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NATIONAL RESEARCH UNIVERSITY HIGHER SCHOOL OF ECONOMICS
as a manuscript
Oksana Zinchenko
NEUROBIOLOGICAL MECHANISMS OF SOCIAL PUNISHMENT AS A
COOPERATION PROMOTER
PhD Dissertation
for the purpose of obtaining academic degree
Doctor of Philosophy in Psychology HSE
Academic supervisor:
Vasily Klucharev
Candidate of Biological Sciences
Moscow 2019
Table of contentsIntroduction.......................................................................................................................3
Research goals...................................................................................................................9
Structure of the work.......................................................................................................10
Key results.......................................................................................................................11
Provisions for the defense...............................................................................................14
Conclusion.......................................................................................................................15
Acknowledgements.........................................................................................................16
References.......................................................................................................................17
Attachments.....................................................................................................................21
Attachment A. Article “Brain responses to social norms: Meta-analyses of fMRI studies”.........................................................................................................................21
Attachment B. Article “Neurobiological mechanisms of fairness-related social norm enforcement: a review of interdisciplinary studies”.....................................................21
Attachment C. Article “Commentary: The Emerging Neuroscience of Third-Party Punishment”.................................................................................................................21
Attachment D. Article “The role of the temporoparietal and prefrontal cortices in third-party punishment: a tDCS study”........................................................................21
2
Introduction
Social norms and the mechanisms of their enforcement: behavioral findings
Human society greatly depends on social norms, which work as a mechanism
supporting cooperation. Social norms can be defined as implicit or explicit rules that are
formed to govern interactions within groups and that are considered appropriate within
a society. Some examples of social norms include common courtesy and culturally
appropriate manners (Sherif and Sherif, 1953). Importantly, in human societies
cooperation is mainly based on social norms (Fehr and Fischbacher, 2004).
Different kinds of social norms regulate individual behavior, one of which is the
norm of fairness (Elster, 1989). The norm of fairness in democratic societies is usually
considered a norm of equality (Elster, 1989). A common approach to investigate social
norms is to use interactive economic games, such as the ultimatum game introduced by
Güth and colleagues (1982; see Gabay et al., 2014 for a review), the Prisoner’s dilemma
(Dickinson et al, 2015), and the dictator game (Tammi, 2013). Such games allow
different distributions of financial transfers between players. For instance, in the dictator
game there are two players, one of whom (the dictator) is given the opportunity to
distribute monetary units (MUs) between herself and another player (the recipient)
(Tammi, 2013). Behavioral studies have robustly demonstrated that many people who
play economic games prefer fair distributions to unequal ones (Guth et al., 1982;
Kahneman et al., 1986; Forsythe et al., 1994; Engel, 2010).
However, people do not always conform to social norms and sometimes tend to
violate them to maximize their own interests. Such violations usually meet with
increasing social pressure to conform to the norms. Psychological studies suggest that
the violation of social norms could result in the exclusion of the norm violator from the
group or in other less harsh forms of social disapproval (Schachter, 1951; Sherif, Sherif,
1953). It follows that the behavior conflicting with social norms can have dramatic
3
consequences. Social disapproval and social exclusion enforce norm compliance; in
fact, even the possibility of such sanctions could increase norm compliance (Ruff et al.,
2013).
Such behavior—a tendency to spend one’s own resources to punish norm
violations (e.g., unfair distributions of MUs that violate the norm of fairness)—is called
social punishment (Fehr, Fischbacher, 2004; Ruff et al., 2013). Social punishment can
be demonstrated experimentally based on material costs only, for example, when people
spend some MUs from their own budget to punish a norm violator. It can also be
expressed as social disapproval (Carpenter, Seki 2011; Masclet et al. 2003; Guala,
2012), which is more common in social life (e.g., reprimands, social exclusion, etc.).
Behavioral economics studies suggest that social punishment is usually meted out by
individuals who are directly affected by the norm violations of others (i.e., second
parties). Yet, individuals who are not directly affected by the norm violations of others
(third parties) are also willing to punish norm violators at their own expense (Fehr,
Fischbacher, 2004). It has been shown that norm violation behavior (such as unfair
behavior in the case of the norm of fairness) leads to negative emotions, such as anger
(Batson et al., 2007; Pedersen, 2012), guilt (Wagner et al, 2011), and embarrassment
(Melchers et al., 2015), that could drive individuals to punish their opponent at the
expense of monetary reward or to consider the opponent guilty. Overall, social
punishment as the “propensity of cooperative individuals to spend some of their
resources penalizing norm violators” (Zinchenko, Klucharev, 2017) is the main
mechanism supporting social norms in large social groups.
Neurofunctional model of social norms and norm violations
Because social norms are so important in maintaining social order, further
investigation is crucial to understand the roots of human behavior in different social
contexts. Montague and Lohrehz (2007) propose a neurofunctional model of social
norms based on a review of studies exploring neural correlates of adherence to shared
social norms. They suggest that the brain can flexibly adjust behavior according to
existing social norms, similar to other forms of adaptive behavior. To successfully4
interact with others in any social group, the following steps are necessary: 1) to have a
representation of the norm, 2) to have a mechanism detecting violations of this norm,
and 3) to have the chance to look at the current situation from a third-party perspective
to be able to maintain norm compliance (Montague, Lohrenz, 2007; Xiang et al., 2013).
We adopted this model to perform the first meta-analysis of neuroimaging studies of
social norms (Zinchenko, Arsalidou, 2018).
Third-party punishment as a mechanism of norm enforcement: a comparison with
second-party punishment and the model of neural activation
In addition to investigating social norms in general, it is particularly critical to
study the mechanisms of enforcement, implementation, and compliance, including
social punishment. Third-party punishment is a special form of social punishment that is
unique to human culture (Riedl et al., 2012) and that has not been observed in other
primates, including chimpanzees. While the majority of neuroimaging studies
investigate the neural basis of second-party punishment, there are not many studies
about the neural mechanisms of third-party punishment. Importantly, third-party
punishment is crucial for establishing cooperation in larger social groups. Therefore,
studies of third-party punishment are of practical importance and are relevant in the
modern urbanized world.
Neuroimaging and brain stimulation studies provide some insights on the neural
mechanisms of third-party punishment. It has been shown that second- and third-party
punishment have different neural mechanisms (Strobel et al., 2011) and that only some
regions, such as the ventral striatum, share a common activation for both types of
punishment (Stallen et al., 2018). For instance, the lateral prefrontal cortex (LPFC)—
and its subpart the DLPFC—is casually involved in both types of social punishment but
in slightly different ways. The right LPFC (rLPFC) is involved in both voluntary and
sanction-induced norm compliance in the case of second-party punishment (Ruff et al,
2013). In the case of third-party punishment, rDLPFC activity correlates with the
evaluation of the responsibility for committing norm violations (Buckholtz et al., 2008).
In particular, the emotional evaluation of the personal responsibility that results in third-5
party punishment correlates with activity of the amygdala, the medial prefrontal cortex,
and the posterior part of the cingulate cortex (Buckholtz et al., 2008).
Neuroimaging studies suggest that several distinct brain networks are consistently
recruited during third-party punishment (Krueger, Hoffman, 2016). According to
Krueger and Hoffman’s model (2016), these brain networks include the central-
executive, mentalizing, and salience networks. The mentalizing network is responsible
for the ability to imagine thoughts and possible actions of others and mainly relies on
individual experience, while the activity of the central-executive network is required for
our cognitive control, working memory, task-switching, planning, etc. Hypothetically,
in accordance with the predictions of Krueger and Hoffman’s model, third-party
punishment decisions start with the activation of the salience network (insula,
amygdala, and dorsal anterior cingulate), which allows the detection of norm violations
and consequently generates an aversive response. Next, the default mode network (TPJ,
dorsomedial prefrontal cortex or dMPFC) integrates the perceived harm and inference
of intentions into an assessment of blame. Finally, the central executive network
(DLPFC) converts the blame signal into a specific punishment decision.
Neural mechanisms of third-party punishment: neuroimaging and brain
stimulation studies
Most previous studies focus on the brain correlates of third-party punishment and
practically ignore the interactions between the large-scale brain networks. A recent
brain stimulation study shows that transcranial magnetic stimulation (rTMS) of the
rDLPFC increased third-party punishment, while psychometric methods have provided
evidence of a correlation between an individual empathy index and the intensity of
third-party punishment (Brune et al., 2012). These results may suggest that the DLPFC
integrates all signals from the previous steps of the decision-making process, including
the emotional emphatic responses.
It follows that suppression of the DLPFC should lead to increased third-party
punishment only if the activity of the DLPFC underlies the final evaluation of the costs
6
of the punishment decision. If so, suppression of the DLPFC should decrease the
perceived costs of social punishment and therefore increase third-party punishment. The
previous TMS study did not disentangle material and moral costs (Brune at al., 2012);
third parties punished the norm violator and helped the victim at the same time.
Therefore, the role of the DLPFC in third-party punishment remains largely unclear.
Considering other main brain regions from the model (Krueger, Hoffman, 2016),
the previous brain stimulation studies provided a more coherent interpretation of the
role of the rTPJ in third-party punishment. It has been shown that rTMS of the rTPJ
decreases third-party punishment of outgroup members (Baumgartner et al., 2014). This
supports Krueger and Hoffman’s model (2016) of third-party punishment and indicates
the vital role of the rTPJ in the processing of emotional information during social
punishment. This interpretation is in line with extensive meta-analyses that
demonstrated the involvement of the rTPJ in mentalizing and empathy (Van Overwalle,
2009; Garrigan, Adlam, Langdon, 2016).
A seminal functional magnetic resonance imaging (fMRI) study of third-party
punishment has demonstrated a functional interaction between the rDLPFC and the
rTPJ (Buckholtz et al., 2008). This study suggests that the activation of the rTPJ before
a punishment decision is followed by simultaneous deactivation of the rDLPFC and
results in the follow-up activation of the rDLFPC when the final decision is made.
Taking into account these findings (Buckholtz et al., 2008), we speculate that the
chronometry of the third-party punishment decision is as follows. The information
about the harm (a degree of norm violation) and the intentions (intentional versus
unintentional norm violations) are processed in the salience network (anterior cingulate,
anterior insula) and the mentalizing network (rTPJ). Subsequently, the resulting
information is transferred to the DLPFC to calculate the final decision, considering the
context of the situation and the self-maximization (if the punishment decision is costly).
Recent neuroimaging studies focus not only on the functional role of the exact
brain region but also on the interaction between different brain regions (e.g., Treadway
et al., 2014; Bellucci et al., 2017). Similarly, Feng and colleagues (2018) analyze7
resting-state fMRI data using graph theory and support Krueger and Hoffman’s model
of the key brain nodes involved in third-party punishment. Another fMRI study
investigates task-related brain activity and supports the main role of the mentalizing
(TPJ and dMPFC) and central-executive (LPFC) systems in third-party punishment
(Bellucci et al., 2017). Importantly, this study demonstrates that the dMPFC receives
the incoming signals only from the TPJ, while the activity of the dMPFC and its
functional co-activation with the dLPFC correlate with the degree of third-party
punishment (Bellucci et al., 2017). According to these findings, the TPJ is considered to
be an integrative node, receiving the information from other sub-regions.
The primary role of the mentalizing and central-executive networks in third-party
punishment is supported by traumatic brain injury studies. Glass and colleagues (2016)
show that damage to these cortical regions decreased the intensity of third-party
punishment and altruistic compassion. However, to date the functional connectivity
before or during social punishment has not been investigated using electrophysiological
methods with high time resolution. To our knowledge, the electroencephalogram studies
reported only the inter-brain connectivity between the receiver’s and the punisher’s
brain activity during third-party punishment using a hyperscanning approach (Astolfi et
al., 2015; Ciaramidaro et al., 2018).
In summary, we reviewed the key neuroimaging studies of social norms and
social norm enforcement, focusing particularly on social punishment and third-party
punishment. We identified the following gaps in the research on social norms and social
punishment, which we addressed in a series of studies: 1) no meta-analyses have been
performed to identify the key brain regions concordantly activated in relation to
representations of social norms and their violations; 2) previous studies robustly
demonstrated the role of the mentalizing and central-executive networks in third-party
punishment, but brain stimulation has not been used to demonstrate a causal relationship
between the aforementioned networks and third-party punishment or to investigate
interaction between the mentalizing and central-executive networks.
8
Research goals
1) To perform a meta-analysis of neuroimaging studies of fMRI modality to identify
the key regions related to information processing in social norms (the representation of
social norms and norm violations).
2) To perform a brain stimulation study to investigate the functional interactions of the
rDLPFC and the rTPJ during third-party punishment decisions.
3) To identify the functional roles of the rDLPFC and the rTPJ in third-party
punishment decisions.
9
Structure of the work
The PhD thesis consists of three main parts which are presented in the following
papers:
Part I (Meta-analysis). Zinchenko O. O., Arsalidou M. Brain responses to social
norms: Meta-analyses of fMRI studies // Human Brain Mapping. 2018. Vol. 39. No. 2.
P. 955-970
Part II (Neurocognitive model of third-party punishment). Zinchenko O. O.,
Belyanin A., Klucharev V. Neurobiological mechanisms of fairness-related social norm
enforcement: a review of interdisciplinary studies. Zh. Vyssh. Nerv. Deiat. 2018. 67(6),
16-27.
Part III (Brain stimulation study). Zinchenko O. O., Klucharev V. Commentary:
The Emerging Neuroscience of Third-Party Punishment // Frontiers in Human
Neuroscience. 2017. No. 11. P. 1-3; Zinchenko O. O., Belyanin, A., Klucharev V.
(2019). The role of the temporoparietal and prefrontal cortices in third-party
punishment: a tDCS study // Psychology. Journal of the Higher School of Economics.
10
Key results
Part I (Meta-analysis). We identified concordant activations in the functional
magnetic resonance imaging (fMRI) studies for the social norm representations and
norm violation using meta-analytic approach (Zinchenko, Arsalidou, 2018). For the
general map of the brain responses to social norms we detected five clusters: the largest
cluster was found in the right insula (Brodmann Area, BA 13), followed by the left
medial frontal gyrus (BA 32) that extended to the cingulate gyrus (BA 32), right
superior and middle frontal gyri (BA 9 and BA 10). Other regions included the left
insula and claustrum. Regions of significant concordance specifically for ‘social norm
representations’ included the left anterior cingulate and right medial frontal gyrus (BA
10). The meta-analysis of ’norm violation’ category revealed five suprathreshold
clusters were detected for norm violation, with the one with the highest likelihood of
being detected in the right insula (BA 13), followed by other regions: right cingulate
gyrus (BA 32), left insula (BA 13) and claustrum, and right middle and superior frontal
gyri (BA 9 and 10). While compared to norm violation, social norm representation
showed greater concordance in the anterior cingulate gyri (BA 32) and right medial
frontal gyrus (BA 10), whereas compared to social norm representation, norm violation
shows greater concordance in the right insula and claustrum and more dorsal parts of
the cingulate gyrus (BA 24, 32). To sum up, the findings suggest that rDLPFC plays
key role in social norm representations and the detection of norm violation.
Part II (Neurocognitive model of third-party punishment). In accordance with our
research goals, we performed a systematic review of behavioral, neuroimaging, and
brain stimulation studies to identify the main open research questions in the third-party
punishment research. The results that were briefly described in the Introduction section
of this thesis were published in Zinchenko, Belyanin, and Klucharev (2018). Based on
the previous fMRI study (Buckholtz et al., 2008), we speculated that an enhancement of
TPJ activity with the simultaneous suppression of DLPFC activity should lead to
increased third-party punishment due to the possible enhancement of the antagonistic
11
TPJ–DLPFC interaction. Therefore, we suggested that a simultaneous application of
tDCS to the TPJ and DLPFC should enhance such antagonistic interaction between
these two regions and increase third-party punishment. Such a behavioral effect of tDCS
could reflect changes in the functional connectivity between the TPJ and the DLPFC.
Therefore, a combined non-invasive brain stimulation–neuroimaging study is needed to
uncover the neural dynamics underlying third-party punishment.
Part III (Brain stimulation study). Based on the results of our review paper, we
formulated the new research hypotheses, which have been published in Zinchenko and
Klucharev (2017). Therefore, we conducted a tDCS experiment in which we tested the
classic stimulation protocols with anodal tDCS stimulation of the rDLPFC and the rTPJ
separately and the novel simultaneous stimulation protocol of the enhancement of TPJ
activity with the simultaneous suppression of DLPFC activity. However, we observed
only a trend relating to the effect of the joint stimulation tDCS protocol (p=0.055).
When the rTPJ was activated and the rDLPFC was simultaneously deactivated, we
observed a trend of increased third-party punishment. We suggest that tDCS is not the
ideal method to study interactions of the rDLPFC and rTPJ. In the future, online
transcranial alternating current stimulation could be used to study the synchronization
and desynchronization of these brain regions. Nevertheless, we observed the effect of
the anodal stimulation of the rTPJ, which led to decreased punishment for moderately
unfair splitting of the resources (p=0.006). A recent study involving anodal tDCS of the
rTPJ shows that subjects were assigned less blame for accidental harm during a moral
judgment task (Sellaro et al., 2015), while a meta-analysis suggests that the rTPJ
showed significant activation when one makes one’s own moral decisions (Garrigan,
Adlam, Langdon, 2016). Overall, rTPJ activity can reflect an analysis of the
consequences of the third-party’s own decision and of how harmful it would be for
others. Therefore, anodal stimulation of the rTPJ area could exaggerate the latter
process and consequently lead to diminished punishment.
One of the important findings of our tDCS study is that anodal tDCS had an
effect on moderately unfair splitting of the resources (30:10) only: when third-party
12
punishment of unfair splits created a Pareto optimal distribution of MUs (10:10:10) and
it was impossible to improve the income of one player without worsening the incomes
of the other players, while the punishment in other conditions led to advantageous and
disadvantageous inequity. Pareto optimality is a state of allocation of resources where it
is impossible to improve the income of one player without worsening the incomes of the
other players. Therefore, in our study social punishment for other splits (0:40, 15:25,
20:20, 25:15, 35:5, and 40:0) would lead to advantageous and disadvantageous inequity.
Following this, we suggest that anodal tDCS led to decreased moral costs, which
resulted in decreased punishment.
13
Provisions for the defense
1) According to our meta-analysis of fMRI studies, social norm representation is
robustly associated with activity of the anterior cingulate and right DLPFC, while norm
violation is associated with the activation of the right insula and claustrum.
2) The Krueger and Hoffman model (2016), along with the results of our extensive
systematic review and our meta-analysis, suggests the key role of the DLPFC and the
TPJ in monitoring social norms and their enforcement. However, according to our tDCS
study, anodal tDCS of the rDLPFC does not lead to changes in third-party punishment.
3) According to the tDCS study, anodal tDCS of the rTPJ decreases third-party
punishment for moderately unfair splitting of the resources. We suggest that during the
dictator game rTPJ activity underlies the initiation of the decision to punish, while
activation of the rDLPFC becomes important in the latest stages of decision making.
14
Conclusion
We conducted the first meta-analysis of neuroimaging studies on social norms
and their violations. The results suggest that social norm representation is linked to the
activation of the anterior cingulate gyri and the rDLPFC and that norm violations are
coded by the activation of the right insula and claustrum. Based on this, we proposed a
neurocognitive model of social norms for healthy adults suggesting that the
temporoparietal-medial-prefrontal circuit controls the emotional responses to norm
violations and regulates the subsequent punishment of norm violators. The results of the
brain stimulation study suggest that anodal tDCS of the rTPJ decreases the third-party
punishment for moderately unfair splitting of the resources, while joint stimulation of
the rTPJ (by anodal tDCS) and rDLPFC (by cathodal tDCS) produces only a marginal
effect. This study demonstrates that anodal tDCS of the rTPJ decreases third-party
punishment for moderately unfair behavior when the participants have an opportunity to
restore equality in their social groups. Overall, the study findings support the critical
role of the temporoparietal-medial-prefrontal circuit in third-party punishment. These
findings can be used in future studies on social norms and the mechanisms of their
enforcement in healthy subjects.
