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99
CHAPTER
5 The Motivational Architecture of
Emotions
Marco Del Giudice
Abstract
Evolutionary research on emotion is increasingly converging on the idea that emotions
can be understood as superordinate coordination mechanisms. Despite its plausibility
and heuristic power, the coordination approach fails to explicitly address the relations
between emotions and motivation. This chapter aims to ll this conceptual gap. The
author argues that the current view of emotions as coordination mechanisms should be
extended— and partially revised— to include motivational systems as an additional control
layer, responsible for the activation and deactivation of specic emotions in the pursuit
of domain- specic goals. The extended coordination approach proposed in this chapter
facilitates the analysis of folk emotion categories; helps clarify the distinction between
emotions and moods; suggests new ways to think about emotion regulation; and provides
a more natural interface to model the link between emotions and personality.
Key Words: emotions, emotion regulation, mood, motivational system, personality
Evolutionary research on emotion is increasingly converging on the idea that emotions can
be understood as superordinate coordination mechanisms or coordination programs (Al- Shawaf
et al., 2016; Al- Shawaf & Lewis, 2017; Nesse, 1990, 2004; Sznycer, Cosmides, et al., 2017;
Tooby & Cosmides, 1990, 2008, Chapter 2 in this volume). In a nutshell, emotions evolved
to solve the coordination problem— the adaptive problem of how to orchestrate large suites of
cognitive, physiological, and behavioral mechanisms so as to produce ecient but exible
responses to recurrent tness- relevant situations. What we call emotions are organismic modes
of operation that fulll this crucial coordination function; the same applies to other feelings
that are not usually categorized as emotions, such as hunger and sexual arousal.
Importantly, coordination mechanisms do not map in a one- to- one fashion on folk cat-
egories such as “anger” or “fear,” which are often used to refer to multiple mechanisms with
somewhat distinct features and functions (Al- Shawaf et al., 2016; Sznycer, Cosmides, et al.,
2017; see also Fiske, 2020; Scarantino, 2012). And because emotions have evolved through
a complex history of divergence and progressive specialization, they are best described as a
multitude of overlapping neurocomputational mechanisms with somewhat fuzzy boundaries
(Nesse, 1990, 2004, 2020). us, simple taxonomies based on a small number of distinct,
sharply dierentiated emotions are inevitably limited and articial; while an adaptationist
approach is the best way to “carve emotions at their functional joints” (Sznycer, Cosmides,
et al., 2017), there are going to be multiple reasonable ways to do the carving, and inherent
uncertainty about the number of mechanisms and their exact boundaries. e coordination
marco del giudice100
approach suggests that the classic distinction between “basic” and “non- basic” emotions is
not functionally meaningful (Al- Shawaf et al., 2016; Cosmides & Tooby, 2000); however, it
is compatible with some recent updates of basic emotion theory, most notably the reformed
version proposed by Scarantino (2015; see also Keltner et al., 2019).
e coordination approach to emotions is biologically plausible, heuristically powerful,
and integrative in scope. I believe it has the potential to become the “standard model” in bio-
logically oriented emotion research. However, the model is still incomplete in many respects,
and some important issues have remained unaddressed so far. Perhaps the biggest conceptual
gap concerns the relations between emotions and motivation. Proponents of the coordination
approach have argued that one of the functions of emotion programs is to regulate the individ-
ual’s motivational priorities (e.g., safety is prioritized when fear is activated; Al- Shawaf et al.,
2016; Cosmides & Tooby, 2000; Tooby & Cosmides, 2008). e same authors have invoked
the concept of motivational systems— computational mechanisms that regulate behavior and
decision- making in tness- relevant domains. For example, Tooby and colleagues (2008) dis-
cussed the sexual and altruistic motivational systems as examples of mechanisms that rely on
the “kinship index,” a hypothetical internal regulatory variable (IRV) that tracks the estimated
genetic relatedness between the focal individual and other people (e.g., siblings). e sexual
system is associated with emotions of lust and disgust, whereas the altruism system is associ-
ated with love and closeness. ey argued that “[a] high kinship index produces feelings of
disgust when accessed by the sexual motivation system at the possibility of sexual contact with
the person, and impulses to help when accessed by the system regulating altruistic motiva-
tions” (p. 256). is seems to imply that motivational systems activate emotional programs
in response to goal- relevant situations. However, the authors also stated that anger orches-
trates the activity of “downstream” motivational systems that regulate cooperation and aggres-
sion (p. 266). In other papers, emotions such as pride are described as systems that include
“motivational subcomponents” (e.g., Sznycer, Cosmides, et al., 2017). While the coordination
approach postulates a tight coupling between emotions and motivational systems, the nature
of this relation is not clearly specied. As noted by Beall and Tracy (2017), the concept of
emotions as coordination mechanisms overlaps substantially with the concept of motivational
systems in the literature on motivation (e.g., Kenrick et al., 2010), but the two have not been
explicitly connected by evolutionary scholars.
In this chapter, I aim to ll this gap. I argue that the current view of emotions as coordina-
tion mechanisms should be extended— and partially revised— to include motivational systems
as an additional (second- order) control layer, responsible for the activation and deactivation of
specic emotions (Aunger & Curtis, 2013; Beall & Tracy, 2017; Bowlby, 1982; Del Giudice,
2018; Gilbert, 1989, 2005; Lichtenberg et al., 1992; Scott, 1980). Motivational systems regu-
late the pursuit of key biological goals and coordinate emotions in the service of those goals.
From a computational perspective, they take up some of the functions that have been ascribed
to emotion mechanisms, including the detection and evaluation of tness- relevant situations.
is reconceptualization has important theoretical implications: just like emotions can solve
the basic coordination problem, motivational systems can eciently solve the higher- order
problems that arise in the pursuit of exible, context- sensitive coordination. Motivational
systems contribute to the robustness and evolvability of psychological architectures, by serv-
ing as central nodes in a regulatory network with a hierarchical “bow- tie” structure (Csete
& Doyle, 2004). A motivational- systems perspective facilitates the analysis of folk emotion
categories, and helps clarify the distinction between emotions and moods. At the end of the
chapter, I illustrate how the extended coordination approach suggests new ways to think about
the motivational architecture of emotions101
emotion regulation, and provides a more natural interface to model the link between emotions
and personality.
Motivational Systems
Historical Roots of the Concept
e theory of motivational systems originates in the psychology of the early 20th century,
most notably in McDougall’s concept of instincts (1908). McDougall took a strikingly modern
approach (Boden, 1965) and described instincts as goal- directed processes that orient atten-
tion and perception (cognitive component), give rise to emotional experiences (aective com-
ponent), and elicit specic action tendencies (conative component). On this view, instincts
are not rigid or stereotyped— on the contrary, they motivate learning and enable adaptive
behavioral change. McDougall (1908) proposed six “primary” human instincts, each with
an associated primary emotion: ight (fear), repulsion (disgust), curiosity (wonder), pugnacity
(anger), self- abasement or subjection (negative self- feeling), self- assertion or self- display (positive
self- feeling or elation), and the parental instinct (tenderness). Four additional instincts lacked
a clearly dened emotional component: reproduction, gregarious instinct, acquisition, and con-
struction. From a functional perspective, McDougall’s cognitive- aective- conative processes
can be likened to the emotional coordination mechanisms envisioned by present- day evolu-
tionary scholars.
In his later work, McDougall (1932) switched from “instincts” to “propensities,” to
avoid the former’s connotations of deterministic rigidity. He also expanded the list to
include appeal or help- seeking, laughter, a migratory propensity, and some basic physiologi-
cal motivations (food- seeking, comfort, and rest/ sleep). By that time, however, the popular-
ity of instinct theories in psychology was fading. ere were a number of reasons for this
reversal of fortune. To begin with, some theorists had started using the concept of instincts
in a circular fashion, raising doubts about the explanatory status of this approach. More
importantly, behaviorism was on the rise, and its proponents kept attacking instinct theo-
ries as old- fashioned and unscientic.1 On a deeper level, it seemed impossible to reconcile
McDougall’s “purposive” and goal- oriented view of the mind with a truly “mechanistic”
explanation of behavior (see Heckhausen, 2018; Krantz & Allen, 1967; McDougall, 1921,
1924; Scheer & Heckhausen, 2018). Instinct- like constructs like “needs” and “ergs”
would keep resurfacing in the eld of personality (e.g., Cattell, 1957; Maslow, 1954;
Murray, 1938), but for most psychologists, the concept was going to remain scientically
suspect, if not outright taboo.
At the same time that they were being (prematurely) abandoned in psychology, instincts
were taking center stage in the emerging discipline of ethology. Building on Craig’s (1918)
appetitive- consummatory model, Tinbergen (1951) advanced the notion that instincts can be
redened as hierarchically organized structures of behavior. For example, the stickleback sh’s
reproductive instinct includes the sub- instincts of ghting, nest building, mating, and o-
spring care; each of these sub- instincts can trigger a set of appropriate consummatory behav-
iors (e.g., chasing, biting, threatening as ghting behaviors). Tinbergen’s seminal contribution
was expanded and recast in the framework of control systems theory (cybernetics), yielding
the concept of behavioral or motivational systems. (In the biological literature, these two labels
are essentially synonymous, and I use them interchangeably in this chapter.) Behavioral/ moti-
vational systems were conceptualized as hierarchies of feedback- regulated processes, with dedi-
cated goals and subgoals, that control the sequencing of behavior through complex loops of
activation and inhibition (Baerends, 1976; McFarland, 1971, 1974; Toates & Archer, 1978).
marco del giudice102
Conceptual Developments
While behavioral systems theory has informed decades of animal research, ethologists have
generally avoided the issue of emotions and aective states (Burghardt, 2019; Burghardt &
Bowers, 2017). But as ethological ideas started to lter back into psychology, the connection
between the operation of behavioral systems and the experience of emotions became an impor-
tant topic of investigation. e key contributions in this respect were made by Scott (1980)
and Bowlby (1982). In Bowlby’s model, feelings are experienced in relation to the activation of
a behavioral system, the progress of current behavior in relation to the system’s “set goal,” and
the eventual consequences of behavior (success vs. failure to achieve the set goal).2 For exam-
ple, the attachment system in infants and children has the set goal of maintaining the proximity
and/ or availability of the caregiver (and the ultimate function of ensuring the child’s survival).
e system is activated by perceived dangers or separations (with feelings of anxiety, fear, dis-
tress, loneliness), and successfully deactivated by the attainment of proximity and protection
(with feelings of relief, comfort, and “felt security”). Lack of progress in reaching proximity
(e.g., because of an inconsistent or insensitive caregiver) can elicit anger and protest behaviors
(e.g., crying, yelling), whereas protracted failure of the system leads to sadness, despair, and
ultimately emotional detachment.
