Intellect As Distinct From Openness...

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J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.
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J Pers Soc Psychol. 2009 November ; 97(5): 883–892. doi:10.1037/a0016615.
Intellect as distinct from Openness: Differences revealed by fMRI

of working memory
Colin G. DeYoung,
Department of Psychology, University of Minnesota
Noah A. Shamosh,
Department of Psychology, Yale University
Adam E. Green,
Department of Psychology, Yale University
Todd S. Braver, and
Department of Psychology, Washington University
Jeremy R. Gray
Department of Psychology and Interdepartmental Neuroscience Program, Yale University
Abstract
Relatively little is known about the neural bases of the Big Five personality trait Openness/Intellect.
This trait is composed of two related but separable aspects, Openness to Experience and Intellect.
On the basis of previous behavioral research (DeYoung, Peterson, & Higgins, 2005), we
hypothesized that brain activity supporting working memory (WM) would be related to Intellect but
not Openness. To test this hypothesis we used fMRI to scan a sample of 104 healthy adults, as they
performed a difficult WM task. Intellect (and not Openness) was found to correlate with WM
accuracy and with accuracy-related brain activity, in left lateral anterior prefrontal cortex and
posterior medial frontal cortex. Neural activity in these regions mediated the association between
Intellect and WM performance, implicating these regions in the neural substrate of Intellect. Intellect
was also correlated significantly with scores on tests of intelligence and working memory capacity,
but the association of Intellect with brain activity could not be entirely explained by cognitive ability.
Keywords
Intellect; Openness; Prefrontal Cortex; Frontopolar Cortex; Working Memory; Intelligence
The five factor model or Big Five classifies personality traits into five broad domains:
Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness/Intellect. The
compound label for the last of these traits reflects an old debate about how best to characterize
the content of this domain, with some researchers preferring “Openness to Experience” (e.g.,
Costa & McCrae, 1992a) and others “Intellect” (e.g., Goldberg, 1993). This debate has been
Correspondence concerning this article should be addressed to Colin G. DeYoung; Psychology Department, University of Minnesota,
75 East River Rd., Minneapolis, MN 55455. cdeyoung@umn.edu; or to Jeremy R. Gray, Department of Psychology, Yale University,
Box 208205, New Haven, CT 06520; jeremy.gray@yale.edu.
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DeYoung et al. Page 2
largely resolved conceptually by the observation that “Openness” and “Intellect” describe two
related but separable aspects of the larger domain (Johnson, 1994; Saucier, 1992). Lexical
studies make it clear that both aspects are well represented in natural language and that content
related to both appears among the terms loading on a single Big Five factor (e.g., Goldberg,
1990; Saucier, 1992). There are a many terms in English that describe Intellect: intellectual,
intelligent, philosophical, erudite, clever, etc.1 There are also many terms (though perhaps
fewer) that describe Openness to Experience: artistic, perceptive, poetic, fantasy-prone, etc.
Additionally, there are many terms that could characterize people high in Intellect or Openness
or both: imaginative, original, complex, innovative, etc. (indeed, Saucier (1994) proposed that
“Imagination” might be a better single label for the domain as a whole). Johnson (1994)
demonstrated that, of the facets (sub-traits) in the Revised NEO Personality Inventory (NEO
PI-R; Costa & McCrae, 1992b), the Ideas and Aesthetics facets were the purest markers of the
lexical Openness/Intellect factor, and he suggested that interests in truth and beauty are
complementary qualities at the heart of the Openness/Intellect domain. McCrae and Costa
(1997) argued that this domain reflects the “depth, breadth, and permeability of consciousness.”
Perhaps Openness reflects these qualities of consciousness in relation to sensory or perceptual
information, whereas Intellect reflects them in relation to abstract or semantic information.
The identification of Openness and Intellect as the two major aspects of this domain of
personality was recently given empirical support by a factor analysis of 15 scales measuring
facets of Openness/Intellect (DeYoung, Quilty, & Peterson, 2007). The covariance of those
facets indicated the presence of two correlated factors, clearly recognizable as Openness and
Intellect. Each factor was strongly marked by six facets, suggesting their similarity in
importance to the larger domain. The two factors were further characterized by examining their
correlations with over 2000 items of the International Personality Item Pool (Goldberg,
1999). This analysis revealed that Intellect encompasses traits reflecting intellectual
engagement and perceived intelligence (e.g. “Avoid philosophical discussions” (reversed);
“Am quick to understand things”), whereas Openness encompasses traits reflecting artistic and
contemplative qualities related to engagement in sensation and perception (e.g., “Believe in
the importance of art”; “See beauty in things that others might not notice”) (DeYoung et al.,
2007). Having established the existence of these two related but separable aspects of Openness/
Intellect, one important concern is discriminant validity. How do these two aspects differ from
each other in their associations with other variables?
Levels of personality organization below the Big Five are associated with unique genetic
variance (Jang et al., 2002; Jang, McCrae, Angleitner, Riemann, & Livesley, 1998), suggesting
that it may be possible to identify biological systems that differentiate Intellect from Openness.
The present study tested the hypothesis that brain function associated with working memory
(WM) performance would be related to Intellect but not Openness. Intellectual individuals
seem more likely than those who are simply open to experience to utilize brain systems that
allow for successful processing of information during WM tasks with a high cognitive demand.
This hypothesis was derived in part from behavioral research relating facets of Openness/
Intellect to a battery of tests of WM and other cognitive functions associated with prefrontal
cortex (PFC) (DeYoung, Peterson, Higgins, 2005). That study, like the present one, measured
the Big Five using the NEO PI-R (Costa & McCrae, 1992b), which divides each of the Big
Five into six facets. Four of these facets (Fantasy, Aesthetics, Feelings, and Actions) clearly
mark the Openness aspect of the domain, in factor analysis (DeYoung et al., 2007). The Ideas
facet, however, is a good marker of Intellect (DeYoung et al., 2007) and was the facet most
strongly associated with performance on the WM battery (DeYoung et al., 2005).2 The sixth
1Similar collections of terms have defined the Openness/Intellect factor in other languages, except when terms reflecting intellectual
ability have been intentionally omitted (e.g., Dutch, Italian), in which case the factor tends to tilt more toward content related to
unconventionality (John, Naumann, & Soto, 2008).

