PS63CH12-Frith ARI 31 October 2011 11:58
Mechanisms of
Social Cognition
Chris D. Frith1,3
and Uta Frith2,3
1
Wellcome Trust Center for Neuroimaging and 2
Institute of Cognitive Neuroscience,
University College London, WCIN 3AR United Kingdom, and 3
Center of Functionally
Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark;
email: c.frith@ucl.ac.uk, u.frith@ucl.ac.uk
Annu. Rev. Psychol. 2012. 63:287–313
First published online as a Review in Advance on
August 11, 2011
The Annual Review of Psychology is online at
psych.annualreviews.org
This article’s doi:
10.1146/annurev-psych-120710-100449
Copyright c 2012 by Annual Reviews.
All rights reserved
0066-4308/12/0110-0287$20.00
Keywords
observational learning, imitation, reputation, teaching, mentalizing,
meta-cognition
Abstract
Social animals including humans share a range of social mechanisms that
are automatic and implicit and enable learning by observation. Learning
from others includes imitation of actions and mirroring of emotions.
Learning about others, such as their group membership and reputation,
is crucial for social interactions that depend on trust. For accurate
prediction of others’ changeable dispositions, mentalizing is required,
i.e., tracking of intentions, desires, and beliefs. Implicit mentalizing is
present in infants less than one year old as well as in some nonhuman
species. Explicit mentalizing is a meta-cognitive process and enhances
the ability to learn about the world through self-monitoring and reflection,
and may be uniquely human. Meta-cognitive processes can also
exert control over automatic behavior, for instance, when short-term
gains oppose long-term aims or when selfish and prosocial interests collide.
We suggest that they also underlie the ability to explicitly share
experiences with other agents, as in reflective discussion and teaching.
These are key in increasing the accuracy of the models of the world that
we construct.
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Contents
INTRODUCTION. . . . . . . . . . . . . . 288
What Is Social About Social
Cognition?. . . . . . . . . . . . . . . . . 288
The Importance of
Comparative Studies. . . . . . . . 289
The Importance of Implicit
Processes . . . . . . . . . . . . . . . . . . 289
LEARNING THROUGH
OBSERVING OTHERS. . . . . . 291
Learning About Places. . . . . . . . . 291
Learning About Objects . . . . . . . 292
Learning About Actions . . . . . . . 292
Learning About Agents . . . . . . . . 292
NEURAL MECHANISMS OF
LEARNING THROUGH
OBSERVATION . . . . . . . . . . . . . 293
Association Learning . . . . . . . . . . 293
Reward Learning. . . . . . . . . . . . . . 293
Gaze Following . . . . . . . . . . . . . . . 294
Mirroring . . . . . . . . . . . . . . . . . . . . . 294
COSTS AND BENEFITS OF
OBSERVATIONAL
LEARNING. . . . . . . . . . . . . . . . . . 294
Self-Interest and Copying. . . . . . 294
Prosocial Effects of Copying . . . 295
Alignment . . . . . . . . . . . . . . . . . . . . 295
Group Decisions . . . . . . . . . . . . . . 295
Group Identity . . . . . . . . . . . . . . . . 296
LEARNING ABOUT OTHER
MINDS . . . . . . . . . . . . . . . . . . . . . . 296
Taking Account of Other
Individuals . . . . . . . . . . . . . . . . . 296
Tracking Past Behavior to
Predict Future Actions. . . . . . 297
Reputation and Audience
Effect . . . . . . . . . . . . . . . . . . . . . . 297
Tracking Mental States . . . . . . . . 298
Tracking Other Points
of View . . . . . . . . . . . . . . . . . . . . 299
Tracking False Beliefs . . . . . . . . . 299
COSTS AND BENEFITS OF
LEARNING ABOUT
OTHER MINDS . . . . . . . . . . . . . 300
The Dark Side of
Mentalizing . . . . . . . . . . . . . . . . 300
Helping Behavior . . . . . . . . . . . . . 300
Mutual Trust . . . . . . . . . . . . . . . . . 300
EXPLICIT PROCESSES IN
SOCIAL COGNITION AND
THEIR MECHANISMS. . . . . . 301
Top-Down Modulation of
Competing Implicit
Processes . . . . . . . . . . . . . . . . . . 301
Executive Control of Social
Cognition. . . . . . . . . . . . . . . . . . 301
Verbal Instruction . . . . . . . . . . . . . 302
Teaching . . . . . . . . . . . . . . . . . . . . . 302
SHARING EXPERIENCES:
THE IMPORTANCE OF
META-COGNITION . . . . . . . . 303
Reflective Discussion . . . . . . . . . . 303
Reflective Discussion of Action
Changes Behavior . . . . . . . . . . 304
Reflective Discussion of
Sensations Creates a More
Accurate Model of the
World . . . . . . . . . . . . . . . . . . . . . 304
The Neural Basis of
Meta-Cognition. . . . . . . . . . . . 304
CONCLUSIONS. . . . . . . . . . . . . . . . 305
INTRODUCTION
What Is Social About
Social Cognition?
Consider the red-footed tortoise. These are
not social animals. They live lives of almost
complete isolation, apart from the brief interactions
necessary for reproduction. And yet
they can learn to perform a difficult detour task
simply by observing an experienced conspecific
(Wilkinson et al. 2010). Imagine a hive of
bees. Bees are undeniably social animals.
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Remarkably, their social behavior is governed
by rules that allow them to share knowledge
and make group decisions (Visscher 2007).
Like the tortoise, human beings can learn a lot
from simply observing others even when this
behavior has no deliberate communicative intent
and when social information is being used
just like any other publicly available information
in the environment (Danchin et al. 2004).
But also, like bees, human beings cooperate
and can make group decisions that are better
than those made by individuals (Couzin 2009).
Gaining benefit by watching and interacting
with conspecifics—and even other species—is
widespread among animals, including humans
(Galef & Laland 2005, Leadbeater & Chittka
2007). We review work that shows that
by following others and by observing their
choices it is possible to learn not only about
places, but also about actions, objects, and
other agents. This is very useful because by
observing what happens to others, we can learn
without experiencing potentially disastrous
errors. We also discuss cognitive processes that
enable deliberate communication, teaching,
and cooperation but are beyond the capacities
of tortoises and bees. These are processes that
enable individuals to understand one another
with a high degree of precision. They are often
referred to as mentalizing or having a theory
of mind. A largely implicit form of mentalizing
is likely to be involved in perspective taking
and tracking the intentional states of others,
and this has been claimed for a variety of social
animals as well as humans (e.g., Clayton et al.
2007). It is only the explicit form of mentalizing
that appears to be unique to humans (see
Apperly & Butterfill 2009 for a discussion of
the two forms of mentalizing). We point out
that explicit mentalizing is closely linked to
meta-cognition: the ability to reflect on one’s
action and to think about one’s own thoughts.
This ability, we argue, confers significant benefits
to human social cognition over and above
the contribution from the many powerful
implicit processes that we share with other social
species. However, these abilities also have
Mentalizing: implicit
or explicit attribution
of mental states to
others and self (desires,
beliefs) in order to
explain and predict
what they will do
Meta-cognition:
reflection on mental
states, including own
mental states
(introspection); others’
mental states (popular
psychologizing);
mental states in
general (philosophy of
mind)
emerged as the end result of a long evolutionary
process.
The Importance of
Comparative Studies
Neural mechanisms, which have evolved to allow
social interaction, need to be studied systematically
across species, and most of this work
still remains to be done. In this review we do
not go into details of such mechanisms when
pertinent reviews already exist. This is the case
in particular for general learning mechanisms,
which are also fundamental to social learning.
These involve conditioning and associative and
instrumental learning (see, e.g., Schultz 2008).
This comparative approach to social cognition
can identify processes in common across
species. It can also help identify the nature
of those processes that are dramatically more
highly developed in humans.
We passionately believe that social cognitive
neuroscience needs to break away from a
restrictive phrenology that links circumscribed
brain regions to underspecified social processes.
Although we build on such links, as shown in
Table 1, we are committed to the idea that it is
necessary to develop a mechanistic account of
these processes. In this review we provide some
pointers toward such accounts.
