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Personality Neuroscience and the Five-Factor
Model
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Personality Neuroscience and the Five-Factor Model
Timothy A. Allen and Colin G. DeYoung
The Oxford Handbook of the Five Factor Model of Personality
Edited by Thomas A. Widiger
Abstract and Keywords
Personality psychology seeks both to understand how individuals differ from one another
in behavior, motivation, emotion, and cognition and to explain the causes of those
differences. The goal of personality neuroscience is to identify the underlying sources of
personality traits in neurobiological systems. This chapter reviews neuroscience research
on the traits of the Five-Factor Model (the Big Five: Extraversion, Neuroticism,
Openness/Intellect, Conscientiousness, and Agreeableness). The review emphasizes the
importance of theoretically informed neuroscience by framing results in light of a theory
of the psychological functions underlying each of the Big Five. The chapter additionally
reviews the various neuroscientific methods available for personality research and
highlights pitfalls and best practices in personality neuroscience.
Keywords: personality, Five-Factor Model, neuroscience, neurobiology, Cybernetic Big Five Theory, individual
differences, traits
Introduction
Personality psychologists pursue at least three fundamental questions regarding human
nature: First, how do individuals meaningfully differ from one another? Second, what are
the causes of these individual differences? And third, what are their consequences? In
relation to the first question, a major problem historically was identification of the most
important dimensions of variation in personality. The emergence of the Five-Factor Model
(FFM) or “Big Five” has gone a long way toward solving this problem (Costa & McCrae,
1992; Goldberg, 1990; John, Naumann, & Soto, 2008; Markon, Krueger, & Watson, 2005).
Subject: Psychology, Personality and Social Psychology,
Neuropsychology
Online Publication Date: Jan
2016
DOI: 10.1093/oxfordhb/9780199352487.013.26
Oxford Handbooks Online
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The discovery of five consistent broad dimensions of covariation among specific traits, in
both lexical and questionnaire assessments of personality, has allowed the field to begin
moving beyond questions of taxonomy toward the systematic accumulation of evidence
regarding the causes and consequences of trait differences. At this point, the
consequences of variation in the Big Five have been studied extensively; the five factors—
Extraversion, Neuroticism, Openness/Intellect, Conscientiousness, and Agreeableness—
matter for many life outcomes, in academic and industrial success, in relationships, in
physical and mental health, etc. (Ozer & Benet-Martinez, 2006). Their causes are not as
thoroughly researched, however, and this chapter reviews the progress that has been
made in identifying the neurobiological basis of the Big Five.
Personality neuroscience rests on the premise that all reasonably persistent individual
differences in thought, cognition, motivation, and emotion (that is, personality) must
entail patterns of consistency in the functioning of the brain (DeYoung, 2010b; DeYoung
& Gray, 2009). From this perspective, the brain is the proximal source of all personality
characteristics, and it is only by affecting the brain that more distal influences in the
genome and environment are able to influence personality. As a result, two major goals of
personality neuroscience are to identify the neural substrates of personality and to better
understand how genetic and environmental forces, over the course of development,
create the relatively stable patterns of brain function that produce personality. So far,
more progress has been made on the first of these goals than on the second.
The rise of neuroscience technologies for brain imaging and molecular genetics has led to
a rapid proliferation of empirical reports over the past decade. Research in personality
neuroscience has employed many different personality measures, behavioral tasks, and
neurobiological techniques to shed light on the workings of the human system, and it can
be difficult to integrate all of these into a coherent understanding. Here, we take
advantage of the fact that the FFM can categorize most personality trait measures in
order to synthesize findings from personality neuroscience over the past several decades.
We begin by describing the various tools available for personality neuroscience. Previous
reviews have highlighted a number of methodological limitations in personality
neuroscience research to date (DeYoung, 2010b; Yarkoni, 2015). We echo many of these
cautions and make a concerted effort, throughout the chapter, to highlight
methodologically rigorous research and to provide caveats regarding findings that are
suggestive but flawed.
After reviewing methods, we discuss theories of the psychological functions underlying
each of the Big Five. Beyond brain scanners and gene-identification chips, theory is one
of the most important tools in personality neuroscience. Atheoretical research is
sometimes published in this field, examining associations of personality traits with brain
structure or function or genetic variation in a purely exploratory manner, but such an
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approach often makes it difficult to achieve sufficient statistical power, given the need to
correct for multiple statistical tests when examining associations throughout large
portions of the brain. It also increases the temptation to develop post hoc explanations of
findings, even when they may be merely false positives. Theoretical approaches to the
FFM can provide hypotheses to guide research in personality neuroscience.
Methodological Issues in Personality Neuroscience
Personality neuroscience, at the intersection of two fields, must contend with the
limitations of measurement in both. Most measurement of personality relies on self-
reports using questionnaires. Better questionnaire assessment can be achieved by
collecting informant reports from knowledgeable peers, in addition to self-reports
(Connelly & Ones, 2010; Vazire, 2010). Still, questionnaires do not exhaust the possible
methods of personality assessment. Various behavioral and cognitive tasks may also be
used to assess stable personality traits. Because the FFM was discovered and established
in questionnaire data, we focus primarily on such data in this chapter. Nonetheless, we
believe nonquestionnaire methodologies are likely to grow in importance in personality
neuroscience (and personality psychology more generally), as researchers attempt to
capture consistencies in thought, behavior, emotion, and motivation in more diverse
ways.
Whereas personality psychology is largely dominated by a single type of measure,
neuroscience is a field burgeoning with technologies that allow researchers to explore
previously inaccessible details of the structure and function of the human brain.
Neurobiological methods in personality neuroscience mostly fall into five general
categories:
1. Neuroimaging techniques. The most prominent and frequently used method in
personality neuroscience is magnetic resonance imaging (MRI), which creates
images of the brain based on the magnetic properties of different tissue types. MRI
is popular not only because it is noninvasive but also because, in addition to
measuring brain structure, it can also be used to measure brain function, by taking
advantage of the fact that blood flow and oxygen use increase with neural activity.
The blood-oxygen-level-dependent (BOLD) signal from functional MRI (fMRI),
therefore, can be used to indicate when different regions of the brain are more or
less active.
Researchers most often use fMRI while participants are engaged in some computerized
task in the scanner. One limitation of task-based fMRI is that relative rather than
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absolute levels of neural activation must be studied; activation during the task of interest
(or during a particular type of event within a task) must be contrasted with activation
during other parts of the scan (which could be a control task, a resting period, or other
events within the same task). Increasingly, however, fMRI researchers are also
investigating patterns of functional connectivity, rather than relative activation, which do
not require a contrast between tasks. Functional connectivity refers to the patterns of
temporal synchrony between different parts of the brain. If brain regions show a similar
temporal pattern of activation and deactivation during some portion of a scan, they are
said to be functionally connected. Analysis of functional connectivity during periods of
rest in the scanner has demonstrated that brain networks that are spontaneously active
closely resemble networks that are activated by specific tasks (Laird et al., 2011; Smith et
al., 2009). This discovery has led to an effort to map the major networks of the brain
using functional connectivity, and the resulting maps provide useful clues about the
brain’s large-scale functional organization (Choi, Yeo, & Buckner, 2012; Yeo et al., 2011).
One of these networks in particular is worth introducing briefly here because of its rather
opaque label, the “default network” (also called the “default mode network”), and
because of its importance for several personality traits. The default network received its
label because it was discovered more or less by accident as a function of the fact that
neural activation must be studied through contrasts (Buckner, Andrews-Hanna, &
Schacter, 2008). In contrasts of task versus rest, it was noted that a particular set of
brain regions was frequently more active during rest than during task. Hence, this
pattern of activation was considered the brain’s default mode, what the brain is likely to
do when participants are asked simply to rest and not to attend to external demands.
Subsequent research has determined that the default network is responsible for
simulating experience in a variety of contexts, including times when we remember events
in the past, imagine the future (or any other hypothetical state), take on another person’s
perspective, or evaluate ourselves (Andrews-Hanna, Smallwood, & Spreng, 2014). These
are the kinds of things that people tend to do when they are not engaged by their
immediate surroundings and their minds are free to wander, but these processes can also
be engaged by specific tasks (e.g., memory or perspective-taking tasks). Here is a case in
which the limitation that task-based analysis of fMRI requires a contrast between two
conditions led to an important discovery.
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Another neuroimaging technique, positron emission tomography (PET), has also been
used in personality neuroscience. It has the great advantage of allowing measurement of
receptors for particular neurotransmitters but the disadvantage of being invasive, as it
requires injection of radioactive tracers into the bloodstream. Both MRI and PET are
valuable for their spatial resolution.
2. Electrophysiological techniques. Electroencephelography (EEG) measures neural
activity by recording electrical activity along the scalp. It has a much higher
temporal resolution than fMRI, capable of tracking differences in brain activity on
the order of milliseconds (as opposed to seconds for fMRI), but has greatly reduced
spatial resolution. Other electrophysiological techniques, such as
electrocardiography and assessment of electrodermal activity, use peripheral
nervous system activity to draw inferences about brain processes related to emotion
and motivation.
3. Molecular genetics. Variation in the genes that build the brain can be measured
through analysis of DNA. Commonly used molecular genetic techniques in
personality neuroscience include candidate gene studies, in which particular genes
are investigated because of their hypothesized relevance to personality, and genome-
wide association studies (GWAS), in which the entire genome is scanned for variation
associated with some trait or traits (see also the chapter by Jarnecke and South).
4. Psychopharmacological manipulation. Specific chemicals can be administered as
drugs in an attempt to implicate a given neurotransmitter, receptor, or other brain
molecule in the expression of a trait. Effects of the manipulation are examined either
on behavior or on some neurobiological assay. If the effects of the manipulation are
moderated by the trait, or vice versa, this implicates the targeted molecule in the
trait.
5. Assays of endogenous psychoactive substances. Measurements of substances such
as hormones or neurotransmitter metabolites, in blood, saliva, urine, or spinal fluid,
can be used to implicate specific neurobiological systems in personality.
The expense of neuroimaging contributes to the largest methodological problem in the
field: low statistical power. Many studies are published with samples that are far too
small for good research on individual differences. A study of 461 structural MRI studies
published between 2006 and 2009 found the median power to be only 8% (Button et al.,
2013; Ioannidis, 2011). Another study reported, in a random sample of 241 neuroimaging
papers published after 2007, the median sample size was just 15 for one-group studies
and 14.75 for each group in two-group studies (Carp, 2012). This trend undoubtedly
accounts for some of the inconsistencies that exist in findings in personality neuroscience
(DeYoung, 2010b; Yarkoni, 2009, 2015). Fifteen is a small sample even for studying many
of the within-person effects that are most commonly researched in neuroimaging, in
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which brain activity in one condition is compared to that in another. Fifteen (or even 30)
is ridiculously small for the study of individual or group differences, and yet many MRI
papers have reported correlations of personality traits with neural variables in samples
smaller than 20. Correlations in small samples are highly susceptible to outliers and to
sampling variability more generally. Further, small sample sizes increase the likelihood
that a given sample will fail to represent variation across the full distribution of the trait
of interest, especially in the tails of the distribution (Mar, Spreng, & DeYoung, 2013). The
likelihood of accurately assessing a correlation in a small sample is very low (Schonbrodt
& Perugini, 2013). Whenever possible, therefore, we focus our review in this chapter on
studies with larger sample sizes.
