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Taking decisions plays a pivotal role in daily life and comprises a complex process of assessing and weighing short-term and long-term costs and benefits of competing actions. Decision-making has been shown to be affected by factors such as sex, age, genotype, and personality. Importantly, also the social environment affects decisions, both via social interactions (e.g., social learning, cooperation and competition) and social stress effects. Although everyone is aware of this social modulating role on daily life decisions, this has thus far only scarcely been investigated in human and animal studies. Furthermore, neuroscientific studies rarely discuss social influence on decision-making from a functional perspective such as done in behavioral ecology studies. Therefore, the first aim of this article is to review the available data of the influence of the social context on decision-making both from a causal and functional perspective, drawing on animal and human studies. Also, there is currently still a gap between decision-making in real life where influences of the social environment are extensive, and decision-making as measured in the laboratory, which is often done without any (deliberate) social influences. However, methods are being developed to bridge this gap. Therefore, the second aim of this review is to discuss these methods and ways in which this gap can be increasingly narrowed. We end this review by formulating future research questions.
published: 26 June 2013
doi: 10.3389/fnhum.2013.00301
Social modulation of decision-making: a cross-species
Ruud van den Bos
, Jolle W . Jolles
and Judith R. Homberg
Department of Organismal Animal Physiology, Faculty of Science, Radboud University Nijmegen, Nijmegen, Netherlands
Department of Zoology, University of Cambridge, Cambridge, UK
Department of Cognitive Neuroscience, Centre for Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, UMC St. Radboud, Nijmegen,
Edited by:
Agustin Ibanez, Institute of
Cognitive Neurology, Argentina
Reviewed by:
René San Martín, Levine Science
Research Center, USA
Koji Jimura, Tokyo Institute of
Te ch n o l o g y, Japan
Ruud van den Bos, Department of
Organismal Animal Physiology,
Radboud University Nijmegen,
Heyendaalseweg 135, NL-6524 AJ
Nijmegen, Netherlands
Taking decisions plays a pivotal rol e in daily life and comprises a complex process
of assessing and weighing short-term and long-term costs and benefits of competing
actions. Decision-making has been shown to be affected by factors such as sex, age,
genotype, and personality. Importantly, also the social environment affects decisions,
both via social interactions (e.g., social learning, cooperation and competition) and social
stress effects. Although everyone is aware of this social modulating role on daily life
decisions, this has thus far only scarcely been investigated in human and animal studies.
Furthermore, neuroscientic studies rarely discuss social inuence on decision-making
from a functional perspective such as done in behavioral ecology studies. Therefore, the
first aim of this article is to review the available data of the influence of the social context
on decision-making both from a causal and functional perspective, drawing on animal
and human studies. Also, there is currently still a gap between decision-making in real
life where influences of the social environment are extensive, and decision-making as
measured in the laboratory, which is often done without any (deliberate) social influences.
However, methods are being developed to bridge this gap. Therefore, the second aim of
this review is to discuss these methods and ways in which this gap can be increasingly
narrowed. We end this review by formulating future research questions.
Keywords: decision-making, translational research, socialenvironment,stress,psychological,humans,animals
Decision-making plays a pivotal role in daily life and comprises a
complex process of assessing and weighing short-term and long-
term costs and benefits of competing actions. The output of the
decision-making process, i.e., which action is to be taken, is deter-
mined by an interaction between impulsive or emotionally based
systems, responding to immediate (potential) rewards and losses
or threats, and reflective or cognitive control systems controlling
long-term goals (Bechara, 2005; de Visser et al., 2011). Decision-
making is influenced by many factors. However, whereas factors
such as sex, age, genotype, and personality have been extensively
investigated and discussed (reviews; Crone and van der Molen,
2004; Overman, 2004; Overman et al., 2004; de Visser et al.,
2011; Homberg, 2012; van den Bos et al., 2013a), relatively little
attention has been paid to the crucial moderating effect of social
context on decision-making. This is all the more surprising given
that decisions in real life are often strongly influenced by the social
environment and involve direct and indirect social interactions.
The social environment may affect decision-making in dif-
ferent ways. For instance, decisions may directly involve social
partners such as when deciding to share knowledge or goods with
others or to provide support (review; Rilling and Sanfey, 2011).
Furthermore, subjects may adjust their decisions depending on
who is with them or who they consider as their reference-point
at the time of the decision. For instance, in the case of so called
“conformity behavior, ” subjects change their behavior to match
that of the rest of the group (Morgan and Laland, 2012). Finally,
the social environment may influence decisions globally by “set-
ting the atmosphere. For instance, the social environment may
breathe a tense or relaxed atmosphere, which influences the indi-
vidual’s emotional state and thereby its decisions (review; Starcke
and Brand, 2012). While studies in the field of behavioral ecology
have provided elaborate understanding of functional aspects of
the social context of decision-making behavior, studies in the eld
of neuroscience have begun to provide information on the causa l
aspects and the neural substrate underlying decision-making
behavior in a social context. Still, crosstalk between these fields
rarely occurs. Researchers in both fields may benefit from insights
from both domains that will enable progress toward a common
understanding of the social modulating role on decision-making.
Therefore, the first aim of our review is to discuss the influence
of the social context on decision-making both from a causal and
functional perspecti ve, dra wing on animal and human studies.
Currently , there is still a gap between decision-making in real
life where influences of the social environment are extensive, and
decision-making as measured in the laboratory, which is often
done without any (deliberate) social influences. Subjects may for
instance be less disturbed by stressful conditions when in com-
pany of friends or relatives with thereby little effect on their
decisions in real life, while showing high levels of stress and
concomitant effects on decision-making in the laboratory when
tested singly . While these laboratory findings may be important
Frontiers in Human Neuroscience June 2013 | V olume 7 | Article 301 | 1
van den Bos et al. Decision-making in a social context
for studying ba sic mechanisms of e.g., the effects of stress on
decision-making (Preston et al., 2007; Lighthall et al., 2009; van
den Bos et al., 2009), they hamper for instance assessing the value
and general applicability of laboratory findings to the function-
ing of people, such as patients, in daily life. Furthermore, they
miss out the important impact the social environment may nor-
mally play on individual and group decision-making. However,
decision-making under laboratory conditions in humans is that
it is difficult to create ecologically valid conditions. Therefore,
monitoring real life effects of the social environment on decision-
making would be a significant step forward. In rodents, home-
cage experimental set-ups, which allow for careful manipulation
of brain-behavior relationships in social settings, have been devel-
oped as means of bridging precisely this gap. Therefore, a second
aim of our review is to discuss these developments in methodol-
ogy to address the question of the effect of the social environment
on decision-making.
