The Dynamics of Personality Approach (DPA): Twenty Tenets for Uncovering the Causal Mechanisms of Personality

Abstract

Over the last few decades, most personality psychology research has been focused on assessing personality via scores on a few broad traits and investigating how these scores predict various behaviors and outcomes. This approach does not seek to explain the causal mechanisms underlying human personality and thus falls short of explaining the proximal sources of traits as well as the variation of individuals' behavior over time and across situations. On the basis of the commonalities shared by influential process-oriented personality theories and models, we describe a general Dynamics of Personality Approach (DPA). The DPA relies heavily on theoretical principles applicable to complex adaptive systems that self-regulate via feedback mechanisms, and parses the sources of personality in terms of various psychological functions relevant in different phases of self-regulation. Thus, we consider personality to be rooted in individual differences in various cognitive, emotional-motivational, and volitional functions, as well as their causal interactions. In this article, we lay out twenty tenets for the DPA that may serve as a guideline for integrative research in personality science.

Full text

The Dynamics of Personality Approach (DPA): 20 Tenets for Uncovering the Causal
Mechanisms of Personality
MARKUS QUIRIN
1,2
*, MICHAEL D. ROBINSON
3
, JOHN F. RAUTHMANN
4
, JULIUS KUHL
5
, STEPHEN J. READ
6
, MATTIE TOPS
7
and COLIN G. DEYOUNG
8
1
Technical University of Munich, Munich, Germany
2
PFH Göttingen, Göttingen, Germany
3
North Dakota State University, Fargo, ND USA
4
Bielefeld University, Bielefeld, Germany
5
Osnabrück University, Osnabrück, Germany
6
University of Southern California, Los Angeles, CA USA
7
Leiden University, Leiden, The Netherlands
8
University of Minnesota, Minneapolis, MN USA
Abstract: Over the last few decades, most personality psychology research has been focused on assessing personality
via scores on a few broad traits and investigating how these scores predict various behaviours and outcomes. This
approach does not seek to explain the causal mechanisms underlying human personality and thus falls short of
explaining the proximal sources of traits as well as the variation of individuals’behaviour over time and across sit-
uations. On the basis of the commonalities shared by influential process‐oriented personality theories and models, we
describe a general dynamics of personality approach (DPA). The DPA relies heavily on theoretical principles appli-
cable to complex adaptive systems that self‐regulate via feedback mechanisms, and it parses the sources of personality
in terms of various psychological functions relevant in different phases of self‐regulation. Thus, we consider person-
ality to be rooted in individual differences in various cognitive, emotional–motivational, and volitional functions, as
well as their causal interactions. In this article, we lay out 20 tenets for the DPA that may serve as a guideline for
integrative research in personality science. © 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Key words: dynamics of personality approach (DPA); systems theory; cybernetics; within‐person variability; person-
ality structure; personality processes; personality functions; cybernetic big five theory; personality systems interac-
tions theory; virtual personalities model; computational modelling; personality neuroscience / predictive and
reactive control systems theory
What kind of theoretical framework is most adequate to com-
prehensively understand and explain human personality?
This question has not lost its relevance from the origins
of personality psychology as an academic discipline up to
the present day (Corr, 2020). Over the last half century,
personality psychology has been predominantly focused on
developing consensual descriptions of personality, largely
on the basis of taxonomies created by factor analysis, and
using those descriptive models to investigate what outcomes
are predicted by trait measures as well as how those scores
change over time. In the last decade, however, personality
researchers have become increasingly interested in investi-
gating the causal mechanisms and processes underlying
personality functioning (e.g. Baumert et al., 2017;
DeYoung, 2015; Mõttus, Condon, Wood, & Epskamp, 2018;
Rauthmann, 2015, 2020; Robinson, Klein, & Persich, 2019;
Shoda, Wilson, Chen, Gilmore, & Smith, 2013; Wood,
Gardner, & Harms, 2015), reviving an earlier phase of
theoretical development (e.g. Atkinson & Birch, 1970;
Block, 1995; Cervone & Shoda, 1999; Kuhl, 1994; Kuhl &
Atkinson, 1986; Mischel & Shoda, 1995; Vallacher, Read,
& Nowak, 2002). Such research is focused on resolving ma-
jor questions about the sources of variation in personality
and about how individuals’behaviours and experiences
vary from situation to situation despite the fact that personal-
ity traits are themselves relatively stable (DeYoung &
Weisberg, 2018).
A number of process‐oriented models and theories of per-
sonality have been developed during the last two decades
(e.g. Collins, Jackson, Walker, O’connor, & Gardiner, 2017;
Fajkowska, 2015; Mayer, 2015; Revelle & Condon, 2015;
Sosnowska, Kuppens, De Fruyt, & Hofmans, 2019; Van
Egeren, 2009), which demonstrates the increasing interest
*Correspondence to: Markus Quirin, Department of Psychology, School of
Management, Technical University of Munich, Arcisstraße 21, 80333 Mu-
nich, Germany.
E‐mail: m.quirin@tum.de
Paper draft submitted to a Special Issue of the European Journal of Person-
ality: New approaches toward conceptualizing and assessing personality,
René Mõttus, David Condon, Dustin Wood, Sacha Epskamp (Eds.)
European Journal of Personality,Eur. J. Pers. 34: 947–968 (2020)
Published online 23 August 2020 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/per.2295
Handling editor: EJP Guest Editor
Received 21 June 2019
Revised 27 June 2020, Accepted 3 July 2020
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
This is an open access article under the terms of the Creative Commons Attribution License, which
permits use, distribution and reproduction in any medium, provided the original work is properly cited.

in uncovering the mechanisms underlying personality. Some
models have also been developed by authors of the present
work (DeYoung, 2015; Kuhl, 2000a,b; Kuhl, Quirin, &
Koole, 2020; Tops, IJzerman, & Quirin, 2020; Quirin, Tops,
& Kuhl, 2019; Read et al., 2010; Read, Droutman, &
Miller, 2017; Tops, Boksem, Luu, & Tucker, 2010; Tops,
Montero‐Marín, & Quirin, 2016). These models and theories
differ in important respects, but all endorse a systems–
theoretical approach in one way or the other, referring to con-
trol theory, cybernetics, cognitive architectures based on neu-
ral networks, or other theories of complex dynamic and
adaptive systems. Here, we refer to the common assumptions
of these different approaches as the dynamics of personality
approach (DPA). The DPA attempts to explain the structure
and dynamics of human personality as a complex adaptive
system that is able to regulate its own behaviour and experi-
ence via feedback processes (e.g. Carver & Scheier, 1998;
Powers, 1973).
We intend to highlight the importance of systems–
theoretical thinking for personality psychologists and,
throughout this article, formulate specific tenets for a solid
DPA that can serve to tie different dynamic approaches to-
gether, which could be instrumental for future research on
personality and its underlying mechanisms (Table 1). Be-
yond that, we believe that the DPA constitutes a meaningful,
cross‐disciplinary framework for conceptualizing phenom-
ena (e.g. person–situation interactions, psychopathology) at
the intersection of personality psychology and neighbouring
disciplines, thus furthering the integration of personality psy-
chology with social, motivational, clinical psychology, and
beyond. Our goal here is not to present a single definite the-
ory of personality but rather to present the important compo-
nents of an approach to the development of any such theory.
We start by reviewing general systems–theoretical princi-
ples and types of mechanisms that are necessary for a basic
understanding of the DPA. Next, we delineate psychological
functions (i.e. cognitive, affective‐motivational, and voli-
tional) as central elements in the DPA that correspond to in-
dividual difference variables and postulate that any DPA
model should explicate how these functions causally interact
with each other within the individual. By using some exam-
ples, we will also demonstrate how these functions might be
linked to commonly studied personality traits. Subsequently,
we will argue that these psychological functions evolved to
optimize efficient goal pursuit and thus play specific roles
in different phases of self‐regulation. After highlighting that
Table 1. Tenets for the DPA
Tenet 1 The DPA aims to understand the proximal causes of personality‐related phenomena.
Tenet 2 Feedback loops are a defining mechanism of the DPA; individuals adapt their functioning on the basis of the results of their
behaviour.
Tenet 3 Goals, understood broadly as desired states, can be conscious or unconscious, as can be the mechanisms applied to achieve goals.
Tenet 4 The DPA addresses within‐person variables that fluctuate dynamically in response to changing goals and changing situations. We
refer to these within‐person variables as psychological functions.
Tenet 5 DPA models must specify how stable between‐person differences emerge from the interactions of psychological functions and are
generated by between‐person variation in some relatively stable parameters of the dynamic mechanisms that govern within‐person
self‐regulation.
Tenet 6 Psychological functions comprise cognitive (basic vs. higher level), emotional–motivational, and volitional variables.
Tenet 7 Important individual differences exist in the readiness with which individuals engage in and maintain specific psychological
functions.
Tenet 8 DPA models should address interactions among these psychological functions and individual differences in them.
Tenet 9 Explaining how, when, and why psychological functions relate to each other to produce a common trait is a central issue for the
DPA.
Tenet 10 Objective or indirect measures are necessary in assessing the mechanisms underlying personality functioning, to avoid confusing
the phenomena to be explained (behaviour and experience) with the explanatory mechanisms.
Tenet 11 It is heuristically useful to describe self‐regulation in terms of a sequence of phases or stages.
Tenet 12 Switching between phases of self‐regulation can be facilitated by volition, such as the flexible upregulation and downregulation of
emotions and other functions.
Tenet 13 Emotional–motivational, cognitive, and volitional functions can be considered to have evolved to serve a particular purpose in
self‐regulation and thus to be of differing importance in different self‐regulation phases.
Tenet 14 All humans share an evolutionarily developed, nomothetic structure of functional variables and operations, and the functional
requirements of that structure are in part summarized by a set of self‐regulation phases.
Tenet 15 Individual differences largely stem from differing tendencies in how readily individuals enter and exit specific self‐regulation
phases or from the degree to which they apply certain functions within these phases.
Tenet 16 The DPA encompasses a personality‐by‐situations view by considering moment‐to‐moment transactions of individuals with
situations.
Tenet 17 To investigate person–environment transactions systematically, it is indispensable to understand and describe the characteristics of
situations as they constitute affordances for affective, motivational, cognitive, or volitional functions within individuals.
Tenet 18 Neuroscientific insights are helpful for advancing our understanding of the causal structure of human personality.
Tenet 19 The same kind of overt behaviour may stem from different underlying functions on different occasions.
Tenet 20 Computational modelling constitutes a prime method for deciphering the causal network of mechanisms underlying personality and
the variability of behaviour.
Note: DPA, dynamics of personality approach.
948 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

the investigation of temporal dynamics of person‐by‐
situation interactions is key to the DPA, we discuss its com-
patibility with neuroscientific research and models (e.g. pre-
dictive coding). Not least, we advocate computational
modelling as a prime method in the DPA, which can be
used to investigate the causal mechanisms of personality.
Throughout the paper, we mention tenets, 20 in total, that
we deem central for the DPA and related research (also Ta-
ble 1). By doing so, we demonstrate how the DPA has the po-
tential to integrate approaches investigating variations
between individuals as well as approaches investigating var-
iations within a person over time.
THE DPA: A SYSTEMS–THEORETICAL ACCOUNT
OF PERSONALITY PROCESSES AND STRUCTURE
The DPA uses systems (or ‘control’or ‘cybernetic’) theory
(Carver & Scheier, 1998; Powers, 1973; Wiener, 1948) as a
general framework to conceptualize, analyse, and (eventu-
ally) computationally simulate personality and its function-
ing. Although systems theory has an extremely broad
scope, here, we are referring to it as it applies to complex
adaptive systems. In this context, systems theory describes
and explicates general principles of complex systems, human
beings included, that self‐regulate their behaviour across
time and situations, through feedback processes. It consti-
tutes a general explanatory framework that provides princi-
ples to organize the understanding of causal relationships
between variables involved in complex adaptive systems.
Therefore, it largely abstracts from the materials (e.g. blood
vessels, neural networks, or computer circuit boards) that
serve as a basis for the causal transference of information
within a system. Indeed, systems–theoretical principles are
independent of the system at hand, be it an ecosystem, a so-
cietal system, a group of interacting agents, a plant or ani-
mal’s body, a robot, or the human psyche. Hence, systems
theory can be applied in personality research to analyse the
network of causal mechanisms underlying personality func-
tioning, or even to implement it in a computer program or a
robot to simulate a human’s personality and behaviour.
One of the major challenges for explanatory theories in
personality psychology is to explain how personality traits
can be stable and persistent while behaviour changes from
moment to moment in response to both situational cues and
fluctuations in processes within the person (e.g. changes in
the strength of motives). Part of the appeal of systems
theory is that it provides tools for explaining the relative
stability of systems despite their constant interaction with
their environments. This particularly applies to systems of
so‐called operational (or operative, autopoetic) closure (e.g.
Luhmann, 1992), which typically have definable borders that
distinguish them from their environment, such as the bound-
aries of the human body, and which maintain a network of
variables that are causally connected to each other via conge-
neric operations. These systems interact with their environ-
ments by taking in information (input) that triggers internal
operations and by producing outcomes (behavioural output)
that causally influence the environment (Rauthmann, 2016).
Thus, there is a constant state of dynamic interplay and
change between the system and its environment.
By adopting this systems–theoretical orientation, the
DPA differs from descriptive, trait approaches to personality,
which typically focus on what is stable in the person and
use that to predict other individual differences in traits,
outcomes, and so forth. Descriptive approaches typically
attempt to provide an economical taxonomy of personality
dimensions (or types) deriving from the covariation of be-
haviour and experience observed between individuals, most
often identified by factor analyses of questionnaire ratings.
The DPA is not opposed to the descriptive approach and,
in fact, can be complementary to it, attempting to identify
causal mechanisms that lead to the patterns of covariation
identified by descriptive taxonomies. Neither are typical
descriptive approaches necessary for the DPA, however, as
researchers may focus mainly on individual difference pa-
rameters that are prominent or particularly meaningful in
their theoretical account.
Systems theory can also be integrated with ideas about
evolutionary function to address questions about why vari-
ables evolved to relate to each other in a certain way (e.g.
Lukaszewski, 2013). This can be fruitful in the DPA because
evolutionary considerations (e.g. of adaptation and fitness)
can be used to generate hypotheses about a proximate causal
network of variables or to integrate the hypothesized account
of mechanism within a broader socio‐biological context by
adding the question of the distal ‘why’to the question of
the proximal ‘how’. Despite this potentially helpful add‐on
of evolutionary perspectives, the core of the DPA, as we
see it, is primarily to disentangle proximal rather than distal
causes of personality‐related phenomena (Tenet 1, Table 1).
In systems theory applied to proximal causes, the causal
network of variables can be analysed structurally (‘conceptu-
ally’) or quantitatively (‘mathematically’) (Bischof, 2016).
Structural systems analysis posits questions about the causal
structure of variables in the form of ‘Does X cause Y?’,
‘Does Y cause X?’,or‘Do X and Y engage in mutual causa-
tion?’as well as questions about the extent to which such
causal relationships have facilitatory or inhibitory relation-
ships. When three or more variables are under consideration,
the topological structure of their causal relationships can as-
sume more complex forms, such as a chain, a mesh, a fork
(bifurcation), or the well‐known feedback loop. These forms
can be depicted as so‐called signal flow graphs (Ma-
son, 1953)—the bedrock of virtually all graphical depictions
of causal relationships (e.g. Pearl, 2009; Spirtes, Glymour, &
Scheines, 2000). A signal flow graph consists of a network
of directed branches that connect at nodes and which have
been used in theories of human personality as well (e.g.
Lee, 2012). Accordingly, in the DPA, the structure of person-
ality refers to the nomothetic network of causal relationships
among within‐person variables (psychological functions;
refer to succeeding discussion) rather than to the
between‐person covariance pattern of traits.
Structural systems analysis can be considered a prerequi-
site for quantitative systems analysis, which attempts to de-
termine mathematically describable relationships between
variables in a network (Bischof, 2016; Powers, 1978). For
Dynamics of personality approach 949
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