Bowlby’s theory of motivation was extended by Gilbert (1989) and Lichtenberg and
colleagues (1992). In addition to attachment, Lichtenberg’s list of motivational mechanisms
includes defensive, exploration/ competence, sexuality, caregiving, and aliative systems. Gilbert’s
work is focused on interpersonal relations, with an emphasis on what he calls the “social men-
talities” related to care eliciting (attachment), caregiving, social ranking (competition), formation
of alliances (cooperation, aliation), and mating/ sexuality (Gilbert, 2005). Along similar lines,
Bugental (2000) argued for the existence of ve basic systems that regulate social relationships
in our species: attachment, mating, reciprocity, hierarchical power, and coalitional group (a sys-
tem that has the goal of acquiring and defending shared group resources, and is involved in
intergroup conict). Bugental also tracked the emergence and development of these systems
across the life span and considered their possible neurobiological correlates.
e model of motivation that emerges from this tradition has some notable implica-
tions. First, a motivational system can have multiple, thematically related goals, rather than
a single overarching goal. For instance, the goals of a system that regulates status/ dominance
relations may include improving, maintaining, and displaying one’s status, as well as deferring
or submitting to higher- status individuals (e.g., Gilbert, 1989, 2005). ese narrower moti-
vations can be thought of as subsystems within a broader neurocomputational mechanism.
Second, motivational systems can embody sophisticated and context- sensitive operation rules,
that respond exibly to the state of the environment and draw on internal representations
(including internal regulatory variables) and models of the world (e.g., inferences about the
caregiver’s intentions and emotions, expectations about the caregiver’s likely response, repre-
sentations of the child’s worth and value to the caregiver). ird, motivational systems can
reciprocally potentiate and inhibit each other’s activity, and thus achieve a degree of collective
self- organization without the intervention of other prioritization mechanisms; for example,
when the attachment system is activated, it quickly suppresses play and curiosity- driven explo-
ration (Bowlby, 1982). Fourth, a given motivational system is not tied to a single emotion,
but to a set of characteristic emotions (both “positive” and “negative”). Dierent emotions
are activated depending on contextual factors, internal representations, and the moment- to-
moment consequences of the individual’s actions. Finally, emotions may be shared by more
than one system. For example, anger— or, quite possibly, multiple domain- specic variants
of the “anger” program— can be triggered in the context of attachment, but also in those of
the motivational architecture of emotions103
status competition, aggressive defense, pair bonding, or reciprocal exchange (in response to
cheating and betrayal). One implication is that aective labels have low motivational speci-
city; simply knowing that someone feels “angry” says relatively little about their goals and
motivational state.
A distinct and highly inuential approach to motivation is Panksepp’s research program
on “basic emotional systems” (Panksepp, 1998, 2005, 2011; Davis & Panksepp, 2018).
Working from a neurobiological and comparative perspective, Panksepp used a broad array
of evidence from animal research to describe seven emotional systems shared by all mammals,
which give rise to basic emotions or “core emotional feelings.” ese mechanisms are mainly
implemented by subcortical circuits; they are labeled RAGE (anger/ rage), FEAR, PANIC (sepa-
ration panic/ sadness), LUST, CARE (care/ nurturance), PLAY (joy), and SEEKING (a general-
ized appetitive/ exploratory system that regulates reward seeking). More recently, Toronchuk
and Ellis (2013) suggested that the model should be expanded to include two additional
systems, DISGUST and POWER/ dominance.
One notable contribution of this research program is the attempt to specify in some
detail how dierent systems interact by potentiating or inhibiting one another’s activity. For
example, Panksepp (1998) drew on neurobiological and pharmacological evidence to argue
that RAGE inhibits the activity of FEAR, PANIC, and SEEKING, whereas FEAR potenti-
ates the other three systems. An important limitation of the model— which self- consciously
echoes McDougall’s instinct theory— is the assumed one- to- one correspondence between each
system and one specic emotion, which precludes the strategic exibility and computational
richness of the multi- emotion systems theorized by Bowlby and others. Another limitation is
the insistence that, to be truly “basic,” emotional systems must be shared across all mammalian
species. Each species faces somewhat distinctive adaptive problems, and humans have evolved
complex forms of social interaction that make them unique among mammals and primates
(e.g., Borgerho Mulder & Beheim, 2011; Hrdy & Burkart, 2020, Chapter 47 in this vol-
ume; Kaplan et al., 2009; Pinker, 2010; Quinlan, 2008). us, humans can be expected to
possess species- specic motivations and emotions, as well as many unique variations on pan-
mammalian motives (Al- Shawaf et al., 2016; Aunger & Curtis, 2013).
Some Recent Contributions
In the last 10 years or so, there have been several notable contributions based on the concept of
behavioral/ motivational systems, including integrative works on the systems underlying care-
giving (Brown et al., 2012; Schaller, 2018), pair- bonding (Fletcher et al., 2015), dominance
and status (Anderson et al., 2015; Johnson et al., 2012), and play (Pellis et al., 2019). Woody
and Szechtman (2011) presented a detailed analysis of the security system (or precaution system;
Boyer & Liénard, 2006), a motivational system specialized to prevent rare, potential threats
and associated with anxiety and apprehension (in contrast with fear triggered by imminent
threats).
In their evolutionary renovation of Maslow’s ever- popular “pyramid of needs” (1954),
Kenrick and colleagues (2010) described seven fundamental motives— immediate physiological
needs, self- protection, aliation, status/ esteem, mate acquisition, mate retention, and parenting.
e ordering of the motives reects both their cognitive priority (higher to lower precedence)
and their timing of emergence during the life course (earlier to later development). Each
motive is served by one or more motivational systems, which in turn are composed by “(a) a
template for recognizing a particular class of relevant environmental threats or opportunities,
(b) inner motivational/ physiological states designed to mobilize relevant resources, (c) cog-
nitive decision rules designed to analyze trade- os inherent in various prepotent responses,
marco del giudice104
and (d) a set of responses designed to respond to threats or opportunities represented by the
environmental inputs (i.e., to achieve adaptive goals)” (Kenrick et al., 2010, p. 306). Neel and
colleagues (2016) developed the framework by adding disease avoidance as distinct from fear-
based self- protection, and replacing parenting with a broader motive of kin care.
Although Kenrick and colleagues repeatedly implied that evolved responses to threats and
opportunities include the experience of feelings, they remained vague about the specic nature
and adaptive role of those feelings. Beall and Tracy (2017) set out to complete the framework
by linking the activation of each fundamental motivation with the onset of a distinct emo-
tion: fear for self- protection, happiness for aliation, pride for status/ esteem, lust for mate
acquisition, romantic love for mate retention, and tenderness for parenting/ kin care. In line
with the coordination approach, the emotion triggered by a motivational system orchestrates
cognition, physiology, and behavior so as to reach the system’s adaptive outcome (eectively
working as an “eector” of the system). Beall and Tracy made a valuable contribution by
explicitly linking the concept of motivational systems with the coordination approach to emo-
tions. As they themselves acknowledged, the idea that complex computational mechanisms
like the status/ esteem system are associated with just one characteristic emotion (instead of
multiple emotions, including not only pride but also shame, embarrassment, etc.) is prob-
lematic and should be revised. Luckily, more sophisticated models of motivation are readily
available (see above) and can be integrated within the same basic framework.
Drawing on a century of literature on this topic, Aunger and Curtis (2013) presented
a biologically informed taxonomy of human motivational systems (which they labeled
“motives”). eir list comprises hunger, comfort, fear, disgust, lust, attract (a system specialized
for mating competition), love (pair- bonding), nurture, aliate, status, justice (a system that
regulates reciprocal exchange), hoard (resource acquisition), create, curiosity, and play. I made
a similar attempt to present an organized taxonomy of motivation in a book on psychopathol-
ogy (Del Giudice, 2018). e admittedly partial list of systems I proposed includes aggression,
fear, security, disgust, status, mating, attachment, caregiving, pair bonding, aliation, reciprocity,
acquisition, play, and curiosity.
A Map of Human Motivational Systems
Even though dierent scholars have proposed somewhat dierent taxonomies of motivational
systems, there are more commonalities than dierences. If one excludes “physiological needs”
like hunger, thirst, and thermoregulation, human motivations can be related to ve broad cat-
egories of adaptive problems: (a) prevention and avoidance of physical hazards; (b) acquisition
and enhancement of resources (including “embodied” resources such as knowledge and skills;
Kaplan et al., 2000, 2007); (c) mating and reproduction; (d) relations with kin; and (e) rela-
tions within and between groups. Each of these categories comprises several specic problems
that can be solved by specialized motivational systems, each equipped with domain- relevant
goals and algorithms.
Figure 5.1 shows a partial map of human motivational systems, derived from recent syn-
theses of the literature (mainly Aunger & Curtis, 2013; Del Giudice, 2018; Kenrick et al.,
2010). I briey describe each of the systems later in this section. Note that, while the taxon-
omy in Figure 5.1 has enough support to serve as a useful starting point, it is also provisional in
many respects. Some systems (e.g., fear, attachment) have been studied extensively for decades,
and we have detailed information on their adaptive goals, activating cues/ situations, operating
rules, associated emotions, neurobiological bases, and developmental patterns; other putative
systems (e.g., acquisition, creation) are understood only in their generalities, or represent plau-
sible but still largely hypothetical adaptations.
the motivational architecture of emotions105
How Many Systems?
Questions about the “right” number of constructs are as old as the psychology of motivation;
lack of agreement on the number of human instincts was a contributing factor to the wan-
ing of instinct theories in the 1920s (Krantz & Allen, 1967; Scheer & Heckhausen, 2018).
A hundred years later, we are much better equipped to deal with this problem, having real-
ized that the evolution of complex biological mechanisms (including the brain) proceeds by
reuse, duplication, partial dierentiation, and gradual accrual of function (see Barrett, 2012,
2015a). is intricate process of “descent with modication” does not deliver neatly packaged
mechanisms with simple, well- specied functions— instead, it produces overlapping mecha-
nisms with somewhat indistinct boundaries, multiple functions, and a great deal of redun-
dancy (Nesse, 2020). Moreover, most computational mechanisms are composed of simpler
components or sub- processes, some of which may be shared with other mechanisms. e
bottom line is that, as in the case of emotions, it may not be possible to converge on a single,
unambiguous taxonomy of motivational systems; there will always be multiple defensible ways
to draw boundaries between related systems, and multiple levels of resolution to describe the
same computational processes (Kenrick et al., 2010). In fact, the problem of “how many
motivational systems there are” is essentially the same problem of “how many emotions there
are”— only somewhat more tractable, because there are many fewer motivational systems than
distinguishable emotions.