Given the foregoing, in the following sections we will discuss
how the social environment may modulate decision-making and
how this can be incorporated in experimental studies. In sec-
tion Decision-making in a social context, we will discuss direct
and indirect social influences on decision-making, while in sec-
tion Social stress and decision-making the effects of social stress
on decision-making are addressed. Where possible we link a
causal and functional perspective and discuss underlying neu-
ral substrates. In section Laboratory studies and real-life studies
we will (briefly) discuss ways to incorporate the social environ-
ment into studies of decision-making. We end this review (section
Concluding remarks) with a brief summary of the main issues
addressed and define (some) future questions.
Humans are an exceptionally successful species, both in the num-
ber of individuals and in our flexibility to expand to the range
of environments and situations in which we live. A major fac-
tor underlying this success boils down to our complex social
life as we have the ability to acquire valuable knowledge and
skills from others through social learning and teaching and build
upon this generation after generation (Boyd and Richerson, 1985;
Laland et al., 2011 ). In our daily life we constantly make deci-
sions based on our personal information and experience as well
as that of others, i.e., social learning. Our behavior may be
restricted through social conformity (Asch, 1956), or promoted
or enhanced through facilitation (Zajonc, 1965). Furthermore,
often the decisions of multiple individuals may result in collective
behavior, such as the synchronization of applause (Néda et al.,
2000), or have to be made jointly to reach a consensus (Conradt
and Roper, 2005; D yer et al., 2008). Living with others comes
with the potential benefit of cooperation (Fehr and Fischbacher,
ited (Davies et al ., 2012). Finally, an individual’s decisions may
be indirectly influenced by the social environment, by affecting
an individual’s emotional state. Importantly, the modulating role
of the social environment is strongly affected by an individual’s
characteristics and personality as well as that of its group mates
(Webster a nd Wa rd, 2011).
To ful l y und e rs t a nd th e ro le o f soc i al mo d ula t io n o n d e ci s ion -
making, it is important to consider it from both a causal and
functional perspective (Tinbergen, 1963; see e.g., Morgan and
Laland, 2012). In neuropsychology, functional explanations are
rarely taken into account while this behavioral ecological perspec-
tive may help to understand how the behavior of individuals is
adapted to the social environment in which they live (Davies et al.,
2012). A growing list of behaviors once described as uniquely
human have now been described in a range of animals, such as
teaching (Franks and Richardson, 2006; Thornton and McAuliffe,
2006), culture (see Laland, 2008; Laland et al., 2011), and confor-
mity (Whiten et al., 2005; Galef and Whiskin, 2008; Jolles et al.,
2011), which provide us with new insights into our own behav-
ior. Therefore, the next few sections are focused on a behavioral
ecological perspective with links to relevant human and animal
laboratory studies. However, as the human literature on social
decision-making has been reviewed elsewhere, we limit ourselves
to the most relevant human experimental studies (see e.g., Fehr
and Fischbacher, 2004a,b; Lieberman, 2007; Frith and Singer,
2008; Behrens et al., 2009; Rilling and Sanfey, 2011).
For social species, like humans, the social en vironment plays a
critical role in day-to-day decision-making, such as where to live,
what to eat and with whom to mate, and may affect their emo-
tional state (see section Observational fear learning). Decisions
can be based on either personal experience and/or informa-
tion gathered by others (Da nchin et al., 2004)andthrough
“social learning, individuals may for example learn how (obser-
vational learning) to deal with a resource or where it is located
(local enhancement; Thorpe, 1956; Webster and Laland, 2012).
Although social learning may involve several different learning
mechanisms (Laland, 2008)onlysomerelyonadvancedcognitive
abilities (Galef, 1988; Heyes, 1994)andmostcasesappeartoresult
from very simple processes (Galef, 1988). Indeed, although social
learning may seem particular to humans, animals from a broad
range of species gather and exploit information generated by
others (review; Galef, 1988; Heyes, 1994; Heyes and Galef, 1996).
performed with rats (review; Galef and Giraldeau, 2001; Galef,
to learn where, what, how and even when to eat (Galef and
Giraldeau, 2001). Both the social information provided by visual
and olfactory cues from conspecifics provide a strong basis for
individual foraging decisions. Just by observing conspecifics, rats
quickly locate food and join to feed with them (see Galef and
Giraldeau, 2001). This is further intensified by deposited olfac-
tory cues on both the food and the location of the food (Galef,
2007), which may for example enable young rats to learn what
foods are best to eat as they may not be able to figure this out
by themselves (see Galef, 2007). In particular the olfactory cues
via the breath of conspecifics may result in these socially induced
food pr eferences that may overrule personal pr eferences (Galef
and Whiskin, 2008; Jolles et al., 2011)andevenreverselearned
aversions to foods (Galef, 1986).
To a cc ur atel y ma k e dec i si o n s, i ndiv id u a ls n ee d to co n st a ntl y
weigh the costs and benefits of private and social information
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van den Bos et al. Decision-making in a social context
and need to be selective when and whom to copy (Galef, 1995;
Laland, 2004). Social learning may be beneficial as it allows indi-
viduals to acquire relevant information without having the risk or
costs associated with individual learning. However, social infor-
mation may be outdated, for example when the environment is
highly variable, or less valuable, when the environment is very sta-
ble (Boyd and Richerson, 1985). Thus, relative reliance on social
and individual learning can be viewed as involving a trade-off
between accuracy and cost (Boyd and Richerson, 1985; Laland,
2004; Kendal et al., 2005). For example, Dally and colleagues
(2008) showed that rooks selectively consumed the same food
as a demonstrator when the foods were novel, but not when
the foods were familiar. Likewise, Galef and Whiskin showed
that the great er the discrepancy between private and social infor-
mation, the less likely the subject is to behave in accord with
the socially acquired information (Ga lef and Whiskin, 1998).