15
Acknowledgements
I would like to express my sincere gratitude to my advisor, Professor Vasily
Klucharev, for giving me opportunity to conduct and complete the PhD study at the
Centre for Cognition and Decision Making.
My sincere thanks goes to Dr. Marie Arsalidou, who is my coauthor on “Brain
responses to social norms: Meta-analyses of fMRI studies”, for the valuable
contribution.
I would like to thank Dr. Alexis Belyanin, Dr. Matteo Feurra and Dr. Anna
Shestakova for their valuable comments and help while designing and conducting these
studies.
Many thanks to Dr. Beatriz Martin-Luengo and Dr. Ksenia Panidi for reviewing
this thesis.
I acknowledge HSE University Basic Research Program and Russian Academic
Excellence Project '5-100' for the financial support through the research unit.
16
References
Astolfi, L., Toppi, J., Casper, C., Freitag, C., Mattia, D., Babiloni, F., Ciaramidaro, A.,
Siniatchkin, M. Investigating the neural basis of empathy by EEG hyperscanning
during a Third Party Punishment // Conf Proc IEEE Eng Med Biol Soc. 2015.
5384-5387.
Baumgartner, T., Götte, L., Gügler, R., Fehr, E. (2012). The mentalizing network
orchestrates the impact of parochial altruism on social norm enforcement //
Human Brain Mapping. T. 33. № 6. С. 1452-1469.
Baumgartner, T., Schiller, B., Rieskamp, J., Gianotti, L. R., Knoch, D. Diminishing
parochialism in intergroup conflict by disrupting the right temporo-parietal
junction // Soc Cogn Affect Neurosci. 2014. T. 9. № 5. C. 653-660.
Bellucci, G., Chernyak, S., Hoffman, M., Deshpande, G., Dal Monte, O., Knutson, K.,
Grafman, J., Krueger, F. Effective connectivity of brain regions underlying third-
party punishment: Functional MRI and Granger causality evidence // Soc
Neurosci. 2017. T. 12. № 2. C. 124-134.
Bendor J, Swistak P. The Evolution of Norms // American Journal of Sociology. 2001.
T. 106. № 6. C. 1493–1545.
Brüne, M., Scheele, D., Heinisch, C., Tas, C., Wischniewski, J., Güntürkün, O.
Empathy moderates the effect of repetitive transcranial magnetic stimulation of
the right dorsolateral prefrontal cortex on costly punishment // PloS ONE. 2012.
T. 7. № 9. e44747.
Buckholtz, J.W., Asplund, C.L., Dux, P.E., Zald, D.H., Gore, J.C., Jones, O.D., Marois,
R. The neural correlates of third-party punishment // Neuron. 2008. T. 60. № 5.
C. 930-940.
Buckholtz, J.W., Marois, R. The roots of modern justice: cognitive and neural
foundations of social norms and their enforcement // Nat Neurosci. 2012. T.15, №
5. C. 655-661.
17
Ciaramidaro, A., Toppi, J., Casper, C., Freitag, C. M., Siniatchkin, M., Astolfi, L.
Multiple-Brain Connectivity During Third Party Punishment: an EEG
Hyperscanning Study // Scientific Reports. 2018. T. 8. №1. C. 6822.
Dickinson, D. L., Masclet, D., Villeval, M. C. Norm enforcement in social dilemmas:
An experiment with police commissioners // Journal of Public Economics. 2015.
T. 126. C. 74– 85.
Eickhoff, S., Laird, A., Grefkes, C., Wang, L., Zilles, K., Fox, P. Coordinate‐based
activation likelihood estimation meta‐analysis of neuroimaging data: A random‐
effects approach based on empirical estimates of spatial uncertainty // Human
Brain Mapping. 2009. T. 30. № 9. C. 2907– 2926.
Eickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F., Fox, P. T. Activation likelihood
estimation revisited // NeuroImage. 2012. T. 59. № 3. C. 2349– 2361.
Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L., Fox, P. T. Implementation
errors in the GingerALE Software: Description and recommendations // Human
Brain Mapping. 2017. T. 38. № 1. C. 7– 11.
Elster, J. Social Norms and Economic Theory // The Journal of Economic Perspectives.
1989. T. 3. № 4. C. 89–117.
Fehr, E., Fischbacher, U. Third-party punishment and social norms // Evol. Hum.
Behav. 2004. T. 25. № 2. C. 63–87.
Gabay, A. S., Radua, J., Kempton, M. J., Mehta, M. A. The ultimatum game and the
brain: A meta‐analysis of neuroimaging studies // Neuroscience and
Biobehavioral Reviews. 2014. T. 47. C. 549– 558.
Garrigan, B., Adlam, A.L., Langdon, P.E. The neural correlates of moral decision-
making: A systematic review and meta-analysis of moral evaluations and
response decision judgements // Brain Cogn. 2016. T. 108. C. 88-97.
Guth, W., Schmittberger, R. Schwarze, B. An experimental analysis of ultimatum
bargaining // Journal of Economic Behavior and Organization. 1982. T. 3. № 4.
C. 367-388.
Krueger, F. Hoffman, M. The Emerging Neuroscience of Third-Party Punishment //
Trends in Neurosciences. 2016. T. 39. № 8. C. 499-501.18
Melchers, M., Markett, S., Montag, C., Trautner, P., Weber, B., Lachmann, B., Reuter,
M. Reality TV and vicarious embarrassment: An fMRI study // Neuroimage.
2015. T. 109. C. 109– 117.
Montague, P. R., Lohrenz, T. To detect and correct: Norm violations and their
enforcement // Neuron. 2007. T. 56. № 1. C. 14– 18.
Nitsche, M. A., Paulus, W. Excitability changes induced in the human motor cortex by
weak transcranial direct current stimulation // The Journal of physiology. 2000. T.
527. C. 633-639.
Nitsche, M.A., Paulus, W. Sustained excitability elevations induced by transcranial DC
motor cortex stimulation in humans // Neurology. 2001. T. 57. № 10. C. 1899-
1901.
Nitsche, M.A., Nitsche, M.S., Klein, C.C., Tergau, F., Rothwell, J.C., Paulus, W. Level
of action of cathodal DC polarisation induced inhibition of the human motor
cortex // Clin Neurophysiol. 2003. T. 114. № 4. C. 600-604.
Paulus, W. Transcranial electrical stimulation (tES - tDCS; tRNS, tACS) methods //
Neuropsychol Rehabil. 2011. T. 21. № 5. C. 602-617.
Ruff, C.C., Ugazio, G., Fehr, E. Changing social norm compliance with noninvasive
brain stimulation // Science. 2013. T.342. № 6157. C. 482-484.
Pedersen, E. J. The roles of empathy and anger in the regulation of third‐party
punishment // Open Access Theses. 2012. 377.
Riedl, K., Jensen, K., Call, J., Tomasello, M. No third-party punishment in chimpanzees
// Proceedings of the National Academy of Sciences of the United States of
America. 2012. T. 109. № 37. C. 14824-14829.
Sellaro, R., Güroglu, B., Nitsche, M.A., van den Wildenberg, W.P., Massaro, V.,
Durieux, J., Hommel, B., Colzato, L.S. Increasing the role of belief information
in moral judgments by stimulating the right temporoparietal junction //
Neuropsychologia. 2015. T. 77. C. 400-408.
Schachter, S. Deviation, rejection, and communication // Journal of Abnormal
Psychology. 1951. T. 46, № 2. C. 190– 207.
19
Sherif, M., Sherif, C. W. Groups in harmony and tension. An integration of studies on
intergroup relations // New York: Harper and Brothers. 1953.
Stallen, M., Rossi, F., Heijne, A., Smidts, A., De Dreu, C. K.W., Sanfey, A.G.
Neurobiological Mechanisms of Responding to Injustice // J. Neurosci. 2018. T.
38. № 12. C. 2944-2954.
Strobel, A., Zimmermann, J., Schmitz, A., Reuter, M., Lis, S., Windmann, S., Kirsch, P.
Beyond revenge: Neural and genetic bases of altruistic punishment //
Neuroimage. 2011. T. 54. № 1. C. 671–680.
Tammi, T. Dictator game giving and norms of redistribution: Does giving in the dictator
game parallel with the supporting of income redistribution in the field? // The
Journal of Socio‐Economics. 2013. T. 43. C. 44–48.
Treadway, M.T., Buckholtz, J.W., Martin, J.W., Jan, K., Asplund, C.L., Ginther, M.R.,
Jones, O.D., Marois, R. Corticolimbic gating of emotion-driven punishment // Nat
Neurosci. 2014. T. 17. № 9. C. 1270–1275.
Van Overwalle, F. Social cognition and the brain: a meta-analysis // Hum Brain Mapp.
2009. T. 30. № 3. C. 829-858.
Wagner, U., N'diaye, K., Ethofer, T., Vuilleumier, P. Guilt‐specific processing in the
prefrontal cortex // Cerebral Cortex. 2011. T. 21. № 11. C. 2461– 2470.
Xiang, T., Lohrenz, T., Montague, P. R. Computational substrates of norms and their
violations during social exchange // Journal of Neuroscience. 2013. T. 33. №3. C.
1099– 1108.
Zinchenko, O., Klucharev, V. Commentary: The Emerging Neuroscience of Third-Party
Punishment // Frontiers in Human Neuroscience. 2017. T.11. C. 512.
Zinchenko O., Arsalidou M. Brain responses to social norms: Meta-analyses of fMRI
studies // Hum. Brain Mapp. 2018. Т. 39. № 2. C. 955-970.
Zinchenko, O., Belianin, A., Klucharev, V. Neurobiological Mechanisms of Fairness-
Related Social Norm Enforcement: a Review of Interdisciplinary Studies //
Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P. Pavlova. 2018. T. 68. № 1. C.
16-27.
20
Attachments
Attachment A. Article “Brain responses to social norms: Meta-analyses of fMRI
studies”.
Attachment B. Article “Neurobiological mechanisms of fairness-related social norm
enforcement: a review of interdisciplinary studies”.
Attachment C. Article “Commentary: The Emerging Neuroscience of Third-Party
Punishment”.
Attachment D. Article “The role of the temporoparietal and prefrontal cortices in third-
party punishment: a tDCS study”.
21
Attachment A. Article “Brain responses to social norms: Meta-analyses of fMRI
studies”.
Social norms have a critical role in everyday decision-making, as frequent
interaction with others regulates our behavior. Neuroimaging studies show that
social-based and fairness-related decision-making activates an inconsistent set of
areas, which sometimes includes the anterior insula, anterior cingulate cortex, and
others lateral prefrontal cortices. Social-based decision-making is complex and
variability in findings may be driven by socio-cognitive activities related to social
norms. To distinguish among social-cognitive activities related to social norms, we
identified 36 eligible articles in the functional magnetic resonance imaging (fMRI)
literature, which we separate into two categories (a) social norm representation and
(b) norm violations. The majority of original articles (>60%) used tasks associated
with fairness norms and decision-making, such as ultimatum game, dictator game,
or prisoner's dilemma; the rest used tasks associated to violation of moral norms,
such as scenarios and sentences of moral depravity ratings. Using quantitative
meta-analyses, we report common and distinct brain areas that show concordance
as a function of category. Specifically, concordance in ventromedial prefrontal
regions is distinct to social norm representation processing, whereas concordance
in right insula, dorsolateral prefrontal, and dorsal cingulate cortices is distinct to
norm violation processing. We propose a neurocognitive model of social norms for
healthy adults, which could help guide future research in social norm compliance
and mechanisms of its enforcement.
R E S E A R CH AR T I C L E
Brain responses to social norms: Meta-analyses of fMRI studies
Oksana Zinchenko1 | Marie Arsalidou2,3
1Centre for Cognition and Decision Making,
National Research University Higher School
of Economics, Moscow, Russian Federation
2Department of Psychology, National
Research University Higher School of
Economics, Moscow, Russian Federation
3Department of Psychology, Faculty of
Health, York University, Toronto, Canada
Correspondence
Oksana Zinchenko, Centre for Cognition
and Decision Making, National Research
University Higher School of Economics,
101000, Moscow, 3 Krivokolenny Pereulok,
Russian Federation.
Email: [email protected]
Funding information
The study has been funded by the Russian
Academic Excellence Project ’5-100’ to OZ.
MA was supported by the Russian Science
Foundation #17-18-01047.
AbstractSocial norms have a critical role in everyday decision-making, as frequent interaction with others
regulates our behavior. Neuroimaging studies show that social-based and fairness-related decision-
making activates an inconsistent set of areas, which sometimes includes the anterior insula, ante-
rior cingulate cortex, and others lateral prefrontal cortices. Social-based decision-making is
complex and variability in findings may be driven by socio-cognitive activities related to social
norms. To distinguish among social-cognitive activities related to social norms, we identified 36 eli-
gible articles in the functional magnetic resonance imaging (fMRI) literature, which we separate
into two categories (a) social norm representation and (b) norm violations. The majority of original
articles (>60%) used tasks associated with fairness norms and decision-making, such as ultimatum
game, dictator game, or prisoner’s dilemma; the rest used tasks associated to violation of moral
norms, such as scenarios and sentences of moral depravity ratings. Using quantitative meta-
analyses, we report common and distinct brain areas that show concordance as a function of cate-
gory. Specifically, concordance in ventromedial prefrontal regions is distinct to social norm
representation processing, whereas concordance in right insula, dorsolateral prefrontal, and dorsal
cingulate cortices is distinct to norm violation processing. We propose a neurocognitive model of
social norms for healthy adults, which could help guide future research in social norm compliance
and mechanisms of its enforcement.
K E YWORD S
brain mapping, functional magnetic resonance imaging, social norms, prefrontal cortex, humans,
cognition, norm violation
1 | INTRODUCTION
Most of us benefit by following social norms to some degree. Social
norms are spoken or unspoken rules of behavior that are formed within
group situations and are considered appropriate within a society. For
instance, common courtesy and culturally appropriate manners for
cooperative actions and bilateral exchange can be referred to as social
norms (Sherif & Sherif, 1953). Because we live, act, and interact among
others in a society we often have to equipoise our personal wants and
social norms (Cialdini, Reno, & Kallgren, 1990; Bicchieri 2016). Devia-
tion from social norms is often met with increasing pressure to con-
form. From early studies we know that if social expectations remain
unmet, deviation from social norms often results with exclusion of the
norm violator from the group or higher likelihood of reduced payoffs to
the norm violator (Schachter, 1951; Sherif & Sherif, 1953). Thus, going
against social norms has critical consequences. In other words, threat-
ening norm violators with some form of social punishment enforces
norm compliance. Punishment is usually given by individuals who are
directly affected by norm violations of others (i.e., second parties), yet
individuals who are not directly affected by norm violations of others
(i.e., third parties) are also willing to give punishment (Fehr & Fisch-
bacher, 2004).
A common approach to investigate norm violation and norm
enforcement is by using interactive economic games (Camerer, 2003;
Fehr & Camerer, 2007; Sanfey, 2007), such as the Ultimatum Game
introduced by (G€uth et al. 1982; see Gabay, Radua, Kempton, & Mehta,
2014 for a meta-analysis), the Prisoner’s Dilemma (Dickinson, Masclet,
& Villeval, 2015), and the Dictator Game (Tammi, 2013). Behavioral
findings suggest that unfair treatment leads to negative emotions, such
as anger (Batson et al., 2007; Pedersen, 2012), guilt (Wagner, N’diaye,
Ethofer, & Vuilleumier, 2011), and embarrassment (Melchers et al.,
2015) that drive individuals to punish their opponent at the expense of
monetary reward or consider the opponent guilty. Performance on
tasks with monetary outcomes highly depends on the participant’s
Hum Brain Mapp. 2018;39:955–970. wileyonlinelibrary.com/journal/hbm VC 2017Wiley Periodicals, Inc. | 955
Received: 4 July 2017 | Revised: 24 October 2017 | Accepted: 10 November 2017
DOI: 10.1002/hbm.23895
understanding of relative and absolute fairness/unfairness of the situa-
tion at hand. In case of unfair situations, participants presented with
economic paradigms, such as the Ultimatum Game are asked to accept
or reject financial offers depending on subjective equality of the
offered distribution. Some researchers identify this rejection rate to
reflect social punishment (Sanfey, Rilling, Aronson, Nystrom, & Cohen,
2003; Tabibnia, Satpute, & Lieberman, 2008). For instance, rejection
rate in an Ultimatum Game gradually increases as the proposer’s offer
becomes lower, such that a lower proposal is perceived as more unfair
(Corradi-Dell’Acqua et al., 2013; Rilling & Sanfey, 2011). In other
words, decision-making in this situation is influenced by social norms
and differs according to the responder’s understanding of norms and
their violation, consistent with the claim that social norms are self-
enforcing (Young, 2015). Overall, behavioral studies show that conse-
quences of social norms are related to both social violations and social
punishment; however, it is difficult to parse out the underlying mecha-
nisms of such complex processes with behavioral paradigms alone.
Many functional magnetic resonance imaging (fMRI) and lesion
studies examined the brain correlates of social norms (e.g., Dimitrov,
Phipps, Zahn, & Grafman, 1999; Koenigs et al., 2007; Buckholtz & Mar-
ois, 2012; Sanfey & Chang, 2008; Harl�e, Chang, Wout, & Sanfey, 2012;
Wright, Symmonds, Fleming, & Dolan, 2011; Hu et al., 2016). An
advantage of functional neuroimaging is that it allows for tracking of
continuous processes as healthy participants are working on tasks. A
recent fMRI meta-analysis on the Ultimatum Game distinguishes two
brain systems responsible for norm enforcement behavior; an intuitive-
emotional system, also called System 1, involving the anterior insula
and ventromedial prefrontal cortex and a cognitive-rational system, or
System 2, involving ventrolateral, dorsomedial, left dorsolateral pre-
frontal cortices, and rostral anterior cingulate cortices (Feng, Luo, &
Krueger, 2015). Regions involved in System 1 represent a drive to pun-
ish norm violators, whereas System 2 is responsible for cognitive con-
trol and suppression of economic self-interest (i.e., to save money;
Feng et al., 2015). It has been suggested that both these systems would
underline quick detection of norm violations, evaluation of benefits,
and costs of punishment to decide its necessity, supporting the dual
process theory (Kahneman, 2003; Kahneman, 2011). The meta-analysis
by Feng et al. (2015) provides knowledge on the brain correlates
related to the Ultimatum Game (Feng et al., 2015); however, does not
distinguish between different aspects of social norms and focuses only
on a single task. Critically, some of Feng et al. (2015) methodological
choices may be problematic or outdated. First, the analyses include
data from children and adults (i.e., G€uro�glu et al., 2011; White et al.,
2013) when behavioral evidence show differences in performance
between children, adolescents and adults (Hamlin, Wynn, Bloom, &
Mahajan, 2011; Steinbeis, Bernhardt, & Singer, 2012). Second, the anal-
yses used a low number of studies (i.e.,<17) with a threshold for multi-
ple comparison control (i.e., false discovery rate, FDR) that is currently
not recommended practice (Eickhoff, Laird, Fox, Lancaster, & Fox,
2017). Moreover, according to GingerALE developers, older versions of
the software (i.e., older than 2.3.6) had a computational error, which
did not appropriately control thresholding procedures (Eickhoff et al.,
2017). Thus, an understanding of how healthy adults process social
norms is still lacking. In this article, we focus on social norms and possi-
ble distinctions among (a) social norm representation and (b) norm
violations.
1.1 | Theoretical approach
To frame our hypotheses, we adopted a recent neurofunctional model
of social norms (Montague & Lohrenz, 2007). Montague’s model is a
product of a classic review of research studies that explored brain
activity related to adherence to shared social norms (Montague & Loh-
renz, 2007; Xiang, Lohrenz, & Montague, 2013). They proposed that
the brain could flexibly adjust behavior according to social norms in
order to develop a program of further behavior. To interact with others
in any social group, the following circumstances are required: (a) a rep-
resentation of a well-known norm as a behavioral rule about something
that is expected to be true (e.g., Montague & Lohrenz, 2007), (b) the
possibility to detect any violations of this norm, and (c) a chance to
look at an ongoing situation from a third-party perspective so to act
and make congruent decisions to maintain norm compliance (e.g., Mon-
tague & Lohrenz, 2007; Xiang et al., 2013). Because norm compliance
is not always voluntary and mostly requires sanction inducement, social
punishment is used for social norm enforcement. In line with this model
that predicts differential brain regions for mental processes associated
with social norms, for our meta-analyses we differentiate among social
norms into two subcategories: (a) social norm representation and (b)
norm violation, and expected different brain regions to underlie these
processes.
