Personality Neuroscience and the FFM 1
Personality Neuroscience and the Five Factor Model
Timothy A. Allen
University of Minnesota
Colin G. DeYoung
University of Minnesota
To appear in: Widiger, T. A. (Ed.). Oxford Handbook of the Five Factor Model. New York: Oxford
University Press.
Contact Information:
Timothy A. Allen, Institute of Child Development, University of Minnesota, 51 East River Road,
Minneapolis, MN 55408; E-mail: [email protected]
Colin G. DeYoung, Department of Psychology, University of Minnesota, 75 East River Rd., Minneapolis,
MN, 55455; E-mail: [email protected]
Personality Neuroscience and the FFM 2
Abstract
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 neuroscience 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
Personality Neuroscience and the FFM 3
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 & McRae, 1992; Goldberg, 1990; John, Naumann, & Soto, 2008; Markon, Krueger, &
Watson, 2005). 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.
Personality Neuroscience and the FFM 4
The rise of neuroscience technologies for brain imaging and molecular genetics has led to a rapid
proliferation of empirical reports over the last 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 last 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, 2014). 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 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
Personality Neuroscience and the FFM 5
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 non-questionnaire 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 tell 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 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
Personality Neuroscience and the FFM 6
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 “default mode network”), and 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, & Schachter, 2008). In contrasts of task versus rest, it was noted that a particular set of
brain regions were 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 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, & Spring, 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 where the limitation that task-based analysis of fMRI requires a contrast between
two conditions led to an important discovery.
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 impressive spatial resolution.
(2) Electrophysiological techniques. Electroencephelography (EEG) measures neural activity by
recording electrical activity along the scalp. It has 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
Personality Neuroscience and the FFM 7
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.
(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 like 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 that, 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 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
Personality Neuroscience and the FFM 8
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 where 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
Personality Neuroscience and the FFM 9
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 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 are between r = .2 and .3 (Hemphill, 2003) and that the average effect size in
personality research has been estimated at .21 (Richard, Bond, & Stokes-Zoota, 2003), researchers should
attempt to ensure that they have 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 a voxel-level threshold
of p < .01 for most neuroimaging research on personality (subsequently corrected 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 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
Personality Neuroscience and the FFM 10
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 out another, non-independent 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 (Fanelli, 2012;
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).
Personality Neuroscience and the FFM 11
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 non-linear 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.uni-jena.de/vbm/segmentation/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 almost 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,
Personality Neuroscience and the FFM 12
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, Angelitner, & Strelau, 1997); rather, they are indicative of the fact that complex traits are
massively polygenic, influenced by many thousands of variations in the genome, 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
not yet 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 explanation,
but in order to develop 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, one can then derive corresponding neurobiological
hypotheses.
Personality Neuroscience and the FFM 13
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, 2014; 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, 2014).
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 variation in the parameters
of evolved cybernetic mechanisms (DeYoung, 2014). (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 or not 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.
Personality Neuroscience and the FFM 14
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. 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.
Further, 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 (Digman, 1997; DeYoung, 2006; 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.
[Insert Figure 1 and Table 1 about here.]
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, 2010, 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
Personality Neuroscience and the FFM 15
of these traits reflects a different kind of stability: low Neuroticism reflects emotional stability,
Conscientiousness motivational stability, and Agreeableness 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, &
Hikasaka, 2010; DeYoung, 2013). Though not as widespread in the brain 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, 2014). 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,
2014).
The third level of traits labeled in Figure 1 are 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 PI-R, a popular measure of the FFM, identifies six facets for each, its 30 facets were
derived rationally through 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 it 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
Personality Neuroscience and the FFM 16
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 non-genetic 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, 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
Personality Neuroscience and the FFM 17
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, one
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 four-year-old 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 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, 2014). Here CB5T builds on the work of Depue and
Collins (1999), who argued that sensitivity to incentive reward mediated by the dopaminergic system is
Personality Neuroscience and the FFM 18
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. 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 and Stemmler, 2006; Wacker,
Chavanon, & Stemmler, 2006; Wacker, Mueller, Pizzagalli, Hennig, & Stemmler, 2013). 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, one 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 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.
Personality Neuroscience and the FFM 19
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, 2014). 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
(DeYoung et al., 2013; Depue & Morrone-Strupinsky, 2005). Whereas Assertiveness encompasses traits
like 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 most well established findings are merely
preliminary to a thorough mechanistic understanding.
Personality Neuroscience and the FFM 20
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 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 neurons encode 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, 2014; 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 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, 2014).
Personality Neuroscience and the FFM 21
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 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 the 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; Liu et al., 2013; Kapogiannis et al., 2012). 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 under-powered 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 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
Personality Neuroscience and the FFM 22
(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 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 one, therefore, abandon the idea of linking Extraversion to hemispheric asymmetry?
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
Personality Neuroscience and the FFM 23
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 agentic Extraversion and
posterior versus frontal resting delta/theta activity (Wacker & Gatt, 2010).
Several fMRI studies with samples around N = 40 to 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
Personality Neuroscience and the FFM 24
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 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, 2014).
Uncertainty is innately threatening because the inability to predict the outcome of 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, 2011; 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. Largely because
Neuroticism is the major personality risk factor for psychopathology (Lahey, 2009), more neuroscience
research is being conducted on Neuroticism than any other trait in the FFM. To parse this research, CB5T
draws on Gray and McNaughton’s (2000) theory that Neuroticism 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 interpretation of existing research on Neuroticism and
inform hypotheses in future research.
Personality Neuroscience and the FFM 25
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 like 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
Personality Neuroscience and the FFM 26
apparent in both studies, and the 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; Zuckerman, 2005; White & Depue, 1999). Other
understudied neurotransmitters involved in stress responses may influence Neuroticism as well. One
extensive study using a variety of neuroscience methods linked trait anxiety with 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, Hoppman, & Klumb, 2010; Polk et al., 2005), as well as 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.
Personality Neuroscience and the FFM 27
Interestingly, an overabundance of cortisol is known to potentiate excitotoxic cell death in
neurons (Sapolsky, 1994), a fact which Knutson, Momenan, Rawlings, Fong, and Hommer (2001)
suggested as a possible explanation for their and others’ finding that Neuroticism is negatively related to
global measures of brain volume, such as volume of cerebral gray matter, 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.
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., 2013), only 7 of them were larger than 25, and only
one was larger than 60. Meta-analysis cannot solve the problems created by under-powered 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. (2013) 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., 2012),
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
Personality Neuroscience and the FFM 28
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., 2009; 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, which
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).
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 thickness of this region to
be 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
Personality Neuroscience and the FFM 29
above of positive correlations between Extraversion and nearby regions of the VMPFC. Another study
that examined cortical area as well as thickness found Neuroticism to be 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 Holmes et al.’s (2012) sample, this is likely to be the only region of the cortex
where 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 Neuroticism to be negatively associated with other regions of 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 emotion regulation that are characteristic of Neuroticism, as this
region is part of the default network crucially involved in self-evaluation and emotion regulation
(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 or not 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 medial PFC response when viewing blocks of sad facial expressions, but
not fearful or happy facial expressions (though in a small sample; N = 29).
The emotion regulation hypothesis is also consistent with a number of studies of both functional
and structural connectivity, which have found Neuroticism to predict reduced connectivity between
frontal cortical regions and the amygdala (sometimes in conjunction with increased connectivity of
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
amygdala and PFC regions while viewing neutral, fearful, and happy faces. Servaas et al. (2013) found
Neuroticism to be negatively correlated with the synchrony of amygdala and hippocampus with
Personality Neuroscience and the FFM 30
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 in fronto-parietal, 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 Neuroticism to be
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 the 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,
Gyrdeland, 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.
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
Personality Neuroscience and the FFM 31
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-infared 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 damage 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 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,
like anxiety and depression, which are linked to passive avoidance. In contrast, traits in the Volatility
subfactor, like 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 Neuroticism to predict
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
Neuroticism to be 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 Neuroticism to predict
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.
Personality Neuroscience and the FFM 32
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 (by pressing a button that enlarged the image,
creating the illusion of approach) or to avoid them (by pressing a button that reduced the image in size).
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 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 inhbition when approaching any stimulus,
whereas Volitality 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, 2015). 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 just to one aspect of Openness/Intellect.