Moreover , Brown and colleagues (2008) showed that personal
and social information about spatial choices are combined in a
rats working memory and both the quality of the food avail-
able and the memory of a familiar conspecifics behavior affect
an individual’s tendency to visit spatial locations in a radial-arm
The trade-off between accuracy and costs is nicely illustrated
by the difference in public information use of two closely related
species o f sticklebacks. Coolen and colleagues (Coolen et al.,
lic information and foraged at the areas they observed others to
have better feeding rates, three-spined sticklebacks ignored this
information and relied in their decisions on their own experi-
ence. This difference in social information use may be explained
by the relative difference in costs of self-acquired information
between the two stickleback species. The robust defenses that
three-spined but not nine-spined sticklebacks have, such as large
spines and armored body plates, allows them to sample alterna-
tive food patches directly in relatively better safety, as reflected by
the increased time nine-spines spent hidden amongst vegetation
(Laland, 2008).
When the presence of group mates affects the behavior of
an individual, allowing or causing them to engage in certain
behaviors at a different rate, or to perform behaviors that they
would not perform at all if they were alone, this is called social
facilitation (Zajonc, 1965). For example, in animals it has been
shown that the presence of others may result in higher activ-
ity (Griffiths and Foster, 1998; Webster et al., 2007), incr eased
foraging (Webster et al. , 2 007; Da lly et al., 2 008)andprovide
scrounging opportunities (review; Gir aldeau and Caraco, 2000).
For example, conform to human work, studies on rats have shown
that the great er the number of models and the greater their
uniformity in behavior, the more likely a naive subject will act
in accord with the information that conspecifics provide (Galef
and Whiskin, 1995). These changes in behavior can probably be
ascribed to proximate mechanisms such as greater anti-predator
benefits of larger g roups (review ; Krause and Ruxton, 2002),
investment in vigilance and/or increased competition (review;
Beauchamp, 2003). This is nicely illustrated by two studies in
ravens (Stöwe et al., 2006a,b)whichshowedthatwhenindivid-
uals were alone compared to in a group, they approached a novel
object faster but spent less time close to and manipulating it.
Although the social group ena bled individuals to decrease time
investment in vigilance, they may have a higher approach latency
because individuals might wait for the other to take the risk and
Social learning theory suggest that in most circumstances where
natural selection favors reliance on social learning, conformity is
favored and individuals, both humans and other animals, should
adopt the behavior of the majority (Boyd and Richerson, 1985;
Laland, 2004). This particular form of social modulation on
decision-making is especially important as it has been argued
to be a major mechanism in human cultural evolution (Boyd
and Richerson, 1985; Efferson et al., 2008). One of the earliest
described studies on human conformity was performed by Asch
(1955, 1956).Inaveryinuentialpaper,Asch (1955) described
how adults would be w illing to abandon their own perceptual
judgment in a simple visual task and go with the overtly false
alternative as a result of group normative behavior. Since then a
huge number of studies has replicated these kinds of findings (see
Bond and Smith, 1996; Morgan and Laland, 2012). Interestingly,
the extent of conformity behavior seems to be strongly dependent
on the situation. Namely, if a par ticipant has to make a public
response and is face-to-face with the majority, there is a strong
normative influence of conformity, whereas it is weaker when
participants make a private response and indirectly communi-
cate with the majority (Bond, 2005). Furthermore, conformity
behavior may be dependent on task difficulty and its importance
(Baron, 1996), group size (Asch, 1955; Bond, 2005)andculture
(Bond and Smith, 1996)amongothers.
Recently , several studies have addressed the neurobiological
basis of conformity (see also Morgan and Laland, 2012). For
instance, studies using mental rotation and auditory tasks (Berns
et al., 2005, 2010)showedthatsocialinformationmayaffect
brain regions classically associated w ith perception as well as
the processing areas associated with each task, suggestion that
social information was affecting the subjects’ perception as well as
decision-making (see Morgan and Laland, 2012). Moreo ver, it has
been shown that while cingulate areas are involved in monitoring
the difference between private and public information (Klucharev
et al., 2009), the ventral striatum is involved in the tendency
to adjust one’ s behavior to the social information (Burke et al.,
2010; Campbell-Meiklejohn et al., 2010), which may be related
to rewarding aspects of being in line with the behavior of others
(Klucharev et al., 2009; Burke et al., 2010; Campbell-Meiklejohn
et al., 2010).
Conformity has been described in a wide range of animal
species including fish (Laland and Williams, 1998; Day et al.,
2001; Pike and Laland, 2010), rats (Galef and Whiskin, 2008;
Jolles et al., 2011)andprimates(Whiten et al., 2005; Dindo et al.,
2009)(seeWeb ster a nd Ward, 2011 for a review). For example,
Laland and Williams (1997) showed that guppies preferentially
chose a foraging route they had previously observed demonstra-
tors use despite an equally valid available alternative. Individuals
may base these kind of conformity decisions on heuristic rules of
social attraction (Webster and La land, 2012)suchastoapproach
Frontiers in Human Neuroscience June 20 13 | Volume 7 | Article 301 | 3
van den Bos et al. Decision-making in a social context
others (e.g., Laland and Williams, 1997), to approach larger over
smaller groups (e.g., Lachlan et al., 1998; Day et al., 2001)and
to approach groups that produce cues indicative of higher for-
aging success (e.g., Coolen et al., 2003, 2005). These tendencies
are likely to benefit animals in most cases as it allows them to
detect food without having to p ay the costs of sampling the envi-
ronment directly (see e.g., Pitcher et al., 1982; Day et al., 2001).
However , sometimes this conforming to the behavior of others
may come with opportunity costs. For example, individual fish
may discover a visually isolated food patch faster and exploit it
for longer than when a group of conspecifics is present (Webster
and Laland, 2012), and smaller groups may discover a hidden
food patch more quickly than larger ones (Day et al., 2001). The
reliance on social information may sometimes even result in indi-
viduals to base their decisions on maladaptive information, such
as rats consuming less palatable and sodium-deficient diets based
on the breath of conspecifics (Galef, 1986), and even aft er the
source of information is removed, such as guppies that kept on
using energetically costly routes to food patches despite shorter
alternatives available (Laland and Williams, 1998).
