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Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
A sensorimotor control framework for understanding emotional
communication and regulation
Justin H G Williams
*, Charlotte F Huggins
, Barbra Zupan
, Megan Willis
Tamsyn E Van Rheenen
, Wataru Sato
, Romina Palermo
, Catherine Ortner
, Martin Krippl
, Joanne M Dickson
, Chiang-shan R. Li
, Leroy Lowe
University of Aberdeen, Institute of Medical Sciences, Foresterhill, AB25 2ZD, Scotland, United Kingdom
Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, QLD 4702, Australia
Australian Catholic University, School of Psychology, ARC Centre for Excellence in Cognition and its Disorders, Sydney, NSW 2060, Australia
University of Melbourne, Melbourne Neuropsychiatry Centre, Department of Psychiatry, 161 Barry Street, Carlton, VIC 3053, Australia
Kyoto University, Kokoro Research Centre, 46 Yoshidashimoadachicho, Sakyo Ward, Kyoto, 606-8501, Japan
University of Western Australia, School of Psychological Science, Perth, WA, 6009, Australia
Thompson Rivers University, Department of Psychology, 805 TRU Way, Kamloops, BC V2C 0C8, Canada
Otto von Guericke University Magdeburg, Faculty of Natural Sciences, Department of Psychology, Universitätsplatz 2, Magdeburg, 39106, Germany
Leiden University, Cognitive Psychology, Pieter de la Court, Waassenaarseweg 52, Leiden, 2333 AK, the Netherlands
Edith Cowan University, Psychology Department, School of Arts and Humanities, 270 Joondalup Dr, Joondalup, WA 6027, Australia
Yale University, Connecticut Mental Health Centre, S112, 34 Park Street, New Haven, CT 06519-1109, USA
Neuroqualia, Room 229A, Forrester Hall, 36 Arthur Street, Truro, Nova Scotia, B2N 1X5, Canada
Our research team was asked to consider the relationship of the neuroscience of sensorimotor control to the
language of emotions and feelings. Actions are the principal means for the communication of emotions and
feelings in both humans and other animals, and the allostatic mechanisms controlling action also apply to the
regulation of emotional states by the self and others. We consider how motor control of hierarchically organised,
feedback-based, goal-directed action has evolved in humans, within a context of consciousness, appraisal and
cultural learning, to serve emotions and feelings. In our linguistic analysis, we found that many emotion and
feelings words could be assigned to stages in the sensorimotor learning process, but the assignment was often
arbitrary. The embodied nature of emotional communication means that action words are frequently used, but
that the meanings or senses of the word depend on its contextual use, just as the relationship of an action to an
emotion is also contextually dependent.
This review on the neuroscience of action and aﬀect is being un-
dertaken as part of the ‘The Human Aﬀectome Project’, a 2016 initiative
organised by the non-proﬁt organisation Neuroqualia. The project aims
to produce a series of overarching reviews that can summarise much of
what is currently known about aﬀective neuroscience while simulta-
neously exploring the language that we use to convey feelings and
emotions. The project is comprised of twelve teams organised into a
taskforce focused on the development of a comprehensive and in-
tegrated model of aﬀect that can serve as a common focal point for
aﬀective research in the future.
To that end, our team was speciﬁcally tasked to review the neu-
roscience research related to actions, the way that people communicate
feelings that relate to actions, and whether or not the feelings terms that
people convey in communication might inform the way we approach
action-related neuroscience research.
The evolutionary origins of feelings and emotions lie in their critical
role in regulating behaviour, and so consequently, emotions are tied
closely to actions. One might consider these regulatory behaviours to be
of two types. Firstly, there are those behaviours that serve to regulate
an individual’s behaviour so that he or she can maintain a
Received 21 December 2018; Received in revised form 22 January 2020; Accepted 11 February 2020
E-mail address: Justin.email@example.com (J.H.G. Williams).
Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
Available online 15 February 2020
0149-7634/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
physiologically healthy state. Secondly, many animals live in social
groups of varying size and complexity, and so additional behaviours are
designed to inﬂuence the behaviour of others. Humans stand out as
being diﬀerent to other animal species in the way that actions relate to
emotions, and this paper will review these diﬀerences. We consider that
there may not be fundamental diﬀerences but that these are largely a
matter of degree. Most obviously, whereas in other animals, actions are
highly limited in their variability, in humans they are highly ﬂexible
and adaptive to variation in context. The exception occurs in states of
mental disorder where action patterns may become stereotyped and
Given that humans are a highly social species, living in large, so-
cially complex societies, the action repertoire for social communication
has become particularly extensive. In addition to language, we com-
municate our emotions to one another through actions such as body
posture, speech (tone, volume or intonation), facial expression and
hand gestures (Vaessen et al., 2018), and complex control systems have
evolved to serve this function. In humans, emotional states are con-
structed through a self-awareness of actions, associated with contextual
and cultural learning. Flexibility depends upon complex interaction
between a subcortical network serving associative learning and cortical
mechanisms allowing for contextual inﬂuence and sensorimotor
learning. Potential conﬂicts between outcomes of intentions at varying
levels within a hierarchical system of goals are managed, and systems
constantly learn and become updated through evaluation and appraisal
In this review we aim to consider how the relationship between
action and emotion has evolved in humans, examining how an en-
hanced capacity for ﬂexible motor control and motor learning has re-
sulted in a complex system for the communication and regulation of
emotional expression and communication, involving cultural learning
and consciousness. We consider what happens to this capacity in states
of disorder and disease, and how, through embodied cognition, action
words are often employed to describe emotions and feeling states.
Firstly though, we will consider two principles that underpin our
review, the ﬁrst of which is expressed through predictive models of
perception, action, and cognition, which argue for an active inference
account of the mind (Friston, 2010;Clark, 2013).
Within active inference accounts, the primary goal of the brain is to
maintain allostasis. Allostasis is a term used to describe how the body
maintains stability through change. It diﬀers slightly from homeostasis
in allowing learning and anticipatory responding to vary set-levels of
parameters in order for the organism to adapt to its environment, rather
than keeping predetermined levels constant (Ramsay et al., 2014). In
the brain, allostasis is achieved primarily by comparing bottom-up
sensory inputs from the world and body to top-down ‘predictions’about
the world and body (Clark, 2013). Mismatch between feedback and
feedforward processes gives rise to ‘prediction errors’, presenting po-
tential risks to stability. These prediction errors trigger action in order
to address the cause of the error and restore equilibrium. Rather than
perception being a blank canvas onto which the state of the world is
painted, perception is the result of comparing predictions about the
world to actual sensory inputs. When an organism detects a discrepancy
between predicted and experience inputs, this brings key threats to
homeostasis to attention, triggering action to address these issues.
These actions are usually intentional which means that they are
enacted with a plan to achieve a speciﬁc goal. Therefore, all actions
have a motivation or an emotional value, which result in the action
being planned to achieve a desirable goal with an associated sensory
feedback. This process has been learned from birth ever since the infant
starts to act on his or her environment in an eﬀort to achieve desirable
ends. This learning process utilises feedback-feedforward processes.
Internal sensorimotor models are encoded in the brain which associate
internal models for action patterns with those that encode their sensory
consequences and goal-achievement. Consequently, ideation of the ac-
tion’s goal triggers an associated motor plan, which determines the
selection, sequences, and power of muscular contractions that form
actions, needed to achieve the goal. The action itself triggers a range of
sensory consequences, occurring across all modalities, whether visual,
tactile, vestibular or kinaesthetic, creating further feedback. This
feedback is then compared to the predicted consequences of any
planned action, actively inferring causes of any error and modifying the
internal sensorimotor model, so that prediction error is decreased to an
acceptable range, actions are coordinated to achieve goals in an optimal
fashion, and allostasis is maintained.
The second principle is that internal models of goal-sensorimotor
relationships are organised in hierarchies. Any action can be reduced to
a set of speciﬁc muscular contractions which combine to form simple
actions and then complex actions, which are enacted to serve short term
plans, and ﬁnally longer-term plans. The longer-term plans may be so
distal to the actions taken to achieve them that they are largely in-
dependent of these individual actions. Consider for example, the com-
bination of muscle contractions required to grasp a door handle and
open the door, which may be serving an immediate goal of leaving a
room. This may be serving a higher goal of leaving a meeting, which
could be an act which communicates an expression of a desire to leave a
group. At each level wider contextual factors characterise the intention.
In this review we consider the relationship of sensorimotor control
mechanisms to feelings and emotion, before considering whether feel-
ings and emotion words can inform the way in which we approach
research related to sensorimotor control. The principles are summarised
in Fig. 1. One notable feature of this model is the omission of something
speciﬁcally called “executive function”since the function of control is
an integral aspect of the relationship between higher levels of the
hierarchy of the sensorimotor-goal model and limbic associations. Si-
milarly, we do not, at this stage, make a distinction between social and
non-social actions, but assume that the strong motivational value of
interpersonal function will mean that limbic associations will be par-
ticularly important in the development of those sensorimotor goal re-
lationships. Finally, we do not discuss anatomical correlates at this
point though these are considered in more detail in Section 4.