To illustrate, Gilbert (2005) described a single sexual system that covers everything from
sexual desire to romantic love; Kenrick and colleagues (2010) separated mate acquisition (sex-
ual desire and attraction) from mate retention (including pair- bonding); while Aunger and
Curtis (2013) drew a subtler distinction between systems that regulate sexual desire (lust),
mate attraction and competition (attract), and pair- bonding (love). ere is no doubt that
sexual desire and romantic love share some deep functional connections; however, they can
occur independently of one another, have dierent emotional constellations and evolutionary
Hazards
(security)
(cooperation,
justice) (social ranking,
hierarchical power)
(sexuality, lust, attract,
mate acquisition) (parenting,
nurture) (care-eliciting)
(love, mate retention)
(exploration)
(coalitional group,
alliances)
Reproduction
Group
Resources
Kin
Fear
Precaution Defensive
aggression
Disgust
Reciprocity
Status
Curiosity
Acquisition
[Predation]
Affiliation
[Creation]
(hoard)
Play
Mating
Caregiving Attachment
Pair-bonding
Figure 5.1. A partial map of human motivational systems, grouped into ve broad categories of adaptive problems.
Some alternative labels used in the literature are shown in parentheses. e systems in square brackets are still mostly
hypothetical but warrant further investigation.
marco del giudice106
histories, and serve dierent goals within the broader task of reproduction (more on this
below). I believe there is a strong case for treating mating and pair- bonding as distinct moti-
vational systems that can become activated separately or in combination; but it is also possible
to regard them as part of a larger, composite system with phylogenetically older and newer
components. In the process of pair- bonding, passionate love gives way to aection and “loving
attachment” (Tennov, 1999). ese two phases of pair- bonding are emotionally distinct, and
may or may not be best described as outputs of the same system. e case of sexual desire vs.
mate attraction is even less clear- cut, and the decision to postulate one or two systems becomes
more arbitrary (at least in the present state of knowledge) and dependent on one’s prefer-
ences for “lumping” vs. “splitting.” is ambiguity is a predictable consequence of the organic,
evolved complexity of motivational mechanisms (Nesse, 2020).
Needless to say, these complications should not deter researchers from trying to map
our species’ motivational systems (and associated emotions) as accurately and meaningfully
as possible. Evolutionary task analysis (Al- Shawaf, 2016; Al- Shawaf & Lewis, 2017; Lewis
et al., 2017; Tooby & Cosmides, 2015) is a powerful tool to identify putative systems and
draw functional distinctions among them— especially in combination with a rich database of
behavioral, neurobiological, and comparative/ phylogenetic evidence. is is no dierent from
how evolutionary scholars approach the analysis and classication of individual emotions, as
exemplied in many chapters of this volume.
A focus on the adaptive tasks and computational logic of motivational systems is essential
to overcome the shortcomings of atheoretical correlational methods, such as factor analysis
and principal component analysis (PCA). Patterns of correlations among multiple types of
behaviors, emotional experiences, and so forth can be informative and heuristically useful; but
when they are used to make inferences about the mechanisms that give rise to those behaviors,
emotions, etc., correlational analyses are severely limited and can be downright misleading (see
also Lukaszewski et al., 2020). e output of a motivational system is not xed, but condi-
tional on the nature of the current situation (e.g., threat vs. opportunity) and the individual’s
success or failure relative to the system’s goal. ese appraisals and the emotional responses
they trigger are further modulated by individual dierences in the system’s working parameters
and the value of the relevant regulatory variables. Activation of attachment needs can lead to
vigorous crying but also withdrawal and detachment; a challenge to one’s status can lead to
pride and elevation but also shame, submission, or defeat. In other words, a system can be
functionally coherent, but this coherence may not translate into simple patterns of correla-
tions among the system’s outputs. For example, infants’ attachment behaviors in response to
separation require at least two dimensions of variation to be adequately summarized (Fraley
& Spieker, 2003). Moreover, between- person correlations do not simply reect the dynam-
ics of individual systems, but also patterns of co- activation and inhibition between multiple
systems and individual dierences at various timescales. And when the measured indicators
include emotions, the use of standard labels makes it impossible to detect functional distinc-
tions within folk categories such as “anger” or “anxiety.”
Given all the above, it is rather unlikely that the dimensions identied by factor analysis
or PCA will correspond to specic mechanisms. In practice, the situation is even worse: rst,
determining the “correct” number of dimensions to retain is an ill- specied task with no
straightforward solution (see Del Giudice, 2021); and second, standard algorithms for rotat-
ing factors/ components are designed to seek a “simple structure” in the data— a hopelessly
unrealistic assumption for many complex biological systems (Lykken, 1971).3 To illustrate,
Brasini and colleagues (2020) performed factor analysis on a pool of behaviors and emotions
selected to indicate the activation of seven motivational systems (attachment, caregiving, rank
the motivational architecture of emotions107
competition, sexuality, cooperation, aliation, and social play). Unsurprisingly, the analysis
failed to clearly identify the hypothesized systems; instead, it returned some composite factors
(e.g., a “prosociality” factor mixing caregiving and cooperation; an “insecurity” factor mixing
attachment, submission, and shame), as well as a separate factor for dominant and high- status
behaviors. Because correlational methods are intrinsically limited in their ability to answer
questions about mechanisms and processes (especially in the absence of strong theoretical
models), the same problems arise in the study of personality (Baumert et al., 2017; Borsboom
et al., 2009; Davis & Panksepp, 2018; Lukaszewski et al., 2020; more on this below).
In the remainder of this section, I outline the motivational systems shown in Figure 5.1.
I want to stress that this is only intended as a brief summary, far from an in- depth evolution-
ary and computational analysis. For more detailed overviews, see Aunger and Curtis (2013);
Bugental (2000); Kenrick and colleagues (2010); and Toronchuk and Ellis (2013), in addition
to the literature cited in each subsection.4
Fear System
e fear system is an ancient defensive mechanism that motivates organisms to avoid or escape
immediate threats. is system can be activated by a multitude of cues and situations, and
many specic fears are acquired through learning. However, some types of stimuli elicit fear
with no need for learning (e.g., sudden loud noises) or after minimal exposure (e.g., snakes,
spiders, angry male faces; LoBue & Rakison, 2013; Mallan et al., 2013; Öhman, 2009). Tonic
and attentive immobility are important components of the fear system. Attentive immobil-
ity or “freezing” occurs in preparation for escape or ghting; tonic immobility is a kind of
paralysis or fainting without loss of consciousness, a last resort defense when harm is inevitable
(Hagenaars et al., 2014; Roelofs, 2017). In contrast, successful escape/ avoidance triggers feel-
ings of safety and relief.
Defensive Aggression System
Aggression is a basic motivation to harm or threaten other organisms, including— but not lim-
ited to— individuals of the same species. Aggression is often deployed as a defensive strategy
in response to immediate threats to oneself, one’s kin, or one’s allies. Defensive aggression has
been labeled as reactive, aective, emotional, etc.; it is marked by intense arousal, anger, or rage,
and can be triggered by high levels of fear (Panksepp, 1998, 2011). For this reason, defensive
aggression and fear are sometimes discussed together as part of a unitary “ght- or- ight” or
“ght- ight- freeze” system (e.g., Corr et al., 2013; Corr & Krupić, 2017). However, aggressive
motivations are not always defensive. A prime example of proactive, instrumental, or predatory
aggression is hunting, which involves extreme aggression toward prey but no anger. In fact,
“proactive” aggression can be accompanied by feelings of pleasure and excitement (Chester,
2017; Chichinadze et al., 2011; Panksepp, 1998).
In humans, proactive aggression is also a key component of group conicts and wars, in
the course of which the enemy is dehumanized and eectively treated like prey (Wrangham,
1999, 2018). Proactive aggression can be employed to reinforce dominance hierarchies, take
or steal resources, and more generally control the behavior of others. Whereas defensive
aggression can be meaningfully treated as a distinct motivational system (in analogy with fear,
disgust, etc.), I concur with Aunger and Curtis (2013) that— generally speaking— proactive
aggression is best understood as a behavioral tactic in the service of other motivations (e.g.,
status, acquisition). On the other hand, humans have a long evolutionary history as predators,
and a number of cognitive adaptations that seem to be specialized for interactions with prey
(Barrett, 2015b). One can tentatively hypothesize the existence of a specialized motivational
marco del giudice108
system for predation, which is activated both during hunting/ shing and in intergroup con-
icts (Figure 5.1). A predation system would most likely develop in a sex- specic way, and may
be only fully expressed in boys and men.
Precaution System
Like the fear system, the precaution system is a mechanism designed to protect organisms from
threats. e crucial dierence is that fear is triggered by immediate threats, whereas precaution-
ary motivations are activated by potential threats— that is, threats that are comparatively rare
and hard to detect but may have catastrophic consequences, such as dangerous predators or
contaminating pathogens (Boyer & Liénard, 2006; Woody & Szechtman, 2011). Immediate
threats evoke fear and escape/ ght behaviors; in contrast, activation of the precaution system is
marked by anxiety, wariness, and repetitive behaviors such as checking and exploration, which
help gather further information about the presence of potential risks. Indeed, the precaution
system tends to inhibit fear, preventing ight/ panic responses to permit cautious exploration
(Grae, 2004). e precaution system is activated by subtle and indirect cues of danger; but
the absence of a potential threat is hard or even impossible to determine with certainty, and
there are no clear signals indicating whether precautionary behaviors have been successful.
us, the system is not deactivated by situational cues, but by the precautionary behaviors
themselves, provided that they have been correctly executed (Woody & Szechtman, 2011). In
my previous work (Del Giudice, 2018), I adopted Woody and Szechtman’s label of “security
system,” but “precaution” (Boyer & Liénard, 2006) is more transparent and less likely to gen-
erate confusion with aliation and attachment.
Disgust System
e disgust system is a defensive mechanism whose original function is preventing contact
with pathogens and/ or toxins through ingestion of contaminated foods, drinks, or waste
products; manipulation of contaminated objects; and contact with infected people or animal
pathogen vectors (pathogen disgust; Curtis, 2011; Toronchuk & Ellis, 2013). Pathogen disgust
promotes physical avoidance, expulsion (e.g., vomiting), and cleaning behaviors. Disgust can
also trigger activation of the precaution system, and the two systems often work in synergy.
