Social Cognitive and Affective Neuroscience
, 2021, 745–760
doi: https://doi.org/10.1093/scan/nsab024
Advance Access Publication Date: 25 February 2021
Original Manuscript
Special Issue: Computational Methods in Social
Neuroscience
Seven computations of the social brain
Tanaz Molapour,
1
Cindy C. Hagan,
1
Brian Silston,
2
Haiyan Wu,
1
,
3
,
4
Maxwell Ramstead,
5
,
6
,
7
Karl Friston,
7
and Dean Mobbs
1
,
8
1
Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA,
2
Department of Psychology, Columbia University, New York, NY 10027, USA,
3
CAS Key Laboratory of Behavioral
Science, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 10010, China,
4
Department of Psychology, University of Chinese Academy of Sciences, Beijing, 10010 China,
5
Division of
Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A2,
Canada,
6
Culture, Mind, and Brain Program, McGill University, Montreal, Quebec H3A 1A2, Canada,
7
Wellcome
Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK,
and
8
Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA
Correspondence should be addressed to Dean Mobbs, Department of Humanities and Social Sciences, California Institute of Technology, 1200 E
California Blvd, HSS 228-77, Pasadena, CA 91125, USA. E-mail:
dmobbs@caltech.edu
.
Abstract
The social environment presents the human brain with the most complex information processing demands. The compu-
tations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and nonverbal
signals and involve multiple cognitive systems, including memory, attention, emotion and learning. This occurs dynam-
ically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core
operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently—from mul-
tiple modalities—when inferring what to do next. We deconstruct the social brain and outline the key components entailed
for successful human–social interaction. These include (i) social perception; (ii) social inferences, such as mentalizing; (iii)
social learning; (iv) social signaling through verbal and nonverbal cues; (v) social drives (e.g. how to increase one’s status);
(vi) determining the social identity of agents, including oneself and (vii) minimizing uncertainty within the current social
context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects
of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates
information from the social world.
Key words:
mentalizing; social signaling; active inference; external/internal self
Introduction
At
∼
300000 years of age, the human brain is relatively young.
Yet, its mid-Paleolithic introduction was preceded by millions
of years of evolution. Through this process, over phyloge-
netic timescales, the brain has slowly acquired models of an
increasingly complex social world, the accumulation of which
has resulted in the human brain we possess today. The evolu-
tion of the human brain evolves the exploitation of group-living
strategies, which benefit both the individual and the group
(
Silston
et al.
, 2018
). In short, humans have evolved a set of
Received:
5 October 2019;
Revised:
1 December 2020;
Accepted:
24 February 2021
© The Author(s) 2021. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
http://creativecommons.org/licenses/by/4.0/
),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
745
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behavioral and neural systems that facilitate group living and
successful social interaction. These systems must be sufficiently
flexible to navigate the fleeting social environment (
Lehmann
et al.
, 2007
), to track the behaviors, interactions and inten-
tions of others, and to accumulate this information over time
to inform and make appropriate social decisions. To under-
stand the recruitment of specific neural systems and predict the
behaviors of others, we must also account for contextual factors
and sociocultural dynamics. To enable adaptive forms of social
interaction, the human brain must be dynamic, efficient and
attentive to—and capable of—the deployment of appropriate
social behaviors in a variety of social contexts.
Like all nervous systems, the human brain has evolved pri-
marily for survival, i.e. its main function is the guidance of
situationally appropriate forms of action, which maintain it in
the neighborhood of states that characterize the human phe-
notype (
Cisek, 1999
;
Mobbs
et al.
, 2015
;
Badcock
et al.
, 2019
).
However, the relative size, ability and metabolic demand of the
brain—and its unique capacity for language and mentalizing—
suggest that the selection pressures, to which humans are sub-
ject, relate primarily to the constraints on group living. Indeed,
a large portion of the human brain is dedicated to social cogni-
tion. For example, brain imaging and neuropsychological studies
of individuals with brain damage suggest that the extrastri-
ate cortices—including the visual fusiform cortex—comprise
regions that specialize in the processing of faces and bodies
(
Kanwisher and Yovel, 2006
). Social attention and the dynamic
features of the human face (e.g. expression and emotion) are
key elements of social interaction and encompass the supe-
rior temporal sulcus (STS;
Haxby
et al.
, 2002
;
Hagan
et al.
, 2009
,
2013
).
As one ascends cortical hierarchies, computational pro-
cesses become more distributed and complex. Inferences con-
cerning the mental state and intentions of others appear to
engage the temporoparietal junction (TPJ), the temporal pole as
well as the medial prefrontal cortex (PFC). The emotional states
of others map onto one’s own affective and interoceptive cir-
cuitry (
Singer
et al.
, 2004
). The perception of threat to another
engages the amygdala (
Adolphs
et al.
, 1995
), while the perception
of another’s joy engages the reward circuitry (
Mobbs
et al.
, 2009
).
Social motivation is an important driver of social actions; how-
ever, information processing pathways have been found to differ
between individuals from different cultures, underscoring the
complexity, sociocultural variability and plasticity of the organ
enabling human social cognition (
Han and Northoff, 2008
). This
brief introduction to the social brain suggests that social behav-
ior involves a diverse yet interconnected network (i.e. a het-
eroarchy) in the human brain and involves several specialized
hubs, each with its own specialization, and each working in
concert to accomplish global computations (
Anderson, 2014
).
In this paper, we outline seven key computations with which
the social brain contends in social interaction. These include (i)
social perception, (ii) social inferences, (iii) social learning, (iv)
social signaling, (v) social drives, (vi) social identity and group
membership and, finally, (vii) integrating interoceptive, exte-
roceptive and proprioceptive signals within the social context.
These challenges suggest that social behavior is a cognitively
complex and metabolically demanding process, which involves
highly interconnected systems that pass messages over both
short- and long-range connections (i.e. intrinsic and extrinsic
connectivity, respectively). We argue that while it is important to
examine these different computations, in order to better under-
stand the true nature of the human social brain, we must first
understand how the brain integrates multimodal information,
and in turn, how this integration underwrites the enormous
variety of social behaviors.
Social perceptual systems
The human sensory system, as all other sensory systems,
views the external world through the lens of evolved adaptions
(
Haselton
et al.
, 2015
). Some have argued that identity is cru-
cial to social interaction and that, therefore, it is not surprising
that a specialized system has evolved to perceive social signals,
such as facial expression, body stance, language, tone of voice
and chemosensory signals (
Haselton
et al.
, 2015
). Research from
cognitive neuropsychology—as well as human brain imaging—
has demonstrated that the brain has specialized systems that
process information about faces, bodies, odors and biological
sounds and movements and that the human body has coevolved
along with these cognitive adaptations (
Kanwisher and Yovel,
2006
;
de Gelder
et al.
, 2010
). This is borne out by a host of
adaptations (both morphological and cognitive) that, in humans,
are hard wired. For instance, it has been shown that new-
borns have the propensity to attend to faces and determine the
chemosensory signals of the mother (
Johnson
et al.
, 1991
). Even
as infants, humans have a propensity to track the gaze of their
conspecifics (
Batki
et al.
, 2000
); this is a cognitive adaptation that
coevolved in humans with a complementary phenotypic trait,
namely, our highly visible white sclera (
Henrich, 2016
). Neu-
roimaging studies have shown that we engage distinct neural
circuits when distinguishing between those who are similar and
dissimilar to ourselves (
Mitchell
et al.