Although conformity of the basic “follow the majority” kind
has been demonstrated in a variety of species of which Pike
and Lalands (2010) study on public information use in stickle-
backs provides compelling evidence, only a few animal studies
(Whiten et al., 2005; Galef and Whiskin, 2008; Jolles et al., 2011)
have investigated the situation where conformity overrides the
discovery of valid alternative means (cf. Asch, 1955, 1956). In
atwo-actiondiffusionstudyinchimpanzees,Whiten and col-
leagues (2005) showed that although some individuals discovered
an alternative technique to free trapped food items to the one
seeded in their group, they later re-converged on the norm of
their group, demonstrating conformity in the face of discovering
tence of this type of conformity in rats (Galef and Whiskin, 2008;
Jolles et al., 2011). Rats were given the opportunity to learn that
two diets differed in palatability. They were subsequently exposed
to a demonstrator that had eaten the less palatable food and
were thereafter exposed to the same diets again. By simply being
exposed to the odors in the breath of a conspecific for 30 min,
individuals considerably decreased their preference for the more
palatable food. Interestingly, despite similar initial preferences
and similar social information, some rats were more resistant to
changing their preference in relation to private and social infor-
mation than others (Jolles et al., 2011), suggesting a different
sensitivity to conflicting information (cf. Klucharev et al., 2009).
Both humans and many group living animals exhibit com-
plex, coordinated, group patterns, such as lanes of traffic flow
in human crowds (Helbing and Molnar, 1995)andthethree-
dimensional movements of fish shoals (Couzin and Krause,
2003). Through collective action, individuals can enhance their
capacity to detect and respond to salient features of the environ-
ment, resulting in more accurate decision-making (Couzin, 2009)
without the need of explicit signals or complex communication
(Couzin et al., 2005; Dyer et al., 2008). The common property
of these phenomena is self-organization, suggesting that much
of complex group behavior may be coordinated by relatively
simple interactions among the members of the group (review;
Couzin and Krause, 2003). Indeed, recently studies have begun
to reveal that collective decision-making mechanisms across ani-
mal species, from insects to bir ds and even humans, seem to
share similar functional characteristics (Couzin and Krause, 2003;
Conradt and Roper, 2005; Sumpter, 2006). For example, Helbing
and colleagues (Helbing and Molnar, 1995; Helbing et al., 2000)
have shown that simple rules such as “try to minimize travel
time, “ avoid collisions” and “mov e in the direction of other
people may help explain pedestrian movements on busy streets
and in life-threatening situations. Similar patterns have been
described for non-human animals including the spectacular trails
of ants on foraging trips (Couzin and Franks, 2003), the collec-
tive movements of starlings (Ballerini et al., 2008), and social
interactions in shoaling fish (Herbert-Read et al., 2011).
In some cases group decisions are the result of a consensus
reached by the individuals in the group (Conradt and Roper,
2005). Humans make these kinds of decisions all the time, from
agreements in groups of a few people, to large-scale international
conventions and political elections. However, also amongst non-
human animals consensus decision-making is very common, such
as travel routes in navigating birds use and the timing of activities
(review; Conradt and Roper, 2003, 2005). In many situations con-
flicts may exist between the preferences of different individuals
(Couzin et al., 2005). How ever, all individuals in the group have to
decide on the same action because the group will fall apar t unless
aconsensusisreached(Conradt and Roper, 2005), resulting in
and Ruxton, 2002). In line with theoretical predictions (Couzin
et al., 2005), it has now been demonstrated that only a small
proportion of knowledgeable individuals is needed to influence
the direction of movement of the whole group, such as has been
shown for nest site choice in social insect colonies (Franks et al.,
2003; Seeley, 2003), the foraging movements in golden shiner fish
(Reebs, 2000), and humans moving to a target without the use of
verbal communications or obvious signaling (Dyer et al., 2008).
An important way to understand social decision-making in
humans and other social animals is to look at it in terms of
costs and benefits, not only to the actor as indicated above, but
also to the recipient in the social context (Hamilton, 1964; West
et al., 2007; Davies et al., 2012). For this it is important to keep
in mind that via natu ral selection those genes are favored that
increase an organisms ability to survive and reproduce (fitness).
Therefore, individuals will often attempt to act in such a way as
to receive immediate, selfish benefits, which may often result in
competition or mutualistic cooperation. This is nicely illustrated
by the Prisoner’s Dilemma (PD; Axelrod and Hamilton, 1981)in
which individuals can either cooperate or defect. Both individ-
uals would benefit from mutual cooperation but both are also
tempted to cheat, as it would be more rewarding to the individ-
ual. Therefore, irrespective of the other player’s choice, it pays
to defect. This raises the problem why cooperation is so com-
mon among human and animal societies (see West et al., 2007)
and why individuals not act selfishly all the time and exploit
Frontiers in Human Neuroscience ww June 2013 | Volume 7 | Article 301 | 4
van den Bos et al. Decision-making in a social context
the cooperative behavior of others (see Davies et al., 2012). In
many cases, the cooperating individual simply acts selfishly and
gains an immediate benefit, but thereby provides by-product ben-
efits to its group mates, such as the benefits of an increased
group size, i.e., reduced chance of predation, due to helping
behavior in meerkats (Clutton-Brock, 2002). When on the other
hand cooperation is altruistic—costly to the cooperator and ben-
eficial to the recipient—cooperating individuals may still gain
selfish benefits in the long term by using conditional strategies
(Stevens and Hauser, 2004), such as cooperating only with rela-
tives (kin selection; Hamilton, 1964), interacting only with those
that have cooperated previously (reciprocity; Triver s, 1971;see
Clutton-Brock, 2009), or under enforcement (Frank, 2003).