Over evolutionary history, the disgust system has been co- opted and dierentiated to deal with
other kinds of threats (Tybur et al., 2013). In particular, sexual disgust is designed to prevent
sexual contact with partners that would be detrimental to tness, for example because they
are too old, too genetically similar (e.g., siblings and other close kin), or prone to sexually
transmitted diseases (e.g., highly promiscuous individuals). Finally, disgust in our species is
deeply connected to morality: violations of moral norms and taboos can elicit disgust and feel-
ings of uncleanliness and contamination. A likely function of moral disgust is to motivate and
coordinate social distancing from (and/ or condemnation of ) individuals who violate moral
rules (Tybur et al., 2013). While failure to avoid contact with repulsive objects leads to intense
physical discomfort, motivational failures in the sexual and moral domains may also evoke
evaluative emotions such as shame and guilt.
Status System
In animals, dominance motivational systems have two complementary functions: (a) enhanc-
ing, defending, and displaying one’s social rank; and (b) when necessary, submitting to higher-
ranking individuals to avoid punishment and retaliation (Toronchuk & Ellis, 2013). In our
species, social hierarchies reect both physical dominance and skill- based prestige; the more
general concept of a “status system” covers both aspects, emphasizing the complex nature of
the motivational architecture of emotions109
human competition (see Anderson et al., 2015; Aunger & Curtis, 2013; Cheng et al., 2010;
Cheng et al., 2013; Johnson et al., 2012; Maner, 2017). e status system is activated by chal-
lenges to one’s dominance rank or prestige (from provocations and disrespectful acts to situa-
tions that involve social judgments), but also by opportunities to rise in the social hierarchy;
depending on the nature of the situation and the person’s current rank and capabilities, the
associated emotions may include anger, (performance) anxiety, envy, hope, and excitement.
e main emotions triggered by success are pride and condence, whereas failure tends to
elicit shame, anger, frustration, and sadness. Importantly, voluntary deference to high- status
individuals (leaders, teachers, etc.) can evoke a range of positive emotions such as admiration
and awe (Keltner et al., 2006). e concept of a status system absorbs the motivational func-
tions that have been ascribed to the emotional mechanisms of pride and shame, such as pro-
moting and advertising the achievement of socially valued goals/ characteristics (Sznycer, 2019;
Sznycer, Al- Shawaf, et al., 2017; see also Durkee et al., 2019). Dominance competition often
elicits aggression, and the two systems are deeply connected on a functional level (Anderson
et al., 2015; Toronchuk & Ellis, 2013).
Mating System
e mating system plays a critical role in reproduction by motivating sexual behavior, from
courtship and mate choice to intercourse. e system is activated by the presence or prospect
of attractive partners and/ or rivals; the emotional constellation of mating is varied, ranging
from arousal, desire, excitement, and pleasure to embarrassment, anxiety, and shame (e.g.,
Al- Shawaf et al., 2016; Toronchuk & Ellis, 2013). In a broader perspective, it is important to
note that mating and sexuality can be put in the service of other motivations and goals— for
example, reinforcing a long- term bond, enhancing one’s social status, exerting dominance, or
exchanging sex for gifts and other resources (e.g., Gangestad & Haselton, 2015; Meston &
Buss, 2007).
Attachment System
Like most young mammals, infants and children are vulnerable and depend on adults for feed-
ing and protection. e attachment system is designed to monitor and maintain the proxim-
ity and availability of caregivers (see above). In infancy and childhood, attachment has high
motivational priority, consistent with its critical role in ensuring survival. When activated, the
attachment system inhibits play and curiosity; conversely, the presence of an available attach-
ment gure works as a “secure base” for exploration (Cassidy, 2016). In our species, attach-
ment has been co- opted as a building block of close relationships in adulthood, including
those with romantic partners and friends (Fletcher et al., 2015; Mikulincer & Shaver, 2016;
Zeifman & Hazan, 2016). Specically, most intimate relationships involve an attachment
component, as they provide comfort, reassurance, and safety in times of distress.
Caregiving System
Mirroring the biological function of the attachment system, the caregiving system motivates
parents and other caregivers to protect and nurture their dependent young (Brown et al.,
2012; Cassidy, 2016; Panksepp, 1998, 2011; Schaller, 2018). As a species, humans show many
features of cooperative breeding: across societies, care and protection are provided not just by
parents, but by multiple individuals including older siblings, grandparents, and friends (Hrdy,
2005; Kramer, 2010). us, caregiving motivations need not be restricted to one’s biological
ospring. Caregiving is primarily activated by displays of immaturity, vulnerability, and/ or
distress (e.g., crying, cute baby- like features) and can trigger a range of emotions: tenderness,
marco del giudice110
“anxious solicitude,” protectiveness, as well as parental love and pride. Failures of the caregiv-
ing motivation can trigger powerful negative emotions of sadness and guilt (e.g., Gilbert,
Chapter 15 in this volume).
Pair- Bonding System
Pair- bonding is a central feature of human mating. It has plausibly evolved from the inte-
gration of sexual attraction with attachment and caregiving— two motivations rooted in
parent– child relations— and reused to enable long- term bonding between sexual partners.
In part, romantic love can be seen as a blend of emotions associated with these three sys-
tems; at the same time, the psychology of love also shows unique features and evidence of
functional specialization. For example, being in love temporarily inhibits the desire for
alternative sexual partners, thus working as a credible signal of interest and a “commitment
device” in view of shared parental investment (Doherty & Brumbaugh, Chapter 11 in this
volume; Eastwick, 2009; Fletcher et al., 2015; Gangestad & ornhill, 2007; Quinlan,
2008). Also, love is powerfully associated with jealousy, an emotional mechanism designed
to prevent indelity by partners (Buss, 2013). For these reasons, it makes sense to postu-
late a specialized pair- bonding system with the specic goal of forming and maintaining
long- term couple relationships (Aunger & Curtis, 2013; Kenrick et al., 2010; see also
Barbaro, 2020).
Aliation System
Aliation is a key motivational substrate of group living; its function is to enable and sustain
long- term relationships with extended kin and other group members, including friends and
allies. As with pair bonding, the psychology of aliation overlaps with that of attachment; at
the same time, friendship and group membership are suciently distinct from parent– child
relations to warrant the idea of a specialized motivational system (Aunger & Curtis, 2013;
Bugental, 2000; Kenrick et al., 2010). e aliation system can be activated not only by
the perception of shared interests and goals, but also by threatening situations, lack of social
resources (isolation, rejection), and intergroup conict. Successful aliation evokes feelings
of security and belonging, promotes the formation of a shared group identity, and sustains
cooperation and reciprocity.
Reciprocity System
While the aliation system promotes aective bonding with other group members, the reci-
procity system deals with cooperation and with the exchange of favors and resources. Its main
tasks are selecting cooperation partners, optimizing joint and personal benets, and monitor-
ing/ enforcing fairness (Bugental, 2000; Keltner et al., 2006). Even though extensive coopera-
tion networks of non- kin are unique to humans, other primates do engage in more limited
forms of reciprocity, for example in the context of grooming and food sharing (Engelmann
et al., 2015; Jaeggi et al., 2013; Jaeggi & Gurven, 2013). e reciprocity system can be acti-
vated by opportunities such as the presence of a capable and trustworthy partner, or by threats
such as cheating and unfairness. e corresponding emotions include trust, benevolence, sus-
piciousness, and moral indignation. In humans, reciprocity is supported by specialized cogni-
tive mechanisms that monitor violations of rules and keep track of partners’ contributions
and reputations over time (Cosmides & Tooby, 2015). While successful exchanges engender
satisfaction and gratitude, failures of reciprocity may arouse intense anger and contempt or
guilt, depending on whether one is the victim or the perpetrator.
the motivational architecture of emotions111
Acquisition System
An obvious but sometimes overlooked characteristic of our species is the extent to which we
store and accumulate resources for future use. Material wealth— in the form of land, cattle,
houses, or money— provides immediate adaptive benets as it improves both mating success
(especially in men) and the survival of children (see Borgerho Mulder & Beheim, 2011;
Nettle & Pollet, 2008). Moreover, stored resources reduce risk by working as a buer against
periods of scarcity and can be passed down from one generation to the next, with cumula-
tive eects on long- term tness (Borgerho Mulder et al., 2009; Winterhalder et al., 1999).
Unsurprisingly, humans have strong motivations to acquire resources, accumulate them, and
defend them against theft, as well as a distinct psychology of ownership based on emotions
such as desire, envy, and greed. e acquisition system likely has its evolutionary roots in the
mechanisms that mediate foraging and food hoarding (Aunger & Curtis, 2013; Preston, 2014;
Preston & Vickers, 2014). e specic goals of the acquisition system depend on its interac-
tion with other motivations such as mating and pair- bonding. For example, saving resources
for future family needs in the context of a long- term relationship is not the same as acquiring
costly luxury goods to boost success in courtship and short- term mating (Griskevicius et al.,
2007; Sundie et al., 2011).
Aunger and Curtis (2013) argued that humans possess a specic motive to improve and
maintain their habitat, making it more conducive to survival and reproduction. e rele-
vant behaviors include building dwellings, removing parasites and other dangers, tidying and
repairing habitat, and producing tools and artifacts. While there is little evolutionary work on
creation as a motivational system, the construct is plausible enough to be tentatively included
in the map of human motivation (Figure 5.1).
Curiosity System
Acquiring knowledge and exploring new environments have long been recognized as funda-
mental motives in animals (Aunger & Curtis, 2013; Loewenstein, 1994). Information- seeking
is essential to building models of the world and improving one’s ability to make inferences
and predictions (Gottlieb et al., 2013). In humans, knowledge can be used to build prestige or
increase one’s value as a social partner. Far from being a “cold” cognitive task, the acquisition
of information is regulated by a wide range of emotions and feelings, from excitement and
surprise to boredom, frustration, and anxiety. Curiosity and exploration are often discussed in
association with play, and play is certainly a powerful way to gather information about one-
self, other people, and the environment. Even pretend play based on unrealistic scenarios can
play a critical role in building sophisticated causal models of the world (Weisberg & Gopnik,
2013). However, there are many ways to acquire knowledge that do not rely on play; moreover,
language permits massive transfer of information without the need for rsthand experience. In
humans, adaptations for learning seem to be matched by adaptations for teaching, the deliber-
ate transmission of knowledge and skills (Csibra & Gergely, 2006; Fogarty et al., 2011).