example, motivation researchers may ask whether the rela-
tionship between strength of incentive‐driven approach moti-
vation and the distance from the incentive is linear or
non‐linear (e.g. logarithmic), how strong its slope is
(Miller, 1944), or how within‐system (person) variables
causally relate to each other during so‐called transition states
(instability between two stable, balanced states), as typically
described by differential equations (Bischof, 2016). Quanti-
tative systems analysis, as typically realized by computa-
tional modelling (refer to succeeding section), thus allows
for predictions of a system’s complex dynamic behaviour
and puts the hypothesized causal network of variables to
the test.
SELF‐REGULATION VIA NEGATIVE FEEDBACK
CONTROL: AN ELEMENTARY DPA PRINCIPLE
In self‐regulation, the system controls a variable or set of var-
iables physically instantiated within itself. Feedback loops
are a defining mechanism of the DPA because an adaptive
system autonomously attempts to adjust a current state to a
target state, thereby pursuing goals, which requires feedback
concerning its states (Tenet 2) (e.g. Carver & Scheier, 1998;
Powers, 1973). In the simplest case of a loop involving two
variables, a variable A causally influences a variable B,
which in turn causally influences A. If this happens in a
way that the value of A remains relatively stable or ‘in bal-
ance’, the causal structure is called a homeostatic loop, with
B exerting negative feedback control over A (as compared
with a positive feedback loop that leads to an increasing di-
vergence of the variables’values: Carver & Scheier, 1998).
Homeostatic processes are common in organisms—for ex-
ample, to maintain variables like temperature within ranges
conducive to life. However, not all negative feedback pro-
cesses are homeostatic, as some reduce the distance between
the system’s current state and a goal state that has not previ-
ously been achieved (e.g. obtaining a promotion within one’s
company, or becoming better at regulating one’s emotions).
Negative feedback control, then, is involved whenever one
seeks to change a current state to a desired state. In negative
feedback, the system attempts to reduce the discrepancy be-
tween these states, and detection of that discrepancy consti-
tutes error. For example, humans may strive for food,
security, autonomy, and arousal to meet a particular person,
to win a game, or to simply relax, all to some desired degree.
The system’s representation of the desired state or outcome,
the value or value range toward which negative feedback
leads, is what can be described as a goal in cybernetic theory.
In the cybernetic or control theory tradition, the term
‘goal’is typically used to designate any desired outcome
state that the system is striving for (DeYoung &
Weisberg, 2018). This contrasts with the manner in which
goals are typically thought of in psychology, where distinc-
tions are often made between goals, intentions, motives,
and needs. Within the latter tradition, goals may signify the
conscious representation of a desired outcome state such as
passing an exam (Elliot & Fryer, 2008), and intentions may
signify prospective actions to obtain the desired outcome
(Cottini & Meier, 2020; Goschke & Kuhl, 1993). Not least,
the concept of motive, instead of focusing on one specific en-
tity such as a desired outcome or a prospective action, de-
notes an associative network of possible actions, outcomes,
and goals that satisfy or frustrate a particular need
(McClelland, 1985).
Compared with goals, needs can be conceived of in terms
of desired outcomes that are evolved components of human
nature and rooted in deeper organismic states. For example,
the goal to make friends with somebody may be rooted
in the deep organismic need for affiliation (relatedness),
which in turn may be integrated in an associative motive net-
work of relevant incentives, actions, and outcomes (Deci &
Ryan, 2011; Kuhl et al., 2020; McClelland, 1985). Notably,
goals, intentions, needs, and motives are interconnected and
can shift over time, both phasically and tonically, which ren-
ders complex systems like humans capable of extensive ad-
aptation. For example, a particular goal or motive (e.g. the
motive for affiliation) may be aroused (disinhibited) in the
presence of a key affording incentive or situation, as when
appetite emerges with the occurrence of food or with the on-
set of eating. This corresponds to a phasic change in the goal
from pre‐stimulus to post‐stimulus exposure, and the amount
of change in that goal can be characterized in terms of indi-
vidual differences in a motive or its activation potential
(McClelland, 1985). Likewise, internal developmental and
learning processes may shift a goal tonically or even perma-
nently. It is worth pointing out that the DPA perspective ex-
tends the idea of goals to unconscious as well as conscious
control processes, so long as the system employs some sort
of feedback control to reach a desired state (Tenet 3).
At a minimum, a cybernetic control system requires a
representation of the goal state of the controlled variable (in
complex systems like organisms, this desired target state
can change over time), a sensor of the current state of that
system that allows comparison between the two (or ‘error de-
tection’) via feedback, and a set of operators that actively ad-
justs the system’s behaviour in such a way as to move the
current state toward the goal state. Most organisms, and cer-
tainly human beings, incorporate other more complicated
modes of self‐regulation, including positive feedback (in
which discrepancies are increased instead of reduced) and
feedforward (in which predictions about the future state are
used to control action). In systems capable of self‐regulation,
however, these other types of control are bounded by nega-
tive feedback processes, which provide a standard to strive
for to keep the system within its adaptive range.
Typically, individuals pursue multiple goals (including
intentions, motives, and needs) over a given period of time,
with only one or two being active in working memory at
any given time. Quiescent goals can become reactivated at
any time, however. This occurs when actively remembering
goals that one is not currently pursuing or upon passive ex-
posure to cues relevant to a goal, especially when those cues
suggest that a goal may be thwarted. Such processes fre-
quently lead to the occurrence of several feedback control
processes overlapping within a particular stretch of time
(Carver & Scheier, 1998). In fact, the possibility that activa-
tion of motivational states can vary over time and situations
950 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

is a defining feature of organismic systems as compared
with many technological systems, which continually work
on preset goals, as in the earliest cybernetic models (Wie-
ner, 1948). Among humans, motivational states can even be-
come chronically deactivated when an individual has not
worked on them for a long time or when the pursuit of related
goals has been problematic in the past (Brandstätter & Herr-
mann, 2018; Rasmussen, Wrosch, Scheier, & Carver, 2006).
PSYCHOLOGICAL FUNCTIONS AS THE BASIC
PERSONALITY COMPONENTS IN THE DPA
Personality trait variables—as favoured by descriptive,
factor‐analytical approaches—refer to between‐person vari-
ables, conceived as changing only very slowly or slightly
within a person under normal circumstances (Specht
et al., 2014). The DPA, in contrast, is primarily (yet not ex-
clusively) focused on within‐person variables that fluctuate
dynamically in response to changing goals and changing sit-
uations. We refer to these distinct within‐person (or ‘pro-
cess’) variables as psychological functions (Tenet 4). Rather
than using the term ‘function’in an evolutionary sense, it is
used here to refer to the manner in which a process contrib-
utes to the goal‐directed functioning of the system (DeYoung
& Krueger, 2018). Cognitive functions, for example, refer to
sensorimotor control, analytical thinking, memory, attention,
holistic thought, and so on—universal human processes
that can be more or less activated or inhibited at any
given moment.
Despite focusing on psychological functions, any com-
prehensive DPA must address stable individual differences
in addition to internal dynamic processes, or it would not
be an approach to personality. Thus, DPA models must spec-
ify how stable individual differences emerge from the inter-
actions of within‐person functions and are generated by
between‐person variation in some relatively stable parame-
ters of the dynamic mechanisms that govern within‐person
self‐regulation (Tenet 5). Descriptive traits often encompass
various variables (e.g. the Big Five domains, aspects, or
facets) that may correlate on a population level but may be
caused not by one single underlying causal function (e.g.
Wood et al., 2015), but by the operation of multiple psycho-
logical functions and their causal interplay (e.g. several dis-
tinct cognitive and motivational processes are likely to
contribute to openness to experience: DeYoung, 2015).
Additionally, it is possible that different functional interac-
tions may produce the same level of a behavioural trait in
different people.
Personality traits are often defined as relatively stable
patterns of emotion, motivation, cognition, and behaviour
(e.g. DeYoung, 2015; McAdams & Pals, 2006; Wilt &
Revelle, 2009). Here, however, we would like to bring a
slightly different perspective to bear, that is, to explain per-
sonality in terms of the pattern of cognitive, emotional–
motivational, and volitional functions, but not behaviour in
itself (Tenet 6). This approach has at least two important im-
plications. First, we discuss volition as a distinct category of
psychological functions in order to highlight the fact that
particular functions may be voluntarily engaged or disen-
gaged at any particular moment of time, thus stressing the
notion of top‐down control in the study of individual
differences.
Second, because observable behaviour is not an explana-
tory psychological function but rather an output or outcome,
we will not discuss behaviour itself as a psychological func-
tion in any narrow sense. Making such distinctions allows
the DPA to meaningfully explore relationships between
function‐level constructs (e.g. emotion/motivation, cogni-
tion, and volition), how they are activated by stimuli and sit-
uations, and how they determine behavioural responses
(Fajkowska, 2015; Smillie, 2013). Our suggestion to separate
psychological functions from behaviour is also driven by the
fact that outwardly similar behaviours can be caused by dif-
ferent underlying psychological functions, and the same psy-
chological functions may lead to different behaviours
(Funder, 1991). For example, an individual may approach
strangers (typically conceived of as an extraverted behav-
iour) in order to reduce one’s anxiety, to ask for help, or
out of boredom (Berlyne, 1960). Behaviour may nevertheless
point to operations or psychological functions if accurately
observed over time and in a variety of appropriate situations.
Behaviour and experience ultimately result from the com-
plex interplay of psychological functions, the activation
levels of which can fluctuate from moment to moment as a
reaction to internal (e.g. goals and motives) and external (sit-
uational) cues. Although all functions we consider here are
present in each individual, they are nevertheless subject to in-
dividual differences in their operation. These differences
largely refer to the readiness with which individuals engage
in and maintain specific psychological functions (Tenet 7;
e.g. in analytical thinking as a cognitive function, or affilia-
tion as a motivational function). In what follows, we provide
an overview of these classes of psychological functions and
discuss relevant individual differences for each one (Table 2).
Models developed through the DPA (at least those aiming to
be integrative or comprehensive) should address causal inter-
actions among these functions and individual differences in
them (Tenet 8).
Emotion and motivation
Motivations are inclinations of the system to move toward
particular goal states. Emotion and motivation are difficult
to separate, as emotions typically have a motivational com-
ponent (i.e. they incline people toward particular actions or
types of action, or disrupt an action: Frijda, 2016), and moti-
vations typically have emotional components. Although hu-
man beings experience a wide range of emotions, any DPA
model will need to deal at least with the basic emotional–
motivational categories of reward and punishment. From
the perspective of the DPA, rewards are associated with pos-
itive emotions and indicate movement toward or accomplish-
ment of goals. By contrast, punishments are associated with
negative emotions and signify threats or other aversive
events (e.g. failure to accomplish one’s goals).
Two kinds of rewards should be distinguished, which
relate to two different motivational phases. Specifically,
Dynamics of personality approach 951
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John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

whereas incentive rewards indicate progress toward a
goal, hedonic (or ‘consummatory’) rewards signal that
the goal has been achieved. Incentive rewards, which
gain their power through mesolimbic dopamine circuits
(Berridge, 2007), induce feelings of desire (or ‘wanting’),
which trigger effort and associated emotions like excitement.
By contrast, hedonic rewards induce feelings of enjoyment
or relaxation resulting from goal attainment (e.g. liking of
food, hugs, orgasm, or even completed work), which can
be linked to functioning of the endogenous opiate system
(Berridge, 2007). Accordingly, desire for incentives supports
goal pursuit, whereas hedonic enjoyment serves as a signal
that the pursued rewards were worth the effort, thus stimulat-
ing future goal pursuit of the same class of incentives.
Incentive and hedonic rewards can refer to different
stimuli/categories that activate or inhibit cognitive or voli-
tional functions differentially. For example, social rewards
and object‐oriented rewards activate different motives (e.g.
of affiliation, power, and achievement). As another example,
a preponderance of object‐related reward sensitivity in in-
fants predicts impairment later in development, whereas
early person‐related reward sensitivity predicts subsequent
facilitation of the development of self‐regulatory skills
(Kochanska, Aksan, Penney, & Doobay, 2007).
Similar to rewards, we can distinguish between two kinds
of punishments (e.g. DeYoung & Weisberg, 2018). Specifi-
cally, threats are cues that indicate the possibility of a future
goal thwarting, including aversive physical stimulation, and
they typically evoke negative emotions such as fear, anxiety,
and worry. By contrast, defeats reference the immediate ex-
perience of failure and other types of goal thwarting and typ-
ically give rise to negative emotions such as frustration,
dejection, helplessness, and hopelessness. Threat is typically
considered to elicit fear or anxiety, whereas defeat is
typically considered to elicit frustration or depression. Rela-
tive to trait anxiety, trait depression shows a unique pattern
of associations with other traits suggesting reduced motiva-
tion and lower dopaminergic activity (DeYoung, 2013).
Anxiety is linked to increased error sensitivity, which can
also be linked to threat rather than defeat (Higgins, 1997;
Kuhl, 2000a; Kuhl et al., 2020). Anxiety is highly
co‐morbid with depression; however, once depression pre-
dominates over anxiety, individuals show decreased rather
than increased error sensitivity (Weinberg, Kotov, &
Proudfit, 2015). This makes sense because ceasing to care
about goals decreases their salience and therefore decreases
the degree to which they can trigger error signals in relation
to the present state.
The specific emotion that a person experiences when
goals are achieved or thwarted is also likely to depend
on the nature of the goal or motive involved (e.g.
object‐related or social; affiliation or power). For example,
unsatisfied attachment needs can give rise to feelings
of loneliness or existential anxiety, which may trigger
proximity‐seeking behaviours. By contrast, unsatisfied or
thwarted power motivation can lead to feelings of oppres-
sion, which might motivate assertion. Hence, even though re-
ward and punishment are basic event types, they can give
rise to a wide diversity of experiences and corresponding be-
haviours, because people pursue a wide range of different
motives and goals (McClelland, 1985).
Individual differences in motivation and emotion
Individual differences in motivation and emotion may be seen
as core to a number of commonly studied personality traits (al-
though not all commonly studied traits, as our analysis in the
following sections will suggest). Among these is the Big Five
trait of neuroticism, which is linked to all sorts of negative
(threat‐related and punishment‐related) emotions. Indeed,
the tendency toward negative emotions appears to constitute
the core of this trait (Watson, 2000). Extraversion is not as
purely focused on emotion as neuroticism is but nonetheless
appears to encompass the tendency to experience many posi-
tive, reward‐related emotions; and such emotions may be cen-
tral to the trait (Watson, 2000). The reinforcement sensitivity
theory tradition (Corr, 2004) seeks to assess traits related to
behavioural approach sensitivity, behavioural inhibition sen-
sitivity, and fight–flight–freeze sensitivity that describe emo-
tional and motivational responses to rewarding and punishing
stimuli. And many emotions are simply measured as traits in
their own right by asking people about their typical levels of
anxiety, anger, curiosity, shame, pride, and so forth.
Cognition
Some psychological functions are primarily cognitive in na-
ture. These include basic functions related to attention and
sensorimotor processes, which can be identified among
all vertebrates, as well as higher‐level functions that are more
evolutionarily recent, such as analytical‐propositional versus
holistic–associative thinking (see Anderson, 1983, vs.
Rumelhart, McClelland, & PDP Research Group, 1986, for
theoretical models of propositional vs. holistic processing).
One functional aspect of attention relevant for personality
function is conscious error detection (error awareness). Error
Table 2. DPA taxonomy of psychological functions
Process level Examples
Volition Emotion regulation, self‐control, self‐regulation
High‐level cognition Analytical‐sequential and holistic–contextual thought
Motivation and emotion Incentive and hedonic reward, threat and defeat punishment
Low‐level cognition Sensorimotor control, error detection
Note: DPA, dynamics of personality approach.
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detection occurs when there is a discrepancy between perfor-
mance and expectation, which initiates an orienting response,
narrows attention toward the mismatch, and facilitates de-
tailed, conscious processing. Narrowed, conscious attention
is associated with analytical rather than holistic processing
(Hsieh, Yu, Chen, Yang, & Wang, 2020) and is facilitated
by negative and positive (arousing) stimuli, albeit more
strongly by negative ones (e.g. Easterbrook, 1959; Kazén,
Kuhl, & Quirin, 2015; Pool, Brosch, Delplanque, &
Sander, 2016). By contrast, a broad scope of attention, which
is present in the absence of perceived errors or concrete ex-
pectations, facilitates the consideration of contextual vari-
ables as well as the interoception of emotional preferences
and values (which has been referred to as self‐access; e.g.
Quirin & Kuhl, 2018). At the same time, a broad scope of at-
tention lowers the likelihood of consciously detecting dis-
crepancies, details, embedded objects or task‐irrelevant
information (Davis & Cochran, 2017; Hsieh et al., 2020).
Broad attentional scope is often facilitated by positive affect
(Fredrickson, 2001; Lindquist, Satpute, Wager, Weber, &
Barrett, 2015) and especially by positive emotions related
to hedonic rewards (as incentive rewards narrow one’s focus
on the goal: Gable & Harmon‐Jones, 2008; Pool et al., 2016).
Another basic class of cognitive function is sensorimotor
processing and coordination (Lehéricy et al., 2006;
Takeshima & Gyoba, 2014). Sensorimotor processing can
operate without conscious attention or deliberation, for ex-
ample, when stimulus–response patterns become automati-
cally elicited as, for example, in non‐verbal social
interaction such as sensorimotor synchronization, emotional
contagion, or intuitive parenting (Boccia, Piccardi, Di
Marco, Pizzamiglio, & Guariglia, 2016; Dumas, Nadel,
Soussignan, Martinerie, & Garnero, 2010; Keller, Chasiotis,
& Runde, 1992; Miller, Xia, & Hastings, 2019). Sensorimo-
tor coordination relies on peripheral (‘preconscious’) percep-
tion of stimulus or own‐body locomotion, as supported by
the dorsal visual stream (Ungerleider & Mishkin, 1982), or
the mirror‐neuron system in the context of interpersonal co-
ordination and imitation (Endedijk, Meyer, Bekkering,
Cillessen, & Hunnius, 2017). Sensorimotor processing can
be relevant in the consideration of expertise or competence
and may also play important roles in the manner in which
practiced goals lead to practiced responses.
High‐level cognitive functions, by contrast, allow individ-
uals to create action scripts and models of the world, which
can facilitate solving complex problems. Such models are
useful in decision making because they allow the organism
to anticipate likely future states, in part by anticipating the re-
sults of one’s actions. Among human beings, such models
can be holistic/associative, keeping track of what patterns
of sensory inputs typically co‐occur, or they can be analytical
(i.e. causal, logical, and propositional), developing models of
the rules that govern transitions between states and the range
of conditions currently obtained based on those rules (e.g.
Epstein, 2003; Kahneman, 2003; Lieberman, 2003; Strack
& Deutsch, 2004). Sometimes, it can be useful to process in-
formation (conceptual thought or attention) in a slow, analyt-
ical way, which can help one avoid mistakes. At other times,
it can be useful to process information in a holistic–
associative way (e.g. to make a quick decision if necessary).
Analytical processing is a cognitive function well suited
for planning sequences of subgoals needed to reach an end
goal. By contrast, holistic–associative processing facilitates
experiential absorption in any activity, whether directed to-
ward a concrete goal or not (Kuhl & Kazén, 2008;
Tellegen, 1981, 1982; Tellegen & Atkinson, 1974).
Individual differences in cognition
Although we have described analytical versus holistic think-
ing as two general modes of information processing, such
dual‐process frameworks have most heuristic value when
one recognizes the multiplicity of cognitive processes at both
conscious and automatic levels. To encourage greater speci-
ficity as well as applicability to the personality realm, it
may be important to develop more differentiated models
that specify functions within each of the larger dual‐process
categories. For example, the personality trait intellect
(which relates to analytical or reflective thinking: Kaufman
et al., 2010; Strack & Deutsch, 2004) entails some ambiguity
from a personality functions standpoint because its manifes-
tations can reflect both cognitive functions (e.g. efficiency or
sensitivity of a cognitive subsystem) or motivational func-
tions related to interest (e.g. DeYoung, 2015; Smillie,
Varsavsky, Avery, & Perry, 2016). Similarly, openness to ex-
perience refers to some processes that have a cognitive basis,
such as in detecting new patterns in sensory and perceptual
activity (e.g. with respect to art or music), but also has an
emotional component involving hedonic enjoyment of
those patterns. These examples highlight the fact that most
traits involve multiple or different types of psychological
function, although one may predominate (e.g. Pytlik Zillig,
Hemenover, & Dienstbier, 2002). Explaining how, when,
and why such functions relate to each other to form a
common trait can be considered a central issue for the DPA
(Tenet 9).
The scope of one’s associative processing, which has
been analysed in experimental research (e.g. Kuhl, 2000a;
Kuhl et al., 2020; Quirin, Düsing, & Kuhl, 2013), can also
be considered a cognitive function that is likely to contribute
to personality tendencies related to openness, with highly
open people having a larger scope of associative processing.
There is also evidence that individuals differ in low‐level
cognitive functions. For example, extraversion has been re-
lated to faster sensorimotor processing as identified by elec-
troencephalographic research (De Pascalis, Sommer, &
Scacchia, 2018; Stahl & Rammsayer, 2008). Similarly, neu-
roticism could centrally involve sensitivities related to error
detection (Olvet & Hajcak, 2008).
Volition
In contrast to motivation, which implicates basic drives re-
lated to approach and avoidance (which are endemic to all
living organisms: Schneirla, 1959), volition, sometimes de-
scribed as ‘self‐control’(Baumeister, 2014), refers to the pur-
poseful regulation of mental activities that will facilitate
one’s intended goals in the context of competing goals (e.g.
distractions or temptations). Volition, as we describe it here,
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Eur. J. Pers. 34: 947–968 (2020)