Personality Neuroscience and the FFM 33
The curiosity and innovation that is common to both Openness and Intellect is 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, 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 pre-conscious 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 probably reflective of dopaminergic function (much like examination of the FRN in
Personality Neuroscience and the FFM 34
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 & 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 didn’t
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), one 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, 2015). Two relatively small functional connectivity studies offer
Personality Neuroscience and the FFM 35
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 Openness/Intellect to be 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 6-9
years that found that Openness/Intellect was negatively correlated with age-related decline in gray matter
volume in the right inferior parietal lobule, a region linked to intelligence and creativity (Taki et al.,
2013). 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
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. 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 (Chmielewski et al., 2013; DeYoung et al., 2012). In combination, these pieces of evidence
Personality Neuroscience and the FFM 36
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-naïve 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., 2013; DeYoung et al., 2012).
Conscientiousness
CB5T posits that the function of Conscientiousness is to facilitate the pursuit of non-immediate
goals and rule-based behavior (DeYoung, 2014). 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, 2012). The two aspects of
Conscientiousness are Industriousness, reflecting the ability and tendency to suppress disruptive impulses
Personality Neuroscience and the FFM 37
and persist in working toward non-immediate 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).
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 unsurprising 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,
Personality Neuroscience and the FFM 38
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.
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 Conscientiousness to be 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 damage 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, 2015; 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 variation 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
Personality Neuroscience and the FFM 39
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., 2012). The second, known as the ventral attention or salience network, is a good candidate as
a substrate of Conscientiousness (DeYoung, 2014). Its broad function appears to entail reorienting
attention away from distractions and toward stimuli important for one’s goals (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 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 regions where 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 variation 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 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
Personality Neuroscience and the FFM 40
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 lateral PFC but 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 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 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 declines in
Conscientiousness to be 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.
Personality Neuroscience and the FFM 41
Agreeableness
CB5T posits that cooperation and altruism—that is, the processes of coordinating one’s own
goals with those of others—are the core functions underlying Agreeableness. This entails that
Agreeableness should be associated with the ability and tendency to understand the perspectives of others
and to adjust one’s 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 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 taking advantage of others. In surveying the 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).
Personality Neuroscience and the FFM 42
MRI research suggests two general types of neural process 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 one were having a similar experience as 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 and others’ pain), whereas default network regions are involved in recruiting those pain-related
regions by decoding others’ experience (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 similar brain systems to physical pain, and
one larger fMRI study found that trait empathy predicted greater functional connectivity of anterior insula
with PFC and limbic regions while watching videos of others’ suffering (Bernhardt, Klimecki, Leiberg, &
Singer, 2013). (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., 2013). 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 empathy to be widely positively correlated with white matter integrity
in tracts connecting affective, perceptual, and action oriented brain regions, which is potentially consistent
Personality Neuroscience and the FFM 43
with the sophisticated integration of different types of information necessary for both understanding and
sharing others’ emotional experience (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 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 two 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, 2014). 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
Personality Neuroscience and the FFM 44
Much new personality neuroscience research has appeared in recent years, as is evident when
comparing this chapter with previous reviews of the field (DeYoung, 2010; 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 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 will 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.
Personality Neuroscience and the FFM 45
References
Adelstein, J. S., Shehzad, Z., Mennes, M., DeYoung, C. G., Zuo, X.-N., Kelly, C., … Milham, M. P.
(2011). Personality is reflected in the brain’s intrinsic functional architecture. PloS One, 6(11),
e27633.
Aghajani, M., Veer, I. M., van Tol, M.-J., Aleman, A., van Buchem, M. A., Veltman, D. J., … van der
Wee, N. J. (2014). Neuroticism and extraversion are associated with amygdala resting-state
functional connectivity. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 836–848.
Amodio, D. M., Master, S. L., Yee, C. M., & Taylor, S. E. (2008). Neurocognitive components of the
behavioral inhibition and activation systems: Implications for theories of self-regulation.
Psychophysiology, 45(1), 11–19.
Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated
thought: Component processes, dynamic control, and clinical relevance. Annals of the New York
Academy of Sciences, 1316, 29–52.
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry--the methods. NeuroImage, 11, 805–
821.
Banissy, M. J., Kanai, R., Walsh, V., & Rees, G. (2012). Inter-individual differences in empathy are
reflected in human brain structure. NeuroImage, 62(3), 2034–2039.
Baron-Cohen, S., & Wheelwright, S. (2004). The empathy quotient: An investigation of adults with
Asperger Syndrome or high functioning Autism, and normal sex differences. Journal of Autism and
Developmental Disorders, 34(2), 163–175.
Barrós-Loscertales, A., Meseguer, V., Sanjuán, A., Belloch, V., Parcet, M. A., Torrubia, R., & Avila, C.
(2006). Behavioral inhibition system activity is associated with increased amygdala and
hippocampal gray matter volume: A voxel-based morphometry study. NeuroImage, 33(3), 1011–
1015.
Benjamin, J., Li, L., Patterson, C., Greenberg, B. D., Murphy, D. L., & Hamer, D. H. (1996). Population
and familial association between the D4 dopamine receptor gene and measures of novelty seeking.
Nature Genetics, 12(1), 81–84.
Bernhardt, B. C., Klimecki, O. M., Leiberg, S., & Singer, T. (2014). Structural covariance networks of the
dorsal anterior insula predict females’ individual differences in empathic responding. Cerebral
Cortex, 24(8), 2189–2198.
Berridge, K. C., Robinson, T. E., & Aldridge, J. W. (2009). Dissecting components of reward: “Liking”,
“wanting”, and learning. Current Opinion in Pharmacology, 9(1), 65–73.
Bjørnebekk, A., Fjell, A. M., Walhovd, K. B., Grydeland, H., Torgersen, S., & Westlye, L. T. (2013).
Neuronal correlates of the five factor model (FFM) of human personality: Multimodal imaging in a
large healthy sample. NeuroImage, 65, 194–208.
Block, J. (1995). A contrarian view of the five-factor approach to personality description. Psychological
Bulletin, 117(2), 187–215.
Bookstein, F. L. (2001). “Voxel-based morphometry” should not be used with imperfectly registered
images. NeuroImage, 14(6), 1454–62.
Bradley, R., Binder, E., Epstein, M., Tang, Y., Nair, H., Liu, W., … Ressler, K. (2008). Influence of child
abuse on adult depression: Moderation by the corticotropin-releasing hormone receptor gene.
Archives of General Psychiatry, 65(2), 190–200.
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control:
Rewarding, aversive, and alerting. Neuron, 68(5), 815–834.
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate
cortex. Science, 307(5712), 1118–1121.
Brown, S. M., Manuck, S. B., Flory, J. D., & Hariri, A. R. (2006). Neural basis of individual differences
in impulsivity: Contributions of corticolimbic circuits for behavioral arousal and control. Emotion,
6(2), 239–245.
Personality Neuroscience and the FFM 46
Brummett, B. H., Boyle, S. H., Kuhn, C. M., Siegler, I. C., & Williams, R. B. (2008). Associations among
central nervous system serotonergic function and neuroticism are moderated by gender. Biological
Psychology, 78(2), 200–203.
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: Anatomy,
function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.
Bunge, S. A., & Zelazo, P. D. (2006). A brain-based account of the development of rule use in childhood.
Current Directions in Psychological Science, 15(3), 118–121.
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M.
R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature
Reviews. Neuroscience, 14(5), 365–76.
Canli, T., Sivers, H., Whitfield, S. L., Gotlib, I. H., & Gabrieli, J. D. E. (2002). Amygdala response to
happy faces as a function of extraversion. Science, 296(5576), 2191.
Canli, T., Zhao, Z., Desmond, J. E., Kang, E., Gross, J., & Gabrieli, J. D. E. (2001). An fMRI study of
personality influences on brain reactivity to emotional stimuli. Behavioral Neuroscience, 115(1),
33–42.
Carp, J. (2012). The secret lives of experiments: Methods reporting in the fMRI literature. NeuroImage,
63(1), 289–300.
Carver, C. S., Johnson, S. L., & Joormann, J. (2008). Serotonergic function, two-mode models of self-
regulation, and vulnerability to depression: What depression has in common with impulsive
aggression. Psychological Bulletin, 134(6), 912–943.
Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior. New York: Cambridge
University Press.
Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses
to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social
Psychology, 67(2), 319–333.
Chang, L., Connelly, B. S., & Geeza, A. A. (2012). Separating method factors and higher order traits of
the Big Five: A meta-analytic multitrait–multimethod approach. Journal of Personality and Social
Psychology, 102(2), 408–426.
Chavanon, M.-L., Wacker, J., & Stemmler, G. (2011). Rostral anterior cingulate activity generates
posterior versus anterior theta activity linked to agentic extraversion. Cognitive, Affective, &
Behavioral Neuroscience, 11(2), 172–185.