facet, Values, does not mark either aspect strongly but was also found to be associated with
WM.
The present study employed functional magnetic resonance imaging (fMRI) of individuals
performing a difficult WM task, in order to test the hypothesis that Intellect (but not Openness)
would be associated with WM performance and with brain activity supporting WM
performance. Use of a large sample (by the standards of neuroimaging research) enabled the
identification of brain regions in which individual variation in neural activity predicted WM
performance. Because this analytic approach identifies regions with meaningful variability, it
appears particularly promising for personality neuroscience (DeYoung & Gray, 2009), in
contrast to approaches that identify regions consistently activated in the sample as a whole.
Two brain regions of particular interest in our analyses were the left lateral region of anterior
PFC (aPFC; also called frontopolar cortex) and the region of posterior medial frontal cortex
(pMFC) that encompasses the dorsal anterior cingulate cortex (ACC). Left aPFC appears to
support the abstract integration of information from multiple cognitive operations (Gilbert et
al., 2006; Green, Fugelsang, Kraemer, Shamosh, & Dunbar, 2006; Ramnani & Owen, 2004)
and has been implicated in intelligence (Jung & Haier, 2007). Several neuroimaging studies
have implicated left lateral aPFC in abstract integration, as distinct from more basic WM
processes such as maintenance and manipulation of information (Reynolds, West, & Braver,
2008). For example, activity in left aPFC has been associated with abstract, relational
integration in analogical reasoning (Green et al., 2006). Left aPFC appears similarly important
for integration during mathematical problem solving (De Pisapia, Slomski, & Braver, 2007),
matrix reasoning (Christoff et al., 2001), and episodic memory (Reynolds, McDermott, &
Braver, 2006). In a previous study of the sample examined here (Shamosh et al., 2008), we
found that activity in left lateral aPFC was associated with individual differences in WM,
intelligence, and the tendency to prefer larger, delayed rewards over smaller, immediate
rewards. The association of left lateral aPFC with WM and intelligence suggests the hypothesis
that this brain region is associated with the trait of Intellect.
The pMFC region is of interest because it is reliably engaged in WM tasks (Cabeza & Nyberg,
2000; Owen, McMillan, Laird, & Bullmore, 2005) and appears to be involved in the cognitive
functions of monitoring performance during goal-directed activity (Ridderinkhof, Ullsperger,
Crone, & Nieuwenhuis, 2004) and detecting the likelihood of error (Brown & Braver, 2005).
Monitoring one’s performance during cognitive tasks seems likely to be associated with
Intellect, especially given the degree to which Intellect reflects the tendency to be motivated
and engaged by intellectual activities. We therefore hypothesized that this region might be
among the neural correlates of Intellect. Finally, other parts of the canonical WM network
(Cabeza & Nyberg, 2000; Owen et al., 2005; Wager & Smith, 2003), such as the dorsolateral
PFC and parietal cortex, which have also been associated with tests of intelligence (Gray &
Thompson, 2004), may also be associated with Intellect, though our predictions regarding these
regions were less specific than those for left aPFC and pMFC.
A key question in testing our hypotheses is whether any association of Intellect with brain
function is due to the association of both Intellect and WM with intelligence. The Ideas facet
of the NEO PI-R, our marker for Intellect, is the facet most consistently associated with scores
on intelligence tests (DeYoung et al., 2005; Furnham, Dissou, Sloan, & Chamorro-Premuzic,
2The fact that the NEO PI-R contains only one clear Intellect facet and four clear Openness facets is due to the inventory’s history and
does not constitute evidence that Intellect is peripheral to the larger Openness/Intellect domain. The facets of the NEO PI-R were derived
rationally; those for Openness to Experience were developed prior to McCrae and Costa’s (1985) attempt to harmonize the NEO with
the lexical Big Five; and McCrae and Costa have consistently resisted the idea that Intellect might be a valid interpretation of content in
this domain (e.g., McCrae & Costa, 1997). As noted above, however, considerable evidence in both lexical and questionnaire research
indicates that Intellect is just as central to the larger domain as Openness.