is distinct from our descriptions of cognition or motivation in
that it involves superordinate control mechanisms that
orchestrate elements of both cognition (e.g. knowledge rep-
resentation and thinking) and motivation/emotion (Kuhl &
Fuhrmann, 1998).
Volition functions not only by counteracting immediate
impulses and action tendencies that might compromise the
pursuit of a current goal but also through processes that
upregulate or downregulate emotions that could facilitate
or interfere with one’s efforts. In other words, volition in-
cludes elements of emotion regulation (Gross, 2014;
Koole, 2009; Kuhl, 2000a). In a volitional process, one
might quiet emotional states that would interfere with one’s
efforts (e.g. anxiety), while cultivating other emotions when
those emotions would be advantageous (Shah, Friedman, &
Kruglanski, 2002; Tamir, 2016). Thus, volition may benefit
from emotional intelligence, which includes capacities to cre-
ate emotional experiences that are suited to the task at hand
(Cohen & Andrade, 2004; Salovey & Grewal, 2005).
Individual differences in volition
Based on different theoretical or empirical (e.g. factor‐
analytical) approaches, a number of individual differences
relevant to volition have been postulated. The Big Five factor
conscientiousness reflects a general tendency toward being
industrious, organized, self‐disciplined, and orderly. Other
volitional individual difference constructs include those de-
scribed as ‘regulatory focus’, which can be either toward pro-
motion, a mode of goal pursuit in which one strives to obtain
rewards, or prevention, a mode of goal pursuit in which one
strives to avoid negative outcomes, including defeats and
failures to fulfil obligations (Higgins, 1997). One can also
distinguish modes of volition that involve constraint and ri-
gidity (e.g. self‐discipline or self‐control) versus context
sensitivity and flexibility (Kuhl & Fuhrmann, 1998). For ex-
ample, in the latter mode, the person might pause in the pur-
suit of a particular goal, and even pursue a different goal, in
order to regain energy for later pursuing the goal at hand.
Likewise, one can use volition to regulate cognitive modes,
for example, to overcome analytical means of apprehending
a problem, in the service of intuitive approaches that might
be more suited to it (Epstein, 2003).
Emotion regulation, which is subsumed here under voli-
tion, has been conceptualized in various ways (Koole, 2009).
Some frameworks distinguish styles or types of emotion reg-
ulation, such as the distinction between reappraisal and sup-
pression (Gross & John, 2003). Other frameworks focus on
the ability (or flexibility) to disengage from negative or in-
tensify positive emotional states and concomitant thoughts
in the service of goal pursuit (Beckmann & Kuhl, 1984;
Koole & Jostmann, 2004; Kuhl, 1994; Quirin, Kuhl, &
Düsing, 2011). Measures of volitional emotion regulation
are moderately related to neuroticism (Barańczuk, 2019;
Diefendorff, Hall, Lord, & Strean, 2000; Ng & Diener, 2009;
Southward, Altenburger, Moss, Cregg, & Cheavens, 2018),
which makes theoretical sense because the ability to regulate
negative emotion will influence how frequently and intensely
one experiences it (Lahey, 2009). However, in a DPA, we ar-
gue that one should distinguish descriptive traits from the
underlying functions that may produce them. Therefore,
one should distinguish processes involved in initial emo-
tional reactivity from subsequent processes related to regulat-
ing those initial reactions, even though both are likely to
contribute to neuroticism (Koole, 2009; Kuhl, 2000a; also
Gross & Feldman‐Barrett, 2011).
Critical remarks
Among other contributions, the DPA highlights the fact that
different research literatures typically focus on different psy-
chological functions. In contrast to approaches focusing on
one or a few specific functions, the DPA encourages investi-
gating individual differences in multiple functions, in an inte-
grated manner (refer to preceding discussion, Tenet 8).
Ideally, this would involve all or most psychological func-
tions (or at least classes of psychological functions) and de-
tailed observations concerning the manner in which the
different functions interact, whether in producing human be-
haviour or in predicting regularities in experience. That being
said, we do not claim that our rough classification of psycho-
logical functions is the only one possible. Rather, we merely
suggest that the present taxonomy into cognitive, emotional–
motivational, and volitional functions appears to be both
plausible and useful (e.g. see Kuhl et al., 2020, for a taxon-
omy differentiating seven functional levels).
However, the DPA’s focus on explaining personality
by within‐person functions and their interplay also implies
the necessity of a multimethod approach (Robinson
et al., 2019). This is important because questionnaire mea-
sures, both trait and state, are limited with respect to the iden-
tification and measurement of within‐person psychological
mechanisms (Robinson & Wilkowski, 2015). Indeed, indi-
viduals are often unaware of the mechanisms that produce
their behaviours (Wilson & Dunn, 2004) and self‐reports
of processing or ability often correlate relatively weakly
with objective indices of processing or ability (e.g. Mayer,
Salovey, & Caruso, 2008; Paulhus, Lysy, & Yik, 1998).
Hence, objective or indirect measures are necessary in
assessing the mechanisms that we have highlighted (Tenet
10). Such measures may include neuroscientific assessments,
which will be discussed subsequently.
PHASES OF SELF‐REGULATION
We have already mentioned that the behaviour of
self‐regulating systems involves, at a minimum, the detection
of distance from a goal and the engagement of an operator
that can move the system toward the goal when a mismatch
is detected. In most animals, and especially in ones as com-
plicated as human beings, however, more functions or sub-
systems are required to characterize the process of control
adequately. People must select between multiple goals and
plan which of them will govern behaviour at which given
moment. Often, they also must select which of multiple pos-
sible actions will be engaged to move toward a goal. More-
over, whereas some functions can work in parallel, such
as implementing an automatic, well‐learned behaviour and
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Eur. J. Pers. 34: 947–968 (2020)