, 2006
;
Mobbs
et al.
, 2009
;
Sui
et al.
, 2013
;
Lockwood
et al.
, 2018
), determine social status,
infer who to cooperate with and even whom to dehumanize
(
Harris and Fiske, 2006
). To survive, people need an accurate per-
ceptual system to infer states of affairs in a social and cultural
econiche (
Table 1
).
While the existence of functionally specialized systems that
allow us to account for these remarkable perceptual abilities
remains contentious, it is clear that there is overlap in the neu-
ral circuits involved in inferring information from faces. The
face processing system is often portrayed as a hierarchically
organized system. In this system, the STS, the occipital face
area (OFA) and the fusiform face area (FFA) have been found
to be a part of the so-called core network for face percep-
tion (
Haxby
et al.
, 2000
;
Fox
et al.
, 2009
;
Kadosh
et al.
, 2011
).
The link between the OFA and FFA has been associated with
processing facial identity, whereas the link between OFA-STS
has been associated with processing the dynamic aspects of
the face that contribute to recognition (e.g., expression) (
Gob-
bini and Haxby, 2007
;
Olivares et al., 2015
). The STS has
been proposed as a hub, comparator and integration center
for a host of functions, which situates it as a major contrib-
utor to social processing and behaviors (
Hagan
et al.
, 2009
,
2013
). More fine-grained investigation by
Deen
et al.
(2015)
and
Lahnakoski
et al.
(2012)
suggests that anterior and posterior
parts of the STS are nodes in different circuits subserving spe-
cific components of social information processing, with some
subareas participating in multiple circuits corresponding to dif-
ferent categories of social input. These authors characterize
the anterior region of the STS as part of a circuit involved in
processing communicative signals and the posterior region as
a social processing control node that is connected with areas
implicated in attentional control. This structure is important
for action understanding but is not necessarily activated in
non-action-oriented mentalizing (i.e. false belief tasks) (
Gob-
bini
et al.
, 2007
). In addition to these ‘core’ areas, the extended
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Table 1.
Examples of how the human perceptual system has evolved to decipher perceptual cues across diverse social landscapes.
Detecting social danger.
Humans are particularly attentive to social expressions of threat, whether by direct expression of anger or indi-
rectly via the observation of fear in others (
Calder
et al.
, 2011
). Although humans are only minimally affected by predatory attacks from
other animals, our predatory defense systems have evolved to cope with social threats arising from members of our own species. In our
social environment, an angry face—or antagonistic tone of voice—presents robust cues that others are aggressive and possibly dangerous
(
Ceravolo
et al.
, 2016
).
Detecting kin and group members.
The detection of kinship and of conspecifics is crucial for survival in humans. Evolutionary models show
that people favor behaviors that benefit others who share genes. Kin detection is certainly observed in more basal species and increases
exponentially in complexity as one moves to more socially complex creatures.
Dawkins (1989)
proposed the ‘green beard effect’, suggesting
that animals, and potentially humans, possess recognition alleles that aid in the visual detection of genetically similar individuals.
Detecting disease and health.
Especially before the invention of modern antibiotics, it was critical to avoid highly infectious diseases, such
as ebola, smallpox and influenza (i.e. contamination fears). According to the disease-avoidance model, disgust functions to protect us from
contiguous diseases (
Oaten
et al.
, 2009
). Studies indicate that people can detect disease from both physical cues (e.g. others’ appearances
and behaviors) and psychological cues (e.g. ‘depressed’
vs
‘not depressed’). Facial (e.g. facial masculinity and maturity), vocal (e.g. pitch and
tone of voice) and body (e.g. motion and movement and speed) features can signal physical strength/weakness (
Fink
et al.
, 2007
;
Sundelin
et al.
, 2015
;
Von Kriegstein
et al.
, 2006
).
Fitness and beauty.
Most females and males want to copulate with those that exude beauty and health, which is a proxy for ‘good genes’
(
Buss, 2016
). Facial attractiveness is a facial attribute that conveys significant biological advantages (
Shen
et al.
, 2016
) [e.g. as expressed in
mating success (
Pashos and Niemitz, 2003
), earning potential (
Frieze
et al.
, 1991
) and longevity (
Henderson and Anglin, 2003
)]. There is a long
line of research showing that the waist–hip ratio is a predictive measure of female attractiveness (
Singh, 1993
), while height, body shape and
penis size in males predict female attraction (
Mautz
et al.
, 2013
).
Trust and cheaters.
The ability to spot cheats, free-riders and the complementary capacity to trust others and evaluate the grounds for such
trust is crucial for mutualism. Several studies have shown that some faces are perceived as more trustworthy than others (
Winston
et al.
,
2013
).
Stirrat and Perrett (2010)
showed that men with greater facial width were more likely to exploit the trust of others. This suggests
that facial phenotypes provide good indicators of another’s trustworthiness.
Rhodes
et al.
(2013)
found that women are better at predicting
unfaithfulness than men and that perceived masculinity was the most dominant cue in detecting cheaters.
Barkow,Cosmides and Tooby
(1992
,
2004)
have proposed the existence of a cheater-detection module, and this has been supported by research showing that people have
enhanced memory for cheaters (
Bell and Buchner, 2009
); similar proposals include a module for evaluating the trustworthiness of others, a
so-called suspicion system (
Gold and Gold, 2015
).
Protection and competence.
Todorov
et al.
(2005)
showed that ratings of a political candidate’s face predicted electoral success. Others have
shown that ratings of leadership ability from CEO faces predicted company profits (
Rule and Ambady, 2008
). It has been demonstrated that
ratings of perceived competence of others (i.e., their ability to protect us) in a potentially threatening situation is a crucial component of
threat assessment, which can influence levels of anxiety and defensive actions. For example, functional MRI studies show that under threat
of pain, neural systems involved in pain anticipation show reduced activity when subjects rate others as higher in competence (
Tedeschi
et al.
, 2015
). This suggests that inferences of competence act as predictors of protection and reduce the expectation of physical harm.
Status and dominance.
Alan Fiske has proposed that during social interactions, individuals rank authority by ‘attending to their linear order’.
Nonhuman primates will pay to view social images of high-status individuals (
Deaner
et al.
, 2005
). Our own work has indicated that people
show more conformity to individuals with higher reputations—manipulated by reputation ratings in uncertainty decisions (
Qi
et al.
, 2018
).
systems [limbic areas, auditory regions and regions involved
when processing theory of mind (ToM)] work together with the
‘core’ system to provide more complete face-driven process-
ing, which includes the processing of social information (
Haxby
et al.
, 2000
). Growing evidence suggests an important role for the
anterior inferior temporal (aIT) lobe in face processing, which
appears to support facial recognition (
Kriegeskorte
et al.
, 2007
;
Rajimehr
et al.
, 2009
;
Nestor
et al.
, 2011
;
Pyles
et al.
, 2013
).
The crucial role of the aIT in face recognition has been fur-
ther supported by a study involving individuals with congenital
prosopagnosia. This study showed a significant reduction in the
volume of white/gray matter in the anterior IT cortex, which
was correlated with deficits in face recognition (
Behrmann
et al.
, 2007
).