The Importance of Implicit Processes
One of the proudest achievements of human
beings is the ability to reflect on themselves and
their past, present, and future. This tends to obscure
the fact that most of our cognition occurs
automatically and without awareness. It comes
as a surprise that even such sophisticated social
processes as group decision and mentalizing can
occur automatically and can happen without
a deliberate attempt to achieve that decision,
individually or collectively. Here we follow the
tradition of cognitive psychologists who make a
fundamental distinction between implicit (automatic,
unconscious) processes and processes
that generate explicit, conscious products (e.g.,
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Table 1 Neural mechanisms underpinning processes relevant to social cognition∗
Mechanism
Relevant brain
regions Social processes
1. Reward
learning
Updating estimated value of
reward through prediction
error signals, whether
about primary reinforcers
or money (e.g., Peters &
Buchel 2010)
Ventral striatum Social rewards
Ventromedial PFC/
medial OFC
Smiling face (Lin et al. 2011)
Gaining status (Zink et al. 2008)
Gaining reputation (Izuma et al. 2010a)
Agreement of others (Campbell-Meiklejohn et al. 2010)
Being imitated (Schilbach et al. 2010)
Observing mimicry (K¨uhn et al. 2010)
Experiencing fairness & cooperation (Tabibnia &
Lieberman 2007)
Sight of cooperative person (Singer et al. 2004a)
Reward for similar other (Mobbs et al. 2009)
Social modulation of reward value
Object value affected by others (Campbell-Meiklejohn
et al. 2010)
Value of cooperation modified by knowledge of
intentions (Cooper et al. 2010)
2. Imitation
Who to
imitate
Orienting to agents and
faces (Klein et al. 2009)
Posterior STS
FFA and posterior STS
Perception of biological motion (Puce & Perrett 2003)
Facial identity & eye gaze (Hoffman & Haxby 2000)
How to
imitate
Linking observed to
executed behavior via
associative learning (e.g.,
Heyes 2011)
IFG and IPL
ACC and anterior
insula Lateral
interparietal area
Mirroring action (Rizzolatti & Craighero 2004)
Mirroring emotion (Singer et al. 2004b)
Gaze following (Shepherd et al. 2009)
When to
imitate
Representing the value of
social information
LIP Signaling value of gaze following (Klein et al. 2008)
3. Tracking
intentions
Predictive coding: updating
estimated intention
through prediction, error
signals relating expected to
observed behavior (e.g.,
Kilner et al. 2007)
pSTS/TPJ (and
mPFC)
Implicit mentalizing
Monitoring own actions (Miele et al. 2011)
Monitoring others’ actions (Pelphrey et al. 2004,
Saxe et al. 2004)
Monitoring others’ trustworthiness (Behrens et al. 2008)
Monitoring others’ generosity (Cooper et al. 2010)
Monitoring influence on others (Hampton et al. 2008)
4. Supervisory
system
Top-down biasing of
competition between
low-level processes (Beck
& Kastner 2009)
dlPFC Overcoming race prejudice (Cunningham et al. 2004)
Overcoming response to unfairness (Kirk et al. 2011)
Overriding trial-and-error learning of reputation by
instructed knowledge (Li et al. 2011)
dlPFC and ACC Managing conflicting information about emotional
states (Zaki et al. 2010)
(Continued )
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Table 1 (Continued )
Mechanism
Relevant brain
regions Social processes
5. Meta-cognition Reflection on our
knowledge about the
mental states of self and
others (Perner & Lang
1999)
mPFC Explicit mentalizing or theory of mind (Van Overwalle
2009)
Intentional stance (Gallagher et al. 2002)
Mentalizing stance (Hampton et al. 2008)
Impression formation (Mitchell et al. 2006)
Monitoring own reputation (what others think of us)
(Bengtsson et al. 2009, Izuma et al. 2010b)
Reflection on
communication
Communicative signaling (Kampe et al. 2003)
Communicative pointing (Cleret de Langavant et al.
2011)
Estimation of the
reliability of our
knowledge (Lau 2007)
Anterior PFC (BA10) Judgment of perception (Fleming et al. 2010)
Judgment of agency (Miele et al. 2011)
mPFC Judgment of strategy of others (Coricelli & Nagel 2009)
Uncertainty about partner’s strategy (Yoshida et al. 2010)
∗
This table is organized in terms of mechanisms and processes rather than brain regions. We have restricted our list to five mechanisms for which there is
some evidence of the specific neural processes involved. We attempt to specify the mechanisms through connected brain systems rather than
circumscribed brain regions. However, at this stage our knowledge is so limited that we still end up with list of brain regions. Note that since we are
emphasizing mechanisms rather than localized functions, the same brain region is sometimes linked with more than one mechanism. The social processes
that are enabled by the five selected mechanisms are listed in the right hand column.
Abbreviations: ACC, anterior cingulate cortex; dlPFC, dorsolateral prefrontal cortex; FFA, fusiform face area; IPL, inferior parietal lobe; mPFC, medial
prefrontal cortex; OFC, orbital frontal cortex; pSTS, posterior part of the superior temporal sulcus; TPJ, temporo-parietal junction;
vmPFC, ventromedial prefrontal cortex; vSTR, ventral striatum.
Kahneman & Frederick 2002). Many recent
reviews of social cognition have emphasized
the same distinction (Adolphs 2009), although
it is by no means straightforward to categorize
behavior in this way (Heyes 2011).
Explicit processes can be recognized
through their interference with currently
ongoing activity. Implicit processes can be
recognized when people cannot report the
stimulus that elicits their behavior or are
unaware of the behavior that is elicited.
However, many cognitive abilities that seem so
evidently “explicit” actually work just as well
without awareness, as shown, for instance, in
Dijksterhuis’s (2006) study of complex decision
making. Furthermore, the study of explicit
processes in nonhumans is extremely difficult
(see the thoughtful discussion of this problem
in relation to the study of declarative memory
by Murray & Wise (2010). We certainly cannot
assume that explicit cognition is unique to
humans.
LEARNING THROUGH
OBSERVING OTHERS
Learning About Places
Fish are among the many animals that learn
about the location of food by observing the behavior
of others. Here is an example. An individual,
isolated nine-spined stickleback learns
that food can be found on the left side of a tank
(private information) and will therefore swim
to the left when given the choice. But, after
a delay of seven days, if he can observe other
fish feeding on the right side of the tank (public
information), he will swim to the right (van
Bergen et al. 2004). This is presumably because
the private information the fish has about food
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pSTS: the posterior
part of the superior
temporal sulcus
FFA: fusiform face
area
being on the left is now too old and unreliable.
Examples of social influence on foraging behavior
can be observed in many other animals
(Galef & Giraldeau 2001), including humans.
A related process is gaze following, through
which we automatically look at the place toward
which someone else is looking. This example
of social influence has been demonstrated in
ravens, goats, dogs, and primates (reviewed in
Zuberb¨uhler 2008). Gaze following is reliably
used by human infants to learn about objects
and events from around one year of age (Flom &
Johnson 2011). In adult humans, gaze following
seems to be automatic, in the sense that people
follow the gaze of another person even when
this behavior runs counter to their intentions.
Bayliss & Tipper (2006) used a target-detection
task to show that individuals would follow the
gaze of another person even when that person
persistently looked in the wrong direction.
Intriguingly, although participants were unable
to stop themselves in their gaze following and
were unaware of the contingency, they did register
a socially relevant fact: They rated the person
who looked in the wrong direction as less
trustworthy.
Learning About Objects
Animals need to distinguish between nice objects
that should be approached and dangerous
objects that should be avoided. Here again,
learning commonly occurs through observation.
For example, in a series of experiments,
Mineka and colleagues have demonstrated that
rhesus monkeys acquire a fear of snakes very
quickly by observing another monkey showing
fear toward a snake (see, e.g., Mineka &
Ohman 2002). Fear conditioning through ob-
servationhasalsobeendemonstratedinhumans
(Olsson et al. 2007). But learning about objects
is not just restricted to fear conditioning. For
example, objects looked at by other people are
preferred more than objects that do not receive
attention (Bayliss et al. 2006).
Learning About Actions
Many animals learn which actions to perform
by observing others (Huber et al. 2009). For
example, chimpanzees will imitate a demonstrated
sequence of actions to gain access to
food in a puzzle box (see Whiten et al. 2009).
Wild mongoose pups learned, by observing an
adult, to open plastic containers of food (modified
Kinder eggs) by either smashing them on
the ground or biting them (M¨uller & Cant
2010). Both actions are equally effective at getting
access to the food inside the container.
However, the action chosen by the pups was determined
by what they saw the adult do. Such
imitative learning can also be seen in human
infants from around the age of one year (e.g.,
Carpenter et al. 1998). By this age infants will
imitate both instrumental and arbitrary actions
when they see an adult interacting with a novel
object. For example, they quickly learn that
they can press buttons on remote controls and
phones with remarkable ease.