2. Comparative psychology of emotion expressed in action
The expression of emotion through action reﬂects the biological
continuity of emotional communication with other animals. Behaviours
can convey emotion explicitly through displays, such as when an an-
imal shows aggression or courtship behaviour, or passively, such as
when an animal reveals its distress. In many species, the emotional
expressions of conspeciﬁcs aﬀect observers’actions.
Humans lie at the end of a continuum in their ability to vary their
expressions of emotion, through a combination of intentional and au-
tomatic control of actions controlling body posture, manual gesture,
facial movements or vocalisation. Apart from the vertebrates, most
other animal species are relatively inﬂexible regarding their beha-
vioural repertoire including motor patterns or vocalisations and rely on
ﬁxed, innate patterns of action. Most animals have ﬁxed patterns of
vocal expression that are largely innate (the chicken is a good example).
However, many primates do adjust their actions or vocalisations to
their observers, and some species may even ﬂexibly mimic the vocali-
sations of other species including those of predators or for instance of
human speech. The parrot is the most obvious example of that, but the
phenomenon has been observed in other species too (e.g., seals pro-
ducing speech-like sounds; Ralls et al., 1985). Vocal learning is ob-
served in several distantly related mammalian species including bats,
cetaceans, elephants and seals (Chakraborty and Jarvis, 2015). Some
birds are comparable to humans in having a capacity for an extensive
and constantly variable vocal repertoire (e.g., songbirds). Whether this
extends to other aspects of action is a question hardly explored but
research in raven and parrot species, shows that they adjust their social
behaviours and related actions when knowing that they are being ob-
served by conspeciﬁcs.
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
To facilitate emotion transmission, humans have evolved ex-
ceptionally communicative faces where all the expressive parts are
enlarged and accentuated (Kobayashi and Kohshima, 1997;Kret, 2015;
Kret and Tomonaga, 2016;Tomasello et al., 2007). As well as evolving
a high level of intentional motor control over expression, human facial
features are adapted to maximising expressiveness. For example, our
visible eye-white, which facilitates emotion expression and gaze fol-
lowing, is larger than in other species, likewise lip colour and eyebrow
size are emphasised. Using our unique collection of facial features,
humans express their emotions explicitly through emotional signals
that can be subject to intentional control (such as smiling). These
emotional signals may also combine with other forms of expression that
may be under a lesser degree of intentional control or awareness such as
pupil dilation (Harrison et al., 2009;Kret et al., 2013a,b;Kret et al.,
2014,2015;Kret and De Dreu, 2017), blushing (Dij et al., 2009;Leary
et al., 1992), and other subtle autonomic cues (Kret, 2015;Levenson
and Gottmann, 1983;Reed et al., 2013).
Given the degree to which humans have evolved a capacity to ex-
press emotion so clearly, one might expect that the emotional signals
would be unambiguous. However, the opposite is more often the case,
and the emotion being conveyed by any particular form of expression
depends on a variety of factors. For example, the smile is an expression
seen across cultures, which participants in lab experiments are sup-
posed to link with the label ‘happy’. However, oftentimes it is not
happiness that is expressed in a smile, but something completely dif-
ferent (e.g., nervousness or contempt). For instance, when participants
were shown the facial expression of an athlete winning a medal at the
Olympics (thus a truly happy person), they were not able to say whe-
ther the athlete had won or lost the competition (Aviezer et al., 2012).
In this case, examination of the way that expressions are used by
other species may throw a light on their adaptive function. To continue
with the example of the smile, this expression stems from the so-called
‘bared teeth display’, an expression shared amongst primates (Van
Hooﬀ, 1976;Waller et al., 2006). It is shown when a primate is afraid,
but also to signal subordination to a more dominant individual. The
smile still has this function in humans –during interactions between a
person high, and a person low in power, it is the latter smiling most
(Hecht and LaFrance, 1998). In humans, the smile has become ritua-
lised, and next to expressing nervousness or subordination, commu-
nicates aﬃliation, love and aﬀection. Over our lifetime, we learn when
to use or reciprocate this expression and when to inhibit it (Hess and
Another example of a facial expression long-thought to be a uni-
versal expression of emotion is that of fear. However, there is no
universal agreement on what constitutes a fearful expression. The ex-
pression associated with fear in Western stimuli (e.g., Ekman, 1993)is
instead interpreted as threatening and aggressive in other societies such
as in a case study of the people of the Trobriand Islands of Papua New
Guinea (Crivelli et al., 2016). This expression is also a common image in
apotropaic art featuring threat displays that are meant to ward oﬀharm
and deter evil (Kret and Straﬀon, 2018). Representations of this ex-
pression in apotropaic art generally show staring or bulging eyes, ﬂared
nostrils, open mouth, ﬂaunted tongue, face distortions, and very often
bared fangs or teeth (Emigh, 2011).
The lack of universal agreement on what constitutes a fearful ex-
pression is also signiﬁed in popular validated facial expression stimulus
sets used in psychology, including the one used in the study by Crivelli
et al. (2016), which intermix diﬀerent facial expressions in the category
‘fear’. The faces of some of the actors in the set show widened eyes in
combination with the display of upper and lower teeth, similar to the
primate bared teeth display (Andersson, 1980;Waller et al., 2006),
whilst others within the same face set show the typical expression
epitomised by Edvard Munch’s painting ‘The Scream’or the ‘Home
Alone’ﬁlm poster; the eyes are enlarged and the mouth is wide open
but the teeth do not show. Still other stimuli show a mixture of the two
(Kret and Straﬀon, 2018). This example demonstrates why it is im-
portant to make a clear distinction between diﬀerent negative expres-
sions –they likely have diﬀerent evolutionary origins and their mean-
ings are context-dependent. The bared teeth face, like other threat
displays, probably evolved from the ritualisation of attack or pre-ﬁght
movements or intentions, such as biting. In contrast, the gasping face
most likely evolved as a fear display from screaming or calling beha-
viour (Andersson, 1980).
The study of facial expressions in our closest living relatives, the
chimpanzees and bonobos, along with studies in more distantly related
species such as macaques, can help resolve such ambiguities.
Behavioural observations have demonstrated that non-human primate
emotional expressions and human emotional expressions can play si-
milar functional roles. For example, human infants use a pout face to
solicit their mother’s attention, and a similar facial expression can be
found in infant chimpanzees for the same bonding functions (Blurton
Jones, 1971;van Lawick-Goodall, 1968). Therefore, cultural appro-
priation of expressions to serve conventional understandings of asso-
ciated emotional states would seem to be one factor that has led to a
diﬀerence in the emotion associated with an expression in some pri-
mate groups to that of humans. Notably, the exception of pouting oc-
curs in infants who are the least exposed to cultural inﬂuences.
Another factor that may be important in modifying the meaning of a
Fig. 1. Schema of components of internal model of sensorimotor relationships involved in the encoding and enactment of actions with emotional values.
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
facial expression is bodily expression. In fact, bodily expression of
emotion may be just as important as facial expression. However the
existing literature has largely focused on posed facial expressions
(Adolphs, 2002;Dimberg, 1982;Ekman, 1993;Ekman and Rosenberg,
1997;Frijda, 2016;Hess and Bougeois, 2010), which may aﬀect the
ecological-validity of the ﬁndings (Kret, 2015). In fact, two of the most
illustrious theoreticians of emotion, Darwin and James, discussed
whole-body expressions at great length. Darwin famously included
postural descriptions in ‘The expression of the emotions in man and
animals’(Darwin, 1872), and James (1890) investigated recognition of
emotion with photographs of whole-body posture.
Faces and bodies are equally salient and familiar in daily life and
often convey some of the same information; when they do not, it is
oftentimes the body that reveals expressers’genuine feelings (i.e.,
Aviezer et al., 2012; for a review, see de Gelder et al., 2010). In recent
decades researchers have taken up the issue of bodily expression re-
cognition, and results from several behavioural experiments using in-
dependent stimulus sets now allow us to conclude that recognition
performance for bodily expressions is similar for face and body stimuli
(Kret et al., 2013a,b;Kret et al., 2011;de Valk et al., 2015).
In conclusion, most other animals show relatively limited range of
emotional expressions, and tight correlations between emotional state,
body posture and facial expressions. However, the capacity for social
learning and intentional control over expression results in a departure
from these relationships. Such a departure can result in more ﬂexible
behaviours, such as the diverse repertoires of vocalisation in songbirds.
In humans who have evolved an especially strong capacity for inten-
tional control and social learning, facial expressions of emotion may be
modiﬁed to convey varied and subtle meanings in the context of bodily
expression, autonomic reactivity, and cultural convention.
3. Interoception and action in emotion
Modern theory increasingly recognises emotion as providing im-
portant inﬂuences upon action to make it serve an adaptive function, in
much the same way as cognition and perception (Moors and Fischer,
2019). This stands in great contrast to the previous thinking of emotions
as maladaptive disruptors of decision-making and action. In fact, it is
increasingly recognised that the common principles of motor learning
and control are likewise applicable to the awareness and regulation of
emotions. More importantly, the ‘active inference’principles of sen-
sorimotor control discussed in the introduction can also be applied to
emotion. Within active inference accounts of emotion, emotion re-
presents the inferred causes of a ‘prediction error’(Barrett et al., 2016),
wherein emotions represent inference about why there is a discrepancy
between the expected and experienced sensory input. This prediction
error then arises as a readiness for action (Ridderinkhof, 2017). Emo-
tions act as motivators for action, through facilitating ideation of goals
(Moors et al., 2017), and thus are not separate from cognition and
perception, but rely upon the same processes.