Individuals may help relatives as this may increase their genetic
representation in future generations, and thus their fitness, as rel-
atives share genes by common descent (see further Hamilton,
1964; West et al., 2007; Davies et al., 2012). If individuals pref-
erentially help those that have helped them or those that help
others, also known as reciprocity, the short-term cost of being
cooperative is outweighed by the long-term benefit of receiving
cooperation (Tr ivers, 1971). Although the PD has shown that
when individuals meet only once it is better for individuals to
defect than to cooperate, some form of cooperation may be stable
if there is a chance both players will meet again because the long-
term benefits of cooperation may outweigh the short-term benefit
of defecting (Axelrod and Hamilton, 1981). Indeed, experimental
work on both humans (Fehr and Fischbacher, 2003)androdents
(Rutte and T aborsky, 2007, 2008; Viana et al., 2010)hasshown
that individuals c ooperate at higher levels in repeated interac-
tions. For example, Rutte and Taborsky showed that rats that
were trained to pull a stick in order to produce food for a part-
ner pulled more often for an unknown partner after they were
helped than if they had not received help before (generalized reci-
procity; Rutte and Taborsky, 2007)andmoreoftenfromapartner
they received help from (direct reciprocity; Rutte and T aborsky,
2008). Furthermore, Schneeberger and colleagues (2012) showed
that, similar to human PD studies, rats pro vided more food to
cooperative partners than to defectors and that furthermore, this
was dependent on costs: when rats experienced experimentally
increased resistance to pull the stick of the apparatus a nd deliver
food to the social partner, they reduced their help. It remains
unclear, however, to what extent these behaviors may potentially
be ascribed to simpler processes such as conditioned place pref-
erence. For example, rats have been shown to prefer a social
partner over an empty space (Trezz a et al ., 20 09)andtocooperate
80% of the time if they have the choice to act either alone or in
cooperation with a social partner to obtain food pellets (Tso or y
et al., 2012). Indeed, although reciprocity has attracted a huge
amount of attention, it is thought to be generally unimportant
outside humans (Hammerstein, 2003; Stevens and Hauser, 2004)
as in most cases cooperation can be explained by more simple
mechanisms such as by-product-benefits (Hammerstein, 2003;
Clutton-Brock, 2009). Nevertheless, it shows that (lab) rodents
may provide a good model system to investigate the mechanisms
and development of cooperation (Łopuch and Popik, 2011).
Finally, enforcement or punishment may alter the benefit/cost
ratio of helping and thereby favor cooperation (Frank, 2003).
The consequences of punishment are nicely illustrated in cleaner
fish. Cleaner fish remove parasites on the body of other species
of fish that cannot remove the parasites themselves. Although,
the cleaner fish prefer to eat parts of their clients’ tissue they
rarely perform this cheating behavior as their hosts may punish
them by chasing them or by swimming away (Bshary and Grutter,
2002). What may be special about human cooperation is that we
have the capacity to establish and enforce social norms (Fehr and
Fischbacher, 2003, 2004a,b)becauseoursocietiesarebasedon
large-scale cooperation among genetically unrelated individuals
(Henrich et al., 2003). For example, human research investigating
the conditional cooperation on social norms has shown that sub-
jects increase their contribution to the public good if the average
contribution of the other group members increases (see Fehr and
Fischbacher, 2004a). Moreover, third-party punishment experi-
ments in which the PD is extended with a passive third party
has shown that these individuals punish not-c ooperating play-
ers despite a cost to themselves and that moreover, defection was
punished much more severely if the other player cooperated than
if they both defected (Fehr and Fischbacher, 2004b).
When individuals act selfishly under situations of lim-
ited resources, competition may occur between individuals.
Competing individuals have to weigh the competitive efforts
against expected benefits as well as the intensity of the con-
flict. Individuals may compete by exploitation and/or by resource
defense (Davies et al., 2012). Importantly, the best way for an
individual to behave often depends on what its competitors are
doing (review; Davies et al., 2012), which will therefore result in
ary stable strategy (EES; Maynard Smith and Price, 1973). Under
ideal free distributions in which individuals are free to go where
they want and have complete information about the availability
of resources (Fretwell, 1972), individuals will distribute them-
selves in such a way that all individuals have the same rate of
resource acquisition. For example, people queuing at the check-
out area of the supermarket will often decide to choose the shorter
and faster queues, ultimately resulting in all queues being of more
or less equal length. However, in most cases individuals may not
be free to go where they want as better competitors will occupy
the richer habitats. This situation is very common in the natural
world (see Davies et al., 2012). For example, although ducks have
been shown to occur in stable distributions of individuals among
foraging sites (Harper, 1982), some ducks were better competi-
tors than others and grabbed most of the food (Harper, 1982).
Importantly, defense of a resource has costs as well as benefits and
individuals should only behave territorial when the benefits are
greater than the costs. This may also help explain why often vari-
able competitive behavior can be found within a popula tion, such
as producers and scroungers in a foraging context (Giraldeau and
Caraco, 2000), as the costs and benefits may be different between
Insight in the neural mechanisms underlying cooperation and
competition is increasing (see Rilling and Sanfey, 2011; Huettel
and Kranton, 2012). For example, a neur oimaging study of the
Prisoner’s Dilemma has shown that mutual cooperation led to
increased activation in reward regions (Rilling et al., 2002), poten-
tially explaining how cooperative social relationships may be
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van den Bos et al. Decision-making in a social context
sustained while inhibiting the impulse to act selfishly. Many social
decision-making studies have used the Ultimatum Game in which
two players split a sum of money, one play er proposes a divi-
sion, and the other can accept or reject this. For example, it has
been shown that both unfair offers and their rejection elicited
activity in br ain areas related to emotion, such as the ante-
rior insular cortex, suggesting an important role for emotions
in social decision-making related to cooperation (Sanfey et al.,
2003). Furthermore, alpha- and theta-oscillations in prefrontal
areas have been found to be sensitive to social risk and to under-
lie fine-tuning regulation of social decisions (Billeke et al., 2012).
be affected by the relationship between the players has shown
that when the proposer was a friend rather than an unknown
person, unfair offers were much less frequently rejected. The ante-
rior prefrontal cortex plays an important role in these kind of
interpersonal economic interactions (Campanhã et al., 2011).