One method for increasing power in smaller samples is the use of extreme groups, in
which participants very high and very low on the trait of interest are recruited based on a
previous assessment of that trait. This is likely to yield a larger effect size (the difference
between high and low groups on the biological variable of interest) than the correlation
across the full range of the trait. This tactic has pitfalls, however. First, the degree to
which the expected effect size increases is unpredictable, making power calculations
difficult. Second, it prevents any meaningful analysis of variables other than the trait
used for selection and may alter the effects of covariates in unpredictable ways. We
recommend an extreme-groups design only in cases in which a single, clear hypothesis is
being tested, funds are limited, and any covariates are handled at the time of recruitment
rather than in analysis. Important covariates, such as gender and age, should be balanced
when recruiting the extreme groups. Crucially, something that should never be done is to
analyze a subset of a larger existing sample by identifying extreme groups within it and
excluding the rest of the participants from the analysis even though they have all relevant
variables assessed. Nor should a continuous variable ever be dichotomized (or
trichotomized) and analyzed as if it were a categorical variable. These strategies entail an
unacceptable loss of power compared to analyzing continuous variables in the whole
sample (MacCallum, Zhang, Preacher, & Rucker, 2002).
Chronically low power in personality neuroscience has a number of important
implications. Most obvious of these is increased Type II error rates—that is, failures to
detect real effects as significant. Two-thirds of the significant effects reported in
psychology are smaller than r = .3 (Hemphill, 2003), and there is no reason to assume
that effects in personality neuroscience should be larger. An observed correlation of .3
will not be significant at p < .05 with a sample size less than 40, and, with a sample of 40,
the power to detect a true correlation of .3 is only about 50%, meaning that a Type II
error would result about half the time, as the observed correlation fluctuates due to
sampling variability. Given that the middle third of effect sizes in psychology is between r
= .2 and .3 (Hemphill, 2003) and that the average effect size in personality research has
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been estimated at .21 (Richard, Bond, & Stokes-Zoota, 2003), researchers should attempt
to ensure that they have the power to detect effects of at least r = .2. To have 80% power
to detect a correlation of .2 at p < .05 requires a sample of 194.
One promising strategy for acquiring sufficiently large samples in MRI is to aggregate
across many smaller studies of different tasks by including standard structural scans or
brief resting-state scans in each study. If a database of subjects’ contact information is
maintained, this method can be used to carry out new MRI studies of individual
differences without collecting additional MRI data (Mar et al., 2013). Even with a large
sample, however, the need to carry out large numbers of statistical tests to examine the
whole brain can lead to problems with power. MRI studies typically divide the brain into a
three-dimensional grid of small “voxels” and often involve testing whether an effect is
present in thousands of individual voxels. In whole-brain analyses, researchers sometimes
choose a stringent threshold for significance at the voxel level (e.g., p < .001) and then
correct to p < .05 for the analysis as a whole based on the size of clusters (adjacent
significant voxels). This can lead to Type II errors because the effect of interest may not
be large enough to achieve significance at p < .001 in any voxel, even in a sample large
enough to detect the same effect at a higher p-value. We recommend setting a voxel-level
threshold that will be capable of detecting effects equivalent to r = .2 or smaller, given
one’s sample size (then subsequently correcting to p < .05 for the whole analysis).
A less well-known but perhaps even more troubling result of low power is that it
increases the proportion of significant results that are Type I errors, false positives
(Green et al., 2008; Yarkoni, 2009, 2015). As the sample size decreases, sampling
variability increases and precision decreases. Even if the true effect is zero, in small
samples it is more likely to be sufficiently misestimated as to appear significant. Testing
effects in many small samples and publishing only those that are large enough to achieve
significance is a recipe for the publication of many false positives, which then distort the
literature and are likely to mislead other researchers (Button et al., 2013). When the true
effect is not zero, low power still has the pernicious effect of artificially inflating
significant effect sizes, a problem that is exacerbated in MRI and other methods that
involve making many statistical tests in the same study. Estimates of the effect will vary
across voxels, and in small samples it is likely that only voxels that greatly overestimate
the effect will be significant (Yarkoni, 2009). This leads not only to overestimated effect
sizes, but also to the false impression that effects are localized to very narrow regions of
the brain, when the true effects are likely to be much weaker but to be present in much
broader swathes of brain tissue (Yarkoni, 2015). The situation is made even worse when
researchers identify voxels of interest using a significance test (a threshold) with some
neural variable and then aggregate across those voxels before inappropriately carrying
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out another, nonindependent significance test involving that variable (Vul, Harris,
Winkielman, & Pashler, 2009).
Beyond small samples, another potential cause of inconsistencies within the
neuroimaging literature is the wide variability in the methods that researchers employ.
Carp’s (2012) review of recent neuroimaging studies indicated that nearly all (223 of 241)
of the reviewed studies reported using different analytical techniques. Even using
different versions of the same software package for MRI analysis or using the same
version on different computers can lead to different results (Gronenschild et al., 2012).
The wide range of methods available may contribute to the presence of excess
significance bias within the neuroimaging literature—and the psychological literature
more generally (Ioannidis, 2011; Jennings & Van Horn, 2012). One reason for the
disproportionate number of significant findings may be selective reporting bias, in which
researchers try multiple analytical methods and choose one that yields the most
statistically significant results, or those best matching their hypotheses, even when other
analytical methods may not support such a conclusion (Ioannidis, 2011). These practices
increase Type I error. Of course, the great variety of methods available can lead to Type
II errors as well, if methods are chosen that obscure effects of interest (Henley et al.,
2010).
A related issue is simply that some methods are better than others, but their relative
quality is not always clear or widely known. In the area of structural MRI, for example,
the most common method for assessing the relative volume of different brain structures
is voxel-based morphometry (VBM). In VBM, structural brain images are spatially
normalized (deformed) to match a template brain, partitioned into gray and white matter,
and smoothed so that each voxel reflects the average percentage of gray matter within
itself and the voxels surrounding it (Ashburner & Friston, 2000). VBM has been criticized
on several grounds. First, it has been noted that if registration to the template were
perfect, there would be no individual differences for VBM to detect; thus, the method
relies problematically on imperfections in processing the data (Bookstein, 2001). Further,
because VBM relies on the density of gray matter in each voxel, it may accurately detect
differences in structure only near the gray–white matter boundary and, even there, only
when the differences are not expressed on an axis parallel to the boundary (Bookstein,
2001; Davatzikos, 2004). Finally, VBM is poor at detecting nonlinear differences in brain
morphology, which are likely to be common (Davatzikos, 2004). A better approach to
structural MRI may be deformation- or tensor-based morphometry (TBM), using the
nonlinear portion of the transformation that aligns each brain image to the template
brain as the index of relative local volume (e.g., DeYoung et al., 2010). Newer versions of
the VBM toolbox in the software program SPM integrate this TBM method as an option
under the label “modulation” (see http://dbm.neuro.unijena.de/vbm/segmentation/
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modulation/), and we recommend selecting modulation for nonlinear effects in any VBM
study of personality. The fact that many structural MRI studies of the FFM have used
VBM without modulation may account for some of their inconsistency.
Neuroimaging is not the only area of personality neuroscience in which low power and
inconsistent findings are problems. In molecular genetics, well-replicated findings are
rare. The first candidate gene studies of personality were published 20 years ago
(Benjamin et al., 1996; Ebstein et al., 1996), linking a particular polymorphism of the
dopamine D4 receptor gene (DRD4) to both Extraversion and Novelty Seeking (a complex
trait reflecting primarily low Conscientiousness but also high Extraversion and potentially
also low Agreeableness and high Openness/Intellect; DeYoung & Gray, 2009). A later
meta-analysis of 36 studies found both effects to be nonsignificant, although a different
polymorphism in the same gene appeared to be associated with Novelty Seeking but not
Extraversion (Munafo, Yalcin, Willis-Owen, & Flint, 2008). Such failures to replicate are
typical of candidate gene studies, which is perhaps not surprising given that well-
powered GWAS studies in much larger samples have also largely failed to identify genetic
variants associated with the Big Five (de Moor et al., 2012; Terracciano et al., 2008).
These failures do not indicate a lack of genetic influences on personality (the Big Five are
substantially heritable; Johnson & Krueger, 2004; Loehlin, McCrae, Costa, & John, 1998;
Riemann, Angleitner, & Strelau, 1997); rather, they are indicative of the fact that
complex traits are massively polygenic—that is, influenced by many thousands of
variations in the genome—with most having only a miniscule effect on the trait in
question (Munafo & Flint, 2011). Superficially, candidate gene studies of personality may
seem to have reasonably large sample sizes, often in the hundreds, but these are
probably often nowhere near large enough given the tiny effects of interest. It seems
likely that the situation with the FFM will resemble that with schizophrenia: once sample
sizes for GWAS exceeded 30,000, many genes began to be robustly implicated (Need &
Goldstein, 2014). Because GWAS studies of the FFM are still not that large, the current
review will largely ignore molecular genetic findings and will usually provide caveats
when they are cited.
Theories of Psychological Function in the FFM
The FFM has long been criticized for being descriptive rather than explanatory (e.g.,
Block, 1995). We would argue that the establishment of an accurate descriptive model is
not a flaw but rather a prerequisite for good science. Nonetheless, having identified the
major dimensions of personality, the field must next strive to explain them. Personality
neuroscience is aimed at neurobiological explanations, but in order to develop
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neurobiological hypotheses it is very helpful to begin with theories of the psychological
functions underlying each of the Big Five. Based on what is known about how different
psychological functions are carried out by the brain, it is possible to derive corresponding
neurobiological hypotheses.
Decades of behavioral and biological research on personality have led to the development
of a number of theories specifying the psychological functions associated with each of the
Big Five (Denissen & Penke, 2008; DeYoung, 2015a; MacDonald, 1995; Nettle, 2006,
2007; Van Egeren, 2009). These theories come to very similar conclusions about each of
the five dimensions, and this level of agreement suggests that the available data point
fairly clearly toward some broad conclusions. For the purposes of this chapter, we will
adopt the perspective of the most thoroughly elaborated of these theories, Cybernetic Big
Five Theory (CB5T; DeYoung, 2015a).
Cybernetics is the study of goal-directed, self-regulating systems (Carver & Scheier,
1998; Wiener, 1965). It is a useful and perhaps even necessary approach for
understanding living systems (Gray, 2004). CB5T defines personality traits as
“probabilistic descriptions of relatively stable patterns of emotion, motivation, cognition,
and behavior, in response to classes of stimuli that have been present in human cultures
over evolutionary time” and attributes the existence of traits to variations in the
parameters of evolved cybernetic mechanisms (DeYoung, 2015a). (Importantly, CB5T
recognizes that these parameters are influenced by both genetic and environmental
forces; the substantial heritability of the Big Five does not render them impervious to life
experience.) The cybernetic mechanisms that underlie traits allow people to identify
goals, to be motivated to attain goals, to select and carry out appropriate actions to move
toward their goals, to interpret feedback about the current state of the world (including
the organism itself), and to detect whether the current state matches their goal state.
CB5T adopts a MIMIC (multiple indicators, multiple causes) approach (cf. Kievit et al.,
2012), which posits that a shared psychological function causes covariance among the
specific traits (the multiple indicators) that are encompassed by each of the Big Five, but
that this psychological function is instantiated by complex brain systems with many
parameters (the multiple causes) that vary to create individual differences in that
function. In other words, CB5T does not attempt to identify just a single biological
parameter responsible for a given trait because it recognizes that various biological
mechanisms with many parameters contribute to any given psychological function.