In daily life, emotional experiences are almost always accompanied
by physical action, whether through unintentional facial expressions or
intentional actions to either communicate the emotional state or ad-
dress its causes. Evolutionary theory suggests that emotions are, at their
core, adaptations that motivate action to beneﬁt the organism
(Damasio, 2018). This can be as simple as the fear that motivates an
individual to run from danger, or as complex as the sympathy and guilt
that motivates an individual to donate to charity. In this way, emotion
is closely tied to action and perception.
Yet a major factor that makes human actions so complex and vari-
able is the existence of subjective awareness –the ability to be con-
sciously aware of one’s own emotional experiences. Subjective aware-
ness allows humans to consciously control how emotions are expressed
through action, resulting in greater variability of emotional expressions,
as well as variability in how emotions inﬂuence actions and decision-
making (Moors and Fischer, 2019). More nuanced awareness of one’s
own emotions facilitates the more eﬀective selection of appropriate
emotional responses, but also more accurate perception of others'
emotional actions. Yet this relationship is unlikely to be unidirectional –
the imitation and production of emotional actions during development
also seems to be related to developing more sophisticated and nuanced
emotion concepts, leading to greater awareness of the emotions of the
self and other.
It is increasingly argued that awareness of one’s own emotions is
variable across individuals, and this variability has socioemotional and
clinical implications (Smidt and Suvak, 2015). This variability is best
understood through the constructed theory of emotion (Barrett, 2017),
which posits that emotions are not categorical ‘natural kind’experi-
ences with limited associated action plans, but dimensional experiences
constructed through consolidating bodily sensations with contextual
information and prior learning. Subsequently, individual variation in
emotional self-awareness can be attributed to sensitivity to bodily
sensations, as well as individual and cultural learning. In this way, more
ﬁne-tuned interoception –the sense of the emotional state of the body –
is associated with more nuanced awareness of one’s own emotions
The association between interoception and emotional self-aware-
ness has been demonstrated by empirical studies examining accuracy in
perceiving one’s own heartbeats. More accurate heartbeat perception is
associated with greater ability to identify and describe one’s own
emotions in adults (Herbert et al., 2011) and children (Koch and
Pollatos, 2014), as well as in people with autism spectrum disorder
(Shah et al., 2016). This association has also been replicated with other
measures of interoceptive sensitivity, such as the ability to discriminate
between similar levels of muscular strain (Murphy et al., 2018). Inter-
vention work targeting interoceptive abilities has also been found to
improve emotional self-awareness in healthy participants (Bornemann
and Singer, 2017), suggesting a causal nature to this relationship. Such
ﬁndings illustrate both that emotional experiences are inherently em-
bodied, and that degree of conscious awareness of these experiences is
dependent upon sensitivity to physical sensation.
Given the importance of physical sensation to the conscious ex-
perience of emotion, it would follow that motor action and its kinaes-
thetic feedback are likewise important. Most of the work on action and
emotion has focused on how motor actions relate to the perception of
emotion in others. For instance, imitating viewed facial expressions
facilitates faster and more accurate emotional recognition (Wood et al.,
2016). Social learning models suggest that mimicking others’actions
allows us to share in their subjective experience, and mimicry early in
life facilitates development of an understanding of how actions relate to
subjective experiences (Decety and Meyer, 2008). In support of this
argument, Niedenthal et al. (2012) report that paciﬁer use in male in-
fants is associated with lower emotional intelligence later in life,
through inhibiting infants’abilities to mimic the facial expressions of
others, thus limiting opportunities for social and emotional learning.
Cross-sectional research has likewise found associations between
emotional self-awareness and emotional actions. Poorer emotional self-
awareness is associated with diminished ability to imitate and sponta-
neously produce emotional facial expressions (Trinkler et al., 2017), as
well as lower expressivity in social and non-social situations (Wagner
and Lee, 2008). Such ﬁndings indicate how conscious awareness of
one’s own emotions facilitates more diverse and eﬀective emotional
communication. This is likely as subjectivity facilitates more reﬁned
conscious control of emotional actions.
The association between subjective awareness of emotions and
conscious control of emotions can be seen in emotional regulation re-
search. Identifying emotions is considered a fundamental step in ef-
fective emotional regulation (Gross, 2015). Furthermore, more diﬀer-
entiated awareness of emotions is associated with more frequent
emotional regulation (Barrett et al., 2001). Greater emotional aware-
ness and regulation has also been found to be associated with greater
social success (Kimhy et al., 2016), as this allows for individuals to
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
select more appropriate emotional actions. Likewise, diﬃculties inter-
preting and imitating motor actions is associated with greater emo-
tional self-awareness diﬃculties (Brezis et al., 2017).
The subjectivity of emotion facilitates the intentional cognitive
control of emotional actions, which in turn regulates emotional ex-
periences. Moreover, this subjectivity leads to a wide diversity in
emotional actions and expressions –the same emotion can result in
many diﬀerent actions, and the same action may be associated with
many diﬀerent emotions. This variability is reﬂected in diversity of
emotional language, particularly in the frequency and diversity of
emotion terms relating to action.
The relationship between emotion and action can also be seen on
the neural level. The next section further details how these relations can
be seen in the brain, before further discussing its relation to emotional
regulation and psychopathology.
4. Brain bases for emotional communication
Ample neuroscientiﬁc evidence in monkeys and in humans has
shown that the cortical sensorimotor regions, speciﬁcally the premotor
and parietal cortices, are involved in emotional communication (Sato
et al., 2015;Trautmann-Lengsfeld et al., 2013). These studies were
inspired by the discovery of mirror neurons in monkeys. Single-unit
recording studies in monkeys revealed that speciﬁc neurons of the
ventral premotor cortex discharge both when the monkey executes
speciﬁc hand actions and when it observes experimenters performing
similar actions (di Pellegrino et al., 1992). These neurons have been
named mirror neurons (Gallese et al., 1996;Rizzolatti et al., 1996).
Later, mirror neurons were also found in the parietal cortices (Fogassi
et al., 2005). As the superior temporal sulcus (and its adjacent temporal
regions) contains neurons that respond during the observation of ac-
tions (Perrett et al., 1985), this region is thought to provide input to the
mirror neurons in the premotor and parietal regions. Some researchers
have proposed that these regions constitute a functional network, as the
mirror neuron system, and are involved in important social cognitive
functions, such as imitation and intention understanding (e.g., Williams
et al., 2001). Hamilton (2008) proposed that the superior temporal
region, parietal region, and inferior frontal gyrus represent the visual,
goal, and motor features, respectively.
Direct evidence from a single-unit recording study in monkeys re-
vealed that the neurons in the ventral premotor cortex discharge during
observation of emotional facial communication, such as lip smacking
(Ferrari et al., 2003). Several neuroimaging studies using functional
magnetic resonance imaging (fMRI) in humans have conﬁrmed the
involvement of the premotor or parietal cortex in the processing of
dynamic emotional facial expressions (Arsalidou et al., 2011;LaBar
et al., 2003;Sato et al., 2004;Schultz and Pilz, 2009;Trautmann et al.,
For example, in one fMRI study (Sato et al., 2004), brain activity
was measured during observation of dynamic facial expressions, static
expressions, and dynamic mosaic images. The results revealed that
certain regions in the mirror neuron system, including the inferior
frontal gyrus (the human homologue of the ventral premotor cortex in
the monkey; Rizzolatti and Arbib, 1998), inferior parietal lobule, and
superior temporal sulcus, were more active in response to dynamic
facial expressions than to static expressions and dynamic mosaics.
Some other studies showed that observation of dynamic bodily
gestures also activated the premotor or parietal cortex (Grèzes et al.,
2007;Kret et al., 2011). Electroencephalography and magnetoence-
phalography studies have supported the rapid activation of premotor
and parietal regions in response to dynamic emotion expressions. For
example, an electroencephalography study reported activation of the
inferior frontal gyrus within 200−300 ms in response to dynamic
emotional (happy and disgusted) facial expressions, compared with
dynamic neutral expressions (Trautmann-Lengsfeld et al., 2013).