1.2 | Social norm representation
We define social norm representation as commonly expected appropri-
ate behavior in a certain situation (i.e., shared norms; Cialdini & Gold-
stein, 2004). Our category for social norm representation includes both
moral and social norms as a kind of normative attitudes as both moral
and social norms are accepted rules or normative principles. Here we
categorize experiments (i.e. contrasts) as belonging to social norm rep-
resentation if the action in the task possesses the social preferences
(“good” versus “bad,” or neutral etc.) or if a comparison between social
and non-social domain has been made (“moral” versus “semantic,” etc.).
Unlike social norm representation studies, which reflect voluntary
actions individuals do because they think they are appropriate (Bic-
chieri, 2016), social conformity studies have participants confronted
with direct peer pressure (Wei, Zhao, & Zheng, 2013; Zaki, Schirmer, &
Mitchell, 2011). Studies of social conformity were not included as a
separate category because of insufficient studies (i.e.,<17 experi-
ments; Eickhoff et al., 2017). We are interested in global normative
judgments that regard others, such as fairness-related norms modeled
in the Ultimatum Game (Brennan, Gonz�alez, G€uth, & Levati, 2008),
norms of equality (Elster, 1989), and others used to maintain social
order. Recent studies report that the ventromedial prefrontal cortex,
critical for social cognition, strongly correlates with distinguishing
“good” and “bad” in the moral domain (Bechara, Damasio, & Damasio,
2000; Dimitrov et al., 1999; Heekeren et al., 2005; Koenigs et al.,
956 | ZINCHENKO AND ARSALIDOU
2007). Such moral evaluations reflect internal representations of social
norms (Prehn et al., 2008). Although most social norm studies report
activity in the prefrontal cortex, the location is inconsistent: orbitofron-
tal (e.g., Koenigs and Tranel, 2007) and dorsolateral (e.g., Lieberman,
2007 for review; Prehn et al., 2008; Yoder and Decety, 2014) cortices.
Based on past literature, we expect that concordant brain locations
responding to “social norm representation” will be revealed in dorsolat-
eral prefrontal cortex.
1.3 | Norm violation
We define norm violation as behavioral deviations from shared social
norms (i.e., inappropriate behavior). Norm violations of another person
could affect the observer’s self-concept by threatening his or her social
identity (Melchers et al., 2015). Many functional neuroimaging studies
focus on brain responses to norm violation situations and how norm
violations influence decision-making. Specifically, they examine per-
ceived unfairness/fairness (Buckholtz & Marois, 2012; Sanfey & Chang,
2008), negative moral emotions—guilt (Wagner et al., 2011) or embar-
rassment as a consequence of norm violation (Takahashi et al., 2004,
2008). Some fMRI studies examining norm violations show key areas
being active in the insula (Denke, Rotte, Heinze, & Schaefer, 2014;
G€uro�glu, Bos, Dijk, Rombouts, & Crone, 2011; Sanfey et al., 2003) and
others in the orbitofrontal and dorsomedial cortex (Wagner et al.,
2011), yet others highlight activity in the cingulate cortex (Denke et al.,
2014; G€uro�glu et al., 2011). Considering this broad representation of
activation following norm violation processing, a meta-analysis is
needed to quantitatively verify which areas are concordantly active.
Based on previous findings, we expect that tasks in the “norm viola-
tion” category will show concordant brain locations in insular and cin-
gulate cortices.
2 | MATERIALS AND METHODS
2.1 | Literature search and article selection
The literature was searched using the standard engines of Web of Sci-
ence (http://apps.webofknowledge.com/), Scopus (https://www.sco-
pus.com/home.uri), and PubMed (https://www.ncbi.nlm.nih.gov/
pubmed/). We looked for keywords (fMRI and norm violation), and
(fMRI and social norms) on April 3, 2017. This search yielded a total of
181 articles. After removing duplicates, articles were subjected to a
series of selection criteria (Figure 1). First, articles needed to report
experiments with human participants that used fMRI or PET to study
tasks related to social norms (social norm representation, norm viola-
tion) and be written in English. These resulted in 116 articles that
underwent full-text review. Articles that reported no fMRI data, only
region of interest (ROI) results, only patient data, data on children,
reviews, and articles with irrelevant tasks were excluded. Articles,
which survived these criteria, underwent a full text review and were
screened for healthy adults, reporting stereotaxic coordinates (Talairach
or Montreal Neurological Institute, MNI) of whole-brain, within-group
results using random effects analysis. To keep methodology constant,
we included only experiments that used subtraction contrasts (i.e.,
A>B) and excluded experiments that addressed relations with specific
task ratings (i.e., “negative correlation with unfairness level”). We also
searched the references of all articles that passed the selection criteria
and identified 14 additional articles that were eligible. Thus, data from
a total of 36 articles were eligible for these meta-analyses.
To control within-group effects, a single experiment (i.e., contrast)
from each article reporting coordinates relating to overall social norms
was selected (Table 1). Experiments were further grouped into the two
categories based on careful evaluation of the task description and
responses required by the participant, as explained below. Social norm
representations were defined as behavioral rules implicitly given, rather
than explicitly given (i.e., laws and policies), related to maintenance of
social order, such as criteria of fairness, moral beauty, and willingness
to help. Eighteen articles reported experiments related to social norm
representation by examining social integration and “good” actions such
as maintenance of social integration and understanding morality (Table
1). The majority of articles used tasks that investigated social norms
through the evaluation of social welfare and exchange of financial
resources: these included the Public Goods Game (Cooper, Kreps,
Wiebe, Pirkl, & Knutson, 2010), Ultimatum Game (Civai, Crescentini,
Rustichini, & Rumiati, 2012; Corradi-Dell’Acqua et al., 2013; Harlé &
Sanfey, 2012; Servaas et al., 2015; Tabibnia et al., 2008; Tomasino
et al., 2013; Wu, Zang, Yuan, & Tian, 2015; Zhou, Wang, Rao, Yang, &
Li, 2014), Trust Game (Delgado, Frank, & Phelps, 2005), and a Dictator
Game (Feng et al., 2016; Strobel et al., 2011; Hu, Strang, & Weber,
2015). Players interacting in the Ultimatum and Dictator Games have
different roles. In the Dictator Game, a dictator is given the opportunity
to distribute points between himself and a recipient, whereas in the
Ultimatum Game a recipient could accept or reject a dictator’s offer. In
the Dictator Game the dictator’s offer remains unchanged. A dictator’s
50–50 offer is considered as normatively fair, whereas any deviation
(e.g., 60–40) is considered unfair. It has been shown that people not
only prefer fair distributions (G€uth, Schmittberger, & Schwarze, 1982)
but also tend to spend their own resources to prevent norm violation
at their own cost (Fehr & Fischbacher, 2004). Four articles used linguis-
tic material ratings (moral versus semantic; Heekeren et al., 2005; Moll,
Oliveira-Souza, Bramati, & Grafman, 2002; Prehn et al., 2008). One
article used a social interaction task (Yoder and Decety, 2014). For suf-
ficient power to detect sized effects in ALE meta-analyses, a minimum
of 17 studies are needed (Eickhoff et al., 2017), thus, due to lack of
experiments we did not examine moral (n54) and social (n514) norm
representations separately.
Norm violation was defined as behavioral actions of not following
behavioral rules related to the maintenance of social order. For this cat-
egory, we selected contrasts related to activity elicited by negative
emotions associated to situations with violation of norms, such as per-
ception of embarrassing stories related to themselves or others, and
unfair behavior related to the participant’s self or the whole social
group. Twenty nine articles used such tasks. These paradigms involved
unfair behaviors (Baumgartner, Knoch, Hotz, Eisenegger, & Fehr, 2011;
Cooper et al., 2010; Civai et al., 2012; Delgado et al., 2005; Feng et al.,
2016; Gospic et al., 2011; Guo et al., 2013; Guo et al., 2014; Halko,
ZINCHENKO AND ARSALIDOU | 957
Hlushchuk, Hari, & Sch€urmann, 2009; Harl�e et al., 2012; Hu et al.,
2016; Kirk, Downar, & Montague, 2011; Sanfey et al., 2003; Servaas
et al., 2015; Wu et al., 2015; Zheng et al., 2015) with more focus at dis-
advantageous inequity, which directly violate social norms (Fliessbach
et al., 2012) and unfair behavior in terms of cooperation, like Prisoner’s
Dilemma (Rilling et al., 2008). In the case of the Prisoner’s Dilemma
task, two players decide to cooperate or betray each other, decisions
that directly influence the player’s budgets. Specifically, the fairest deci-
sion is to cooperate as both players increase their budgets slightly. If
one player betrays the other the betrayer has higher gains than the
other player. The worse circumstance is when both players betray each
other, which results in loss of resources for both players. Others pre-
sented participants with personal embarrassing-norm violation stories
(Berthoz et al., 2002), sentences about moral depravity (Takahashi
FIGURE 1 PRISMA flowchart for identification and eligibility of articles (template by Moher et al., 2009) [Color figure can be viewed atwileyonlinelibrary.com]
958 | ZINCHENKO AND ARSALIDOU
TABLE1
Descriptive
inform
ationofstud
iesan
dco
ntrastsused
inthemeta-an
alyses
Firstau
thor,ye
arN
FEdu
cation
Han
dedn
ess
Age
Task
Contrast
Foci
Categ
ory
Bau
mgartne
r,2011
32
0n/r
Right
21.66
2.2
Ultim
atum
game
Unfair>
fair
17
NV
32
0n/r
Right
21.66
2.2
Ultim
atum
game
Fair>
unfair
4NR
Berthoz,
2002
12
0n/r
Right
19–3
7Persona
l-im
persona
l,em
barrassing
-violation
stories
Intentional
viola-
tion>norm
alstories
18
NV
Civai,2012
19
12
n/r
Right
n/r
Seco
nd-party
andthird-pa
rty
splitting
task
Uneq
ual
>eq
ual
5NV
19
12
n/r
Right
n/r
Seco
nd-party
andthird-pa
rty
splitting
task
Equal
>uneq
ual
3NR
Coope
r,2010
38
18
Unive
rsity
Right
18–4
6Pub
licgo
ods
game
Low
>high(donationco
ndi-
tion)
2NV
38
18
university
Right
18–4
6Pub
licgo
ods
game
High>low
(donationco
ndi-
tion)
1NR
Corrad
i-Dell’A
cqua
,2013
23
9n/r
n/r
18–3
5Ultim
atum
game
Ultim
atum
game>free
win
17
NR
Delgado
,2005
12
n/r
n/r
Right
26.646
4.11
Trust
game
Share>
keep
8NR
12
n/r
n/r
Right
26.646
4.11
Trust
game
Kee
p>
share
2NV
Den
ke,2014
17
8n/r
n/r
256
3.54
Observationofno
rm-
violationbe
havior’s
sce-
nariosan
dno
rm-
confirmingbe
havior
Immoral>
moral
3NV
Fen
g,2016
22
11
n/r
n/r
22.9
61.6
Third-party
punishmen
tpa
radigm
inmone
tary
task
Unfair>
Fair
19
NV
22
11
n/r
n/r
22.9
61.6
Third-party
punishmen
tpa
radigm
inmone
tary
task
Fair>
unfair
16
NR
Fliessba
ch,2012
64
32
n/r
n/r
22–3
3Adv
antage
ous/disad
vanta-
geous
payo
ffs
DI>
E1
NV
Gospic,2011
17
12
n/r
Right
23.7
64.2
Ultim
atum
game
(u>f)placebo
4NV
Guo
,2013
21
n/r
n/r
n/r
22.446
3.49
Ultim
atum
game
Unfair>
fair
13
NV
Guo
,2014
18
5n/r
Right
21.0
62.10
Ultim
atum
game
Unfair>
fair
10
NV
Halko
,2009
23
812ye
arsof
scho
olin
g/un
iversity
Right
22–4
6Ultim
atum
game
Unfair>
fair(noco
mpetition)
12
NV
Haren
ski,2006
10
10
n/r
Right
18–2
9W
atch
-decreasepictureev
a-luationtask
Watch
moral>
odd–
even
baseline
7NV
Harl� e,2012
38
23
n/r
n/r
18–7
0Ultim
atum
game
Unfair>
fair
12
NV
38
23
n/r
n/r
18–7
0Ultim
atum
game
Fair>
unfair
3NR
(Continues)
ZINCHENKO AND ARSALIDOU | 959
TABLE1
(Continue
d)
Firstau
thor,ye
arN
FEdu
cation
Han
dedn
ess
Age
Task
Contrast
Foci
Categ
ory
Hee
keren,
2005
12
2n/r
Right
25.756
1.54
Ling
uistic
materialrating
(moral\im
moral,seman
ti-
cally
correct\inco
rrect)
Moraldecision>seman
tic
decision
8NR
Hu,
2015
36
24
n/r
n/r
22.726
2.85
Dictatorgame
“Help”>
“help_control”
10
NR
Hu,
2016
23
n/r
Unive
rsity
Right
19–2
5Ultim
atum
game
Unfair>
fair
3NV
Luo,2006
20
11
n/r
n/r
20–3
6Illeg
alan
dlegalbe
havioral
scen
arios’observation
Illeg
al>
legal
9NV
Kirk,
2011
40
21
n/r
n/r
n/r
Ultim
atum
game
Unfair>
fair(controls)
11
NV
Melch
ers,2015
60
39
Unive
rsity
n/r
22.9565.38
Perceptionofem
barrass-
men
ts’film
san
dno
rmal
film
s
Vicariousem
barrassmen
tfilm
s>co
ntrolfilm
s6
NV
Moll,
2002
74
n/r
Right
30.3
64.7
Moralan
dno
n-moralsen-
tenc
esjudg
emen
tMoral>
neu
tral
3NR
Prehn
,2008
23
23
Unive
rsity
Right
25.176
6.56
Sociono
rmativean
dgram
-matical
judg
emen
tsrating
Socio-norm
ativejudg-
men
ts>gram
matical
judg-
men
ts
6NR
Rilling,
2008
20
15
n/r
Right
21.26
2.9
Prisone
r’sDilemma
Unreciprocated>reciproca-
tedco
operation
6NV
Sanfey
,2003
19
0n/r
n/r
n/r
ultimatum
game
Unfair>
fair
17
NV
Schreibe
r,2012
19
n/r
Unive
rsity
n/r
20–2
3Perceptionofno
rm-violating
versus
norm
-consistent
images
Norm
-violatingve
rsusnorm
-co
nsisten
tIm
ages
5NV
Servaas,2015
120
120
Unive
rsity
Right
18–2
5Ultim
atum
game
Unfair>fair
32
NV
120
120
Unive
rsity
Right
18–2
5Ultim
atum
game
Fair>
unfair
4NR
Tab
ibnia,
2008
12
9Und
ergrad
uate
Right
21.8
Ultim
atum
gamemodifica-
tion
Highfairness>
low
fairness
8NR
Takah
ashi,2008
15
n/r
n/r
Right
20.16
0.8
Senten
ces(neu
tral,moral
beau
ty,an
dmoralde
prav-
ity)
Moraldep
ravity-neu
tral
2NV
15
n/r
n/r
Right
20.16
0.8
Senten
ces(neu
tral,moral
beau
ty,an
dmoralde
prav-
ity)
Moralbea
uty
>neu
tral
5NR
Tomasino,2013
17
0n/r
Right
27.356
3.88
Ultim
atum
game
Fair>
unfair
3NR
Tread
way,2014
41
n/r
n/r
n/r
n/r
Scen
ariosrating
(neu
tral,
emotiona
llyha
rmful)
Intentional>unintentional
(allsubjects)
18
NV
Wagne
r,2011
18
18
n/r
n/r
25–3
0Emotionco
nditions
task
Guilt
>sham
e1sadness
2NV
(Continues)
960 | ZINCHENKO AND ARSALIDOU
et al., 2008), scenarios with norm-violations (Denke et al., 2014),
picture evaluation task with scenes with moral norm violations
(Harenski and Hamann, 2006), norm violated behavior’s scenarios (Luo
et al., 2006), embarrassing content (Melchers et al., 2015), norm-
violating images (Schreiber and Iacoboni, 2012), a task that modulated
social interaction (Yoder and Decety, 2014), different scenarios based
on intentional and unintentional norm violation (Treadway et al., 2014),
and a task that modulated of emotions related to social content (Wag-
ner et al., 2011). Overall, 29 experiments included in this category
reflect social emotions associated with norm violation.
2.2 | Meta-analysis
Activation likelihood estimation (ALE) is a meta-analysis method, which
can be used for whole-brain, random-effects voxel-wise imaging analy-
sis (Eickhoff et al., 2009, 2017; Eickhoff, Bzdok, Laird, Kurth, & Fox,
2012). For our study, we used GingerAle version 2.3.6 (freely available
at brainmap.org/ale). It uses foci combined from different studies to
create a probabilistic map of activation that is thresholded and com-
pared against a null distribution at a voxel-by-voxel level.
This map provides the clusters (peak and volume) that have a signif-
icant likelihood of being detected across studies within a stereotaxic
coordinate space. Specifically, activation likelihood estimates are calcu-
lated for each voxel by modeling each coordinate with an equal weight-
ing using a 3-D Gaussian probability density function. ALE values can be
thresholded using a cluster-forming (i.e., in terms of magnitude) and a
cluster-level (i.e., in terms of the size of the cluster) criterion. Thus, ALE
values provide information about statistical maps of estimated activation
regarding tasks included in the analyses. To create ALE maps we used
contrast coordinates (i.e., experiments) reported in eligible, previously
published fMRI studies that include experiments on social norms “over-
all” and subcategories for (a) social norm representation and (b) norm
violation. The overall analysis (i.e., social norms overall) allows for identi-
fying concordance at the single study level with higher power as more
studies are included in this analysis; however, contrast analysis allows
for examining the conjunction and differences between the subcatego-
ries. Montreal Neurological Institute (MNI) coordinates were trans-
formed into Talairach coordinates. Significance is assessed using a
cluster-level correction for multiple comparisons at p5 .05 and a
cluster-forming threshold p< .001 (Eickhoff et al., 2012, 2017). A con-
trast analysis was performed on the thresholded ALE maps of social
norm representation and norm violation categories to identify concord-
ance that was common (i.e., conjunction) and different for these
categories. Because ALE maps are already thresholded for multiple com-
parisons a threshold of uncorrected 0.01, with 5000 permutations, mini-
mum volume 50 mm2 was used (e.g., Arsalidou, Pawliw-Levac, Sadeghi,
& Pascual-Leone, 2017; Sokolowski, Fias, Mousa, & Ansari, 2017).
3 | RESULTS
Articles included in the meta-analyses report data on 993 participants
(Table 1). Eight articles did not report gender; of the remaining articles,
52% were female participants. About a half of articles that reportedTABLE1
(Continue
d)
Firstau
thor,ye
arN
FEdu
cation
Han
dedn
ess
Age
Task
Contrast
Foci
Categ
ory
Wu,
2015
32
24
Unive
rsity
Right
22.316
2.35
Ultim
atum
game,
dictator
game
Unfair>
fairUltim
atum
Gam
e1
NV
32
24
Unive
rsity
Right
22.316
2.35
Ultim
atum
game,
dictator
game
FairUltim
atum
Gam
e>
unfair
Ultim
atum
Gam
e
3NR
Yode
r,2014
40
21
n/r
n/r
216
2So
cial
interactions’m
odu
la-
tiontask
Bad
>go
odactions
9NV
40
21
n/r
n/r
216
2So
cial
interactions’m
odu
la-
tiontask
Good>
bad
actions
15
NR
Zhe
ng,2015
25
18
n/r
Right
21.446
3.38
Ultim
atum
game
Uneq
ual
>eq
ual
15
NV
Zho
u,2014
28
15
Unive
rsity
Right
25.076
3.35
Ultim
atum
game
Unfair>
fair
4NV
28
15
Unive
rsity
Right
25.076
3.35
Ultim
atum
game
Fair>
unfair
1NR
ZINCHENKO AND ARSALIDOU | 961
handedness (42%) tested participants who were right-handed (100%).