Chavanon, M.-L., Wacker, J., & Stemmler, G. (2013). Paradoxical dopaminergic drug effects in
extraversion: Dose- and time-dependent effects of sulpiride on EEG theta activity. Frontiers in
Human Neuroscience, 7, 117.
Cherbuin, N., Windsor, T. D., Anstey, K. J., Maller, J. J., Meslin, C., & Sachdev, P. S. (2008).
Hippocampal volume is positively associated with behavioural inhibition (BIS) in a large
community-based sample of mid-life adults: The PATH through life study. Social Cognitive and
Affective Neuroscience, 3(3), 262–269.
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., …
Thompson, P. M. (2009). Genetics of brain fiber architecture and intellectual performance. The
Journal of Neuroscience, 29(7), 2212–2224.
Chmielewski, M., Bagby, R. M., Markon, K., Ring, A. J., & Ryder, A. G. (2014). Openness to
experience, intellect, schizotypal personality disorder, and psychoticism: Resolving the controversy.
Journal of Personality Disorders, 28(4), 483–499.
Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012). The organization of the human striatum estimated by
intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 2242–2263.
Christensen, J. C., Shiyanov, P. A., Estepp, J. R., & Schlager, J. J. (2014). Lack of association between
human plasma oxytocin and interpersonal trust in a prisoner’s dilemma paradigm. PloS One, 9(12),
e116172.
Personality Neuroscience and the FFM 47
Churchwell, J. C., & Yurgelun-Todd, D. A. (2013). Age-related changes in insula cortical thickness and
impulsivity: Significance for emotional development and decision-making. Developmental
Cognitive Neuroscience, 6, 80–86.
Coan, J.A., & Allen, J. J. B. (2003). Frontal EEG asymmetry and the behavioral activation and inhibition
systems. Psychophysiology, 40(1), 106–114.
Coan, J. A., Allen, J. J. B., & McKnight, P. E. (2006). A capability model of individual differences in
frontal EEG asymmetry. Biological Psychology, 72(2), 198–207.
Cohen, M. X., Young, J., Baek, J.-M., Kessler, C., & Ranganath, C. (2005). Individual differences in
extraversion and dopamine genetics predict neural reward responses. Cognitive Brain Research,
25(3), 851–861.
Connelly, B. S., & Ones, D. S. (2010). An other perspective on personality: Meta-analytic integration of
observers’ accuracy and predictive validity. Psychological Bulletin, 136(6), 1092–1122.
Cooper, A. J., Duke, E., Pickering, A. D., & Smillie, L. D. (2014). Individual differences in reward
prediction error: Contrasting relations between feedback-related negativity and trait measures of
reward sensitivity, impulsivity and extraversion. Frontiers in Human Neuroscience, 8, 248.
Costa, P. T., Jr., & McCrae, R. R. (1992). NEO PI-R Professional Manual. Odessa, FL: Psychological
Assessment Resources.
Cremers, H. R., Demenescu, L. R., Aleman, A., Renken, R., van Tol, M. J., van der Wee, N. J., ... &
Roelofs, K. (2010). Neuroticism modulates amygdala—prefrontal connectivity in response to
negative emotional facial expressions.Neuroimage, 49(1), 963-970.
Cremers, H. R., van Tol, M.-J., Roelofs, K., Aleman, A., Zitman, F. G., van Buchem, M. A., … van der
Wee, N. J. A. (2011). Extraversion is linked to volume of the orbitofrontal cortex and amygdala.
PloS One, 6(12), e28421.
Cunningham, W. A, Arbuckle, N. L., Jahn, A., Mowrer, S. M., & Abduljalil, A. M. (2010). Aspects of
neuroticism and the amygdala: Chronic tuning from motivational styles. Neuropsychologia, 48(12),
3399–3404.
Davatzikos, C. (2004). Why voxel-based morphometric analysis should be used with great caution when
characterizing group differences. NeuroImage, 23(1), 17–20.
Davidson, R. J. (1998). Anterior electrophysiological asymmetries, emotion, and depression: Conceptual
and methodological conundrums. Psychophysiology, 35(5), 607–614.
Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional
approach. Journal of personality and social psychology,44(1), 113–126.
de Moor, M. H., Costa, P. T., Terracciano, A., Krueger, R. F., De Geus, E. J., Toshiko, T., ... & Metspalu,
A. (2010). Meta-analysis of genome-wide association studies for personality. Molecular
psychiatry, 17(3), 337–349.
De Pascalis, V., Cozzuto, G., Caprara, G. V., & Alessandri, G. (2013). Relations among EEG-alpha
asymmetry, BIS/BAS, and dispositional optimism. Biological Psychology, 94(1), 198–209.
Deacon, T. W. (1997). What makes the human brain different? Annual Review of Anthropology, 26(1),
337–357.
Denissen, J. J. A., & Penke, L. (2008). Motivational individual reaction norms underlying the five-factor
model of personality: First steps towards a theory-based conceptual framework. Journal of Research
in Personality, 42(5), 1285–1302.
Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine,
facilitation of incentive motivation, and extraversion. Behavioral and Brain Sciences, 22(3), 491–
517.
Depue, R. A., & Fu, Y. (2013). On the nature of extraversion: Variation in conditioned contextual
activation of dopamine-facilitated affective, cognitive, and motor processes. Frontiers in Human
Neuroscience, 7, 288.
Depue, R. A., Luciana, M., Arbisi, P., Collins, P., & Leon, A. (1994). Dopamine and the structure of
personality: Relation of agonist-induced dopamine activity to positive emotionality. Journal of
Personality and Social Psychology, 67(3), 485–498.
Personality Neuroscience and the FFM 48
DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant sample. Journal of
Personality and Social Psychology, 91(6), 1138–1151.
DeYoung, C. G. (2010a). Impulsivity as a personality trait. In K. D. Vohs & R. F. Baumeister (Eds.),
Handbook of self-regulation: Research, theory, and applications (2nd ed., pp. 485–502). New York:
Guilford Press.
DeYoung, C. G. (2010b). Personality neuroscience and the biology of traits. Social and Personality
Psychology Compass, 4(12), 1165–1180.
DeYoung, C. G. (2013). The neuromodulator of exploration: A unifying theory of the role of dopamine in
personality. Frontiers in Human Neuroscience, 7, 762.
DeYoung, C. G. (2014). Cybernetic Big Five Theory. Journal of Research in Personality. Online
publication before print. http://dx.doi.org/10.1016/j.jrp.2014.07.004.
DeYoung, C. G. (2015). Openness/Intellect: A dimension of personality reflecting cognitive exploration.
In M. L. Cooper & R. J. Larsen (Eds.), The APA handbook of personality and social psychology:
Personality processes and individual differences (Vol. 4, pp. 369–399). Washington, DC: American
Psychological Association.
DeYoung, C. G., Cicchetti, D., & Rogosch, F. A. (2011). Moderation of the association between
childhood maltreatment and neuroticism by the corticotropin-releasing hormone receptor 1 gene.
Journal of Child Psychology and Psychiatry, 52(8), 898–906.
DeYoung, C. G., Cicchetti, D., Rogosch, F. A., Gray, J. R., Eastman, M., & Grigorenko, E. L. (2011).
Sources of cognitive exploration: Genetic variation in the prefrontal dopamine system predicts
openness/intellect. Journal of Research in Personality, 45(4), 364–371.
DeYoung, C. G., & Gray, J. R. (2009). Personality neuroscience: Explaining individual differences in
affect, behavior, and cognition. In P. J. Corr & G. Matthews (Eds.), The Cambridge handbook of
personality (pp. 323–346). New York: Cambridge University Press.
DeYoung, C. G., Grazioplene, R. G., & Peterson, J. B. (2012). From madness to genius: The
openness/intellect trait domain as a paradoxical simplex. Journal of Research in Personality, 46(1),
63–78.
DeYoung, C. G., Hirsh, J. B., Shane, M. S., Papademetris, X., Rajeevan, N., & Gray, J. R. (2010). Testing
predictions from personality neuroscience. Brain structure and the big five. Psychological Science,
21(6), 820–828.
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2002). Higher-order factors of the Big Five predict
conformity: Are there neuroses of health? Personality and Individual Differences, 33(4), 533–552.
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2005). Sources of openness/intellect: Cognitive and
neuropsychological correlates of the fifth factor of personality. Journal of Personality, 73(4), 825–
858.
DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the
big five. Journal of Personality and Social Psychology, 93(5), 880–896.
DeYoung, C. G., Quilty, L. C., Peterson, J. B., & Gray, J. R. (2014). Openness to experience, intellect,
and cognitive ability. Journal of Personality Assessment, 96(1), 46–52.