Neuroimaging studies have suggested that activity in the mirror
neuron system regions during observation of dynamic emotional ex-
pressions is related to the matching of observation and execution of
actions (Carr et al., 2003;Hennenlotter et al., 2005;Kircher et al., 2013;
Leslie et al., 2004;Likowski et al., 2012;van der Gaag et al., 2007). For
example, Likowski et al. (2012) measured facial electromyography and
fMRI simultaneously during observation of dynamic emotional facial
expressions and found a positive association between facial muscle
activity and activity in certain brain regions, including the inferior
frontal gyrus. Hennenlotter et al. (2005) evaluated common patterns
among brain regions in the observation and execution of smiling facial
expressions and found shared activation in brain regions, such as the
inferior frontal gyrus
Theoretical and empirical studies have explored the functional
networking patterns of brain regions in the mirror neuron system
during emotional communication. Hamilton (2008) proposed that the
superior temporal region, parietal region, and inferior frontal gyrus
represent the visual, goal, and motor features, respectively. In this
model, mimicry can be implemented by direct connectivity from the
superior temporal gyrus region to the inferior frontal gyrus, and goal-
directed imitation can be accomplished by connectivity among the su-
perior temporal sulcus region, parietal region, and inferior frontal
gyrus. Sato et al.’s (2012,2015) fMRI and magnetoencephalography
studies applied dynamic causal modelling analysis to brain activity data
obtained during observation of dynamic facial expressions versus dy-
namic mosaic images and found that the optimal model accounting for
the data involved bidirectional (feedforward and feedback) modulatory
connectivity between the superior temporal sulcus region and inferior
frontal gyrus, which was accomplished as early as 200 ms after stimulus
onset. Engelen et al.’s (2018) combined stimulation and fMRI study
revealed that that the inferior parietal lobule communicates with the
premotor cortex, as well as a number of other regions, including the
amygdala, when processing the emotional content of actions. In short,
these data suggest that the premotor and parietal cortices are involved
in emotional communication and are possibly responsible for the
matching observations with execution of emotional actions.
Beyond these sensorimotor regions, substantial neuroscientiﬁc evi-
dence indicates an extended cortical and subcortical network, including
the amygdala, insula, anterior cingulate gyrus, and orbitofrontal cortex
(OFC). The evidence further suggests that, as in the case of the cortical
mirror neuron system, these regions can be activated by mirroring ac-
tions (see meta-analysis by Molenberghs et al., 2012a).
The amygdala has been consistently implicated in the recognition,
and experience, of emotion from faces, voices and bodies (Schirmer and
Adolphs, 2017) and forms part of a neural network enabling context-
appropriate social behaviours (Adolphs, 2010). Mimicking smiles has
been linked to activity in the amygdala, as well as the striatum (Lee
et al., 2006;Schilbach et al., 2008) and the amygdala is a key com-
ponent of the Simulation of Smiles model, in which embodied simula-
tion can be used to understand diﬀerent types of smiles (Niedenthal
et al., 2010). In humans, the amygdala and motor-related areas are co-
activated when perceiving emotions (e.g., Van den Stock et al., 2011).
In addition, there are direct pathways between the amygdala and cor-
tical motor areas, linked to emotion-related brain structures (such as
the STS and OFC) involved in emotional communication (Grèzes et al.,
The insula is activated both in the experience of disgust (as evoked
by unpleasant odours) and the observation of disgusted facial expres-
sions (Wicker et al., 2003). Weakened spontaneous expressions of dis-
gust in response to odours (Hayes et al., 2009a), and reduced ability to
voluntarily pose disgusted expressions (Hayes et al., 2009b) and imitate
at least some basic facial expressions (Trinkler et al., 2011) has also
been reported in patients with Huntington’s disease, which is associated
with a loss of volume in key social network structures, including the
amygdala and insular cortex (Kordsachia et al., 2017). More recently,
Braadbaart et al. (2014) have argued that the insula plays an important
role in learning facial expressions, which would make this structure
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
sensitive to mismatches between observed and imitated facial expres-
Empathy has been argued to be based on an action-perception
mechanism, with ‘aﬀective’empathy, such as mimicry and emotional
contagion (which is likely to be shared across species) likely to be as-
sociated with neural systems involved in sensation, movement and
emotion (i.e., premotor-parietal, temporal and subcortical regions;
Ferrari and Coudé, 2018). Meta-analysis has shown that the anterior
insular cortex, along with medial and anterior cingulate cortex are in-
volved in empathy for pain and the direct experience of pain (Lamm
et al., 2011). Along similar lines, a recent study used fMRI to compare
representations of self and others' expressions of pain and found se-
lectively greater activity for ‘self’pain-related stimuli in the anterior
mid-cingulate cortex, a region critical for pain perception and re-
cognition (Benuzzi et al., 2018). Interestingly, areas of the insula and
amygdala were more active during emotional expressions from a mo-
ther’s own child than another child, and brain responses were corre-
lated with an indirect measure of empathy (Lenzi et al., 2009).
One way to recognise emotions in others might be to internally si-
mulate the emotional state (e.g., Decety and Chaminade, 2003;Gallese,
2003;Goldman and Sripada, 2005;Keysers and Gazzola, 2007;
Niedenthal et al., 2010;Winkielman, 2010). Simulation could occur
relatively automatically and involve neural substrates that were acti-
vated for both recognition and experience (Heberlein and Atkinson,
2009). Simulation might be more important for understanding dy-
namic, ambiguous expressions than prototypical ones (Niedenthal and
Ric, 2017;Rychlowska et al., 2014;Sato and Yoshikawa, 2007) and
might also be more eﬀective for some people than others (Hess and
Fischer, 2014;Niedenthal and Ric, 2017). Heberlein and Atkinson
(2009) suggest that evidence is consistent with shared substrates for
emotion recognition and experience in the amygdala and OFC, but less
clearly consistent with a simulation model (c.f. somatosensory cortices).
However, by prioritising and enhancing processing emotional in-
formation (at least visual, but perhaps also auditory) the amygdala and
OFC could be inﬂuencing simulation processes in other parts of the
network (Heberlein and Atkinson, 2009). Finally, Williams (2013)
suggests that systems for goal-directed action are connected with
amygdala-orbitofrontal circuits central to emotional learning.
5. The hierarchical organization of motor control and emotional
For animals with a limited range of stereotyped behavioural re-
sponses to either rewarding or aversive conditions, the relationship
between conditioning and sensorimotor systems can be made relatively
easily. However, in humans, the expression and communication of
emotional responses is highly ﬂexible and ever-evolving in relation to
cultural demands. This requires another level of control over action
execution which is intentional and self-aware. In this section we con-
sider two actions. First, we consider a monkey in a motor learning
experiment, moving a robotic arm to guide a cursor onto a target on the
computer screen to acquire a juice reward. Secondly, we consider a
parent witnessing her child stepping dangerously out into the road. Her
initial reaction is to generate and exhibit fear, but she realises this re-
action might scare the child and make things worse. Therefore, some
cognitive control involving self-awareness is likely to moderate the
initial sensorimotor response.
As discussed in the introduction, for any action, we plan (goal set-
ting), execute the plan (action), and adjust the action according to
feedback (error detection and learning) until the desired outcome is
achieved. In the motor learning experiment, where macaque monkeys
move a robotic arm to acquire a juice reward, the monkeys manage this
easily, achieving optimal performance within just a few tens of trials of
training. A clockwise force ﬁeld is then turned on, and the reach tra-
jectory is perturbed in the force direction such that the monkeys fail to
reach the target in time. Sensing the change, the monkeys learn to adapt
to the force ﬁeld and again in a few tens of trials, can accomplish the
task –the movement trajectory becomes straight and dynamics reﬂects
an optimal proﬁle. The monkeys appear to have learned to reset the
movement strategy to accomplish the goal. If at this point the force ﬁeld
is removed, movement trajectories are once again deviated, and the
monkeys start another adaptation cycle to accommodate the pertur-
Neuronal activities have been recorded from motor cortical struc-
tures in these experiments (Li et al., 2001;Padoa-Schioppa et al., 2002,
2004). In the motor cortex, ensemble neuronal activities encode the
target direction and movement synergy and these activities are aligned
when no force ﬁeld is present. When a force ﬁeld is turned on, the
ensemble activities initially align with the movement kinematics (target
location) but gradually change to reﬂect the dynamics rather than the
desired kinematics of the upcoming movement. Thus, the neuronal
activities reﬂect a sensorimotor or kinematics–dynamics transformation
to meet the desired goal. This simple example highlights the core
component processes of motor control: goal (to reach the target; spe-
ciﬁed in kinematics), movement (muscle synergy required to execute
the movement or dynamics), error detection and post-error learning
There are a few issues worth considering from these motor learning
studies. First, movement control is hierarchical only in the sense that
the sequence of events unfolds in time but not that the processes of
control are unamenable to change. Second, when the environment is
stable, hierarchical interaction is established in favour of goal to action
translation rather than outcome monitoring and goal resetting. As goal
action translation becomes most expedient, a habit is formed and the
behavioural contingency may transpire without awareness. Third,
much of neuroscience research have focused on understanding the
neural processes subserving the “linear”chain of command and less is
known about the mechanisms serving how the outcome resets the goal.
In this example of motor control the goal is clear –monkeys must
reach the target in order to obtain juice reward. Whereas this applies to
many of the actions one routinely performs, social and emotional
communication is a diﬀerent matter. Then there are conﬂicting goals
and behaviour needs to be optimised to meet these goals. If we now
consider the example in which a parent whose child is about to step
dangerously out into the road, she faces a conﬂict between exhibiting a
prepotent response and an anticipation of the eﬀects of her behaviour
on that of another person, which conﬂicts with the prepotent response.
Therefore, and additional level of cognitive control is employed to
moderate the initial reaction.