Social inferential systems
Individuals use social perceptual systems to form general
impressions of others; however, people can use mentalizing
skills to make social inferences. A key process in successful
social interactions is integrating body language cues, verbal
information and context to furnish insight into another’s mind.
Tamir and Thornton’s 3D model which suggests a three-layer
structure in which the first layer describes others’ observable
actions and the second and third layers concern their mental
states and traits, respectively (
Tamir and Mitchell, 2012
). They
propose that the probabilistic trajectories within, and between,
these layers offer an explanation for how people might use their
social knowledge to predict others’ futures.
Adjacent to the 3D is the interactive mentalizing theory
(IMT), which proposes that during dynamic social interaction,
four key processes are in play: (i) meta-cognition: confidence
about one’s mentalizing ability (e.g. how confident Agent A is
about their inference of another’s thoughts and intentions; (ii)
first-order mentalizing: mentalizing of another’s mental states
(e.g. what Agent A thinks Agent B’s thoughts and intentions
are), (iii) personal second-order mentalizing: mentalizing of self-
generated mental states from the perspective of others (e.g.
how much insight Agent A thinks Agent B has into his/her
own thoughts and intentions) and (iv) collective mentalizing,
where we conform to what we believe another agent thinks
about Agent B (e.g. Agent A infers that Agent C thinks that
agent B has bad intentions) (
Wu
et al.
, 2019
). The latter aspect
has been developed under the rubric of ‘thinking through other
minds’ (
Veissière
et al.
, 2019
). The IMT model proposes that
people are prone to this type of bias; especially, when their
confidence (metacognition) is low (
Qi
et al.
, 2018
). During real-
time social interactions, these four mentalizing components
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interact to update beliefs about another’s intentions (
Wu
et al.
,
2019
). The IMT, therefore, suggests that multiple computa-
tions are involved in social inferences (i.e. integration of social
information).
These theories support a network that encodes social knowl-
edge, which includes thinking about mental states, making
inferences about others’ beliefs, thinking about the context
including groups of people (
Mitchell
et al.
, 2006
;
Ames
et al.
, 2008
;
Saxe and Kanwisher, 2013
). This network includes dorsome-
dial prefrontal cortex (dMPFC), ventromedial prefrontal cortex
(vmPFC), medial parietal cortex, TPJ and the anterior temporal
lobes (ATLs). The medial PFC is a brain area involved in mental-
izing but has also been implicated in person perception, action
monitoring, expectations and metacognition (
Amodio and Frith,
2006
). The temporal poles and TPJ are also components of
the mentalizing circuit. Activity in the TPJ has been associ-
ated with inferring the mental states of others (from one’s own
perspective) but is also associated with cues indicating agency
more generally (
Wurm and Schubotz, 2018
). For example,
Saxe
and Kanwisher (2013)
found that descriptions of mental states
recruited the TPJ, but physical descriptions of people did not—
and
Castelli
et al.
(2000)
found TPJ activation in a task in which
moving shapes appeared to possess intentionality but not for
simple goal-directed actions or randomly moving shapes. While
the involvement of STS and TPJ is supported by neuroimaging
and brain lesion work (
Samson
et al.
, 2004
;
Saxe
et al.
, 2004
),
the exact role of these brain regions is still unclear. It is possi-
ble that STS is involved in action observation and understanding
and TPJ is involved in inferring different mental states (e.g. effort
during action observation). Other brain regions thought to com-
prise the ToM network that include the precuneus and posterior
cingulate, which are associated with self-referential thoughts
and cognitions, such as feelings of causation or attribution to
oneself (
Cabanis
et al.
, 2013
). Much like STS, the anterior and pos-
terior parts of the precuneus appear to underwrite different pro-
cesses in social inference. For example, in an attributional bias
task, the posterior precuneus is associated with self-reference
in general, while self-attributed positive
vs
negative sentences
elicited activation of the anterior part of the precuneus (
Cabanis
et al.
, 2013
). The precuneus is also involved in updating state
self-esteem by transforming others’ evaluation of oneself into
state self-esteem, thereby relating to the mentalizing system for
subjective evaluation regarding others (
Kawamichi
et al.
, 2018
).
Social learning systems
The philosopher Gilbert Ryle proposed that a boy can learn chess
by simply ‘watching the moves made by others’ (The Mind:
p41). Social learning is a major benefit when living in groups,
accelerating overall learning and leading to adaptive solutions
that can be passed on to offspring and other conspecifics over
developmental timescales. Animals that cannot imitate others
are confined to the rules of individual learning (
Richerson and
Henrich, 2012
).
Whiten (2005)
suggests that social learning pro-
vides a ‘secondary inheritance system’, where our capacity to
learn from others lowers the cost of acquiring information first-
hand, including learning about dangers, cheaters and the best
locations to forage [a complementary account from the per-
spective of human evolutionary biology is provided by
Henrich
(2016
)]. Therefore, specialized brain systems seem to exist that
support the computations involved in social learning. Below we
will outline selected findings on how the brain signals self- and
other-referenced social learning.
The Anterior Cingulate Cortex.
The anterior cingulate cortex
(ACC) has been proposed to be an integrative area relating to
social learning systems (
Lockwood
et al.
, 2020
). Specifically, the
ACC seems to be involved during social decision-making, reflect-
ing information processing about self, other, or both (
Apps and
Ramnani, 2014
;
Lockwood
et al.
, 2015
;
Apps
et al.
, 2016
;
Hill
et al.
,
2016
). In one recent study, the whole ACC was lesioned in rhe-
sus monkeys where they found specific disruption of learning
which stimuli rewarded others, but not the self, while previ-
ously learned stimuli were still intact (
Basile
et al.
, 2020
). These
findings indicate the importance of the ACC when acquiring
prosocial preferences from vicarious reinforcement. Moreover,
neuroimaging studies in humans suggest an important divi-
sion between social and nonsocial subregions within the ACC,
namely the sulcus (ACCs) and gyrus (ACCg) (
Chang
et al.
, 2013
;
Apps
et al.
, 2016
;
Joiner
et al.
, 2017
;
Kendal
et al.
, 2018
). Sev-
eral studies have found that the ACCg plays an important role
in evaluating the behaviors of others, estimating other’s level of
motivation and error processing, whereas the ACCs responds to
self-relevant reward signals and prediction errors (
Apps
et al.
,
2016
;
Chang and Sanfey, 2013
;
Hill
et al.
, 2016
;
Lockwood
et al.
,
2016
). Learning about reward probability from vicarious and per-
sonal experiences does seemingly recruit other neural systems
where the information gets combined when making decisions.
The Ventromedial Prefrontal Cortex.
The vmPFC is also impli-
cated in vicarious reward learning (
Mobbs
et al.
, 2009
), vicarious
prediction errors (
Burke
et al.
, 2010
) and vicarious fear learn-
ing (
Olsson
et al.
, 2007
;
Olsson and Phelps, 2007
). These studies
point to the PFC as another crucial player in social learning.
Although the exact processes are unknown,
Price and Boutilier
(2003)
have put forward a Bayesian imitation model of the
PFC, stating that humans (and possibly other animals) com-
bine the information learned through the observation of oth-
ers with existing knowledge afforded by personal experiences
(also see
Dunne and O’Doherty, 2013
) and behave accordingly.
