Personality Neuroscience and the FFM 1 - Mindsways … Neuroscience and the FFM 1 Personality Neuroscience and the Five Factor Model Timothy A. Allen University of Minnesota Colin

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]


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


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.


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


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


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


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


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


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


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).


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,

http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/


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.


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.


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


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


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


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


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.


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.


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).


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


(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


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


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.


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


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.


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


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


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


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


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.


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.


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


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


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


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


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,


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


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


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.


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).


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


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


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.


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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.


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.


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.


Figure 1.

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