5.1. Conﬂict control
Cognitive control facilitates decision making in a changing en-
vironment. One of the cardinal features of cognitive control is the
ability to learn from the outcome of our actions and revise our
knowledge of world and action plans accordingly. This ability to learn
and change is supported by a brain system that integrates moment-to-
moment information into our behavioural repertoire. By exploring the
changing environment, individuals strengthen behavioural routines
that lead to positive outcomes and revise those in association with
negative consequences. Cognitive control is particularly critical in the
face of conﬂicting goals.
In the laboratory, investigators have combined brain imaging or
electrophysiological recording and a variety of behavioural tests (e,g.,
go/no-go; Simon, Stroop ﬂankers; stop signal task) to examine the
neural processes of cognitive control. Here we use recent studies of the
stop signal task (SST) as an example to highlight the neural circuits of
cognitive control and the interactive nature of regional processes to
support optimal performance in the face of conﬂicting goals.
In the SST, a frequent “go”signal instructs participants to quickly
respond (by pressing a button) and an occasional “stop”signal (1/3 or
1/4 in frequency) instructs participants to withhold the response. The
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
stop signal follows the go signal and the time interval –stop signal
delay (SSD) –determines how diﬃcult it is for participants to withhold
the response. With a long SSD, the motor command to respond has
likely been relayed to the muscle and reached a “point of no return”
(Logan, 2015), and a stop error ensues. In a typical SST experiment, the
SSD is adjusted stop-trial by stop-trial either pseudo-randomly or fol-
lowing a staircase procedure, so that participants achieve success in
only half of the stop-trials. There are two main reasons in manipulating
this variable in the SST. First, it would allow the computation of the
stop signal reaction time –the time needed to stop a motor response –
using the race model (Logan et al., 1984). Second, there will be a suf-
ﬁcient number of error trials, so one can examine the neural processes
underlying error detection and post-error behavioural adjustment
(Chang et al., 2014;Ide and Li, 2011;Li et al., 2008a,b). Participants
are confronted with two conﬂicting goals in the SST –a speeded re-
sponse to the go-signal to meet a time window and a cautious act so the
response can be withheld when the stop-signal appears. As a result of
these conﬂicting goals, participants typically ﬂuctuate in go-trial reac-
tion time (RT) and slow down after committing a stop-error –an ob-
servation termed post-error slowing. That is, compared to a go-trial that
follows another go-trial, the go-trial that follows a stop-error (or stop
success) trial is prolonged in RT. There are diﬀerent accounts of why
participants slow down following a conﬂict (stop-trial), including di-
version of attention (Van der Borght et al., 2016) and conﬂict-elicited
control. Without going into the details of the debate, here we elaborate
on the behavioural and neural evidence in support of conﬂict-elicited
In a series of studies, investigators posited that, because stop-trial
occurs randomly but inﬂuences go-trial RT, it is possible that in-
dividuals track the occurrences of stop-trial and slow down in response
when they anticipate a stop signal. Using a Bayesian model, Yu and
colleagues estimated the trial by trial likelihood of stop signal or P
(Stop) and showed that a higher P(Stop) is associated with prolonged
go-trial RT –an observation termed “sequential eﬀect.”(Yu and Cohen,
2008). That is, participants proactively prolong the response if they
anticipate that a stop signal will occur. This provides a strategy to ne-
gotiate the conﬂicting goals between speedy and cautious go-responses.
Combining fMRI and the Bayesian model of SST performance, stu-
dies have delineated the neural correlates of conﬂict anticipation, RT
slowing, and unsigned prediction error or the absolute discrepancy
between anticipated and actual outcome –a surprise signal. Regional
activities in response to P(Stop) are located in the anterior pre-sup-
plementary motor area (pre-SMA) and bilateral inferior parietal cortices
(Hu et al., 2015). RT slowing engages the posterior pre-SMA and bi-
lateral anterior insula, the latter of which has an acknowledged role in
conﬂict awareness (Ullsperger et al., 2010). Importantly, using a
Granger causality analysis, investigators are able to demonstrate di-
rectional inﬂuence of P(Stop) on RT activities. An event-related po-
tential study in combination with source reconstruction conﬁrmed
these ﬁndings (Chang et al., 2017). Thus, these studies together support
proactive control of motor response in the SST. Further, a distinct area
of the medial prefrontal cortex (mPFC) –in the dorsal anterior cingulate
cortex –responds to unsigned prediction error, highlighting the func-
tional heterogeneity of the mPFC (Hu et al., 2015;Ide et al., 2013). An
important question pertaining to the control hierarchy concerns the
roles of prediction error signal in driving SST performance and remains
The aforementioned studies describe how we proactively control
motor response in anticipation of conﬂicting goals. A complementary
process of cognitive control is the reaction evoked by an infrequent,
behaviourally relevant stimulus. In the SST, the stop signal appears
infrequently and is highly relevant, as it instructs an interruption of the
motor command. In imaging studies of the SST this issue is commonly
addressed by computing the stop signal reaction time (SSRT), as esti-
mated from the race model, and identifying its neural correlates on the
basis of between-subject analyses. A circuit involving right inferior
frontal cortex, anterior pre-SMA and subcortical structures including
the caudate nucleus has been identiﬁed in supporting reactive response
inhibition (Cai et al., 2017;Duann et al., 2009;Li et al., 2006). Brain
regions within this circuit interact to respond to the stop signal and
interrupt the motor action. RT slowing following an error can therefore
also be conceived in terms of a reactive process. That is, error signals
may engage cerebral processes of control and prolongs RT in the next
Earlier imaging studies demonstrated that a cortical-thalamic-cere-
bellar-cortical circuit, in congruence with known anatomical con-
nectivity, supports the reactive response of post-error slowing
(Hendrick et al., 2010;Ide and Li, 2011;Li et al., 2008b). Thus, there
are both reactive and proactive processes that subserve the hierarchy of
cognitive control. In contrast with motor control, which we illustrate
with a force ﬁeld learning experiment in macaque monkeys, cognitive
control requires constant outcome monitoring and resolving conﬂicting
goals, and engages multiple loops of reactive and proactive control
circuits. The hierarchical nature of cognitive control can only be
meaningfully considered with this complexity in mind.
A few issues can be considered in contrasting cognitive and motor
control (though we might equally refer to high level and low level
motor control). First, motor control often comes with a clearly set goal
whereas cognitive control is demanded in situations where multiple
goals are in place and often in conﬂict. Second, with a set goal, motor
control may become a routine with repeated practice or a habit that is
“closed-loop”and expressed without concomitant awareness. Cognitive
control, in contrast, is often engaged to override a habit and requires
active monitoring (and awareness) of performance to be eﬀective.
Finally, these control mechanisms involve very distinct circuits even
when the same sensory (input) and motor (output) modalities are en-
gaged. These mechanisms exist across primate species and similar me-
chanism located in the dorsal aspect of the anterior cingulate and
prefrontal cortex, show many similarities in serving cognitive control
(Mansouri et al., 2017).
5.2. Hierarchical control of complex actions
One of the other diﬀerences between the monkey and the parent in
our examples is the involvement of other psychological processes such
as the recall of several behavioural rules and conventions the mother
may have learnt. An important aspect of motor control is the way that a
set of actions and rules can be integrated to determine a coherent re-
sponse, by incorporating abstract concepts into the organisation of re-
sponse. Importantly, studies have converged to suggest a hierarchical
organization or rostrocaudal gradient in the frontal cortex with the
rostral and caudal regions respectively supporting more abstract and
concrete representations (Azuar et al., 2014;Badre and D’Esposito,
2009;Badre et al., 2008;Koechlin et al., 2003). It is suggested that
more rostral regions may be critical for progressively later stages of
perception and action (Fuster and Bressler, 2012).
This complexity of cognitive functional organisation has also been
construed in terms of the time scale of activities. It is posited that the
frontal cortex embodies a rostro-caudal hierarchy that is sensitive to
diﬀerent time scales of environmental dynamics, with caudal and ros-
tral regions each engaging faster (shorter time scale) and slower dy-
namics (Badre, 2008;Botvinick, 2008;Fuster, 2004;Koechlin and
Hyaﬁl, 2007;Koechlin et al., 2003; see, however, Zhang and Rowe,
2015). The time scale of activities has also been explored for neural
circuits beyond the prefrontal cortex. For instance, by creating distinct
narratives with word changes while preserving the grammatical struc-
ture across stories, investigators reported diﬀerent neural responses
between the stories that gradually increased along the hierarchy of
processing timescales (Yeshurun et al., 2017). In early perceptual au-
ditory cortex the diﬀerences in neural responses between stories were
relatively small. In contrast, in areas with the longest integration win-
dows, such as the precuneus, temporal parietal junction, and medial
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
frontal cortices, there were large diﬀerences in neural responses be-
tween stories. Further, this gradual increase in neural diﬀerences be-
tween the stories was correlated with an area's ability to integrate in-
formation over time. These ﬁndings suggest that hierarchical control of
complex mental act may unfold according to the temporal scales at
which component processes take place.