Four articles did not report the age of the participants. When an age
range was given, the median of the age range was used in calculating
the average of the sample, which was a 23.8966.28 year. Twenty-five
percent of the articles reported the education level of participants, and
100% of the participants were reported to have some university educa-
tion (undergraduate or graduate). Bottom of Figure 1 shows the num-
ber of articles, number of experiments, and number of foci included in
each meta-analysis.
3.1 | ALE maps
3.1.1 | Social norms (overall)
All tasks related to social norms show concordance in five clusters
(Table 2). The largest cluster with the highest ALE value is found in the
right insula (BA 13). The second largest cluster is found in the left
medial frontal gyrus (BA 32) that extended to the cingulate gyrus (BA
32). Prefrontal activity is also observed in the right superior and middle
frontal gyri (BA 9 and BA 10). Other regions include the left insula and
claustrum.
3.1.2 | Social norm representation
Social norm representations show concordance in a cluster that
includes the left anterior cingulate and right medial frontal gyrus (BA
10; Figure 2 and Table 2).
3.1.3 | Norm violation
Five suprathreshold clusters were detected for norm violation (Figure 2
and Table 2). The one with the highest likelihood of being detected is
in the right insula (BA 13). Other regions include right cingulate gyrus
(BA 32), left insula (BA 13) and claustrum, and right middle and superior
frontal gyri (BA 9 and 10).
3.1.4 | Social norm representation versus norm violation
No common clusters survive the conjunction between social norm rep-
resentation and norm violation. Compared to norm violation, social
norm representation shows greater concordance in the anterior cingu-
late gyri (BA 32) and right medial frontal gyrus (BA 10), whereas com-
pared to social norm representation, norm violation shows greater
concordance in the right insula and claustrum and more dorsal parts of
the cingulate gyrus (BA 24, 32; Table 2).
4 | DISCUSSION
We examined neural correlates of social norms using quantitative ALE
meta-analyses. Processing tasks that assess social norms show con-
cordance mainly in frontal regions with clear significant distinctions
between instances of social norm representation and norm violation.
Specifically, our results reveal two key findings. First, norm violation
tasks show that the area with the highest likelihood of being active is
FIGURE 2 Brain maps demonstrating significant ALE values for each category. Left5 left. Note: coordinates are in Talairach space. Cluster-levelcorrection p5 .05 for multiple comparisons with cluster forming threshold p< .001 [Color figure can be viewed at wileyonlinelibrary.com]
962 | ZINCHENKO AND ARSALIDOU
the insula, along with dorsolateral prefrontal regions and dorsal parts of
the cingulate gyrus. Secondly, social norm representations rely on activ-
ity mainly in ventromedial prefrontal regions; medial frontal and ante-
rior cingulate gyri. Findings are in agreement with Montague and
Lohrenz (2007) hypothesis, which suggests that different systems
underlie different social norm processes. We did not replicate any of
the concordance of posterior brain regions observed by Feng et al.
(2015), likely because these were smaller clusters that did not survive
our cluster-level correction for multiple comparisons. Alternatively, lack
of concordance in posterior regions may be due to visual-spatial heter-
ogeneity in task paradigms assessing “social norms.” Importantly, we
show that according to contrast analyses, the anterior cingulate and
medial frontal gyri are significantly more concordant for social norm
representation processing, whereas the right insula, dorsolateral pre-
frontal, and the dorsal cingulate cortices are significantly more concord-
ant to norm violation processing.
4.1 | Social norm representation
Social norm representation tasks show concordant activity in the left
anterior cingulate (BA 32) extended to right medial frontal gyrus (BA
10). BA 10 is mainly implicated in inferences of another person’s
TABLE 2 Concordant areas for each category
Talairach coordinates
Category Volume mm3 ALE value x y z Brain area BA
Social norms (overall) 4016 0.0480 34 18 4 Right insula 13
3392 0.0429 24 10 46 Left medial frontal gyrus 320.0342 4 20 38 Right cingulate gyrus 320.0306 4 22 28 Right cingulate gyrus 32
1808 0.0270 230 20 8 Left insula 130.0253 230 14 22 Left claustrum0.0251 234 14 0 Left insula 13
1736 0.0276 36 40 20 Right middle frontal gyrus 100.0236 34 26 32 Right middle frontal gyrus 9
936 0.0297 8 52 24 Right superior frontal gyrus 9
Social norm representation 1184 0.0163 24 50 22 Left anterior cingulate 10
0.0116 10 48 6 Right medial frontal gyrus 10
Norm violation 4368 0.0472 34 18 4 Right insula 13
4192 0.0339 6 18 38 Right cingulate gyrus 320.0329 24 10 48 Left superior frontal gyrus 60.0306 4 22 28 Right Cingulate gyrus 320.0203 26 28 30 Left cingulate gyrus 9
1912 0.0258 230 20 8 Left insula 130.0239 230 14 22 Left claustrum
1464 0.0236 34 26 32 Right middle frontal gyrus 90.0175 34 42 22 Right middle frontal gyrus 100.0150 42 22 40 Right middle frontal gyrus 8
856 0.0255 6 52 24 Right superior frontal gyrus 9
Social norm representation>norm violation
1080 3.7190 2 45 2.7 Right anterior cingulate gyrus 32
3.5400 4 48 2 Right anterior cingulate gyrus3.3528 21 52 22 Left anterior cingulate gyrus3.2389 4 52 0 Right medial frontal gyrus 103.0902 5 51 24 Right medial frontal gyrus 10
Norm violation> socialnorm representation
2848 3.7190 40.3 19.2 3.9 Right insula 13
3.5401 37 21 2 Right insula 133.2389 37 13 9 Right insula 133.1560 34.8 10.8 3.7 Right insula 13
1976 3.7190 6.7 24.3 27.3 Right anterior cingulate gyrus 243.5400 0 26 26 Left cingulate gyrus 323.3528 22 30 30 Left cingulate gyrus 323.2389 6.5 20.6 35.2 Right cingulate gyrus 323.0902 9.3 16 36.7 Right cingulate gyrus 322.9478 26 24 26 Left anterior cingulate gyrus 24
616 3.0902 35.3 24.7 36.7 Right middle frontal gyrus 92.9112 34.9 23.1 32.2 Right middle frontal gyrus 92.7703 41 22 42 Right middle frontal gyrus 8
Conjunction between norm violationand social norm representation
No clusters found
ZINCHENKO AND ARSALIDOU | 963
intentions, mostly social intentions (Ciaramidaro et al., 2007; Frith &
Frith, 2003). The “gateway hypothesis” states that BA 10 activity sup-
ports mechanisms that allow individuals to react to environmental stim-
uli based not on immediate perceptual information but on self-
generated and maintained representations (Burgess, Dumontheil, & Gil-
bert, 2007). Thus, this region seems to be mainly involved in processes
of relational integration by manipulation of self-generated information
and highly abstract information (Christoff et al., 2001; Christoff, Kera-
matian, Gordon, Smith, & Mädler, 2009). Previous fMRI and transcra-
nial direct current stimulation (tDCS) findings propose the right lateral
prefrontal cortex to play a key role in the behavioral control and judg-
ment between fair and selfish responses (Ruff, Ugazio, & Fehr, 2013).
The findings suggest that activity in the dorsal anterior cingulate cortex
has been implicated in processing the detection and appraisal of social
processes, such as exclusion and “social pain” phenomenon (Corradi-
Dell’Acqua, Tusche, Vuilleumier, & Singer, 2016; Dedovic, Slavich, Mus-
catell, Irwin, & Eisenberger, 2016; Kawamoto, Ura, & Nittono, 2015).
We suggest that prefrontal BA 10 serves to support abstract represen-
tation of existing norms. It would be interesting to examine the involve-
ment of these regions in newly formed social norm representations.
4.2 | Norm violation
Processing norm violations elicits activity in the insular cortex. The
anterior insula is generally considered as a relay station that sends
interoceptive information to the cortex (Menon & Uddin, 2010; Seeley
et al., 2007; Taylor, Seminowicz, & Davis, 2009). However, its activity
is also associated in all sorts of cognitive and affective activities (Duer-
den et al., 2013; Uddin, 2015). Social-emotional tasks reveal activity in
the anterior-ventral insula, while cognitive tasks elicited activation in
the anterior-dorsal part (Kurth, Zilles, Fox, Laird, & Eickhoff, 2010). It
was suggested that the insula also plays a role in fairness-related
behavior (Moll, Oliveira-Souza, & Zahn, 2008; Corradi-Dell’Acqua,
2013). In particular, the right insula (BA 13) and anterior cingulate have
been also shown to activate to first-hand and vicarious experiences of
unfairness (Cheng et al., 2015; Cheng et al., 2017), lie evaluation (Lelie-
veld, Shalvi, & Crone, 2016), and detection of distributional inequity in
economic tasks (Zhong, Chark, Hsu, & Chew, 2016), which could be
explained as a violation of social norms. Patients with damaged insula
have abnormal expressions of trust in economics tasks such as the
Trust Game, which leads to an inability to detect norm violation effec-
tively (Belfi, Koscik, & Tranel, 2015). Moreover, insular activation has
an indirect influence on social preferences as aversive emotional states
increase the frequency of receiving unfair monetary offers, which cor-
respond to worse detection of norm violation (Harl�e et al., 2012).
We also find concordance in the anterior cingulate cortex. Previ-
ous findings also suggest that anterior cingulate cortex is implicated in
social behavior and possibly processing costs and benefits (Apps, Rush-
worth, & Chang, 2016). Specifically, the anterior cingulate cortex acti-
vates when processing rewards that other people receive (Lockwood,
Apps, Roiser, & Viding, 2015) and when others make decisions related
to prediction error (Apps, Green, & Ramnani, 2013). It was shown that
anterior cingulate cortex to anterior insula connectivity may also reflect
basic prosocial motivation (Hein, Morishima, Leiberg, Sul, & Fehr,
2016). Furthermore, people with higher egoistical motivation, who
more frequently violate social norms, have weaker connectivity
between these regions (Hein et al., 2016). This is consistent with the
hypothesis that the cingulate gyrus and insula are involved in conver-
sion of affective goals into cognitive goals (Arsalidou & Pascual-Leone,
2016) as a feeling of effort in cognitively demanding situations (Arsali-
dou et al., 2017). A generic role of the insula as part of a salience net-
work has been suggested (Menon & Uddin, 2010; Uddin, 2015). We
propose that the role of the insula in norm violation may be related to
a generic sense of cognitive demand related to the “inequity encoding”
(Hsu, Anen, & Quartz, 2008) and fairness-related behavior.
Other areas related to norm violation include the right cingulate
gyrus and left claustrum. A meta-analysis suggests that cingulate cortex
is implicated in six domains according to the activation’s map: attention,
pain, language, action execution, emotions, and memory (Torta &
Cauda, 2011). The cingulate cortex has received extensive attention in
its role in social norms and was studied under paradigms of altruistic
punishment in social dilemmas (Fehr & Camerer, 2007; Feng et al.,
2016; Sanfey, Loewenstein, McClure, & Cohen, 2006). Moreover, the
dorsal anterior cingulate cortex through its strong connectivity with the
insula could be related to the detection of social norm violations during
conflict monitoring and moral context evaluation (G€uro�glu et al., 2011;
Denke et al., 2014). Therefore, in processing norm violations, we sug-
gest that the role of the dorsal cingulate to be a hub for information
where signals are sent to the insula to help evaluate possible norm vio-
lation; such process would not be pertinent during social norm
representation.
Norm violation studies also show concordant activity in the right
middle frontal gyrus (BA 10). The middle frontal gyrus BA 10 has been
associated with general abstract representations that require process-
ing of internally generated information (Christoff & Gabrielli, 2000;
Chrisoff et al., 2009). Specific to social cognition, the right lateral pre-
frontal cortex has been shown to be linked to processing of context-
dependent social interaction regulated by norms of fairness in case of
financial exchange (Ruff et al., 2013), understanding of social standards
related to fairness norms and good reputation (Knoch, Pascual-Leone,
Meyer, Treyer, & Fehr, 2006; Knoch, Schneider, Schunk, Hohmann, &
Fehr, 2009), and inferences of another person’s intentions, mostly
social intentions (Ciaramidaro et al., 2007; Frith & Frith, 2003). Evi-
dence from lesions studies and meta-analysis show that BA10 is also
involved in performance theory of mind tasks, and social cognition in
general (Gilbert et al., 2006; Roca et al., 2011). We suggest that BA 10
contribution to processing social norms could be related to the detec-
tion of norm violation and the possibility to process knowledge about
the existing norm. According to the “gateway hypothesis” (Burgess
et al., 2007), BA 10 contributes to forming self-generated representa-
tions that are not necessarily environmentally based. This hypothesis is
consistent with claims that suggest BA 10 to process highly abstract
information (Christoff et al., 2001; Christoff et al., 2009). Based on this
literature, we suggest that BA 10 contribution to processing social
norms could be related to the detection of norm violation and the pos-
sibility to process knowledge about the existing norm. This
964 | ZINCHENKO AND ARSALIDOU
interpretation is also consistent with Montague and Lohrenz (2007)
model that suggests the existence of specific brain representations to
keep information about existing social norms.
Another significantly concordant region for norm violation is the
claustrum, a region adjacent to the insula. The claustrum due to the
numerous input–output connections with limbic, prefrontal, sensory,
motor, and associative cortices was assumed to act as a cross-modal
integrator (Goll, Atlan, & Citri, 2015). It has also to been identified as a
key region of a network that supports consciousness (Koubeissi, Barto-
lomei, Beltagy, & Picard, 2014). Claustrum activation is also reported in
studies of fairness-related inequity during decision-making (Nihonsugi,
Ihara, & Haruno, 2015); however, its role was not semantically defined.
A meta-analysis reports that claustrum is involved in general empathy
and pain-related empathy processing (Gu, Hof, Friston, & Fan, 2013).
Concordant activity in this region supports its nonrandom appearance
in social cognition studies. Although further work is needed to clearly
define the functions of the claustrum, some evidence point to its spe-
cial impact on social behavior. Studies show that claustrum activity is
related to processes such as fear recognition (Stein, Simmons, Fein-
stein, & Paulus, 2007) and associative learning in animals (Chachich &
Powell, 2004), and multimodal information processing and emotional
responses (Bennett & Baird, 2006) in healthy humans. Anatomically the
volume of the claustrum is deficient in clinical populations that suffer
from socio-cognitive deficits. For instance, claustrum volume in autism
patients is 22% reduced compared to healthy children from 4 to 8
years (Wiegel et al., 2014). Examination of altered connectivity in indi-
viduals with autism and comparison with behavioral performance sug-
gest that claustrum and its network interactions could significantly
contribute in social and communication development (Wiegel et al.,
2014). Owing to this multimodal integration, we propose that the
claustrum could integrate aversive emotional signals and signals from
associative cortices in norm violation processing.
Our analysis also found concordant activity in left superior frontal
gyrus (BA 6). The superior frontal gyrus (BA 6) has been linked to
higher cortical functions such as internal guidance of the behavior and
its control (Luria, 1966), hand motor representations (Vara et al., 2014),
and working memory (Wager & Smith, 2003 for review; du Boisguehe-
neuc et al., 2006). Specifically, it is suggested that this region is
involved in fronto-parietal cortical network associated with attention,
working memory, episodic retrieval, and conscious perception (Naghavi
& Nyberg, 2005 for review). In addition, an activation of cingulate cor-
tex/superior frontal cortex has been found during processing psycho-
logical self (Hu et al., 2016). It has been suggested that cingulate cortex
is involved in conflict monitoring (Botvinick, Cohen, & Carter, 2004),
which could be applicable for socially driven interactions (Lavin et al.,
2013). Thus, cingulate cortex activation during norm violation process-
ing could be related to direct conflict monitoring and evaluation with
self-reference (i.e., what does norm violation mean for me). We suggest
that activation of the left superior frontal gyrus in the same cluster as
the right cingulate gyrus corresponds with the need to continuously
monitor and adjust information about others behavior in working mem-
ory relevant to norm violation processing. Overall, concordant fMRI
findings suggest that activation of the lateral prefrontal cortex during
affective information processing, of the anterior cingulate cortex during
monitoring any conflict, and of the insula during emotional processing
of aversive signals and responses to unfairness may be involved into
driving a motivation to act against norm violations.
5 | LIMITATIONS
Data presented here represent concordance across fMRI studies that
investigated social norms overall and as two different subcategories in
healthy adults. Optimally, further cortical differentiation would be pos-
sible with additional social norm subcategories such as social norm rep-
resentation in social and moral domains and social conformity.
However, an insufficient number of experiments did not allow for
examining concordance in these subcategories (i.e., n<17; Eickhoff
et al., 2017). Second, we examine the activity to various tasks that may
elicit a differentiated brain response, such as moral paradigms (scenario
ratings) and classic economic tasks (e.g., Ultimatum Game, Trust Game).
In the future, as more experiments become available, it could be possi-
ble to distinguish between these domains within social norms. Task
characteristics (e.g., visual-spatial features and task demands such as
economic games versus reading and rating tasks) could also influence
heterogeneity across studies. Importantly, our goal was to identify
common patterns in brain locations related to social norms regardless
of task specificities, and our results show that this concordance is
observed in anterior brain areas. Last, a shortcoming of the ALE
method is that it does not use effect sizes (as seed-based mapping
(SDM; Radua & Mataix-Cols, 2012; Radua et al., 2012)). Although there
are no available methods for performing robustness analyses with Gin-
gerALE, simulations of ALE analyses have been performed to test sensi-
tivity, ensuing cluster sizes, number of incidental clusters, and statistical
power (Eickhoff et al., 2016). They did so by systematically varying the
overall number of experiments and experiments activating the simu-
lated “true” location (Eickhoff et al., 2016 for details) and a recom-
mended minimum number of experiments to reach sufficient power
(n517–20; Eickhoff et al., 2017). Despite these shortcomings, the cur-
rent meta-analyses present new knowledge on the topic of social
norms with a meta-analytic methodology that provides coordinates in
stereotaxic space, which is advantageous to standard reviews.
6 | CONCLUSION
Social norms are fundamental for our daily social interactions and our
meta-analyses show that different aspects associated with social norms
elicit activity in distinct brain regions. The right anterior cingulate and
medial frontal gyri (BA 10) are critical for social norm representation
(social and moral), whereas the insula, dorsolateral, and dorsal cingulate
cortices are key for processing norm violation. Stereotaxic coordinates
reported here can serve as a normative adult framework for targeted
future studies and may be beneficial for studies investigating social
norm compliance and enforcement in patients with disorders such as
autism spectrum disorder.
ZINCHENKO AND ARSALIDOU | 965
CONFLICT OF INTEREST
The authors declared that they have no conflict of interest.
ORCID
Oksana Zinchenko http://orcid.org/0000-0002-7976-3224
Marie Arsalidou http://orcid.org/0000-0001-9879-3894
REFERENCES
Apps, M. A., Green, R., & Ramnani, N. (2013). Reinforcement learning sig-
nals in the anterior cingulate cortex code for others’ false beliefs.
NeuroImage, 64, 1–9. https://doi.org/10.1016/j.neuroimage.2012.09.
010.
Apps, M. A. J., Rushworth, M. F. S., & Chang, S. W. C. (2016). The ante-
rior cingulate gyrus and social cognition: Tracking the motivation of
others. Neuron, 90(4), 692–707.
Arsalidou, M., & Pascual-Leone, J. (2016). Constructivist developmental
theory is needed in developmental neuroscience. NPJ Science of
Learning, 1, 16016. https://doi.org/10.1038/npjscilearn.2016.16.
Arsalidou, M., Pawliw-Levac, M., Sadeghi, M., & Pascual-Leone, J. (2017).
Brain areas associated with numbers and calculations in children:
Meta-analyses of fMRI studies. Developmental Cognitive Neuroscience.
Batson, C. D., Kennedy, C. L., Nord, L. A., Stocks, E. L., Fleming, D. Y. A.,
Marzette, C. M., . . . Zerger, T. (2007). Anger at unfairness: Is it moral
outrage? European Journal of Social Psychology, 37(6), 1272–1285.https://doi.org/10.1002/ejsp.434.