Play System
Play behaviors are widespread in mammals and absorb a large fraction of juveniles’ time and
energy. e overarching function of play is to enable self- training in a range of adaptive skills;
ghting, parenting, and foraging are prominent recurring themes across species. More specic
functions are regulating neuromuscular development, learning how to cope with unexpected
events, and testing the limits of one’s abilities (Burghardt, 2005; Byers & Walker, 1995; Spinka
et al., 2001). While playful motivation in mammals seems to be mediated by a specialized
mechanism (Panksepp, 1998, 2005, 2011; Pellis et al., 2019), it always works in synergy
marco del giudice112
with other motivational systems that provide the momentary goals of play and the relevant
behavioral/ emotional repertoires. For example, rough- and- tumble play stems from the play-
ful coordination of the fear, defensive aggression, predation, and status systems (see Pellis
et al., 2019). Cognitive skills are also exercised through play, as when children play games of
memory, numbers, and language (e.g., Locke & Bogin, 2006). Finally, play promotes social
bonding in synergy with the aliation system (Toronchuk & Ellis, 2013) and can also be an
eective way to display skills and other attractive traits (e.g., strength, intelligence) to potential
allies and partners.
Extending the Coordination Approach
e standard coordination approach to emotions makes certain assumptions about the com-
putational architecture of emotions. Specically, emotion mechanisms are thought to comprise
two kinds of components: (a) situation- detecting algorithms that monitor for situation- dening
cues, and perform computations of variable complexity to identify the presence of the acti-
vating situation; and (b) coordination programs that signal to the downstream mechanisms
(e.g., memory, attention, autonomic, and other physiological systems), switching them into
the appropriate functioning mode. Because more than one emotion- eliciting situation may
occur at the same time (e.g., an animal may be hungry and being attacked by a predator),
emotion mechanisms are supervised by prioritizing algorithms that determine the degree of
compatibility between multiple emotion modes, and resolve conicts by giving priority to the
most important or pressing situations (Cosmides & Tooby, 2000; Tooby & Cosmides, 2008,
Chapter 2 in this volume). is architecture is sketched in Figure 5.2A. Note that, for clarity,
the gure depicts emotions and motivational systems as separate mechanisms with clear- cut
boundaries; as I discussed earlier, this is a dramatic simplication of reality.
A motivational- systems perspective suggests some modications to the standard approach,
as illustrated in Figure 5.2B. Most notably, motivational systems take up the task of detect-
ing situations, and subsequently activate dierent emotions depending on the state of the
organism and its environment vis à vis the system’s goal(s). is revision has two main conse-
quences. First, emotion mechanisms are eectively reduced to coordination programs. Second,
situation- detecting algorithms are decoupled from emotion mechanisms, so that similar situa-
tions may give rise to dierent emotions depending on the motivational state of the individual,
while situations pertaining to dierent motivational domains may trigger the same emotion
(possibly with alternative motivation- specic “avors”).
In addition to detecting goal- relevant situations, motivational systems monitor the prog-
ress of current behavior in relation to the active goals, evaluate situations in terms of success vs.
failure, and strategically deploy emotions in order to increase the chances of success, avoid fail-
ure, or deal with failure and mitigate its costs if necessary. In the diagram of Figure 5.2B, these
computational tasks are carried out by goal pursuit/ evaluation algorithms. Note that multiple
motivational systems may make use of the same information to perform their computations.
For example, information about the possession of socially valued traits such as attractiveness
and trustworthiness (see Sznycer & Lukaszewski, 2019) is going to aect evaluations (and the
intensity of the corresponding emotions) across a number of distinct motivational domains—
from status, reciprocity, and aliation to mating and pair- bonding (Scrivner et al., Chapter 4
in this volume).
Figure 5.2B makes the additional assumption that control signals ow in one particular
direction, that is, from motivational systems to emotion mechanisms but not vice versa (i.e.,
emotions do not directly coordinate motivational systems). is simplifying assumption is
open to revision, as future research unveils the computational logic of various motivational
the motivational architecture of emotions113
Situation
cues
Situation
cues
Downstream
mechanisms
Downstream
mechanisms
Emotion mechanism
Emotion mechanism
Situation-detecting
algorithm
Coordination
program
Coordination
program
Emotion mechanism
Emotion mechanism
Emotion mech.
Emotion mech.
Emotion mech.
Emotion mech.
Emotion mechanism
Prioritizing
algorithms
Mood
mechanisms
Motivational system
Goal pursuit/
evaluation
algorithms
Motivational system
Situation-detecting
algorithms
(a)
(b)
Figure 5.2. Schematic diagram of the computational architectures underlying (A) the standard coordination
approach; and (B) the extended coordination approach, in which emotion mechanisms are themselves coordinated
by a layer of motivational systems. Each motivational system detects a range of situations, integrates them over
time, evaluates them in relation to its specic goals, activates the appropriate emotions, and modulates the activity
of other systems. Note that a given emotion may be activated by more than one motivational system, and thus may
play a role in the pursuit of more than one adaptive goal. Moods are produced by superordinate mechanisms that
use information from motivational systems (and/ or other inputs, such as the immune system) to assess/ predict the
state of the organism and its environment on a more global scale, and to regulate the activity of several motivational
systems at once.
marco del giudice114
systems. To be clear, even if emotions do not directly control motivational systems, they still
“motivate behavior” in the sense of activating certain action tendencies. e point is that,
according to this model, motivational goals are processed upstream of emotions rather than
downstream (e.g., pride is triggered by the successful pursuit of status- enhancement goals).
e extended architecture in Figure 5.2B is characterized by a hierarchical “bow- tie”
structure, in which a large number of inputs and outputs ow through a small set of common,
highly conserved processes that form the “knot” of the tie (Csete & Doyle, 2004). In this case,
the central knot corresponds to the layer of motivational systems. Bow- tie architectures are
ubiquitous in biological systems, from genetic regulation and immunity to cellular and neural
signaling (Doyle & Csete, 2011; Kitano, 2004; Kitano & Oda, 2006). e compact size of the
knot (e.g., a small set of regulatory genes or second messenger molecules) permits rapid, e-
cient control of the entire system in response to challenges and uctuations. At the same time,
the comparatively weak linkages between the central knot and input/ output processes increase
both the exibility of the system and its evolvability. For example, a given motivational system
can easily evolve to process dierent situation cues, or trigger additional emotions that were
previously specic to other systems, with few or no changes to its set- goals and core algorithms
(“plug- and- play modularity”).
e Achilles’ heel of bow- tie architectures lies in the same features that make them ver-
satile and robust to perturbations— that is, the small size and centralized control function of
the knot. If knot processes get damaged or successfully hijacked (for example by parasites), the
consequences can be catastrophic. As a result, central processes are more tightly regulated than
the ones in the periphery, and tend to evolve at a much slower pace (Csete & Doyle, 2004;
Kitano, 2004). ese properties of bow- tie architectures could have interesting implications
for the study of motivation and emotion from a phylogenetic and comparative perspective.
Higher- Order Coordination Problems
In the standard coordination approach, the need to postulate the existence of prioritizing
algorithms (Figure 5.2A) points to what I will call the second- order coordination problem.
Emotions evolved to eciently coordinate a large number of psychological and physiological
mechanisms, by “centralizing” the detection of situations and the generation of appropriate
activity patterns. However, there are not just a handful of emotion mechanisms, but doz-
ens of them— and hence dozens of potential modes of operation for the organism, many
of which have mutually contradictory eects;5 hence, the second- order problem of how to
coordinate the activity of this teeming multitude of emotion mechanisms, resolving potential
conicts and maintaining a coherent sequence of behavior. In the standard approach, this role
is fullled by prioritizing algorithms, whose architecture and functional properties are left
unspecied. In the extended approach I am proposing, motivational systems directly control
the activation of emotion mechanisms, and thus solve the second- order coordination problem
without the need for a dedicated supervisory system.
Although motivational systems solve the second- order coordination problem, they still
necessitate ways to resolve conicts and prioritize certain goals over others, giving rise to a
third- order coordination problem. is may sound like innite regress, but it is not: as the
number of mechanisms that have to be coordinated shrinks, it becomes possible to use coor-
dination strategies that would be impractical or intractable at lower levels of the control
hierarchy. For example, cross- modulation (e.g., reciprocal inhibition between functionally
incompatible systems) may allow motivational systems to self- coordinate to a certain degree,
and make it possible to “arbitrate” simple priority conicts without the intervention of super-
ordinate mechanisms. Cross- modulation is feasible within a relatively small set of motivational
the motivational architecture of emotions115
systems, but would become cumbersome (and potentially unworkable) if scaled up to dozens
of emotion mechanisms with thousands of potential connections among them.
Moods as Third- Order Coordination Programs
e concept of higher- order coordination problems shines new light on the old and perplex-
ing question of what dierentiates emotions from moods. Phenomenologically, moods are
long- lasting and have a diuse rather than focused quality; unlike emotions, they do not
have a specic cause or triggering object, and do not prompt specic behaviors or action
tendencies (Beedie et al., 2005; Gendolla, 2000). At the same time, they have a powerful (if
nonspecic) impact on motivation, and dispose people to appraise new situations in aect-
congruent ways (e.g., attributing hostile intentions to others when one is in an irritable mood;
see Siemer, 2009).
Current biological models of mood resonate with Nesse’s (1990) suggestion that mood
encodes a global estimate of the “propitiousness” of the environment, or the expected rate of
reward per unit of eort invested (a plausible internal regulatory variable). Similarly, Morris
(1992) framed mood as a system that regulates goal- directed behavior so as to maintain a
balance between goal- relevant resources and demands; mood improves when (personal and
environmental) resources are more plentiful than necessary to meet demands, and deteriorates
when resources are perceived as inadequate. Nettle and Bateson (2012) argued that, as organ-
isms experience rewards and punishments, they revise their estimates of the probability of the
two types of outcomes, and adjust the thresholds that determine how easily a new situation is
perceived as a potential reward or a potential threat. In this model, the organism’s “core” mood
reects the settings of two separate thresholds for detecting/ responding to rewards and pun-
ishments (e.g., a depressed mood corresponds to high thresholds for both rewards and pun-
ishments). Trimmer and colleagues (2013) proposed a somewhat dierent two- dimensional
scheme that distinguishes between the organism’s general positive vs. negative expectations
and its level of preparedness to act (e.g., a depressed mood corresponds to a combination of
negative expectations and low preparedness). In his later work, Nesse (2004) focused on the
relation between high vs. low mood and the rate of progress in the pursuit of domain- specic
goals. Across domains, reaching one’s goals faster than expected elicits positive moods, which
in turn facilitate investing more eort; whereas the perception that goals keep getting farther
away despite increasing eort is a trigger for low mood and depression, which promote dis-
engagement and eort withdrawal. Eldar and colleagues (2016) echoed these ideas with the
notion that mood is especially inuenced by prediction errors, and specically by positive
vs. negative discrepancies between expected and actual outcomes (e.g., rewards). In the same
paper, they argued that mood encodes the momentum of recent outcomes (i.e., their improving
or declining trend), and noted that forming global expectations about future rewards based
on specic events can be adaptive if dierent sources of reward (e.g., material resources, social
status, sexual partners) tend to be correlated with one another.