Learning About Agents
How do animals distinguish agents from objects?
Agents move of their own intention and
have motion patterns that are different from
moving physical objects. Specific brain structures
in the temporal lobe are involved in detecting
this difference. Thus, in humans, perception
of biological motion is subserved by a
circumscribed brain region, the posterior part
of the superior temporal sulcus (pSTS; Puce
& Perrett 2003; see Table 1), and the ability
to distinguish this motion from other kinds of
motion is exquisitely tuned.
Over and above detecting animacy, animals
need the ability to detect agents who
might be friend or foe. This is critical for survival
even immediately after birth. One would
therefore expect to see specially adapted neural
mechanisms that require little if any learning.
Indeed, newly hatched chicks exhibit a spontaneous
preference for biological motion patterns,
and this mechanism facilitates imprinting
(Vallortigara et al. 2005).
Agents also typically have faces, and their
eyes give cues to what they are interested in.
A posterior region of STS is specialized for
analysis of facial movements, while invariant
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aspects of faces are analyzed in the fusiform
gyrus [fusiform face area (FFA)] (Haxby et al.
2000; see Table 1).
Another important cue to agency is contingent
responding. Human infants and adults
alike are likely to treat a shapeless object
as being animate if the object moves or makes
noises that are contingent on their own actions
( Johnson 2003). Learning about other individuals
and how to interact with them is vital for
all social animals. This is most particularly relevant
for mate choice. Na¨ıve female fruit flies
will choose as partners male flies they have seen
mating with experienced females (Mery et al.
2009). Effects of observation on mate choice are
also seen in guppies and quail (White 2004).
Well-disposed agents need to be distinguished
from those who are not. Pre-verbal
human infants (aged 6 to 10 months), after
observing agents interacting with each other,
prefer those who help others to those who
hinder others (e.g., Jacob & Dupoux 2008).
Learning about the status of conspecifics is also
important for knowing whom to approach and
whom to avoid. Many animals can infer social
rank by observation alone. For instance, fish
learn whom not to pick a fight with through
observation (Grosenick et al. 2007).
NEURAL MECHANISMS
OF LEARNING THROUGH
OBSERVATION
Association Learning
It seems plausible that learning through observation
(see Table 1) could be built from the basic
mechanisms of association learning (Catmur
et al. 2010). Mineka & Cook (1993) report that
the model monkey’s behavior on seeing a snake
elicits fear in the observer monkey. For the observer
monkey, the fear response of the model
monkey acts as an unconditioned stimulus
(through emotional contagion) and elicits the
fear response. Through classical conditioning
the snake becomes associated with the fear response
in the observer monkey. Note, however,
that classical conditioning has strict limitations
vmPFC:
ventromedial
prefrontal cortex
vSTR: ventral
striatum
forged by evolution (Breland & Breland 1961).
Thus, a potentially dangerous thing like a
snake readily triggers conditioning of the fear
response in the observer monkey but an innocuous
flower does not (Cook & Mineka 1989).
Fear conditioning through observation in
humans presumably uses the same mechanism
(Olsson & Phelps 2004). It can occur even when
the conditioned stimulus is presented subliminally,
which suggests that the learning depends
on an implicit process. Indeed, we suspect that
most, if not all, of the learning processes we
have discussed so far are examples of implicit
processes.
Reward Learning
Conditioning mechanisms can be applied to
other kinds of social learning through observation.
We know that places and objects acquire
value through being associated with reward.
We go to the places and approach the objects
with the highest value. A simple extension of
this mechanism would entail that places and
objects should gain value if they are approached
by others and lose value if they are avoided
by others. Evidence for such a simple model
comes from the finding that in many species
including fish and rodents, the probability that
an observer will adopt a particular behavior
increases monotonically with the proportion
of potential models exhibiting that behavior
(Pike & Laland 2010).
Social learning seems to involve the same
neural systems as nonsocial reward-based learning.
Lin and colleagues (2011) have shown
that learning for social rewards involves the
same neural system as learning for money, with
values being represented in ventromedial prefrontal
cortex (vmPFC) and reward prediction
errors being represented in the ventral striatum
(vSTR). Furthermore, this same rewardlearning
system is activated when we see others
choosing the objects we like. Neural signals in
this system that reflect how much we value an
object increase when we know that others also
value this object (Campbell-Meiklejohn et al.
2010; see Table 1).
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Imitation: used in
automatic learning by
observation, including
repeating the actions
(mimicry) and aiming
for the goals of another
agent (emulation)
Gaze Following
Investigations of the neural basis of gaze
following indicate something of the complexity
of the mechanisms involved when we imitate
actions (Klein et al. 2009). At least three highly
automatic components are necessary. The observer
must first recognize an agent and orient
toward the face and eyes. This is probably mediated
by a long-established subcortical route.
The observer must then work out the target of
the gaze from the position of the agent’s eyes. It
is not sufficient simply to imitate the eye movement
since the observer and the agent have
different viewpoints. The lateral interparietal
area (LIP), a brain region previously linked to
attention and saccade planning, is likely to have
a role in this computation. Mirror neurons have
been located here, which fire when a monkey
looks in the preferred direction of the neuron
and also when observed monkeys look in this
direction (Shepherd et al. 2009). In addition,
the observer must believe that gaze following is
likely to lead to a valuable outcome. Here again
LIP neurons seem relevant because they signal
the value of social (and nonsocial) information,
but only when this information is relevant to
decisions about orienting (Klein et al. 2008).
Mirroring
The aspect of learning through observation
that has been most extensively investigated at
the neural level relates to action. When we
learn about actions from observing others, we
are effectively learning to copy them. Forms
of copying are often referred to as imitation,
mimicry, and emulation, each emphasizing different
aspects of copying a model. The process
underlying these forms of copying has recently
been reinterpreted as a direct result of a specifically
social neural mechanism associated with
mirror neurons. Rizzolatti & Craighero (2004)
were the first to identify such neurons in the
ventral premotor cortex (F5) of the rhesus monkey.
Although there is still some controversy
about the role of this mechanism as a facilitator
or a product of imitation (Heyes 2001), the
discovery of mirror neurons has had a major
impact on our understanding of the nature and
role of imitation. The tendency to imitate the
actions of others is likely to be automatic. This
is shown by the finding that a high workingmemory
load facilitates rather than hinders
behavioral imitation (van Leeuwen et al. 2009).
Nevertheless, imitation can be controlled in a
top-down fashion, and we do not imitate the
actions of everyone we observe (Spengler et al.
2010).
Perceiving an emotional response of another
person elicits the same emotional response in
ourselves. This is also called emotional contagion
and allows us to share the emotion of
the person we are observing (de Vignemont
& Singer 2006), a prerequisite of empathy.
Emotional contagion supplies a basic conditioning
mechanism through which we can learn
from others on the basis of their emotional
expressions.
COSTS AND BENEFITS OF
OBSERVATIONAL LEARNING
Self-Interest and Copying
How important is observational learning in
comparison with nonsocial alternatives such as
trial-and-error learning? Laland and colleagues
(Rendell et al. 2010) assessed this question by
means of a computer tournament in which
participants proposed strategies for combining
learning by observation (copying) with learning
by direct experience (trial and error) in order
to acquire adaptive behavior in a complex
environment. The most successful strategy relied
almost exclusively on copying. Why was
copying so successful in this context? First, the
observer avoids having to make errors that are
an essential part of trial-and-error learning. In
addition, demonstrators selectively perform the
actions that they have found to be most beneficial
for themselves. Therefore, they effectively
and inadvertently act as a filter to provide the
information that is most useful for an observer.
Copying is a highly adaptive means of gaining
knowledge (Rendell et al. 2010).
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Prosocial Effects of Copying
While learning from observation can serve
purely short-term self-interest, contagion and
copying can also bias us toward the long-term
interests of our group (which also serves
self-interest). This effect is seen in experiments
that reveal subtle effects of copying. If we are
covertly mimicked, we tend to like that person.
Furthermore, we become more helpful to people
in general (van Baaren et al. 2004). Similar
effects have been demonstrated in monkeys,
who are more likely to approach and share
food with an imitator (Paukner et al. 2009).
These effects are likely to be unconscious.