One advantage of CB5T over the other, similar theories cited above is that it specifies
mechanisms for traits at three levels of the personality hierarchy, not just the Big Five
(Figure 1 and Table 1). The fact that personality is structured hierarchically means that
the Big Five are not the only traits of interest in personality psychology or neuroscience.
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They are merely the most prominent major dimensions of covariation among more
specific traits. The variance of those more specific traits, below the Big Five in the
hierarchy, is not fully explained by the Big Five, in either phenotypic or genotypic
analysis (Jang et al., 1998, 2002). This means that, in addition to investigating
mechanisms for the Big Five, personality neuroscience should also investigate
mechanisms that differentiate specific traits within each of the Big Five domains.
Click to view larger
Figure 1. A personality trait hierarchy based on the Five-Factor Model. First (top) level:
metatraits. Second level: Big Five domains. Third level: aspects. Fourth level: facets.
The minus sign indicates that Neuroticism is negatively related to Stability.
Additionally, the Big Five themselves are not entirely independent; they show relatively
weak but consistent correlations with each other. Based on these correlations, a
considerable body of research demonstrates the existence of two higher-order factors
above the Big Five in the trait hierarchy, called metatraits (DeYoung, 2006; Digman,
1997; McCrae et al., 2008). When modeled using ratings from multiple informants, the
correlation between the metatraits is near zero, suggesting that there is no nonartifactual
“general factor of personality” above them (Chang, Connelly, & Geeza, 2012; DeYoung,
2006; Revelle & Wilt, 2013). CB5T includes hypotheses regarding the mechanisms
associated with the metatraits, as well as a level of traits below the Big Five, in addition
to the Big Five themselves.
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Table 1. Psychological Functions Hypothesized to Be Associated with Each of the
Traits Labeled in Figure 1
Trait Cybernetic Function
Metatraits
Stability Protection of goals, interpretations, and strategies from
disruption by impulses.
Plasticity Exploration: creation of new goals, interpretations, and
strategies.
Big Five
Extraversion Behavioral exploration and engagement with specific rewards
(i.e., goals to approach).
Neuroticism Defensive responses to uncertainty, threat, and punishment.
Openness/Intellect Cognitive exploration and engagement with information.
Conscientiousness Protection of nonimmediate or abstract goals and strategies
from disruption.
Agreeableness Altruism and cooperation; coordination of goals,
interpretations, and strategies with those of others.
Aspects
Assertiveness Incentive reward sensitivity: drive toward goals.
Enthusiasm Consummatory reward sensitivity: enjoyment of actual or
imagined goal attainment.
Volatility Active defense to avoid or eliminate threats.
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Withdrawal
(anxiety,
depression)
Passive avoidance: inhibition of goals, interpretations, and
strategies, in response to uncertainty or error.
Intellect Detection of logical or causal patterns in abstract and
semantic information.
Openness to
Experience
Detection of spatial and temporal correlational patterns in
sensory and perceptual information.
Industriousness Prioritization of nonimmediate goals.
Orderliness Avoidance of entropy by following rules set by self or others.
Compassion Emotional attachment to and concern for others.
Politeness Suppression and avoidance of aggressive or norm-violating
impulses and strategies.
Adapted with permission from DeYoung (2015a).p
The metatraits, Stability and Plasticity, are not given a separate section in this chapter
because most of the evidence for their biological basis comes from studies of the Big Five
considered individually, rather than in terms of their shared variance, and these studies
will be reviewed in the sections on each of the Big Five. This evidence suggests that
serotonin influences Stability and dopamine influences Plasticity (DeYoung, 2006, 2010b,
2013). Serotonin stabilizes information processing in many brain systems, helping to
maintain ongoing cybernetic function by facilitating both resistance to disruption by
impulses and focus on ongoing goals (Carver et al., 2008; Gray & McNaughton, 2000;
Spoont, 1992). Stability represents the shared variance of Conscientiousness,
Agreeableness, and low Neuroticism. Each of these traits reflects a different kind of
stability: low Neuroticism reflects emotional stability, Conscientiousness reflects
motivational stability, and Agreeableness reflects social stability (maintaining social
harmony). Serotonergic neurons project from the raphe nuclei in the brainstem to
innervate most cortical and subcortical brain structures, making serotonin well poised to
influence the broad range of personality traits implicated in Stability.
Dopamine facilitates exploration, approach, learning, and cognitive flexibility in response
to unexpected rewards and cues indicative of the possibility of reward (Bromberg-Martin,
Matsumoto, & Hikosaka, 2010; DeYoung, 2013). Though not as widespread in the brain
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as serotonin, it nonetheless influences most subcortical and frontal cortical structures.
Plasticity represents the shared variance of Extraversion and Openness/Intellect, and
CB5T posits that it reflects a general tendency toward exploration (DeYoung, 2013,
2015a). Whereas Extraversion reflects behavioral exploration and sensitivity to specific
rewards, Openness/Intellect reflects cognitive exploration and sensitivity to the reward
value of information. The metatraits are important from a cybernetic perspective because
they represent variation in the prioritization of two of the broadest needs of any
cybernetic system that must survive in a complex and changing environment: (1) to move
toward goals consistently (Stability) and (2) to generate new interpretations, strategies,
and goals in order to adapt to the environment (Plasticity) (DeYoung, 2006, 2015a).
The third level of traits labeled in Figure 1 is described as aspects of the Big Five,
whereas the unlabeled traits at the lowest level of the hierarchy are known as facets
(DeYoung, Quilty, & Peterson, 2007). No consensus exists regarding the number and
identity of facets within each Big Five dimension. Although the NEO Personality
Inventory-Revised (PI-R), a popular measure of the FFM, identifies six facets for each, its
30 facets were derived rationally through a review of the personality literature, rather
than empirically (Costa & McCrae, 1992), and other instruments assess different FFM
facets (e.g., Goldberg, 1999). CB5T focuses on the aspect level of the trait hierarchy,
between the Big Five and their facets, because this level was empirically derived and,
thus, is likely to capture the most important distinctions within each of the Big Five
(DeYoung et al., 2007). This level of the trait hierarchy was first detected in a behavioral
genetic analysis of twins, in which two genetic factors were needed to model the
covariance of the six NEO PI-R facets in each domain (Jang et al., 2002). If the Big Five
were the next level of the hierarchy above the facets, only a single genetic factor should
have been necessary for each domain. In a different sample, similar factors were
subsequently found in nongenetic factor analysis, using 15 facet scales for each domain,
rather than six (DeYoung et al., 2007). The resulting 10 factors were characterized
empirically, based on their correlations with over 2000 items from the International
Personality Item Pool (Goldberg, 1999), and a public-domain instrument, the Big Five
Aspect Scales (BFAS), was created to measure them (DeYoung et al., 2007). Whenever
possible in the following review, we distinguish between the two aspects in terms of their
neurobiological correlates.
Table 1 lists the cybernetic functions hypothesized by CB5T to be associated with each of
the labeled traits in Figure 1. An important caveat is that even the functions associated
with the aspects may themselves be broken down into various interacting psychological
mechanisms, each of which is likely to be instantiated within the brain in different ways
(Yarkoni, 2015). Some of these mechanisms will be associated with specific facets, but
even these are likely to be further decomposable into multiple mechanisms. For example,
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the passive avoidance mechanisms associated with the Anxiety facet of the Withdrawal
aspect of Neuroticism involve increased vigilance (attention to both the external
environment and information in memory), involuntary inhibition of behavior, and
increased arousal of the sympathetic nervous system, all of which have distinct,
identifiable neural circuits (Gray & McNaughton, 2000). Further, specific mechanisms
may be involved in multiple traits, so that the mapping of traits to brain systems will be
many-to-many (Yarkoni, 2015; Zuckerman, 2005).
Another caveat is that the hierarchy depicted in Figure 1 is oversimplified in one
important way: it depicts personality as having a simple hierarchical structure, with no
cross-loadings. If the diagram were entirely accurate as is, traits beneath Stability could
not be related to traits beneath Plasticity, but this is not the case at the levels below the
Big Five (Costa & McCrae, 1992; DeYoung, 2010b; Hofstee, de Raad, & Goldberg, 1992).
For example, Politeness is negatively related to Assertiveness, and Compassion is
positively related to Enthusiasm (DeYoung, Weisberg, Quilty, & Peterson, 2013). These
cross-connections are potentially important for biological models of personality. In
relation to the example just mentioned, testosterone may be at least partly responsible
for the covariation of Assertiveness and Politeness, given that it is related to both of these
dimensions (DeYoung et al., 2013; Turan, Guo, Boggiano, & Bedgood, 2014).
The cybernetic perspective on the FFM has a number of advantages for personality
neuroscience. First, the hypothesized functions for each trait provide a ready jumping-off
point for hypotheses about brain function. Second, it describes traits as the product of
variation in a set of integrated mechanisms, which is consistent with the fact that the
brain is a single complex adaptive system with many interacting subsystems. Considering
the interactions among these mechanisms may help to explain the relations among traits
as well as their manifestation in behavior. Third, by focusing on the psychological
functions underlying the Big Five, rather than just their superficial manifestation in
behavior and experience, we can more easily connect research on personality in
childhood and adulthood. All five factors appear to be present relatively early in
childhood, even though their exact manifestations in behavior shift with age (Shiner &
DeYoung, 2013). For example, a 4-year-old child high in Openness/Intellect is unlikely to
be interested in poetry or philosophy but is nonetheless likely to express the tendency
toward cognitive exploration through curiosity and imaginative play. By using the FFM in
developmental research, personality neuroscience can shed light on the ontogeny of
personality. Finally, this perspective helps to link human research with the wealth of
knowledge from neuroscience research in other species, in which the brain can be
observed and manipulated more directly. The Big Five can be used to describe individual
differences in other species (Gosling & John, 1999), and, despite important evolutionary
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change, much of the anatomy and cybernetic function of the brain has been conserved by
evolution, especially across mammalian species.
Extraversion
CB5T posits that sensitivity to reward is the core function underlying Extraversion,
enabling the individual to be energized by goals (DeYoung, 2013, 2015a). Here CB5T
builds on the work of Depue and Collins (1999), who argued that sensitivity to incentive
reward mediated by the dopaminergic system is the primary driver of Extraversion.
Depue and Collins were themselves influenced by Gray’s Reinforcement Sensitivity
Theory, which posited a Behavioral Approach System (BAS) that mediates the relation
between sensitivity to incentive reward and ensuing approach behavior (Gray, 1982; Gray
& McNaughton, 2000; Pickering & Gray, 1999). Although Gray initially hypothesized that
impulsivity was the personality trait most closely reflecting BAS sensitivity, evidence has
accumulated that Extraversion is a better candidate, and the questionnaire most
commonly used to measure BAS sensitivity shows reasonable convergent validity with
Extraversion (Carver & White, 1994; Pickering, 2004; Quilty, DeYoung, Oakman, &
Bagby, 2014; Smillie, Pickering, & Jackson, 2006; Wacker, Mueller, Hennig, & Stemmler,
2012). All of these theories highlight the central role of the neurotransmitter dopamine in
the brain’s reward system. (Depue & Collins, 1999; DeYoung, 2013; Pickering & Gray,
1999; Smillie, 2008).