DeYoung, C. G., Shamosh, N. A., Green, A. E., Braver, T. S., & Gray, J. R. (2009). Intellect as distinct
from openness: Differences revealed by fMRI of working memory. Journal of Personality and
Social Psychology, 97(5), 883–892.
DeYoung, C. G., Weisberg, Y. J., Quilty, L. C., & Peterson, J. B. (2013). Unifying the aspects of the big
five, the interpersonal circumplex, and trait affiliation. Journal of Personality, 81(5), 465–475.
Digman, J. M. (1997). Higher-order factors of the big five. Journal of Personality and Social Psychology,
73(6), 1246–1256.
Du, L., Bakish, D., Ravindran, A. V, & Hrdina, P. D. (2002). Does fluoxetine influence major depression
by modifying five-factor personality traits? Journal of Affective Disorders, 71(1-3), 235–241.
Ebstein, R. P., Novick, O., Umansky, R., Priel, B., Osher, Y., Blaine, D., … Belmaker, R. H. (1996).
Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait
of novelty seeking. Nature Genetics, 12(1), 78–80.
Personality Neuroscience and the FFM 49
Everhart, D. E., Demaree, H. A., & Harrison, D. W. (2008). The influence of hostility on
electroencephalographic activity and memory functioning during an affective memory task. Clinical
Neurophysiology, 119(1), 134–143.
Farr, O. M., Hu, S., Zhang, S., & Li, C. S. R. (2012). Decreased saliency processing as a neural measure
of Barratt impulsivity in healthy adults. NeuroImage, 63(3), 1070–1077.
Forbes, E. E., Brown, S. M., Kimak, M., Ferrell, R. E., Manuck, S. B., & Hariri, A. R. (2009). Genetic
variation in components of dopamine neurotransmission impacts ventral striatal reactivity associated
with impulsivity. Molecular Psychiatry, 14(1), 60–70.
Forbes, C. E., Poore, J. C., Krueger, F., Barbey, A. K., Solomon, J., & Grafman, J. (2014). The role of
executive function and the dorsolateral prefrontal cortex in the expression of neuroticism and
conscientiousness. Social neuroscience,9(2), 139–151.
Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal
activity distinguishes human dorsal and ventral attention systems. Proceedings of the National
Academy of Sciences of the United States of America, 103(26), 10046–10051.
Freeman, H. D., & Gosling, S. D. (2010). Personality in nonhuman primates: A review and evaluation of
past research. American Journal of Primatology, 72(8), 653–671.
Frokjaer, V. G., Mortensen, E. L., Nielsen, F. A., Haugbol, S., Pinborg, L. H., Adams, K. H., … Knudsen,
G. M. (2008). Frontolimbic serotonin 2A receptor binding in healthy subjects is associated with
personality risk factors for affective disorder. Biological Psychiatry, 63(6), 569–576.
Fuentes, P., Barrós-Loscertales, A., Bustamante, J. C., Rosell, P., Costumero, V., & Ávila, C. (2012).
Individual differences in the behavioral inhibition system are associated with orbitofrontal cortex
and precuneus gray matter volume. Cognitive, Affective, & Behavioral Neuroscience, 12(3), 491–
498.
Gale, A., Edwards, J., Morris, P., Moore, R., & Forrester, D. (2001). Extraversion-introversion,
neuroticism-stability, and EEG indicators of positive and negative empathic mood. Personality and
Individual Differences, 30(3), 449–461.
Garcia-Banda, G., Chellew, K., Fornes, J., Perez, G., Servera, M., & Evans, P. (2014). Neuroticism and
cortisol: Pinning down an expected effect. International Journal of Psychophysiology, 91(2), 132–
138.
Gerritsen, L., Geerlings, M., Bremmer, M., Beekman, A., Deeg, D., Penninx, B. W. J. H., & Comijs, H.
(2009). Personality characteristics and hypothalamic-pituitary-adrenal axis regulation in older
persons. The American Journal of Geriatric Psychiatry, 17(12), 1077–1084.
Gilbert, S. J., Spengler, S., Simons, J. S., Steele, J. D., Lawrie, S. M., Frith, C. D., & Burgess, P. W.
(2006). Functional specialization within rostral prefrontal cortex (area 10): A meta-analysis. Journal
of Cognitive Neuroscience, 18(6), 932–948.
Goldberg, L. R. (1990). An alternative “description of personality”: The big-five factor structure. Journal
of Personality and Social Psychology, 59(6), 1216–1229.
Goldberg, L. R. (1996). Evidence for the big five in analyses of familiar English personality adjectives.
European Journal of Personality, 10, 61–77.
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-
level facets of several five-factor models. In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf
(Eds.), Personality psychology in Europe (Vol. 7, pp. 7–28). Tilburg, The Netherlands: Tilburg
University Press.
Gosling, S. D., & John, O. P. (1999). Personality dimensions in nonhuman animals: A cross-species
review. Current Directions in Psychological Science, 8(3), 69–75.
Grabe, H., Schwahn, C., Appel, K., Mahler, J., Schulz, A., Spitzer, C., … Volzke, H. (2010). Childhood
maltreatment, the corticotropin-releasing hormone receptor gene and adult depression in the general
population. American Journal of Medical Genetics. Part B: Neuropsychiatric Genetics, 153B(8),
1483–1493.
Gray, J. A. (1982). The neuropsychology of anxiety: An inquiry into the functions of the septo-
hippocampal system. New York: Oxford University Press.
Personality Neuroscience and the FFM 50
Gray, J. A. (2004). Consciousness: Creeping up on the hard problem. New York: Oxford University
Press.
Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the function of
the septo-hippocampal system. New York: Oxford University Press.
Green, A. E., Fugelsang, J. A., Kraemer, D. J. M., Shamosh, N. A., & Dunbar, K. N. (2006). Frontopolar
cortex mediates abstract integration in analogy. Brain Research, 1096(1), 125–137.
Green, A. E., Munafo, M. R., DeYoung, C. G., Fossella, J. A., Fan, J., & Gray, J. R. (2008). Using
genetic data in cognitive neuroscience: From growing pains to genuine insights. Nature Reviews
Neuroscience, 9, 710–720.
Gronenschild, E. H. B. M., Habets, P., Jacobs, H. I. L., Mengelers, R., Rozendaal, N., van Os, J., &
Marcelis, M. (2012). The effects of FreeSurfer version, workstation type, and Macintosh operating
system version on anatomical volume and cortical thickness measurements. PloS One, 7(6), e38234.
Haas, B. W., Constable, R. T., & Canli, T. (2008). Stop the sadness: Neuroticism is associated with
sustained medial prefrontal cortex response to emotional facial expressions. NeuroImage, 42(1),
385–392.
Haas, B. W., Omura, K., Amin, Z., Constable, R. T., & Canli, T. (2006). Functional connectivity with the
anterior cingulate is associated with extraversion during the emotional Stroop task. Social
Neuroscience, 1(1), 16–24.
Haas, B. W., Omura, K., Constable, R. T., & Canli, T. (2007). Is automatic emotion regulation associated
with agreeableness? A perspective using a social neuroscience approach. Psychological Science,
18(2), 130–132.
Harmon-Jones, E. (2004). Contributions from research on anger and cognitive dissonance to
understanding the motivational functions of asymmetrical frontal brain activity. Biological
Psychology, 67(1-2), 51–76.
Harmon-Jones, E., & Allen, J. J. B. (1997). Behavioral activation sensitivity and resting frontal EEG
asymmetry: Covariation of putative indicators related to risk for mood disorders. Journal of
Abnormal Psychology, 106(1), 159–163.
Harmon-Jones, E., & Allen, J. J. B. (1998). Anger and frontal brain activity: EEG asymmetry consistent
with approach motivation despite negative affective valence. Journal of Personality and Social
Psychology, 74(5), 1310–1316.
Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity
in emotion-related phenomena: A review and update. Biological Psychology, 84(3), 451–462.
Harris, S. E., Wright, A. F., Hayward, C., Starr, J. M., Whalley, L. J., & Deary, I. J. (2005). The
functional COMT polymorphism, Val 158 Met, is associated with logical memory and the
personality trait intellect/imagination in a cohort of healthy 79 year olds. Neuroscience Letters,
385(1), 1–6.
Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. The American
Psychologist, 58(1), 78–79.
Henley, S. M. D., Ridgway, G. R., Scahill, R. I., Klöppel, S., Tabrizi, S. J., Fox, N. C., & Kassubek, J.