In contrast, when people are aware that they
are being imitated (see Bailenson et al. 2008),
they experience high levels of discomfort and
thus the prosocial effects do not occur. At the
neural level there is evidence that mimicry is
rewarding. When we observe someone else
being imitated, activity increases in rewardrelated
regions such as vmPFC (K¨uhn et al.
2010). When others choose the same song
that we have just chosen ourselves, because we
liked it, a reward area of our brain is activated.
Furthermore it is exactly the same area in the
vSTR that is activated when we actually receive
the desired song (Campbell-Meiklejohn et al.
2010). Thus, there is reward in being endorsed
by others, and this may result in reinforcing
group-oriented behavior and conformity.
When is it a good idea to stop learning
by observation and learn instead by trial and
error? This will depend upon an implicit
cost-benefit analysis. As long as our own
knowledge is sufficient for achieving success
(e.g., knowing the location of a food source),
we will continue to exploit that knowledge.
Once that knowledge becomes unreliable (e.g.,
the food source at that location is depleted),
we switch to learning by observing others.
Finally, when the knowledge acquired by
observation becomes unreliable, we start to
explore innovative choices on our own (Laland
2004). This is an extension into the social world
of the exploit/explore dichotomy developed
in models of reinforcement learning (Sutton
& Barto 1998). In observational learning,
this may become a trichotomy of exploit own
knowledge–exploit others’ knowledge–explore.
Alignment
A necessary consequence of learning by observation
is the formation of behavioral similarity
across a population. This is most obviously the
case when learning about actions. When we
interact with others, we often automatically imitate
their behavior (Chartrand & Bargh 1999).
In the case of verbal interactions this alignment
can occur at many levels. During a productive
discourse, speakers will automatically tend to
align their posture, their speech rate, their
choice of words, and their syntactic forms
(Garrod & Pickering 2009). This alignment
enhances communication (e.g., Adank et al.
2010). But language does not always have to
be involved. Alignment has a similar advantage
for any joint action, where two players need to
coordinate their behavior (Sebanz et al. 2006).
Alignment in synchronized tapping can be
manifest in mutual adjustments occurring at
the level of 1 or 2 ms (Konvalinka et al. 2010).
Group Decisions
Social insects, such as ants and bees, make successful
group decisions by collating information
from several individuals (Couzin 2009).
One way in which such group decisions can
be achieved relies on learning through observation,
e.g., the process through which a swarm
of honeybees uses information from scouts to
locate a new nesting site (Visscher 2007).
The mechanism by which a group decision
can be made in the absence of any central coordination
is an example of the more general
principle of herding, through which complex
group behavior can emerge from simple local
interactions, which can occur automatically and
without awareness (Raafat et al. 2009). In humans,
Dyer and colleagues (2008) have shown
how a few informed individuals, the equivalent
of scouts in the case of bees, can guide a group to
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a target without verbal communication or any
obvious signaling.
Group Identity
There is a value to learning about people as
types as opposed to people as individuals. It
provides prior knowledge to guide our behavior
when confronted with someone we have
never met before. Inevitably, this prior knowledge
feeds into stereotypes and prejudices, and
these play a major role in in-group cohesion
and, conversely, in out-group hostility.
A child might observe her mother being
gracious when approached by a member of
an in-group and ungracious when approached
by a member of an out-group. Thus, through
emotional contagion, human infants can learn
to favor members of the in-group. The use
of stereotypes is already present by age three
(Hirschfeld 1996). At the neural level, prejudice
and stereotyping are likely to be underpinned
by brain areas associated with evaluative
processing (vmPFC and amygdala; Quadflieg
et al. 2009). As mentioned already, conformity
itself is rewarding and thus may provide a basis
for both producing and confirming stereotypic
behavior (Richerson & Boyd 2001).
Of great relevance for the study of group
conformity is “overimitation.” Children and
chimpanzees have been studied when they
learn, by observation, how to open a puzzle
box to get a reward. The model in these studies
performs additional actions that are irrelevant
for getting to the reward. Nevertheless,
children of three to five years persist in imitating
the irrelevant actions, even in situations
whentherearecountervailingtaskdemandsand
even in the face of direct warnings (Lyons et al.
2011). Adults perform the task with even more
emphasis on conformity with the irrelevant actions
(McGuigan et al. 2011). Chimpanzees, on
the other hand, are much less likely to imitate
the irrelevant components of the action and go
to the reward as quickly as possible (Horner
& Whiten 2005). The faithful copying of actions,
which overrides getting a primary reward
by other means, is a striking feature of human
culture and provides a means for creating distinct
group identities, emphasizing not so much
what we do but rather the way we do it.
LEARNING ABOUT
OTHER MINDS
Taking Account of Other Individuals
Arguably the most important and valuable
aspect of social cognition is learning about
other agents not just as types but also as individuals.
Stereotypes often result in inaccurate
predictions and do not take account of changeable
predispositions. The relevant cognitive
processes in dyadic interactions have been
extensively investigated by social psychologists,
and an account of their likely neural basis can
be found in a review by Liebermann and colleagues
(2002). Here we consider changeable
attributes of others, such as their current status
and their beliefs, knowledge, and intentions, all
attributes that need to be continually updated.
How is it possible to keep track of the status
of several individuals at the same time? The
term “keeping track of” is a spatial metaphor.
However, it may actually reflect the evolutionary
origins of the mechanism involved.
Many animal species, including, for example,
monkeys and dolphins, emit sounds as they
move about in groups, foraging for food (see
Boinski & Garber 2000 for many examples). It
is important for their survival that everyone in
the group keeps in close contact, but they cannot
necessarily see each other. The problem
can be solved if all the group members emit
frequent calls. If these calls are sufficiently individualistic,
then each member of the group
knows roughly where the other individuals are.
The implication of this idea is that each individual
has an internal map of the relative locations
of the other members of the group that is
continually updated.
A similar principle can be applied to aspects
of individuals in the group other than their positions
in space. For example, the relative status
of the individuals in a group can be represented
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spatially by distances along a line. For humans
at least, status does seem to be represented this
way. Differences in both numerical magnitude
and social status scale with activity in the inferior
parietal cortex (Chiao et al. 2009). Knowledge
of status also helps us to keep track of who
is currently allied with whom (Fiske 2010). We
also need to keep track of who has the most
relevant knowledge. Young children at first are
mainly concerned with learning by observing
their parents, whom they trust implicitly. However,
from about age eight, they switch to copying
the local expert instead (Henrich & Broesch
2011).
Tracking Past Behavior to Predict
Future Actions
The human face provides many clues as to how
a person will behave. First there is the emotional
expression that is reflected in the continually
changing configuration of a face. From
this information we can tell whether a person is
fearful or happy even if we have never met that
person before. Many studies have now demonstrated
that this can happen in the absence of
any awareness of the expression (e.g., Dimberg
et al. 2000). Another kind of information can
also be inferred from the faces of unfamiliar
people through fixed configurations of the face,
such as the width of the jaw. This information
relates to dispositions such as dominance and
trustworthiness (Oosterhof & Todorov 2008).
The third kind of information relates to the
faces of people we know. When we recognize
that this is the face of Fred, we know from past
experience with this individual how he is likely
to behave. Through our interactions we continually
update this knowledge, and it is our
knowledge of the person that is more important
than his facial appearance (Todorov et al.
2007).
There is some validity to the beliefs we have
about the personality of our friends. There
is much greater agreement between close acquaintances
about the personality traits of an individual
(and with the individual’s self-ratings)
than between strangers who have interacted
with the individual only once (Funder & Colvin
1988). This ability may depend on association
learning mechanisms through which we locate
everyone we know in a “personality space,”
with these locations being continually updated
(Todorov 2011).
We also implicitly learn from subtle cues
in social interactions. Imagine playing the
game of stone-paper-scissors. In some cases,
the behavior of your opponent enables you
to predict what his response would be. For
example, a very brief eyebrow movement
might consistently precede the choice of stone.
Participants can learn to use such a cue because
it makes them more likely to win on these
trials (Heerey & Velani 2010). This effect was
observed even when participants had no idea
which cue had been predictive, as revealed in
a debriefing session after the game.
Learning about people can certainly occur
without awareness (see also Todorov &
Uleman 2003). Such learning is likely to be procedural
and semantic (i.e., association learning)
rather than episodic. This is suggested by the
observation that patients with amnesia associated
with hippocampal damage can still acquire
person knowledge, but only if the damage does
not extend into the amygdala and the temporal
pole (Todorov & Olson 2008).