The association of variation in dopaminergic function with Extraversion is one of the best
established findings in personality neuroscience (see also the chapter by Wilt and
Revelle). A number of empirical studies have demonstrated that Extraversion moderates
the effects of pharmacological manipulation of the dopaminergic system (Chavanon,
Wacker, & Stemmler, 2013; Depue, Luciana, Arbisi, Collins, & Leon, 1994; Mueller et al.,
2014; Rammsayer, 1998; Rammsayer, Netter, & Vogel, 1993; Wacker, Chavanon, &
Stemmler, 2006; Wacker, Mueller, Pizzagalli, Hennig, & Stemmler, 2013; Wacker &
Stemmler, 2006). In a particularly impressive demonstration, a recent study by Depue
and Fu (2013) used Pavlovian conditioning in human participants to show that high
Extraversion was associated with greater sensitivity to the rewarding effects of
dopamine. To understand the meaning of this association, we must understand the
difference between incentive and consummatory reward (DeYoung, 2013). An incentive
reward is a cue that one is moving toward a goal, whereas a consummatory reward is the
actual attainment of a goal. Dopamine is responsible for the drive to attain rewards in
response to incentive cues but not for the hedonic enjoyment of reward; this distinction
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has been described in terms of “wanting” versus “liking” (Berridge, Robinson, &
Aldridge, 2009). Whereas the dopaminergic system is responsible for wanting, the opiate
system is responsible for liking (Peciña, Smith, & Berridge, 2006), and the association of
Extraversion with dopamine reflects only that Extraversion is linked to desire for reward,
not enjoyment of reward.
Nonetheless, questionnaire and behavioral research indicates that Extraversion involves
not only increased wanting, but also increased liking of rewards. Positive emotionality is
a facet of Extraversion describing energized positive emotions such as excitement,
enthusiasm, and elation that have a clear hedonic component, and research indicates that
Extraversion predicts the amount of these positive emotions that people experience in
response to incentively rewarding stimuli (Smillie, Cooper, Wilt, & Revelle, 2012). This
suggests that Extraversion might be related to opiate function as well as to dopamine.
CB5T posits that the two aspects of Extraversion, Assertiveness and Enthusiasm, reflect
the difference between wanting and liking, with Assertiveness reflecting wanting rather
than liking and Enthusiasm reflecting primarily liking and only secondarily wanting
(DeYoung, 2015a). Enthusiasm appears to reflect liking in an incentive context, with
opiate release providing the positive hedonic feelings that accompany dopaminergic
activity (DeYoung, 2013). Research on dopamine is consistent with this hypothesis, as
measures of Assertiveness (usually called “agentic Extraversion” in this literature) appear
to be more strongly related to dopaminergic variables than do measures of Enthusiasm
(often called “affiliative Extraversion”) (Mueller et al., 2014; Wacker et al., 2012).
Further, one study found that Social Closeness, a good marker of Enthusiasm, moderated
the effects of an opiate manipulation (Depue & Morrone-Strupinsky, 2005; DeYoung et
al., 2013). Whereas Assertiveness encompasses traits such as drive, leadership, initiative,
and activity, Enthusiasm encompasses both sociability or gregariousness and positive
emotionality (DeYoung et al., 2007).
In sum, existing research strongly supports the hypothesis that dopamine is an important
substrate of Extraversion, especially Assertiveness, and shows some preliminary support
for the hypothesis that the opiate system is also important for Extraversion, particularly
Enthusiasm. Note that the strong support for the dopamine hypothesis leaves much
unknown about the specific parameters of the dopaminergic system that contribute to
Extraversion (e.g., parameters related to the density of different dopamine receptors,
mechanisms of neurotransmitter synthesis, or clearance from the synapse). This is
indicative of the state of personality neuroscience in general, in which even the best
established findings are merely preliminary to a thorough mechanistic understanding.
Electroencephalographic (EEG) research on a phenomenon known as the “feedback-
related negativity” (FRN) also supports the hypothesis that Extraversion reflects
dopaminergically driven sensitivity to incentive reward. The FRN is an EEG waveform
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that appears 200–350 milliseconds after receiving feedback about an outcome and
appears to be generated by the dorsal anterior cingulate cortex (ACC) in response to
dopaminergic signaling of deviations from the expected value of the outcome (Sambrook
& Goslin, 2015). Animal research has shown that one type of dopaminergic neuron
encodes a prediction error learning signal by spiking in response to better-than-expected
outcomes and dropping below baseline levels of activity in response to worse-than-
expected outcomes (Bromberg-Martin, Matsumoto, & Hikosaka, 2010). The FRN shows
the same pattern (becoming most negative for worse-than-expected outcomes and least
negative for better-than-expected outcomes), indicating that it is a prediction error signal
driven by dopamine (Proudfit, 2015; Sambrook & Goslin, 2015). Several studies have
shown that Extraversion (sometimes measured with the BAS sensitivity scale) is
correlated with FRN amplitude following reward (Bress & Hajcak, 2013; Cooper, Duke,
Pickering, & Smillie, 2014; Lange, Leue, & Beauducel, 2012; Smillie, Cooper, &
Pickering, 2011). Implicating dopamine more directly, Mueller et al. (2014) showed that
agentic Extraversion was associated with FRN magnitude following failure (i.e., a worse-
than-expected outcome), but only when the task was incentivized, and the association
was eliminated by the administration of a dopamine D2 receptor antagonist (a drug that
blocks one type of dopamine receptor).
Turning to neuroimaging research, and considering the brain as a whole, the most
obvious hypothesis about Extraversion is that it should be associated with function and
structure in regions of the brain that are part of the reward system, including the
ventromedial prefrontal cortex (VMPFC; often called the orbitofrontal cortex, OFC), the
nucleus accumbens (often described as the ventral striatum), the caudate nucleus (part of
the dorsal striatum), the ACC, and the midbrain regions from which dopaminergic
neurons project (substantia nigra and ventral tegmental area [SN/VTA]). That
Extraversion should be associated with amygdala function is another important
hypothesis for neuroimaging, stemming from the observation that the amygdala is crucial
for processing emotional salience related to rewarding as well as threatening stimuli
(Stillman, Van Bavel, & Cunningham, 2015).
Several fMRI studies have supported these hypotheses, showing that Extraversion
predicts neural activation in some or all of these structures in response to emotionally
positive or rewarding stimuli (Canli, Sivers, Whitfield, Gotlib, & Gabrieli, 2002; Canli et
al., 2001; Cohen, Young, Baek, Kessler, & Ranganath, 2005; Mobbs, Hagan, Azim, Menon,
& Reiss, 2005; Schaefer, Knuth, & Rumpel, 2011). All of these studies, however, had
samples smaller than 20, rendering their evidentiary value questionable at best. Well-
powered, task-based, fMRI studies of the link between Extraversion and reward are
needed. A recent study with a sample of 52 is a step in the right direction, showing that
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Extraversion predicted neural activity in the nucleus accumbens during anticipation of
gaining five dollars (Wu, Samanez-Larkin, Katovich, & Knutson, 2014).
In contrast to the functional studies just mentioned, structural MRI studies with larger
sample sizes are beginning to appear, and the most replicated finding for Extraversion is
that it is associated positively with regional volume in VMPFC, a brain area that appears
to be crucial for maintaining representations of the value of stimuli (Cremers et al., 2011;
DeYoung et al., 2010; Omura, Constable, & Canli, 2005). One of the largest such studies,
which used the BAS sensitivity scale rather than a more standard measure of
Extraversion, found a positive association with VMPFC in women but found a significant
negative association in men (Li et al., 2014). Other studies have not replicated the
association at all (Bjørnebekk et al., 2013; Hu et al., 2011; Kapogiannis et al., 2013; Liu et
al., 2013). Variation in the populations studied and methods employed could be at least
partly responsible for differing results. Further, all of these studies reported whole brain
analyses, rather than focusing on the VMPFC as a region of interest. Whole brain
analyses require corrections for multiple tests that could have rendered even the larger
studies underpowered to detect a true association. Additional large primary studies,
targeted hypothesis testing, and meta-analyses will be needed to provide accurate
estimates of this effect. Associations of Extraversion with volume in other brain regions
have been even more inconsistent.
A recent PET study also provided some evidence of an association between Extraversion
and VMPFC, showing that Positive Emotionality (PEM), as measured by the
Multidimensional Personality Questionnaire (MPQ), was positively associated with
resting-state glucose metabolism in this region (Volkow et al., 2011). MPQ-PEM is a
broader construct than its label would suggest, consisting of subscales measuring Social
Potency and Social Closeness, which are good measures of Extraversion, but also
subscales measuring Well-Being (Extraversion and Neuroticism) and Achievement
(Assertiveness, Conscientiousness, and Openness/Intellect) (DeYoung, 2013; DeYoung et
al., 2013; Markon, Krueger, & Watson, 2005). Although it is primarily a measure of
Extraversion, some caution is warranted about whether findings will generalize to more
traditional Extraversion measures. Another recent study that used this measure and
found a positive association between PEM and left amygdala volume is worth mentioning
here because of its sample size: N = 486 (Lewis et al., 2014).
Resting EEG hemispheric asymmetry, in which one frontal lobe of the brain is more
active than the other, is another phenomenon that has been linked to Extraversion and to
the motivation to approach that is characteristic of response to incentive reward.
Considerable evidence suggests that the left hemisphere is biased toward information
processing associated with approach motivation and behavior (Davidson, 1998; Harmon-
Jones, Gable, & Peterson, 2010). For the left hemisphere to be chronically more active
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than the right, therefore, might reflect a general tendency toward approach that could be
manifested in increased Extraversion. Indeed, a number of studies have found that
Extraversion, or more specifically its Assertiveness aspect, is related to greater left-
dominant asymmetry (Amodio, Master, Yee, & Taylor, 2008; Coan & Allen, 2003; De
Pascalis, Cozzuto, Caprara, & Alessandri, 2013; Harmon-Jones & Allen, 1997; Schmidt,
1999; Sutton & Davidson, 1997). However, failures to replicate have been reported as
well, and a meta-analysis found no evidence for the effect (Wacker, Chavanon, &
Stemmler, 2010).
Should the idea of linking Extraversion to hemispheric asymmetry be abandoned,
therefore? Perhaps not; a recent study of an all-male sample found that the BAS
sensitivity scale predicted resting-state asymmetry only for participants interacting with
a female experimenter whom they rated as attractive (Wacker et al., 2013). Another
much smaller EEG study found an analogous effect; a trait measure of positive affect that
is strongly linked to Extraversion was associated with asymmetry only in a condition of
positive mood as opposed to negative or neutral mood (Coan, Allen, & McKnight, 2006).
These studies suggest that the association of Extraversion with hemispheric asymmetry
may be detectable only when positive emotional states related to incentive motivation are
activated. This possibility is consistent with many trait theories, including CB5T, which
posit that traits represent the tendency to respond in particular ways to particular classes
of stimuli. Without the presence of a relevant stimulus, the trait may not be manifest, and
individual differences in behavior or neural activity may not be apparent.
Interestingly, one EEG effect measured during rest appears to be more robustly
associated with Extraversion than hemispheric asymmetry. Meta-analysis has shown that
agentic Extraversion is associated with increased posterior versus anterior theta activity
at centerline electrode sites (Koehler et al., 2011; Wacker et al., 2010). (Frequency bands
in EEG are labeled with the names of Greek letters.) This finding has been extended to
the delta frequency band as well, and this theta/delta anterior–posterior difference
appears to reflect activity in the rostral ACC and to be associated with processing of
reward and salience information (Chavanon, Wacker, & Stemmler, 2011; Knyazev, 2010;
Wacker & Gatt, 2010; Wacker et al., 2010). The association of the anterior–posterior EEG
index with Extraversion has been linked empirically to dopaminergic function. Several
studies have shown that the association of Extraversion with increased posterior–anterior
difference is either negated or reversed when subjects are administered a dopamine
antagonist prior to the EEG recording (Chavanon et al., 2013; Wacker et al., 2006), and a
study combining EEG with molecular genetics found that variation in the catechol-O-
methyltransferase (COMT) gene (which produces an enzyme that metabolizes dopamine
in the synapse and varies in efficiency depending on genotype) was associated with both
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agentic Extraversion and posterior versus frontal resting delta/theta activity (Wacker &
Gatt, 2010).