From the standpoint of the extended coordination approach, moods are easily under-
stood as the product of third- order coordination mechanisms that (a) receive information
from motivational systems about success and failure in the pursuit of domain- specic goals
(together with other inputs that encode the state of the organism, for example its immuno-
logical condition, energetic balance, and level of fatigue); (b) compute integrative estimates
of the present/ future state of the organism in relation to its environment, for example based
on the momentum of recent outcomes; and (c) strategically modulate the functioning of
multiple motivational systems— not just by generically “activating” or “inhibiting” them, but
also by selectively inuencing their sensitivity to threats vs. opportunities (as in the threshold
marco del giudice116
model by Nettle and Bateson, 2012). Computationally, some of these modulation eects may
be construed as changes in the settings of global or motivation- specic regulatory variables.
According to this model (Figure 5.2B), moods act as superordinate programs that function to
coordinate the activity of motivational systems. ey aect cognition, behavior, and physiol-
ogy on a broad scale, but do so indirectly through the action of multiple motivational systems
and the corresponding emotions (see also Morris, 1992). To the extent that motivational sys-
tems directly modulate one another, some aspects of the phenomenology of moods may reect
self- coordination instead of regulation by superordinate programs; precisely how much top-
down regulation is needed to produce moods will become clearer as we learn more about the
dynamic interplay between motivational systems.
e extended coordination approach accommodates the main insights of other biological
models and accounts for key aspects of the phenomenology of moods, including the combina-
tion of high motivational potency and low motivational specicity. It also provides a simple,
principled answer to the long- standing question of what the dierence is between emotions
and moods. Both are coordination adaptations; but emotions are rst- order coordination
mechanisms activated by motivational systems, whereas moods are third- order coordination
mechanisms whose primary function is to modulate the activity of motivational systems.
From this vantage point, some putative emotions such as lassitude (the feeling of being sick;
Schrock et al., 2020, Chapter 16 in this volume) should be classied more precisely as moods.
Lassitude does not entail specic goals or action tendencies; but when triggered by cues of
infection, it modulates a wide range of motivational systems, including the ones that control
mating, parenting, hunger, and thermoregulation (Schrock et al., 2020). e eects on cogni-
tion and behavior are profound, and can last for days or even weeks (i.e., until the acute phase
of the infection is resolved).
The Sequence Integration Problem
In many cases, the meaning of emotion- eliciting situations is not entirely determined by
immediate circumstances, but depends on the preceding sequence of situations, outcomes,
and emotions. Winning an unlikely victory after suering humiliation and shame does not
just arouse pride and satisfaction, but intoxicating feelings of triumph. In fact, important
situations like revenge, betrayal, and reconciliation are dened by their place within thematic
sequences of events and emotions, which can be represented as movements toward and away
from certain motivational goals. is adds a layer of complexity to the task of detecting and
evaluating situations, raising what I will call the sequence integration problem. In principle, it
would be possible to solve the sequence integration problem with a complex system of regu-
latory variables whose values are updated and accessed by individual emotion mechanisms.
However, a control layer of motivational systems addresses this problem in a more straight-
forward way. Tracking goals over time and evaluating new events in relation to those goals are
crucial functions of motivational systems; sequence integration arises naturally out of these
functions, without the need for additional computational machinery.
A Note on Feedback vs. Feedforward Control
Both the classic ethological perspective and contemporary theories of self- regulation (e.g.,
Carver & Scheier, 2013; DeYoung, 2015; DeYoung & Krueger, 2018; Revelle & Condon,
2015) emphasize the critical role of feedback control in the pursuit of goals. Feedback control-
lers work by reducing the discrepancy between the current state of the world (as sensed and
interpreted by the controller) and a desired state or “set point.” e set point can be static
(homeostasis) but need not be— it is possible for a feedback controller to track a “moving
the motivational architecture of emotions117
target” that changes based on previous events and/ or predictions about the future state of the
world (allostasis; Sterling & Eyer, 1988).
Here I want to briey note that feedback regulation is not the only possibility, and suggest
that the goal- pursuit algorithms employed by motivational systems will often include both
feedback and feedforward processes. Instead of continuously self- correcting based on the con-
sequences of previous actions, feedforward controllers anticipate the future state of the system,
and execute the appropriate action(s) without further course correction. In the simplest forms
of feedforward control, no actual predictions are computed and the response has a xed and
“ballistic” quality, as in the case of rapid protective reexes (e.g., blinking, pain- induced limb
retraction). In more sophisticated instances, the controller computes a model of the system
and uses the resulting prediction to generate an action, or a prespecied sequence of actions
(see Albertos & Mareels, 2010). A thermostat that turns on and o a heater to maintain
the target temperature within a house is a classic example of feedback control. A device that
automatically turns on the heater at a certain time in the evening to preempt an (expected)
temperature drop during the night would be an example of feedforward control based on a
simple model of the system. (For an introduction to the basic concepts of control theory, see
Del Giudice, 2015: Del Giudice et al., 2018.)
Feedback and feedforward regulation have complementary strengths and weaknesses. For
example, feedforward controllers are more resistant to noise and delays in the system, but are
unable to deal with unanticipated events; feedback controllers can function without an accu-
rate model of the system, but can only respond to events “after the fact,” without the ability
to anticipate them (Albertos & Mareels, 2010; Bechhoefer, 2005). In many situations, com-
bining the two strategies yields dramatically improved performance, and I see no reason why
motivational systems should not take advantage of this option (see also Tops et al., 2010; Tops
et al., 2021). To give a simple example, encountering a dangerous predator at night activates
the fear system and the emotion of fear, which in turn may promote escape behaviors (Tooby
& Cosmides, 2008). Like other avoidance goals, escaping from danger can be described as a
feedback- regulated process, in which the intensity of fear and the urge to ee diminish as one
moves farther away from the threat (Ballard et al., 2017). However, this feedback mechanism
is vulnerable to noisy information— e.g., the predator may be closer than it seems, it may be
hiding in the dark, or there may be other predators lurking in the surroundings. At least ini-
tially, the escape response is more likely to operate under feedforward control (just run away
as fast as possible); indeed, the optimal strategies for defensive mechanisms that deal with
uncertain dangers almost invariably involve an initial feedforward phase (Shudo et al., 2003).
Likewise, the (feedback- regulated) goal of increasing the distance from the threat may be sup-
plemented by the (feedforward- regulated) goal of reaching a safe hiding place or some other
known refuge. In many cases, a regulatory system that combines the strengths of feedback and
feedforward control is going to outcompete a system that relies on just one of these principles.
Other Benets of an Extended Approach
roughout this section, I have emphasized the theoretical benets of extending the coordi-
nation approach to include a central role for motivation. Another advantage of an extended
approach is that it makes it easier to “carve emotions at their functional joints” (Sznycer,
Cosmides, et al., 2017; see also Scarantino, 2015), using motivational systems as a guide
to plausible functional distinctions. For example, the folk category of “anger” may be use-
fully analyzed in view of the distinct adaptive problems posed by reciprocal cooperation, pair-
bonding, parent– child attachment, status competition, and defensive aggression. I speculate
that, when viewed through this lens, the recalibration theory of anger (Sell et al., 2009; Sell
marco del giudice118
et al., 2017; Sell et al., Chapter 8 in this volume) will turn out to apply only to some domains,
or will require modications to match the specics of the various motivational systems that
deploy “angry” emotions. To illustrate: open deance by a subordinate may signal a bid for
dominance; the implications of this gesture go beyond the fact that the subordinate does not
place enough weight on the welfare of the dominant individual, and an adaptive response
should take this into account. e anger expressed by infants and children toward unrespon-
sive caregivers is not amplied by self- perceived formidability (Sell et al., 2009), but by self-
perceived vulnerability— and the associated behaviors also function to display heightened
vulnerability and immaturity, rather than strength and competence. Anger and rage in the
context of defensive aggression may lack a recalibration function altogether, and terminate
when the aggressor is gone or incapacitated. Are these “varieties” of anger produced by the
same neurocomputational mechanism, or by distinct mechanisms? Answering this question is
going to require sustained research eort, and the task will be much facilitated by a working
map of the main motivational systems and their adaptive logic.
Conversely, the conceptual precision and careful analytic style that characterize the coor-
dination approach could greatly improve the current understanding of motivational systems.
To begin, the lists of emotions associated with the activation, success, and failure of most
motivational systems are plausible but still impressionistic (and most likely incomplete). ere
is urgent need for a ne- grained, empirically rigorous map of the emotional constellations
of human motivations. Similarly, existing attempts at specifying the computational logic of
human motivational systems are no more than bare- bones outlines, heuristically useful but
lacking in detail and precision (e.g., Bugental, 2000; Kenrick et al., 2010). Needless to say,
the computational logic of a motivational system is likely to be more complex than that of a
single emotion, as it involves more elaborate decision rules and goal- directed control strategies.
Hence, the toolkit of the coordination approach will need to be supplemented, for example
with concepts and models from mathematical control theory.
Another problem that would benet from detailed computational analysis concerns the
nature of the interplay between multiple systems. e vague notion that motivational systems
“activate” or “inhibit” one another (e.g., Bowlby, 1982; Panksepp, 1998) is rooted in simplistic
cybernetic and/ or neurobiological models, and should be updated with a modern understand-
ing of psychological adaptations. To illustrate, inhibition of a system may be understood as a
change in its general threshold for activation, but also as a change in the evaluation of certain
activating cues, a strategic adjustment of the system’s goals or criteria for success/ failure, a
selective suppression of some emotional responses, and so forth. Evolutionary models of emo-
tion and motivation naturally complement one another, and there are no good reasons to
maintain a separation between these areas of research (Beall & Tracy, 2017).