(2010). Pitfalls in the use of voxel-based morphometry as a biomarker: examples from Huntington
disease. American Journal of Neuroradiology, 31(4), 711–719.
Hennig, J. (2004). Personality, serotonin, and noradrenaline. In R. M. Stelmack (Ed.), On the
psychobiology of personality: Essays in honor of Marvin Zuckerman (pp. 379–408). New York:
Elsevier.
Hill, J., Inder, T., Neil, J., Dierker, D., Harwell, J., & Van Essen, D. (2010). Similar patterns of cortical
expansion during human development and evolution. Proceedings of the National Academy of
Sciences of the United States of America, 107(29), 13135–13140.
Hirsh, J. B., & Inzlicht, M. (2008). The devil you know: Neuroticism predicts neural response to
uncertainty. Psychological Science, 19(10), 962–967.
Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012). Psychological entropy: A framework for understanding
uncertainty-related anxiety. Psychological Review, 119(2), 304–320.
Personality Neuroscience and the FFM 51
Hofstee, W., de Raad, B., & Goldberg, L. R. (1992). Integration of the big five and circumplex
approaches to trait structure. Journal of Personality and Social Psychology, 63(1), 146–163.
Holmes, A. J., Lee, P. H., Hollinshead, M. O., Bakst, L., Roffman, J. L., Smoller, J. W., & Buckner, R. L.
(2012). Individual differences in amygdala-medial prefrontal anatomy link negative affect, impaired
social functioning, and polygenic depression risk. The Journal of Neuroscience, 32(50), 18087–
18100.
Hu, X., Erb, M., Ackermann, H., Martin, J. A., Grodd, W., & Reiterer, S. M. (2011). Voxel-based
morphometry studies of personality: Issue of statistical model specification-effect of nuisance
covariates. NeuroImage, 54(3), 1994–2005.
Hyde, L. W., Gorka, A., Manuck, S. B., & Hariri, A. R. (2011). Perceived social support moderates the
link between threat-related amygdala reactivity and trait anxiety. Neuropsychologia, 49(4), 651–
656.
Iidaka, T., Matsumoto, A., Ozaki, N., Suzuki, T., Iwata, N., Yamamoto, Y., … Sadato, N. (2006).
Volume of left amygdala subregion predicted temperamental trait of harm avoidance in female
young subjects. A voxel-based morphometry study. Brain Research, 1125(1), 85–93.
Ioannidis, J. P. A. (2011). Excess significance bias in the literature on brain volume abnormalities.
Archives of General Psychiatry, 68(8), 773–780.
Itoh, K., & Izumi, A. (2005). Affiliative bonding as a dynamical process: A view from ethology.
Behavioral and Brain Sciences, 28(3), 355–356.
Jackson, J., Balota, D. A., & Head, D. (2011). Exploring the relationship between personality and
regional brain volume in healthy aging. Neurobiology of Aging, 32(12), 2162–2171.
Jang, K. L., Livesley, W. J., Angleitner, A., Riemann, R., & Vernon, P. A. (2002). Genetic and
environmental influences on the covariance of facets defining the domains of the five-factor model
of personality. Personality and Individual Differences, 33(1), 83–101.
Jang, K. L., Livesley, W. J., Riemann, R., Vernon, P. A., Hu, S., Angleitner, A., … Hamer, D. H. (2001).
Covariance structure of neuroticism and agreeableness: A twin and molecular genetic analysis of the
role of the serotonin transporter gene. Journal of Personality and Social Psychology, 81(2), 295–
304.
Jang, K. L., McCrae, R. R., Angleitner, A., Riemann, R., & Livesley, W. J. (1998). Heritability of facet-
level traits in a cross-cultural twin sample: Support for a hierarchical model of personality. Journal
of Personality and Social Psychology, 74(6), 1556–1565.
Jennings, R. G., & Van Horn, J. D. (2012). Publication bias in neuroimaging research: Implications for
meta-analyses. Neuroinformatics, 10(1), 67–80.
John, O., Naumann, L., & Soto, C. (2008). Paradigm shift to the integrative big five trait taxonomy. In O.
P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research (pp.
114–158). New York: Guilford Press.
Johnson, J. A. (1994). Clarification of factor five with the help of the AB5C model. European Journal of
Personality, 8(4), 311–334.
Johnson, W., & Krueger, R. F. (2004). Genetic and environmental structure of adjectives describing the
domains of the big five model of personality: A nationwide US twin study. Journal of Research in
Personality, 38(5), 448–472.
Jung, R. E., Grazioplene, R., Caprihan, A., Chavez, R. S., & Haier, R. J. (2010). White matter integrity,
creativity, and psychopathology: Disentangling constructs with diffusion tensor imaging. PloS One,
5(3), e9818.
Kalbitzer, J., Frokjaer, V. G., Erritzoe, D., Svarer, C., Cumming, P., Nielsen, F. A., … Knudsen, G. M.
(2009). The personality trait openness is related to cerebral 5-HTT levels. NeuroImage, 45(2), 280–
285.
Kamarck, T. W., Haskett, R. F., Muldoon, M., Flory, J. D., Anderson, B., Bies, R., … Manuck, S. B.
(2009). Citalopram intervention for hostility: Results of a randomized clinical trial. Journal of
Consulting and Clinical Psychology, 77(1), 174–188.
Personality Neuroscience and the FFM 52
Kapogiannis, D., Sutin, A., Davatzikos, C., Costa, P., & Resnick, S. (2013). The five factors of
personality and regional cortical variability in the Baltimore longitudinal study of aging. Human
Brain Mapping, 34(11), 2829–2840.
Kievit, R. A., van Rooijen, H., Wicherts, J. M., Waldorp, L. J., Kan, K.-J., Scholte, H. S., & Borsboom,
D. (2012). Intelligence and the brain: A model-based approach. Cognitive Neuroscience, 3(2), 89–
97.
Kirschbaum, C., Bartussek, D., & Strasburger, C. (1992). Cortisol responses to psychological stress and
correlations with personality traits. Personality and Individual Differences, 13(12), 1353–1357.
Knutson, B., Momenan, R., Rawlings, R. R., Fong, G. W., & Hommer, D. (2001). Negative association of
neuroticism with brain volume ratio in healthy humans. Biological Psychiatry, 50(9), 685–690.
Knyazev, G. G. (2010). Antero-posterior EEG spectral power gradient as a correlate of extraversion and
behavioral inhibition. The Open Neuroimaging Journal, 4, 114–120.
Koehler, S., Wacker, J., Odorfer, T., Reif, A., Gallinat, J., Fallgatter, A. J., & Herrmann, M. J. (2011).
Resting posterior minus frontal EEG slow oscillations is associated with extraversion and DRD2
genotype. Biological Psychology, 87(3), 407–413.
Koelsch, S., Skouras, S., & Jentschke, S. (2013). Neural correlates of emotional personality: A structural
and functional magnetic resonance imaging study. PloS One, 8(11), e77196.
Kranzler, H., & Feinn, R. (2011). A CRHR1 haplotype moderates the effect of adverse childhood
experiences on lifetime risk of major depressive episode in African-American women. American
Journal of Medical Genetics. Part B: Neuropsychiatric Genetics, 156(8), 960–968.
Kumari, V., Cotter, P. A., Mulligan, O. F., Checkley, S. A., Gray, N. S., Hemsley, D. R., … Gray, J. A.
(1999). Effects of d-amphetamine and haloperidol on latent inhibition in healthy male volunteers.
Journal of Psychopharmacology, 13(4), 398–405.
Lahey, B. B. (2009). Public health significance of neuroticism. American Psychologist, 64(4), 241–256.
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., … Fox, P. T. (2011).
Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience,
23(12), 4022–4037.
Lamm, C., Decety, J., & Singer, T. (2011). Meta-analytic evidence for common and distinct neural
networks associated with directly experienced pain and empathy for pain. NeuroImage, 54(3), 2492–
2502.
Lange, S., Leue, A., & Beauducel, A. (2012). Behavioral approach and reward processing: results on
feedback-related negativity and P3 component. Biological Psychology, 89(2), 416–425.
Lemogne, C., Gorwood, P., Bergouignan, L., Pélissolo, A., Lehéricy, S., & Fossati, P. (2011). Negative
affectivity, self-referential processing and the cortical midline structures. Social Cognitive and
Affective Neuroscience, 6(4), 426–433.
Lewis, G. J., Panizzon, M. S., Eyler, L., Chen, C., Neale, M. C., Jernigan, T. L., … Franz, C. E. (2014).
Heritable influences on amygdala and orbitofrontal cortex contribute to genetic variation in core
dimensions of personality. NeuroImage, 103, 309–315.