Several fMRI studies with samples around N = 40–50 have reported associations of
Extraversion with resting-state functional connectivity. Their results have not been very
similar, but, then, neither have their methods: one examined connectivity only between
the amygdala and other brain regions (Aghajani et al., 2014), one examined connectivity
with nine seed regions on the medial surface of the cortex (Adelstein et al., 2011), one
examined connectivity only within the default network (Sampaio, Soares, Coutinho,
Sousa, & Goncalves, 2014), and one examined connectivity of the midbrain dopaminergic
SN/VTA with other brain regions (Passamonti et al., 2015). With such heterogeneous
methods and small samples, it is hard to draw conclusions. The most compelling
Extraversion findings, from these studies, were that it was positively associated with (1)
connectivity between the amygdala and several other regions involved in basic emotional
and motivational processes (Aghajani et al., 2014) and (2) connectivity between SN/VTA
and the striatum, both key components of the dopaminergic reward system (Passamonti
et al., 2015).
Neuroticism
CB5T posits that Neuroticism reflects individual differences in the sensitivity of defensive
distress systems that become active in the face of threat, punishment, and uncertainty
(DeYoung, 2015a). Uncertainty is innately threatening because the inability to predict the
outcome of an action or perception may indicate that one does not understand the
current situation sufficiently to be confident in the progress toward one’s goals—
sometimes including goals as fundamental as survival (Gray & McNaughton, 2000; Hirsh,
Mar, & Peterson, 2012; Peterson & Flanders, 2002). Indeed, one EEG study found that
for people high in Neuroticism, ambiguous feedback about task performance produced a
more negative FRN even than negative feedback (whereas the opposite was true for
people low in Neuroticism), consistent with the theory that Neuroticism is associated
with aversion to uncertainty (Hirsh & Inzlicht, 2008).
Individuals high in Neuroticism are prone to emotional responses to stress that foster
avoidant or defensive behavior, including anxiety, depression, anger, irritability, and
panic (see also the chapter by Tackett and Lahey). Largely because Neuroticism is the
major personality risk factor for psychopathology (Lahey, 2009), more neuroscientific
research is being conducted on Neuroticism than on any other trait in the FFM. To parse
this research, CB5T draws on Gray and McNaughton’s (2000) theory that Neuroticism
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reflects the joint sensitivity of a behavioral inhibition system (BIS), which responds to
threats in the form of conflicts between goals (e.g., approach–avoidance conflict or any
other conflict that generates uncertainty), and a fight–flight–freeze system (FFFS), which
responds to threats without conflict—that is, when the only motivation is to escape or
eliminate the threat. Much is known about the neurobiology of the BIS and FFFS in the
brainstem, hypothalamus, and limbic system, which can aid in the interpretation of
existing research on Neuroticism and inform hypotheses in future research.
CB5T posits that variations in the BIS and FFFS are likely to be reflected differentially in
the two aspects of Neuroticism. Withdrawal (related to BIS) reflects the shared variance
of traits related to anxiety and depression, which involve passive avoidance, the tendency
to slow or inhibit behavior to avoid potential punishment or error. Volatility (related to
FFFS) encompasses traits related to irritability, anger, emotional lability, and the
tendency to get upset easily, which involve active defensive responses. In research on
children, similar factors have been described as anxious distress and irritable distress
(Rothbart & Bates, 1998; Shiner & Caspi, 2003). Neuroticism is often studied using scales
such as the BIS sensitivity scale (Carver & White, 1994), Cloninger’s Harm Avoidance,
and various measures of trait anxiety (most of which appear to measure something
broader than just the anxiety facet). Most such scales measure either a combination of
Withdrawal and Volatility or just Withdrawal. To identify existing neuroscience research
specifically relevant to Volatility requires focusing on measures of anger or hostility as
emotional traits (though not actual aggression, which is more strongly related to
Agreeableness than Neuroticism).
The neurotransmitters serotonin and noradrenaline modulate both the BIS and the FFFS
and, therefore, are likely candidates as contributors to Neuroticism (Gray &
McNaughton, 2000). Several lines of evidence implicate serotonin in Neuroticism.
Serotonergic drugs are used to treat many disorders with symptoms reflecting severe
Neuroticism, including depression, anxiety and panic disorders, and intermittent
explosive disorder. In clinical depression, selective serotonin reuptake inhibitors (SSRIs)
have been shown to reduce Neuroticism, and this reduction appears to mediate the
improvements in depressive symptoms caused by SSRIs (Du, Bakish, Ravindran, &
Hrdina, 2002; Quilty, Meusel, & Bagby, 2008; Tang et al., 2009). A clinical trial has also
shown that an SSRI can reduce irritability and anger (Kamarck et al., 2009). Three PET
studies have found that Neuroticism predicts variation in serotonin receptor or
transporter binding (Frokjaer et al., 2008; Takano et al., 2007; Tauscher et al., 2001),
although only the most recent of these used a sample large enough to be of much
interest. Two studies have shown that response to a fenfluramine pharmacological
challenge (which assesses central serotonergic function) is associated with Neuroticism;
however, gender differences in the effect were apparent in both studies, and the
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direction of effect was not consistent for men (Brummett, Boyle, Kuhn, Siegler, &
Williams, 2008; Manuck et al., 1998). Both studies were too small to assess effects
separately by gender with much confidence. Molecular genetic studies implicating
serotonergic genes in Neuroticism are inconclusive (Munafo et al., 2009). A small body of
research exists to suggest an association of noradrenaline and Neuroticism, which may
be more specific to fear and anxiety, and this hypothesis could use more research
(Hennig, 2004; White & Depue, 1999; Zuckerman, 2005). Other understudied
neurotransmitters involved in stress responses may influence Neuroticism as well. One
extensive study using a variety of methods in neuroscience linked trait anxiety with a
variation in levels of neuropeptide Y, which is released under stress and modulates
anxiety and pain (Zhou et al., 2008).
Substantial evidence documents a link between Neuroticism and increased activation of
the hypothalamic–pituitary–adrenal (HPA) axis, which regulates the body’s stress
response under the control of both BIS and FFFS (Zobel et al., 2004). Corticotropin-
releasing hormone (CRH) is the proximal activator of the HPA axis, and several studies of
variation in the CRH receptor 1 gene have linked it to depression or Neuroticism in
individuals maltreated as children, though results are complex and may differ by race and
type of maltreatment (Bradley et al., 2008; DeYoung, Cicchetti, & Rogosch, 2011; Grabe
et al., 2010; Kranzler et al., 2011; Polanczyk et al., 2009). A better established link is
between Neuroticism and levels of cortisol, the stress hormone released from the adrenal
cortex at the culmination of the stress response initiated by CRH. Neuroticism is
positively associated with baseline levels of cortisol (Garcia-Banda et al., 2014; Gerritsen
et al., 2009; Miller, Cohen, Rabin, Skoner, & Doyle, 1999; Nater, Hoppmann, & Klumb,
2010; Polk et al., 2005) as well as with blunted cortisol responses to specific stressors
(Netter, 2004; Oswald et al., 2006; Phillips, Carroll, Burns, & Drayson, 2005; but see
Kirschbaum, Bartussek, & Strasburger, 1992; Schommer, Kudielka, Hellhammer, &
Kirschbaum, 1999, for failures to replicate). This pattern suggests that people high in
Neuroticism tend to be not only chronically stressed but also less able to engage the
resources necessary to cope with specific stressful situations.
Interestingly, an overabundance of cortisol is known to potentiate excitotoxic cell death
in neurons (Sapolsky, 1994), a fact that Knutson, Momenan, Rawlings, Fong, and
Hommer (2001) suggested as a possible explanation for their findings and those of others
that Neuroticism is negatively related to global measures of brain volume, such as the
volume of cerebral gray matter, the ratio of brain volume to intracranial volume, and total
brain volume (Bjørnebekk et al., 2013; Jackson, Balota, & Head, 2011; Liu et al., 2013).
The chronic stress associated with high Neuroticism may damage the brain as a whole.
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The threat and punishment systems that control HPA activation are the obvious neural
candidates to underlie Neuroticism, and evidence from both functional and structural
MRI supports this broad hypothesis. Until recently, most fMRI studies reporting that
Neuroticism predicts neural responses to aversive stimuli used samples so small as to
preclude confidence in their results. Of 21 samples in a recent meta-analysis of these
effects (Servaas et al., 2013b), only seven of them were larger than 25, and only one was
larger than 60. Meta-analysis cannot solve the problems created by underpowered
samples because meta-analytic conclusions are likely to be biased by their inclusion. One
study not included in this meta-analysis, with a sample of 52, found that Neuroticism
predicted right insula activation in anticipation of a loss of five dollars, and that this
insula activation showed trait-like stability over a period of 2.5 years (Wu et al., 2014).
Many theoretical accounts of the neurobiology of Neuroticism highlight a role for the
amygdala, given its central role in BIS, FFFS, and mobilization of negative affect and
stress responses. Although the meta-analysis by Servaas et al. (2013b) did not implicate
the amygdala, some larger fMRI studies have found associations between Neuroticism
and amygdala response to aversive stimuli, although methods have differed and the
findings cannot be easily integrated. One study reported that Neuroticism predicted a
slower decrease in amygdala activity after viewing aversive images (N = 120; Schuyler et
al., 2014), and another reported that Neuroticism was positively correlated with
amygdala activity in response to aversive images, but only in participants generally
lacking in social support (N = 103; Hyde, Gorka, Manuck, & Hariri, 2011). A region
considered part of the “extended amygdala,” known as the bed nucleus of the stria
terminalis (BNST), has been specifically linked to anxious vigilance, and its activation to a
persistent threat cue was predicted by Neuroticism (Somerville, Whalen, & Kelley, 2010;
N = 50).
Structural neuroimaging studies linking Neuroticism to amygdala volume have been
inconsistent, much like studies of Extraversion and VMPFC volume. Several studies have
found a positive correlation (Barros-Loscertales et al., 2006; Iidaka et al., 2006; Koelsch,
Skouras, & Jentschke, 2013), but several others have not (Cherbuin et al., 2008; DeYoung
et al., 2010; Fuentes et al., 2012; Liu et al., 2013). Luckily, in this case, a nearly definitive
study has been carried out in a sample of over 1000 people that found that Neuroticism
scores based on the average of several commonly used questionnaire measures were
indeed correlated with amygdala volume (controlling for total brain volume), albeit
weakly (r = .1; Holmes et al., 2012). Only one other subcortical structure, the
hippocampus, was also significantly correlated with Neuroticism (r = .1), which is salient
both because the hippocampus is a core component of the BIS and because resting-state
hippocampal activity has previously been linked to Neuroticism using PET (Gray &
McNaughton, 2000; Sutin, Beason-Held, Dotson, Resnick, & Costa, 2010).