Implications for Emotion Regulation
From “Emotion Regulation” to “Motivation Regulation”
After decades of research, emotion regulation remains a scientic puzzle. e slow progress
on this topic is largely due to a persistent neglect of function beyond immediate proximate
concerns. With few exceptions, work in this area has been guided by the “hedonic assumption”
that people are motivated by a desire to feel good and avoid feeling bad, and the related notion
that emotion regulation is “adaptive” if it leads to more positive (or less negative) feelings (see
Aldao, 2013; Erber & Erber, 2000; Tamir, 2009, 2016). ese ingrained assumptions have
been challenged by another line of work, showing that people have multiple reasons to change
their emotional state in ways that are potentially counter- hedonic (e.g., getting important
work done, eliciting help and compassion, displaying empathy, matching the emotions of
the motivational architecture of emotions119
other group members; see Tamir, 2016). While this more realistic approach has been gaining
traction in recent years, the focus is still on immediate goals; so far, there have been very few
attempts to understand emotion regulation from an explicitly adaptationist perspective. Kisley
(Chapter 39 in this volume) has started to lay the groundwork for this enterprise. Here I adopt
the working assumption that, while emotions coordinate the state of the organism to deal with
recurrent adaptive problems, other cognitive mechanisms have a (limited) ability to second-
guess emotional responses based on the unique features of a situation (see Kisley, Chapter 39).
For example, certain situations may cause detection errors, and trigger emotions that are inap-
propriate or harmful. (is is especially likely when situation cues are ambiguous, inconsistent,
or occur in evolutionarily novel contexts.) In other cases, algorithms correctly detect the cur-
rent situation, but circumstances make it undesirable to express the relevant emotions or act
on them (e.g., because doing so would incur social costs, or interfere with other prioritized
goals). In yet other cases, the situation may pose multiple contradictory demands, causing a
regulatory conict that cannot be resolved by low- level arbitration processes.
My goal is not to delve into the details of these scenarios, but to suggest a general shift
in perspective that may facilitate their functional and evolutionary analysis. Specically,
many phenomena that are currently studied under the rubric of emotion regulation can be
understood more accurately and fruitfully as instances of motivation regulation. Consider the
social situations in which people prefer to “feel bad” for instrumental reasons. One common
example is that people who prepare for competitions and other conictual interactions (e.g.,
negotiations with strangers) often want to feel at least somewhat angry (e.g., Tamir & Ford,
2012; Tamir et al., 2008; Tamir et al., 2013). I argue that what people are trying to do is not
to feel anger per se, but to strategically upregulate specic motivational systems that include
anger as a characteristic emotion. In this case, “feeling angry” likely corresponds to the threat-
mode activation of either the status system (“dominance challenge”) or the reciprocity system
(“unfairness”). Note how a motivational perspective helps one to move beyond the folk con-
cept of anger, and consider alternative functional accounts of the same self- reported emotions.
is perspective also suggests new hypotheses about the mechanisms of regulation and their
proximate functions. To illustrate: activating the status system before a competitive interaction
may work not just for its communicative eects (e.g., van Kleef et al., 2004), but also because
it indirectly inhibits the reciprocity and aliation systems— thus making one less inclined to
compromise with the adversary, or less receptive to manipulative cues of aliation.
Regulation Strategies
It is instructive to apply the same lens to the literature on emotion- regulation strategies (Gross,
2015; McRae & Gross, 2020). Cognitive reappraisal involves reinterpreting or re- evaluating
the situation to change the way one thinks about it. In some cases, “reinterpreting the situation”
means downregulating the motivational system activated by the situation, by re- evaluating the
meaning of ambiguous or inconsistent cues (“he didn’t mean to disrespect me; he was just in
a hurry”). In other cases, it means activating a new system whose activity is incompatible with
that of the previous one (e.g., caregiving instead of status: “he didn’t mean to disrespect me;
he’s nervous and depressed because his daughter is sick— poor guy!”). e activation of incom-
patible motivations may also underlie distraction strategies, when the distracting thoughts and/
or actions are not merely neutral but engage a dierent motivational system. Indeed, strategies
that belong to dierent categories according to current taxonomies may share functional com-
monalities when viewed through a motivational lens.
A motivational perspective could have interesting implications for the ecacy of dierent
self- regulation strategies. For example, strategies that exploit the interplay between dierent
marco del giudice120
motivational systems may be especially eective, compared with strategies that lack that func-
tional leverage. Also, the success of a certain strategy may depend not just on the specic emo-
tion that one is experiencing, but on the role played by that emotion in the economy of the
relevant motivational system. When an emotion is triggered by the activation of a system by
certain situation cues, it should be relatively easy to deactivate the system through reappraisal,
as long as the cues are suciently weak or ambiguous. But when a negative emotion marks the
failure of a system at the end of a sequence of goal- directed actions, reappraisal may become
signicantly harder, as it demands a complete re- evaluation of the entire course of events and
their psychological meaning.
Finally, the model I presented in this chapter may help clarify the dierences between the
regulation of emotions (or motivations) and that of moods (e.g., Erber & Erber, 2000; Morris,
2000). As third- order coordination programs, moods are not driven by specic events, but
by integrative evaluations of the state of the organism in relation to the environment. In this
sense, they are harder to regulate than emotions/ motivations, and less susceptible to targeted
strategies such as reappraisal and suppression. On the other hand, the fact that mood mecha-
nisms integrate over multiple inputs— including the immune system, digestive system, etc.—
creates some opportunities for regulation that are not available for lower- order mechanisms.
For example, it becomes possible to employ compensatory strategies, so that success in one
motivational domain balances out failure in another. Just as importantly, the range of potential
regulation mechanisms broadens to include physiological channels such as sleep, eating, and
exercise. As Morris (2000) noted, “e most reliable form of mood repair is probably a good
night’s sleep” (p. 201).
Implications for Personality and Emotion
The Motivational Basis of Personality
e idea that motivations are the basic building blocks of personality has a long history (e.g.,
Cattell, 1957; Murray, 1938), and is gaining renewed popularity as the eld begins to move
from the description of individual dierences to genuine, process- based explanation (e.g.,
Corr & Krupić, 2017; Davis & Panksepp, 2018; Dweck, 2017; Read et al., 2010; Read et al.,
2017; Schultheiss, 2020). In an inuential paper, Denissen and Penke (2008) argued that
individual dierences in the Big Five traits of the Five Factor Model (Neuroticism/ Emotional
Stability, Conscientiousness, Agreeableness, Extraversion, and Openness to Experience;
McCrae & Costa, 2003) reect dierences in “motivational reaction norms,” or response
patterns to specic classes of evolutionarily relevant situational cues. Ashton and Lee (2001,
2007) linked the six traits of the HEXACO model to ve behavioral domains with a bio-
logical interpretation: reciprocal altruism/ cooperation (Agreeableness and Honesty- Humility),
kin altruism (Emotionality), social engagement (Extraversion), task- related engagement
(Conscientiousness), and idea- related engagement (Openness to experience). While these
authors did not explicitly discuss motivational systems, the domains they described show
some correspondences with more detailed models of human motivation, like the one I have
presented in this chapter.
ese and similar models (such as DeYoung’s [2015] “cybernetic Big Five theory”) share
a fundamental limitation: because they take factor- analytic traits at face value, they cannot
provide a mechanistic, process- focused explanation of personality. Human behavior is not
controlled by a handful of general- purpose mechanisms, but by large number of specialized
adaptations— certainly much larger than ve or six (Michalski & Shackelford, 2010). e
traits described by factor- analytic models arise from patterns of covariation among multiple
mechanisms, including— but not limited to— motivational systems. Covariation between
the motivational architecture of emotions121
mechanisms can be explained at various levels of analysis, both proximate (e.g., shared genetic
and environmental inuences, regulation by the same hormones/ neuromodulators, reliance
on shared regulatory variables, reciprocal activation/ inhibition) and ultimate (e.g., synergistic
eects on tness, coordinated expression of life history strategies; see Del Giudice, 2018).
Correlated mechanisms produce patterned behaviors, which are then ltered through evolved
heuristics for person perception, translated into intuitive person- description concepts, and
imperfectly captured by the lexical terms of human languages (Buss, 2011; Lukaszewski,
2020). To be sure, correlations among lexical descriptors can be quite informative; but they
provide very little information about the structure, number, and function of the underly-
ing psychological mechanisms (Davis & Panksepp, 2018; Lukaszewski, 2020; Lukaszewski
et al., 2020).
e solution to this problem is to invert the direction of analysis, and leverage our
knowledge of psychological mechanisms to reconstruct personality from the bottom up
(“ground- up adaptationism”; Lukaszewski, 2020). Neel and colleagues (2016) took an ini-
tial step in this direction, by assessing Kenrick et al.’s (2010) fundamental motives and
correlating them to a host of other individual- dierence variables (including the Big Five).
However, these authors did not include emotions in the picture. In contrast, Davis and
Panksepp (2011, 2018) sought to build an alternative model of personality based on puta-
tive basic emotional systems such as RAGE, SEEKING, CARE, and PLAY. is approach
to personality puts emotions front and center; unfortunately, it adopts a simplistic con-
ception of the link between motivation and emotion, and covers only a small portion of
the human motivational landscape (see above for details). For these reasons, I view Davis
and Panksepp’s model as an interesting proof of concept, but not a realistic candidate for a
general theory of personality.
Even if they do not explicitly include emotions, the computational models of personal-
ity developed by Read and colleagues (2010, 2017, 2020) deserve special attention. In these
models, personality arises from the behavior of multiple motivational systems that interact
with situational aordances and are able to learn from experience. is conception of person-
ality agrees very well with the approach I am proposing, and I see the authors’ computational
approach as an important step in the right direction (see also Revelle & Condon, 2015). Still,
the specics of the models reveal some notable theoretical limitations. To begin, the lists of
motivational systems included in the models are somewhat ad hoc and do not follow a prin-
cipled taxonomy. Second, the models lack an explicit theory of how dierent systems interact
with one another. ird, all the motivational systems in these models employ the same control
algorithm, regardless of their adaptive domain and specic goals. e algorithm is based on a
feedback loop, without the possibility of anticipatory feedforward control. Finally, Read and
colleagues introduce a separation between “approach motives” such as hunger, dominance,
and aliation; and “avoidance motives” such as avoidance of harm, rejection, and interper-
sonal conict. is is a major conceptual problem, because many motivational goals require
both approach and avoidance, depending on the situation and the state of the organism (e.g.,
approach food when hungry, avoid it when satiated; approach subordinates if dominant, avoid
dominants if subordinate). It is reasonable to postulate that, when emotion programs are
activated by motivational systems, the behavioral adaptations they orchestrate include some
general- purpose mechanisms that promote approach vs. avoidance of salient stimuli. Stated
dierently, approach and avoidance mechanisms may function as common behavioral path-
ways for the action of multiple domain- specic motivations. On the other hand, treating
approach and avoidance as distinct categories of motivations is a confusing move, and I believe
it will prove a theoretical dead end (see also Davis & Panksepp, 2018).6
marco del giudice122
Motivation as the Bridge between Personality and Emotion
If personality is largely the product of individual dierences in motivation (in combination
with other regulatory processes; e.g., Tops et al., 2010; Tops et al., 2021; Volk & Masicampo,
2020), the extended coordination approach suggests a two- pronged strategy for bridging per-
sonality and emotion. First, one should think about personality in explicitly motivational
terms, without assuming the functional coherence of standard personality traits. As a rule,
factor- analytic traits arise from the (correlated) activity of multiple motivational systems.