2007; McCrae, 1993; Moutafi, Furnham, & Crump, 2003, 2006). Although WM is not identical
to intelligence, the two are strongly related (Conway, Kane, & Engle, 2003), suggesting that
WM forms a key part of the cognitive substrate of intelligence. Additionally, the neural
substrates of intelligence and WM are at least partially overlapping (Gray, Chabris, & Braver,
2003; Gray & Thompson, 2004; Kane & Engle, 2002).
If Intellect, like intelligence, is associated with the neural substrates of WM, what will that
finding add to our knowledge? We addressed this question empirically, using ability tests of
intelligence. As noted above, measures of Intellect reflect perceived intelligence as well as
intellectual engagement. Neither perceived intelligence nor intellectual engagement can be
considered identical, or even strongly related, to intelligence as measured by ability tests
(correlations are typically in the range of .2 to .3; e.g., Ackerman & Heggestad, 1997; Paulhus,
Lysy, & Yik, 1998). Intellectual engagement reflects motivation, interest, and enjoyment in
intellectual pursuits, without necessarily reflecting cognitive ability (though ability could
encourage engagement and vice versa). The items of the Ideas facet were selected to reflect
intellectual engagement rather than perceived intelligence (Costa & McCrae, 1992b), and Ideas
is the NEO PI-R facet most strongly related to measures of Typical Intellectual Engagement,
r = .77 (Ackerman & Goff, 1994) and the similar construct Need for Cognition, r = .78
(Cacioppo, Petty, Feinstein, & Jarvis, 1996). Nonetheless, the fact that both intellectual
engagement and perceived intelligence tend to be positively associated with tests of ability
makes it important to test whether any association with Intellect is due to intelligence measured
as an ability.
There are two possibilities for the outcome of an analysis of the relations among Intellect,
intelligence, and brain activity supporting WM performance (assuming the correctness of our
initial hypothesis that Intellect is associated with WM-related brain activity). First, any
association between Intellect and WM-related brain activity may be eliminated by controlling
for intelligence, which would mean that self-rated Intellect is associated with WM-related brain
activity only because it reflects intelligence with some limited degree of accuracy. Second, the
association may be independent of intelligence, which would indicate that the brain activity in
question is probably associated with intellectual engagement, rather than, or in addition to,
intelligence. We used mediation tests to examine the relations among these constructs. As an
even more specific test of the degree to which Intellect is associated with WM-related brain
activity for reasons other than cognitive ability, we included measures of WM capacity, in
addition to tests of intelligence.
Method
Participants
Right-handed participants were recruited from Washington University and the surrounding
community, in St. Louis, Missouri, to participate in a neuroimaging study. (There is no overlap
with the sample described by Gray et al. (2003); there is substantial overlap with the samples
described by Shamosh et al. (2008), and Fales et al. (2008).) The experimental protocol was
approved by the Washington University Medical Center Human Subjects Committee. All
participants gave informed consent and were screened for history of neurological or psychiatric
disorders and use of psychoactive drugs. Participants (N = 107) were selected for the present
study if they had completed the NEO PI-R and had complete imaging data. One of these
participants was excluded because performance on the WM task was not significantly better
than chance (d' = 0.31, 3.19 SDs below the mean). Another participant was excluded for
omitting more than 20 responses in multiple blocks of the task. Finally, the participant with
the highest WM score was excluded as an outlier (d' = 3.80, 3.24 SDs above mean, the next
highest score being only 2.12 SDs above the mean). This left 104 participants (59 female),
ranging in age from 18 to 40 years (M = 22.67, SD = 5.12), who were included in all analyses

reported below. There were no significant gender differences for any of the variables examined
in this study.
Measures
Personality—Personality was assessed using the NEO PI-R, a standard instrument for
measuring the Big Five, with excellent reliability and validity (Costa & McCrae, 1992b).
Cronbach’s Alphas for the Big Five domains were: N = .92, = .87, O = .89, A = .91, C = .91.
Cronbach’s Alphas for the six facets of Openness/Intellect were: Fantasy = .84, Aesthetics = .
81, Feelings = .67, Actions = .66, Ideas = .84, Values = .70.
Working memory—WM was assessed during fMRI using a 3-back WM task. Participants
were required to press one button if the item presented on the screen was identical to that
presented 3 trials previously, and another button if the item was different. The task was made
additionally difficult by the inclusion of lure trials, in which the stimulus matched one seen
previously (and hence was familiar in the context of the task) but did not match the one 3 back.
Trial proportions were 31% targets, 19% lures, and 50% non-lure/non-targets. Participants
performed this task in six functional scanning runs, each comprising two blocks of 32 trials
(64 total trials per functional run) lasting 2 seconds each. The first 3 trials of each block were
discarded because no match was possible, leaving 58 trials per run. Runs alternated between
using faces and concrete nouns as stimuli, with order counterbalanced across participants; all
face stimuli displayed a mildly positive (smiling) expression, and all nouns were emotionally
neutral. Every run was preceded by a short video; two of these videos involved positive emotion
inductions, two involved negative emotion inductions, and two were emotionally neutral. The
order of video presentation was counterbalanced, and we do not focus on this variable in our
analyses. (All significant effects reported in Table 1 below remained significant as main effects
when controlling for stimulus type and emotion condition in a series of 2 × 3 repeated measures
ANCOVA, run post hoc.) 3-back performance was assessed by the signal detection measure
of accuracy, d', averaged across runs. Cronbach’s Alpha for d' across the six runs was .84.
Blocks of the 3-back task in which participants omitted responses to more than 15 trials were
excluded from all analyses. For 6 participants, 3-back accuracy (and associated brain activity)
was therefore computed based on 5 blocks. Accuracy was not significantly correlated with
reaction times on the task, nor was reaction time correlated with any of the other variables
examined in the study.
In addition, participants completed four WM span tasks outside of the scanner. These tasks
required participants to keep information in mind despite interference (Conway et al., 2003).
Two were verbal and two were spatial: Operation span required keeping several words in mind
over a short delay while doing math problems. Reading span required keeping several letters
in mind while reading sentences out loud and judging their meaningfulness. Symmetry span
required keeping several spatial locations in mind while making symmetry judgments. Rotation
span required keeping the size and direction of several arrows in mind while making unrelated
judgments that required mental rotation. For all four tasks, keeping more items in mind resulted
in higher scores, on a metric ranging from 0.00 to 1.00. Factor analysis showed that all four
tasks loaded strongly on a single factor accounting for 70% of total variance (loadings ranged
from .81 to .86). WM capacity (WMC) was defined as the average score across all four
measures (α = .86).
Intelligence—Participants completed four standard measures of intelligence: Raven’s