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Given the small effects detected by Holmes et al. (2012), previous inconsistencies are
likely to reflect a lack of statistical power. Another possibility is that the amygdala effect
is suppressed because it differs for different subfactors of Neuroticism. One study found
that a measure of trait anger was associated negatively with left amygdala volume
(Reuter, Weber, Fiebach, Elger, & Montag, 2009). Although this study was small (N = 47)
and, therefore, may have misestimated the correlation of anger with amygdala volume, it
does raise the possibility that facets encompassed by Volatility might show a different
association with amygdala volume than those encompassed by Withdrawal.
In addition to the volume of subcortical structures, Holmes et al. (2012) also examined
cortical thickness and found that Neuroticism was negatively associated with the
thickness of a region of left rostral ACC and adjacent medial PFC (r = – .1). Interestingly,
in a subset of 206 members of their sample who completed additional questionnaire
measures, Holmes et al. (2012) found that the thickness of this region was correlated (r =
– .2) with measures of social dysfunction that appear to assess low Extraversion (perhaps
blended with Neuroticism). This finding represents a notable parallel to the findings
described above of positive correlations between Extraversion and nearby regions of the
VMPFC. Another study that examined cortical area as well as thickness found that
Neuroticism was associated negatively with cortical area in a very similar region of ACC
and medial PFC in the right hemisphere (Bjørnebekk et al., 2013).
Given the size of the sample of Holmes et al. (2012), this is likely to be the only region of
the cortex in which thickness is associated with Neuroticism; however, other types of
structural measures may nonetheless implicate additional cortical regions. Two studies of
volume instead of thickness, with samples over 100, have found that Neuroticism was
negatively associated with other regions of the PFC (DeYoung et al., 2010; Fuentes et al.,
2012). Reduced volume and thickness in the medial PFC may be linked to the low self-
esteem and poor regulation of emotion that are characteristic of Neuroticism, as this
region is part of the default network crucially involved in self-evaluation and regulation of
emotion (Andrews-Hanna et al., 2014). Three fMRI studies are consistent with this
hypothesis: Lemogne et al. (2011) found that Neuroticism was associated with increased
activation of both the medial PFC and the posterior cingulate cortex and adjacent
precuneus (another core hub of the default network) when participants judged whether
negative pictures were related to themselves. Williams et al. (2006) found that
Neuroticism predicted age-related decreases in medial PFC responses to happy faces and
increases in responses in that region to fear faces. And Haas, Constable, and Canli (2008)
found that Neuroticism was associated with activity in medial PFC when viewing blocks
of sad facial expressions, but not fearful or happy facial expressions (though in a small
sample; N = 29).
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The emotion regulation hypothesis is also consistent with a number of studies of both
functional and structural connectivity, which have found that Neuroticism predicts
reduced connectivity between frontal cortical regions and the amygdala (sometimes in
conjunction with increased connectivity of the amygdala with other limbic regions). In
functional studies, methods vary and results are hard to integrate; larger samples would
be helpful. Mujica-Parodi et al. (2009) reported reduced synchrony between the amygdala
and PFC regions while viewing neutral, fearful, and happy faces. Servaas et al. (2013a)
found that Neuroticism was negatively correlated with the synchrony of amygdala and
hippocampus with dorsomedial and dorsolateral PFC during a scan preceded by criticism
from the experimenter (prerecorded to ensure standardization) relative to a standard
resting-state scan. In a more typical resting-state study by the same group, Neuroticism
was associated with weaker functional connections throughout the brain, including
connections in frontoparietal, sensory, and default mode networks, but with stronger
connectivity between affective regions, including the amygdala, hippocampus, and insula
(Servaas et al., 2015). This is not entirely consistent with smaller resting-state studies
that found that Neuroticism was negatively associated with connectivity of the amygdala
with temporal lobe regions and the insula (Aghajani et al., 2014) and positively associated
with connectivity in the default network (between dorsomedial PFC and the precuneus;
Adelstein et al., 2011). Finally, a larger resting-state study (N = 178) found that
Neuroticism was positive associated with connectivity between the amygdala and
fusiform gyrus (a region crucial for visual processing of faces), which may be related to
the fact that Neuroticism is associated with greater neural reactivity to negative facial
expressions (Cremers et al., 2010).
Structural studies have found a more consistent pattern of reduced connectivity
associated with Neuroticism. Structural connectivity is measured in MRI using diffusion
tensor imaging (DTI) to assess the integrity of the white matter (axon) tracts that connect
different parts of the brain. Neuroticism is associated with reductions in white matter
integrity in tracts connecting cortical and subcortical regions (Bjørnebekk et al., 2013;
Taddei, Tettamanti, Zanoni, Cappa, & Battaglia, 2012; Westlye, Bjørnebekk, Grydeland,
Fjell, & Walhovd, 2011; Xu & Potenza, 2012).
Interestingly, although Holmes et al. (2012) did not examine structural or functional
connectivity, they did find that in individuals scoring highest in Neuroticism (more than
one standard deviation above the mean), cortical thickness in the ACC and medial PFC
region was negatively correlated with amygdala volume (whereas they were unrelated in
the rest of the sample). In sum, the evidence suggests that Neuroticism is associated with
an imbalance between control of behavior and experience by subcortical negative
emotional systems versus frontal cortical systems.
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Another consistent finding regarding Neuroticism comes from EEG research
demonstrating a pattern of greater activation in the right frontal lobe relative to the left
when viewing stimuli and while at rest (Gale, Edwards, Morris, Moore, & Forrester,
2001; Shackman, McMenamin, Maxwell, Greischar, & Davidson, 2009; Sutton &
Davidson, 1997), and this has been confirmed by meta-analysis (Wacker et al., 2010).
Similarly, near-infrared reflection spectroscopy (a technique that uses light to measure
regional cerebral oxygenated hemoglobin) has shown that cerebral blood flow in the right
frontal lobe is positively correlated with Neuroticism during anticipation of a shock
(Morinaga et al., 2007). A lesion study, comparing 199 brain-damaged patients to 50
healthy controls using MRI, found that focal damage to the left dorsolateral prefrontal
cortex was associated with higher scores on Neuroticism, especially the anxiety facet
(Forbes et al., 2014). Lesions of the left hemisphere lead to dominance of right
hemisphere function. Whereas most evidence suggests that the association of
Neuroticism with lateralization is driven by differences in frontal activation, one large
EEG study found a similar effect in posterior portions of the right hemisphere (Schmidtke
& Heller, 2004).
Importantly, not all components of Neuroticism show the same association with
hemispheric asymmetry. The right-dominant asymmetry appears to apply only to traits in
the Withdrawal subfactor, such as anxiety and depression, which are linked to passive
avoidance. In contrast, traits in the Volatility subfactor, such as anger-proneness and
hostility, which involve active defense, are associated with greater left-dominant frontal
asymmetry (Everhart, Demaree, & Harrison, 2008; Harmon-Jones, 2004; Harmon-Jones &
Allen, 1998).
Bearing in mind the caveat that different aspects of Neuroticism may show different
relations to hemispheric asymmetry, it is worth considering two non-EEG studies that
found that Neuroticism predicted hemispheric asymmetry in connectivity. (Importantly,
most global measures of Neuroticism—including those used in these two studies—
emphasize Withdrawal more than Volatility.) Madsen et al. (2012) found that Neuroticism
was associated with higher right, relative to left, white matter integrity in the major
white matter tract (the cingulum) connecting limbic regions. Cremers et al. (2010) found
that Neuroticism predicted reduced synchrony between the left amygdala and medial
PFC when viewing negative versus neutral emotion faces, but increased synchrony
between these structures in the right hemisphere.
We conclude this section with a call for more studies that explicitly distinguish between
Withdrawal and Volatility. One otherwise exemplary study unfortunately used a sample of
only 18 (Cunningham et al., 2010), but its innovative methodology is worth describing, in
the hope of encouraging replication attempts in larger samples. Participants in fMRI
viewed positive, negative, and neutral images and were required either to approach them
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(by pressing a button that enlarged the image, creating the illusion of approach) or to
avoid them (by pressing a button that shrank the image). Withdrawal was found to
predict amygdala reactivity to approach relative to avoidance (independently of stimulus
valence), whereas Volatility was found to predict amygdala reactivity to negative stimuli
relative to neutral and positive stimuli (independently of behavioral direction). These
findings, if replicated, would support the hypothesis that Withdrawal reflects sensitivity
to conflict (especially approach–avoidance conflict), thus leading to increased vigilance
and behavioral inhibition when approaching any stimulus, whereas Volatility reflects
sensitivity to all negatively valenced proximal stimuli.
Openness/Intellect
CB5T posits that Openness/Intellect reflects individual differences in the cognitive
exploration that generates new interpretations of experience in terms of causal and
correlational patterns and connections. Cognition here is conceived broadly to include
both reasoning and perceptual processes (DeYoung, 2015b). People high in Openness/
Intellect are imaginative, curious, innovative, perceptive, thoughtful, and creative. The
trait’s compound label stems from the debate about whether to label it “Openness to
Experience” or “Intellect” (Costa & McCrae, 1992; Goldberg, 1990). This debate has been
resolved by the recognition that these two labels capture two major distinct subfactors of
the trait, with Intellect reflecting cognitive engagement with abstract information and
ideas (intellectual interests) and Openness reflecting cognitive engagement with
perceptual and sensory information (artistic and aesthetic interests) (DeYoung et al.,
2007; DeYoung, Grazioplene, & Peterson, 2012; Johnson, 1994; Saucier, 1992). When we
refer to “Openness/Intellect,” we are referring to the broad FFM dimension; when we
refer to either “Intellect” or “Openness” alone, we are referring to just one aspect of
Openness/Intellect (see also the chapter by Sutin).
The curiosity and innovation that are common to both Openness and Intellect are likely to
be driven by dopamine—specifically, a type of dopaminergic neuron that codes for
salience instead of value, is activated by both positive and negative information, and
innervates different brain regions than do the value-coding neurons implicated in
Extraversion (Bromberg-Martin et al., 2010; DeYoung, 2013). The evidence for
dopaminergic involvement in Openness/Intellect is more circumstantial than the evidence
for Extraversion, although there have been two molecular genetic studies showing
associations with the DRD4 and COMT genes in three samples (DeYoung, Cicchetti,
Rogosch, Gray, & Grigorenko, 2011; Harris et al., 2005). The adult sample investigated
by DeYoung et al. (2011) exhibited an interaction effect between DRD4 and COMT, which,
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if replicated, could explain the failure of these genes to be identified in larger GWAS
studies of the FFM.
The original hypothesis that dopamine is involved in the biological substrate of Openness/
Intellect was based on several lines of indirect evidence (DeYoung, Peterson, & Higgins,
2002, 2005): (1) the involvement of dopamine in curiosity and exploratory behavior is
well-established in animal research (Panksepp, 1998); (2) dopamine is involved in the
working-memory attentional mechanisms that allow maintenance and manipulation of
information in short-term memory, and Openness/Intellect (specifically its Intellect
aspect) is the only FFM trait positively associated with working memory ability (DeYoung
et al., 2005, 2009); and (3) Openness/Intellect is associated with reduced latent
inhibition, an automatic preconscious process that blocks stimuli previously categorized
as irrelevant from entering awareness (Peterson & Carson, 2000; Peterson, Smith, &
Carson, 2002). Dopamine is the primary neuromodulator of latent inhibition, with
increased dopaminergic activity producing reduced latent inhibition (Kumari et al., 1999),
and Openness/Intellect may reflect individual differences in the automatic tendency to
perceive salient information in everyday experience.