There is also some evidence that person
knowledge is accessed automatically whenever
we see a familiar face (Todorov et al. 2007). If a
particular kind of behavior has been associated
with a particular face, then the mere presentation
of that face elicits stronger brain activity
compared with a novel face. Such activity was
observed in anterior paracingulate cortex and
the STS regardless of the nature of the specific
behavior associated with the face. We speculate
that this reflects taking an intentional stance toward
people we know (see Table 1). As yet,
however, we lack models of how person representations
are rapidly updated in the brain.
Reputation and Audience Effect
Species whose interactions are characterized by
indirect reciprocity benefit from keeping track
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Audience effect/
reputation
management:
behavior of agents to
indicate that they are
cooperative and
trustworthy while
being observed
mOFC: medial
orbitofrontal cortex
mPFC: medial
prefrontal cortex
TPJ: temporoparietal
junction
of reputation. They are more likely to cooperate
with a partner who has a reputation for cooperativeness.
For example, clients of the reef cleaner
fish, labroides dimidiatus, eavesdrop on cleaning
sessions and spend more time next to cleaners
known to be cooperative. Furthermore, the
cleaners behave more cooperatively when they
are being observed (Bshary & Grutter 2006).
The audience effect means that we behave
differently when we believe ourselves to be observed.
For example, to ensure that others will
continue to cooperate with us we need to maintain
our own reputation for being cooperative
(Tennie et al. 2010). Cooperative behavior
rapidly declines in trust games where the players
are anonymous (Milinski et al. 2002). Under
conditions of anonymity, there is no longer
any need to guard one’s reputation. On the
other hand, a watching pair of eyes, even in
the form of a photograph, is sufficient to increase
prosocial behavior (Bateson et al. 2006).
This is presumably an automatic effect because
people would be well aware that a photograph
cannot record their behavior or damage their
reputation.
The evidence for audience effects in cleaner
fish suggests that these effects need not
depend upon complex cognitive processes.
However, such high-level processes are likely
to have an additional role in humans (Bshary
& Bergmuller 2008). Social psychologists
have long studied the processes by which the
presence of an audience improves performance
(Zajonc 1965). This may in part be due to an
increase in arousal when others are present,
but there is also evidence that the presence
of others increases reflective self-focus and
attention to ideal behavioral standards (Carver
& Scheier 1981).
How do humans keep track of the trustworthiness
of others? This question has been investigated
using economic games, such as iterated
prisoner’s dilemma or trust games. These studies
show that the brain’s reward system [vSTR
and medial orbitofrontal cortex (mOFC)] (see
Table 1) is activated by reciprocated cooperation
(Rilling et al. 2004) and also by fair behavior
(Tabibnia et al. 2008). Participants in
the study of Phan and colleagues (2010) rapidly
learned about the cooperativeness and fairness
of the people they were playing with during a
trust game involving iterative exchanges. Positive
reciprocity robustly activated vSTR and
mOFC. In an earlier study by Singer and colleagues
(2004a) using a similar paradigm, the
mere presentation of the face of a cooperative
partner elicited activity in reward areas.
Izuma and colleagues (2010b) scanned participants
while they made self-disclosures. Activity
in medial prefrontal cortex (mPFC) during selfdisclosure
was greatly enhanced by the presence
of an audience, as was activity in vSTR.
It seems plausible from these observations
that we keep track of others’ reputation for
cooperativeness on the basis of the same fundamental
learning mechanisms that we use to
learn about objects. Behrens and colleagues
(2008) specifically investigated the mechanism
by which we update this knowledge. This was
a learning study, where a human advisor provided
information about where a reward was
likely to be found. However, the reliability of
the advice was continually varied. The results
suggest that the same computational mechanism
was engaged for tracking the location of
the reward as for tracking the reliability of the
advisor: associative learning updated through
prediction errors. However, whereas prediction
errors associated with reward learning were associated
with activity in the vSTR, those relating
to social learning were associated with
activity in mPFC and temporo-parietal junction
(TPJ), regions previously associated with
mentalizing (i.e., the ability to attribute mental
states to others) (Van Overwalle 2009; see
Table 1).
Tracking Mental States
Although experience of their past behavior can
help us to predict what people are likely to do
next in various situations, we can do better. It is
extremely useful to know something about their
current intentions, desires, knowledge, and beliefs.
Because these mental states are variable,
keeping track of them enhances the accuracy of
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our predictions. For example, people will not
reach for something they cannot see, and their
actions will be determined more by what they
know, i.e., believe to be the case rather than
what is actually the case. Thus, for successful interactions,
it pays to keep track of people’s continually
changing inner states relating to their
goals and their knowledge. There is increasing
evidence that keeping track of these mental
states also occurs automatically and without the
need for awareness.
Tracking Other Points of View
Samson, Apperly, and their colleagues (2010)
have shown that observers were slowed down
when they had to report the number of dots
visible to them in the presence of another agent
who could not see all of these dots. In fact, the
other agent was an avatar placed in a schematic
room with variable numbers of dots on its walls.
This result suggests that we automatically take
note of the fact that others may have knowledge
that differs from our own knowledge due to
their different point of view. The idea that this
process is automatic was confirmed by a subsequent
experiment looking at the effects of cognitive
load (Qureshi et al. 2010). Here, the addition
of a cognitive load did not stop participants
from automatically taking note of the avatar’s
perspective. However, cognitive load did impair
participants’ capacity to switch between
their own and the avatar’s perspective when
they were explicitly required to do so. Remarkably,
while we automatically keep track of the
knowledge of others, it requires cognitive effort
to deliberately take their perspective. Awareness
into this deliberate process dawns between
the ages of two and four years (Flavell 1992).
Tracking False Beliefs
There is evidence that it is possible not only to
automatically keep track of what other people
see but also what they (invisible to the eye)
believe. The critical insight here is the recognition
that people’s behavior is determined
by their beliefs rather than by physical reality,
even if this belief happens to be false. This
insight is a result of mentalizing or having
a theory of mind. Although the latter label
suggests a conscious process, it is important to
bear in mind that there is both an implicit and
an explicit version of theory of mind, and this is
why we prefer the term mentalizing. Why is it
useful to track other people’s beliefs about the
state of the world? This knowledge allows you
to predict what they are going to do much better
than if you used your own belief about the
world. Children from around the age of four to
six years are aware that own beliefs and others’
beliefs of the same state of affairs can be interestingly
different (Wellman et al. 2001). They
can also work out implications. With greater
experience, adolescents and adults are increasingly
able to manipulate other people’s beliefs,
for instance through persuasion or deception.
There is now evidence that already in the
first and second year of life infants have an implicit
recognition of false belief (Kov´acs et al.
2010). These results suggest that infants (and
adults) automatically note when someone has a
different belief from themselves. There is also
increasing evidence for the ability to keep track
of the knowledge of others in nonhuman animals
including birds (e.g., Bugnyar 2011), although
there is still controversy to what extent
this is the case for chimpanzees (Call &
Tomasello 2008).
As yet we know very little about the physiological
underpinnings of the implicit processes
by which we keep track of the mental states of
others. There is a large literature on mentalizing
tasks implicating mPFC and TPJ/pSTS
(e.g., Van Overwalle 2009), but all of these
studies involve explicit processes. There are
also several studies on visual perspective taking.
Many of these also implicate TPJ (see Spengler
et al. 2010 for a useful review of links between
perspective taking and mentalizing) but, here
again, only for explicit processes.
The mechanisms underlying mentalizing
are likely to involve the same predictive coding
principles as are involved in vision (Kilner et al.
2007). On the basis of an estimated intention,
the behavior of the agent is predicted. The
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estimated intention can then be updated on
the basis of prediction errors. Activity in pSTS
reflects such prediction errors (e.g., Behrens
et al. 2008). Mechanisms that support explicit
forms of mentalizing probably differ from
those involved in implicit forms, but systematic
distinctions between these forms have yet to
be delineated (Apperly & Butterfill 2009).
COSTS AND BENEFITS
OF LEARNING ABOUT
OTHER MINDS
The Dark Side of Mentalizing
Corvids, who show implicit mentalizing, use
this ability in selfish and antisocial ways: Its
main purpose seems to be to avoid sharing
food with other corvids (Clayton et al. 2007).
There are many parallels in human societies.
Getting the better of others through scheming
and lying is typical for human societies, and
Machiavellianism has been used to describe the
main outcome of mentalizing (Byrne & Whiten
1988). However, at the same time, mentalizing
abilities, both implicit and explicit, are used
in the service of reciprocal communication and
cooperation.