Rodent work has also provided interesting insights into the
emotional and neurobiological bases of competition. For exam-
ple, water-deprived rats in a p air competing for a single source
of water quickly establish a firm relationship during which one
rat drinks consistently more (the dominant rat) than the other
(the submissive rat). However, interestingly, when the animals are
exposed to severe stress, the dominants becomes less dominant,
and when their submissive cagemates are administered anxiolyt-
ics, they increase their access to resources at the expense of that
obtained by dominants (Joly and Sanger, 1991). One brain area
in particular seems to play a central role in the cost-benefit deci-
sion making related to competition: the anterior cingulate cortex
(ACC). For example, the ACC is implicated in action selection
and action outcome and effort monitoring, as well as signaling
the use of social information (Rudebeck et al., 2006). Hillman and
Bilkey (2012) provided rats with a choice whether to physically
compete with a peer for a large food reward or not to compete
and to obtain a small reward. It was found that ACC neurons elec-
trophysiologically responded to competitive effort costs, assisting
the rats in goal-directed decision making under social competitive
conditions (Hillman and Bilkey, 2012).
Decision-making can be strongly influenced by the way the social
environment affects an individual’s emotional state. An impor-
tant example of this is social learning of fear (reviewed by Olsson
and Phelps, 2007). Learning about potentially harmful stimuli
and events is important in shaping adaptive behavior, which
may be less risky if learned socially through observation and
social communication. Experimentally, social fear learning can be
assessed by subjecting an observer to another individual who is
undergoing cued threatening experiences, which may elicit physi-
ological and behavioral responses in the observer as if undergoing
the threat him/herself. Fear responses acquired through condi-
tioning and observation of a distressed model were expressed to
both seen and unseen (backwardly masked) conditioned stimuli,
whereas, fear responses acquired through verbal communica-
tion were expressed only to seen conditioned stimuli (Olsson
and Phelps, 2004). This indicates that the route of social infor-
mation transmission affects how information is perceived. Also
genotype affects social fear learning. Carriers of the low activ-
ity variant of the common serotonin transporter polymorphism
displayed more cued fear responses compared to high activity
variant carriers when subjected to an observational fear learn-
ing paradigm in which subjects had to view a mo vie in which
models received shocks in the presence of a conditioned stimulus
(Cri¸san et al., 2009). Furthermore, personality has been inves-
tigated as modulator of social fear learning using a paradigm
in which participants watched mock panic attacks while emo-
tional (e.g., fear and panic) and skin conductance levels were
assessed. It was found that emotional avoidance and anxiety sensi-
tivity were positively associated with more self-reported fear and
more severe panic symptoms t o the challenge proc edure (Kelly
and Forsyth, 2009). Similarly, Hooker et al. (2008) found that
trait neuroticism enhanced social fear learning. Finally, there are
sex differences in observational fear conditioning using modeled
“mock” panic attacks as an unconditioned stimulus and an asso-
ciated neutral cue as conditioned stimulus, as women reported
more distress to the conditioned stimulus (Kelly and Forsyth,
2007). Mechanistically, social fear learning shares neural features
with classical conditioned fear, including the involvement of the
amygdala, but also requires higher-level reflective mental state
attribution, like involvement by the anterior cingulate cortex and
the anterior insula (see Olsson and Phelps, 2007; Olsson et al.,
2007; Olsson and Ochsner, 2008).
Next to humans, observational fear learning has been shown
in a large number of species (see Olsson and Phelps, 2007)but
most animal studies have been performed with rodents, show-
ing that both visual, au ditory as well as olfactory stimuli play
an important role in social transfer of fear. For example, Jeon
et al. (2010) demonstrated that mice observing demonstrators
undergoing foot-shock stress displayed increased contextual con-
ditioned freezing when subsequently placed in the observing
chamber. This process was reduced, but not occluded, when an
opaque partition was placed between the observer and demon-
strator. In rodents in particular, olfactory cues play an important
role, especially related to alarm pheromones. These may change
autonomic activity and increase defensive and r isk assessment
behaviors (Kiyokawa et al., 2004, 2006)andareexcretedinthe
rats perianal region, especially by allogrooming, as seen during
the social interaction between the demonstrator and observer
rats (Knapska et al., 2010). Also, distr ess vocalizations affect fear
learning. For example, when a conditioned stimulus was coupled
to aversive 22 KHz ultrasonic vocalizations (USVs), observers dis-
played conditioned freezing (Chen et al., 2009)andthenumber
of 22 KHz-USVs emitted by a fearful demonstrator was positively
associated w ith the conditioned freezing response displayed by the
observer (hr and Schwar tin g, 2008). In line with human stud-
ies, familiarity between the observer and demonstrat or results in
higher observational fear learning (Chen et al., 2009; Jeon et al.,
2010). Interestingly, not only fear or distress itself can be socially
transmitted amongst rats and mice, also the predictive value of
the conditioned stimulus itself. Bruchey and colleagues (2010)
demonstrated that observer rats acquire a freezing response by
observing fear-conditioned demonstrators, i.e., being exposed to
the conditioned stimulus in the absence of the foot-shock. Thus,
the observers responded to the conditioned stimulus as if they
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van den Bos et al. Decision-making in a social context
had experienced foot-shocks themselves. Whereas fear can be
socially transmitted by social interaction between a previously
stressed demonstrator and a naive conspecific, it has also been
demonstrated that observation of a non-fearful demonstrator
mouse inhibited subsequent recall of a context-shock association
in observers (Guzmán et al., 2009). Thus, it seems that previous
experience with a fear-naive demonstrator buffered’ fear con-
ditioning in observers (Panksepp and Lahvis, 2011), providing
strong evidence for socio-emotional influences on the behavioral
response to threat.