Baumgartner, T., Knoch, D., Hotz, P., Eisenegger, C., & Fehr, E. (2011).
Dorsolateral and ventromedial prefrontal cortex orchestrate norma-
tive choice. Nature Neuroscience, 2, 14(11), 1468–1474. https://doi.org/10.1038/nn.2933.
Bechara, A., Damasio, H., & Damasio, A. (2000). Emotion, decision mak-
ing and the orbitofrontal cortex. Cerebral Cortex (New York, N.Y.:
1991), 10(3), 295–307. https://doi.org/10.1093/cercor/10.3.295.
Belfi, A. M., Koscik, T. R., & Tranel, D. (2015). Damage to the insula is
associated with abnormal interpersonal trust. Neuropsychologia, 71,
165–172. https://doi.org/10.1016/j.neuropsychologia.2015.04.003.
Bennett, C. M., & Baird, A. A. (2006). Anatomical changes in the emerg-
ing adult brain: A voxel-based morphometry study. Human Brain Map-
ping, 27, 766–777.
Berthoz, S., Armony, J. L., Blair, R. J., Dolan, R. J. (2002). An fMRI study
of intentional and unintentional (embarrassing) violations of social
norms. Brain: A Journal of Neurology, 125(8), 1696–1708. https://doi.org/10.1093/brain/awf190.
Bicchieri, C. (2016). Diagnosing norms. Norms in the wild (1st ed.) (pp. 4–6). Oxford, United Kingdom: Oxford University Press.
Boisgueheneuc, F. D., Levy, R., Volle, E., Seassau, M., Duffau, H., Kinking-
nehun, S., & Dubois, B. (2006). Functions of the left superior frontal
gyrus in humans: A lesion study. Brain: A Journal of Neurology, 129
(12), 3315–3328. https://doi.org/10.1093/brain/awl244.
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring
and anterior cingulate cortex: An update. Trends in Cognitive Sciences,
8(12), 539–546. https://doi.org/10.1016/j.tics.2004.10.003.
Brennan, G., Gonz�alez, L. G., G€uth, W., & Levati, M. V. (2008). Attitudes
toward private and collective risk in individual and strategic choice
situations. Journal of Economic Behavior & Organization, 67, 253–262.
Buckholtz, J. W., & Marois, R. (2012). The roots of modern justice: Cog-
nitive and neural foundations of social norms and their enforcement.
Nature Neuroscience, 15(5), 655–661. https://doi.org/10.1038/nn.
3087.
Burgess, P. W., Dumontheil, I., & Gilbert, S. J. (2007). The gateway
hypothesis of rostral prefrontal cortex (area 10) function. Trends in
Cognitive Sciences, 11(7), 290–298. https://doi.org/10.1016/j.tics.
2007.05.004.
Camerer, C. (2003). Behavioral game theory: Experiments in strategic inter-
action. New York, NY: Russell Sage Foundation.
Chachich, M. E., & Powell, D. A. (2004). The role of claustrum in Pavlov-
ian heart rate conditioning in the rabbit (Oryctolagus cuniculatus):
Anatomical, electrophysiological, and lesion studies. Behavioural Neu-
roscience, 118, 514–525.
Cheng, X., Zheng, L., Li, L., Guo, X., Wang, Q., Lord, A., & Yang, G.
(2015). Power to punish norm violations affects the neural processes
of fairness-related decision making. Frontiers in Behavioral Neuro-
science, 9. https://doi.org/10.3389/fnbeh.2015.00344.
Cheng, X., Zheng, L., Li, L., Zheng, Y., Guo, X., & Yang, G. (2017). Anterior
insula signals inequalities in a modified Ultimatum Game. Neuro-
science, 348, 126–134. https://doi.org/10.1016/j.neuroscience.2017.02.023. Epub 2017 Feb 20.
Christoff, K., & Gabrieli, J. D.E. (2000). The frontopolar cortex and
human cognition: Evidence for a rostrocaudal hierarchical organiza-
tion within the human prefrontal cortex. Psychobiology, 28, 168–186.
Christoff, K., Prabhakaran, V., Dorfman, J., Zhao, Z., Kroger, J. K., Holy-
oak, K. J., & Gabrieli, J. D. (2001). Rostrolateral prefrontal cortex
involvement in relational integration during reasoning. NeuroImage,
14(5), 1136–1149. https://doi.org/10.1006/nimg.2001.0922.
Christoff, K., Keramatian, K., Gordon, A. M., Smith, R., & Mädler, B.
(2009). Prefrontal organization of cognitive control according to lev-
els of abstraction. Brain Research, 1286, 94–105. https://doi.org/10.1016/j.brainres.2009.05.096.
Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of
normative conduct: Recycling the concept of norms to reduce litter-
ing in public places. Journal of Personality and Social Psychology, 58(6),
1015–1026. https://doi.org/10.1037//0022-3514.58.6.1015.
Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and
conformity. Annual Review of Psychology, 55(1), 591–621. https://doi.org/10.1146/annurev.psych.55.090902.142015.
Ciaramidaro, A., Adenzato, M., Enrici, I., Erk, S., Pia, L., Bara, B., & Walter,
H. (2007). The intentional network: How the brain reads varieties of
intentions. Neuropsychologia, 45(13), 3105–3113. https://doi.org/10.
1016/j.neuropsychologia.2007.05.011.
Civai, C., Crescentini, C., Rustichini, A., & Rumiati, R. I. (2012). Equality
versus self-interest in the brain: Differential roles of anterior insula
and medial prefrontal cortex. NeuroImage, 62(1), 102–112. https://
doi.org/10.1016/j.neuroimage.2012.04.037.
Cooper, J. C., Kreps, T. A., Wiebe, T., Pirkl, T., & Knutson, B. (2010).
When giving is good: Ventromedial prefrontal cortex activation for
others’ intentions. Neuron, 67(3), 511–521. https://doi.org/10.1016/j.
neuron.2010.06.030.
Corradi-Dell’Acqua, C., Civai, C., Rumiati, R. I., & Fink, G. R. (2013). Dis-
entangling self- and fairness-related neural mechanisms involved in
the ultimatum game: An fMRI study. Social Cognitive and Affective
Neuroscience, 8(4), 424–431. https://doi.org/10.1093/scan/nss014
Corradi-Dell’Acqua, C., Tusche, A., Vuilleumier, P., & Singer, T. (2016).
Cross-modal representations of first-hand and vicarious pain, disgust
and fairness in insular and cingulate cortex. Social Cognitive and Affec-
tive Neuroscience, 7, 10904. https://doi.org/10.1038/ncomms10904.
Dedovic, K., Slavich, G. M., Muscatell, K. A., Irwin, M. R., & Eisenberger,
N. I. (2016). Dorsal anterior cingulate cortex responses to repeated
social evaluative feedback in young women with and without a his-
tory of depression. Frontiers in Behavioral Neuroscience, 10, 64.
966 | ZINCHENKO AND ARSALIDOU
Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005). Perceptions of moral
character modulate the neural systems of reward during the trust
game. Nature Neuroscience, 8(11), 1611–1618. https://doi.org/10.
1038/nn1575.
Denke, C., Rotte, M., Heinze, H., & Schaefer, M. (2014). Belief in a just
world is associated with activity in insula and somatosensory cortices
as a response to the perception of norm violations. Social Neuro-
science, 9(5), 514–521. https://doi.org/10.1080/17470919.2014.
922493.
Dickinson, D. L., Masclet, D., & Villeval, M. C. (2015). Norm enforcement
in social dilemmas: An experiment with police commissioners. Journal
of Public Economics, 126, 74–85. https://doi.org/10.1016/j.jpubeco.
2015.03.012.
Dimitrov, M., Phipps, M., Zahn, T. P., & Grafman, J. (1999). A thoroughly
modern gage. Neurocase, 5(4), 345–354. https://doi.org/10.1080/
13554799908411987.
Duerden, E. G., Arsalidou, M., Lee, M., & Taylor, M. J. (2013). Lateraliza-
tion of affective processing in the insula. NeuroImage, 78, 159–175.
Eickhoff, S., Laird, A., Grefkes, C., Wang, L., Zilles, K., & Fox, P. (2009).
Coordinate-based activation likelihood estimation meta-analysis of
neuroimaging data: A random-effects approach based on empirical
estimates of spatial uncertainty. Human Brain Mapping, 30(9), 2907–2926. https://doi.org/10.1002/hbm.20718.
Eickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F., & Fox, P. T. (2012). Acti-
vation likelihood estimation revisited. NeuroImage, 59, 2349–2361.https://doi.org/10.1016/j.neuroimage.2011.09.017.
Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K.,
Fox, P. T., . . . Eickhoff, C. R. (2016). Behavior, sensitivity, and power
of activation likelihood estimation characterized by massive empirical
simulation. NeuroImage, 137, 70–85. https://doi.org/10.1016/j.neuro-image.2016.04.072.
Eickhoff, S. B., Laird, A. R., Fox, P. M., Lancaster, J. L., & Fox, P. T.
(2017). Implementation errors in the GingerALE Software: Descrip-
tion and recommendations. Human Brain Mapping, 38, 7–11. https://
doi.org/10.1002/hbm.23342.
Elster, J. (1989). Social norms and economic theory. The Journal of Eco-
nomic Perspectives, 3(4), 89–117.
Fehr, E., & Fischbacher, U. (2004). Third-party punishment and social
norms. Evolution and Human Behavior, 25(2), 63–87. https://doi.org/
10.1016/s1090-5138(04)00005-4.
Fehr, E., & Camerer, C. F. (2007). Social neuroeconomics: The neural cir-
cuitry of social preferences. Trends in Cognitive Sciences, 11(10), 419–
427. https://doi.org/10.1016/j.tics.2007.09.002.
Feng, C., Luo, Y., & Krueger, F. (2015). Neural signatures of fairness-
related normative decision making in the ultimatum game: A
coordinate-based meta-analysis. Human Brain Mapping, 36(2), 591–602. https://doi.org/10.1002/hbm.22649.
Feng, C., Deshpande, G., Liu, C., Gu, R., Luo, Y. J., & Krueger, F. (2016).
Diffusion of responsibility attenuates altruistic punishment: A func-
tional magnetic resonance imaging effective connectivity study.
Human Brain Mapping, 37(2), 663–677. https://doi.org/10.1002/hbm.
23057.
Fliessbach, K., Phillipps, C. B., Trautner, P., Schnabel, M., Elger, C. E.,
Falk, A., & Weber, B. (2012). Neural responses to advantageous and
disadvantageous inequity. Frontiers in Human Neuroscience, 6. https://
doi.org/10.3389/fnhum.2012.00165.
Frith, U., & Frith, C. D. (2003). Development and neurophysiology of
mentalizing. Philosophical Transactions of the Royal Society B: Biological
Sciences, 358(1431), 459–473. https://doi.org/10.1098/rstb.2002.
1218.
Gabay, A. S., Radua, J., Kempton, M. J., & Mehta, M. A. (2014). The ulti-
matum game and the brain: A meta-analysis of neuroimaging studies.
Neuroscience & Biobehavioral Reviews, 47, 549–558. https://doi.org/10.1016/j.neubiorev.2014.10.014.
Gilbert, S. J., Spengler, S., Simons, J. S., Steele, J. D., Lawrie, S. M., Frith,
C. D., & Burgess, P. W. (2006). Functional specialization within rostral
prefrontal cortex (Area 10): A meta-analysis. Journal of Cognitive Neu-
roscience, 18(6), 932–948. https://doi.org/10.1162/jocn.2006.18.6.
932.
Goll, Y., Atlan, G., & Citri, A. (2015). Attention: The claustrum. Trends in
Neurosciences, 38(8), 486–495. https://doi.org/10.1016/j.tins.2015.
05.006.
Gospic, K., Mohlin, E., Fransson, P., Petrovic, P., Johannesson, M., &
Ingvar, M. (2011). Limbic justice—Amygdala involvement in immediate
rejection in the ultimatum game. PLoS Biology, 9(5). https://doi.org/
10.1371/journal.pbio.1001054.
Gu, X., Hof, P. R., Friston, K. J., & Fan, J. (2013). Anterior insular cortex
and emotional awareness. The Journal of Comparative Neurology, 521
(15), 3371–3388. https://doi.org/10.1002/cne.23368.
Guo, X., Zheng, L., Zhu, L., Li, J., Wang, Q., Dienes, Z., & Yang, Z. (2013).
Increased neural responses to unfairness in a loss context. Neuro-
Image, 77, 246–253. https://doi.org/10.1016/j.neuroimage.2013.03.
048.
Guo, X., Zheng, L., Cheng, X., Chen, M., Zhu, L., Li, J., . . . Yang, Z. (2014).
Neural responses to unfairness and fairness depend on self-contribu-
tion to the income. Social Cognitive and Affective Neuroscience, 9(10),
1498–1505. https://doi.org/10.1093/scan/nst131.
G€uro�glu, B., Bos, W. V., Dijk, E. V., Rombouts, S. A., & Crone, E. A.
(2011). Dissociable brain networks involved in development of fair-
ness considerations: Understanding intentionality behind unfairness.
NeuroImage, 57(2), 634–641. https://doi.org/10.1016/j.neuroimage.
2011.04.032.
G€uth, W., Schmittberger, R., & Schwarze, B. (1982). An experimental
analysis of ultimatum bargaining. Journal of Economic Behavior &
Organization, 3(4), 367–388.
Halko, M. L., Hlushchuk, Y., Hari, R., & Sch€urmann, M. (2009). Competing
with peers: Mentalizing-related brain activity reflects what is at stake.
NeuroImage, 46(2), 542–548.
Hamlin, J. K., Wynn, K., Bloom, P., & Mahajan, N. (2011). How infants
and toddlers react to antisocial others. Proceedings of the National
Academy of Sciences of the United States of America, 108(50), 19931–19936.
Harenski, C. L., & Hamann, S. (2006). Neural correlates of regulating neg-
ative emotions related to moral violations. NeuroImage, 30(1), 313–324. https://doi.org/10.1016/j.neuroimage.2005.09.034.
Harl�e, K. M., & Sanfey, A. G. (2012). Social economic decision-making
across the lifespan: An fMRI investigation. Neuropsychologia, 50(7),
1416–1424. https://doi.org/10.1016/j.neuropsychologia.2012.02.
026.
Harl�e, K. M., Chang, L. J., Wout, M. V., & Sanfey, A. G. (2012). The neu-
ral mechanisms of affect infusion in social economic decision-making:
A mediating role of the anterior insula. NeuroImage, 61(1), 32–40.
https://doi.org/10.1016/j.neuroimage.2012.02.027.
Heekeren, H. R., Wartenburger, I., Schmidt, H., Prehn, K., Schwintowski,
H., & Villringer, A. (2005). Influence of bodily harm on neural corre-
lates of semantic and moral decision-making. NeuroImage, 24(3),
887–897. https://doi.org/10.1016/j.neuroimage.2004.09.026.
Hein, G., Morishima, Y., Leiberg, S., Sul, S., & Fehr, E. (2016). The brains
functional network architecture reveals human motives. Science, 351
(6277), 1074–1078. https://doi.org/10.1126/science.aac7992.
ZINCHENKO AND ARSALIDOU | 967
Hsu, M., Anen, C., & Quartz, S. R. (2008). The right and the good: Dis-
tributive justice and neural encoding of equity and efficiency. Science
(New York, N.Y.), 320(5879), 1092–1095. https://doi.org/10.1126/sci-ence.1153651.
Hu, C., Di, X., Eickhoff, S. B., Zhang, M., Peng, K., Guo, H., & Sui, J.
(2016). Distinct and common aspects of physical and psychological
self-representation in the brain: A meta-analysis of self-bias in facial
and self-referential judgements. Neuroscience & Biobehavioral Reviews,
61, 197–207. https://doi.org/10.1016/j.neubiorev.2015.12.003.
Hu, J., Blue, P. R., Yu, H., Gong, X., Xiang, Y., Jiang, C., & Zhou, X.
(2015). Social status modulates the neural response to unfairness.
Social Cognitive and Affective Neuroscience, 11(1), 1–10. https://doi.
org/10.1093/scan/nsv086.
Hu, Y., Strang, S., & Weber, B. (2015). Helping or punishing strangers:
Neural correlates of altruistic decisions as third-party and of its rela-
tion to empathic concern. Frontiers in Behavioral Neuroscience, 9.
https://doi.org/10.3389/fnbeh.2015.00024.
Kahneman, D. (2003). A perspective on judgement and choice. The Amer-
ican Psychologist, 58, 697–720.
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus
and Giroux.
Kawamoto, T., Ura, M., & Nittono, H. (2015). Intrapersonal and interper-
sonal processes of social exclusion. Frontiers in Neuroscience, 9, 62.
Kirk, U., Downar, J., & Montague, P. R. (2011). Interoception drives
increased rational decision-making in meditators playing the ultima-
tum game. Frontiers in Neuroscience, 5, 49. http://doi.org/10.3389/
fnins.2011.00049
Knoch, D., Pascual-Leone, A., Meyer, K., Treyer, V., & Fehr, E. (2006).
Diminishing reciprocal fairness by disrupting the right prefrontal cor-
tex. Science, 314(5800), 829–832. https://doi.org/10.1126/science.
1129156.
Knoch, D., Schneider, F., Schunk, D., Hohmann, M., & Fehr, E. (2009).
Disrupting the prefrontal cortex diminishes the human ability to build
a good reputation. Proceedings of the National Academy of Sciences,
106(49), 20895–20899. https://doi.org/10.1073/pnas.0911619106.
Koenigs, M., & Tranel, D. (2007). Irrational economic decision-making
after ventromedial prefrontal damage: Evidence from the ultimatum
game. Journal of Neuroscience, 27(4), 951–956. https://doi.org/10.
1523/jneurosci.4606-06.2007.
Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M.,
& Damasio, A. (2007). Damage to the prefrontal cortex increases util-
itarian moral judgements. Nature, 446(7138), 908–911. https://doi.
org/10.1038/nature05631.
Koubeissi, M. Z., Bartolomei, F., Beltagy, A., & Picard, F. (2014). Electrical
stimulation of a small brain area reversibly disrupts consciousness.
Epilepsy & Behavior, 37, 32–35. https://doi.org/10.1016/j.yebeh.
2014.05.027.
Kurth, F., Zilles, K., Fox, P. T., Laird, A. R., & Eickhoff, S. B. (2010). A link
between the systems: Functional differentiation and integration
within the human insula revealed by meta-analysis. Brain Structure
and Function, 214(5–6), 519–534. https://doi.org/10.1007/s00429-
010-0255-z
Lavin, C., Melis, C., Mikulan, E., Gelormini, C., Huepe, D., & Iba~nez, A.
(2013). The anterior cingulate cortex: An integrative hub for human
socially-driven interactions. Frontiers in Neuroscience, 7. https://doi.
org/10.3389/fnins.2013.00064.
Lelieveld, G., Shalvi, S., & Crone, E. A. (2016). Lies that feel honest: Dis-
sociating between incentive and deviance processing when evaluat-
ing dishonesty. Biological Psychology, 117, 100–107. https://doi.org/10.1016/j.biopsycho.2016.03.009.
Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core
processes. Annual Review of Psychology, 58(1), 259–289. https://doi.org/10.1146/annurev.psych.58.110405.085654.
Lockwood, P. L., Apps, M. A. J., Roiser, J. P., & Viding, E. (2015). Encod-
ing of vicarious reward prediction in anterior cingulate cortex and
relationship with trait empathy. The Journal of Neuroscience, 35(40),
13720–13727.
Luo, Q., Nakic, M., Wheatley, T., Richell, R., Martin, A., & Blair, R. J.
(2006). The neural basis of implicit moral attitude—An IAT study
using event-related fMRI. NeuroImage, 30(4), 1449–1457. https://doi.org/10.1016/j.neuroimage.2005.11.005.
Luria, A. R. (1966). Higher cortical functions in man. New York: Basic
Books.
Melchers, M., Markett, S., Montag, C., Trautner, P., Weber, B., Lachmann,
B., & Reuter, M. (2015). Reality TV and vicarious embarrassment: An
fMRI study. NeuroImage, 109, 109–117. https://doi.org/10.1016/j.
neuroimage.2015.01.022.