Helping Behavior
Helping is extremely widespread among social
animals and does not depend on keeping
track of others’ mental states. However, human
infants show evidence of a more refined and
flexible helping behavior. For example, infants
of 18 months spontaneously helped an adult
by opening a cupboard door when the adult’s
hands were full (Warneken & Tomasello 2006).
Chimpanzees also showed some evidence of this
kind of helping behavior, but in a much reduced
form. In another study, Liszkowski et al. (2007)
showed that infants age 12 months would point
more often to an object whose location an adult
did not know than to an object whose location
was known. Again, such helpful actions depend
upon infants keeping track of what others know.
Mutual Trust
One of the main socially beneficial effects
of keeping track of other minds is enhanced
and flexible cooperation. As predicted by formal
evolutionary theory (Axelrod & Hamilton
1981), we seem to expect, at least to start with,
that our partner in a game will be cooperative
(Andreoni & Miller 1993). Furthermore,
if trust can be established, then players will do
better. The mechanism underlying trust building
through tracking of partners’ intentions to
cooperate has been studied using the model of
Rousseau’s stag and rabbit hunt game (Skyrms
2003). In this game a small reward is gained by
catching a rabbit, and this can be obtained regardless
of what the other person does. A much
bigger reward can be obtained by catching a
stag, and this reward is obtained only if both
partners choose to cooperate. But there is always
the risk that one partner will not cooperate.
Successful cooperation in this game depends
upon players making inferences about the
beliefs of their partner and also making inferences
about what their partner believes about
them, e.g., they believe that their partner will
cooperate when their partner believes that they
will cooperate. This is an example of the recursive
process that seems to lie at the heart of
many human social interactions.
According to a recently developed computational
model of this game, players need to
estimate the depth of inference being made
by their partner in order to optimize success
(i.e., achieve cooperation; Yoshida et al.
2008). This suggests that the model is also relevant
to reciprocal communication. We have
referred to this as “closing the loop” (Frith
2007). Reciprocal communication plays an important
role in teaching, as both teacher and
learner need to keep track of the differences
in their state of knowledge. However, whether
these processes are implicit or explicit remains
to be determined. A scanning study (Yoshida
et al. 2010) found that activity in mPFC
was related to participants’ uncertainty about
their partner’s depth of inference. We believe
that the approach offered by computational
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functional magnetic resonance imaging using
models such as the one applied to the stag hunt
game could lead to a much more precise formulation
of the role of the brain regions involved
in mentalizing.
EXPLICIT PROCESSES
IN SOCIAL COGNITION AND
THEIR MECHANISMS
Clearly, many processes associated with social
cognition occur automatically and without
awareness. We have shown that these are typically
present in many species including humans
and are also present in very young human
infants. What is there left to do for explicit processes?
We suggest a particular role for explicit
processes in fostering social interactions, which
may be unique to humans. These processes
are characterized not simply by awareness but
also by reflective awareness. By this we mean
the meta-cognitive ability to think about our
thinking. We argue that it is through reflective
awareness that humans manage to outstrip the
performance of other social species.
Top-Down Modulation of Competing
Implicit Processes
It is a truism that our senses are bombarded
by many signals, from which just a few must
be selected for controlling our actions. This
selection can be achieved bottom-up by direct
competition between the many different signals.
However, it can also be achieved top-down
by prior biases that are applied to some signals
and not to others. This is the biased competition
model, whose neural basis has been
analyzed in some detail (see Beck & Kastner
2009). The same principles as apply to physical
information about the world also apply to social
information.
Executive Control of Social Cognition
There is continual competition between the
various implicit processes relevant to social
interactions, such as self-interest versus group
dlPFC: dorsolateral
prefrontal cortex
ACC: anterior
cingulate cortex
interest or short-term gain versus long-term
gain. This competition can also be biased by
top-down, executive processes.
One important role for these executive
processes is to overcome biases such as race
prejudice. We all tend to have an implicit fear
of out-group members (Phelps et al. 2000)
and fail to show empathy for people in the
out-group (e.g., Avenanti et al. 2010). For
example, if white observers are shown the faces
of unknown black people for 30 ms, activity
is typically elicited in the amygdala. That this
result occurs with such a short presentation
time suggests that the processing of the face
occurs without awareness. Furthermore, the
amygdala activity is correlated with measures
of the strength of the observer’s implicit race
prejudice. However, if the faces are shown for
525 ms, then activity is elicited in dorsolateral
prefrontal cortex (dlPFC), and the amygdala
response is reduced (Cunningham et al. 2004).
This result suggests that explicit executive
processes, instantiated in frontal cortex, can
modulate the automatic evaluation of people.
More direct evidence for a role for executive
processes in resolving conflicts between different
social processes comes from a study by
Zaki and colleagues (2010). Participants were
scanned while making inferences about the
emotional states of agents. Relevant information
was available from two sources: silent video
clips of people depicting positive or negative
autobiographical events and written sentences
describing such events. The idea was that the
video clips would activate the brain’s mirror
system while the sentences would engage the
brain’s mentalizing system. The pattern of
brain activity associated with these different
cues confirmed this expectation. In the incongruent
condition, activity was elicited in areas
associated with executive control [dlPFC and
anterior cingulate cortex (ACC)], and there was
also evidence that this control was associated
with biasing toward a participant’s preferred
source of information. These results demonstrate
that social processes are subject to topdown
executive control using the same mechanisms
as nonsocial processes (see Table 1).
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Ostensive gestures:
deliberately signaling
the intention to
communicate; signals
can be verbal (call,
prosody) and
nonverbal (look,
touch)
Verbal Instruction
Just as we can learn by observing others, we can
also learn from what others tell us. Work from
Milinski’s group shows that reputation, conveyed
through word of mouth, can have a strong
effect on group behavior. In a trust game an
effect was found even when participants could
access the same information by direct observation
(Sommerfeld et al. 2007). The same effect
can be observed at the neural level. Delgado
and colleagues (2005) found that participants
would make more risky investment choices
with partners when they had been told that the
partners had a good reputation. In addition, in
this case the activation in the caudate nucleus
that normally reflects whether the partner cooperates
or defects on a trial-by-trial basis was
significantly reduced. The indirect spreading
of information about others is known as gossip
and is an important part of human communication
(Spacks 1982). It seems that gossip reduces
our reliance on the feedback mechanisms underlying
trial-and-error learning, as is typical
of a top-down mechanism (Li et al. 2011).
Verbal instruction can also affect how
we respond to objects, not just people. The
instruction “When you see the blue square,
you will receive at least one shock” is sufficient
to induce fear of the blue square. But, in this
case, the feared object does not elicit a response
when presented subliminally (Olsson & Phelps
2007). It seems that learning through verbal instruction
depends upon explicit top-down brain
processes. These do not establish an automatic
response to objects that we believe to be
threatening but of which we have no direct
experience.
Teaching
Most human learning arguably occurs through
deliberate teaching rather than mere observation
and is greatly dependent on the use
of language. However, the human advantage
of learning from a teacher extends even to
situations in which language is not involved.
Teaching, in a broad sense understood
as actively helping a learner to benefit from
the teacher’s experience, has been observed
in a variety of animals including ants and
bees (Hoppitt et al. 2008). Meerkats provide
a vivid example (Thornton & Clutton-Brock
2011). To kill and eat a scorpion without
being stung requires considerable skill. So the
mother prepares this food in line with her
pups’ gradually increasing abilities. At first,
mothers find and kill scorpions, then bring
them to the pups. As the young meerkats
grow up, their mothers disable the scorpions
rather than killing them. They remove their
deadly sting and then present the live scorpion.
Through such teaching, the young meerkats
eventually learn to kill a scorpion. This kind of
instruction does not depend on keeping track
of constantly changing mental states. Instead
it is finely attuned to the physical states of the
pups, which are signaled by the pitch of their
vocalization. It is this vocalization that triggers
the adult’s food preparation behavior.
We would argue that in humans the continuous
tracking of mental states enables a more
flexible type of instruction (referred to as natural
pedagogy; Csibra & Gergely 2006). In adultchild
interactions in particular, deliberate and
explicit teaching occurs, with both adult and
child knowing when teaching is intended. This
usually involves ostensive gestures, such as eye
contact or the high-pitched speech mode of
“motherese” (Senju & Csibra 2008). In such
interactions the child is not simply learning
by observation. A shared intention has been
formed between the adult, the child, and—
importantly—also the object of their intention.