planning future actions, or unattended goal selection while
consciously engaging in a different task (Dijksterhuis &
Nordgren, 2006), people typically have great difficulty en-
gaging in two or more motor actions at the same time, if
those are directed toward multiple goals. Moreover, some op-
erations such as planning typically need to be accomplished
before the action can be executed, and sometimes require a
noticeable amount of time. Accordingly, self‐regulation can
be described in terms of a sequence of phases or stages, heu-
ristically at least (Tenet 11) (DeYoung & Weisberg, 2018;
Kazén & Quirin, 2017; Kuhl, 2000b; Kuhl et al., 2020).
These phases necessitate the operating of specific functions,
and individuals are assumed to differ in the degree to which
they tend (or are able) to effectively use these functions dur-
ing these phases, or to facilitate switching between them (e.g.
use holistic processing during goal selection, or positive af-
fect to facilitate action).
Self‐regulation models have proposed various numbers
of phases, depending on the degree of resolution of the com-
plexity of the model and what phenomena they most directly
aim to explain (DeYoung, 2015; Kuhl, 2000b; Van
Egeren, 2009). Because we take these serial, phasic models
to be necessarily heuristic, minor differences among them
are unproblematic, as all conform to the same basic regula-
tory dynamics involved in the feedback control of human ac-
tion. To illustrate the relevance of self‐regulation phases in
the DPA, we will here refer to the well‐known Rubicon
model of action phases (Heckhausen & Gollwitzer, 1987)
as it provides much common ground for the different models
of self‐regulation proposed. This model specifies four major
phases (Figure 1): (i) goal selection phase, (ii) planning
phase, (iii) action phase, and (iv) evaluation phase (for simi-
lar conceptualizations, see DeYoung, 2015; Kuhl, 1984).
In the first phase, individuals select a goal that will gov-
ern their behaviour. Goals are selected, consciously or non‐
consciously, from various potentially competing candidates,
on the basis of urges, preferences, situational cues, and utili-
tarian concerns that influence the activation level of goals.
When a goal is sufficiently active, the person will attempt
to select an action that will make progress toward the goal.
If a promising action is considered but cannot immediately
be implemented, a goal may be kept in memory in the form
of an intention. After this second phase of action selection
and planning, the individual takes initiative to perform the
action when the situation is adequate (in contrast, procrasti-
nation would be one way a transition to the next stage can
fail), which is continuously monitored by sensorimotor con-
trol processes during the third phase. In the fourth phase, the
action and its outcomes (i.e. the current state) are interpreted
and compared with the desired goal state. If a match is regis-
tered, the individual engages in a new goal selection, cycling
back to Phase 1. By contrast, if the individual becomes aware
of a mismatch, they revisit the action plan (planning) or dis-
engage from the unmet goal (goal selection), and individual
differences exist in the experience of a mismatch (error sen-
sitivity) and the readiness with which goals are maintained
or abandoned (e.g. Kuhl, 2000). Evaluative experiences
concerning goal success, as well as what means were suc-
cessful and which benefits and costs were experienced, can
then be integrated in autobiographical memory to update
Figure 1. Dynamics of personality: phases of self‐regulation, adaptive functions, and individual differences.
Dynamics of personality approach 955
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the knowledge base for future goal and action selections,
with individual differences existing in the readiness of
updating (Tops, IJzerman, & Quirin, 2020). In organizational
contexts (March, 1991), the latter process has also been re-
ferred to as ‘exploration’, which contrasts with ‘exploitation’,
primarily referring to the phases of planning and
implementation.
Although error detection is described in the model as a
separate phase between action and goal selection, in reality,
the organism continuously carries out monitoring of potential
discrepancies between expectations or desires and what
is perceived. Becoming aware of these errors can thus inter-
rupt all other stages, prompting conscious, focused attention
toward discrepancies or even promote disengagement
from a problematic goal (Brandstätter & Herrmann, 2018;
Kuhl, 1981), which constitutes one example of the heuristic
nature of this model. Another example can be seen in
the possibility that a goal might be abandoned without ever
proceeding to action, if anticipatory evaluations of possible
action outcomes, in the planning phase, leads to disengage-
ment from an apparently unattainable goal.
Psychological functions in self‐regulation phases
Emotional–motivational, cognitive, and volitional functions
can be considered to have evolved to serve a particular pur-
pose within the context of self‐regulation, aiming to foster
the attainment of goals. They are therefore of differing im-
portance in different self‐regulation phases (Tenet 12; Fig-
ure 1). For example, motivational aspects matter strongly
in the goal selection phase, as preferences for different goals
have to be weighed (Heckhausen & Gollwitzer, 1987;
Kuhl, 1984; Read, Smith, Droutman, & Miller, 2017). By
contrast, volitional functions come into play more heavily
when a decision of a goal has been made and distractions
or low levels of motivation render action planning, enact-
ment, and goal maintenance difficult. With respect to
low‐level cognition, sensorimotor functions are inherently
relevant to implementation in the action phase (Lehéricy
et al., 2006; Takeshima & Gyoba, 2014). Error awareness
plays a particular role in the evaluation phase and is typi-
cally accompanied by an immediate negative emotional re-
sponse upon conscious detection of a deviation from
expectations in goal progress. As to high‐level cognitive
functions, broad holistic thought is often involved in taking
many pros and cons of different goal alternatives simulta-
neously into consideration during the goal selection phase,
whereas analytical thought is particularly important in the
planning phase (Kuhl, 2000; Kuhl et al., 2020; Quirin
et al., 2019).
Notably, moving from one self‐regulation phase to the
next, which entails a relative deactivation of functions
strongly involved in one phase (e.g. analytical thought) and
an activation of functions related to the next phase
(Gollwitzer, 2012), is facilitated by volitional functions such
as emotion regulation (Tenet 13; Kazén & Quirin, 2017, for
an overview). For example, the ability to flexibly upregulate
positive emotions (in switching from planning to action) and
to downregulate negative emotions (in detaching from error
detection) ensures adaptive and smooth transitions between
self‐regulation phases and may thus foster everyday func-
tioning and mental health (Kuhl, 2000a,b; Kuhl et al., 2020).
Individual differences in self‐regulation phases
The DPA assumes a nomothetic structure of functional vari-
ables and operations that all humans share owing to evolu-
tion and also assumes that the functional requirements of
that structure are summarized, in an important part, by the
set of regulation phases just described (Tenet 14). Neverthe-
less, individual differences exist in the settings of functional
parameters, and these create differences in personality. Ac-
cordingly, an understanding of general principles of psycho-
logical (i.e. cognitive, emotional–motivational, and
volitional) functioning, which may be derived from psycho-
logical areas other than personality psychology, is imperative
to an adequate understanding of individual differences (note
that additional functional variables that are not human uni-
versals may exist in addition to such a nomothetic structure).
Sources of individual differences can be seen in the read-
iness with which individuals enter (or the steadiness with
which they linger in) specific self‐regulation phases (Tenet
15). For example, associative processing, interoceptive
awareness of emotions and personal preferences (‘self‐ac-
cess’; Quirin & Kuhl, 2018), as well as accessibility of auto-
biographical memories are typically required for making
adequate decisions. Therefore, openness to experience,
which has been found to be associated with high capacity
of these functions (Kaufman, 2013; Rasmussen &
Berntsen, 2010; Rosenberg et al., 2016), may be linked to
the selection phase. By contrast, industriousness is conceptu-
ally linked to the planning phase of self‐regulation and to
ready engagement of related psychological functions such
as prioritization and self‐discipline. Moreover, evidence ex-
ists that individuals high in neuroticism engage more in error
detection and linger in the evaluation phase, producing rumi-
nation (Whitmer & Gotlib, 2013), which is also (likely) rele-
vant to the orderliness aspect of conscientiousness (e.g.
Yovel, Revelle, & Mineka, 2005). As reported earlier, indi-
viduals high in extraversion may show faster sensorimotor
processing as relevant for the action phase. Extraversion is
also related to stronger responses to so‐called reward predic-
tion errors, in which the system detects that things have gone
better than anticipated (e.g. Smillie et al., 2019; Wacker &
Smillie, 2015).
Individual differences can also derive from the engage-
ment of different functions or mechanisms to produce out-
comes in a specific self‐regulation phase (Figure 1). For
example, individuals may differ in their tendency to apply
holistic (vs. analytical) thinking during goal selection, their
tendency to be more or less perfectionistic in the planning
phase, their tendency to linger in a state of action implemen-
tation, and their tendency to detect errors at smaller degrees
of discrepancy between desired and observed states in the
evaluation phase or to adequately update their experiential
knowledge structure to avoid repetition errors (i.e. to learn).
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Neuroticism and extraversion are likely associated with
sensitivity to mismatch in opposite directions, with neuroti-
cism leading to greater sensitivity when outcomes are worse
than expected, prompting negative affect, and extraversion
leading to greater sensitivity when outcomes are better
than expected, prompting positive affect (Rusting &
Larsen, 1997). Given that all biological systems have limited
energy resources, it would not be adaptive to continuously
engage operations to reduce very small discrepancies. Thus,
some tolerance must be built into the system, and the error
detection mechanism should not be too sensitive. How sensi-
tive it should be, however, is presumably a question with no
definite answer from an evolutionary perspective, as the op-
timal sensitivity values may fluctuate across environments,
thereby preserving variance in the population (Nettle, 2006).
This line of reasoning explains why the error detection
threshold differs between individuals, in relation to both
positive and negative mismatches, and thus sensitivity of
the error system leads to individual difference in widely stud-
ied personality traits. Not least, it should be noted that this
within‐person functionality of emotions facilitating phase
switches as mentioned previously may stimulate research in
personality psychology to more intensely investigate interac-
tions of neuroticism or extraversion with traits relating to
volitional functions (Baumann, Kaschel, & Kuhl, 2007; Rob-
inson & Gordon, 2011).
INTERACTIONS AMONG FUNCTIONS:
TEMPORAL AND SITUATIONAL DYNAMICS AND
THEIR ASSESSMENT
Systems constantly exchange information with their environ-
ments, which means that they obtain perceptual (and meta-
bolic) inputs via a variety of channels and also themselves
influence the environment by their behaviour. Behavioural
outputs then feed back to create the individual’s perception
of resulting environmental changes and concomitant emo-
tions and cognition. Accordingly, as a systems–theoretical
approach, the DPA encompasses a personality‐by‐situations
view by considering moment‐to‐moment transactions of in-
dividuals with situations (Tenet 16). This view is not new
as influential originators of our discipline advocated its im-
portance and recommended investigation of how behaviour
varies within the person over time (Allport, 1937; Cat-
tell, 1957; Lewin, 1935; Murray, 1938). Notwithstanding
these early exhortations, a lack of large‐scale methodologies
to investigate occasions over time led researchers to focus
primarily if not exclusively on between‐person variables for
many decades.
To investigate person–environment transactions system-
atically, one must understand the characteristics of situations
and how they can affect psychological functions within an
individual (Tenet 17). Therefore, personality researchers
have begun to systematically and comprehensively
taxonomize the psychological characteristics of situations
that may transact with individuals’personality traits to pro-
duce experience and behaviour. For example, the DIA-
MONDS model (Rauthmann et al., 2014) encompasses
eight continuous dimensions that can be used to characterize
situations: Duty (work needs to be done), Intellect (intellec-
tual engagement or problem solving is possible), Adversity
(someone is under threat), Mating (potential mates can be
courted), pOsitivity (the situation encompasses or gives rea-
son to expect rewards), Negativity (the situation could entail
punishments and negative affect), Deception (mistrust could
be an issue), and Sociality (meaningful social interactions are
possible or relations can be built). Situation characteristics
constitute affordances for arousing affective, motivational,
cognitive, or volitional processes. The instruments developed
on the basis of this taxonomy (e.g. Rauthmann et al., 2014)
have already been used to describe, predict, or understand,
for example, situations encapsulated in tweets (Serfass &
Sherman, 2015), changes of situations within and between
people (Rauthmann & Sherman, 2016), mean‐level changes
of situation characteristics across the lifespan (Brown &
Rauthmann, 2016), and affect and self‐reported behaviours
in experience sampling studies (Sherman, Rauthmann,
Brown, Serfass, & Jones, 2015).
Of special interest to the DPA are dynamic network
modelling approaches to situations within everyday life, typ-
ically using the experience sampling method (Rauthmann &
Sherman, 2016; Sherman et al., 2015). In this method, partic-
ipants are prompted by a smartphone app or another device
to provide short ratings on their experience and behaviour
at intervals across the day for some extent of time (e.g. sev-
eral days or weeks) (e.g. Csikszentmihalyi & Larson, 2014;
Fleeson, 2012). Despite its advantages and potential as a
DPA method (e.g. reduced retrospective memory biases,
possibility to assess behavioural variability), experience sam-
pling approaches possess some limitations that pose chal-
lenges for the DPA. First, the experience sampling method
cannot easily be used to determine the causal relationships
between two variables (although cross‐lagged panel analyses
can provide some evidence in this direction: Beck & Jack-
son, 2019; Epskamp, Waldorp, Mõttus, & Borsboom, 2018).
Second, frequent responding in experience sampling may be
challenging or simply not feasible for many participants.
Third, many automatic processes and mechanisms cannot
easily be assessed via self‐report. Lastly, research that aggre-
gates measures of psychological functions (e.g. affect) over
time without taking their interactions among each other and
with situational conditions in account cannot readily be sub-
sumed under a DPA, given that the latter involves investiga-
tion of the causal structure of within‐person processes.
Complex parameters or dynamic indices may be preferable
but also problematic in terms of the information they convey
and their predictive abilities relative to simpler measurements
like mean and standard deviation (e.g. Dejonckheere
et al., 2019).
Given the limitations of the experience sampling method-
ology, a multimethod approach is necessary to potentiate
insights into the dynamics of personality. Specifically, exper-
imental research is enormously useful for investigating
person‐by‐situation interactions (along with computational
modelling, as discussed subsequently). In experiments, situa-
tions with specific characteristics (e.g. those described in the
DIAMONDS model) may be manipulated and interactive
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effects of personality on affective‐motivational, cognitive, or
volitional responses may be investigated. For example, ex-
periments have been used to dissociate facets of broad factors
such as extraversion (Depue & Morrone‐Strupinsky, 2005;
Revelle, Humphreys, Simon, & Gilliland, 1980), validate
measures of motives (McClelland, 1985), and investigate
the cognitive mechanisms underlying personality traits (e.g.
Moeller, Robinson, & Bresin, 2010; Robinson et al., 2019;
Smillie et al., 2016), as well as emotion regulation abilities
(Radtke, Düsing, Kuhl, Tops, & Quirin, 2020; Quirin
et al., 2011) and strategies (Gross, 2014; Gross &
John, 2003).
THE IMPORTANCE OF NEUROSCIENTIFIC
EVIDENCE
As the brain is the primary material generator of mental pro-
cesses, personality functioning included, neuroscientific in-
sights are helpful for advancing our understanding of the
causal network structure of human personality (Tenet 18).
Specifically, empirical evidence on how the structure and
function of brain systems are linked to psychological
constructs and to individual variation in them can help us
identify which variables must be distinguished and what
functions may explain reasonable amounts of variance in hu-
man behaviour (Allen & DeYoung, 2017). An early example
of this principle in action is that insights into the functioning
of the brain (e.g. action potentials) acted as a source of inspi-
ration for later artificial neural networks (Hebb, 1949). At the
same time, insights into brain functioning can put boundaries
on theoretically possible mechanisms underlying personality
and thus channel our hypotheses on personality functioning
into a meaningful direction.
It should be noted that, although very useful, neuroscien-
tific research is not necessary for theory development in
personality research, a statement that follows from the
systems–theoretical principle that functional causal networks
can largely be abstracted from the material substrate (e.g.
neural structures) that instantiates it. Accordingly, the causal
network of mechanisms underlying personality may be
deciphered by other means as well, using measurement of
psychological characteristics (through both introspective
and external observations of behaviour) and their dynamic
computer simulation (refer to succeeding discussion). None-
theless, the DPA is compatible with neuroscience and can be
used to develop models that are consistent with current neu-
roscientific knowledge (e.g. Allen & DeYoung, 2017). Gen-
erally, the authors agree that DPA models should be in
accordance, at least at some level of abstraction, with what
is known about human brain functioning.
A more specific benefit of neuroscience for the DPA de-
rives from the fact that the same kind of overt behaviour
may stem from different underlying functions on different
occasions (Tenet 19) (Funder, 1991). The studies on the re-
lationship between extraversion and sensorimotor function-
ing mentioned previously (De Pascalis et al., 2018; Stahl
& Rammsayer, 2008) provide a good example because they
could identify sensorimotor differences for introverted
versus extraverted individuals via electroencephalography
but not via behavioural measures such as reaction times or
self‐reports. Accordingly, neuroscientific research can ad-
vance our knowledge about functions that need to be differ-
entiated in explaining personality when mere self‐report or
observation will not suffice. In this vein, patterns of brain
activation and knowledge about their associated psycholog-
ical functions can be considered objective data that may,
sometimes at least (Poldrack, 2011), measure the operation
of a function and their underlying systems more readily than
behavioural assessments can. In addition to brain activation
patterns, some functions may be measured with implicit
measures of cognition, motivation, and affect (e.g. Kazén
et al., 2015; Quirin, Kazén, & Kuhl, 2009), but these can
also be combined with assessment of brain activation for
cross validation (e.g. Quirin & Lane, 2012; Suslow
et al., 2015).
Two recent developments in human neuroscience are par-
ticularly compatible with the DPA. The first is a shift from a
region‐oriented to a network‐oriented approach. Instead of
approaching the brain in terms of specific regions assumed
to carry out computations for specific tasks, researchers are
increasingly recognizing that many psychological functions
are carried out by distributed networks of regions that operate
in relative synchrony—which are termed large‐scale brain
networks. Many of these networks are evident in patterns of
synchrony (known as functional connectivity) regardless of
whether the person is at rest or engaged in various tasks
(Krienen, Yeo, & Buckner, 2014). In addition, some broad
networks have been identified in many distinct contexts,
have known associations with broad psychological functions,
and can be used as a common atlas for human neuroscience
(Uddin, Yeo, & Spreng, 2019).
This scheme is highly compatible with the DPA, which
asserts that psychological functioning can be parsed in terms
of a set of broad regulatory functions (e.g. prioritizing goals
and monitoring for errors) that are important for the pursuit
of goals, no matter which goal is being pursued (although
note that different goals may additionally recruit more spe-
cific mechanisms or modules that are specialized for the type
of goal in question). Thus, one fruitful DPAwould be to iden-
tify psychological functions that link specific traits with spe-
cific neural networks, such as with respect to the hypothesis
that conscientiousness depends on a neural network that spe-
cializes in prioritizing goals (Rueter, Abram, MacDonald,
Rustichini, & DeYoung, 2018). Because of its relevance
for personality functioning, large‐scale brain networks
have been considered to underlie so‐called personality
systems (Quirin et al., 2019; also Tops, Quirin, Boksem, &
Koole, 2017).
The second major development is a move toward focus-
ing on predictive processing as the basis of virtually all
brain function. This approach is described in different
models by terms like predictive coding, predictive control,
or the Bayesian brain (e.g. Clark, 2013; Fitch, 2014; Rao
& Ballard, 1999). These models postulate that, at every
level of the brain’s hierarchical organization, neural struc-
tures predict bottom‐up input (i.e. from structures closer to
sensory input) and themselves send signals to higher levels
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of organization only inasmuch as the actual input differs
from the predicted input. This allows efficient information
processing because only deviations from expectations re-
quire additional processing. At the functional level, we can
say that the brain anticipates sensory inputs on the basis of
both pre‐existing perceptual expectations and goals for de-
sired perceptual states. These schemata form the priors that
are used to make predictions (Tops et al., 2010). Accord-
ingly, the brain is continuously concerned with minimizing
‘prediction errors’rather than with representing the world
in some more comprehensive manner. It continuously mon-
itors the external and internal environment for information
that is discrepant with its schemata (i.e. for prediction errors,
with most of them not becoming consciously available to
the individual).
Predictive processing can be found across many different
levels of organization in the brain, ranging from communica-
tion between adjacent sections of cortex all the way up to in-
teractions among large‐scale networks and structures. Thus,
predictive coding and its associated feedback circuits are
considered not only to play a role in low‐level cognition
such as perception and sensorimotor function (Franklin &
Wolpert, 2011) but to constitute a much more general
systems–theoretical principle of the brain (e.g. Friston, 2005).
As such, predictive coding can be usefully applied in
explaining phenomena such as mismatch sensitivity in
language processing (e.g. Friederici, Pfeifer, & Hahne, 1993)
as well as reward processing as mediated by
mesocorticolimbic dopamine release (Schultz, 2013).
Similarly, Gray and McNaughton (2000) have argued that,
at one of the highest levels of functional organization, a net-
work centred on the hippocampus serves as a comparator to
detect potential conflicts between psychological goals, a cen-
tral mechanism implicated in behavioural inhibition and neu-
roticism (Allen & DeYoung, 2017). However, it should be
noted that not all prediction errors (e.g. at the level of basic
motor or perceptual processes) are relevant to the more
complex psychological functions associated with these
personality dimensions.
In the context of such broad principles, it is important to
note that individual differences in psychological traits may
stem from variation in a wide range of neural parameters.
These include patterns of connectivity and interaction within
or between networks at both structural and functional levels,
the computational efficiency of different networks, and vari-
ations in neurotransmitter functioning (which can be related
to the synthesis and metabolism of the neurotransmitter in
question and to the density, distribution, and efficiency of
its receptors). Undoubtedly, there are many other categories
of neural parameters in which variation could also be conse-
quential for variations in personality. An important principle
when considering the sources of personality traits in brain
function is that they will have a many‐to‐many mapping (Al-
len & DeYoung, 2017; Yarkoni, 2015). That is, any given
neural parameter may affect multiple psychological functions
(which in turn may contribute to multiple broad personality
traits), and any given personality trait is likely to reflect a
combination of multiple psychological functions and varia-
tion in even more neural parameters.
AN EXAMPLE OF A DPA ANALYSIS: THE CASE OF
ERROR DETECTION, NEGATIVE EMOTION,
NEUROTICISM, AND EMOTION REGULATION
To illustrate how the DPA, as an inherently functional ap-
proach, applies to research across psychological and neurobi-
ological levels of analysis, we focus on an example from a
specific phase of self‐regulation: the comparison of the pres-
ent state to the desired state in the evaluation phase. This in-
volves detecting mismatches between the current perceived
state and the various goals of the individual, which can be
described as ‘error detection’, a function that has been exten-
sively investigated using neuroscientific methods.
At any given moment, individuals expect to experience a
particular set of perceptions, based simultaneously on a
model of the likely conditions of the world and a model of
the desired state of the world, entailing goal states of the in-
dividual. Errors can come in the form of deviation from ex-
pectations stemming from either model. In cybernetic
terms, errors are threats because they signal an increase in
uncertainty regarding whether the system will be able to
reach its goals. Uncertainty is therefore innately threatening
(although also innately promising, as a cue for exploration:
Berlyne, 1960; DeYoung, 2013; Heine, Proulx, &
Vohs, 2006; Jonas et al., 2014). Variation in the sensitivity
of error detection should therefore be correlated with varia-
tion in defensive reactions to uncertainty, threat, and
punishment.
The psychological core of such defensive reactions is
negative emotions, and a general tendency to experience neg-
ative emotions of all kinds has long been recognized as a ma-
jor dimension of personality, with labels such as neuroticism,
negative emotionality, and dispositional negativity, which all
refer to the notion that people who are prone to experience
one negative emotion also tend to be prone to experience var-
ious other negative emotions (DeYoung, 2015; Shackman
et al., 2016). One potential explanation for this general ten-
dency is precisely that all such emotions ultimately stem
from error detection. Consistent with the idea that neuroti-
cism is associated with uncertainty aversion, one electroen-
cephalographic study found that individuals high in
neuroticism had a neural reaction to ambiguous feedback
about task performance that was even stronger than their re-
sponse to negative feedback, whereas the opposite was true
for individuals low in neuroticism (Hirsh & Inzlicht, 2008).
In general, persons with high levels of internalizing (a gen-
eral risk for disorders like anxiety and depression that is dif-
ficult to distinguish from neuroticism statistically: Griffith
et al., 2010) appear to display higher error detection sensitiv-
ity, as indexed by electroencephalography (Olvet &
Hajcak, 2008).
Of course, any specific negative emotion involves addi-
tional features that differentiate it from other negative emo-
tions. This illustrates the concept of trait hierarchy, in this
case pertaining to emotion, in which more specific traits (of-
ten called ‘facets’) have unique causes as well as causes
shared with other related traits (e.g. sadness and guilt both
join a general negative affect factor but are distinct from each
other: Watson, 2000). Further, the causes of any specific trait
Dynamics of personality approach 959
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Eur. J. Pers. 34: 947–968 (2020)