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and con-
trol: A network model of insula function. Brain Structure and Function,
214(5–6), 655–667. https://doi.org/10.1007/s00429-010-0262-0.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred
reporting items for systematic reviews and meta-analyses: the PRISMA
statement. The BMJ, 339, b2535. http://doi.org/10.1136/bmj.b2535.
Moll, J., Oliveira-Souza, R. D., Bramati, I. E., & Grafman, J. (2002). Func-
tional networks in emotional moral and nonmoral social judgments.
NeuroImage, 16(3), 696–703. https://doi.org/10.1006/nimg.2002.
1118.
Moll, J., Oliveira-Souza, R. D., & Zahn, R. (2008). The neural basis of
moral cognition: Sentiments, concepts, and values. Annals of the New
York Academy of Sciences, 1124, (1), 161–180. https://doi.org/10.
1196/annals.1440.005.
Montague, P. R., & Lohrenz, T. (2007). To detect and correct: Norm vio-
lations and their enforcement. Neuron, 56(1), 14–18. https://doi.org/10.1016/j.neuron.2007.09.020.
Naghavi, H. R., & Nyberg, L. (2005). Common fronto-parietal activity in
attention, memory, and consciousness: Shared demands on integra-
tion? Consciousness and Cognition, 14(2), 390–425. https://doi.org/10.1016/j.concog.2004.10.003.
Nihonsugi, T., Ihara, A., & Haruno, M. (2015). Selective increase of
intention-based economic decisions by noninvasive brain stimulation
to the dorsolateral prefrontal cortex. Journal of Neuroscience, 35(8),
3412–3419. https://doi.org/10.1523/jneurosci.3885-14.2015.
Pedersen, E. J. (2012). The roles of empathy and anger in the regulation
of third-party punishment. Open Access Theses, 377.
Prehn, K., Wartenburger, I., M�eriau, K., Scheibe, C., Goodenough, O. R.,
Villringer, A., . . . Heekeren, H. R. (2008). Individual differences in
moral judgment competence influence neural correlates of socio-nor-
mative judgments. Social Cognitive and Affective Neuroscience, 3(1),
33–46. https://doi.org/10.1093/scan/nsm037.
Radua, J., & Mataix-Cols, D. (2012). Meta-analytic methods for neuroi-
maging data explained. Biology of Mood &Amp; Anxiety Disorders, 2(6),
Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M.,
Cardoner, N., & Surguladze, S. (2012). A new meta-analytic method
for neuroimaging studies that combines reported peak coordinates
and statistical parametric maps. European Psychiatry, 27, 605–611.
Rilling, J. K., Goldsmith, D. R., Glenn, A. L., Jairam, M. R., Elfenbein, H. A.,
Dagenais, J. E., & Pagnoni, G. (2008). The neural correlates of the
affective response to unreciprocated cooperation. Neuropsychologia,
46(5), 1256–1266. https://doi.org/10.1016/j.neuropsychologia.2007.
11.033.
968 | ZINCHENKO AND ARSALIDOU
Rilling, J. K., & Sanfey, A. G. (2011). The neuroscience of social decision-
making. Annual Review of Psychology, 62, 23–48. https://doi.org/10.1146/annurev.psych.121208.131647.
Roca, M., Torralva, T., Gleichgerrcht, E., Woolgar, A., Thompson, R., Duncan,
J., & Manes, F. (2011). The role of Area 10 (BA10) in human multitasking
and in social cognition: A lesion study. Neuropsychologia, 49(13), 3525–3531. https://doi.org/10.1016/j.neuropsychologia.2011.09.003.
Ruff, C. C., Ugazio, G., & Fehr, E. (2013). Changing social norm compli-
ance with noninvasive brain stimulation. Science (New York, N.Y.), 342
(6157), 482–484. https://doi.org/10.1126/science.1241399.
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D.
(2003). The neural basis of economic decision-making in the ultima-
tum game. Science (New York, N.Y.), 300(5626), 1755–1758. https://doi.org/10.1126/science.1082976.
Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006).
Neuroeconomics: Cross-currents in research on decision-making.
Trends in Cognitive Sciences, 10, 108–116.
Sanfey, A. G. (2007). Social decision-making: Insights from game theory
and neuroscience. Science (New York, N.Y.), 318(5850), 598–602.https://doi.org/10.1126/science.1142996.
Sanfey, A. G., & Chang, L. J. (2008). Multiple systems in decision making.
Annals of the New York Academy of Sciences, 1128, 53–62. https://doi.org/10.1196/annals.1399.007.
Schachter, S. (1951). Deviation, rejection, and communication. Journal of
Abnormal Psychology, 46, 190–207. https://doi.org//10.1037/
h0062326.
Schreiber, D., & Iacoboni, M. (2012). Huxtables on the brain: An fMRI
study of race and norm violation. Political Psychology, 33(3), 313–330. https://doi.org/10.1111/j.1467-9221.2012.00879.x
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H.,
Kenna, H., & Greicius, M. D. (2007). Dissociable intrinsic connectivity
networks for salience processing and executive control. Journal of
Neuroscience, 27(9), 2349–2356. https://doi.org/10.1523/jneurosci.
5587-06.2007.
Servaas, M. N., Aleman, A., Marsman, J. C., Renken, R. J., Riese, H., &
Ormel, J. (2015). Lower dorsal striatum activation in association with
neuroticism during the acceptance of unfair offers. Cognitive, Affec-
tive, & Behavioral Neuroscience, 15(3), 537–552. https://doi.org/10.
3758/s13415-015-0342-y
Sherif, M., & Sherif, C. W. (1953). Groups in harmony and tension. An inte-
gration of studies on intergroup relations. New York: Harper &
Brothers.
Sokolowski, H. M., Fias, W., Mousa, A., & Ansari, D. (2017). Common
and distinct brain regions in both parietal and frontal cortex support
symbolic and nonsymbolic number processing in humans: A func-
tional neuroimaging meta-analysis. NeuroImage, 146, 376–394.
Stein, M. B., Simmons, A. N., Feinstein, J. S., & Paulus, M. P. (2007).
Increased amygdala and insula activation during emotion processing
in anxiety-prone subjects. American Journal of Psychiatry, 164, 318–327.
Steinbeis, N., Bernhardt, B., & Singer, T. (2012). Impulse control and
underlying functions of the left DLPFC mediate age–related and
age–independent individual differences in strategic social behavior.
Neuron, 73, 1040–1051.
Strobel, A., Zimmermann, J., Schmitz, A., Reuter, M., Lis, S., Windmann,
S., & Kirsch, P. (2011). Beyond revenge: Neural and genetic bases of
altruistic punishment. NeuroImage, 54(1), 671–680. https://doi.org/
10.1016/j.neuroimage.2010.07.051.
Tabibnia, G., Satpute, A. B., & Lieberman, M. D. (2008). The sunny side
of fairness: Preference for fairness activates reward circuitry (and
disregarding unfairness activates self-control circuitry). Psychological
Science, 19(4), 339–347. https://doi.org/10.1111/j.1467-9280.2008.
02091.x
Takahashi, H., Yahata, N., Koeda, M., Matsuda, T., Asai, K., & Okubo, Y.
(2004). Brain activation associated with evaluative processes of guilt
and embarrassment: An fMRI study. NeuroImage, 23(3), 967–974.https://doi.org/10.1016/j.neuroimage.2004.07.054.
Takahashi, H., Kato, M., Matsuura, M., Koeda, M., Yahata, N., Suhara, T.,
& Okubo, Y. (2008). Neural correlates of human virtue judgment. Cer-
ebral Cortex, 18(8), 1886–1891. https://doi.org/10.1093/cercor/
bhm214.
Tammi, T. (2013). Dictator game giving and norms of redistribution:
Does giving in the dictator game parallel with the supporting of
income redistribution in the field? The Journal of Socio-Economics, 43,
44–48. https://doi.org/10.1016/j.socec.2013.01.002.
Taylor, K. S., Seminowicz, D. A., & Davis, K. D. (2009). Two systems of
resting state connectivity between the insula and cingulate cortex.
Human Brain Mapping, 30(9), 2731–2745. https://doi.org/10.1002/
hbm.20705.
Tomasino, B., Lotto, L., Sarlo, M., Civai, C., Rumiati, R., & Rumiati, R. I.
(2013). Framing the ultimatum game: The contribution of simulation.
Frontiers in Human Neuroscience, 7, 337.
Torta, D., & Cauda, F. (2011). Different functions in the cingulate cortex,
a meta-analytic connectivity modeling study. NeuroImage, 56(4),
2157–2172. https://doi.org/10.1016/j.neuroimage.2011.03.066.
Treadway, M. T., Buckholtz, J. W., Martin, J. W., Jan, K., Asplund, C. L.,
Ginther, M. R., & Marois, R. (2014). Corticolimbic gating of emotion-
driven punishment. Nature Neuroscience, 17(9), 1270–1275. https://doi.org/10.1038/nn.3781.
Uddin, L. Q. (2015). Salience processing and insular cortical function and
dysfunction. Nature Reviews. Neuroscience, 16(1), 55–61. https://doi.
org/10.1038/nrn3857.
Vara, A. S., Pang, E. W., Vidal, J., Anagnostou, E., & Taylor, M. J. (2014).
Neural mechanisms of inhibitory control continue to mature in ado-
lescence. Developmental Cognitive Neuroscience, 10, 129–139.https://doi.org/10.1016/j.dcn.2014.08.009.
Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working
memory: A meta-analysis. Cognitive, Affective, & Behavioral Neuro-
science, 3(4), 255–274. https://doi.org/10.3758/cabn.3.4.255.
Wagner, U., N’diaye, K., Ethofer, T., & Vuilleumier, P. (2011). Guilt-spe-
cific processing in the prefrontal cortex. Cerebral Cortex (New York, N.
Y.: 1991), 21(11), 2461–2470. https://doi.org/10.1093/cercor/
bhr016.
Wei, Z., Zhao, Z., & Zheng, Y. (2013). Neural mechanisms underlying
social conformity in an ultimatum game. Frontiers in Human Neuro-
science, 7. https://doi.org/10.3389/fnhum.2013.00896.
White, S. F., Brislin, S. J., Meffert, H., Sinclair, S., & Blair, R. J. R. (2013).
Callous-unemotional traits modulate the neural response associated
with punishing another individual during social exchange: A prelimi-
nary investigation. J Pers Disord 27, 99.
Wiegel, J., Mory�s, J., Kowia�nski, P., Ma, S. Y., Kuchna, I., Nowicki, K., . . .
Wisniewski, T. (2014). Chapter 8 - Delayed development of the
claustrum in autism. In J. Smythies, L. Edelstein, & V. Ramachandran
(Eds.), The claustrum (pp. 225–235). San Diego: Academic Press. ISBN
9780124045668.
Wright, N. D., Symmonds, M., Fleming, S. M., & Dolan, R. J. (2011). Neu-
ral segregation of objective and contextual aspects of fairness. The
Journal of Neuroscience: The Official Journal of the Society for Neuro-
science, 31(14), 5244–5252. https://doi.org/10.1523/jneurosci.3138-10.2011.
ZINCHENKO AND ARSALIDOU | 969
Wu, Y., Zang, Y., Yuan, B., & Tian, X. (2015). Neural correlates of deci-
sion making after unfair treatment. Frontiers in Human Neuroscience,
9. https://doi.org/10.3389/fnhum.2015.00123.
Xiang, T., Lohrenz, T., & Montague, P. R. (2013). Computational substrates of
norms and their violations during social exchange. Journal of Neuroscience,
33(3), 1099–1108. https://doi.org/10.1523/jneurosci.1642-12.2013.
Yoder, K. J., & Decety, J. (2014). The good, the bad, and the just: Justice
sensitivity predicts neural response during moral evaluation of
actions performed by others. Journal of Neuroscience, 34(12), 4161–4166. https://doi.org/10.1523/jneurosci.4648-13.2014
Young, H. P. (2015). The evolution of social norms. Annual Review of Eco-
nomics, 7, 359–387.
Zaki, J., Schirmer, J., & Mitchell, J. P. (2011). Social influence modulates
the neural computation of value. Psychological Science, 22(7), 894–900. https://doi.org/10.1177/0956797611411057.
Zheng, L., Guo, X., Zhu, L., Li, J., Chen, L., & Dienes, Z. (2015). Whether
others were treated equally affects neural responses to unfairness in
the Ultimatum Game. Social Cognitive and Affective Neuroscience, 10
(3), 461–466. https://doi.org/10.1093/scan/nsu071.
Zhong, S., Chark, R., Hsu, M., & Chew, S. H. (2016). Computational sub-
strates of social norm enforcement by unaffected third parties. Neu-
roImage, 129, 95–104. https://doi.org/10.1016/j.neuroimage.2016.
01.040.
Zhou, Y., Wang, Y., Rao, L.-L., Yang, L., & Li, S. (2014). Money talks: Neu-
ral substrate of modulation of fairness by monetary incentives. Fron-
tiers in Behavioral Neuroscience, 8, 150.
How to cite this article: Zinchenko O, Arsalidou M. Brain
responses to social norms: Meta-analyses of fMRI studies. Hum
Brain Mapp. 2018;39:955–970. https://doi.org/10.1002/hbm.
23895
970 | ZINCHENKO AND ARSALIDOU
Attachment B. Article “Neurobiological mechanisms of fairness-related social
norm enforcement: a review of interdisciplinary studies”.
Modern neuroimaging studies begin to explore neurobiological mechanisms of
social norms enforcement. Several regions of frontal lobes and temporo-parieto-
occipital cortex play a key role in decision making of social punishment of
fairness’ norm violation. The cutting–edge methods of brain stimulation allow to
change frequency and intensity of social punishment in different economic tasks
(games). The analysis of modern studies show that brain mechanisms of decision
making to punish non–cooperative individual requires further investigation with
brain stimulation methods to differentiate the role of frontal and temporo-
parietooccipital regions and clarify its interaction.
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16
ВВЕДЕНИЕ
Наличие норм является важной особенно-стью социального поведения человека. С их помощью достигается не только упорядочен-ность совместных действий, но и оптимизация поведения отдельного индивида [Cialdini et al., 1990]. Современные исследования социальных норм становятся все более междисциплинар-ными, объединяя экономические [Camerer et al., 2004; Fehr, Fischbacher, 2004], социально психологические [Sherif et al., 1953; Hogg et al., 2006] и нейробиологические подходы [Spitzer
et al. 2007; Rilling et al., 2008]. Задачей насто-ящей работы является анализ исследований поддержания социальных норм, комбини-рующих современные методы картирования головного мозга и подходы поведенческой экономики.
В последнее десятилетие большое коли-чество исследований было посвящено ней-робиологическим механизмам социального наказания некооперативных индивидов, на-рушающих социальные нормы в непосред-ственном двустороннем взаимодействии. Од-нако количество исследований социального
DOI: 10.7868/S0044467718010021
Ключевые слова: социальные нормы, социальное наказание, наказание третьей стороной, дорсолатеральная префронтальная кора, дорсомедиальная префронтальная кора, темен-но-височно-затылочная область, транскраниальная магнитная стимуляция (ТМС), транс-краниальная электрическая стимуляция постоянным током (ТЭС), функциональная маг-нитно-резонансная томография (фМРТ).
Современные нейроимиджинговые исследования начинают приоткрывать нейробиологи-ческие механизмы следования социальным нормам. Настоящий обзор анализирует нейро-имиджинговые исследования поддержания социальной нормы справедливости с помощью социального наказания, с акцентом на социальном наказании третьей стороной ввиду ма-лой изученности этого феномена. Анализ литературы свидетельствует, что процесс соци-ального наказания за нарушение нормы справедливости поддерживается распределенной активностью ряда областей коры головного мозга: (а) системой оценки социальных норм и (б) системой ментализации, с регионами-интеграторами информации в лобных отделах и теменно-височно-затылочной области мозга соответственно. Однако анализ современ-ных исследований показывает, что нейробиологические механизмы принятия решения о социальном наказании некооперативного индивида требуют дополнительного изучения. Актуальным для будущих исследований представляется прояснение механизма взаимодей-ствия вышеописанных нейронных сетей методами стимуляции отделов мозга и методами магнито- и электроэнцефалографии.
Поступила в редакцию 12.12.2016 г. Принята в печать 22.05.2017 г.
1 Центр нейроэкономики и когнитивных исследований, Национальный исследовательский университет “Высшая школа экономики”, Москва, Россия.
2Лаборатория экспериментальной и поведенческой экономики, Национальный исследовательский университет “Высшая школа экономики”, Москва, Россия.
3 Центр нейроэкономики и когнитивных исследований, Национальный исследовательский университет “Высшая школа экономики”, Москва, Россия.
e-mail: [email protected], [email protected], [email protected] 3
© 2018 О. О. Зинченко1, А. В. Белянин2, В. А. Ключарев3
НЕЙРОБИОЛОГИЧЕСКИЕ МЕХАНИЗМЫ ПОДДЕРЖАНИЯ СОЦИАЛЬНОЙ НОРМЫ СПРАВЕДЛИВОСТИ: ОБЗОР
МЕЖДИСЦИПЛИНАРНЫХ ИССЛЕДОВАНИЙ
УДК 159.91
ОБЗОРЫ, ТЕОРЕТИЧЕСКИЕ И ДИСКУССИОННЫЕ СТАТЬИ
НЕЙРОБИОЛОГИЧЕСКИЕ МЕХАНИЗМЫ ПОДДЕРЖАНИЯ СОЦИАЛЬНОЙ НОРМЫ… 17
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наказания, осуществляемого сторонним на-блюдателем, т.н. третьим лицом, ограничено. Важно отметить, что именно наказание нару-шителей норм третьей стороной (человеком, наблюдающим за нарушением норм “со сторо-ны”) является ключевым для стабилизации ко-операции в больших группах индивидов [Fehr, Fischbacher, 2004]. Поэтому в нашей работе мы провели анализ исследований нейробиологи-ческих механизмов социального наказания третьей стороной для выяснения ключевых си-стем мозга, вовлеченных в поддержку нормы справедливости в социальных группах.
СОЦИАЛЬНЫЕ НОРМЫ. КАК ПОЯВЛЯЕТСЯ СОЦИАЛЬНОЕ
НАКАЗАНИЕ
Социальные нормы предписывают поведе-ние, ориентированное не на личные матери-альные интересы, а на следование определен-ным установкам или предпочтениям, одобря-емым обществом. Нормы способны влиять на поведение и приводить к сильным отклонени-ям наблюдаемых результатов от равновесных предсказаний классической экономической теории. Нормы являются частью группового наследия – общества или малой группы, и их существование поддерживается членами этой социальной группы – выражением поощре-ния за следование или наказанием за откло-нение от них [Elster, 1989]. Помимо когнитив-ного компонента (усвоенного ожидания “как следует себя вести”), важен и эмоциональный компонент норм, ведь отклонение от социаль-ной нормы порождает ряд социальных эмо-ций, таких как стыд и вина, которые, в свою очередь, становятся мощными мотиваторами действий. В задачах распределения ресурсов важную роль играет норма равенства деле-жа: такие равные дележи большинство лю-дей склонно считать справедливыми, причем даже в тех случаях, когда они могут перерас-пределить ресурсы в свою пользу [Elster, 1989; Kahneman et al., 1986]. Более того – несправед-ливость дележа также может быть источником определенного психологического дискомфор-та, поэтому стремление к справедливости мо-жет также рассматриваться в качестве способа максимизации личной выгоды [Deutsch, 1985, сh.11; Elster, 1989; Messick, Sentis, 1983].
В экономических играх социальная нор-ма справедливости и отклонения от нее
моделируются распределениями финансо-вых трансферов между игроками. Так, напри-мер, в классической экспериментальной игре в “Диктатора” участвуют два человека, один из которых (“диктатор”) принимает едино-личное решение о том, как распределить ус-ловный “бюджет” (очки, баллы, деньги и пр.) между собой и другим игроком (“получате-лем”). Норма справедливости в данном случае диктует дележ очков между участниками в со-отношении 50:50, тогда как дележ 70:30 будет восприниматься как отклонение от нормы, а дележ 90:10 – как вопиюще несправедливый [Forsythe et al., 1994; Qu et al., 2014; Sun et al., 2015]. Результаты мета-анализа, основанно-го на поведении более чем 20 000 пар игро-ков по всему миру, показывают, что “диктато-ры” в среднем склонны отдавать получателям 28.35% полученного бюджета [Engel, 2010], де-монстрируя наличие как эгоистических, так и кооперативных мотивов.