Li, Y., Qiao, L., Sun, J., Wei, D., Li, W., Qiu, J., … Shi, H. (2014). Gender-specific neuroanatomical
basis of behavioral inhibition/approach systems (BIS/BAS) in a large sample of young adults: A
voxel-based morphometric investigation. Behavioral Brain Research, 274, 400–408.
Liu, W.-Y., Weber, B., Reuter, M., Markett, S., Chu, W.-C., & Montag, C. (2013). The big five of
personality and structural imaging revisited: A VBM-DARTEL study. Neuroreport, 24(7), 375–380.
Loehlin, J. C., McCrae, R. R., Costa, P. T., & John, O. P. (1998). Heritabilities of common and measure-
specific components of the big five personality factors. Journal of Research in Personality, 453(32),
431–453.
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization
of quantitative variables. Psychological Methods, 7(1), 19–40.
MacDonald, K. (1995). Evolution, the five-factor model, and levels of personality. Journal of Personality,
63(3), 525–567.
Personality Neuroscience and the FFM 53
MacLean, K. A., Johnson, M. W., & Griffiths, R. R. (2011). Mystical experiences occasioned by the
hallucinogen psilocybin lead to increases in the personality domain of openness. Journal of
Psychopharmacology, 25(11), 1453–1461.
Madsen, K. S., Jernigan, T. L., Iversen, P., Frokjaer, V. G., Mortensen, E. L., Knudsen, G. M., & Baaré,
W. F. C. (2012). Cortisol awakening response and negative emotionality linked to asymmetry in
major limbic fibre bundle architecture. Psychiatry Research, 201(1), 63–72.
Mahoney, C. J., Rohrer, J. D., Omar, R., Rossor, M. N., & Warren, J. D. (2011). Neuroanatomical
profiles of personality change in frontotemporal lobar degeneration. The British Journal of
Psychiatry, 198(5), 365–732.
Manuck, S. B., Flory, J. D., Ferrell, R. E., Mann, J. J., & Muldoon, M. F. (2000). A regulatory
polymorphism of the monoamine oxidase-A gene may be associated with variability in aggression,
impulsivity, and central nervous system serotonergic responsivity. Psychiatry Research, 95(1), 9–
23.
Manuck, S. B., Flory, J. D., McCaffery, J. M., Matthews, K. A., Mann, J. J., & Muldoon, M. F. (1998).
Aggression, impulsivity, and central nervous system serotonergic responsivity in a nonpatient
sample. Neuropsychopharmacology, 19(4), 287–299.
Markon, K. E., Krueger, R. F., & Watson, D. (2005). Delineating the structure of normal and abnormal
personality: An integrative hierarchical approach. Journal of Personality and Social Psychology,
88(1), 139–157.
Matsuo, K., Nicoletti, M., Nemoto, K., Hatch, J. P., Peluso, M. A. M., Nery, F. G., & Soares, J. C. (2009).
A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Human Brain
Mapping, 30(4), 1188–1195.
McCrae, R. R., Yamagata, S., Jang, K. L., Riemann, R., Ando, J., Ono, Y., … Spinath, F. M. (2008).
Substance and artifact in the higher-order factors of the big five. Journal of Personality and Social
Psychology, 95(2), 442–455.
Mehrabian, A., & Epstein, N. (1972). A measure of emotional empathy. Journal of Personality, 40(4),
525–543.
Meier, B. P., Robinson, M. D., & Wilkowski, B. M. (2006). Turning the other cheek. Agreeableness and
the regulation of aggression-related primes. Psychological Science, 17(2), 136–142.
Miller, G., Cohen, S., Rabin, B., Skoner, D. P., & Doyle, W. J. (1999). Personality and tonic
cardiovascular, neuroendocrine, and immune parameters. Brain, Behavior, and Immunity, 13(2),
109–123.
Mobbs, D., Hagan, C., Azim, E., Menon, V., & Reiss, A. L. (2005). Personality predicts activity in
reward and emotional regions associated with humor. Proceedings of the National Academy of
Sciences of the United States of America, 102(45), 16502–16506.
Morinaga, K., Akiyoshi, J., Matsushita, H., Ichioka, S., Tanaka, Y., Tsuru, J., & Hanada, H. (2007).
Anticipatory anxiety-induced changes in human lateral prefrontal cortex activity. Biological
Psychology, 74(1), 34–38.
Mueller, E. M., Burgdorf, C., Chavanon, M. L., Schweiger, D., Wacker, J., & Stemmler, G. (2014).
Dopamine modulates frontomedial failure processing of agentic introverts versus extraverts in
incentive contexts. Cognitive, Affective, & Behavioral Neuroscience, 14, 756–768.
Mujica-Parodi, L. R., Korgaonkar, M., Ravindranath, B., Greenberg, T., Tomasi, D., Wagshul, M., …
Malaspina, D. (2009). Limbic dysregulation is associated with lowered heart rate variability and
increased trait in healthy adults. Human Brain Mapping, 30(1), 47–58.
Munafò, M. R., & Flint, J. (2011). Dissecting the genetic architecture of human personality. Trends in
Cognitive Sciences, 15(9), 395–400.
Munafò, M. R., Freimer, N. B., Ng, W., Ophoff, R., Veijola, J., Miettunen, J., … Flint, J. (2009). 5-
HTTLPR genotype and anxiety-related personality traits: A meta-analysis and new data. American
Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 150B(2), 271–281.
Personality Neuroscience and the FFM 54
Munafò, M. R., Yalcin, B., Willis-Owen, S. A., & Flint, J. (2008). Association of the dopamine D4
receptor (DRD4) gene and approach-related personality traits: Meta-analysis and new data.
Biological Psychiatry, 63(2), 197–206.
Mutschler, I., Reinbold, C., Wankerl, J., Seifritz, E., & Ball, T. (2013). Structural basis of empathy and
the domain general region in the anterior insular cortex. Frontiers in Human Neuroscience, 7, 177.
Nater, U., Hoppmann, C., & Klumb, P. (2010). Neuroticism and conscientiousness are associated with
cortisol diurnal profiles in adults—role of positive and negative affect. Psychoneuroendocrinology,
35(10), 1573–1577.
Need, A. C., & Goldstein, D. B. (2014). Schizophrenia genetics comes of age. Neuron, 83(4), 760–763.
Nelson, M. T., Seal, M. L., Phillips, L. J., Merritt, A. H., Wilson, R., & Pantelis, C. (2011). An
investigation of the relationship between cortical connectivity and schizotypy in the general
population. The Journal of Nervous and Mental Disease, 199(5), 348-353.
Netter, P. (2004). Personality and hormones. In R. Stelmack (Ed.), On the psychobiology of personality:
Essays in honor of Marvin Zuckerman (pp. 353–377). New York: Elsevier.
Nettle, D. (2006). The evolution of personality variation in humans and other animals. The American
Psychologist, 61(6), 622–631.
Nettle, D. (2007). Personality: What makes you the way you are. New York: Oxford University Press.
Nettle, D., & Liddle, B. (2008). Agreeableness is related to social-cognitive, but not social-perceptual,
theory of mind. European Journal of Personality, 22(4), 323–335.
Omura, K., Constable, R. T., & Canli, T. (2005). Amygdala gray matter concentration is associated with
extraversion and neuroticism. NeuroReport, 16(17), 1905–1908.
Oswald, L., Zandi, P., Nestadt, G., Potash, J. B., Kalaydjian, A. E., & Wand, G. S. (2006). Relationship
between cortisol responses to stress and personality. Neuropsychopharmacology, 31(7), 1583–1591.
Ozer, D. J., & Benet-Martínez, V. (2005). Personality and the prediction of consequential outcomes.
Annual Review of Psychology, 57, 401–421.
Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotion. New York:
Oxford University Press.
Pardini, D. A., Raine, A., Erickson, K., & Loeber, R. (2014). Lower amygdala volume in men is
associated with childhood aggression, early psychopathic traits, and future violence. Biological
Psychiatry, 75(1), 73–80.
Parkinson, C., & Wheatley, T. (2014). Relating anatomical and social connectivity: White matter
microstructure predicts emotional empathy. Cerebral Cortex, 24, 614–625.
Passamonti, L., Terracciano, A., Riccelli, R., Donzuso, G., Cerasa, A., Vaccaro, M., … Quattrone, A.
(2015). Increased functional connectivity within mesocortical networks in open people.
NeuroImage, 104, 301–309.
Peciña, S., Smith, K. S., & Berridge, K. C. (2006). Hedonic hot spots in the brain. The Neuroscientist,
12(6), 500–511.