Intriguingly, we all expect, infants included,
that we are taught something important about
the object, not about a fleeting and precise moment
of interpersonal interaction. In this way
we are continuously teaching each other and
learning from each other about the world.
Teaching here is a cooperative activity of
which social games would also be examples. In
a study by Warneken and colleagues (2006),
18- to 24-month-old children engaged in
cooperative games with adults. At one point in
these games the adult partner stopped participating.
All of the children made at least one
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communicative attempt to re-engage the adult.
Here it is the child rather than the adult who
is using an ostensive gesture to restart the
interaction. In contrast to human children, the
chimpanzees in this study never made any communicative
attempt to re-engage their partner.
We are aware of only two explorations of
the neural basis of ostensive, communicative
gestures. In our group, Kampe et al. (2003)
found that both eye contact with a participant
and calling the participant’s name elicited activity
in anterior rostral medial prefrontal cortex
and in the temporal poles, both regions associated
with mentalizing. Another study investigated
brain activity elicited when individuals
communicated through pointing at an object
compared with just pointing at an object without
the intention to communicate (Cleret de
Langavant et al. 2011). In this study, when
pointing was communicative, the pointers subtly
altered their behavior to take account of the
point of view of the observer. The brain regions
specifically activated by communicative pointing
were right pSTS and right mPFC.
As we have seen, activity in mMPFC seems
to be elicited in many different situations when
we need to think about mental states. In a study
by Mitchell and colleagues (2006), participants
were presented with a series of sentences describing
a person. Some of these sentences were
informative as to the personality of the person
(“he turned down three parties to study for organic
chemistry”) whereas others were not (“he
photocopied the article”). When the task was to
form an impression of the person, both kinds
of sentence elicited activity in MPFC. However,
when the task was simply to remember
the order of the people, only the sentences relevant
to personality activated MPFC. We speculate
that the instruction to form an impression
was a form of ostensive signal, indicating
that the sentences that followed would be relevant
to this task. The activity in the MPFC reflects
the adoption by participants of a mentalizing
stance toward all subsequent information.
In the absence of such an instruction, only the
sentences about personality elicited this stance.
This is consistent with the idea that MPFC has a
Reflective
discussion: mutual
communication of
meta-cognitive
knowledge;
comparison of
confidence in own
perception increases
joint performance
high-level role in top-down biasing toward
treating information as socially relevant. This
speculation is also consistent with the various
studies in which greater activity was elicited in
MPFC when subjects were told that they were
interacting with a person rather than a computer
(e.g., Gallagher et al. 2002).
An even more specific role for this brain region,
consistent with its activation by ostensive
gestures and by the presence of an audience,
might be in linking mental states of the self and
the partner during communicative interactions
(Amodio & Frith 2006). In line with these observations,
Saxe (2006) has suggested that cognitive
processes supported by mPFC may be a
uniquely human form of social cognition (see
Table 1).
SHARING EXPERIENCES:
THE IMPORTANCE OF
META-COGNITION
Reflective Discussion
We have suggested that there is an important
distinction between learning through observation
of others, which can occur without explicit
awareness, and learning through explicit communication
with others. We have discussed
gossip, through which we learn about the reputation
of others, and instruction, through which
we are taught about objects in the world. In this
final section we discuss another major topic of
explicit communicative interactions. We refer
to it as reflective discussion. We frequently
talk to each other about our mental states,
describing our sensory experiences and justifying
our decisions. Such interactions would be
impossible without a special high-level ability,
meta-cognition, which may well be uniquely
human (Metcalfe 2008). Meta-cognition is the
ability to reflect upon our mental states and describe
these states to others. This self-reflection
requires taking a step away from the representations
of the world and of other people, and
this step appears to be a mental “decoupling”
(Leslie 1987). We are normally unaware of
our representations of the world. Instead, we
take for granted that the world is an open book
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directly accessible to our experience (Frith
2007). Meta-cognition allows reflection and
can give us a rare glimpse into the fragility of
our mental world. This makes it possible for
us to recognize representations as just that,
representations (Perner 1991). From this it
follows that other minds may have different
representations, and even more startling, that
our representations of the world might be illusory
or false. Since we all are able to have these
insights, we can discuss them. Thus, reflective
discussions enable us to compare our views of
the world and to create improved shared views
of the world. In this way meta-cognition has
a vital role in the generation of cultural values
and institutions.
Reflective Discussion of Action
Changes Behavior
A major feature of our mental life involves the
vivid experience of being in control of our actions
and choosing one option rather than another.
Yet, this experience seems to be largely
post hoc and has little to do with actual control.
We believe that the value of this experience
arises because we can discuss the sources of our
actions with others (Frith 2010). People readily
explain and justify their decisions, even though
these explanations and justifications may be inaccurate
and self-serving. Johansson and colleagues
(2005) have shown how readily people
will justify a choice even when, unbeknown to
them, it was not the choice they actually made.
The importance of such discussions with other
people is that they enable us to understand better
the factors that determine our own decisions
and can change the way we make decisions in
the future (e.g., Vohs & Schooler 2008).
Reflective Discussion of Sensations
Creates a More Accurate Model
of the World
In humans the explicit meta-cognitive process
of sharing sensory experience enables pairs
of participants to enhance their perception of
basic sensory signals, even beyond the abilities
of the better member of the pair. In our group,
Bahrami and colleagues (2010) studied pairs of
participants collaborating in the performance
of a signal detection task. If the pair disagreed
as to when the signal was presented, then they
had to come up with a joint decision through
discussion. As long as the members of the dyad
had relatively similar perceptual abilities, the
group performance was significantly better
than the better member of the pair. Furthermore,
this group advantage critically depended
upon the occurrence of the discussion. We
believe that this advantage depends on one
or all of the following components. First, the
partners need to reflect on their performance;
second, they need to be able to convey to
each other their confidence in what they have
observed, and third, they have to be able to
compare their reflections.
The Neural Basis of Meta-Cognition
The exploration of the neural basis of our metacognitive
abilities (see Table 1) has barely
begun. However, the frontal cortex is clearly
implicated. In relation to perception, the bilateral
application of TMS to DLPFC has a specific
effect on confidence in perception without
affecting discrimination (Rounis et al. 2010).
This suggests that TMS can cause a reduction
in meta-cognitive sensitivity, presumably by
increasing neural noise in PFC. Lesions of prefrontal
cortex are also associated with greater
effects on subjective report than on objective
performance (Del Cul 2009). Individuals can
differ considerably in their meta-cognitive
sensitivity even when they do not differ in
perceptual sensitivity. People with greater
meta-cognitive sensitivity in a perceptual task
have a greater density of gray matter in anterior
prefrontal cortex (BA10) (Fleming et al. 2010).
In relation to action and the experience of
agency, Lau and colleagues (2004) found that
the requirement to make reports about the
intention to act was associated with activity
in presupplementary motor area (preSMA).
A more recent paper (Miele et al. 2011)
has taken the study of action considerably
further, revealing brain regions relating to a
hierarchy of cognitive processes underlying
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meta-cognition. In this study, right TPJ
activity was associated with the detection of
discrepancies between expected and observed
states, whereas activity in preSMA and rostral
ACC was associated with being in control of
one’s actions (i.e., few violations of expectations).
However, judgments of control (i.e.,
meta-cognitive reflections upon control) were
associated with activity in anterior prefrontal
cortex (BA10), a location close to that identified
as relevant to perceptual meta-cognition in the
study of Fleming and colleagues (2010).
It remains to be explored whether there is an
intimate relationship between meta-cognition
and the executive processes associated with
prefrontal cortex through which competing
automatic social processes are modulated. One
possibility is that the biasing of competition
exerted by top-down control requires explicit
representations of the processes that are
competing for the control of behavior.
CONCLUSIONS
In this review we have emphasized the importance
of comparisons of different species and
the use of an evolutionary framework for understanding
social interaction. Much of human
social behavior derives from the same range of
cognitive processes that can be seen in other
social animals. We distinguished mechanisms
from processes and suggested that it is largely
general mechanisms that enable specifically social
processes. That is, many of the underlying
mechanisms can also be used to solve problems
without social content or social aims. There are
many different social processes, from observational
learning and copying to mentalizing
and reflective discussion. We found that we
share some, but not all, with other species and
that many of the processes are implicit and
automatic. For instance, the mere presence
of others biases us toward group-oriented
behavior, and our behavior is automatically
influenced in the presence of others with a
different perspective. We also share with other
social species the ability automatically to keep
track of the agents we are interacting with,
as well as their status and predispositions.