One fMRI study tested hypotheses derived explicitly from the dopamine theory of
Openness/Intellect. Although dopaminergic activity cannot be studied directly in fMRI,
neural activity can be assessed in regions that are core to the dopaminergic system, with
the inference that activation there is probably reflective of dopaminergic function (much
like the FRN in EEG). Passamonti et al. (2015) examined functional connectivity between
the midbrain SN/VTA, where the dopaminergic system originates, and other brain
regions, not only during resting state but also in two tasks involving sensory experience.
In the first, participants were presented with pleasant food odors through a special
apparatus, contrasted with smelling pure air. In the second, participants viewed
appealing pictures of food, contrasted with viewing a fixation cross. In all three tasks,
Openness/Intellect positively predicted connectivity of SN/VTA with dorsolateral PFC, a
region crucial for voluntary control of attention and working memory. This circuit may
help to explain why people high in Openness/Intellect find sensory experiences
interesting and rewarding.
The association of Intellect with working memory has been demonstrated neurally as well
as behaviorally. An fMRI study using the Ideas facet of the NEO PI-R as a measure of
Intellect found that it was the only facet associated with brain activity predicting
accurate working memory performance in the scanner (DeYoung et al., 2009).
Associations were found in two regions of the PFC, the left frontal pole of the lateral PFC
and a posterior region of the medial PFC. The frontal pole is crucial for integrating the
outputs of various simpler cognitive operations and for making abstract analogies
(Gilbert et al., 2006; Green, Fugelsang, Kraemer, Shamosh, & Dunbar, 2006; Ramnani &
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Owen, 2004). The medial PFC region in question is known to be involved in monitoring
goal-directed performance, which might be particularly important for those high in
Intellect, who are motivated to do well in cognitive tasks (Brown & Braver, 2005;
Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). A PET study, which did not
separate Intellect from Openness, found that Openness/Intellect was associated with
neural activity while participants were at rest, in brain areas not identical to but near the
two areas just described, in regions of lateral PFC and anterior cingulate cortex
associated with working memory and error detection (Sutin, Beason-Held, Resnick, &
Costa, 2009).
Given the centrality of imagination for Openness/Intellect (“Imagination” was even
suggested as an alternative label for the whole dimension; Saucier, 1992), we might
expect that the default network would be an important substrate of the trait, especially
the Openness aspect, which encompasses fantasy-proneness as one of its facets
(DeYoung, 2015b). Two relatively small functional connectivity studies offer some
tentative preliminary support for this hypothesis. One found that Openness/Intellect was
associated with increased connectivity between the main midline hubs of the default
network, in medial PFC and precuneus (Adelstein et al., 2011), whereas the other found
that Openness/Intellect was associated with connectivity in more parietal components of
the default network instead (Sampaio et al., 2014).
Studies of the association of Openness/Intellect with the volume of regions throughout
the brain have been inconsistent, often finding no significant effects despite samples
larger than 100 (Bjørnebekk et al., 2013; DeYoung et al., 2010; Hu et al., 2011;
Kapogiannis et al., 2013; Li et al., 2014; Liu et al., 2013). An MRI study of change in brain
structure in 274 adults (M = 51, SD = 12 years) over a period of 6–9 years found that
Openness/Intellect was negatively correlated with an age-related decline in gray matter
volume in the right inferior parietal lobule, a region linked to intelligence and creativity
(Taki et al., 2013). The volume of this area was previously found to be associated
positively with Openness/Intellect, though in a region too small to be significant after
correction for multiple tests (DeYoung et al., 2010). Clearly, this area would be a sensible
region of interest for future research.
Two DTI studies have found apparently contradictory findings for Openness/Intellect,
which may be reconcilable through consideration of the differences between Openness
and Intellect in their associations with IQ and positive schizotypy or psychoticism
(comprising magical ideation and perceptual aberrations). The first study found a
negative association between Openness/Intellect and white matter integrity in the frontal
lobes (Jung, Grazioplene, Caprihan, Chavez, & Haier, 2010), whereas the second study
found a positive association (Xu & Potenza, 2012). The major difference between the two
studies appears to be that the first controlled for IQ whereas the second did not.
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Importantly, frontal white matter integrity is positively associated with IQ but negatively
related to psychoticism (Chiang et al., 2009; Nelson et al., 2011). Intellect is
independently associated with IQ, whereas Openness is not (DeYoung, Quilty, Peterson,
& Gray, 2014), so controlling for IQ should render the residual Openness/Intellect scores
closer to Openness. Further, Openness is positively related to psychoticism, whereas
Intellect is negatively related to it (Chmielewski et al., 2014; DeYoung et al., 2012). In
combination, these pieces of evidence suggest that Openness and Intellect might be
differentially related to frontal white matter integrity, and future research should
measure them separately.
We close this section by noting the possibility that serotonin may play some role in
Openness/Intellect. A PET study of 50 people (Kalbitzer et al., 2009) found that Openness/
Intellect predicted serotonin transporter binding in the midbrain (whereas Neuroticism
did not). In a sample that small, this finding might simply be a false positive. However,
the involvement of serotonin in Openness/Intellect is rendered more plausible by the fact
that most hallucinogenic drugs act directly on the serotonergic system. A longitudinal
study of 52 hallucinogen-naive adults who received doses of psilocybin (the active
serotonergic agent in hallucinogenic mushrooms) or an active placebo (methylphenidate)
found that participants showed increases in Openness/Intellect following psilocybin but
not placebo (MacLean, Johnson, & Griffiths, 2011). Even more dramatically, Openness/
Intellect remained elevated over a year later for the 30 participants who had had mystical
experiences while on psilocybin. No other FFM traits were affected. Of course, it is
possible that dramatic disruptions of the serotonergic system by hallucinogens might
influence Openness/Intellect even if normal variation in that system does not.
Nonetheless, people high in Openness (especially when also low in Intellect) appear to be
susceptible to cognitive and perceptual distortions of the kind that are greatly
exaggerated in hallucination (i.e., to psychoticism), and these might be associated with
reduced serotonergic function (Chmielewski et al., 2014; DeYoung et al., 2012).
Conscientiousness
CB5T posits that the function of Conscientiousness is to facilitate the pursuit of
nonimmediate goals and rule-based behavior (DeYoung, 2015a). This function is critical
to the successful navigation of human culture, and, indeed, Conscientiousness is typically
the best psychological predictor, after intelligence, of academic and occupational
success, as well as health-promoting behaviors and longevity (Ozer & Benet-Martinez,
2006; Roberts, Lejuez, Krueger, Richards, & Hill, 2014). The two aspects of
Conscientiousness are Industriousness, reflecting the ability and tendency to suppress
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disruptive impulses and persist in working toward nonimmediate goals, and Orderliness,
which involves a tendency to adopt and follow rules, whether these rules are self-
generated or imposed by others (DeYoung et al., 2007; see also the chapter by Jackson
and Roberts).
The low pole of the Conscientiousness dimension is often described as “impulsivity,” but
impulsivity is a complex construct, and multiple types of impulsivity can be identified, not
all of which are equivalent to low Conscientiousness (DeYoung, 2010a). The UPPS model
(Whiteside & Lynam, 2001) identifies four types of impulsivity, of which lack of
Perseverance is the most clearly related to Conscientiousness, being essentially
equivalent to low Industriousness. Lack of Premeditation, the tendency to act quickly
without deliberation, is also clearly linked to Conscientiousness, but it appears to be a
blend of low Conscientiousness and high Extraversion and may therefore have somewhat
different biological substrates than other traits in the Conscientiousness domain. For
example, one fMRI study found that reward-related activity in the ventral striatum was
positively associated with scores on the Barratt Impulsivity Scale, a commonly used
measure that corresponds most closely to lack of Premeditation (Forbes et al., 2009;
Whiteside & Lynam, 2001). This finding seems likely to have been driven by reward-
related variance linked to Extraversion. The other two types of impulsivity in the UPPS
system are Urgency, which reflects the broader Stability metatrait, and Sensation
Seeking, most closely linked to Extraversion (DeYoung, 2010a).
Humans are highly unusual in their ability to follow explicit systems of rules and plan for
the distant future, so it is perhaps not surprising that chimpanzees are the only other
species in which a trait analogous to Conscientiousness has been identified (Freeman &
Gosling, 2010; Gosling & John, 1999). Other species obviously need to inhibit disruptive
impulses, but individual differences in impulse control may simply be reflected in
dimensions analogous to Neuroticism and Agreeableness that are related to more
immediate goals and are influenced by serotonin. As noted above, CB5T hypothesizes that
the variance Conscientiousness shares with Neuroticism and Agreeableness is linked to
serotonin. A fenfluramine challenge study found that Conscientiousness was positively
associated with central serotonergic function in men (Manuck et al., 1998). Another study
failed to replicate this effect, but its sample was only half as large (Brummett et al.,
2008). In a study of 75 men, Manuck, Flory, Ferrell, Mann, and Muldoon (2000) used a
fenfluramine challenge to show that central serotonergic function was negatively
associated with a combined measure of Hostility, Aggression, and lack of Premeditation
(the latter assessed by the Barratt Impulsivity Scale), a composite that is probably a good
indicator of low Stability. Serotonin remains a plausible component of the substrate of
Conscientiousness, but more research is needed.
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Considerable evidence exists to implicate the PFC in Conscientiousness, which is sensible
given the central role of PFC in following rules and maintaining goal representations
(Bunge & Zelazo, 2006; Miller & Cohen, 2001). The PFC is the brain region most
expanded in human evolution (Deacon, 1997; Hill et al., 2010), so this association is
consistent with the fact that only humans and their closest evolutionary relatives appear
to have a distinct trait of Conscientiousness. Multiple MRI studies have found that
Conscientiousness was positively associated with the volume of regions in the
dorsolateral PFC (DeYoung et al., 2010; Jackson et al., 2011; Kapogiannis et al., 2013),
though other studies have not replicated these findings (Bjørnebekk et al., 2013; Hu et
al., 2011; Liu et al., 2013). An MRI study comparing 199 brain-damaged patients to 50
healthy controls found that focal damage to the left dorsolateral prefrontal cortex was
associated with lower scores on Conscientiousness, especially the self-discipline facet,
which is a marker of Industriousness (Forbes et al., 2014).
The association of Conscientiousness with dorsolateral PFC raises an interesting question
about the differentiation of Conscientiousness from other traits that have been linked to
dorsolateral PFC, particularly Intellect, intelligence, and working memory capacity. The
latter three traits are all related and can be grouped together in the Intellect dimension
(DeYoung, 2015b; DeYoung et al., 2009, 2012), whereas Conscientiousness is not related
to either intelligence or working memory (except for a possible weak negative correlation
with intelligence; DeYoung, 2011; DeYoung et al., 2014). We propose that Intellect and
Conscientiousness may reflect variations in two different large-scale neural networks,
both of which involve dorsolateral PFC.