Advanced Progressive Matrices, Set II (APM; Raven et al., 1998), the Cattell Culture Fair
Intelligence Test (Cattell, 1973), the Vocabulary subscale of the WAIS-R (Wechsler, 1997)
and the National Adult Reading Test - Revised (J. R. Blair & Spreen, 1989). Intelligence was
assessed as general cognitive ability (g), computed as the average of participants’ standardized

scores on the four psychometric tasks (α = .88). Common factor analysis revealed that a single
factor explained 74% of the shared variance in these measures, the scree plot indicated no
second factor, and all variables loaded strongly and approximately equally on the first unrotated
factor (range: .79 – .84). Operationalizing g using scores derived from the first unrotated factor
did not change the results appreciably and has the disadvantage of capitalizing on sampling
variability.
fMRI Data Acquisition

Whole-brain images were collected on a 3 Tesla Allegra System (Siemens, Erlangen,
Germany), including T1-weighted MP-RAGE structural images (FOV = 256 mm; 256 × 256
matrix; 1.25 mm thick axial slices) and T2* BOLD functional images (asymmetric spin-echo
echo-planar sequence; TR = 2360 ms; TE = 25 ms; FOV = 256 mm; flip angle = 90°; matrix
= 64 × 64; 4 mm thick axial slices). Each functional run comprised 149 sequential whole-brain
volumes (32 contiguous slices, 4 × 4 mm in-plane resolution). During each functional run, the
inter-trial intervals were jittered across a range of 0 to 4720 msec (0 to 2 TRs) in steps of 2360
msec (1 TR). Each task block was preceded and followed by a resting fixation block of 35
seconds, during which participants were instructed to watch a simple dash that remained at the
center of the screen. Each scanning run began with an unanalyzed 4 TR fixation period that
allowed the scanner to reach steady state.
fMRI Data Analysis

Data were analyzed using Statistical Parametric Mapping 2 (SPM2) software

(http://www.fil.ion.ucl.ac.uk/spm). Each functional run was preprocessed prior to analysis.
Data were realigned using INRIAlign
(http://wwwsop.inria.fr/epidaure/Collaborations/IRMf/INRIAlign.html) to correct for
movement. Images were normalized to Montreal Neurological Institute (MNI) stereotaxic
space using a 12-parameter affine transformation followed by nonlinear warping using basis
functions, resampled into 3 mm isotropic voxels, and smoothed using an 8 mm full-width at
half-maximum Gaussian kernel.
For each participant, a basic contrast, task > fixation, was computed across all six functional
runs.3 Each 32-trial block of 3-back performance was modeled as a boxcar function convolved
with a canonical hemodynamic response function. The magnitude of neural activity at each
voxel was estimated using the general linear model. This contrast produced statistical
parametric maps of the t statistic at each voxel for each subject. These maps of the brain,
indicating the difference in neural activity at each voxel between when participants were
engaged in the WM task and when they were simply focusing on the fixation point, were used
in all subsequent analyses.
As potential neural correlates of Intellect, we identified candidate regions of interest (ROIs) in

which brain activity during the WM task covaried with 3-back accuracy between subjects,
using a group-level random-effects analysis. ROIs were selected if they comprised 15 or more
contiguous voxels in which the task > fixation contrast values correlated with d' at p < .001,
uncorrected. The MarsBar toolbox (http://marsbar.sourceforge.net) was used to define these
ROIs and to extract average percent signal change values from each ROI (computed as the
mean B value across all voxels in the ROI divided by the global signal, or mean across all
3A previous study utilizing the same task in a completely different sample, in order to investigate neural correlates of fluid intelligence,
focused on neural activity associated specifically with lure trials, as a phasic departure from sustained activity (Gray et al., 2003).
Additionally, they identified ROIs on the basis of correlations with fluid intelligence rather than WM accuracy. These differences may
explain their identification of a different set of ROIs. For the present purposes, we were interested in sustained activity throughout the
WM task, rather than phasic activity in lure trials, because the n-back task requires continual maintenance, monitoring, updating, and
integration of information, all of which are likely to be relevant to Intellect.