The development of vicarious learning systems has roots in
representational processes that recruit motor, affective, sen-
sory and cognitive systems associated with first person expe-
riences while observing others performing actions, perceiving
sensations or under distress. The so-called mirror neuron sys-
tem purports to provide a vicarious experience to observers,
though the interpretation of exactly what this system does is
still under debate (
Cook
et al.
, 2014
). While it is clear that
these observations are represented in some regard to areas
that are active when we perform similar actions, how this
information is integrated into action understanding is not well
understood. Nonetheless, the recognition of various actions
of others, together with an explicit representation of their
goals and our own knowledge, seems sufficient to generate a
framework for vicarious learning (for an extensive review, see
Charpentier and O’Doherty, 2018
;
Konovalov
et al.
, 2018
).
Social signaling systems
Thorndike (1920)
proposed that social intelligence rests on two
central properties: the ability to understand others and the
behavioral effectiveness of social actions. Social signals are
driven by the importance of conveying information and are
observed with varying complexity across the animal kingdom
(
Dawkins and Krebs, 1978
). Social signals are conveyed via mul-
timodal cues such as intonation, posture, intensity, gaze direc-
tion, etc., and reduce the asymmetry of information between
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the signaler and receiver. However, they can also be used by
the signaler strategically to promote a desired image for per-
sonal status-seeking. Signaling theory has been used to explain
behavior in several fields including economics (
Spence, 1973
)
as part of game theory, anthropology—with respect to selecting
costly behaviors that otherwise appear irrational—and biology,
as an evolutionarily adaptive strategy to gain or communicate
social status and mitigate potential harm (
Zahavi, 1975
,
1977
;
Grafen, 1990
). Subconscious signals are expressed through body
language, facial expressions, touch or tone of voice. These sig-
nals include brain areas involved in language, motor and control
systems.
Inner self: self-monitoring, metacognition and control.
Lieber-
man (2007)
suggests that one central question for social neu-
roscientists is ‘how do we control ourselves’?.
Baumeister and
Vohs (2004)
propose that humans have an innate capacity to
regulate and alter their social behavior in reference to exter-
nal guidelines. These guidelines include social norms, religion,
morals, contextual rules and the law. Some have even gone so
far as to propose that humans always are thinking in terms
of expectations, and especially, what others expect of us and
what are our personal expectations (
Veissière
et al.
, 2019
). One
important part of this internal process is metacognition, or the
knowledge that we have about our internal cognitive processes,
which plays a key role in the control and monitoring of the
internal-self (see
Metcalfe and Shimamura (1994)
for detailed
review). Successful self-monitoring and control require coordi-
nated activity in prefrontal circuits to override the connection
between the value signal and motivation systems that lead to
action selection.
The inability to control and monitor one’s behavior is typ-
ically impaired in patients with prefrontal damage (
Damasio,
1995
) and susceptible to failure upon depletion of self-regulatory
resources. Regulatory failure has also been associated with
reduced dorsolateral prefrontal cortex (DLPFC) activity and with
functional connectivity between the inferior frontal gyrus (a
region implicated in certain elements of response inhibition)
and the vmPFC and orbitofrontal areas (regions thought to
encode the value of reward) (
Dambacher
et al.
, 2015
;
Stramac-
cia
et al.
, 2015
). Retrieval of meta-goals—or those associated
with personal longer-term outlooks, and unaccomplished by
any single decision or action—may be central in influencing
self-regulatory behaviors. Lateral frontopolar regions are impli-
cated in high-level cognitive monitoring and representation in
the tracking of meta-goals, with medial subdivisions involved
in memory processes that are likely required to retrieve particu-
lar goal information (
Baird
et al.
, 2013
). Together, these internal
processes will determine the behavioral output or the external
presentation of the self.
External presentation of self: speech and nonverbal signals.
Inferences about the internal self set the foundation for social
interaction. The use of language in social interaction is beyond
the scope of this review; however, several core features deserve a
special mention.
Bolinger (1965)
spoke of speech metaphorically
as an ocean, where forces acting on it create surface move-
ments, resembling the ups and downs of the human voice. Like
the ocean, speech conveys voluminous undercurrents, including
assertiveness and confidence through rising pitch; it transmits
emotion through prosodic tone, status through grammatical
accuracy and dialect, and intelligence through vocabulary and
pronunciation. Therefore, what we say and how we say it
are rich sources of social information. This weaving of tran-
sient social information is augmented by visual information
that includes the infinitesimal movements that characterize the
complex facial muscles, movement and directionality of the
eyes, gait, hand gestures, speed of movement, proxemics and so
forth. Humans are acutely aware of how we are viewed by others,
and in many cultures, individuals accumulate and display fine
material belongings to signal wealth, which is a proxy for high
social status. Bourdieu famously argued that material signaling
consisting of one’s ‘symbolic capital’ could be used interchange-
ably with economic capital to acquire social status, including
advantageous positions vis-à-vis access to high-quality mates,
ability to forge advantageous and stable alliances and enhanced
opportunity to acquire additional status (
Bourdieu, 1977
).
While we elicit all these signals, the human brain is encoding
others’ social signals, inferring allowable subsequent behaviors
based on these signals and prior knowledge and is making social
judgments concerning the target individual’s intentions. For
example,
Keltner
et al.
(2014)
have shown that humans exhibit
nonverbal signs of a prosocial character. These signals include
smiles head nods, head tilts, blushing and laughter that col-
lectively may indicate social engagement, warmth and concern
for others (
Keltner
et al.
, 2014
). Another social cue is proximity,
which provides information about the connectedness of peo-
ple, where close others (or those we selectively bond with) place
themselves (and are allowed to place themselves) within our
personal or intimate space (
Hall, 1966
).
Consistency in representations of the inner and external self.
Festinger and Carlsmith (1959)
defined internalization as the
process of matching one’s private self-concept with one’s exter-
nal behavior. Several theories have been advanced to account
for the relationship between internal and external selves. Self-
verification suggests that people act in ways that are consistent
with how they self-identify (
Swann
et al.
, 1987
). This is closely
allied with self-discrepancy theory (SDT;
Higgins, 1987
). SDT
proposes that individuals have an internal self-model, to which
they compare their behavior. Self-guides include the actual
self, ideal self and ought self. SDT further predicts that when
self-guides are incongruent, emotional discomfort will emerge.
Therefore, one goal during social interaction is to minimize
the discrepancy between internal and external states. This is
evident when one feels a mismatch between goals and their
attainment (e.g. rejection). The systems underlying this feeling
may share common neural substrates with dissonance, more
generally, which is assumed to provide an uncomfortable feeling
that motivates our actions and desire to return to a coher-
ent state. Cognitive dissonance, according to (
Festinger, 1962
),
recruits areas involved in error conflict monitoring, notably the
ACC, but also regions associated with affect and memory pro-
cessing, including the insula and precuneus (
Kitayama
et al.
,
2013
;
De Vries
et al.
, 2015
).
Shared reality: rapport forming and social tuning.
Shared
reality theory posits that when we take another person’s per-
spective, we become socially attuned and possess a mutual
understanding (
Hardin and Higgins, 1996
). Rapport is criti-
cal to cooperation and conflict resolution and can be consid-
ered a form of social bonding (see above). Forming a stable
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rapport typically increases the overlap of beliefs and emo-
tional responses between individuals—leading to an intrinsi-
cally rewarding interaction, providing an incentive to expend
significant energy to maintain a positive shared experience. This
shared reality results in affiliative behaviors, social bonding and
shared epistemic needs (
Hardin and Higgins, 1996
) and is cru-
cial for healthy social and psychological functioning (
Echterhoff
et al.