Perry, A., Mankuta, D., & Shamay-Tsoory, S. G. (2015). OT promotes closer interpersonal distance
among highly empathic individuals. Social Cognitive and Affective Neuroscience, 10(1), 3–9.
Peterson, J. B., & Carson, S. (2000). Latent inhibition and openness to experience in a high-achieving
student population. Personality and Individual Differences, 28(2), 323–332.
Peterson, J. B., & Flanders, J. L. (2002). Complexity management theory: Motivation for ideological
rigidity and social conflict. Cortex, 38(3), 429–458.
Peterson, J. B., Smith, K. W., & Carson, S. (2002). Openness and extraversion are associated with
reduced latent inhibition: Replication and commentary. Personality and Individual Differences,
33(7), 1137–1147.
Phillips, A., & Carroll, D. (2005). Neuroticism, cortisol reactivity, and antibody response to vaccination.
Psychophysiology, 42(2), 232–238.
Pickering, A. D. (2004). The neuropsychology of impulsive antisocial sensation seeking personality traits:
From dopamine to hippocampal function? In R. M. Stelmack (Ed.), On the psychobiology of
personality: Essays in honor of Marvin Zuckerman (pp. 453–477). New York: Elsevier.
Personality Neuroscience and the FFM 55
Pickering, A. D., & Gray, J. A. (1999). The neuroscience of personality. In L. A. Pervin & O. P. John
(Eds.), Handbook of Personality (2nd ed., pp. 277–299). New York: Guilford Press.
Polanczyk, G., Caspi, A., Williams, B., Price, T. S., Danese, A., Sugden, K., … Moffitt, T. E. (2009).
Protective effect of CRHR1 gene variants on the development of adult depression following
childhood maltreatment: Replication and extension. Archives of General Psychiatry, 66(9), 978–
985.
Polk, D., Cohen, S., Doyle, W., Skoner, D. P., & Kirschbaum, C. (2005). State and trait affect as
predictors of salivary cortisol in healthy adults. Psychoneuroendocrinology, 30(3), 261–272.
Proudfit, G. H. (2014). The reward positivity: From basic research on reward to a biomarker for
depression. Psychophysiology.
Quilty, L. C., DeYoung, C. G., Oakman, J. M., & Bagby, R. M. (2014). Extraversion and behavioral
activation: Integrating the components of approach. Journal of Personality Assessment, 96(1), 87–
94.
Quilty, L. C., Meusel, L.-A. C., & Bagby, R. M. (2008). Neuroticism as a mediator of treatment response
to SSRIs in major depressive disorder. Journal of Affective Disorders, 111(1), 67–73.
Rammsayer, T. H. (1998). Extraversion and dopamine: Individual differences in response to changes in
dopaminergic activity as a possible biological basis of extraversion. European Psychologist, 3(1),
37–50.
Rammsayer, T., Netter, P., & Vogel, W. H. (1993). A neurochemical model underlying differences in
reaction times between introverts and extraverts. Personality and Individual Differences, 14(5),
701–712.
Ramnani, N., & Owen, A. M. (2004). Anterior prefrontal cortex: Insights into function from anatomy and
neuroimaging. Nature Reviews. Neuroscience, 5(3), 184–194.
Reuter, M., Weber, B., Fiebach, C. J., Elger, C., & Montag, C. (2009). The biological basis of anger:
Associations with the gene coding for DARPP-32 (PPP1R1B) and with amygdala volume.
Behavioural Brain Research, 202(2), 179–183.
Revelle, W., & Wilt, J. (2013). The general factor of personality: A general critique. Journal of Research
in Personality, 47, 493-504.
Richard, F. D., Bond Jr., C. F., & Stokes-Zoota, J. J. (2003). One hundred years of social psychology
quantitatively described. Review of General Psychology, 7(4), 331–363.
Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial
frontal cortex in cognitive control. Science, 306(5695), 443–447.
Riemann, R., Angleitner, A., & Strelau, J. (1997). Genetic and environmental influences on personality:
A study of twins reared together using the self- and peer report NEO-FFI scales. Journal of
Personality, 65(3), 449–475.
Roberts, B. W., Lejuez, C., Krueger, R. F., Richards, J. M., & Hill, P. L. (2014). What is
conscientiousness and how can it be assessed? Developmental Psychology, 50(5), 1315–1330.
Rothbart, Μ., & Bates, J. (1998). Temperament. In W. Damon & N. Eisenberg (Eds.), Handbook of child
psychology: Vol. 3. Social, emotional, and personality development (pp. 105–176). New York:
Wiley.
Sambrook, T. D., & Goslin, J. (2015). A neural reward prediction error revealed by a meta-analysis of
ERPs using great grand averages. Psychological Bulletin, 141(1), 213–235.
Sampaio, A., Soares, J. M., Coutinho, J., Sousa, N., & Gonçalves, O. F. (2013). The big five default
brain: Functional evidence. Brain Structure & Function, 1–10.
Sapolsky, R. M. (1994). Glucocorticoids, stress and exacerbation of excitotoxic neuron death. Seminars in
Neuroscience, 6(5), 323–331. doi:10.1006/smns.1994.1041
Sassa, Y., Taki, Y., Takeuchi, H., Hashizume, H., Asano, M., Asano, K., … Kawashima, R. (2012). The
correlation between brain gray matter volume and empathizing and systemizing quotients in healthy
children. NeuroImage, 60(4), 2035–2041.
Saucier, G. (1992). Openness versus intellect: Much ado about nothing? European Journal of Personality,
6(5), 381–386.
Personality Neuroscience and the FFM 56
Schaefer, M., Knuth, M., & Rumpel, F. (2011). Striatal response to favorite brands as a function of
neuroticism and extraversion. Brain Research, 1425, 83–89.
Schmidt, L. A. (1999). Frontal brain electrical activity in shyness and sociability. Psychological Science,
10(4), 316–320.
Schommer, N., Kudielka, B., Hellhammer, D. H., & Kirschbaum, C. (1999). No evidence for a close
relationship between personality traits and circadian cortisol rhythm or a single cortisol stress
response. Psychological Reports, 84(3), 840–842.
Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilize? Journal of
Research in Personality, 47(5), 609–612.
Schuyler, B. S., Kral, T. R. A., Jacquart, J., Burghy, C. A., Weng, H. Y., Perlman, D. M., … Davidson, R.
J. (2014). Temporal dynamics of emotional responding: Amygdala recovery predicts emotional
traits. Social Cognitive and Affective Neuroscience, 9(2), 176–181.
Scmidtke, J. I., & Heller, W. (2004). Personality, affect and EEG: Predicting patterns of regional brain
activity related to extraversion and neuroticism. Personality and Individual Differences, 36, 717–
732.
Servaas, M. N., Geerligs, L., Renken, R. J., Marsman, J. B. C., Ormel, J., Riese, H., & Aleman, A.
(2015). Connectomics and neuroticism: An altered functional network organization.
Neuropsychopharmacology, 40, 296–304.
Servaas, M. N., Riese, H., Renken, R. J., Marsman, J.-B. C., Lambregs, J., Ormel, J., & Aleman, A.
(2013). The effect of criticism on functional brain connectivity and associations with neuroticism.
PloS One, 8(7), e69606.
Servaas, M. N., van der Velde, J., Costafreda, S. G., Horton, P., Ormel, J., Riese, H., & Aleman, A.
(2013). Neuroticism and the brain: A quantitative meta-analysis of neuroimaging studies
investigating emotion processing. Neuroscience and Biobehavioral Reviews, 37(8), 1518–1529.
Shackman, A. J., McMenamin, B. W., Maxwell, J. S., Greischar, L. L., & Davidson, R. J. (2009). Right
dorsolateral prefrontal cortical activity and behavioral inhibition. Psychological Science, 20(12),
1500–1506.
Shiner, R. L., & Caspi, A. (2003). Personality differences in childhood and adolescence: Measurement,
development, and consequences. Journal of Child Psychology and Psychiatry, 44(1), 2–32.
Shiner, R. L., & DeYoung, C. (2013). The structure of temperament and personality traits: A
developmental perspective. In P. D. Zelazo (Ed.), The Oxford Handbook of Developmental
Psychology, Vol. 2: Self and Other (pp. 113–141). New York: Oxford University Press.
Smillie, L. (2008). What is reinforcement sensitivity? Neuroscience paradigms for approach-avoidance
process theories of personality. European Journal of Personality, 22(5), 359–384.
