’s
Openness/Intellect and Cognitive Ability 28
Table 2
Correlations and descriptive statistics for WAIS-III subtests in Sample 2
Block Design
Matrix Reasoning
Note. N = 189. All correlations significant at p < .01.
Openness/Intellect and Cognitive Ability 29
Table 3
Correlations among Measures of Cognitive Ability and BFAS
Sample 1 (N = 125)
Sample 2 (N = 189)
Note: All correlations greater than .17 are significant at p < .05 in Sample 1, and all
correlations greater than .15 are significant at p < .05 in Sample 2.
Openness/Intellect and Cognitive Ability 30
Table 4.
Regressions of Cognitive Abilities on Intellect and Openness.
Sample 1
Sample 2
*p < .05; **p < .01
Openness/Intellect and Cognitive Ability 31
Note. Sample 1 N = 125 ; Sample 2 N = 189 ; ΔR = incremental R for each predictor
when entered after the other predictor.
... Higgins, 2005;DeYoung, Quilty, Peterson, & Gray, 2014;Nusbaum, & Silvia, 2011). Openness to new experiences combines creativity, curiosity, cultural taste, achievement orientation, and desire to be knowledgeable. ...
... In other words, this trait involves cultural and mental curiosity terms. At this point, 'culture' means valuing art and science, and being sensitive to social values by using a liberal point of view.'Intellect' is defined as learning and analyzing causation(DeYoung et al., 2014). ...
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