, 2009
). A salient feature of the promotion system is affilia-
tive motivation (
Sinclair
et al.
, 2005
). Socially tuned interactions
should produce characteristic social behaviors, including behav-
ioral mirroring. However, social anti-tuning, as engaged by the
prevention system, should be evidenced when people aim to
distance themselves from others, as occurs with out-group or
individuals who perceive themselves to be of lower status than
others (
Sinclair
et al.
, 2005
).
Social motivation system
From amoeba to humans, rewarding states are approached and
pain is avoided (
Higgins, 1997
). Extending this dichotomy social
behavior, regulatory focus theory (RFT) suggests an individ-
ual’s motivation interacts with goal pursuit (
Higgins, 1987
). RFT
parses motivation into either a promotion focus, where one
focuses on nurturance needs and gain
vs
non-gain situations, or
a prevention focus, which emphasizes security needs and non-
loss
vs
loss situations. Therefore, the promotion–prevention
system would be engaged when one attempts to optimize social
drives, through bonding, social tuning and biasing, and social
network formation. For example, when status-seeking is in
progress, promotion would presumably result in socializing
with high-status individuals and prevention through avoiding
or limiting interactions with low-status individuals. Assimila-
tion and allegiance are also important promotion motivators.
These drives would presumably be enabled by the well-known
circuitry involved in motivation, including the dopaminergic
and opioid circuitry in the basal ganglia and ventral tegmental
area (
Berridge and Robinson, 1998
). Further, some theorists have
suggested that the left hemisphere is associated with affiliative
and promotion-type behaviors and parasympathetic activation,
while the right hemisphere produces aggressive, defensive and
prevention-type behaviors and sympathetic activation (
Craig,
2005
). Craig’s model derives from two premises: the fact that
autonomic projections to the heart are asymmetric; and the
idea that the brain, given its high metabolic consumption rate,
requires optimization of energy consumption to perform at its
observed level. This model highlights a key role for the insula,
given its position as a hub and connections with areas subserv-
ing opposing components of the autonomic nervous system.
Social promotion and reward.
In humans, social rewards
tap into the same dopaminergic systems involved in primary
rewards such as food and sex (
Izuma
et al.
, 2008
). Indeed, the
drive to broadcast information about themselves, (
Tamir and
Mitchell, 2012
), to be liked (
Davey
et al.
, 2010
) and to have
a positive reputation (
Izuma
et al.
, 2008
) increase activity in
the dopamine-enriched ventral striatum (VS). In addition to
the VS, the vmPFC has also been widely implicated in social
reward and play an important role in value-based learning and
decision-making in general (
Bartra
et al.
, 2013
). Advice giving
may be one way in which individuals can gain the most basic of
social rewards: acceptance and respect (
Baumeister and Leary,
1995
). This was investigated by examining advice acceptance
and reflected glory (
Mobbs
et al.
, 2015
). In this study, it was
shown that activity increased in VS when one’s advice was
accepted in a three-player advisor–advisee game. Furthermore,
if this advice led to the advisee winning money, activity in the VS
also increased, suggesting that it is rewarding to see others win if
it reflects positively on our advice (
Mobbs
et al.
, 2015
). Therefore,
the human propensity to provide others with advice may act as
a positive, status-enhancing behavior. Another study directly
investigating reward-related neural activity in monetary and
social rewards found common activation in VS during reward
anticipation, but divergent results during reward presentation,
with monetary and social rewards associated with greater tha-
lamic and amygdala activity, respectively (
Rademacher
et al.
,
2010
).
Social prevention and punishment.
The most commonly stud-
ied form of social punishment is that of ostracism. Social pain
and rejection motivate people to avoid exclusions and conform
with others (
Lin
et al.
, 2018
), which involves the same neural
networks (e.g. VS and vmPFC) as when tracking reward sig-
nals, updating value information and motivating people to act
(
Klucharev
et al.
, 2009
;
Zaki
et al.
, 2011
;
Nook and Zaki, 2015
).
In a set of classic studies, Eisenberger, Lieberman and Williams
have shown that when subjects are ignored by other players in
a three-player cyberball catch game, they report feeling social
pain (
Eisenberger
et al.
, 2003
). This feeling of social rejection cor-
relates with increased neural activity in brain regions known to
be involved in physical pain (
Eisenberger
et al.
, 2003
). Other stud-
ies investigating social exclusion have identified the lateral and
medial prefrontal cortex (mPFC), several subregions of the ACC
and insula (
Gunther Moor
et al.
, 2012
). Similar regions have been
found to activate when people feel envy (
Takahashi
et al.
, 2009
)
and guilt (
Chang
et al.
, 2011
). Social punishment and forgive-
ness of excluders has been shown to activate regions implicated
in mentalizing and ToM, (
Will
et al.
, 2015
) including the TPJ,
STS and several areas of the PFC, and the pre-supplementary
motor area. This is likely because it entails taking the perspec-
tive of and making inferences about others’ mental states, both
of which are critical for empathy and cooperation (
Heatherton,
2011
). In third-party determination of appropriate punishments
for crimes committed, some have found activity in the amyg-
dala, mPFC and posterior cingulate cortex (PCC) when subjects
assessed magnitude, and activity in the right dlPFC when deter-
mining culpability (
Sebastian
et al.
, 2011
). The social pain net-
work may work to drive the reward network via retaliation or
revenge.
Affiliation and social bonding systems.
In humans, significant
mother–infant interaction is associated with synchrony in var-
ious biological rhythms such as heartbeat (
Feldman
et al.
, 2011
)
and other autonomic coupling that reflects a shared affective
state (
Ebisch
et al.
, 2012
), although these may be influenced by
attachment security (
Waters and Mendes, 2016
). More recently,
Preston (2013)
has pointed out that mammals are attuned to, and
motivated to help, neonates when they produce signals of dis-
tress. As mentioned above, this drive may be higher in females,
as stress increases tending behaviors (
Taylor
et al.
, 2000
). The
biological mechanisms that underlie the tend–befriend systems
are grounded in the attachment–caregiving system, which is
involved in maternal bonding and rearing. Oxytocin is believed
to be the core biological chemical that facilitates mother–infant
attachment (
Drago
et al.
, 1986
;
Preston, 2013
). In human moth-
ers, viewing their own infant’s faces during fMRI scanning
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resulted in activation of oxytocin-enriched regions of hypotha-
lamus and pituitary gland (
Strathearn
et al.
, 2009
). Others have
shown that images of increasingly cute baby faces result in
increased activity in dopaminergic rewards areas, suggesting
that these images provide an innate primary reward (
Glocker
et al.
, 2009
). Consistent with this model, the insula is modu-
lated by oxytocin signaling (
Riem
et al.
, 2011
) such that increased
signaling upregulates insular activity and downregulates amyg-
dala activity. Most bonding research involves mother–infant
dyads; however, some studies point to gender differences or
lack thereof in the affective and motivational systems that drive
parental bonding behaviors (
Rajhans
et al.
, 2019
).
Group identity and bias
People quickly evaluate and use social categories (e.g. race, gen-
der, status and age)—not always based on perceptual features
as discussed in the section social perceptual systems above—
as a guide on how to interact with others (
Ramstead
et al.