Second, one should think about emotions not as isolated mechanisms, but as eectors of
motivational systems, without assuming the functional coherence of folk emotion labels. A key
insight is that emotions do not correspond to motivations in a simple one- to- one fashion;
instead, they are deployed conditionally, depending on the meaning of a situation in relation
to the system’s adaptive goals.
As an illustration, consider the broad personality trait of Agreeableness in the Five Factor
Model (FFM). People high in Agreeableness are described as kind, trusting, altruistic, and
accommodating. Across countries, girls and women score higher than on this trait than boys
and men (Lippa, 2010; Mac Giolla & Kajonius, 2019; Murphy et al., 2021; Soto et al., 2011).
Agreeableness is associated with low proneness to anger, but high proneness to both guilt and
shame (Cohen et al., 2011; Einstein & Lanning, 1998; Reisenzein & Weber, 2009). It cor-
relates negatively with the experience of “hubristic” or dominance- related pride, and positively
with “authentic” or prestige- related pride (Beall & Tracy, 2020; Cheng et al., 2010; Tracy &
Robins, 2007). From a motivational systems perspective, Agreeableness is a functionally com-
plex trait that reects individual dierences in reciprocity, aliation, and status (specically
dominance- seeking; see Cheng et al., 2010; DeYoung et al., 2013; Graziano & Tobin, 2017).
Beyond this motivational core, Agreeableness is also associated with increased investment in
parental and kin care (caregiving system; Buckels et al., 2015; Neel et al., 2016), reduced mate-
seeking, lack of interest in short- term mating, and high investment in long- term mating and
stable romantic relationships (mating and pair- bonding systems; see Baams et al., 2014; Banai
& Pavela, 2015; Bourdage et al., 2007; Holtzman & Strube, 2013; Neel et al., 2016; Schmitt
& Buss, 2000).7
In functional terms, this pattern of covariation among motivational systems may be
explained as a manifestation of individual dierences along a “fast- slow continuum” of life-
history strategies (see Del Giudice, 2020; Del Giudice et al., 2015; Figueredo et al., 2007; Sela
& Barbaro, 2018). From this perspective, the trade- o between mating and parenting— a cen-
tral aspect of human life- history strategies— drives the observed associations between status,
mating, pair- bonding, and caregiving motivations (e.g., Neel et al., 2016); the future- oriented
nature of slow strategies promotes increased cooperation and aliation in addition to lower
mating and higher parenting eort (see Del Giudice, 2018).
A motivational analysis of Agreeableness indicates that people high on this trait should
experience (and express) less anger in response to violations of reciprocity and aliation, dom-
inance challenges, and threats to long- term relationships (e.g., romantic jealousy; Lukaszewski
et al., 2020). But it also suggests some new hypotheses that run against a simple negative
correlation with anger. For example, high- Agreeableness people may react intensely with pro-
tective (“parental”) anger when their children or other dependents are threatened. And while
they tend to get less angry when they experience unfairness and transgressions, things may
change when the victims are other innocent people (for indirect evidence, see Bizer, 2020). If
these hypotheses were supported, they would also raise interesting questions about the exis-
tence of functionally distinct variants of anger (e.g., how is “caregiving anger” dierent from
“reciprocity anger” or “dominance anger”? Are these variants expressed dierently in males and
the motivational architecture of emotions123
females?). e same kind of reasoning could be used to develop ner- grained hypotheses about
the contexts in which Agreeableness should predict the experience of shame, guilt, and many
other less- studied emotions like gratitude and sexual arousal. A more ambitious goal would be
not just to rene the concept of Agreeableness, but to develop an alternative, functional model
of personality based on a ne- grained understanding of human motivation.
Motivation and Person Perception
e ip side of this view of personality and motivation is that evolved heuristics for person
perception (“dierence- detecting mechanisms”; Buss, 2011) should be designed to make infer-
ences about people’s motivational processes, because this is the level of analysis that aords the
largest predictive payos. An important corollary is that information about people’s emotions
is often going to be interpreted in relation to their (probabilistically inferred) motivational
states. is is already implicit in the evolutionary literature on person perception. To illustrate
the kinds of problems that person- perception algorithms are designed to solve, Lukaszewski
and colleagues (2020) oered a list of questions, including: Who will be a reliable ally or long-
term mate? Who is likely to defect on social contracts? Who will rise in the social hierarchy?
Who is sexually permissive? To a large extent, these questions concern individual dierences in
motivational priorities and in the calibration of specic motivational systems.
In the same paper, Lukaszewski and colleagues presented convergent evidence that expe-
riences, facial expressions, and behaviors associated with anger are systematically translated
into descriptions that map onto the construct of Agreeableness.8 I suggest that people employ
the outputs of the anger program mainly as cues to the motivational processes of the angry
individual. And because motivational systems covary in meaningful patterns, these inferences
should often “spill over” to motivational domains that are not directly tapped by the target
situation. For example, imagine someone who consistently gets angry and aggressive in the
context of cooperative, reciprocal exchanges. at person is also more likely to be driven by
dominance concerns, sensitive to behaviors that could be interpreted as dominance challenges,
unreliable as a long- term romantic partner, interested in short- term mating opportunities, and
so forth. (For evidence that people tend to possess accurate models of the correlations among
personality traits, see Stolier et al., 2020.) In general, motivational inferences are so powerful
precisely because they allow one to make predictions about people’s emotions and behaviors
beyond the current situation, including hypothetical events and unlikely yet tness- critical
scenarios (“would he protect me if someone assaulted us?”). Note that, depending on context,
observed emotions may convey other kinds of predictive information besides motivation— for
example about a person’s beliefs, plans, and social alliances.
Based on the motivational analysis presented earlier, one can advance some hypotheses
about situations in which anger should not trigger inferences of low Agreeableness, or would
do so in a much- attenuated fashion. Possible examples are a parent getting angry at someone
who is threatening their child, and a witness of blatant injustice getting angry at the perpetra-
tor. Note that, in both scenarios, the emotion labeled as “anger” does not match the recalibra-
tion theory of anger (Sell et al., 2009; Sell et al., 2017), except in a loose and indirect sense.
is is a nice example of how motivation and emotion can illuminate each other— and why
they should be studied together as two sides of the same coin.
Conclusion
As this volume clearly testies, the evolutionary study of emotion has made tremendous
progress over the past few decades. e coordination approach has played a major role by
clearing some important conceptual hurdles, emphasizing the computational level of analysis,
marco del giudice124
providing a common language for alternative models, grounding and suggesting productive
directions for empirical research. But motivation and emotion are inextricably linked, and it is
becoming increasingly apparent that a successful theory of emotion requires an explicit theory
of motivation (and vice versa). Here I have taken a step in this direction, by showing how the
theory of motivational systems can be used to extend and partially revise the standard coordi-
nation approach. I hope that other researchers will nd these ideas as exciting as I do, and use
the extended framework as a springboard to rene existing theories, explore new hypotheses,
and draw fruitful connections within and across disciplines.
Acknowledgments
I am grateful to Laith Al- Shawaf, Romina Angeleri, Steve Gangestad, and Daniel Sznycer for
their generous and constructive feedback on this chapter.
Notes
1. On the ideological side, instinct theories were often portrayed as not merely old- fashioned but also politically
conservative; in contrast, behaviorism aligned with the tenets of the Progressive movement, including a view
of human behavior as radically malleable and an unshakable faith in top- down social engineering (see, e.g.,
Burnham, 1960).
2. Note that Bowlby (1982) remained agnostic as to whether feelings cause behavior, or only serve as intra- and inter-
personal signals of the individual’s motivational state. In contrast, Scott (1980) argued that the feelings triggered
during the operation of behavioral systems contribute to motivate specic goal- relevant behaviors.
3. Intuitively, a simple structure obtains when each of the factors/ components shows strong correlations with some
indicators, and near- zero correlations with the remaining ones. For technical discussion, see Browne (2001) and
Sass & Schmitt (2010).
4. Parts of this section are adapted with permission from Del Giudice (2018).
5. Recent large- scale analyses of emotional expressions and self- reports (see Cowen et al., 2019) suggest at least 25–
30 dimensions of variation (extracted with PCA), with denser regions corresponding to “fuzzy” emotion catego-
ries. is is almost certainly a lower bound on the numerosity of emotion mechanisms, because the resolution of
the analysis is limited by the use of folk labels, and the choice of the number of dimensions to retain is somewhat
arbitrary. More generally, these results are based on correlational rather than functional analyses, and suer from
the same problems I have discussed in regard to the numerosity of motivational systems.
6. In Gray and McNaughton’s (2000) reinforcement sensitivity theory (RST), behavior is regulated by three general-
purpose systems that control reward approach (behavioral activation system or BAS), punishment avoidance (ght-
ight- freeze system or FFFS), and approach- avoidance conicts (behavioral inhibition system or BIS). I do not
discuss this theory in detail because it provides an extremely partial account of motivation and emotion. For
reviews of RST as a model of personality, see Corr et al. (2013) and Corr and Krupić (2017). e recent discus-
sion by Corr & Krupić seems compatible with the idea that approach/ avoidance mechanisms function as common
pathways for other, domain- specic motivational systems.
7. As a further indication that Agreeableness is not a functionally unitary construct, the HEXACO model denes
this trait in a somewhat dierent way by excluding sentimentality and including (low) irritability, which is a facet
of Neuroticism in the FFM (see Ashton & Lee, 2007). Both versions of Agreeableness show a similar motiva-
tional prole with respect to reciprocity, aliation, dominance/ status, mating, and pair- bonding; however, the
association with caregiving may be more specic to the FFM version (see Ashton & Lee, 2001, 20017; Ashton
et al., 2010; Bourdage et al., 2007; Lee et al., 2013). Also, the available data suggest that sex dierences on
the HEXACO version of Agreeableness are smaller and less consistent than those on the FFM version (Lee &
Ashton, 2020).
8. Note that these authors employed the HEXACO version of Agreeableness.
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