are likely to be multi‐determined as well. For example,
anxiety is one specific negative emotion related to
uncertainty, which involves increased vigilance, the
involuntary inhibition of behaviour, and the increased
arousal of the sympathetic nervous system, all of which have
distinct, identifiable neural circuits (Gray & McNaugh-
ton, 2000). Variations in trait levels of anxiety could thus de-
rive from differences in any of these circuits (Shackman
et al., 2016).
Nonetheless, specific limbic structures, and especially the
hippocampus and amygdala, have been implicated in the de-
tection of error or conflict and the mobilization of subsequent
negative emotional responses, respectively, and they are
likely to be implicated in neuroticism. Consistent with this
idea, several imaging studies have found that neuroticism
is positively associated with amygdala activation during
tasks involving threatening, uncertain, or ambiguous stimuli
(e.g. Everaerd, Klumpers, van Wingen, Tendolkar, &
Fernández, 2015). A meta‐analysis also found that neuroti-
cism was positively associated with hippocampal activation
during fear learning (Servaas et al., 2013), and several small
positron emission tomography studies have found a link be-
tween resting‐state hippocampal activity and neuroticism
(e.g. Gray & McNaughton, 2000). Encouragingly, as an ex-
ample of cross‐species validation, a study of 238 rhesus
monkeys similarly found that anxious temperament predicted
metabolic activity in the hippocampus (Oler et al., 2010).
Functional magnetic resonance imaging (MRI) studies
further suggest the relevance of interactions between the
amygdala and medial prefrontal cortex (presumably
reflecting emotion regulation). A recent study of over 500 in-
dividuals showed that connectivity between these regions in
response to images of faces expressing negative emotion (a
common tool used to activate the amygdala) was negatively
correlated with neuroticism (Silverman et al., 2019). MRI
studies of structural connectivity have also supported the
idea that reduced influence of prefrontal structures on limbic
structures is one component of neuroticism, consistently
finding a pattern of reduced white matter coherence in axonal
tracts connecting cortical and subcortical regions (e.g.
Bjørnebekk et al., 2013).
The conjunction of functional and structural evidence
suggests that one crucial component of the neural substrate
of (reduced) neuroticism may be the brain systems that
downregulate defensive emotional responses after they have
been triggered by threatening stimuli—that is, emotion regu-
lation abilities or ‘emotional flexibility’. Accordingly, and as
described earlier, negative emotion sensitivity (strongly
linked to the process of error detection) and emotion regula-
tion abilities (here operationalized in terms of individual dif-
ferences in the modulatory processes that follow error
detection) may be considered two different components of
neuroticism. This functional dissociation allows for a person
to possess both high negative emotion sensitivity and high
emotion regulation abilities, which, perhaps paradoxically,
has predicted especially high levels of well‐being in correla-
tional studies (Baumann et al., 2007).
Emotion regulation abilities can be assumed to involve
regions of prefrontal cortex and the adjacent anterior
cingulate cortex—structures that typically show regulatory
influences on the amygdala (Marusak et al., 2016; Silverman
et al., 2019). Because measures of emotion regulation and
neuroticism are typically substantially negatively correlated
(and poor emotion regulation may be considered a compo-
nent of neuroticism, although rarely measured as one of its
facets), the fact that neuroticism is related to reduced connec-
tivity between the amygdala and the prefrontal cortex may be
attributed to low emotion regulation abilities in individuals
with high neuroticism (e.g. Servaas et al., 2013). In fact,
Schlüter et al. (2018) found that emotion regulation
abilities predicted increased connectivity between the dorsal
anterior cingulate and the amygdala. Similarly, Morawetz,
Bode, Derntl, and Heekeren (2017) demonstrated that suc-
cess in emotion regulation (in terms of reappraisal) predicted
increased connectivity between areas related to both emotion
regulation (e.g. inferior frontal gyrus and ventromedial pre-
frontal cortex) and areas related to emotion sensitivity (e.g.
amygdala). These findings are promising and they demon-
strate the potential of the DPA, which (among other things)
considers the manner in which different functions combine
to determine psychological outcomes. Nonetheless, more re-
search is needed to substantiate the incremental predictive
power of emotion regulation abilities over measures of emo-
tion sensitivity in both behavioural and brain research.
IMPORTANCE OF COMPUTATIONAL
MODELLING
From the DPA perspective, nearly all methods applied by
personality psychologists (e.g. correlational and experimen-
tal methods, third‐person observation and introspection, or
even evolutionary considerations) can help to uncover the
processes and mechanisms that are causal for personality
functioning, and each has a distinct significance for different
problems. Here, however, we would like to highlight compu-
tational modelling as a method for the DPA. Computational
modelling can help to decipher the causal network of mech-
anisms underlying personality and the variability of behav-
iour (Tenet 20). Complex self‐regulating systems typically
consist of non‐linear relationships, multiple causal factors
(including person‐by‐situation interactions), and feedback
loops between antecedents and consequences. These features
result in a complexity of mutually influencing variables that
readily leads to dynamic behavioural outcomes surpassing
the predictive potential of static, linear models, as were al-
most exclusively used to predict behaviour during the last
few decades of personality research. Computational model-
ling has been developed to deal with these complexities
and can therefore be considered a prime method for the DPA.
Computational modelling builds upon principles of struc-
tural and quantitative system analyses by creating a mathe-
matical formulation of the causal relationships between
variables that can be used as the foundation of a computer
simulation. Computer simulation then tests which output
the system produces as a function of given systems’inputs
and parameters. Computer models can be instantiated in a
variety of computational architectures such as cybernetic
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John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

control systems models (e.g. Bischof, 1975; Powers, 1973;
Schneider, 2015), neural network models (O’Reilly,
Munakata, Frank, Hazy, & Contributors, 2012; Rumelhart
et al., 1986), or hybrids of these two (e.g. Hunt, Sbarbaro,
Żbikowski, & Gawthrop, 1992; Nguyen & Widrow, 1990).
Computational modelling has already been used to simulate
human personality by models instantiated on the basis of
cybernetic (Atkinson & Birch, 1970; Kuhl & Atkinson, 1986;
Revelle & Condon, 2015) or neural networks (Read
et al., 2010).
In computational modelling, the researcher typically be-
gins by specifying hypotheses about the topological structure
of causal relationships (e.g. as illustrated by signal flow
graphs). Because of the dynamic complexities of interacting
variables, these hypotheses need to be examined by the sim-
ulation of human behaviour using mathematical elaborations
of the functional relationships, which results in time series of
the values of all the variables involved (i.e. input, psycholog-
ical, and output variables). If the resulting system output (be-
haviour) does not readily match the outcome expected by the
researchers, the researchers can make changes in the topolog-
ical structure of the relationships or their parameters until an
acceptable fit is obtained. If such a fit is obtained, the con-
structed structure of the model provides a likely account of
the causal relationships underlying personality functioning.
However, if plausible adjustments cannot be made to the
model that result in acceptable fit, then this state of affairs ar-
gues against the model. Such procedures can also be used to
compare different DPA models with respect to their veridi-
cality (Costantini & Perugini, 2018).
Ideally, predicted behaviour can be compared with empir-
ically assessed data on dynamics of personality such as
time series of observable (i.e. output) variables as recorded
by ambulatory diaries or experience sampling (e.g.
Csikszentmihalyi & Larson, 2014; Fleeson, 2012; Geukes,
Nestler, Hutteman, Küfner, & Back, 2017; Kuhl, Mitina, &
Koole, 2017), or continuous recordings of physiological or
behavioural (e.g. movement) data. The fit between that em-
pirical data and outputs produced by the computerized model
can then be evaluated visually or using various fit indices
(e.g. Pickering & Pesola, 2014). Today, more so than before,
the possibility of gathering large samples of individuals’ex-
perience and behaviour over time (e.g. smartphone and other
electronic diaries) makes it possible to compare computer
simulations to big‐data sets to test the adequacy and accuracy
of the simulation, as well as to continuously adapt the param-
eters of a concrete DPA model and to refine it subsequently.
Here, we present the virtual personality model (Read,
Smith, et al., 2017) as an example of a concrete computa-
tional DPA model. This model uses basic motivational
principles to predict behaviour on the basis of directed infor-
mation flow between nodes in a neural network. Thus, it can
be seen as a mathematical description of the functioning of a
set of interconnected nodes that pass activation to each other
over weighted links (e.g. O’Reilly et al., 2012).
In this model, nodes represent human subsystems consti-
tuting important psychological functions that are arranged in
three operational layers, intermediated by hidden layers.
Specifically, in the first input layer, nodes represent situa-
tional cues and internal states that are associated with two
major motivational systems, reward (approach) and punish-
ment (avoidance), situated on a second layer. Situational cues
identify affordances that indicate the possibility to advance
goals that can activate one of these two systems or even both
(e.g. a novel social situation may induce mixed emotions of
threat and reward as the individual may be rejected or ac-
cepted). And internal state cues identify the status of both
physical and emotional needs. Within the reward and punish-
ment motivation systems, different types of motives are
nested, such as hunger, affiliation (e.g. attachment), or
power. Accordingly, signals from internal states indicating
a certain need and signals from situational cues (indicating
their incentive value) together activate motive‐specific moti-
vational systems. Different cues and internal states can be ac-
tivated in parallel and, thus, activate multiple motives that
then compete for behaviour, with the strongest motive pre-
vailing (e.g. Atkinson & Birch, 1970; see Revelle & Con-
don, 2015, for a conceptually similar approach).
Strongly activated motives in the two motive system
layers then activate behaviour(s) in a third behaviour layer,
where the behaviours aim at either approaching or avoiding
a goal (e.g. Hull, 1943). One way in which individual differ-
ences can be represented is by differing strengths of specific
motive goals and by baseline activations of these two motive
systems (Read, Brown, Wang, & Miller, 2018). As behaviour
may successfully arrive at the consummation of a goal (e.g.
obtaining warmth by a person), behaviour influences in a
feedback control loop consult the availability of situational
cues (e.g. when the other person distances after some inti-
mate contact) and one’s internal state (e.g. reduction of need
for security, or even subsequent increase if the other person
distances). By using different parameters for baseline activa-
tions of the motive systems and by producing dynamics in
behaviour, this computational model can explain how stabil-
ity of between‐person variance in personality factor domains
such as assertiveness (power) or affiliation can be logically
integrated with the occurrence of within‐person variability
over time and across situations (see Read, Droutman, &
Miller, 2017, for such an application).
Note that although the model uses feedback control (as
behaviour feeds back to changes in the availability of exter-
nal rewards and interoceptive states), it does not rely on un-
changing reference points that indicate full goal attainment.
Rather, the values in the network (e.g. goal activations) settle
in a range of points as a function of multiple constraints
deriving from many parallel goals and other parameters of
the system. A major reason for such a conceptualization
can be seen in the fact that this model primarily relies on
the activation of nodes representing motivational functions
or strengths to drive behaviour and does not include cogni-
tive components explicitly comparing the current state with
a reference value in order to prioritize the different goals
using volitional functions.
Whereas the virtual personality model is an appealing
computer model that implements some motivational (e.g.
approach and avoidance) and cognitive functions (i.e. goal
Dynamics of personality approach 961
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Eur. J. Pers. 34: 947–968 (2020)