Whether actions are favoured or discouraged depends upon learning
systems which attribute a valence to their outcomes. Encountered en-
vironmental stimuli are encoded with positive or negative valence and
mediate behavioural changes accordingly, with changes being encoded
in hypothalamic nuclei, which also mediate neuroendocrine stress re-
sponses (Kim et al., 2019). According to traditional learning theory,
when valence is attributed to environmental stimuli, associated beha-
viours are either reinforced or punished, meaning that they either in-
crease or decrease. A range of psychological models propose systems
that explain how these systems might work in humans. (e.g., Davidson,
1992,2000;Davidson et al., 2002;Gray, 1982,1987;Lang and Bradley,
2010). Neurologically based approach and avoidance systems are
thought to mediate emotional sensitivity, personality, positive and ne-
gative aﬀective experiences, and goal- directed behaviour (e.g.,
Davidson, 1998;Fowles, 1988;Lang and Bradley, 2010;Laricchiuta,
2015;McNaughton and Corr, 2014).
Gray’s (1982) early two-system model of motivation proposed a
behaviour inhibition system (BIS) and a behaviour activation system
(BAS). BIS activation is sensitive to anticipation of threatening stimuli
and inhibiting aversive outcomes, and responsible for regulating ne-
gative feelings such as anxiety and fear. The BAS is sensitive to an-
ticipation of reward and approaching appetitive experiences, and re-
sponsible for regulating positive feelings such as hope, elation,
happiness. Within Gray’s model, positive aﬀect (PA) and negative aﬀect
(NA) are viewed as state manifestations of underlying regulatory re-
ward-driven and punishment-driven motivational systems. The BIS is
thought to primarily involve serotonergic and noradrenergic pathways
(Gray, 1994), whereas, the BAS is thought to be mediated by dopami-
nergic pathways (Depue and Iacono, 1989).
Self-report motivational scales designed to assess motivational sys-
tems such as the BIS/BAS are not direct measures of motivation or
underlying neurophysiological activation. Ongoing integrative research
investigating neurological activation, explicit (eﬀortful, awareness) and
implicit (autonomous, spontaneous) motivational, cognitive and aﬀec-
tive processes is required to better understand motivated action. A more
recent theoretical development by McNaughton and Corr (2014) dis-
tinguishes underlying independent motivational systems from more
surface level behaviours based on approach and avoidance interactions
that may lead to the activation of approach-avoidance conﬂicts. Their
model purports that more surface level behaviour may be determined
interactively, even when the underlying approach and avoidance mo-
tivational systems are independent (Corr, 2013).
5.4. Self-regulation and being regulated by others
Further models place the valence systems within a context of
emotion regulation. Higgin’s Regulatory Focus Theory (RFT; Higgins,
2000) and Carver and Scheier’s (1998,2001) self-regulatory models
provide a theoretical framework for investigating the interface between
motivational, cognitive and aﬀective systems involved in goal-directed
action and emotion (Higgins, 2000;Carver and Scheier, 1998,2001).
Rooted in self-regulatory motivational sensitivities, approach and
avoidance goal striving actions represent sustained activity towards
desirable outcomes and away from undesirable outcomes, respectively.
Goal-directed action is guided by the process of ongoing self-regulation
that modulates an individual’s thoughts, aﬀect and attention (e.g.,
Dickson et al., 2017;Winch et al., 2015). In sum, a two-system view of
motivation has persisted over time, even though diﬀerent labels have
been put forward to deﬁne approach and avoidance systems. Approach-
and avoidance-oriented actions and emotional sensitivities in response
to rewarding or threatening stimuli are seen as rooted in speciﬁc neu-
rological brain systems (Gentry et al., 2016;Steinman et al., 2018).
Laricchiuta (2015) posits that brain networks are implicated in in-
stigating approach and avoidance behaviours in reaction to salient
stimuli. Such networks include cerebral nodes interconnected as pre-
frontal cortex, amygdala, hypothalamus, striatum and cerebellum.
There is also evidence that the dopaminergic system and inter-
connected brain regions process positive and negative stimuli to re-
inforce approach and avoidance behaviours (Gentry et al., 2018). Al-
though sensorimotor reactions to appetitive or aversive stimuli are
typically spontaneous and automatic, goal-directed conﬂict, lack of goal
progress or unpleasant emotions may stimulate reﬂective awareness,
goal planning and more eﬀortful cognitive control. McNaughton and
Corr (2014) draw an important distinction between underlying ortho-
gonal motivational systems and possible approach and avoidance in-
teractive surface level behavioural conﬂicts.
A key aspect to emotional regulation is the capacity to be regulated
by others, whether during childhood by adults or by peers. This re-
quires bridges to be built between codings for one’s own emotion-action
states and those of others. We are able to do this by generating sensory
changes in our own body state to identify how someone else is feeling
(Craig, 2003;Seth, 2013), a process controlled by the somatosensory
and prefrontal cortices (Adolphs et al., 2000;de Gelder, 2006;Hornak
et al., 2003;Radice-Neumann et al., 2007). Although these internally
generated emotional responses generally lack intentional control and
awareness, they signiﬁcantly impact our recognition of nonverbal
emotion cues (Naranjo et al., 2011;Neumann et al., 2014). They also
reﬂect our desired outcome for the social interaction we are engaged in
and thus modulate our emotional experiences in response to these cues
(Naranjo et al., 2011;Soussignan, 2002). Our interoceptive response,
desired outcome and ultimate interpretation of the emotional experi-
ence are inﬂuenced by gender, social roles and culture (Chaplin et al.,
2005;Fischer et al., 2004). As outlined in the embodied-contextual
model of emotion, our interpretation of others’feelings is further
mediated by previous experience and the environmental context in
which the interaction took place (Barrett, 2017;Eder, 2017).
The inﬂuence of context becomes more apparent as we develop and
gain more sophisticated cognitive skills. With increased cognition, we
learn that an emotion expression may have multiple (and often con-
ﬂicting) meanings depending on the context in which it is produced. In
response, we learn to rely upon our prior experience and memories to
accurately interpret and respond to the emotion expressions of others
(Boone and Cunningham, 1998;Buck, 1991;de Gelder, 2006). Thus,
recognising and appropriately responding to emotion operates as part
of a feedback system, one in which our analysis of the actions and
movements of others as well as our own internally generated sensory
changes, leads to learning. The responses we receive during social in-
teractions provide feedback and guide our future behaviour –if the
response is a rewarding one, we are more likely to behave similarly in
future social interactions, but if the response is a punishing one, we will
learn to adjust our behaviour to pursue a more positive emotional
outcome (Baumeister et al., 2007;Gendolla, 2017). We are constantly
appraising the meaning of the interaction and modifying our emotional
actions in response (Ridderinkhof, 2017). We then enact cognitive and
motor control to guide future responses in similar contexts through goal
striving actions that result in an (usually desirable) outcome (Griﬃths
et al., 2014;Higgins, 2000). This learning should ultimately contribute
to conscious adaptation that leads us to choose actions that are ap-
propriate within a social context (Baumeister et al., 2007).
It is therefore evidence that the 'regulation' of emotion is directly
concerned with learning patterns of behavioural responses to environ-
mental stimuli such that they minimise the experience of negative va-
lence and maximise the positive. This requires ongoing and iterative
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
motor learning and conditioning. Emotion regulation for humans, in-
volves constant appraisal and reappraisal through explicit or implicit
regulatory processes (Braunstein et al., 2017). People draw from a large
number of diﬀerent strategies in the service of regulating their emotions
(Heiy and Cheavens, 2014), but the neural correlates of emotion reg-
ulation have been studied primarily through fMRI studies of reappraisal
(the cognitive reinterpretation of emotionally evocative events), and
sometimes distraction or expressive suppression (Etkin et al., 2015;
Frank et al., 2014).
Broadly speaking, explicit emotion regulation through reappraisal
recruits frontal cognitive control regions of the brain, including regions
involved in sensorimotor control, with concomitant changes in sub-
cortical regions, including the amygdala and ventral striatum (Ochsner
et al., 2012). A consistent ﬁnding across meta-analyses is that down-
regulation of emotions (particularly through reappraisal) recruits the
dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and
anterior cingulate cortex (Buhle et al., 2014;Etkin et al., 2015;Frank
et al., 2014;Kohn et al., 2014).
These ﬁndings mesh with psychological models of the process of
emotion regulation whereby reappraisal involves working memory and
selective attention to generate and maintain the reappraisal re-
presentation, inhibition to prevent prepotent responses, and monitoring
to assess the eﬀectiveness of the reappraisal response (e.g., Ochsner
et al., 2012). For example, Kohn et al. (2014) note that activation of the
ventrolateral prefrontal cortex also occurs during emotion generation
and appraisal, and, as such may reﬂect emotional salience as well as the
operation of regulatory processes like inhibition. In addition, the
anterior middle cingulate cortex has been described as a limbic motor
control region, involved in controlling motor responses in situations of
reward and punishment (Kohn et al., 2014). In tandem with these re-
gions of activation, explicit down-regulation of negative emotions in-
volves reduced activity in the amygdala, known for signalling the
presence of emotionally-arousing stimuli, and the ventral striatum,
known for representing the reward value of stimuli. Other regions may
also be implicated in explicit emotion regulation. For example, the
supplementary motor area, which also is active during emotional mi-
micry tasks and mental imagery studies, and plays a role in preparatory
motor movement, is noted to be active in up-regulation and down-
regulation of emotions (Etkin et al., 2015;Frank et al., 2014;Kohn
et al., 2014).