voxels in the brain). Percent signal change in each ROI was then averaged across all six
functional runs (mean α across all ROIs = .83), and this index of brain activity was examined
for correlation with personality.
Mediation Tests
Mediation tests indicate whether the association between two variables is due to another
variable or set of variables. In an imaging context, mediation analyses can be used to test
whether activity in a given brain region can plausibly account for the covariation between two
behavioral variables, thereby implicating the region’s function in that association (Gray et al.,
2003). In the present study, a significant mediation by WM-related brain activity of the
association between Intellect and WM accuracy in the 3-back task would indicate that this
brain activity is responsible, at least in part, for the relation between Intellect and WM.
Mediation tests were also used to assess the role of cognitive ability in the associations of
interest. Mediation tests were computed using path analysis in Amos 7.0 (Arbuckle, 2006),
using maximum likelihood estimation and the bootstrap method to test the significance of
indirect effects of personality on WM performance through brain activity (bootstrap N = 2000).
(The bootstrap method replaces the inferior Sobel test; Shrout & Bolger, 2002.) Additionally,
the independence of indirect effects in multiple mediation analysis was tested using the SPSS
multiple mediation macro by Preacher and Hayes (2008).
Results
Before discussing the ROIs associated with d', we present an image of the basic contrast of
task > fixation (Figure 1A), thresholded at p < .001. This figure illustrates the pattern of neural
activity, for the sample as a whole, when participants engaged in the WM task as opposed to
when they simply focused on a fixation cross. The key point is that the canonical WM brain
network (Cabeza & Nyberg, 2000; Owen et al., 2005; Wager & Smith, 2003) was engaged by
the task; group level activations were apparent in lateral PFC, parietal cortex, and regions in
and adjacent to dorsal ACC.
The t values for the contrast of task > fixation at each voxel across the whole brain were then
used as input for a second step of the analysis that identified ROIs for which there were a
positive correlation between activation level and between-subjects variation in d' – in other
words, brain regions where individual differences in neural activity predicted task performance.
Three ROIs were identified based on correlations with d', one ROI of 62 voxels in right superior
parietal cortex (SPC, Brodmann area 7), and ROIs of 20 voxels each in left and right lateral
aPFC (Brodmann area 10).4 Because we had an a priori hypothesis that activity in pMFC
would also be related to Intellect, and because there were strong activations in this region for
the sample as a whole (Figure 1A), we examined whether voxels in this brain region also
showed correlations with performance that were reliable but did not meet our stringent
statistical thresholds (which were employed to protect against false positives in whole-brain
analyses). Indeed, when the statistical threshold was lowered to p < .005, we identified a 21-
voxel ROI in pMFC (Brodmann area 6) superior to dorsal ACC. This ROI is shown with the
other three in Figure 1B, and coordinates of the point of strongest correlation within each ROI
are given in Table 1. Scatterplots of the correlations of d' with average activation in each ROI
are shown in Figure 2. Importantly, the tests that identified these ROIs are statistically
independent of our primary test of interest, which is the association of activation in the ROIs
with facets of Openness/Intellect.
4Because our previous study of these data (Shamosh et al., 2008) used a slightly different subsample, based on the availability of different
measures, we there reported a partially different set of ROIs based on the same selection criteria. As well as nearly identical ROIs in right
superior parietal cortex and left aPFC, that study also reported four additional ROIs and did not report an ROI in right aPFC. We examined
those four additional ROIs in the present sample and found that none of them were correlated with any NEO PI-R variables.

Table 1 shows the correlations of the Big Five and the facets of Openness/Intellect with d',
intelligence, WMC, and brain activity in the four ROIs. Confirming our hypothesis, Intellect,
as represented by the Ideas facet, was significantly correlated with d', g, WMC, and with activity
in left lateral aPFC and pMFC. Additional regressions showed that these associations were not
moderated by gender or age. However, in one of the other two ROIs, an association with
Intellect was moderated by gender (β for Ideas × Gender = .36, p < .05), such that Intellect was
associated with activity in right SPC but only for females. For females, r = .33, p < .05, whereas
for males, r = −.15, p = .33. (Because this moderated association was not predicted, we do not
focus on this ROI in additional analyses.)
No other NEO PI-R variable was associated with both cognitive performance and brain activity.
The Values facet was significantly correlated with d' and g, but was not correlated with WMC
or any of the ROIs. The full Openness/Intellect domain score was correlated with activity in
left lateral aPFC but was not correlated with WM or any other ROI. None of the other NEO
PI-R traits were significantly correlated with WM, g, or activity in the four ROIs.5 As expected,
based on the literature showing their strong behavioral and neural overlap, WM (measured by
both d' and WMC) and g were strongly correlated with each other, and g was significantly
correlated with neural activity in all four ROIs. WMC was correlated with all ROIs except the
one in right lateral aPFC.
Because the Ideas facet was significantly correlated with both d' and brain activity in two ROIs,
we conducted a multiple mediation test to determine whether the association between Intellect
(represented by Ideas) and d' was due to activity in these ROIs (Figure 3). The indirect effect
of Intellect on d' was significant, β = .12 (SE = .047), p < .01, indicating significant mediation
by the two ROIs. Additionally, the indirect effect through each ROI was significant even when
controlling for the other, p < .05 for both (and this remained true when controlling for both
gender and age). Thus, there are at least two independent neural mechanisms by which Intellect
is associated with WM accuracy, one in left lateral aPFC and one in pMFC. The extent of this
mediation can be quantified by noting that there was a 77% decrease in variance in WM
accuracy explained directly by Intellect, after partialling out activity in the two ROIs.
Additional mediation models were run to test whether intelligence or WM mediate the
associations of Intellect with brain activity. Intelligence (g), WMC, and d' were all tested
individually as mediators. Finally, all three were used simultaneously to provide an even more
stringent test of whether cognitive ability accounted for the association between Intellect and
brain activity. Note that there is a serious conceptual difficulty in using d' as a mediator here,
because d' represents performance on the task in which brain activity was assessed and this
performance was caused by that brain activity. In a causal model, therefore, the arrow must
run from brain activity to d', not the other way around. Nonetheless, if one is willing to consider
d' simply as another indicator of a trait of working memory ability, it can be used as an additional
potential mediator, and doing so provides a particularly stringent test of whether the association
of Intellect with brain activity is independent of ability because of the fact that association with
d' was used to select the ROIs in the first place.
5Two previous studies of different samples (Gray & Braver, 2002; Gray, Burgess, Schaefer, Yarkoni, Larsen, & Braver, 2005), have
examined the association of WM accuracy (d') and brain activity in the same task with personality scales measuring sensitivity of the
behavioral inhibition and behavioral activation systems (BIS/BAS scales; Carver & White, 1994). The first found a significant correlation
of BAS with d' (r = .18, p < .05), when examining the first time participants performed the task. The second replicated this finding for
the first administration, but found that the association was not significant when averaging over repeated administrations of the task (r =
−.03). The BIS/BAS scales were administered the current sample; correlations of d' with BIS and BAS were .06 and −.04, respectively,
in the first block, and .08, and −.03 across all blocks (all p > .40). There were no significant correlations between BIS or BAS and the
four ROIs.