Норма справедливости может поддержи-ваться за счет нескольких факторов, среди ко-торых особый интерес исследователей вызыва-ет механизм наказания, или санкций в случае отклонения от социальной нормы для поддер-жания кооперации [Fehr, Fischbacher, 2004]. Исследования показывают, что социальное на-казание применяется индивидами даже в слу-чае однократного взаимодействия, что может объясняться переживанием негативных эмо-ций к “отступникам” – людям, нарушающим социальную норму в рамках взаимодействия [Fehr, Gächter, 2002]. Более того, объем нака-зания – количество инвестированных в нака-зание ресурсов – пропорционален величине отклонения от нормы. Попытка наказать – оказать некое негативное воздействие на того, кто повел себя несправедливо – несовместима с традиционной гипотезой рационального по-ведения – подразумевающей лишь стремление максимизировать собственную выгоду и со-хранить ресурсы. Одна из возможных теорий, почему индивиды применяют социальное на-казание, заключается в существовании “ме-та-нормы”, вызывающей неодобрение третьих лиц в случае отклонения индивида от социаль-ной нормы [Axelrod, 1986; Elster, 1989].
ВИДЫ СОЦИАЛЬНОГО НАКАЗАНИЯ
Социальное наказание можно классифици-ровать по его контексту и адресату – наказание
18 ЗИНЧЕНКО и др.
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в двустороннем взаимодействии (second-party punishment) и наказание третьей стороной (third-party punishment). Наказание в двусто-роннем взаимодействии предпринимается не-посредственным участником взаимодействия (реципиентом) как реакция на несостояв-шееся кооперативное взаимодействие [Fehr, Gächter, 2002; Fehr et al., 2002]. В этом случае второй игрок (реципиент) тратит свои соб-ственные ресурсы на то, чтобы уменьшить вы-игрыш первого игрока (агента), совершившего некооперативное действие. В ситуации эконо-мической игры наказание может выглядеть как отказ от дальнейшего взаимодействия, – на-пример, в “Ультимативной Игре”, в которой агент теряет весь выигрыш, если реципиент отказывается принимать его предложение. В другом варианте наказания реципиент тра-тит собственные ресурсы на штраф, наклады-ваемый им на агента. Как показали исследова-ния, штраф обычно стимулирует реципиента следовать нормам и поддерживать кооперацию в дальнейшем [Fehr, Gächter, 2002; Herrmann et al., 2008].
Наказание третьей стороной осуществляет-ся участником (третьим игроком), который не вовлечен во взаимодействие непосредствен-но, но наблюдает за действиями других игро-ков. В этом случае участник не получает пря-мой выгоды от своих действий, поскольку не участвует в финансовых трансферах, однако может оказывать влияние на ход игры за счет имеющихся ресурсов путем применения штра-фа. Эрнст Фер с коллегами [Fehr et al., 2004] провели поведенческий эксперимент на осно-ве игр “Диктатор” и “Дилемма заключенного”, показавший, что более 60% не вовлеченных в непосредственное взаимодействие участни-ков–наблюдателей третьей стороны проявля-ли тенденцию использовать свой изначальный капитал для “наказания” – уменьшения зара-ботанной суммы агента, который распределил очки несправедливо между собой и реципиен-том [Fehr et al., 2014]. В игре “Диктатор” ре-шение о наказании третьей стороной обычно принималось, когда активный игрок передавал реципиенту менее половины своего бюджета, в то время как в “Дилемме заключенного” – в случаях, когда один участник принимал не-справедливое решение “не кооперировать”, в то время как второй участник кооперировал. В целом, традиционное экономическое пред-ставление о том, что затратные наказания за
собственный счет (без каких–либо матери-альных выгод для наказывающего) не долж-ны наблюдаться, т.к. не несут наказывающе-му никаких выгод, не нашло эмпирического подтверждения [Fehr, Fischbacher, 2004]. Что-бы объяснить подобное поведение, Эрнст Фер и Клаус Шмидт разработали теоретическую модель избегания неравенства, включающую в классическом виде оценку (А) “выгодного” неравенства – при котором бюджет игрока, принимающего решение, превышает бюджеты других игроков, и (Б) “невыгодного” неравен-ства – где бюджет игрока оказывается меньше по сравнению с бюджетами других игроков. “Невыгодное” неравенство, в свою очередь, операционализируется с помощью параметра “зависти”, в то время как “выгодное” – пара-метром “вины” [Fehr, Schmidt, 1999].
Интересно, что частота наказаний “третьей стороной” варьирует в зависимости от опреде-ленных условий. Более половины участников исследований принимали решения наказать (уменьшать выигрыш) участников, которые несправедливо распределяли выигрыши в игре “Диктатор” [Kahneman, Knetsch, Thaler, 1986]. Однако при введении возможности наградить участников, по отношению к которым было принято несправедливое решение, количество наказаний уменьшалось по сравнению с усло-виями, где нельзя было “возместить ущерб” пострадавшей стороне [Turillo et al., 2002]. Ча-вез и соавторы показали, что при предоставле-нии возможностей “возместить ущерб” и “на-казать виновного”, отняв у него определенную сумму, индивиды руководствуются следующи-ми целями: вознаградить равное распределе-ние средств, избегая неравенства; уравнять неравное распределение, сложившееся после принятия решения другим игроками; в слу-чае же, когда возможность компенсации от-сутствовала – индивиды принимали решение об осуществлении наказания [Chavez et al., 2013]. Это свидетельствует о том, что участник в роли третьей стороны предпочитает уравни-вать благосостояние игроков в первую очередь с помощью наград и лишь в отсутствие этой возможности/недостаточной ее эффективно-сти применять наказание путем уменьшения бюджета несправедливого участника.
Показано, что наказание третьей сторо-ной обусловлено определенными эмоцио-нальными процессами. Частота наказаний со
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стороны третьих лиц возрастала, если в ходе эксперимента манипулировался уровень зло-намеренности (за счет модификации интен-ции нарушения социальных норм другим участником – намеренное/ненамеренное на-рушение) и вины (участник наделялся ответ-ственностью за осуществление санкций для исправления несправедливого распределения ресурсов) [Nelissen et al., 2009, Seip et al., 2014]. Так, в случае манипуляции уровнем злонаме-ренности частота наказаний возрастала, если нарушение нормы справедливости было наме-ренным. На наказание третьей стороной влия-ют и другие факторы: социальная дистанция – при ее увеличении сила наказания возрастает [Lieberman, Linke, 2007], репутация – в случае присутствия хотя бы одного наблюдателя ча-стота и наказание третьей стороной значимо возрастает [Kurzban et al., 2007].
Феномен социального наказания поэтапно формируется в онтогенезе. В 5 лет дети про-являют тенденцию взаимодействовать имен-но с индивидами, вне зависимости, следовали ли те ранее норме справедливости: дети пози-тивно взаимодействуют и с кооперативными, и с антисоциальными партнерами. Тогда как 8-летние дети проявляют избирательность, предпочитая взаимодействовать с индивида-ми (другими детьми), которые позитивно ре-агировали на просоциальное поведение и не-гативно реагировали на антисоциальное пове-дение – наказывали детей, нарушавших норму справедливости в игре [Hamlinet et al., 2011]. В возрасте 6 лет дети проявляют тенденцию осуществлять наказание игрока, несправед-ливо распределившего сумму игровых очков, находясь в роли третьей стороны, однако, если процесс наказания подразумевал трату их собственных ресурсов, частота наказаний несколько снижалась. Сравнение результа-тов экспериментов, в которых неравномерное распределение ресурсов игроками происхо-дило из-за эгоистического мотива и, напро-тив, из-за щедрости, показало, что 6–летние дети относительно плохо понимают эгоис-тические намерения при принятии решения [McAuliffe K. et al.,2015]. Судя по всему, эго-истические проявления в играх “Ультиматум” и “Диктатор” спровоцированы не невозмож-ностью понять принципы “что такое хорошо и что такое плохо”, но недостаточно разви-той у детей младшего возраста способностью
контролировать свое поведение и подавлять естественные реакции [Steinbeis et al., 2012].
Таким образом, социальное наказание тре-тьей стороной является динамическим про-цессом, последовательно формирующимся в онтогенезе и появляющимся уже в раннем детском возрасте. Такие факторы, как соци-альная дистанция и репутация, влияют на ча-стоту проявления социального наказания. При возможности возместить ущерб пострадавше-му частота наказаний снижается, из чего сле-дует, что неосознанно социальное наказание выступает как “крайняя мера”, к которой при-бегают участники взаимодействия для поддер-жания социальной нормы справедливости.
НЕЙРОИМИДЖИНГОВЫЕ КОРРЕЛЯТЫ ПРОЦЕССА НАКАЗАНИЯ
В ДВУСТОРОННЕМ ВЗАИМОДЕЙСТВИИ И ТРЕТЬЕЙ СТОРОНОЙ
Процесс принятия решения о необходи-мости наказания за нарушения нормы спра-ведливости может определяться различными мотивами, как, например, избеганием чувства вины и избеганием неравенства [Fehr, Schmidt, 1999; Carpenter, Matthews, 2009]. В контексте экономической игры избегание чувства вины представляет собой нежелание разочаровывать партнеров [Dufwenberg et al., 2000; Charness et al., 2006]. Иная мотивация – избегание нера-венства – заключается в стремлении избегать чрезмерной диспропорции в получаемых аген-том и реципиентом выигрышах [Fehr et al., 1999; Knoch et al., 2007]. Чувство вины агента может быть операционализировано как модуль разности бюджета, который инвестор ожида-ет получить обратно и реальным возвращен-ным “доверенным лицом” бюджетом. Иссле-дования с использованием функциональной магнитно-резонансной томографии (фМРТ) [Chang et al., 2011] показало, что избегание чувства вины сопровождается активацией пре-моторных отделов – преимущественно допол-нительной моторной области, а также темен-но-височно-затылочной области, островковой коры и дорсолатеральной лобной коры.
Другой мотив – избегание неравенства – может быть операционализирован как модуль разности общего бюджета и непотраченных ресурсов в экономической игре [Hsu et al., 2008]. Исследование мотивации избегания
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неравенства с помощью искусственного мани-пулирования неравномерного распределения денежных средств при двустороннем взаимо-действии [Hsu et al., 2008; Tricomi et al., 2010; Haruno et al., 2010; Haruno et al., 2014], проде-монстрировало активацию передней поясной извилины, прилежащего ядра и миндалины, островковой коры и дорсолатеральной лобной коры, когда испытуемым предъявлялось не-равное распределение между ними и партне-ром. В целом, общей мозговой активностью, возникающей и при избегании чувства вины, и при избегании неравенства является акти-вация островковой коры и дорсолатеральной лобной коры.
фМРТ исследования демонстрируют не-которые различия в нейробиологических механизмах, обеспечивающих наказание в двустороннем взаимодействии и осущест-вляемое третьей стороной. Так, активация прилежащего ядра выше в ситуации нака-зания в двустороннем взаимодействии по сравнению с наказанием третьей стороной [Strobel et al., 2011]. Островковая кора так-же активируется сильнее при принятии ре-шения о необходимости наказания “отступ-ника” в двустороннем взаимодействии по сравнению с наказанием третьей стороной. Предполагается, что активность островковой коры, связанная с репрезентацией эмоцио-нальных состояний (преимущественно нега-тивных) и реакции на отклонение от соци-альных норм, играет тем самым важную роль в принятии решения об осуществлении со-циального наказания [Strobel et al., 2011].
Исследование принятия решения об от-ветственности за совершение криминально-го преступления и соответствующем размере наказания также показывает, что активность правой дорсолатеральной лобной коры меня-ется в зависимости от оценки ответственно-сти за совершение преступления, в то время как аффективная реакция коррелирует с ак-тивностью миндалины, дорсомедиальной лобной коры и задней части поясной изви-лины [Buckholtz et al., 2008]. Фенг и соавто-ры [Feng et al., 2016] изучали влияние фено-мена диффузии ответственности на поведе-ние третьих лиц, применяющих социальное наказание. Присутствие нескольких третьих лиц, осуществляющих одну и ту же функцию, когда каждый участник может подсознательно
чувствовать себя менее ответственным за под-держание норм, приводит к снижению ин-тенсивности наказания отклонения от соци-альных норм по сравнению с ситуацией, где наказание производится единственным испы-туемым [Feng et al., 2016]. Этот процесс сопро-вождается активностью дорсомедиальной лоб-ной коры, вероятно, контролирующей величи-ну наказания и получающей входные сигналы от верхней части островковой коры, вентроме-диальной лобной коры и предклинья.
Степень суровости наказания третьей сто-роной также варьирует в зависимости от того, считает ли индивид себя членом группы: в этом случае за отступление от социальных норм назначается сравнительно мягкое нака-зание, нежели за тот же самый “проступок” члену другой группы [Baumgartner T. et al., 2012]. В ситуации необходимости применения наказания к участнику “своей” группы акти-вируются дорсомедиальная лобная кора и те-менно-височно-затылочная область, ответ-ственные за ментализацию – интерпретацию мыслей и действий других игроков, в то время как при наказании членов чужой группы ак-тивировались области мозга, ответственные за принятие решений о необходимости санкций (орбитофронтальная кора, латеральная лобная кора и дорсальная часть хвостатого ядра в пра-вом полушарии). Таким образом, дорсомеди-альная лобная кора, вовлеченная в процессы ментализации, вероятно связана с определе-нием принадлежности нарушителя социаль-ных норм к группе и опосредует аффективное сопровождение процесса социального наказа-ния. Ментализация играет определенную роль в применении наказания – так было показа-но, что сознательное использование страте-гии “постановки себя на место другого” при-водит к повышению интенсивности эмоций при наблюдении за несправедливыми и спра-ведливыми предложениями в игре Диктатор [Gregucci et al., 2012]. В то же время, (дорсо-) латеральная лобная кора также играет важную роль в экономических взаимодействиях, вы-ступая в роли “хаба”, определяющего мораль-ную ответственность и оценку величины нака-зания [Buckholtz et al., 2012].
Другим свидетельством вовлечения медиаль-ных структур лобной доли в процесс осущест-вления социального наказания третьей стороной является электроэнцефалографическое (ЭЭГ)
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исследование [Sun et al., 2015], изучавшее груп-пы испытуемых, различающиеся по уровню по-веденческих проявлений альтруизма, показало большую частоту применения наказания третьей стороной лицами с высоким уровнем поведенче-ского альтруизма, и различия в динамике спец-ифического связанного с событием вызванного потенциала – медиальной негативной фронталь-ной волны (МНФ). Этот компонент, возника-ющий с латентностью 300 мс, отражает эмоци-ональную категоризацию возникающего собы-тия [Fukushima, Hiraki, 2006]. Амплитуда МНФ возрастала у третьей стороны при наблюдении за максимально несправедливым предложением по сравнению со справедливыми финансовыми предложениями. В данном исследовании опреде-ление сильной или слабой предрасположенности к альтруизму осуществлялось после серии игр с финансовыми трансферами, где этим же игро-кам давалась возможность распределять очки от первого лица: так, если в 75% проб участник рас-пределял очки 50:50, ему присваивался высокий уровень альтруизма, средний – при распределе-ниях 70:30, низкий – если участник распределял очки в отношении 90:10. В противоположность этому, у лиц с низким уровнем альтруизма боль-шая амплитуда вызванного потенциала МНФ возникала при наблюдении за справедливыми финансовыми трансферами [Sun et al., 2015].
Современные статистические подходы так-же позволили обнаружить зависимость между объемом и толщиной серого вещества дорсо-медиальной лобной коры и беспристрастно-стью – способностью в равной степени нака-зывать за одинаковое нарушение социальной нормы участников своей и чужой группы, на-ходясь в роли третьей стороны [Baumgartner et al., 2013]. Показано, что чем больше разви-та дорсомедиальная лобная кора, тем меньше выражена беспристрастность, и в большей сте-пени участники в роли третьей стороны нака-зывают игроков чужой группы по сравнению с игроками своей группы. Также с беспри-страстностью позитивно коррелировал и объ-ем (толщина) анатомических связей между дорсомедиальной лобной корой и теменно-ви-сочно-затылочной областью правого полуша-рия [Baumgartner et al., 2015].
Таким образом, дорсолатеральная лоб-ная кора правого полушария, теменно-ви-сочно-затылочная область и дорсомедиаль-ная префронтальная кора могут выступать
ключевыми регионами в обеспечении про-цесса социального наказания. Нейрональные корреляты поддержания норм также обнару-живались в островковой коре, базальных ган-глиях (прилежащем и хвостатом ядрах), перед-ней и задней частях поясной извилины, хотя эти данные в меньшей степени реплицируются последующими исследованиями.
Фокус современных нейроимиджинговых исследований смещается на оценку систем-ных взаимосвязей между различными областя-ми мозга и их роли в обеспечении механизма социального наказания. В более реалистичной задаче – принятие решений о виновности/не-виновности в гипотетических криминальных сценариях и о вынесении соответствующе-го наказания третьей стороной [Belucci et al., 2016] было связано с активностью двух систем головного мозга: системы ментализации (те-менно-височно-затылочная область и дорсо-медиальная префронтальная кора) – обеспе-чивающей способность представить мысли и действия других людей, опираясь на свой собственный опыт и представления, и систе-мы оценки необходимости социального наказания (латеральная часть лобной коры), регулирую-щей рабочую память, контролирующей ког-нитивную гибкость, переключаемость, внима-ние, планирование, принятие решений и др.
Согласно данным нейровизуализации и анализа эффективной коннективности в си-туации применения социального наказания, дорсомедиальная лобная кора (системы мен-тализации) получает входящие сигналы преи-мущественно от височной области, активация которой, а также сила функциональных свя-зей с дорсолатеральной лобной корой, кор-релирует со строгостью наказания [Belucci et al.., 2016]. Эти данные согласуются с иссле-дованиями пациентов: травмы мозга с лока-лизацией в дорсомедиальной префронтальной коре – приводят к нетипичному поведению при необходимости социального наказания – снижению его интенсивности, что сопрово-ждается недостатком альтруистических пе-реживаний, и худшей работой регуляторных функций (внимания, переключаемости, рабо-чей памяти и др.) [Glass et al., 2016]. Мы при-лагаем иллюстративную схему системы оценки необходимости социального наказания и систе-мы ментализации, вовлеченных в обеспече-ние социального наказания третьей стороной
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согласно данным фМРТ и ЭЭГ исследований (см. рис. 1). Предполагается, что эти системы могут взаимодействовать реципрокно – акти-вация системы ментализации сопровождается подавлением системы оценки необходимости социального наказания [Buckholtz et al., 2008].
РАСШИРЕНИЕ ПРЕДСТАВЛЕНИЙ О МЕХАНИЗМАХ СОЦИАЛЬНОГО
НАКАЗАНИЯ С ПОМОЩЬЮ МЕТОДОВ НЕИНВАЗИВНОЙ СТИМУЛЯЦИИ
МОЗГА (ТЭС, ТМС)
Методы неинвазивной стимуляции мозга с помощью магнитного поля – транскрани-альная магнитная стимуляция (ТМС) и транс-краниальная электрическая стимуляция (ТЭС, микрополяризация) – позволяют подавлять (при подавляющей стимуляции) или усили-вать активность нейронов (при возбуждаю-щей) с целью установления каузальной роли
той или иной области мозга в когнитивных процессах при выполнении задачи. Катод-ная стимуляция в ТЭС позволяет временно подавить активность целевой области мозга, в то время как анодная стимуляция – допол-нительно активировать. С помощью методов стимуляции мозга можно продемонстрировать различия в нейробиологических механизмах, вовлеченных в процессы социального наказа-ния в двустороннем взаимодействии и третьей стороной.