Although the mere presence of others may affect the decisions
an individual makes, such as via facilitation and conformity,
this modulating effect is strongly influenced both by the char-
acteristics of the individual as well as that of its group mates,
for instance by social status (Nicol and Pope, 1999), familiar-
ity (see above; Swaney et al., 2001; Jeon et al., 2010), sex (see
Choleris and Kavaliers, 1999; Piyapong et al., 2010)andsocial
relationships between individuals in the group (e.g., Beauchamp,
2000; Schwab et a l., 2008; Jolles et al., 2013b). Furthermore,
consistently expressed behavioral differences between individu-
als that are otherwise similar to one another in terms of age,
size and sex—also known as personality types or coping styles
(Réale et al., 2007; Koolhaas et al., 2010)—may play a particu-
lar large role on individual decision-making (Webster a nd Ward,
2011). For example, bold compared to shy individuals have been
found to be less responsive to changes in their social environment
(Magnhagen and Bunnefeld, 2009)andtheirpartnersbehavior
(Harcourt et al., 2009; Schuett and Dall, 2009), have a lower ten-
dency to join and follow conspecifics (Ward et al., 2004), base
their decisions less on social information (Kurvers et al., 2010)
and display greater initiative in leadership (Harcourt et al., 2009).
It is especially the interplay between these personality traits, indi-
vidual characteristics and the relationships between individuals
that affects an individual’ s decisions (e.g., van Oers et al., 2005;
Schuett and Dall, 2009; Jolles et al., 2013b). Importantly, in this
way individual characteristics and heterogeneity within groups
may ultimately impact the dynamics of group decisions and
behavior and affect the way in which the group as a whole func-
tions in relation to the environment (Webs ter and Ward, 2011 ).
For example, individual differences in risk-taking strongly affect
social feedback between individuals (Harcourt et al., 2009), indi-
viduals may not be uniformly distributed within groups (Jolles
et al., 2013a), and certain individuals may take leadership posi-
tions and thereby determine group decisions (King et al., 2009;
Nagy et al., 2010). Surprisingly, few studies have considered the
impact of individual characteristics and personality tra its on the
social modulation of decision-making. For example, although sex
differences have been described in a wide range of cognitive and
behavioral processes, investigations of sex differences in social
learning are still largely neglected (review: Choleris and Kavaliers,
1999). Furthermore, despite the surge of interest in personality
traits in animals, only in recent times have studies start ed to con-
sider personality in the context of the crucial moderating effect of
the social environment (review: Webster and Ward , 2011). Finally,
both human and non-human studies as well as models on g roup
behavior still seldom consider the impact of such heterogeneity
on the rules underlying their coordination (but see e.g., Jolles
et al., 2013a).
The social environment in which humans and animals live is not
devoid of psychosocial stress. Stressors may entail among others
potential or actual conflicts with conspecifics either in the con-
text of dominance-submission or in competition over (valuable)
resources, the sheer performance of a task in front of conspecifics,
and experiencing or witnessing aggression and violence. To assess
the effects of social stressors on decision-making in the labora-
tory , tests are needed which produce reliable and reproducible
stress-related effects. One such psychosocial test is the Trier Social
Stress Test ( TSST; Kirschbaum et al., 1993) and its variants (e.g.,
group-wise TSST; Von D awans et a l., 201 1).
The TSST has been shown to be very effective in inducing
stress as measured by questionnaires regarding stress, mood and
anxiety as well as parameters indicative of the activation of the
two main stress axes, i.e., hypothalamus-pituitary-adrenocortical
axis (HPA-axis; cortisol) and the sympatho-adrenomedullary
axis (SAM-axis; (nor)adrenaline) (e.g., Kirschbaum et al., 1999;
Kudielka and Kirschbaum, 2005; Nater et al., 2005, 2006; Starcke
et al., 2008; Nater and Rohleder, 2009; van den Bos et al., 2009;
Foley and Kirschbaum, 2010; Cornelisse et al., 2011; Starcke et al.,
2011; Maruyama et al., 2012; Vinkers et al., 2013). This stress
effect is related to the social-evaluative and uncontrollable ele-
ments of the task (Dickerson and Kemeny, 2004): subjects have
to deliver a speech as well as do a difficult arithmetic in front of a
panel that judges their performance without much aprioriknowl-
edge of the procedure. Even anticipating delivery of the speech is
already stressful.
The activation of the SAM-axis is often measured by sali-
vary alpha-amy lase, while activation of the HPA-axis is often
measured by salivary cortisol (Kirschbaum et al., 1999; Kudielka
and Kirschbaum, 2005; Nater et al., 2005, 2006, 2007; van
Stegeren et al., 2006, 2008; Nater and Rohleder, 2009; Foley and
Kirschbaum, 2010; Thoma et al., 2012). While the SAM-axis is
strongly activated during the TSST and returns to baseline imme-
diately or quickly thereafter, HPA-axis activity peaks 10–20 min
after the TSST and returns to baseline ab ou t 60 min thereafter
(e.g., Nater et al., 2005, 2006; Cornelisse et al., 2011; Starcke
et al., 2011; Maruyama et al., 2012; Thoma et al., 2012; Vinkers
et al., 2013). Cortisol levels in men are generally higher than
in women, while in women the menstrual cycle and contracep-
tives in addition have a modulatory effect (Kirschbaum et al.,
1999; Kudielka and Kirschbaum, 2005; Foley and Kirschbaum,
2010; Nielsen et al., 2013;butseeKelly et al., 2008). Thus,
the TSST seems to be a useful laboratory test to delineate the
effects of psychosocial stress on decision-making, when decision-
making tasks are delivered after the TSST. It should be noted
that the Cold Pressor Test has been used as well to delineate the
effects of stress on decision-making. As at first glance the results
between this test and the TSST on decision-making were not
different its effects on decision-making will be included in the
following paragraphs.
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van den Bos et al. Decision-making in a social context
Following the TSST (as well as the Cold Pressor Test) sev-
eral reward-based decision-making tasks have been shown to
be affected (review; Starcke a nd Brand, 2012), i.e., the Iowa
Gambling Task (IGT; Preston et al., 2007; van den Bos et al.,
2009), the Balloon Analogue Risk Task (BART; Lighthall et al.,
2009), the Game of Dice Task (Starcke et al., 2008), delay-
discounting (Lempert et al., 2012)andafinancialdecision-
making task (Porcelli and Delgado, 2009). Social stress paradigms
have not been tested in animals with respect to reward-based
decision-making. However, the data of other t ypes of stress
paradigms reveal similar effects as in humans: stress disrupts
reward-based decision-making tasks in rats (Graham et al., 2010;
Shafiei et al., 2012).