Results of these mediation tests are presented in Table 2 and reveal that, whereas controlling
for cognitive ability did reduce the association of Intellect with activity in left aPFC below
significance, it did not reduce the association of Intellect with activity in pMFC below
significance. Thus, we have evidence that the association between Intellect and activity in
pMFC during a difficult cognitive task is not simply due to the fact that Intellect is associated
with cognitive ability.
Neither g nor WMC could be demonstrated to mediate the association between Intellect and
activity in aPFC (d' did mediate this association, but, as noted above, this is a special case
because d' was used to select the ROI and was causally dependent on the brain activity in
question). In contrast, WMC (and d' but not g) did partially mediate the association between
Intellect and activity in MFC. This means there was not only a significant direct path from
Intellect to MFC, but also a significant and independent indirect path, through working memory
ability.
Discussion
As hypothesized, the Intellect aspect of the Openness/Intellect trait domain (represented by the
Ideas facet of the NEO PI-R) was associated both with performance on a difficult WM task
and with brain activity in two brain regions that supported accuracy in WM. Neural activity in
both left lateral aPFC and pMFC mediated the association of Intellect with WM, suggesting
that one reason why people who describe themselves as more intellectual show better WM
performance is that they tend to engage these brain regions more strongly during difficult
cognitive operations.
The associations of Intellect with WM performance and brain activity were in the range of r
= .2 to .3, which should be considered moderate, based on empirical guidelines for interpreting
effect sizes (Hemphill, 2003; Richard, Bond, & Stokes-Zoota, 2003). Associations of
intelligence (g) and working memory capacity (WMC) with brain activity were in the same
range. Observe that the correlations of brain activity with WM accuracy, during the WM task
itself, were only in the range of .3 to .4 (Table 1). Notably, neural activity in the two PFC
regions accounted for over three quarters of the variance in WM accuracy associated with
Intellect.
Mediation tests were used to determine whether the association of Intellect with brain activity
was independent of cognitive ability, measured both by intelligence tests and by tests of
working memory capacity. The association of Intellect with pMFC activity was indeed
independent of cognitive ability, whereas the association of Intellect with left aPFC activity
was not. By the standards of formal mediation tests, intelligence did not mediate the association
between Intellect and either brain region (and WMC only partially mediated the association of
Intellect with pMFC). Nonetheless, with g or WMC in the model, neither Intellect nor g or
WMC was significantly associated with neural activity in left aPFC. The fact that the effects
of both Intellect and cognitive ability on left aPFC were suppressed relative to their zero-order
magnitude suggests that the shared variance of Intellect and cognitive ability is associated with
activity in this region. This interpretation is sensible when one considers that Intellect subsumes
perceived intelligence as well as intellectual engagement (DeYoung et al., 2007), and to some
limited extent perceived intelligence accurately reflects intelligence as measured by ability
tests (Paulhus et al., 1998). Thus, one plausible source of both Intellect and cognitive ability
is the function of left lateral aPFC, which has been characterized as integrating information
from multiple cognitive operations, across a variety of different cognitive tasks (Gilbert et al.,
2006; Green et al., 2006; Ramnani & Owen, 2004).