6. Development, psychopathology and disordered states
Due to its deep evolutionary roots, our ability to perceive emotion
begins early in life and is thought to be an automatic and spontaneous
component of social interaction (Boone and Cunningham, 1998) con-
nected to early mimicry (Decety and Meyer, 2008). A recent study
showed that newborns appear to be sensitive to dynamic faces ex-
pressing emotions at birth (Addabbo et al., 2018). Infants between ﬁve
and seven months of age start to preferentially attend to fearful faces
rather than happy faces, and disengage attention less readily from
fearful faces, than from happy or neutral faces (Hoehl, 2014). The
various neurological structures (e.g., prefrontal cortex, amygdala) that
control our capacity to recognise and express emotions continue to
develop throughout childhood and adolescence, allowing us to perceive
more nuanced and subtle diﬀerences in the emotions expressed by
others (Herba and Phillips, 2004;Thomas et al., 2007). Hence, the
accuracy in which we can diﬀerentiate between emotions is mediated
by both age and gender, with female children and adolescents showing
more accurate perception than males (Herba et al., 2006;Lawrence
et al., 2015;McClure, 2000). Cognition is also strongly associated with
recognising how someone else is feeling (Lawrence et al., 2015;Thomas
et al., 2007), likely because accurate perception requires simultaneous
processing and integration of many diﬀerent cues.
Our choice of actions within the emotional regulation system can be
signiﬁcantly aﬀected by experience. For instance, studies with infants
and children who have been abused have shown that this aberrant
social experience alters their perception of facial and bodily movements
indicative of anger (Pollak et al., 2000;Pollak and Kistler, 2002;Pollak
and Sinha, 2002). Speciﬁcally, Pollak and his colleagues found children
who were abused to be more in-tune with nonverbal expressions of
anger in their environment. Although this may be an adaptive response
reﬂecting a desire to avoid punishing responses in future interactions,
results of these studies additionally indicate that children who are
abused may attribute anger to expressions intended to elicit a more
sympathetic or positive response. These results indicate that children
who are abused may view even rewarding responses as punishing ones,
and thus respond negatively or withdraw from the interaction, thereby
minimising the frequency and range of social interactions they have
Breakdowns in the feedback system are seen in many neurological
populations where the neuroanatomical circuitry necessary for re-
cognising, analysing, and responding to the facial and bodily move-
ments of others has been damaged. For instance, people with traumatic
brain injury (TBI) who commonly experience damage to the prefrontal
cortex, limbic system, and parietal cortex, have been shown to have
poor social outcomes due to the diﬃculty they have understanding and
identifying their own emotions (i.e., alexithymia; Henry et al., 2006;
Neumann et al., 2014;Williams and Wood, 2010) as well as diﬃculties
in recognising, interpreting, and accurately responding to the nonverbal
emotional expressions of others (Babbage et al., 2011;McDonald, 2005;
Milders et al., 2003;Neumann et al., 2012;Zupan et al., 2014,2016).
Patients with Parkinson’s Disease, resulting in damage to the basal
ganglia, show impaired facial expression recognition, which is linked
with voluntary control of facial muscles (Gray and Tickle-Degnen,
2010;Marneweck et al., 2014). Disruption of amygdala-cortical path-
ways, such as in autism spectrum disorder (Gotts et al., 2012)or
amyotrophic lateral sclerosis (ALS, Passamonti et al., 2013), may also
aﬀect emotional perception and social interaction.
Another way that brain disorder impacts upon the action-emotion
relationship is to diminish ﬂexibility. The diversity, ﬂexibility and
range of action seems to be diminished in psychopathological condi-
tions like schizophrenia, autism spectrum disorder and obsessive com-
pulsive disorder, where behaviours are often quite inﬂexible and ste-
reotyped, and the outward expression of emotion quite ﬁxed.
Schizophrenia for example, is well characterised by negative
symptoms including ﬂat or blunted aﬀect, emotional withdrawal and
apathy. Reduced emotional expressivity in the context of intact sub-
jective emotional experience (Kring and Moran, 2008) has led some to
conceptualise the symptom of blunted aﬀect in schizophrenia as re-
ﬂecting or mirroring abnormality, given the previously described role
of the motor system in the physical action of emotion expression and
the simulation of others’emotive states (Gaebel and Wölwer, 1992).
Several diﬀerent studies show mirror neuron disturbances in schizo-
phrenia (Enticott et al., 2008a;Mehta et al., 2014a), which directly
correlate with negative symptoms such as aﬀective blunting, anhe-
donia, avolition and alogia; as well performance on facial emotion
processing tasks (Kohler et al., 2003,2010;Lee et al., 2014;Turetsky
et al., 2007).
Deﬁcits in facial aﬀect processing are also core to the social cogni-
tive proﬁle of schizophrenia (Kring and Elis, 2013), and are consistently
associated with reduced recruitment of a neural network encompassing
limbic and prefrontal areas including the mirror neuron enriched in-
ferior frontal gyrus, as well as regions in the occipital and temporal
cortex (Gur et al., 2007,2002;Kilner et al., 2009;Leitman et al., 2011;
Taylor et al., 2012). These widespread neural abnormalities and asso-
ciated behavioural deﬁcits appear to reﬂect disruption in the activity
and integration of several systems involved in general face perception,
motor behaviour and emotional states (Eimer et al., 2011;McCleery
et al., 2015;Rossell et al., 2014;Taylor et al., 2012;Van Rheenen et al.,
2017). Relevantly, mirror neuron-related motor system abnormalities
in schizophrenia may result in an inability to adequately mimic and
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
recognise the emotional expressions of others, and thus the extent to
which their emotional state can be internally simulated (Enticott et al.,
2008b;Haker and Rössler, 2009).
Mirror neuron disturbances have been directly linked to poor theory
of mind in schizophrenia (Mehta et al., 2014b). However, it is possible
that this relationship is somehow mediated by other top-down ab-
normalities as the mirror neuron system is likely a predictive system
that is activated not only by the visual representation of an action but
by its goal or intention (Kilner et al., 2007a;Umiltà et al., 2001). In-
deed, mirror neuron activity appears to be moderated by the context in
which an action occurs (Liepelt et al., 2009), as well as the biases of the
observer (Liepelt and Brass, 2010;Molenberghs et al., 2012b). Thus,
intentions inferred by observing actions are biased by prior knowledge,
reﬂecting the outcome of the brain’s attempt to minimise diﬀerences
between what is observed and what is expected (i.e., a prediction error)
(Kilner et al., 2007b;Maranesi et al., 2014;Miall, 2003).
In schizophrenia, aberrant predictive coding may contribute to
symptoms by reducing precision for prior expectations, leading to ab-
normal attentional control over sensory information and altered in-
tegration of top-down and bottom-up input (Adams et al., 2013;
Stephan et al., 2009;Tschacher et al., 2017). Several studies show ab-
normal cognitive control of bottom-up emotional experience in schi-
zophrenia, indicating deﬁcient emotion regulation by lateral prefrontal
control regions that are consistently hypoactive in the presence of
emotionally evocative stimuli, with this hypoactivation perpetuated in
patients with aﬀectively relevant negative symptoms, such as alogia,
avolition and blunted aﬀect (Anticevic et al., 2012;Dichter et al., 2008;
Potkin et al., 2002;Vai et al., 2015). Further, ventrolateral-orbito-
frontal cortex activation during emotion processing does not appear to
be modulated by context in schizophrenia as it is in healthy individuals,
suggesting that patients with schizophrenia do not adequately integrate
prefrontal representations of existing knowledge into their evaluations
of social stimuli (Leitman et al., 2011).
Indeed, it has been shown that negative symptoms in schizophrenia
are associated with an increased tendency to over-weight sensory in-
formation relative to prior expectations when making inferences about
the social actions of others, which results in an inability to accurately
predict others’intentions (Chambon et al., 2011). On the contrary,
when inferring intent during interactions between others and mean-
ingless objects, patients with schizophrenia over-weigh prior expecta-
tions over visual (sensory) information, and this top-down bias corre-
lates with the severity of positive symptoms (Chambon et al., 2011).
Thus, it appears that non-social situations in schizophrenia invoke
heightened conviction in prior beliefs even in the face of contradictory
external evidence –a mismatch of which would normally give rise to a
prediction error that allows for the readjustment of one’s worldview.
This over-reliance on (potentially aberrant) prior beliefs ﬁts with ar-
guments that schizophrenia reﬂects an impaired separation of one’s
own intentions from that of others, resulting in a disconnect between
action and free will that gives rise to positive symptoms involving
passivity experiences (the misattribution of intentions to non-agents) or
paranoia (attributing intent in the absence of any) (Chambon et al.,
With this understanding of the current state of action research as a
backdrop, our team was speciﬁcally tasked to review the language that
people use to express feelings related to action. To better understand,
the range of verbally articulated feelings that are expressed in the
English language, a small task team within the Human Aﬀectome
Project led a computational linguistics research eﬀort to identify feeling
words (Siddharthan et al., 2018).