representation) as well as individual differences in them, it
does not consider other cognitive functions such as holistic
versus analytical processing or volitional functions. Accord-
ingly, goal selection in this model (much like animal models)
is based on the momentary strength of action preferences.
Other models highlight volitional functions for the goal se-
lection phase, which facilitate the prioritization of abstract,
integrated values over motivational strivings, such as via
goal shielding or holistically integrating the latter with moti-
vational strivings on the basis of multiple constraint satisfac-
tion processes as a key feature of parallel processing (Engel
& Kuhl, 2015; Kuhl et al., 2020; Kuhl, Koole, & Quirin,
2015). Future research might attempt to integrate different,
yet not incompatible models to arrive at an integrative com-
putational DPA model that considers the different levels of
cognitive, motivational, and volitional functions, the inter-
play of which is considered to make up an individual’s
personality.
CONCLUSION
The DPA approach refers to a systems–theoretical conceptu-
alization of personality functioning that considers individual
differences in basic and higher‐level cognitive, emotional–
motivational, and volitional functions related to self‐
regulation. We highlighted 20 tenets that we deem useful to
consider when conducting DPA research, which we derived
from the commonalities we identified among models pro-
posed by the authors and by others. The DPA seeks to explain
the causal underpinnings of personality by analysing the
network of within‐person psychological functions. It thus
complements the descriptive, between‐person trait approach,
and even has the potential to explain between‐person variabil-
ity in personality and behaviour. Advancements in this
endeavour can strongly benefit from the consideration of
multiple methods besides questionnaires such as experience
sampling, objective assessment, computational modelling,
experimental research, and neurobiological methods. The
DPA has the potential to strongly connect personality re-
search with neighbouring disciplines such as general, social,
and clinical psychology, as well as the neurosciences.
ACKNOWLEDGEMENT
This work was facilitated by a grant from Templeton Rlg.
Trust (TRT 0119) supporting M. Q. Open access funding
enabled and organized by Projekt DEAL.
REFERENCES
Allen, T. A., & DeYoung, C. G.Widiger, T. A. (Eds.). (2017). Per-
sonality neuroscience and the five factor model. The Oxford
handbook of the Five Factor Model (pp. 1–63). New York, NY,
USA: Oxford University Press.
Allport, G. W. (1937). Personality: A psychological interpretation.
New York, NY, USA: Holt.
Anderson, J. R. (1983). The architecture of cognition. Hillsdale, NJ,
USA: Lawrence Erlbaum Associates, Inc.
Atkinson, J. W., & Birch, D. (1970). On the dynamics of action.
Nederlands Tijdschrift voor de Psychologie en haar
Grensgebieden,25,83–94.
Barańczuk, U. (2019). The five factor model of personality and
emotion regulation: A meta‐analysis. Personality and Individual
Differences,139, 217–227.
Baumann, N., Kaschel, R., & Kuhl, J. (2007). Affect sensitivity and
affect regulation in dealing with positive and negative affect.
Journal of Research in Personality,41, 239–248. https://doi.
org/10.1016/j.jrp.2006.05.002
Baumeister, R. F. (2014). Self‐regulation, ego depletion, and inhibi-
tion. Neuropsychologia,65, 313–319.
Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G.,
Borkenau, P., …Wrzus, C. (2017). Integrating personality struc-
ture, personality process, and personality development. European
Journal of Personality,31, 503–528. https://doi.org/10.1002/
per.2115
Beck, E. D., & Jackson, J. J. (2019). Consistency and change in id-
iographic personality: A longitudinal ESM network study. Jour-
nal of Personality and Social Psychology. Advance online
publication, 118 , 1080–1100. https://doi.org/10.1037/
pspp0000249
Beckmann, J., & Kuhl, J. (1984). Altering information to gain
action control: Functional aspects of human information
processing in decision making. Journal of Research
in Personality,18, 224–237. https://doi.org/10.1016/0092-6566
(84)90031-X
Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York,
NY, USA: McGraw‐Hill. https://doi.org/10.1037/11164‐000.
Berridge, K. C. (2007). The debate over dopamine’s role in reward:
The case for incentive salience. Psychopharmacology,191,
391–431.
Bischof, N. (1975). A systems approach towards the functional con-
nections of attachment and fear. Child Development,46,
801–817. https://doi.org/10.2307/1128384
Bischof, N. (2016). Struktur und Bedeutung: Einführung in die
Systemtheorie [Structure and meaning: Introduction in systems
theory]. Goettingen, Germany: Hogrefe.
Bjørnebekk, A., Fjell, A. M., Walhovd, K. B., Grydeland, H.,
Torgersen, S., & Westlye, L. T. (2013). Neuronal correlates of
the five factor model (FFM) of human personality: Multimodal
imaging in a large healthy sample. NeuroImage,65, 194–208.
https://doi.org/10.1016/j.neuroimage.2012.10.009
Block, J. (1995). A contrarian view of the five‐factor approach to
personality description. Psychological Bulletin,117, 187–215.
https://doi.org/10.1037/0033-2909.117.2.187
Boccia, M., Piccardi, L., Di Marco, M., Pizzamiglio, L., &
Guariglia, C. (2016). Does field independence predict
visuo‐spatial abilities underpinning human navigation? Behav-
ioural evidence. Experimental Brain Research,234,
2799–2807. https://doi.org/10.1007/s00221-016-4682-9
Brandstätter, V., & Herrmann, M. (2018). Goal disengagement and
action crises. In N. Baumann, M. Kazén, M. Quirin, & S. L.
Koole (Eds.), Why people do the things they do (pp. 87–108).
Goettingen, Germany: Hogrefe Publishing.
Brown, N. A., & Rauthmann, J. F. (2016). Situation characteristics
are age graded: Mean‐level patterns of the situational eight
DIAMONDS across the life span. Social Psychological and
Personality Science,7, 667–679. https://doi.org/10.1177/
1948550616652207
Carver, C. S., & Scheier, M. F. (Eds) (1998). On the self‐regulation
of behavior. New York, NY, USA: Cambridge University Press.
https://doi.org/10.1017/CBO9781139174794.
Cattell, R. B. (1957). Personality and motivation structure and mea-
surement. Chicago, IL, USA: World Book Co.
Cervone, D., & Shoda, Y. (1999). The coherence of personality:
Social‐cognitive bases of consistency, variability, and organiza-
tion. New York, NY, USA: Guilford Press.
962 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

Clark, A. (2013). Whatever next? Predictive brains, situated
agents, and the future of cognitive science. Behavioral and
Brain Sciences,36, 181–204. https://doi.org/10.1017/
S0140525X12000477
Cohen, J. B., & Andrade, E. B. (2004). Affective intuition and
task‐contingent affect regulation. Journal of Consumer Research,
31, 358–367. https://doi.org/10.1086/422114
Collins, M. D., Jackson, C. J., Walker, B. R., O’connor, P. J., & Gar-
diner, E. (2017). Integrating the context‐appropriate balanced at-
tention model and reinforcement sensitivity theory: Towards a
domain‐general personality process model. Psychological Bulle-
tin,143,91–107.
Corr, P. J. (2004). Reinforcement sensitivity theory and personality.
Neuroscience & Biobehavioral Reviews,28, 317–332. https://
doi.org/10.1016/j.neubiorev.2004.01.005
Corr, P. J. (2020). A consensual paradigm for personality: Introduc-
tion to special issue. Personality and Individual Differences,152,
109611. https://doi.org/10.1016/j.paid.2019.109611
Costantini, G., & Perugini, M. (2018). A framework for testing cau-
sality in personality research. European Journal of Personality,
32, 254–268. https://doi.org/10.1002/per.2150
Cottini, M., & Meier, B. (2020). Prospective memory monitoring
and aftereffects of deactivated intentions across the lifespan. Cog-
nitive Development,53, 100844. https://doi.org/10.1016/j.
cogdev.2019.100844
Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability
of the experience‐sampling method. In M. Csikszentmihalyi
(Ed.), Flow and the foundations of positive psychology
(pp. 35–54). Dordrecht, The Netherlands: Springer.
Davis, J. K., & Cochran, K. F. (2017). An information processing
view of field dependence‐independence 1. In O. N. Saracho
(Ed.), Cognitive style in early education (pp. 61–78). London,
UK: Routledge. https://doi.org/10.4324/9781315209968‐4.
De Pascalis, V., Sommer, K., & Scacchia, P. (2018). Extraversion
and behavioural approach system in stimulus analysis and motor
response initiation. Biological Psychology,137,91–106. https://
doi.org/10.1016/j.biopsycho.2018.07.004
Deci, E. L., & Ryan, R. M. (2011). Levels of analysis, regnant
causes of behavior and well‐being: The role of psychological
needs. Psychological Inquiry,22,17–22. https://doi.org/
10.1080/1047840X.2011.545978
Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L.,
Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamics
add limited information to the prediction of psychological well‐
being. Nature Human Behaviour,3, 478–491.
Depue, R. A., & Morrone‐Strupinsky, J. V. (2005). A neurobehav-
ioral model of affiliative bonding: Implications for conceptualiz-
ing a human trait of affiliation. Behavioral and Brain Sciences,
28, 313–395.
DeYoung, C. G. (2013). The neuromodulator of exploration: A uni-
fying theory of the role of dopamine in personality. Frontiers in
Human Neuroscience,7. https://doi.org/10.3389/
fnhum.2013.00762
DeYoung, C. G. (2015). Cybernetic Big Five theory. Journal of Re-
search in Personality,56,33–58. https://doi.org/10.1016/j.
jrp.2014.07.004
DeYoung, C. G., & Krueger, R. F. (2018). A cybernetic theory of
psychopathology. Psychological Inquiry. A cybernetic theory of
psychopathology,29,117–138.
DeYoung, C. G., & Weisberg, Y. J. (2018). Cybernetic approaches
to personality and social behavior. In M. Snyder, & K. Deaux
(Eds.), The Oxford handbook of personality and social psychol-
ogy (2nd ed., pp. 387–414). New York, NY, USA: Oxford Uni-
versity Press.
Diefendorff, J. M., Hall, R. J., Lord, R. G., & Strean, M. L. (2000).
Action‐state orientation: Construct validity of a revised measure
and its relationship to work‐related variables. Journal of
Applied Psychology,85, 250–263. https://doi.org/10.1037/0021-
9010.85.2.250
Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious
thought. Perspectives on Psychological Science,1,95–109.
https://doi.org/10.1111/j.1745-6916.2006.00007.x
Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., & Garnero, L.
(2010). Inter‐brain synchronization during social interaction.
PLoS ONE,5, e12166. https://doi.org/10.1371/journal.
pone.0012166
Easterbrook, J. A. (1959). The effect of emotion on cue utilization
and the organization of behavior. Psychological Review,66,
183–201. https://doi.org/10.1037/h0047707
Elliot, A. J., & Fryer, J. W. (2008). The goal construct in psychol-
ogy. In J. Y. Shah, & W. L. Gardner (Eds.), Handbook of
motivation science (pp. 235–250). New York, NY, USA:
Guilford Press.
Endedijk, H. M., Meyer, M., Bekkering, H., Cillessen, A. H. N., &
Hunnius, S. (2017). Neural mirroring and social interaction: Mo-
tor system involvement during action observation relates to early
peer cooperation. Developmental Cognitive Neuroscience,24,
33–41. https://doi.org/10.1016/j.dcn.2017.01.001
Engel, A., & Kuhl, J. (2015). Personality and planning: The inter-
play between linear and holistic processing. In M. D. Mumford,
& M. Frese (Eds.), The psychology of planning (pp. 58–88). Bos-
ton, Mass.: Routledge, Taylor and Francis Group.
Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018).
The Gaussian graphical model in cross‐sectional and time‐series
data. Multivariate Behavioral Research,53, 453–480. https://
doi.org/10.1080/00273171.2018.1454823
Epstein, S. (2003). Cognitive‐experiential self‐theory of personality.
In T. Millon, & M. J. Lerner (Eds.), Comprehensive handbook of
psychology: Personality and social psychology (pp. 159–184), 5.
Hoboken, NJ, USA: Wiley & Sons. https://doi.org/10.1002/
0471264385.wei0507.
Everaerd, D., Klumpers, F., van Wingen, G., Tendolkar, I., &
Fernández, G. (2015). Association between neuroticism and
amygdala responsivity emerges under stressful conditions.
NeuroImage,112, 218–224.
Fajkowska, M. (2015). The complex‐system approach to personal-
ity: Main theoretical assumptions. Journal of Research in Person-
ality,56,15–32.
Fitch, W. T. (2014). Toward a computational framework for cogni-
tive biology: Unifying approaches from cognitive neuroscience
and comparative cognition. Physics of Life Reviews,11,
329–364. https://doi.org/10.1016/j.plrev.2014.04.005
Fleeson, W. (2012). Perspectives on the person: Rapid growth
and opportunities for integration. In K. Deaux, & M. Snyder
(Eds.), The Oxford handbook of personality and social
psychology (pp. 33–63). New York, NY, USA: Oxford
University Press.
Franklin, D. W., & Wolpert, D. M. (2011). Computational mecha-
nisms of sensorimotor control. Neuron,72, 425–442. https://
doi.org/10.1016/j.neuron.2011.10.006
Fredrickson, B. L. (2001). The role of positive emotions in positive
psychology: The broaden‐and‐build theory of positive emotions.
American Psychologist,56, 218–226. https://doi.org/10.1037/
0003-066X.56.3.218
Friederici, A. D., Pfeifer, E., & Hahne, A. (1993). Event‐related
brain potentials during natural speech processing: Effects of se-
mantic, morphological and syntactic violations. Cognitive
Brain Research,1, 183–192. https://doi.org/10.1016/0926-6410
(93)90026-2
Frijda, N. H. (2016). The evolutionary emergence of what we call
“emotions”.Cognition and Emotion,30, 609–620. https://doi.
org/10.1080/02699931.2016.1145106
Friston, K. J. (2005). A theory of cortical responses. Philosophical
Transactions of the Royal Society, B: Biological Sciences,360,
815–836. https://doi.org/10.1098/rstb.2005.1622
Funder, D. C. (1991). Global traits: A neo‐allportian approach to
personality. Psychological Science,2,31–39. https://doi.org/
10.1111/j.1467-9280.1991.tb00093.x
Dynamics of personality approach 963
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