Smillie, L. D., Cooper, A. J., & Pickering, A. D. (2011). Individual differences in reward-prediction-error:
Extraversion and feedback-related negativity. Social Cognitive and Affective Neuroscience, 6(5),
646–652.
Smillie, L. D., Cooper, A. J., Wilt, J., & Revelle, W. (2012). Do extraverts get more bang for the buck?
Refining the affective-reactivity hypothesis of extraversion. Journal of Personality and Social
Psychology, 103(2), 306–326.
Smillie, L. D., Pickering, A. D., & Jackson, C. J. (2006). The new reinforcement sensitivity theory:
Implications for personality measurement. Personality and Social Psychology Review, 10(4), 320–
335.
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F.
(2009). Correspondence of the brain’s functional architecture during activation and rest.
Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–
13045.
Somerville, L. H., Whalen, P. J., & Kelley, W. M. (2010). Human bed nucleus of the stria terminalis
indexes hypervigilant threat monitoring. Biological Psychiatry, 68(5), 416–424.
Spoont, M. R. (1992). Modulatory role of serotonin in neural information processing: Implications for
human psychopathology. Psychological Bulletin, 112(2), 330–350.
Personality Neuroscience and the FFM 57
Stillman, P. E., Van Bavel, J. J., & Cunningham, W. A. (2014). Valence asymmetries in the human
amygdala: Task relevance modulates amygdala responses to positive more than negative affective
cues. Journal of Cognitive Neuroscience, 1–10.
Sutin, A. R., Beason-Held, L. L., Dotson, V. M., Resnick, S. M., & Costa, P. T. (2010). The neural
correlates of neuroticism differ by sex prospectively mediate depressive symptoms among older
women. Journal of Affective Disorders, 127(1-3), 241–247.
Sutin, A. R., Beason-Held, L. L., Resnick, S. M., & Costa, P. T. (2009). Sex differences in resting-state
neural correlates of openness to experience among older adults. Cerebral Cortex, 19(12), 2797–
2802.
Sutton, S. K., & Davidson, R. J. (1997). Prefrontal brain asymmetry: A biological substrate of the
behavioral approach and inhibition systems. Psychological Science, 8(3), 204–210.
Taddei, M., Tettamanti, M., Zanoni, A., Cappa, S., & Battaglia, M. (2012). Brain white matter
organisation in adolescence is related to childhood cerebral responses to facial expressions and harm
avoidance. NeuroImage, 61(4), 1394–1401.
Takano, A., Arakawa, R., Hayashi, M., Takahashi, H., Ito, H., & Suhara, T. (2007). Relationship between
neuroticism personality trait and serotonin transporter binding. Biological Psychiatry, 62(6), 588–
592.
Taki, Y., Thyreau, B., Kinomura, S., Sato, K., Goto, R., Wu, K., … Fukuda, H. (2013). A longitudinal
study of the relationship between personality traits and the annual rate of volume changes in
regional gray matter in healthy adults. Human Brain Mapping, 34(12), 3347–3353.
Tang, T. Z., Derubeis, R. J., Hollon, S. D., Amsterdam, J., Shelton, R., & Schalet, B. (2009). Personality
change during depression treatment. Archives of General Psychiatry, 66(12), 1322–1330.
Tauscher, J., Bagby, R. M., Javanmard, M., Christensen, B. K., Kasper, S., & Kapur, S. (2001). Inverse
relationship between serotonin 5-HT 1A receptor binding and anxiety: A [11C]WAY-100635 PET
investigation in healthy volunteers. American Journal of Psychiatry, 158(8), 1326–1328.
Terracciano, A., Sanna, S., Uda, M., Deiana, B., Usala, G., Busonero, F., ... & Costa, P. T. (2008).
Genome-wide association scan for five major dimensions of personality. Molecular
psychiatry, 15(6), 647–656.
Turan, B., Guo, J., Boggiano, M. M., & Bedgood, D. (2014). Dominant, cold, avoidant, and lonely: Basal
testosterone as a biological marker for an interpersonal style. Journal of Research in Personality, 50,
84–89.
Van Egeren, L. F. (2009). A cybernetic model of global personality traits. Personality and Social
Psychology Review, 13(2), 92–108.
Vazire, S. (2010). Who knows what about a person? The self–other knowledge asymmetry (SOKA)
model. Journal of Personality and Social Psychology, 98(2), 281–300.
Volkow, N. D., Tomasi, D., Wang, G.-J., Fowler, J. S., Telang, F., Goldstein, R. Z., … Alexoff, D.
(2011). Positive emotionality is associated with baseline metabolism in orbitofrontal cortex and in
regions of the default network. Molecular Psychiatry, 16(8), 818–825.
Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of
emotion, personality, and social cognition. Perspectives on Psychological Science, 4(3), 274–290.
Wacker, J., Chavanon, M.-L., & Stemmler, G. (2006). Investigating the dopaminergic basis of
extraversion in humans: A multilevel approach. Journal of Personality and Social Psychology,
91(1), 171–187.
Wacker, J., Chavanon, M.-L., & Stemmler, G. (2010). Resting EEG signatures of agentic extraversion:
New results and meta-analytic integration. Journal of Research in Personality, 44(2), 167–179.
Wacker, J., & Gatt, J. M. (2010). Resting posterior versus frontal delta/theta EEG activity is associated
with extraversion and the COMT VAL(158)MET polymorphism. Neuroscience Letters, 478(2), 88–
92.
Wacker, J., Mueller, E. M., Hennig, J., & Stemmler, G. (2012). How to consistently link extraversion and
intelligence to the catechol-O-methyltransferase (COMT) gene: On defining and measuring
Personality Neuroscience and the FFM 58
psychological phenotypes in neurogenetic research. Journal of Personality and Social Psychology,
102(2), 427–444.
Wacker, J., Mueller, E., Pizzagalli, D. A., Hennig, J., & Stemmler, G. (2013). Dopamine-D2-receptor
blockade reverses the association between trait approach motivation and frontal asymmetry in an
approach-motivation context. Psychological Science, 24(4), 489–497.
Westlye, L. T., Bjørnebekk, A., Grydeland, H., Fjell, A. M., & Walhovd, K. B. (2011). Linking an
anxiety-related personality trait to brain white matter microstructure: Diffusion tensor imaging and
harm avoidance. Archives of General Psychiatry, 68(4), 369–377.
White, T. L., & Depue, R. A. (1999). Differential association of traits of fear and anxiety with
norepinephrine- and dark-induced pupil reactivity. Journal of Personality and Social Psychology,
77(4), 863–877.
Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: Using a structural model
of personality to understand impulsivity. Personality and Individual Differences, 30(4), 669–689.
Wiener, N. (1965). Cybernetics or control and communication in the animal and the machine (2nd ed., p.
212). New York: MIT Press.
Williams, L. M., Brown, K. J., Palmer, D., Liddell, B. J., Kemp, A. H., Olivieri, G., … Gordon, E.
(2006). The mellow years?: Neural basis of improving emotional stability over age. The Journal of
Neuroscience, 26(24), 6422–6430.
Xu, J., & Potenza, M. N. (2012). White matter integrity and five-factor personality measures in healthy
adults. NeuroImage, 59(1), 800–807.
Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical
power-commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298.
Yarkoni, T. (2015). Neurobiological substrates of personality: A critical overview. In M. Mikulincer & P.
R. Shaver (Eds.), APA Handbook of Personality and Social Psychology: Vol. 4. Personality
Processes and Individual Differences (pp. 61–84). Washington D.C.: American Psychological
Association.
Yeo, B., Krienen, F., Sepulcre, J., Sabuncu, M., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011).
The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
Journal of Neurophysiology, 106(3), 1125–1165.
Zhou, Z., Zhu, G., Hariri, A. R., Enoch, M. A., Scott, D., Sinha, R., ... & Goldman, D. (2008). Genetic
variation in human NPY expression affects stress response and emotion. Nature, 452(7190), 997–
1001.
Zobel, A., Barkow, K., Schulze-Rauschenbach, S., Von Widdern, O., Metten, M., Pfeiffer, U., … Maier,
W. (2004). High neuroticism and depressive temperament are associated with dysfunctional
regulation of the hypothalamic-pituitary-adrenocortical system in healthy volunteers. Acta
Psychiatrica Scandinavica, 109(5), 392–399.
Zuckerman, M. (2005). Psychobiology of personality (Second edition, revised and updated). New York:
Cambridge University Press.
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Table 1. Psychological functions hypothesized to be associated with each of the traits labeled in Figure 1.
(Adapted with permission from DeYoung, 2014.)
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 non-immediate 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 non-immediate 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.
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Figure Caption
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.
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Figure 1.