Results were extracted from the Google n-gram corpus (Younes and
Reips, 2019), which includes roughly 8 million books and then manu-
ally annotated by more than one hundred researchers from this project.
This resulted in 9 proposed categories of feelings and a new aﬀective
dataset that identiﬁes 3664 word senses as feelings. Of relevance to this
review is a category related to Actions and “Prospects”, which was
deﬁned as follows:
“Feelings related to goals, tasks and actions (e.g. purpose, inspired),
including feelings related to planning of actions or goals (e.g., ambitious),
feelings related to readiness and capacity of planned actions (e.g. ready,
daunted), feelings related to levels of arousal, typically involving changes to
heart rate, blood pressure, alertness, etc., physical and mental states of
calmness and excitement (e.g. relaxed, excited, etc.), feelings related to a
person’s approach, progress or unfolding circumstances as it relates to tasks/
goals within the context of the surrounding environment (e.g. organised,
overwhelmed, surprised, cautious, etc.), feelings related to prospects (e.g.
afraid, anxious, hopeful, tense, etc.).“
This subset of the results included about 1137 feeling words, in-
cluding 130 words that were judged by individual raters to express AF
within the context of planning (251 words). About 48 anticipatory
feeling words were exclusively related to feelings of fear and anxiety,
whereas about 54 words expressed feelings of optimism, while three
smaller clusters of about 10–15 words expressed feelings of hope, sus-
picion or suspense (see word corpus of the Human Aﬀectome Project).
Although it was not within the scope of this eﬀort to undertake a formal
analysis of this dataset, we reviewed these feelings words and we at-
tempted to roughly organize the words into discernable categories. The
individual word senses and this sorting attempt can be found in the
supplemental data accompanying this review. However, a degree of
caution should be exercised in the interpretation of this sorting eﬀort,
as it was created only to give us an initial sense of how feeling words
related to the various stages of sensorimotor function, as shown in
From our perspective, these feeling words are interesting, relevant
and warrant further study. A signiﬁcant number of words simply de-
scribed general levels of arousal (e.g., calm, aroused), but many feeling
words were very speciﬁc and reﬂected diﬀerent aspects of action-re-
lated thought. For example, feelings related to the Hierarchy of Goals
included having a sense of purpose, immediate physiological needs,
social/moral obligations, external inﬂuences (e.g., social prodding), the
acquisition of resources, competitiveness, sentimentality, and even
fate/superstition. In this area, recent research in monkeys has provided
new insights about the role of the frontopolar cortex in monitoring the
signiﬁcance of current and alternative goals (Mansouri et al., 2017).
Current goal-management models involve arbitration processes be-
tween exploitation and exploration behaviours (Donoso et al., 2014)
and additional research is needed to determine whether humans may
have additional cognitive capacities for the directed exploration of
concurrent alternative strategies (Mansouri et al., 2017). So, this roster
of articulated feelings which appears to help us better understand the
range of goal priorities in humans will be useful when formulating fu-
Other feelings that we reviewed related to Planning and
Coordination. This included competing priorities, the degree of crea-
tivity needed/employed, decision speed, risk involved, readiness, op-
timism/pessimism about prospective outcomes, the degree of ration-
ality (e.g. irrational, rational), inclination towards action (e.g.,
reluctant, undecided, inclined), the degree of caution to be exercised,
the level of aggressiveness employed, and assessments of persistence
(e.g., resistless, persistent). In neuroscience research, the prefrontal
cortex is the primary focus when it comes to planning, executive at-
tention, decision-making, and inhibitory controls (Fuster, 2019), and
many of aspects of planning and coordination have been already been
subjected to a considerable degree of research. However, the full scope
of PFC function is still not well known (see Burgess and Stuss, 2017, for
a historical review), so just having an initial inventory of articulated
feelings in this area is helpful.
Feelings related to the outcomes of actions, assessed the degree of
success, external assistance, luck involved, the predictability of the
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
result and overall acceptability of the outcome. While feelings of sus-
pense reﬂected unresolved circumstances. Finally, feelings related to
outcomes from a personal perspective additionally related freedom,
composure, understanding, skill level and power.
A principal observation made during this categorisation exercise
was that there are many categorical overlaps. Many words will ﬁt into
more than one category because the stages are interdependent to a large
degree. A single word sense may describe the goal of an action as well
as its motivation, whilst motivation is also dependent upon ability (e.g.,
knowing that you can do something successfully is a prerequisite to
having the drive to do it). Given the hierarchical, feedback-dependent
nature of motor control, one may always argue that it is the outcome
that is really the goal of on action. For example, feelings of courage in
the execution of an action may suggest careful judgment of risk in its
planning. A person’s judgement of risk would impact upon motivation.
Alternatively, feelings of courage may be considered to reﬂect that
person’s ability as a character trait, or an attribution that is dependent
upon judgement by others. Indeed, it may be the goal of the person
undertaking these actions to be to be considered courageous by others,
and this may subsequently be the outcome of the action.
Similarly, aﬀordance learning theory argues that the properties of
an object will shape the action that is enacted towards it. Social af-
fordance theory similarly (Marsh et al., 2009) argues that sensorimotor
states are determined by expectations of the consequences of an action.
Therefore, a word used to describe the goal of an action may also de-
scribe the action’s expected consequences on others, as well as the way
that action is performed.
Also, because of the very nature of embodied cognition, action
words often reﬂect emotions or feelings being expressed metaphori-
cally. This is particularly the case for words used to describe how one
person might aﬀect another person. Therefore, words that can be used
to describe a simple action can also be used to describe the nature of
one person’s behaviour towards another. In this respect, words such as
‘snare’or ‘stiﬂe’, which are used to describe the goal of an action as to
have a restricting eﬀect, can also describe social oppositionality.
Another way of explaining this is to consider that some words have
evolved to serve actions at a high level of the action organisational
hierarchy. As such they have more metaphorical properties and will
serve a range of actions, whether they have literal, selﬁsh or social
properties. A word such as emancipate would seem to be free from any
speciﬁc action form but describes the relationship between an action
and its consequences.
Nonetheless, we do think that this inventory of English feeling
words may have some utility. Fig. 2 is not intended as a ﬁnal model,
and it has not been tied to corresponding neuroanatomy. Rather, it is
intended only as an illustration of the ways in which articulated feelings
might be related sensorimotor control. Additional research in each of
these areas will be needed to determine whether our feelings can be
closely tied to these areas of neural function.
In the ﬁrst part of our paper we reviewed the relationship between
emotion and the sensorimotor system. We showed that the
Fig. 2. Schema of how feeling words may relate to stages of sensorimotor function. Emotion word categories are considered sub-categories of action stages (see
Fig. 1) which may have a social or non-social dimension. In the case of inclination and anticipation, further sub-categories are listed.
J.H.G. Williams, et al. Neuroscience and Biobehavioral Reviews 112 (2020) 503–518
sensorimotor system is intrinsic to the communication of emotional
states between individuals, and how the nature of this communication
develops and evolves from expression of simple emotional states to
much more complex communication at a more abstract level and higher
hierarchical level of organisation. Sensorimotor control mechanisms
regulating emotional states have evolved from serving simple allostatic
functions through selection of stereotyped actions and optimised motor
performance in most non-human animals, to having a capacity for
highly ﬂexible, novel and creative responses, drawing upon a wide
range of cultural and physical inﬂuences, operating across diﬀering
temporal scales, to satisfy conﬂicting social and appetitive goals. The
sensorimotor system of representation is key as part of appraisal and
reappraisal systems that modulate action and emotional awareness. In
the second part of this paper, we reviewed the process creating a lin-
guistic framework that categorises feeling words within the context of
these appraisal and reappraisal systems.
Considering the more granular array of feeling words that were
generated in this project, a large proportion of the words in the original
list were considered to describe feelings related to actions. This is un-
surprising given that embodied cognition theory argues that feelings
and emotions are intrinsic to the sensorimotor states that are used to
communicate them. We found that further categorisation of words ac-
cording to action stage was an exercise with limited value with respect
to obtaining a neat and reliable classiﬁcation but was of more value for
learning the reasons why this was the case. It was often found that
words applied to multiple categories, which is unsurprising given that
the meaning of a word is dependent upon its context. This was espe-
cially so where words described action properties at a relatively high
level within the action progress hierarchy. At these high levels, cate-
gorisation would depend upon the tense which is applied, whether it is
considered as being done by the person or to the person, or whether
there is a social context. Multiple classiﬁcations of the same word also
seemed dependent on the integrated nature of sensorimotor control, by
which we mean that even allocating aspects of sensorimotor control to
action stages is arbitrary to some degree. For example, the desire for a
successful outcome is inherent to motivation, which is at least partially
determined by ability and skill. Nevertheless, despite these reserva-
tions, we can conclude that a sensorimotor action framework can throw
a useful light on the classiﬁcation of emotions and feelings.
JHGW and CFH are supported by the Northwood Trust. TEVR was
supported by a National Health and Medical Research Council
(NHMRC) Early Career Fellowship (1088785). RP and MW were sup-
ported by the the Australian Research Council (ARC) Centre of
Excellence for Cognition and its Disorders (CE110001021)
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