, 2016
).
Social groups give individuals a sense of social identity, which is
based on the group to which they belong—and is a strong deter-
minant of how one reacts to the observed outcomes of others.
Others perceived as similar to oneself, and therefore as belong-
ing to the same social category, generate both behavioral and
neural increases in vicarious reward processing, even when oth-
ers are not genetically related (
Mobbs
et al.
, 2009
). Perception of
self-similar others activates neural regions including the ventral
mPFC, which is also recruited during self-referential thought,
while more dorsal areas of the mPFC are associated with per-
ception of dissimilar others (
Mitchell
et al.
, 2006
;
Sul
et al.
, 2015
;
Wittmann
et al.
, 2018
;
Piva
et al.
, 2019
). Social orientation toward
others and ensuing behaviors may be determined in part by the
location of mPFC activation during perception of others. Specific
mPFC location may bifurcate the simulation processing to pro-
ceed under the assumption the other is ‘like me’ or ‘not like me’
(assuming no other inputs). However, activation location can
be shifted toward self-referential representation as a result of
perspective taking of others that may have initially been per-
ceived as dissimilar to oneself (
Ames
et al.
, 2008
;
Nicolle
et al.
,
2012
). This and other evidence suggest that social group cate-
gorizations can be quite flexible in general. This has also been
demonstrated with minimal group paradigms, where individu-
als are randomly assigned to previously unfamiliar social groups
based on arbitrary cues (e.g. a color) associated with a group.
The surprising results indicate how easily biases in favor of arbi-
trary in-groups occur (
Otten, 2016
). However, it should be noted
that evaluative preferences with respect to real groups tend to
be stronger than those observed with minimal groups (
Dunham,
2018
).
Individual responses to socially relevant information can be
biased depending on from whom the information is coming (i.e.
ingroup
vs
outgroup). For example, participants who identified
as strong supporters of a political party rated identical state-
ments as more inspirational if they believed the statements orig-
inated from their ingroup (
vs
outgroup) leaders (
Molenberghs
and Louis, 2018
), while another study found statements pre-
sented from the participant’s ingroup leader (
vs
from the out-
group) were perceived as less contradictory (
Westen
et al.
, 2006
).
Perceived group membership and attitudes toward the ingroup
or outgroup member also contribute to empathy-related behav-
iors towards the ingroup members (
Hein
et al.
, 2010
). This
ingroup empathy bias is modulated in the anterior insula cor-
tex, a region related to the impact of group membership on
neural correlates of fear (
Olsson
et al.
, 2005
;
Haaker
et al.
, 2016
)
and face processing (
Golby
et al.
, 2001
;
Van Bavel
et al.
, 2008
;
Hein
et al.
, 2010
). In contrast to empathy-related in-group bias,
while watching a negatively evaluated outgroup member suffer-
ing pain, the activity of the anterior insula cortex (associated
with empathy) has been found to be decreased, and activity in
nucleus accumbens (NAcc) (associated with reward processing)
was increased, suggesting that watching a negatively evaluated
outgroup member receiving pain was processed in a reward-
related manner (
Hein
et al.
, 2010
).
One perceptual and non-perceptual-based dimension in
group perception that has been extensively investigated is social
status (
Karafin
et al.
, 2004
;
Cloutier
et al.
, 2008
;
Magee and
Galinsky, 2008
;
Zaki
et al.
, 2011
). Inference of status can be
determined through observed demonstrations of skill, knowl-
edge, generosity or prestige-related social competencies (e.g.
affiliative tendency and morality (
Mattan
et al.
, 2017
) (see section
social perceptual system regarding perceptual social status).
Unlike for perceptual-based evaluations, status-based evalua-
tions frequently engage regions known to support person evalu-
ation (e.g. vmPFC) and reward/reinforcement learning (e.g. VS).
Other regions involved in affective responses (e.g. amygdala and
insula) and mentalizing (e.g. dMPFC, TPJ, STS/superior tempo-
ral gyrus (STG) and ATL) has also been associated with status
conveyed through person-knowledge.
Other non-perceptual-based cues, such as personality traits,
the knowledge of a person’s influence over others, their political
opinions or their financial status also influence how group eval-
uations are formed. It has been suggested that the brain tracks
discrepancies between a person’s behavior and the behavior that
is expected based on their trait impressions (e.g. competence,
trustworthiness and generosity:
Boorman
et al.
, 2013
;
Hackel
and Amodio, 2018
;
Morelli
et al.
, 2018
). Several studies have
revealed distinct ways in which the brain tracks the traits of
others—one is associated with the conceptual representation
of others and one tracks the value associated with individual’s
traits. For example, one study found that—based on the positive
or negative feedback received from another person in different
contexts—the value of the person, as well as higher level trait
inferences, is encoded in the VS (
Mende-siedlecki
et al.
, 2013
).
However, the trait inferences additionally involve a broader
network, including right temporoparietal junction (rTPJ), pre-
cuneus, inferior parietal lobule and ventrolateral PFC, regions
previously identified as involved in more explicit forms of trait
updating (
Mende-siedlecki
et al.
, 2013
). Overall, several networks
seem to be involved in group perception involving perceptual,
affective, cognitive systems and ToM (
Eres and Molenberghs,
2013
;
Amodio, 2014
).
Integration of social computations
In reviewing the six computational aspects entailed by social
interactions, we have seen some key themes emerge. First,
processing depends upon distributed brain systems; particu-
larly those involved in perspective-taking, social signals, and
emotional and goal-directed behavior. These systems are exem-
plified by an engagement of face processing in fusiform areas,
action observation in the extended mirror neuron system, sub-
jective value signals in the medial PFC and the striatum, inte-
roceptive inference in the anterior insular, and the extended
reward system including subcortical systems, such as the amyg-
dala. So, what principles could account for this plurality of brain
systems—and what principles could be brought to bear on their
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Fig. 1.
Multiple processes involved in social interaction. Perceptual signals and
inferential processes are influenced by social drives and biases. These draw on
learning systems that update and modify social behavior. Together, these pro-
cesses are integrated to produce an output or social signal (e.g. facial expression
and speech). These output systems are also modulated by control systems that
filter social signals.
functional integration? The goal of this section, therefore, is to
explain the underling processes, as well as the integration, of
perception and inferential system during social interaction (see
Figure 1
).
Active inference.
The account on offer here is based upon the
notion of active inference; namely the view that all action and
perception are in the service of minimizing uncertainty or max-
imizing model evidence (
Friston
et al.
, 2011
,
2017b
). These com-
plementary but equivalent perspectives inherit from a number
of theories; in particular, the Bayesian brain hypothesis (
Knill
and Pouget, 2004
) and the principle of maximum efficiency in
information processing (
Barlow, 1974
;
Optican and Richmond,
1987
). The basic idea is that the brain actively constructs expla-
nations for its sensory inputs, using a hierarchical generative
model—that generates predictions of what would be sensed if
the brain had correctly inferred states of affairs in the external
world [
Gregory, 1980
;
Kahl, R.
(1971)]. There is a large literature
on various neuronal process theories that underwrite this sort
of inference, including predictive coding and belief propagation
in cortical and subcortical hierarchies (
Bastos
et al.
, 2012
;
Fris-
ton
et al.