Functional connectivity maps have identified two strongly interdigitated networks in the
lateral PFC, anterior insula, putamen, ACC and adjacent medial PFC, lateral parietal
cortex, and posterior temporal cortex (Choi et al., 2012; Yeo et al., 2011). The first,
known as the frontoparietal or cognitive control network, is the major substrate of
working memory and intelligence, and parts of it have been associated with both
Openness/Intellect in general and Intellect in particular (DeYoung et al., 2009, 2010; Taki
et al., 2013). The second, known as the ventral attention or salience network, is a good
candidate as a substrate of Conscientiousness (DeYoung, 2015a). Its broad function
appears to entail reorienting attention away from distractions and toward stimuli
important for goal pursuit (Fox, Corbetta, Snyder, Vincent, & Raichle, 2006). It is often
called “ventral” due to research focusing on two important nodes of the network, in the
right inferior frontal gyrus and the temperoparietal junction, but it nonetheless
incorporates regions of the dorsal PFC as well, including the region of the middle frontal
gyrus where Conscientiousness has been found to correlate positively with volume
(DeYoung et al., 2010; Kapogiannis et al., 2013; Yeo et al., 2011). Not only that, but other
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regions in which Conscientiousness has been linked to brain structure and function fall
within this network, as we will now review.
Several studies have linked Conscientiousness or the Barratt Impulsivity Scale to
variations in the anterior insula (in what follows, we describe the impulsivity findings in
terms of “Premeditation,” so that they are keyed in the same direction as
Conscientiousness). One structural MRI study found that Conscientiousness was
negatively associated with white matter volume in the insula and adjacent putamen,
caudate, and ACC (Liu et al., 2013), and another found that the cortical thickness of the
anterior insula was negatively correlated with Premeditation (Churchwell & Yurgelun-
Todd, 2013). In an fMRI study of response inhibition, Premeditation was positively
associated with activation of the anterior insula and lateral frontal cortex on trials when
inhibition was required. It was also associated during those trials with greater functional
connectivity of the right anterior insula with regions of the PFC and visual cortex (Farr et
al., 2012).
Several MRI studies have implicated the dorsal ACC and adjacent medial PFC in
Conscientiousness. One structural study found that Premeditation was negatively related
to volume in the left ACC (Matsuo et al., 2009; this study also found positive associations
with VMPFC volumes). Another found that a measure of Conscientiousness in adolescents
(Effortful Control) predicted a leftward asymmetry in dorsal ACC anatomy (Whittle et al.,
2009). In an fMRI study of response inhibition, Premeditation was negatively associated
with activity in the dorsal ACC and caudate (Brown, Manuck, Flory, & Hariri, 2006). A
resting-state fMRI study found that Conscientiousness was associated with functional
connectivity in the ACC and adjacent medial PFC (Adelstein et al., 2011).
The overall pattern that emerges suggests that Conscientiousness is associated with
greater volume in the lateral PFC but with reduced volume in other areas of the ventral
attention network. This suggests the hypothesis that Conscientiousness depends in part
on the balance between the portions of this network that generate signals of motivational
salience and those that engage in attentional and behavioral control in response to those
signals. This hypothesis is also reasonably consistent with the fMRI finding, mentioned
above, that Premeditation predicted greater connectivity of the insula with the lateral
PFC when response inhibition was required than when it was not (Farr et al., 2012).
Some caution is needed moving forward, however, because Premeditation is a fairly
peripheral Conscientiousness facet, not strongly linked to either Industriousness or
Orderliness (DeYoung, 2010a), so findings may not generalize easily to the broader
Conscientiousness dimension.
We close our discussion of Conscientiousness by noting one brain region that has been
associated with Conscientiousness in multiple studies but has not been identified as part
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of the ventral attention network—namely, the fusiform gyrus. In one large structural MRI
study, Conscientiousness was negatively correlated with white matter volume in the left
fusiform gyrus (Liu et al., 2013). In another, which did not separate gray and white
matter, Conscientiousness was also negatively associated with volume in the fusiform
gyrus (DeYoung et al., 2010). Many studies of brain structure consider gray matter
volume only, and future studies may benefit from considering both gray and white
matter. Finally, a study of personality and neurological change in frontotemporal
dementia found that declines in Conscientiousness were associated with relative
preservation of gray matter in the fusiform gyrus (Mahoney, Rohrer, Omar, Rossor, &
Warren, 2011); this study was quite small (N = 30), but we mention it because of the
interesting parallel with structural studies of healthy adults.
Agreeableness
CB5T posits that cooperation and altruism—that is, the processes of coordinating our own
goals with those of others—are the core functions underlying Agreeableness (see also the
chapter by Graziano and Tobin). This entails that Agreeableness should be associated
with the ability and tendency to understand the perspectives of others and to adjust our
own behavior to accommodate them (Nettle & Liddle, 2008). The most obvious
candidates as a neural substrate for Agreeableness are the many parts of the default
network that are involved in decoding the mental states of others (Andrews-Hanna et al.,
2014). Two resting-state fMRI studies have reported that Agreeableness is positively
associated with functional connectivity among major hubs of the default network
(Adelstein et al., 2011; Sampaio et al., 2014).
Two reasonably large structural MRI studies have found no association of regional brain
volumes with Agreeableness (Bjørnebekk et al., 2013; Liu et al., 2013), and others have
found associations that were not consistent (DeYoung et al., 2010; Hu et al., 2011;
Kapogiannis et al., 2013). Two of the latter studies reported a negative correlation of
Agreeableness with a region of the posterior superior temporal gyrus and sulcus that is
part of the default network and is important for interpreting the actions and intentions of
others by decoding biological motion, but one study found the effect in the left
hemisphere and one in the right (DeYoung et al., 2010; Kapogiannis et al., 2013). Clearly,
further research is necessary on this brain region’s relation to Agreeableness.
The two aspects of Agreeableness are Compassion, reflecting empathy and sympathy (the
tendency to care about others emotionally), and Politeness, the tendency to conform to
social norms and to refrain from belligerence and exploitation of others. In surveying the
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relatively sparse neuroscience research on Agreeableness, it is important to note that
measures of empathy reflect Compassion, whereas measures of aggression reflect low
Politeness (DeYoung et al., 2007, 2013). Compassion scales include the Empathic
Concern subscale (and potentially the Perspective Taking subscale) of the Interpersonal
Reactivity Index (IRI; Davis, 1983), the Balanced Emotional Empathy Scale (Mehrabian &
Epstein, 1972), and the Empathy Quotient (Baron-Cohen & Wheelwright, 2004).
MRI research suggests two general types of neural processes involved in empathy. The
first involves the default network and the ability to simulate the mental states of others.
The second involves what can be called “mirroring”—neural activation that occurs, while
observing someone else, in the same sensory networks that would be active if the
observer were having an experience similar to that of the observed person. The most
studied form of empathy in fMRI is empathy for pain, and here regions of the anterior
insula (involved in integrating emotional and sensory information with cognitive
processes) and the mid-cingulate cortex appear to constitute the circuit that is active in
mirroring (i.e., they are active for both one’s own pain and the pain of others), whereas
default network regions are involved in recruiting those pain-related regions by decoding
the experience of others (Lamm, Decety, & Singer, 2011). A number of fMRI studies of
empathy for pain have reported an association between trait levels of empathy and neural
responses, with inconsistent results. As with many traits, however, most of these studies
have been too small to detect individual differences adequately. In a recent meta-
analysis, for example, none of the 15 studies that examined trait effects had a sample
larger than 30 (Lamm et al., 2011, Appendix B).
Social or emotional pain has been found to activate brain systems similar to physical
pain, and one larger fMRI study found that trait empathy predicted greater functional
connectivity of the anterior insula with the PFC and limbic regions while watching videos
of the suffering of others (Bernhardt, Klimecki, Leiberg, & Singer, 2014). (The default
network, like the ventral attention and frontoparietal networks, includes regions of
anterior insula; Yeo et al., 2011.) Two structural MRI studies found empathy to be
positively associated with regional volume in the anterior insula (Mutschler, Reinbold,
Wankerl, Seifritz, & Ball, 2013; Sassa et al., 2012), but one found no association
(Takeuchi et al., 2014). Another study, with a sample of 118, found a negative correlation
of empathy with anterior insula volume; however, this study used all four subscales of the
IRI as simultaneous predictors, and the process of residualization may have shifted the
meaning of the Empathic Concern subscale (Banissy, Kanai, Walsh, & Rees, 2012). We
would not recommend partialling out shared variance from the IRI subscales without a
clear theoretical justification. One DTI study found that empathy was widely positively
correlated with white matter integrity in tracts connecting affective, perceptual, and
action-oriented brain regions, which is potentially consistent with the sophisticated
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integration of different types of information necessary for both understanding and
sharing the emotional experience of others (Parkinson & Wheatley, 2014).
Agreeableness in general, and Politeness specifically, are likely to be associated with
emotion regulation. Agreeableness predicts suppression of aggressive impulses and other
socially disruptive emotions (Meier, Robinson, & Wilkowski, 2006), and one fairly small
fMRI study found that Agreeableness predicted greater right lateral PFC activation in
response to fearful compared to neutral faces (Haas, Omura, Constable, & Canli, 2007),
which the authors argued might reflect automatic engagement of emotion regulation
when facing stimuli signaling potential threat or conflict. In a structural MRI of 56 men
drawn from a larger cohort studied since childhood, amygdala volume at age 26 was
negatively associated with both current aggression and a history of aggression (Pardini,
Raine, Erickson, & Loeber, 2014).
Inasmuch as Agreeableness involves the ability to suppress aggressive impulses, it is
likely to be facilitated by serotonin (Montoya, Terberg, Bos, & Van Honk, 2012). An
interview-based life history of aggression measure was negatively associated with
serotonin function in men but not women (Manuck et al., 1998), whereas a 2-month trial
on an SSRI significantly reduced aggression in women but not men (Kamarck et al.,
2009). One twin study found that variation in the serotonin transporter gene accounted
for 10% of the genetic correlation between Neuroticism and Agreeableness (Jang et al.,
2001).
Other neurotransmitters likely to be involved in Agreeableness include testosterone and
oxytocin. Testosterone levels appear to be negatively associated with Agreeableness,
particularly Politeness versus Aggression (DeYoung et al., 2013; Montoya et al., 2012;
Turan et al., 2014). Oxytocin is critically involved in processes of social bonding and
attachment. Trait empathy has been found to moderate the effects of acute oxytocin
administration (Perry, Mankuta, & Shamay-Tsoory, 2015). Difficulties in the assessment
of oxytocin levels suggest the need for caution in research on their association with
personality (Christensen, Shiyanov, Estepp, & Schlager, 2014).
Future Directions
Much new personality neuroscience research has appeared in recent years, as is evident
when comparing this chapter with previous reviews of the field (DeYoung, 2010b;
DeYoung & Gray, 2009; Zuckerman, 2005). Further, personality neuroscience research is
improving in quality, allowing this review to be reasonably critical and to focus on larger
studies. Still, because personality neuroscience is such a young field, its future is wide
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open. Very few findings about the neurobiological sources of the FFM are sufficiently
well-supported to have the status of fact. Every trait needs much additional research
before we begin to have anything like a clear picture of the many biological parameters
that account for its variation.
We have two major recommendations for those interested in pursuing personality
neuroscience. First, work with existing or new theories in order to develop specific
testable hypotheses, rather than pursuing purely exploratory research. Readers should be
able to glean from this chapter many hypotheses that can be tested by future research. In
theory-driven research, it will often be advantageous to test associations with regions of
interest in the brain specified a priori. Second, collect samples large enough for good
research on individual differences—near 100 at a minimum, preferably over 200. We
believe that existing theories of the psychological functions underlying the FFM, such as
CB5T, are sufficiently well developed to allow rapid advancement of our understanding of
the biological basis of traits, as long as rigorous methods are employed.
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Timothy A. Allen
University of Minnesota
Colin G. DeYoung
Colin G. DeYoung is Associate Professor, Department of Psychology, University of
Minnesota.