Thus far, only a few studies have been published on the
effects of the TSST on social decision-making related paradigms.
Social stress had no effects on moral decision-making, although
in the stress group it was shown that the higher the salivary
cortisol levels the more egoistic, and thus less altruistic, deci-
sions were taken in highly emotional dilemmas (Starcke et al.,
2011). Furthermore, social stress induced by the TSST increased
pro-social behavior as measured by the Tr ust Game (reciprocal
exchange) and the Dictator Game (altruism) (Takahas hi et a l.,
2007; Von Dawans et al., 2012). Still in the latt er game this
effect seemed to be dependent of whether money was donated
to a person or to an anonymous charity organization as Vinkers
and colleagues (2013) observed that people donated less money
to an organization following the TSST. Finally, altruistic pun-
ishment behavior in the Ultimatum Game was not affected
immediately following the TSST (Vo n D awan s et al. , 2012;
Vinkers et al., 2013); however, it was affected when the task
was administered 75 min after the TSST (Vinkers et al., 2013;
see further below).
The overall impression from these studies is that differences
are present in the consequences of social stress on paradigms that
people play singly and those that involve interaction, even when
virtually, with others. Von D awans an d colleagues (20 12) sug-
gest that this may be related to the workings of oxytocin, which
would be released under stress and modulate the response in
social decision-making in the direction of pro-social behavior
and social support (see Taylor et al ., 2000; Cousino Kl ein a nd
Corwin, 2002; Heinrichs et al., 2003; Foley and Kirschbaum, 2010;
Vinkers et al., 2013;seefurtherbelow).Thelatterwouldlower
the stress-response (Heinrichs et al., 2003; Foley and Kirschbaum,
2010). The data on the stress-related increase in pro-social behav-
ior are in line with the observation that in primate species
behaviors like reconciliation and consolation follow conflicts or
social tension (e.g., Aureli et al., 1989; Koski et al., 2007; Fraser
et al., 2008). These behaviors facilitate recovery from stress and
counterbalance the negative consequences of social conflict on
group-cohesion and may restore internal group-cohesion (Aureli
et al., 1989; Fraser et al., 2008;butseeKoski et al., 2007). For,
maintaining internal cohesion is crucial as to maintain the bene-
fits from group-living, which are related to increased possibilities
to find and exploit food resources as well as lowering predation
risk. Interestingly, oxycotin has been shown to promote in-group
behavior and increase defensive a gg ression toward outsiders (De
Dreu et al., 2010). To what extent this relates to the observation
that altruism in the Dictat or Game was enhanced following social
stress depending upon whether it was in the context of persons
or a charity organization (Takah as hi et al., 20 07; Von D awans
et al., 2012; Vinkers et al., 2013)remainstobestudied.These
data thus provide a link between causal mechanisms and func-
tional mechanisms of pro-social behavior following social stress.
The biological meaning of the data on reward-based decision-
making is discussed in section Timing, coping styles and daily life
Studies directed at dissecting sex differences showed that men
displayed more risk-taking behavior following stress (IGT and
BART), whilst women were more risk-aversive (BART) or became
more task-focused (IGT). These studies also showed that sex dif-
ferences were related to the levels of cortisol. The higher the
levels of cortisol, the more risk-taking behavior was shown by
men (IGT; van den Bos et al., 2009). Women, on the other
hand displayed more risk-aversive or task-focused behavior with
increasing levels of cortisol (BART; Lighthall et al., 2009;IGT;
van den Bos et al., 2009). Data from the IGT also indicated
that women became more risk-taking when levels were too high
(van den Bos et al., 2009;seealsoWitbracht et al., 2012). Thus,
overall these data indicate that stress has a different effect on
reward-based decision-making in men and women with differ-
ent underlying effects of cortisol. This was recently confirmed
using the Cambridge Gambling Task and a job assessment proce-
dure to induce stress: while salivary cortisol levels were positively
correlated with risk-taking behavior in men, they were if any-
thing weakly negatively correlated in women (van den Bos et al.,
2013b). Interestingly, this study also revealed a different relation-
ship between salivary alpha-amylase and risk-taking in men and
women: while in women a positive relationship was found, a neg-
ative relationship existed in men (van den Bos et al., 2013b).
These data underline that differences do exist between men
and women regarding the relationship between stress, neuro-
endocrine changes and decision-making (see also de Visser et al.,
2010; van den Bos et al., 2013a).
Studies on social decision-making have been mainly done
in male only populations (Ta ka ha shi et al ., 2 007; Von Dawans
et al., 2012; Vinkers et al., 2013)ordonotmentionpotential
sex-differences in the data set (Starcke et al., 2011), preclud-
ing therefore to discuss differences between men and women
in this respect. Still, one study using the same social-decision-
making tasks and stress protocol as applied in men, did not
observe an effect of social stress on social-decision making
in women (Koot, unpublished). None of the studies in men
reported a relation with cortisol (Von Dawans et al., 2012;
Vinkers et al., 2013). Furthermore, while one study reported
Dawans et al., 2012), other studies did not observe a corre-
lation between salivary alpha-a mylase and p ro-social b ehavior
(Takaha shi et al., 2007; Vinker s et a l., 2013 ).
The increase in risk-taking behavior in men in reward-related
decision-making may be associated with a loss of top-down con-
trol of prefrontal over subcortical areas, such as mediated by
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van den Bos et al. Decision-making in a social context
the lateral orbitofrontal cortex and dorsolat eral prefrontal cortex
(Piazza and Le Moal, 1997; Arnsten, 1998, 2009; Erickson et al.,
2003; Stark et al., 2006; Wang et al., 2007; Kern et al., 2008;
Dias-Ferreira et al., 2009; Goldstein et al., 2010; Koot et al.,
2011, 2013). Furthermore within the limbic system high levels
of cortisol may shift the balance of the activity of the ventral
striatum (reward-related behavior) and amy gdala (punishment-
related behavior) toward the ventral striatum (Piazza et al., 1993;
Dellu et al., 1996; Piazza and Le Moal, 1997; Pruessner et al.,
2004; Mather et al., 2010; Porcelli et