There are hints that this ability may derive
from spatial tracking ability. We suggest that
mentalizing, that is, tracking the intentions,
knowledge, and beliefs of others, may depend
on predictive coding mechanisms. In the case
of other species, tracking others’ mental states
appears to be limited to certain domains of
interest, for instance, food caching in corvids.
In the case of humans, we are continually
updating our representations of other people’s
constantly changing dispositions, emotions,
intentions, point of view, knowledge, and beliefs.
Though complex, much of this updating
also appears to be automatic and implicit.
What then in social cognition is specific to
human beings? First, through language, humans
have the means of creating processes that
are explicit. Second, humans, in comparison
with other species, have a much greater ability
to exert top-down control over automatic
processes. This is particularly important when
there is competition between different components
of social cognition. Third, humans have
the extraordinary ability to reflect upon their
own mental states. This is a prime example of
meta-cognition, which may well lie at the heart
of conscious awareness.
Finally, how do the explicit, controllable,
and meta-cognitive abilities that human beings
can put to use in the service of social cognition
benefit social interaction? We believe that our
specific communicative abilities, both verbal
and nonverbal, greatly enhance the value of our
social interactions. Unlike other animals, humans
teach and learn in a deliberately interactive
manner and can share intentions and experiences
very effectively. Learning by instruction
can often be even more efficient than learning
by observation. The ability to discuss and share
mental states is perhaps the most valuable of the
social processes we discussed. This ability to
share experiences can enhance the accuracy of
the models of the world that we construct and
thus our potential to make better decisions. It
is this uniquely human kind of social cognition
that makes possible joint endeavors, such as
cultural institutions, arts, and science.
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SUMMARY POINTS
1. Social cognition needs to explain the use of social cues for selfish interest and for groupdirected
altruistic behavior. Even altruistic behavior serves self-interest in the long term.
The automatic effect of social cues (presence of others, being imitated by others) usually
increases prosocial tendencies. The action of social cues is seen in the audience and the
chameleon effects.
2. Learning by observation is largely automatic; it is widespread and has many advantages
over trial-and-error learning. It has benefits for the individual who can avoid making
errors and can make use of others’ experience. It is also of benefit for the group by
making individuals more similar. However, for exploration of novelty, trial-and-error
learning may be necessary.
3. Gossip is an important means to gauge the reliability of potential partners, and it feeds into
reputation management. There is pressure for reputation management to facilitate trust
and cooperation as well as to punish those who break trust. This is usually an automatic
process and is seen in social animals that use the mechanism of indirect reciprocity to
balance selfish and group interests. In the framework of neuroeconomics, there is a
never-ending arms race between investors and free riders.
4. Mentalizing is likely to be based on predictive coding. This mechanism is carried by a
network of frontal and temporo-parietal regions of the brain. An implicit form of mentalizing
is observed in infants under 12 months, and homologous forms have been observed
in other species, e.g., corvids. The explicit form of mentalizing is linked to the development
of meta-cognition and language and is unique to humans, being universally present
beginning at about age 4 to 6 years. Classic false-belief tasks test explicit mentalizing;
looking behavior is used to test implicit mentalizing.
5. An implicit form of teaching young infants is signaled by ostension. Deliberate instruction,
in which both pupil and teacher are aware of the intention to teach, is abundant in
human societies from the time that children reach age 4 to 6 years. Many networks of
the social brain (mentalizing, meta-cognition, mirroring, language) are involved.
6. Meta-cognition plays a crucial role in human social interactions and provides a basis for
human consciousness. Consciously applied top-down processes can control automatic
processes. Prefrontal regions of the human brain and their connections to other cortical
regions are thought to be crucial in this control.
FUTURE ISSUES
1. Find a principled distinction between implicit and explicit processes at the cognitive and
the experimental level.
2. Find a principled distinction between accidental signals that are broadcast publicly and
deliberate signals of communication.
3. Use computational models of mentalizing combined with neuroimaging to lead to a
better understanding of the mechanisms involved.
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4. Elucidate the relationship between meta-cognition and executive function.
5. Elucidate the role of meta-cognition in social interaction.
6. Comparisons of group decision making in social insects and humans will be important
for revealing underlying mechanisms.
7. Study how conflicts are resolved between the use of social signals for an individual’s own
selfish benefit and for group-oriented behavior.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
We are very grateful to the following colleagues who gave very constructive comments on a first
draft of this paper: Ralph Adolphs, Ian Apperly, Sarah-Jayne Blakemore, Cecilia Heyes, William
Hoppitt, Kevin Laland, Matthew Liebermann, Rosalind Ridley, and Matthew Rushworth. Where
we have failed to take their advice this is due to our own obduracy and space limitations. We are
also grateful to Susan Fiske for her encouragement. Our work is supported by the Danish National
Research Foundation through the Interacting Minds project.
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Annual Review of
Psychology
Volume 63, 2012 Contents
Prefatory
Working Memory: Theories, Models, and Controversies
Alan Baddeley p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1
Developmental Psychobiology
Learning to See Words
Brian A. Wandell, Andreas M. Rauschecker, and Jason D. Yeatman p p p p p p p p p p p p p p p p p p p p p31
Memory
Remembering in Conversations: The Social Sharing
and Reshaping of Memories
William Hirst and Gerald Echterhoff p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p55
Judgment and Decision Making
Experimental Philosophy
Joshua Knobe, Wesley Buckwalter, Shaun Nichols, Philip Robbins,
Hagop Sarkissian, and Tamler Sommers p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p81
Brain Imaging/Cognitive Neuroscience
Distributed Representations in Memory: Insights from Functional
Brain Imaging
Jesse Rissman and Anthony D. Wagner p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 101
Neuroscience of Learning
Fear Extinction as a Model for Translational Neuroscience:
Ten Years of Progress
Mohammed R. Milad and Gregory J. Quirk p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 129
Comparative Psychology
The Evolutionary Origins of Friendship
Robert M. Seyfarth and Dorothy L. Cheney p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 153
Emotional, Social, and Personality Development
Religion, Morality, Evolution
Paul Bloom p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 179
vi
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Adulthood and Aging
Consequences of Age-Related Cognitive Declines
Timothy Salthouse p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 201
Development in Societal Context
Child Development in the Context of Disaster, War, and Terrorism:
Pathways of Risk and Resilience
Ann S. Masten and Angela J. Narayan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 227
Social Development, Social Personality, Social Motivation, Social Emotion
Social Functionality of Human Emotion
Paula M. Niedenthal and Markus Brauer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 259
Social Neuroscience
Mechanisms of Social Cognition
Chris D. Frith and Uta Frith p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 287
Personality Processes
Personality Processes: Mechanisms by Which Personality Traits
“Get Outside the Skin”
Sarah E. Hampson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 315
Work Attitudes
Job Attitudes
Timothy A. Judge and John D. Kammeyer-Mueller p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 341
The Individual Experience of Unemployment
Connie R. Wanberg p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 369
Job/Work Analysis
The Rise and Fall of Job Analysis and the Future of Work Analysis
Juan I. Sanchez and Edward L. Levine p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 397
Education of Special Populations
Rapid Automatized Naming (RAN) and Reading Fluency:
Implications for Understanding and Treatment of Reading Disabilities
Elizabeth S. Norton and Maryanne Wolf p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 427
Human Abilities
Intelligence
Ian J. Deary p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 453
Research Methodology
Decoding Patterns of Human Brain Activity
Frank Tong and Michael S. Pratte p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 483
Contents vii
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Human Intracranial Recordings and Cognitive Neuroscience
Roy Mukamel and Itzhak Fried p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 511
Sources of Method Bias in Social Science Research
and Recommendations on How to Control It
Philip M. Podsakoff, Scott B. MacKenzie, and Nathan P. Podsakoff p p p p p p p p p p p p p p p p p p p p 539
Neuroscience Methods
Neuroethics: The Ethical, Legal, and Societal Impact of Neuroscience
Martha J. Farah p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 571
Indexes
Cumulative Index of Contributing Authors, Volumes 53–63 p p p p p p p p p p p p p p p p p p p p p p p p p p p 593
Cumulative Index of Chapter Titles, Volumes 53–63 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 598
Errata
An online log of corrections to Annual Review of Psychology articles may be found at
http://psych.AnnualReviews.org/errata.shtml
viii Contents
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