Gable, P. A., & Harmon‐Jones, E. (2008). Approach‐motivated pos-
itive affect reduces breadth of attention. Psychological Science,
19, 476–482. https://doi.org/10.1111/j.1467-9280.2008.02112.x
Geukes, K., Nestler, S., Hutteman, R., Küfner, A. C. P., & Back, M.
D. (2017). Trait personality and state variability: Predicting indi-
vidual differences in within‐and cross‐context fluctuations in af-
fect, self‐evaluations, and behavior in everyday life. Journal of
Research in Personality,69, 124–138. https://doi.org/10.1016/j.
jrp.2016.06.003
Gollwitzer, P. M. (2012). Mindset theory of action phases. In P. A.
van Lange (Ed.), Handbook of theories of social psychology
(pp. 526–545). Los Angeles, CA, USA: Sage. https://doi.org/
10.4135/9781446249215.n26.
Goschke, T., & Kuhl, J. (1993). Representation of intentions:
Persisting activation in memory. Journal of Experimental Psy-
chology: Learning, Memory, and Cognition,19, 1211–1226.
Gray, J. A., & McNaughton, N. (2000). Fundamentals of the
septo‐hippocampal system. In The neuropsychology of
anxiety: An enquiry into the functions of septo‐hippocampal
system (2nd ed., pp. 204–232). Oxford, UK: Oxford
University press.
Griffith, J. W., Zinbarg, R. E., Craske, M. G., Mineka, S., Rose, R.
D., Waters, A. M., & Sutton, J. M. (2010). Neuroticism as a
common dimension in the internalizing disorders. Psychological
Medicine,40, 1125–1136. https://doi.org/10.1017/
S0033291709991449
Gross, J. J. (2014). Emotion regulation: Conceptual and empirical
foundations. In J. J. Gross (Ed.), Handbook of emotion regulation
(pp. 3–22), 2. New York, NY, USA: Guilford Press.
Gross, J. J., & Feldman‐Barrett, L. (2011). Emotion generation
and emotion regulation: One or two depends on your point
of view. Emotion Review,3,8–16. https://doi.org/10.1177/
1754073910380974
Gross, J. J., & John, O. P. (2003). Individual differences in two
emotion regulation processes: Implications for affect,
relationships, and well‐being. Journal of Personality and Social
Psychology,85, 348–362. https://doi.org/10.1037/0022-
3514.85.2.348
Hebb, D. O. (1949). The organization of behavior. New York, NY,
USA: Wiley.
Heckhausen, H., & Gollwitzer, P. M. (1987). Thought contents and
cognitive functioning in motivational versus volitional states of
mind. Motivation and Emotion,11, 101–120. https://doi.org/
10.1007/BF00992338
Heine, S. J., Proulx, T., & Vohs, K. D. (2006). The meaning main-
tenance model: On the coherence of social motivations. Person-
ality and Social Psychology Review,10,88–110. https://doi.org/
10.1207/s15327957pspr1002_1
Higgins, E. T. (1997). Beyond pleasure and pain. American Psy-
chologist,52, 1280–1300. https://doi.org/10.1037/0003-
066X.52.12.1280
Hirsh, J. B., & Inzlicht, M. (2008). The devil you know:
Neuroticism predicts neural response to uncertainty. Psychologi-
cal Science,19, 962–967. https://doi.org/10.1111/j.1467-
9280.2008.02183.x
Hsieh, S., Yu, Y. T., Chen, E. H., Yang, C. T., & Wang, C. H.
(2020). ERP correlates of a flanker task with varying levels of
analytic‐holistic cognitive style. Personality and Individual Dif-
ferences,153, 109673. https://doi.org/10.1016/j.
paid.2019.109673
Hull, C. L. (1943). Principles of behavior. New York, NY, USA:
Appleton‐Century‐Crofts.
Hunt, K. J., Sbarbaro, D., Żbikowski, R., & Gawthrop, P. J. (1992).
Neural networks for control systems—a survey. Automatica,28,
1083–1112. https://doi.org/10.1016/0005-1098(92)90053-I
Jonas, E., McGregor, I., Klackl, J., Agroskin, D., Fritsche, I.,
Holbrook, C., Nash, K., …Quirin, M. (2014). Threat and defense:
From anxiety to approach. In J. M. Olson, & M. P. Zanna (Eds.),
Advances in experimental social psychology (49th ed.,
pp. 219–286). San Diego, CA, USA: Academic Press.
Kahneman, D. (2003). A perspective on judgment and choice: Map-
ping bounded rationality. American Psychologist,58, 697–720.
https://doi.org/10.1037/0003-066X.58.9.697
Kaufman, S. B. (2013). Opening up openness to experience: A
four‐factor model and relations to creative achievement in the arts
and sciences. The Journal of Creative Behavior,47, 233–255.
https://doi.org/10.1002/jocb.33
Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jiménez, L., Brown,
J., & Mackintosh, N. (2010). Implicit learning as an ability. Cog-
nition,116 , 321–340. https://doi.org/10.1016/j.
cognition.2010.05.011
Kazén, M., Kuhl, J., & Quirin, M. (2015). Personality interacts with
implicit affect to predict performance in analytic versus holistic
processing. Journal of Personality,83, 251–261. https://doi.org/
10.1111/jopy.12100
Kazén, M., & Quirin, M. (2017). The integration of motivation and
volition in personality–systems interactions theory. In S. L.
Koole, M. Kazén, M. Quirin, & N. Baumann (Eds.), Why people
do the things they do: Building on Julius Kuhl’s contributions to
the psychology of motivation and volition (pp. 15–30).
Goettingen, Germany: Hogrefe.
Keller, H., Chasiotis, A., & Runde, B. (1992). Intuitive parenting
programs in German, American, and Greek parents of 3‐month‐
old infants. Journal of Cross‐Cultural Psychology,23,
510–520. https://doi.org/10.1177/0022022192234007
Kochanska, G., Aksan, N., Penney, S. J., & Doobay, A. F. (2007).
Early positive emotionality as a heterogenous trait: Implications
for children’s self‐regulation. Journal of Personality and Social
Psychology,93, 1054–1066.
Koole, S. L. (2009). The psychology of emotion regulation: An in-
tegrative review. Cognition and Emotion,23,4–41. https://doi.
org/10.1080/02699930802619031
Koole, S. L., & Jostmann, N. B. (2004). Getting a grip on your feel-
ings: Effects of action orientation and external demands on intu-
itive affect regulation. Journal of Personality and Social
Psychology,87, 974–990.
Krienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014).
Reconfigurable task‐dependent functional coupling modes cluster
around a core functional architecture. Philosophical Transactions
of the Royal Society, B: Biological Sciences,369, 20130526.
https://doi.org/10.1098/rstb.2013.0526
Kuhl, J. (1981). Motivational and functional helplessness: The mod-
erating effect of state versus action orientation. Journal of Per-
sonality and Social Psychology,40, 155–170. https://doi.org/
10.1037/0022-3514.40.1.155
Kuhl, J. (1984). Volitional aspects of achievement motivation and
learned helplessness: Toward a comprehensive theory of action‐
control. In B. A. Maher (Ed.), Progress in experimental personal-
ity research (pp. 99–171), 13. New York, NY, USA: Academic
Press.
Kuhl, J. (1994). A theory of action and state orientations. In J. Kuhl,
& J. Beckmann (Eds.), Volition and personality: State versus ac-
tion orientation (pp. 9–46). Goettingen, Germany: Hogrefe &
Huber.
Kuhl, J. (2000a). A functional‐design approach to motivation and
self‐regulation: The dynamics of personality systems and interac-
tions. In M. Boekaerts, & P. R. Pintrich (Eds.), Handbook of
self‐regulation (pp. 111–169). San Diego, CA, USA:
Academic Press. https://doi.org/10.1016/B978‐012109890‐2/
50034‐2.
Kuhl, J. (2000b). The volitional basis of personality systems inter-
action theory: Applications in learning and treatment contexts. In-
ternational Journal of Educational Research,33, 665–703.
https://doi.org/10.1016/S0883-0355(00)00045-8
Kuhl, J., & Atkinson, J. W. (1986). Motivation, thought, and action.
New York, NY, USA: Praeger.
Kuhl, J., & Fuhrmann, A. (1998). Decomposing self‐regulation and
self‐control: The volitional components inventory. In J.
Heckhausen, & C. S. Dweck (Eds.), Motivation and
self‐regulation across the life span (pp. 15–49). Cambridge,
964 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

UK: Cambridge University Press. https://doi.org/10.1017/
CBO9780511527869.003.
Kuhl, J., & Kazén, M. (2008). Motivation, affect, and hemispheric
asymmetry: Power versus affiliation. Journal of Personality and
Social Psychology,95, 456–469. https://doi.org/10.1037/0022-
3514.95.2.456
Kuhl, J., Koole, S. L., & Quirin, M. (2015). Being someone: The in-
tegrated self as a neuropsychological system. Social and Person-
ality Psychology Compass,9,115–132.
Kuhl, J., Mitina, O., & Koole, S. L. (2017). The extended trust hy-
pothesis: Single‐attractor self‐contagion in day‐to‐day changes in
implicit positive affect predicts action‐oriented coping and psy-
chological symptoms. Nonlinear Dynamics, Psychology, and Life
Sciences,21, 505–518.
Kuhl, J., Quirin, M., & Koole, S. L. (2020). The functional architec-
ture of human motivation: Personality systems interactions the-
ory. In A. J. Elliot (Ed.), Advances in motivation science 7.
Cambridge, UK: Elsevier.
Lahey, B. B. (2009). Public health significance of neuroticism.
American Psychologist,64, 241–256. https://doi.org/10.1037/
a0015309
Lee, J. J. (2012). Correlation and causation in the study of personal-
ity. European Journal of Personality,26, 372–390. https://doi.
org/10.1002/per.1863
Lehéricy, S., Bardinet, E., Tremblay, L., Van de Moortele, P.‐F.,
Pochon, J.‐B., Dormont, D., Kim, D.‐S., …Ugurbil, K. (2006).
Motor control in basal ganglia circuits using fMRI and brain atlas
approaches. Cerebral Cortex,16, 149–161. https://doi.org/
10.1093/cercor/bhi089
Lewin, K. (1935). A dynamic theory of personality: Selected pa-
pers. New York, NY, USA: McGraw‐Hill.
Lieberman, M. D. (2003). Reflexive and reflective judgment pro-
cesses: A social cognitive neuroscience approach. In J. P. Forgas,
K. D. Williams, & W. von Hippel (Eds.), Social judgments: Im-
plicit and explicit processes (pp. 44–67). New York, NY, USA:
Cambridge University Press.
Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., & Barrett,
L. F. (2015). The brain basis of positive and negative affect: Ev-
idence from a meta‐analysis of the human neuroimaging litera-
ture. Cerebral Cortex,26, 1910–1922.
Luhmann, N. (1992). Die Wissenschaft der Gesellschaft [the art of
society]. Frankfurt am Main, Germany: Suhrkamp.
Lukaszewski, A. W. (2013). Testing an adaptationist theory of trait
covariation: Relative bargaining power as a common calibrator of
an interpersonal syndrome. European Journal of Personality,27,
328–345. https://doi.org/10.1002/per.1908
March, J. G. (1991). Exploration and exploitation in organizational
learning. Organization Science,2,71–87. https://doi.org/
10.1287/orsc.2.1.71
Marusak, H. A., Thomason, M. E., Peters, C., Zundel, C., Elrahal,
F., & Rabinak, C. A. (2016). You say ‘prefrontal cortex’and I
say ‘anterior cingulate’: Meta‐analysis of spatial overlap in
amygdala‐to‐prefrontal connectivity and internalizing
symptomology. Translational Psychiatry,6, e944. https://doi.
org/10.1038/tp.2016.218
Mason, S. J. (1953). Feedback theory—Some properties of signal
flow graphs. Proceedings of the IRE,41, 1144–1156. https://
doi.org/10.1109/JRPROC.1953.274449
Mayer, J. D. (2015). The personality systems framework: Current
theory and development. Journal of Research in Personality,
56,4–14. https://doi.org/10.1016/j.jrp.2015.01.001
Mayer, J. D., Salovey, P., & Caruso, D. R. (2008). Emotional intel-
ligence: New ability or eclectic traits? American Psychologist,
63, 503–517. https://doi.org/10.1037/0003-066X.63.6.503
McAdams, D. P., & Pals, J. L. (2006). A new Big Five: Fundamen-
tal principles for an integrative science of personality. American
Psychologist,61, 204–217. https://doi.org/10.1037/0003-
066X.61.3.204
McClelland, D. C. (1985). How motives, skills, and values deter-
mine what people do. American Psychologist,40, 812–825.
https://doi.org/10.1037/0003-066X.40.7.812
Miller, J. G., Xia, G., & Hastings, P. D. (2019). Resting heart rate
variability is negatively associated with mirror neuron and limbic
response to emotional faces. Biological Psychology,146, 107717.
https://doi.org/10.1016/j.biopsycho.2019.107717
Miller, N. E. (1944). Experimental studies of conflict. In J. M. Hunt
(Ed.), Personality and the behavior disorders (pp. 431–465).
New York, NY, USA: Ronald Press.
Mischel, W., & Shoda, Y. (1995). A cognitive‐affective system the-
ory of personality: Reconceptualizing situations, dispositions, dy-
namics, and invariance in personality structure. Psychological
Review,102, 246–268. https://doi.org/10.1037/0033-
295X.102.2.246
Moeller, S. K., Robinson, M. D., & Bresin, K. (2010). Integrating
trait and social‐cognitive views of personality: Neuroticism, im-
plicit stress priming, and neuroticism‐outcome relationships. Per-
sonality and Social Psychology Bulletin,36, 677–689. https://
doi.org/10.1177/0146167210367487
Morawetz, C., Bode, S., Derntl, B., & Heekeren, H. R. (2017). The
effect of strategies, goals and stimulus material on the neural
mechanisms of emotion regulation: A meta‐analysis of fMRI
studies. Neuroscience & Biobehavioral Reviews,72, 111–128.
https://doi.org/10.1016/j.neubiorev.2016.11.014
Mõttus, R., Condon, D., Wood, D., & Epskamp, S. (2018). Call for
papers: “New approaches toward conceptualizing and assessing
personality”: Joint special issue of European Journal of Psycho-
logical Assessment and European Journal of Personality. Euro-
pean Journal of Psychological Assessment,34, 287–289.
https://doi.org/10.1027/1015-5759/a000493
Murray, H. A. (1938). Explorations in personality. New York, NY,
USA: Oxford University Press.
Nettle, D. (2006). The evolution of personality variation in humans
and other animals. American Psychologist,61, 622–631. https://
doi.org/10.1037/0003-066X.61.6.622
Ng, W., & Diener, E. (2009). Personality differences in emotions:
Does emotion regulation play a role? Journal of Individual Dif-
ferences,30, 100–106. https://doi.org/10.1027/1614-
0001.30.2.100
Nguyen, D. H., & Widrow, B. (1990). Neural networks for
self‐learning control systems. IEEE Control Systems Magazine,
10,18–23. https://doi.org/10.1109/37.55119
Oler, J. A., Fox, A. S., Shelton, S. E., Rogers, J., Dyer, T. D., David-
son, R. J., Shelledy, W., …Kalin, N. H. (2010). Amygdalar and
hippocampal substrates of anxious temperament differ in their
heritability. Nature,466, 864–868.
Olvet, D. M., & Hajcak, G. (2008). The error‐related negativity
(ERN) and psychopathology: Toward an endophenotype. Clini-
cal Psychology Review,28, 1343–1354. https://doi.org/10.1016/
j.cpr.2008.07.003
O’Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., & Contrib-
utors (2012). Computational cognitive neuroscience Wiki book,
1st Edition. Available from: http://ccnbook.colorado.edu
Paulhus, D. L., Lysy, D. C., & Yik, M. S. M. (1998). Self‐report
measures of intelligence: Are they useful as proxy IQ tests? Jour-
nal of Personality,66, 525–554.
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd
ed.). New York, NY, USA: Cambridge University Press. https://
doi.org/10.1017/CBO9780511803161.
Pickering, A. D., & Pesola, F. (2014). Modeling dopaminergic and
other processes involved in learning from reward prediction er-
ror: Contributions from an individual differences perspective.
Frontiers in Human Neuroscience,8, 740. https://doi.org/
10.3389/fnhum.2014.00740
Poldrack, R. A. (2011). Inferring mental states from neuroimaging
data: From reverse inference to large‐scale decoding. Neuron,
72, 692–697. https://doi.org/10.1016/j.neuron.2011.11.001
Dynamics of personality approach 965
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