In contrast to left aPFC, neural activity in pMFC remained significantly associated with
Intellect even after partialling out variance associated with g and working memory. This
suggests that intellectual engagement plays a major role in the association of Intellect with
activity in pMFC. The ROI identified in pMFC lies in a region that is involved in monitoring
goal-directed performance (Ridderinkhof et al., 2004) and is sensitive to the likelihood of error
(Brown & Braver, 2005). This performance monitoring function aids in the resolution of
uncertainty during decisions and in resolving response conflict (Brown & Braver, 2005;
Ridderinkhof et al., 2004). Both uncertainty and response conflict should be frequent in the 3-
back WM task, as participants attempt to decide whether the present stimulus matches the one
exactly three previously and to inhibit the urge to respond to lures. The tendency to monitor
cognitive performance closely and accurately is another plausible source of Intellect as a trait
and one that may reflect not just intelligence, but also one’s motivation and interest in
intellectual activities (i.e., one’s intellectual engagement).
In relation to this conclusion, it is important to note that the function served by pMFC is unlikely
to be specific to difficult working memory operations or even complex cognitive tasks. This
brain region is similarly active during much simpler cognitive tasks that involve response
conflict and/or uncertainty, such as Stroop tasks (Ridderinkhof et al., 2004). (Of course, the
mere fact that the same brain region is active during different tasks does not guarantee that it
is performing similar functions during different tasks, but many cognitive tasks clearly require
the kind of performance monitoring that this region subserves.) In order to distinguish between
brain activity related exclusively to working memory and brain activity that would support
performance on simpler cognitive tasks as well, we would have needed to include a simple
cognitive task as a control condition. However, our concern was to detect neural correlates of
the personality trait Intellect, not to pin down the specific neural correlates of working memory,
and we were, therefore, interested broadly in neural processes that support cognition. Given
that the variance responsible for most of the association between Intellect and pMFC activity
appeared to be related to intellectual engagement, rather than cognitive ability, it seems quite
likely that the Intellect-pMFC association reflects a tendency toward monitoring cognitive
performance that would be heightened across a wide variety of cognitive tasks for those high
in Intellect. This brain region may be modulated by motivation to perform any cognitive task
vigilantly and accurately.
One additional region that was identified as supportive of WM accuracy was associated with
Intellect only in females. For females, but not males, Intellect was associated with activity in
right SPC. We did not hypothesize any such interaction with gender. However, this brain region
was strongly associated with working memory performance and is part of the canonical
working memory network (Cabeza & Nyberg, 2000; Owen et al., 2005; Wager & Smith,
2003), so the finding is reasonably consistent with our hypotheses. Future research might
investigate gender differences in the relation of personality to cognitive and brain function,
building on research showing gender differences in brain function (e.g., Canli, Desmond, Zhao,
& Gabrieli, 2002) and in various forms of cognitive ability, including working memory (e.g.,
Kaufman, 2007).
The one ROI identified that was entirely unrelated to Intellect was in right lateral aPFC, and
this ROI may not be reliable because it did not appear when using the same technique to identify
ROIs in a largely overlapping sample (Shamosh et al., 2008). The left (but not the right) aPFC
ROI is consistent with previous studies of abstract relational integration (Bunge, Helskoga, &
Wendelken, in press; Green et al., 2006) and with a review suggesting that left rather than right
aPFC is predominantly involved in intelligence (Jung & Haier, 2007). Although research on
WM suggests that lateralization in PFC is influenced somewhat by whether visual or verbal
stimuli are used (Wager & Smith, 2003), our WM task employed both visual and verbal stimuli,
in order to identify brain activity involved in WM regardless of modality. Our results are

consistent with previous research, but additional research will be necessary to determine the
significance of the left lateralization of activity in aPFC in relation to complex cognition.
Although Ideas was the only facet of Openness/Intellect associated with brain activity and
performance on all cognitive tasks, the Values facet was associated with g and one WM
performance variable; these associations are consistent with previous research, reporting
similar effect sizes (DeYoung et al., 2005). The finding that the Values facet was associated
with cognitive variables but not brain activity suggests that, compared to Ideas, Values has a
less direct link to the brain activity that supports WM. Nonetheless, the behavioral associations
are worth noting, given related research. Because Values reflects a liberal worldview and its
alternative label is “Liberalism” (Goldberg, 1999), our finding may be relevant to the
characterization of cognitive processes associated with political attitudes. They are consistent
with findings that liberalism, as a political orientation, is associated with higher IQ, relative to
conservatism (Block & Block, 2006; Deary, Batty, & Gale, 2008). Further, one recent study
reported that liberalism was associated with better performance and increased brain activity in
MFC, during a cognitive control task (Amodio, Jost, Master, & Yee, 2007). The Values facet
is not a particularly good marker of Intellect, but it is nonetheless moderately related to Intellect
as well as to Openness (DeYoung et al., 2007), and it may be fruitful to continue investigating
this politically-relevant dimension of personality in future research on the cognitive and neural
correlates of personality.
A question for future research is what brain functions might contribute specifically to Openness
as opposed to Intellect. This might be revealed through fMRI by using a task more directly
relevant to the artistic and contemplative qualities that characterize Openness. Additionally,
future research on Openness and Intellect could benefit from using personality measures
designed specifically to distinguish these two aspects (e.g., the Big Five Aspect Scales;
DeYoung et al., 2007).
Conclusion
Intellect and Openness are separable aspects of one larger domain of personality, which raises
the question of their discriminant validity. To our knowledge, the present study provides the
first assessment of the neural correlates of either aspect. Intellect subsumes traits reflecting
intellectual engagement and perceived intelligence. Consistent with this characterization, we
found that the Ideas facet of the NEO PI-R (a good marker of Intellect) was associated with
measures of intelligence and WM, whereas the facets that strongly mark Openness were not.
This study went beyond previous research (DeYoung et al., 2005) by investigating the
biological sources of the link between Intellect and WM accuracy, and found two regions of
PFC that mediated this association, one in the left anterior PFC, or frontal pole, and the other
in posterior medial frontal cortex. The region in pMFC, which has been implicated in
performance monitoring, was significantly associated with Intellect even after controlling for
intelligence and working memory ability. Neither region was associated with any other
Openness/Intellect facets. These findings demonstrate the possibility of distinguishing two
aspects of a single Big Five domain in terms of neural correlates and suggest that the functions
of pMFC may be an important substrate of Intellect that is distinct from cognitive ability and
is perhaps driven by the motivation to engage with intellectual activities
Acknowledgments
This research was supported by grants from the National Institute of Mental Health to J.R.G. (MH R01 66088) and
C.G.D. (F32 MH077382). The content is solely the responsibility of the authors and does not necessarily represent
the official views of the National Institute of Mental Health or the National Institutes of Health.