, 2017a
;
Shipp, 2016
). From our perspective, there are
two key themes. First, the architecture of the brain recapitulates
the architecture of the generative models used to predict sen-
sory outcomes in all conceivable modalities over which it has
control (
Conant and Ross Ashby, 1970
;
Mansell, 2011
). Second,
if the social brain is associated with this kind of architecture,
it must have some special properties. In other words, if the
brain can predict all the consequences of social interactions, it
means that the requisite generative model must be capable of
generating predictions in the exteroceptive domain (for social
inference based, for example, on facial expressions and nonver-
bal cues); it must be able to predict outcomes in the interoceptive
domain (appropriate for inferences based upon affiliative touch
and autonomic responses during prosocial engagements [
Seth
and Friston, 2016
;
Fotopoulou and Tsakiris, 2017
)]. Finally, it
clearly has to make predictions in the proprioceptive domain
to enable motor acts, particularly, of communication, such as
speech and nonverbal forms of exchange.
Active inference and the self.
In short, the special aspect of the
social brain is that it has to accommodate every consequence
of being a ‘self’. Indeed, the whole notion of minimal selfhood
can be cast as a hypothesis used by the brain to explain for the
myriad of sensory signals encountered during social exchange
(
Limanowski and Blankenburg, 2013
;
Seth and Critchley, 2013
).
Heuristically, what this means is that the brain infers that the
self is the most probable cause of the exteroceptive, intero-
ceptive and proprioceptive sensory signals to which it is privy.
The picture that emerges here is of a deep hierarchical gener-
ative model that generates all modalities. A generative model
is, technically, a probabilistic specification of how causes in the
outside world generate sensory consequences (
Hinton, 2007
).
Conversely, perceptual inference and synthesis corresponds to
Bayesian model inversion, namely, inferring the causes from
sensory consequences. Technically, this involves the maximiza-
tion of the evidence for our models of the sensorium—that can
be articulated as a minimization of variational free energy (i.e.
a mathematical bound on model evidence) (
Dayan
et al.
, 1995
;
Friston
et al.
, 2006
). This can be thought of more simply as
the minimization of surprise or prediction errors through neu-
ronal message passing among different levels of cortical and
subcortical hierarchies.
This view suggests that a generative model that starts with
‘me’ as the cause of my sensations will, when inverted, look
as if I am assimilating and integrating multiple sensory modal-
ities in the exteroceptive and interoceptive domains. If one also
adds proprioception to this inference, I am effectively generat-
ing predictions about my own action, either in the autonomic
or motor domain (
Baker
et al.
, 2009
;
Friston
et al.
, 2011
;
Seth,
2014
). This is referred to as active inference. When the percep-
tual synthesis implied by belief updating under such genera-
tive models includes interoceptive signals—as in affiliative and
nurturing social interactions—we come to the notion of intero-
ceptive inference (
Barrett and Simmons, 2015
;
Fotopoulou and
Tsakiris, 2017
;
Allen
et al.
, 2019
). The term coined above—social
inference—is meant to imply that the sort of active inference
required for social exchange is of the broadest, multimodal
nature conceivable, subsuming interoceptive inference and all
other forms of inference in the service of modeling me and my
interactions with you. On this view, the brain systems reviewed
above start to make perfect sense—as heteroarchical subgraphs
of a hierarchical graphical generative model, ultimately inte-
grated under a supraordinate level of self-modeling. So, what
does this say about how all the subsystems involved coordinate
social perception, inference, communication and learning?
In brief, social perception rests upon exactly the same
systems involved in nonsocial perception, but with a special
emphasis on inferring the sensory cues supplied by ‘creatures
like me’. Social influences, such as mentalizing, can—as the
active inference story goes—be explained by repurposing gen-
erative models of my own behavior to explain yours; much
in the sense of simulation theories and mirror neuron the-
ories reviewed above. Put another way, communication and
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ToM become much easier if we have a shared narrative such
that models of my behavior become models of your behavior—
enabling ‘me’ to efficiently and accurately infer ‘our’ behavior
(
Friston and Frith, 2015
). Clearly, to select the appropriate model
of shared narratives means that I have to first infer that you are
like me. This places the social perception, identity of agents and
group membership center stage, in facilitating this particular
aspect of social inference. That is, I first have to infer that you
are like me before I can use my models of how I would behave to
infer your intentions and state of mind. This high-level form of
active inference comes along with some special considerations
that we now consider in terms of social attention, joint attention
and sensory attenuation.
Self-modeling and mental action.
Above, we considered the
social brain as making inferences about states of affairs in a
social econiche by maximizing the evidence for (or minimiz-
ing the variational free energy of) a hierarchical model of a
world populated by ‘creatures like me’. Mathematically, this
can be described as message passing on a graphical descrip-
tion of the generative model (i.e. a neural network), where this
message passing corresponds to neuronal communication over
extrinsic (between cortical area) connections and the intrin-
sic connectivity of canonical microcircuits (
Bastos
et al.
, 2012
;
Shipp, 2016
;
Friston
et al.
, 2017b
). In predictive coding formu-
lations of this message passing, it is generally assumed that
inference proceeds via reciprocal message passing between the
levels of the hierarchical model. In particular, predictions are
sent down from one level to the next that try to predict represen-
tations on the lower level. The resulting mismatch or prediction
error is then returned to the higher level to induce belief updat-
ing or revisions of Bayesian beliefs encoded by neuronal activ-
ity (
Shipp, 2016
). This recurrent message passing/mediated by
ascending streams of prediction errors and descending counter
streams of predictions looks a lot like recurrent connectivity in
cortical hierarchies in the brain (
Hilgetag
et al.
, 2000
).
So how is this message passing coordinated? In other words,
how do we select those ascending signals that will update
Bayesian belief representations in the right kind of way? Under
active inference, the right kind of way corresponds to Bayes opti-
mal inference, where the various sources of prediction errors
and implicit information are weighted according to their relia-
bility or precision (
Knill and Pouget, 2004
;
Feldman and Friston,
2010
;
Parr and Friston, 2017
). Physiologically, this corresponds
to a delicate and fundamentally important control of postsy-
naptic gain or excitability of the neuronal populations broad-
casting messages from one level to the next. Psychologically,
this has been associated with attentional selection or atten-
tional gain, and indeed, the complement, namely attenuation
(such as in sensory attenuation) (
Kok
et al.
, 2012
;
Brown
et al.
,
2013
;
Wiese, 2017
). In short, the coordination of message passing
in a hierarchical generative model rests upon context-sensitive
predictions of the precision of various sources of information.
In turn, this means that there must be a generative model of
the precision or confidence afforded under different sorts of
information.
This may sound obvious, but it has some profound implica-
tions for the nature of social inference. In brief, it means that
we have the capacity to act upon our own hierarchical inference
by selectively gating different sorts of information in a context-
sensitive fashion. Many people consider this a form of mental
action (
Limanowski and Friston, 2018
), much like the premotor
theory of attention (
Rizzolatti
et al.
, 1987
). In short, mental action
can be regarded as a covert action that samples the right kind
of hierarchical information to make the best inferences about
the (social) world based upon multisensory cues that are decon-
structed in increasingly abstract and amodal levels. There are
three reasons why this particular aspect of social inference has a
special relevance for social cognition. First, forming representa-
tions about the precision or confidence ascribed to the contents
of my representations is, effectively, a belief about beliefs and a
formal sort of metacognition (
Fleming
et al.