Pool, E., Brosch, T., Delplanque, S., & Sander, D. (2016). Atten-
tional bias for positive emotional stimuli: A meta‐analytic inves-
tigation. Psychological Bulletin,142,79–106. https://doi.org/
10.1037/bul0000026
Powers, W. T. (1973). Behavior: The control of perception. Chi-
cago, IL, USA: Aldine.
Powers, W. T. (1978). Quantitative analysis of purposive systems:
Some spadework at the foundations of scientific psychology. Psy-
chological Review,85, 417–435. https://doi.org/10.1037/0033-
295X.85.5.417
Pytlik Zillig, L. M., Hemenover, S. H., & Dienstbier, R. A. (2002).
What do we assess when we assess a Big 5 trait? A content anal-
ysis of the affective, behavioral, and cognitive processes repre-
sented in Big 5 personality inventories. Personality and Social
Psychology Bulletin,28, 847–858. https://doi.org/10.1177/
0146167202289013
Quirin, M., Düsing, R., & Kuhl, J. (2013). Implicit affiliation
motive predicts correct intuitive judgment. Journal of Individual
Differences,34,24–31. https://doi.org/10.1027/1614-0001/
a000086
Quirin, M., Kazén, M., & Kuhl, J. (2009). When nonsense sounds
happy or helpless: The Implicit Positive and Negative Affect Test
(IPANAT). Journal of Personality and Social Psychology,97,
500–516. https://doi.org/10.1037/a0016063
Quirin, M., & Kuhl, J. (2018). The Self‐Access Form (SAF): Devel-
opment and validation in the context of personality functioning
and health. Journal of Individual Differences,39,1–17. https://
doi.org/10.1027/1614-0001/a000244
Quirin, M., Kuhl, J., & Düsing, R. (2011). Oxytocin buffers cortisol
responses to stress in individuals with impaired emotion regula-
tion abilities. Psychoneuroendocrinology,36, 898–904. https://
doi.org/10.1016/j.psyneuen.2010.12.005
Quirin, M., & Lane, R. D. (2012). The construction of emotional ex-
perience requires the integration of implicit and explicit emo-
tional processes. Behavioral and Brain Sciences,35, 159–160.
https://doi.org/10.1017/S0140525X11001737
Quirin, M., Tops, M., & Kuhl, J. (2019). Autonomous
motivation, internalization, and the integrative self: A
self‐regulation framework of interacting neuropsychological sys-
tems. In R. M. Ryan (Ed.), The Oxford handbook of human mo-
tivation (pp. 393–413). New York, NY, USA: Oxford
University Press.
Radtke, E. L., Düsing, R., Kuhl, J., Tops, M., & Quirin, M. (2020).
Personality, stress, and intuition: Emotion regulation abilities
moderate the effect of stress‐dependent cortisol increase on co-
herence judgments. Frontiers in Psychology,11, 339. https://
doi.org/10.3389/fpsyg.2020.00339
Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the vi-
sual cortex: A functional interpretation of some extra‐classical
receptive‐field effects. Nature Neuroscience,2,79–87. https://
doi.org/10.1038/4580
Rasmussen, A. S., & Berntsen, D. (2010). Personality traits and au-
tobiographical memory: Openness is positively related to the ex-
perience and usage of recollections. Memory,18, 774–786.
https://doi.org/10.1080/09658211.2010.514270
Rasmussen, H. N., Wrosch, C., Scheier, M. F., & Carver, C. S.
(2006). Self‐regulation processes and health: The importance of
optimism and goal adjustment. Journal of Personality,74,
1721–1748. https://doi.org/10.1111/j.1467-6494.2006.00426.x
Rauthmann, J. F. (2015). Persönlichkeit als
informationsprozessierendes System: Ein
systemisch‐synergetischer Zugang. Psychology of Everyday Ac-
tivity,8,36–64.
Rauthmann, J. F. (2016). Motivational factors in the perception of
psychological situation characteristics. Social and Personality
Psychology Compass,10,92–108. https://doi.org/10.1111/
spc3.12239
Rauthmann, J. F. (Ed) (2020). The handbook of personality dynam-
ics and processes. Amsterdam, The Netherlands: Elsevier.
Rauthmann, J. F., Gallardo‐Pujol, D., Guillaume, E. M., Todd, E.,
Nave, C. S., Sherman, R. A., Ziegler, M., …Funder, D. C.
(2014). The Situational Eight DIAMONDS: A taxonomy of ma-
jor dimensions of situation characteristics. Journal of Personality
and Social Psychology,107, 677–718. https://doi.org/10.1037/
a0037250
Rauthmann, J. F., & Sherman, R. A. (2016). Situation change: Sta-
bility and change of situation variables between and within per-
sons. Frontiers in Psychology,6. https://doi.org/10.3389/
fpsyg.2015.01938
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2018). The vir-
tual personalities neural network model: Neurobiological under-
pinnings. Personality Neuroscience.,1, e10. https://doi.org/
10.1017/pen.2018.6
Read, S. J., Droutman, V., & Miller, L. C. (2017). Virtual personal-
ities: A neural network model of the structure and dynamics of
personality. In R. R. Vallacher, S. J. Read, & A. Nowak (Eds.),
Computational social psychology (1st ed., pp. 15–37). New
York, NY, USA: Routledge.
Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra,
G., & Miller, L. C. (2010). A neural network model of the struc-
ture and dynamics of human personality. Psychological Review,
117 ,61–92.
Read, S. J., Smith, B. J., Droutman, V., & Miller, L. C. (2017). Vir-
tual personalities: Using computational modeling to understand
within‐person variability. Journal of Research in Personality,
69, 237–249.
Revelle, W., & Condon, D. M. (2015). A model for personality at
three levels. Journal of Research in Personality,56,70–81.
https://doi.org/10.1016/j.jrp.2014.12.006
Revelle, W., Humphreys, M. S., Simon, L., & Gilliland, K. (1980).
The interactive effect of personality, time of day, and caffeine: A
test of the arousal model. Journal of Experimental Psychology:
General,109,1–31.
Robinson, M. D., & Gordon, K. H. (2011). Personality dynamics:
Insights from the personality social cognitive literature. Journal
of Personality Assessment,93, 161–176. https://doi.org/
10.1080/00223891.2010.542534
Robinson, M. D., Klein, R. J., & Persich, M. R. (2019). Personality
traits in action: A cognitive behavioral version of the social cog-
nitive paradigm. Personality and Individual Differences,147,
214–222. https://doi.org/10.1016/j.paid.2019.04.041
Robinson, M. D., & Wilkowski, B. M. (2015). Personality
processes and processes as personality: A cognitive perspective.
In M. Mikulincer, P. R. Shaver, M. L. Cooper, & R. J. Larsen
(Eds.), APA handbook of personality and social psychology.
Personality processes and individual differences (pp. 129–145),
4. Washington, D. C., USA: American Psychological
Association.
Rosenberg, N., Rufer, M., Lichev, V., Ihme, K., Grabe, H.‐J., Kugel,
H., Kersting, A., et al. (2016). Observer‐rated alexithymia and its
relationship with the five‐factor‐model of personality.
Psychologica Belgica,56,118–134. https://doi.org/10.5334/
pb.302
Rueter, A. R., Abram, S. V., MacDonald, A. W., Rustichini, A., &
DeYoung, C. G. (2018). The goal priority network as a neural
substrate of conscientiousness. Human Brain Mapping,39,
3574–3585. https://doi.org/10.1002/hbm.24195
Rumelhart, D. E., McClelland, J. L., & PDP Research Group (Eds)
(1986). Parallel distributed processing: Explorations in the mi-
crostructure of cognition 1. Cambridge, MA, USA: MIT press.
Rusting, C. L., & Larsen, R. J. (1997). Extraversion, neuroticism,
and susceptibility to positive and negative affect: A
test of two theoretical models. Personality and Individual
Differences,22, 607–612. https://doi.org/10.1016/S0191-8869
(96)00246-2
Salovey, P., & Grewal, D. (2005). The science of emotional intelli-
gence. Current Directions in Psychological Science,14,
281–285. https://doi.org/10.1111/j.0963-7214.2005.00381.x
966 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

Schlüter, C., Fraenz, C., Pinnow, M., Voelkle, M. C., Güntürkün,
O., & Genç, E. (2018). Volition and academic achievement: Inter-
individual differences in action control mediate the effects of con-
scientiousness and sex on secondary school grading.
Motivation Science,4, 262–273. https://doi.org/10.1037/
mot0000083
Schneider, M. E. (2015). Motivational development, systems theory
of. In J. D. Wright (Ed.), International encyclopedia of the social
& behavioral sciences (2nd ed., pp. 10120–10125), 2. New York,
NY, USA: Elsevier. https://doi.org/10.1016/B978‐0‐08‐097086‐
8.23027‐4.
Schneirla, T. C. (1959). An evolutionary and developmental theory
of biphasic processes underlying approach and withdrawal. In M.
R. Jones (Ed.), Nebraska symposium on motivation (pp. 1–42).
Lincoln, NE, USA: University of Nebraska Press.
Schultz, W. (2013). Updating dopamine reward signals. Current
Opinion in Neurobiology,23, 229–238. https://doi.org/10.1016/
j.conb.2012.11.012
Serfass, D. G., & Sherman, R. A. (2015). Situations in 140 charac-
ters: Assessing real‐world situations on twitter. PLoS ONE,10,
e0143051. https://doi.org/10.1371/journal.pone.0143051
Servaas, M. N., Van Der Velde, J., Costafreda, S. G., Horton, P.,
Ormel, J., Riese, H., & Aleman, A. (2013). Neuroticism and the
brain: A quantitative meta‐analysis of neuroimaging studies in-
vestigating emotion processing. Neuroscience & Biobehavioral
Reviews,37, 1518–1529. https://doi.org/10.1016/j.
neubiorev.2013.05.005
Shackman, A. J., Tromp, D. P. M., Stockbridge, M. D., Kaplan, C.
M., Tillman, R. M., & Fox, A. S. (2016). Dispositional negativ-
ity: An integrative psychological and neurobiological perspec-
tive. Psychological Bulletin,142, 1275–1314. https://doi.org/
10.1037/bul0000073
Shah, J. Y., Friedman, R., & Kruglanski, A. W. (2002). Forgetting
all else: On the antecedents and consequences of goal shielding.
Journal of Personality and Social Psychology,83, 1261–1280.
Sherman, R. A., Rauthmann, J. F., Brown, N. A., Serfass, D. G., &
Jones, A. B. (2015). The independent effects of personality and
situations on real‐time expressions of behavior and emotion.
Journal of Personality and Social Psychology,109, 872–888.
https://doi.org/10.1037/pspp0000036
Shoda, Y., Wilson, N. L., Chen, J., Gilmore, A. K., & Smith, R. E.
(2013). Cognitive‐affective processing system analysis of
intra‐individual dynamics in collaborative therapeutic assess-
ment: Translating basic theory and research into clinical applica-
tions. Journal of Personality,81, 554–568. https://doi.org/
10.1111/jopy.12015
Silverman, M. H., Wilson, S., Ramsay, I. S., Hunt, R. H., Thomas,
K. M., Krueger, R. F., & Iacono, W. G. (2019). Trait neuroticism
and emotion neurocircuitry: Functional magnetic resonance im-
aging evidence for a failure in emotion regulation. Development
and Psychopathology,31, 1085–1099.
Smillie, L. D. (2013). Extraversion and reward processing. Current
Directions in Psychological Science,22, 167–172. https://doi.
org/10.1177/0963721412470133
Smillie, L. D., Hayley, K. J., Hughes, D. M., Wacker, J., Cooper, A.
J., & Pickering, A. D. (2019). Extraversion and reward‐
processing: Consolidating evidence from an electroencephalo-
graphic index of reward‐prediction‐error. Biological Psychology,
146, 107735. https://doi.org/10.1016/j.biopsycho.2019.107735
Smillie, L. D., Varsavsky, V., Avery, R. E., & Perry, R. (2016). Trait
intellect predicts cognitive engagement: Evidence from a re-
source allocation perspective. European Journal of Personality,
30, 215–226. https://doi.org/10.1002/per.2059
Sosnowska, J., Kuppens, P., De Fruyt, F., & Hofmans, J. (2019). A
dynamic systems approach to personality: The personality dy-
namics (PersDyn) model. Personality and Individual Differences,
144,11–18.
Southward, M. W., Altenburger, E. M., Moss, S. A., Cregg, D. R.,
& Cheavens, J. S. (2018). Flexible, yet firm: A model of healthy
emotion regulation. Journal of Social and Clinical Psychology,
37, 231–251. https://doi.org/10.1521/jscp.2018.37.4.231
Specht, J., Bleidorn, W., Denissen, J. J., Hennecke, M., Hutteman,
R., Kandler, C., & Zimmermann, J. (2014). What drives adult
personality development? A comparison of theoretical perspec-
tives and empirical evidence. European Journal of Personality,
28, 216–230. https://doi.org/10.1002/per.1966
Spirtes, P., Glymour, C. N., & Scheines, R. (2000). Causation, pre-
diction, and search. Cambridge, MA, USA: MIT Press.
Stahl, J., & Rammsayer, T. (2008). Extroversion‐related differences
in speed of premotor and motor processing as revealed by
lateralized readiness potentials. Journal of Motor Behavior,40,
143–154. https://doi.org/10.3200/JMBR.40.2.143-154
Strack, F., & Deutsch, R. (2004). Reflective and impulsive determi-
nants of social behavior. Personality and Social Psychology Re-
view,8, 220–247.
Suslow, T., Ihme, K., Quirin, M., Lichev, V., Rosenberg, N., Bauer,
J., Bomberg, L., …Lobsien, D. (2015). Implicit affectivity and
rapid processing of affective body language: An fMRI study.
Scandinavian Journal of Psychology,56, 545–552. https://doi.
org/10.1111/sjop.12227
Takeshima, Y., & Gyoba, J. (2014). Hemispheric asymmetry in the
auditory facilitation effect in dual‐stream rapid serial visual pre-
sentation tasks. PLoS ONE,9, e104131. https://doi.org/10.1371/
journal.pone.0104131
Tamir, M. (2016). Why do people regulate their emotions? A taxon-
omy of motives in emotion regulation. Personality and Social
Psychology Review,20, 199–222. https://doi.org/10.1177/
1088868315586325
Tellegen, A. (1981). Practicing the two disciplines for relaxation
and enlightenment: Comment on “Role of the feedback signal
in electromyograph biofeedback: The relevance of attention”by
Qualls and Sheehan. Journal of Experimental Psychology: Gen-
eral,110 , 217–226. https://doi.org/10.1037/0096-
3445.110.2.217
Tellegen, A. (1982). Brief manual for the multidimensional person-
ality questionnaire. Unpublished manuscript, University of Min-
nesota, Minneapolis, MN, USA.
Tellegen, A., & Atkinson, G. (1974). Openness to absorbing and
self‐altering experiences (“absorption”), a trait related to hypnotic
susceptibility. Journal of Abnormal Psychology,83, 268–277.
https://doi.org/10.1037/h0036681
Tops, M., Boksem, M. A. S., Luu, P., & Tucker, D. M. (2010). Brain
substrates of behavioral programs associated with self‐regulation.
Frontiers in Cognition,1,1–14.
Tops, M., IJzerman, H., & Quirin, M. (2020). Personality dynamics
in the brain: Individual differences in updating of representations
and their phylogenetic roots. To appear. In J. Rauthmann (Ed.),
The handbook of personality dynamics and processes.
Amsterdam, the Netherlands: Elsevier.
Tops, M., Montero‐Marín, J., & Quirin, M. (2016). Too much of a
good thing: A neuro‐dynamic personality model explaining en-
gagement and its protective inhibition. In S.‐I. Kim, J. Reeve,
& M. Bong (Eds.), Recent developments in neuroscience re-
search on human motivation (pp. 283–319). Bingley, UK: Emer-
ald Group Publishing Limited. https://doi.org/10.1108/S0749‐
742320160000019012.
Tops, M., Quirin, M., Boksem, M. A. S., & Koole, S. L. (2017).
Large‐scale neural networks and the lateralization of motivation
and emotion. International Journal of Psychophysiology,119,
41–49. https://doi.org/10.1016/j.ijpsycho.2017.02.004
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019). Towards a uni-
versal taxonomy of macro‐scale functional human brain net-
works. Brain Topography,32, 926–942. https://doi.org/10.1007/
s10548-019-00744-6
Ungerleider, L. G., & Mishkin, M. (1982). Two cortical visual sys-
tems. In D. J. Ingle, M. A. Goodale, & R. J. W. Mansfield (Eds.),
Analysis of visual behavior (pp. 549–586). Cambridge, MA,
USA: MIT Press.
Dynamics of personality approach 967
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)

Vallacher, R. R., Read, S. J., & Nowak, A. (2002). The dynamical
perspective in personality and social psychology. Personality
and Social Psychology Review,6, 264–273. https://doi.org/
10.1207/S15327957PSPR0604_01
Van Egeren, L. F. (2009). A cybernetic model of global personality
traits. Personality and Social Psychology Review,13,92–108.
https://doi.org/10.1177/1088868309334860
Wacker, J., & Smillie, L. D. (2015). Trait extraversion and dopa-
mine function. Social and Personality Psychology Compass,9,
225–238. https://doi.org/10.1111/spc3.12175
Watson, D. (2000). Mood and temperament. New York, NY, USA:
Guilford Press.
Weinberg, A., Kotov, R., & Proudfit, G. H. (2015). Neural indica-
tors of error processing in generalized anxiety disorder,
obsessive‐compulsive disorder, and major depressive disorder.
Journal of Abnormal Psychology,124, 172–185. https://doi.org/
10.1037/abn0000019
Whitmer, A. J., & Gotlib, I. H. (2013). An attentional scope model
of rumination. Psychological Bulletin,139, 1036–1061.
Wiener, N. (1948). Cybernetics. Scientific American,159,14–19.
Wilson, T. D., & Dunn, E. W. (2004). Self‐knowledge: Its limits,
value, and potential for improvement. Annual Review of
Psychology,55, 493–518. https://doi.org/10.1146/annurev.
psych.55.090902.141954
Wilt, J., & Revelle, W. (2009). Extraversion. In M. Leary, & R.
Hoyle (Eds.), Handbook of individual differences in social behav-
ior (pp. 27–45). New York, NY, USA: Guilford Press.
Wood, D., Gardner, M. H., & Harms, P. D. (2015). How functional-
ist and process approaches to behavior can explain trait covaria-
tion. Psychological Review,122,84–111. https://doi.org/
10.1037/a0038423
Yarkoni, T. (2015). Neurobiological substrates of personality: A
critical overview. In M. Mikulincer, P. R. Shaver, M. L. Cooper,
& R. J. Larsen (Eds.), APA handbook of personality and social
psychology. Personality processes and individual differences
(pp. 61–84), 4. Washington, D. C., USA: American Psychologi-
cal Association.
Yovel, I., Revelle, W., & Mineka, S. (2005). Who sees trees before
forest? The obsessive‐compulsive style of visual attention.
Psychological Science,16, 123–129. https://doi.org/10.1111/
j.0956-7976.2005.00792.x
968 M. Quirin et al.
© 2020 The Authors. European Journal of Personality published by
John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
Eur. J. Pers. 34: 947–968 (2020)