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Figure 1.
Coronal (from the front), axial (from above), and mid-sagital views of (A) neural activity
associated with performing a working memory task, relative to focusing on a fixation point,
for the sample as a whole (N = 104), and (B) four regions where neural activity was associated
with working memory accuracy, in between-subjects analysis.

Figure 2.
Scatterplots showing the correlation of brain activity (percent signal change for task versus
fixation averaged across all voxels in each ROI) during the 3-back working memory task with
accuracy (d') in the task. (See Table 1 for significance.)

Figure 3.
Path analysis demonstrating that working memory-related neural activity in two brain regions
(left lateral anterior prefrontal cortex and posterior medial frontal cortex) mediates the
association of Intellect (the Ideas facet of the NEO PI-R) with working memory accuracy
(d'). Each brain region is an independent mediator at p < .05. The zero-order correlation between
Ideas and d' appears in parentheses. Red paths involve neural variables, black paths do not.

NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Table 1
Correlations of working memory (d' and WMC), intelligence (g), the Big Five, and facets of Openness/Intellect with brain activity in four ROIs (with MNI
and Talairach coordinates).
d' g WMC R SPC L aPFC R aPFC pMFC

DeYoung et al.
MNI 12, −69, 66 −24, 66, 6 24, 66, 18 −3, 6, 66

Talairach 10, −70, 54 −22, 61, 8 20, 61, 19 −3, 3, 58
d' – .58** .51** .43** a .37** a .34** a .31** a

g .58** – .60** .27* .21* .20* .21*
WMC .51** .60** – .30** .19* .09 .27*
Fantasy .03 −.09 −.11 −.06 .16 .10 .03
Aesthetics .03 .06 −.04 .01 .08 .01 −.07
Feelings .00 −.06 −.09 −.06 .08 −.02 .03
Actions .05 −.01 −.04 −.04 .12 −.01 .11
Ideas .23* .27** .19* .14 .21* .06 .27**
Values .23* .33** .12 .07 .15 .10 .08
E −.08 −.13 −.03 −.11 .11 −.05 −.01

A .01 .11 .09 .18 .05 .03 .15
C −.15 −.10 .06 −.01 .02 .10 −.02
N −.05 .09 −.05 .00 −.09 .01 −.09
O/I .14 .09 .01 .02 .25* .10 .10
*
p < .05
**

p < .01
a
These correlations are not independent of the initial statistical test that identified the ROIs. Their designation as significant therefore reflects that initial test.
Note. N = 104. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O/I = Openness/Intellect, WMC = working memory capacity, SPC = superior parietal cortex, aPFC = anterior
prefrontal cortex, pMFC = posterior medial frontal cortex, L = left, R = right.
Page 18
Table 2
Tests of mediation of the associations between Intellect (I) and brain activity by cognitive ability (intelligence
(g) and working memory).
Mediation model Paths Indirect effect

(ROI and mediator) I to ROI Ability to ROI
aPFC
g .17 .16 .045
WMC .18 .16 .030
d' .13 .34** .077**
All 3 .14 – .072
pMFC
g .23* .14 .039
WMC .22* .23* .044*

d' .20* .26* .060**
All 3 .20* – .062
*
p < .05
**
p < .01
Note. N = 104. For path weights from Intellect to abilities, see Table 1. WMC = working memory capacity; All 3 = g, WMC, and d' used as simultaneous
predictors.

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J Pers Soc Psychol. 2009 November ; 97(5): 883–892. doi:10.1037/a0016615.

Intellect as distinct from Openness: Differences revealed by fMRI

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

Intelligence—Participants completed four standard measures of intelligence: Raven’s

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

fMRI Data Acquisition

fMRI Data Analysis

Data were analyzed using Statistical Parametric Mapping 2 (SPM2) software

As potential neural correlates of Intellect, we identified candidate regions of interest (ROIs) in

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

of Educational Psychology 1994;86:150–153.

2007;17:993–1006. [PubMed: 16769743]

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

Furnham A, Dissou G, Sloan P, Chamorro-Premuzic T. Personality and intelligence in business people:

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

d' g WMC R SPC L aPFC R aPFC pMFC

MNI 12, −69, 66 −24, 66, 6 24, 66, 18 −3, 6, 66

d' – .58** .51** .43** a .37** a .34** a .31** a

E −.08 −.13 −.03 −.11 .11 −.05 −.01

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

(g) and working memory).

Mediation model Paths Indirect effect

WMC .22* .23* .044*

J Pers Soc Psychol. Author manuscript; available in PMC 2010 November 1.

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d' – .58 .51 .43 a .37 a .34 a .31 a