, 2012
;
Shea
et al.
,
2014
). As such, it brings us close to a (possibly subpersonal)
form of self-modeling that has an enactive—if covert—aspect.
In fact, one could argue, that any (minimal) sense of self would
be redundant unless it entailed a deployment of mental action
and precision control over hierarchical processing (
Limanowski
and Blankenburg, 2013
;
Limanowski and Friston, 2018
).
The second reason that this form of covert action is partic-
ularly important for the social brain is in communication and
turn-taking (
Wilson and Wilson, 2005
;
Ghazanfar and Takahashi,
2014
). In brief, the ability to engage in verbal exchange, under a
shared narrative, depends upon the alternating augmentation
and attenuation of our sensory signals. This follows from the
need to attenuate the sensed consequences of our own action—
that would otherwise confound the fluent expression of motor
reflexes (and indeed autonomic reflexes). Put simply, if I want
to listen, I have to attenuate my proprioceptive predictions;
otherwise I would find myself speaking (c.f., echolalia). Con-
versely, if I want to speak, I have to suspend that attenuation,
while you are listening; see
Friston and Frith (2015)
for a sim-
ulation of this ‘turn-taking’. Furthermore, to use models of my
own body to infer your intentions based upon what I see you
doing, I have to attenuate the prediction errors that would ensue
from proprioceptive predictions; otherwise I would overtly mir-
ror your movements (i.e. echopraxia); see
Friston
et al.
(2011)
for
a simulation of ‘action understanding’.
Active inference allows for a parsimonious explanation of
many human behavioral tendencies noted above, especially
prosocial behavior and motivation. For example, humans tend
to be motivated to cooperate with conspecifics, especially with
members of their ingroup, and to dislike those from outgroups.
In human social groups, an especially important prior belief is
that other human agents in our ingroup will align their men-
tal states with our own and vice versa. This has been proposed
as one of the prior beliefs that define the human cooperative
phenotype and that make communication possible (
Vasil
et al.
,
2019
). Human cooperation and distinctly human forms of coop-
erative communication, then, are underwritten by the shared
belief—formalized in active inference and harnessed in the gen-
erative models that are species-typical of humans—that ‘we are
the same kind of creature, inhabiting the same cultural niche’
and that therefore ‘we should align with one another’.
There are many other fascinating issues that attend the aug-
mentation and attenuation of precision (i.e. attention) in this
setting, specifically, the notion of joint attention in higher-order
forms of social inference (
Moll and Meltzoff, 2011
). However, we
will conclude this subsection by noting a particularly important
aspect of precision control, namely, its intimate relationship to
emotional inference and interoception.
In brief, much of social interaction has a substantial intero-
ceptive component, hence the frequent reference to the anterior
insular (
Paulus and Stein, 2006
;
Craig, 2013
;
Gu
et al.
, 2013
;
Seth and Friston, 2016
;
Fotopoulou and Tsakiris, 2017
). It may
be that our sense of self and feelings (induced by another) are
inferences that provide the best explanation for the myriad of
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autonomic signals inherent in any prosocial exchange (
Barrett
and Simmons, 2015
;
Fotopoulou and Tsakiris, 2017
). These feel-
ing states both inform and are informed by various levels of
confidence or uncertainty about what will happen next or what
one should do next. This takes us in the direction of emotional
inference and the psychopathology of stress (and avoidance)—
all of which are especially relevant for social inference and
learning (
Peters
et al.
, 2017
). However, we will now close with
a slightly broader perspective that takes us beyond the brain
(and body) but still pursues the overall goal of inference and the
minimization of uncertainty.
The social brain and cultural niche construction.
In recent
years, there has been a move toward generalizing the prin-
ciples of active inference beyond the brain, to cover things
like variational ethology, niche construction and deontic value
(
Bruineberg and Rietveld, 2014
;
Constant
et al.
, 2018
,
2019
;
Badcock
et al.
, 2019
;
Veissière
et al.
, 2019
). This extension nicely
subsumes some of the more encultured aspects of social learn-
ing and inference reviewed above. The basic idea here is that
if one reduces (social) cognition to the minimization of uncer-
tainty (or the maximization of expected model evidence), a
simple explanation for much of ethology and the nongenetic
inheritance described above starts to emerge.
In brief, if we associate model evidence with adaptive fitness,
then natural selection just becomes Bayesian model selection
(
Frank, 2012
). On this view, natural selection is driven by the
imperative for self-evidencing (
Hohwy, 2016
), namely, making
the world as predictable and as learnable as possible. We have
seen beautiful examples of this above, in terms of mimicry and
other forms of socially mediated econiche construction. There
is a formal treatment of this form of cultural niche construction
under active inference that unfolds at two levels. The first is in
a reciprocal exchange between a phenotype and her environ-
ment such that as an agent learns about her world, the world
‘learns’ about the phenotype to which it plays host, in the sense
that it comes to mirror the statistical structure of the actions
of its denizens by accumulating traces of those actions. A com-
pelling example of this is the phenomena of desire paths or
elephant paths: these correspond to paths (e.g. across a field
or park) that are worn down by frequent use. The emergence
of desire paths could be seen in terms of niche construction,
in the sense that they reflect the enacted desires and pre-
dicted (locomotive) behavior of phenotypes. On the other hand,
they also provide ‘deontic’ cues that encourage walking and
the very emergence and maintenance of these paths in and of
themselves (
Constant
et al.
, 2018
), where ‘deontic’ cues are cues
endowed with a shared value for a given community and which
have an obligatory or deontic character. For example, humans
learn to stop at red traffic lights, which function as a deontic cue
that conveys the value of a given policy (in this case, stopping at
a red light) for all enculturated members of the community. In
short, the environment is effectively remembering the sort of
behavior which adaptive phenotypes exhibit. The implicit cir-
cular causality can now be extended to interpersonal exchange
and a similar ‘offloading’ of the sorts of phenotypes found in
this niche—that can be lifted to the level of semiotics (e.g. traf-
fic lights and signs in our lived environments) (
Constant
et al.
,
2019
) and, ultimately, social exchange (
Shea
et al.
, 2014
;
Veis-
sière
et al.
, 2019
). The underlying message here is that the social
brain may be a product of hierarchical inference—not just within
the skull—but in the context of coevolution with conspecifics
and a shared environmental niche. At its heart, all of the pro-
cesses entailed by cultural niche construction and ‘group living’
are quintessentially social.
Concluding remarks
A clear goal of neuroscience and artificial intelligence is to
understand how the brain functions during social interactions.
By dissecting the social brain into its core components and
rebuilding it to examine how these components work together,
we can begin to understand how the human brain computes
input and output signals to form coherent social behaviors. A
future goal of social neuroscience is to provide better psycholog-
ical, computational and anatomical models of the social brain
in action, a goal that will involve innovations in paradigm and
technical development. A great start is to build paradigms that
reflect real social interaction or more immersive social envi-
ronments and use techniques that provide better temporal and
spatial resolution.
Acknowledgements
Dean Mobbs is supported by US National Institute of Mental
Health grant 2P50MH094258 and a Tianqiao and Chrissy Chen
Institute for Neuroscience Award (P2026052); Tanaz Molapour
was supported by Vetenskapsrådet (project 2017-00524).
Conflict of interest
All authors declare no conflict of interest.
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