Фокус внимания исследователей процес-са социального наказания постепенно сме-щается к попытке понять роль корковых ре-гионов-интеграторов, таких, как медиальная и дорсолатеральная области лобной коры и височно-теменно-затылочная область. Изу-чение региона системы ментализации – меди-альной префронтальной коры – с использова-нием ТЭС показало, что эта область вовлече-на в оценку отклонения от социальной нормы
Система оценки необходимости социального наказания
Дорсальная частьхвостатого ядра
Орбитофронтальная кора Латеральная частьпрефронтальной коры
Система ментализации
Теменно-височно-затылочнаяобласть
Дорсомедиальнаяпрефронтальная кора
А
Б
Рис. 1. Ключевые регионы, активирующиеся в процессе применения социального наказания третьей стороной: А. система оценки необходимости социального наказания. Б. система ментализации. Линии указывают на одно-временную активацию данных областей.Fig. 1. Key regions activated during third-party punishment: А. system of determination of the appropriate social punishment; Б. mentalizing system. Lines indicate simultaneous activation.
НЕЙРОБИОЛОГИЧЕСКИЕ МЕХАНИЗМЫ ПОДДЕРЖАНИЯ СОЦИАЛЬНОЙ НОРМЫ… 23
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справедливости в обоих случаях – когда участ-ник вовлечен во взаимодействие сам или ког-да он является третьей стороной [Civai et al., 2015]. Рис. 2 графически иллюстрирует раз-ницу в поведении, вызванную катодной сти-муляцией медиальной префронтальной коры. В ситуации двустороннего взаимодействия при подавлении активности медиальной префрон-тальной коры с помощью катодной стимуля-ции вероятность отвержения несправедливых предложений уменьшается, в то время как в ситуации наказания третьей стороной пода-вление активности этого участка коры вызвало рост отклонения несправедливых предложе-ний [Civai et al., 2015]. Таким образом, изме-нение роли участника в экономической игре демонстрирует различную степень вовлечен-ности медиального префронтального отдела лобной коры.
С помощью ТМС также была показана роль другого региона системы ментализа-ции – теменно-височно-затылочной области правого полушария – в процессе социального
наказания: ингибирующее воздействие на эту область снижало эффект “парохиализма” (parochialism) – более сильных наказаний по отношению к участникам чужой группы по сравнению с участниками своей группы – за одинаковое отклонение от социальной нор-мы [Baumgartner et al., 2014]. Использование опросников эмоционально-личностной сферы показало, что степень интенсивности парохи-ального наказания модулируется стремлени-ем к “возмездию” (retaliation) – склонностью к пропорциональному возмещению потерь. Предполагается, что процесс ментализации, поддерживаемый активностью дорсомедиаль-ной лобной коры и височно-теменно-заты-лочной области, приводит к разному уровню вовлеченности в ситуацию в непосредствен-ном взаимодействии и наблюдении за взаимо-действием других участников. Сознательное использование ментализации приводит к по-вышению интенсивности переживания соци-альных эмоций при наблюдении за неспра-ведливыми и справедливыми предложениями
Агент
Несправедливоепредложение(финансовый трансфер)
Реципиент
Снижение частотысоциального наказания
Увеличение частотысоциального наказания
Наблюдатель(третья сторона)
А
Б
Рис. 2. Влияние катодной стимуляции в ситуации наказания третьей стороной (А). и наказания в двустороннем взаимодействии (Б). В ситуации применения катодной ТЭС на область медиальной префронтальной коры (А) на-блюдается увеличение частоты социального наказания, (Б) наблюдается снижение частоты социального наказания.Fig. 2. The consequences of cathodal tDCS in third-party punishment (А) and second-party punishment (Б). In case of cathodal tDCS applied to medial prefrontal cortex (А) the frequency of social punishment is raised; (Б) the frequency of social punishment is diminished.
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[Gregucci et al., 2012], что отражается в уси-лении тенденции к кооперации в двусторон-нем взаимодействии [см. обзор Krueger et al., 2008]. В ситуации наказания третьей стороной при наблюдении за несправедливыми пред-ложениями участник (третья сторона), ввиду вероятной постановки себя на место другого, применяет наказание как средство упрочения кооперации и изменения поведения индивида, отклоняющегося от социальной нормы.
Однако не меньший интерес со стороны исследователей вызывает установление кау-зальной роли дорсолатеральной лобной коры правого полушария в процессе социального наказания. Показано, что подавляющее воз-действие с помощью ТМС на дорсолатераль-ную лобную кору правого полушария увели-чивает частоту наказаний со стороны третьего лица [Brune et al., 2012]. Однако в ходе развер-нутого статистического анализа с использова-нием данных опросников эмоционально-лич-ностной сферы авторы предположили, что речь идет о механизме эмпатии как триггере принятия решений о необходимости соци-ального наказания. В этом случае дорсолате-ральная лобная кора выступает областью-ин-тегратором эмоциональных сигналов – ответ-ных реакций на несправедливое предложение [Brune et al., 2012]. Усиление активности пра-вой дорсолатеральной лобной коры при помо-щи анодной ТЭС увеличивает частоту приня-тия решений наказать отклонение от социаль-ной нормы в двустороннем взаимодействии, вне зависимости от предшествующего опыта и результатов предыдущих решений [Nihonsugi et al., 2015]. Область дорсолатеральной лобной коры правого полушария, вероятно, также вовлечена в процесс осуществления социаль-ного наказания в двустороннем взаимодей-ствии, что также было показано с применени-ем катодной ТЭС, подавляющей активность нейронов дорсолатеральной лобной коры пра-вого полушария и приводит к уменьшению ко-личества решений наказывать за несправедли-вое распределение материальных благ [Knoch et al., 2008]. С помощью ТЭС также показано, что в условиях отсутствия санкций подавле-ние активности правой латеральной области лобной доли приводит к увеличению финан-совых трансферов – “активному” следованию социальной норме, в то время как в присут-ствии возможности наказания за отклонение от социальной нормы данный тип стимуляции
резко сокращает частоту финансовых транс-феров [Ruff et al., 2013]. Обнаруженный эф-фект сильнее в контексте прямого социально-го взаимодействия по сравнению с ситуаци-ей, когда остальные участники компьютерной игры находились в режиме удаленного досту-па. Из этого следует, что данный регион так-же вовлечен как в добровольное подчинение социальным нормам, так и в принудительное (спровоцированное социальным наказанием).
Ранее мы останавливались на исследова-ниях эффективной коннективности мозговых систем, вовлеченных в процесс социального наказания третьей стороной. Активность си-стемы ментализации (дорсомедиальная лоб-ная кора и височно-теменно-затылочная об-ласть правого полушария) коррелирует с оцен-кой намерений и постановкой себя на место участников взаимодействия, в то время как система оценки необходимости социального наказания (дорсолатеральная префронталь-ная кора) кодирует необходимый уровень на-казания на основе полученной информации от других систем [Krueger et al., 2016]. Согласно данным нейровизуализации, нейронная сеть, вовлеченная в процесс оценки принадлеж-ности участника к своей или чужой группе – дорсомедиальная лобная кора и теменно-ви-сочно-затылочная область, модулирует актив-ность сети принятия решений, включающей орбитофронтальную кору, латеральную пре-фронтальную кору, дорсальную часть хвоста-того ядра, о необходимости вынесения нака-зания в соответствии с социальными нормами. Однако в настоящий момент остается недоста-точно изученной проблема взаимодействия этих сетей в процессе социального наказания, что является актуальной проблемой будущих исследований.
ДАЛЬНЕЙШИЕ ПЕРСПЕКТИВЫ ИССЛЕДОВАНИЯ
НЕЙРОБИОЛОГИЧЕСКИХ КОРРЕЛЯТ СОЦИАЛЬНОГО НАКАЗАНИЯ
Нейроимиджинговые исследования позво-ляют предположить модель взаимодействия системы ментализации и системы оценки не-обходимости социального наказания в про-цессе наказания на основе анализа активации их ключевых регионов височно-теменно-за-тылочной области и дорсолатеральной преф-ронтальной коры, соответственно. Активация
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височной теменно-затылочной области со-провождается деактивацией дорсолатераль-ной префронтальной коры в ходе принятия решения (фазы предрешения) и сменяется ак-тивацией дорсолатеральной префронтальной коры, когда решение о социальном наказании принято и готово к исполнению [Buckholtz et al., 2008]. Активность височно-теменно-за-тылочной области, вероятно, является ней-рональным коррелятом оценки переживаний (перспективы) другого игрока – вовлечения эмпатического ответа по отношению к участ-никам взаимодействия, в то время как ак-тивность дорсолатеральной префронтальной коры соотносится с оценкой “виновности” и необходимости понести наказание за откло-нение от социальной нормы. Таким образом, предполагается, что процесс социального на-казания поддерживается антагонистической работой этих систем.
Остается неясным, вовлечены ли эти реги-оны в непосредственное взаимодействие друг с другом в процессе принятия решения о на-казании третьей стороной, что требует даль-нейшего исследования с помощью методик неинвазивной стимуляции мозга с целью из-учения взаимодействия этих мозговых регио-нов. Проведенный анализ литературы позво-ляет предположить, что подавление работы областей, вовлеченных в систему оценки со-циального наказания третьей стороной, при-ведет к увеличению частоты наказания за счет работы системы ментализации, в то время как подавление системы ментализации приведет к уменьшению частоты социального наказа-ния. Выдвинутая гипотеза требует экспери-ментального подтверждения с помощью мето-дов неинвазивной стимуляции мозга.
ЗАКЛЮЧЕНИЕ
Проведенный анализ исследований позво-ляет заключить, что в процесс осуществле-ния социального наказания третьей стороной вовлечены две ключевые системы головно-го мозга – система ментализации и системы оценки необходимости социального наказания. Регионы-хабы информации этих систем лока-лизованы в дорсомедиальной и теменно-ви-сочно-затылочной коре, и дорсолатеральной лобной коре, соответственно. Фокусом вни-мания в настоящий момент становится дока-зательство каузальной роли вышеописанных
отделов мозга в процессе социального нака-зания третьей стороной с помощью методов неинвазивной стимуляции мозга. Однако вза-имодействие этих нейросетей через регио-ны-интеграторы (дорсолатеральную лобную кору и теменно-височно-затылочную область) при принятии решения о наказании наруши-телей социальных норм практически не ис-следовалось с помощью современных методов когнитивной нейробиологии, таких как ТЭС и ЭЭГ. Перспективным представляется про-ведение исследований, направленных на вы-явления механизмов и принципов взаимодей-ствия между дорсолатеральной лобной корой и теменно-височно-затылочной областями при поддержке испытуемыми нормативного поведения в группе.
Исследование финансировалось в рамках государственной поддержки ведущих универ-ситетов Российской Федерации “5–100”.
СПИСОК ЛИТЕРАТУРЫ
Axelrod R. An Evolutionary Approach to Norms. The Ameri-can Political Science Review. 1986. 80: 4, 1095–1111.
Battigalli P., Dufwenberg M. Guilt in games. American Eco-nomic Review. 2007. 97: 170–176.
Baumgartner T., Gotte L., Gugler R., Fehr E. The Mentalizing Network Orchestrates the Impact of Parochial Altruism on Social Norm Enforcement. Human Brain Mapping. 2012. 33: 1452–1469.
Baumgartner T., Schiller B, Hill C, Knoch D. Impartiality in humans is predicted by brain structure of dorsomedial prefrontal cortex. Neuroimage. 2013. 81: 317–324.
Baumgartner T., Schiller B., Rieskamp J., Gianotti L., Knoch D. Diminishing parochialism in intergroup con-flict by disrupting the right temporo-parietal junction. SCAN.2014. 9: 653–660.
Baumgartner T., Nash K., Hill C., Knoch D. Neuroanatomy of intergroup bias: A white matter microstructure study of individual differences. Neuroimage. 2015. 15. 122: 345–354.
Bellucci G., Chernyak S., Hoffman M., Deshpande G., Dal Monte O., Knutson K., Grafman J., Krueger F. Effective connectivity of brain regions underlying third-party pun-ishment: Functional MRI and Granger causality evi-dence. Soc Neurosci. 2016. 10: 1–11.
Brune M., Scheele D., Heinisch C., Tas C., Wischniews-ki J., Gunturkun O. Empathy Moderates the Effect of
26 ЗИНЧЕНКО и др.
ЖУРНАЛ ВЫСШЕЙ НЕРВНОЙ ДЕЯТЕЛЬНОСТИ том 68 № 1 2018
Repetitive Transcranial Magnetic Stimulation of the Right Dorsolateral Prefrontal Cortex on Costly Punish-ment. PLOS ONE. 2012. 7(9): e44747.
Buckholtz J., Asplund C.L., Dux P.E., Zald D.H., Gore J.C., Jones O.D., and Marois R. The Neural Correlates of Third–Party Punishment. Neuron. 2008. 60: 930–940.
Buckholtz J.W., Marois R. The roots of modern justice: cog-nitive and neural foundations of social norms and their enforcement. Nature Neuroscience. 2012; 15: 655–661.
Carpenter J., Matthews P.H. What norms trigger punishment? Experimental Economics. 2009. 12: 272–288.
Chang L.J., Smith A., Dufwenberg M., Sanfey A.G. Triangu-lating the neural, psychological, and economic bases of guilt aversion. Neuron. 2011. 70: 560–572.
Chavez A., Bicchieri C. Third-party sanctioning and com-pensation behavior: Findings from the ultimatum game, Journal of Economic Psychology. 2013. 39: 268–277.
Civai C., Miniussi C., Rumiati R. Medial prefrontal cortex re-acts to unfairness if this damages the self: a tDCS study. Soc Cogn Affect Neurosci. 2015. 10(8): 1054–1060.
Charness G., Dufwenberg M. Promises and partnership. Econometrica. 2006. 74: 1579–1601.
Dufwenberg M., Gneezy U. Measuring beliefs in an experi-mental lost wallet game. Games Econ Behav. 2000. 30: 163–182.
Grecucci, A., Giorgetta, C., Bonini, N., Sanfey, A.G. Living Emotions, Avoiding Emotions: Behavioral Investigation of the Regulation of Socially Driven Emotions. Frontiers in Psychology. 2012. 3: 616.
Camerer C.F., Fehr E. Measuring Social Norms and Prefer-ences Using Experimental Games: A Guide for Social Scientists. Foundations of Human Sociality. Ed. Hen-rich J.: Oxford Press. 2003. 472 pp.
Cialdini R.B., Reno R.R., Kallgren C.A. A focus theory of nor-mative conduct: Recycling the concept of norms to re-duce littering in public places. Journal of Personality and Social Psychology, 1990. 58: 1015–1026.
Deutsch M. Distributive Justice: A Social–Psychological Per-spective. New Haven: Yale University Press, 1985, 328.
Elster J. Social Norms and Economic Theory. The Journal of Economic Perspectives. 1989. 3 (4): 89–117.
Engel C. Dictator Games: A Meta Study. Preprints of the Max Planck Institute for Research on Collective Goods. 2010 (07).
Fehr E., Schmidt K. A theory of fairness, competition, and co-operation. Q.J. Econ. 1999. 114: 817–868.
Fehr E., Gachter S. Altruistic punishment in humans. Nature. 2002. 415: 137–140.
Fehr E., Fischbacher U., Gachter S. Strong Reciprocity, Hu-man Cooperation and the Enforcement of Social Norms. Hum. Nat. 2002. 13: 1–25.
Fehr E., Fischbacher U. Third–party punishment and social norms. Evol. Hum. Behav. 2004. 25: 63–87.
Fehr E., Tougareva E., Fischbacher U. Do high stakes and competition undermine fair behaviour? Evidence from
Russia. Journal of Economic Behavior and Organization. 2014. 108: 354–363.
Feng C., Deshpande G., Liu C., Gu R., Luo Y.J., Krueger F. Diffusion of responsibility attenuates altruistic punish-ment: A functional magnetic resonance imaging effec-tive connectivity study. Hum Brain Mapp. 2016. 37(2): 663–677.
Forsythe R., Horowitz J.L. H., Savin N.E., Sefton M. Fairness in Simple Bargaining Experiments. Games and Econom-ic Behavior. 1994. 6: 347–369.
Fukushima H., Hiraki K. Perceiving an opponent's loss: gen-der–related differences in the medial–frontal negativity. Soc Cogn Affect Neurosci. 2006. 1 (2): 149–157.
Glass L., Moody L., Grafman J., Krueger F. Neural signatures of third–party punishment: evidence from penetrating traumatic brain injury. Soc Cogn Affect Neurosci. 2016. 11(2): 253–262.
Hamlin J.K., Wynn K., Bloom P., Mahajan N. How infants and toddlers react to antisocial others. PNAS. 2011108 (50): 19931–19936.
Haruno M., Frith C.D. Activity in the amygdala elicited by unfair divisions predicts social value orientation. Nat Neurosci. 2010. 13: 160–161.
Haruno M., Kimura M., Frith C.D. Activity in the nucleus ac-cumbens and amygdala underlies individual differences in prosocial and individualistic economic choices. J Cogn Neurosci. 2014. 26: 1861–1870.
Herrmann B., Thoni C., Gachter S. Antisocial Punishment Across Societies. 2008. Science, 319, 1362–1367.
Hogg M.A., Reid S.A. Social identity, self–categorization, and the communication of group norms. Commun Theory. 2006. 16: 7–30.
Hsu M., Anen C., Quartz S.R. The right and the good: distrib-utive justice and neural encoding of equity and efficiency. Science. 2008. 320: 1092–1095.
Kahneman D., Knetsch J., Thaler R. Fairness and the As-sumptions of Economics. Journal of Business. 1986. 59: 5285–5300.
Knoch D., Nitsche M., Fischbacher U., Eisenegger C., Pascu-al–Leone A., Fehr E. Studying the Neurobiology of So-cial Interaction with Transcranial Direct Current Stimu-lation – The Example of Punishing Unfairness. Cerebral Cortex.2008. 18: 1987–1990.
Kurzban R., DeScioli P., O’Brien E. Audience effects on mor-alistic punishment. Evolution and Human Behavior. 2007. 28: 75–84.
Krueger F., Grafman J., McCabe K. Neural correlates of eco-nomic game playing. Phil. Trans. R. Soc. B. 2008. 363: 3859–3387.
Krueger F., Hoffman, M. The Emerging Neuroscience of Third-Party Punishment. Trends in Neurosciences. 2016, 39. 8: 499–501.
Lieberman D., Linke L. The Effect of Social Category on Third Party Punishment. Evol Psychol. 2007. 5(2): 289–305.
НЕЙРОБИОЛОГИЧЕСКИЕ МЕХАНИЗМЫ ПОДДЕРЖАНИЯ СОЦИАЛЬНОЙ НОРМЫ… 27
ЖУРНАЛ ВЫСШЕЙ НЕРВНОЙ ДЕЯТЕЛЬНОСТИ том 68 № 1 2018
McAuliffe K., Jordan J., Warneken F. Costly third–party pun-ishment in young children. Cognition. 2015. 134: 1–10.
Messick D.M. Sentis K. Fairness, Preference and Fairness Bi-ases. Equity Theory. Ed. Messick D.M., Cook K. New York: Praeger. 1983. 61–94 pp.
Nelissen, R. M., Zeelenberg, M. Moral emotions as determi-nants of third–party punishment: Anger, guilt, and the functions of altruistic sanctions. Judgment and Decision Making. 2009. 4(7): 543–553.
Nihonsugi T, Ihara A., Haruno M. Selective increase of in-tention-based economic decisions by noninvasive brain stimulation to the dorsolateral prefrontal cortex. J Neu-rosci. 2015. 35: 3412–3419.
Qu L., Dou W., Cheng Y., Qu C. The processing course of conflicts in third–party punishment: an event–related potential study. PsyCh J. 2014. 3: 214–221.
Rilling J., King–Casas B., Sanfey A. The neurobiology of so-cial decision–making. Current Opinion in Neurobiology. 2008. 18 (2): 159–165.
Ruff С., Ugazio G., Fehr E. Changing Social Norm Compli-ance with Noninvasive Brain Stimulation. Science. 2013. 342: 482 p.
Sherif M., Sherif C.W. Groups in Harmony and Tension; An Integration of Studies in Intergroup Relations. Harper; 1953. 316.
Seip E.C., Van Dijk W.W., Rotteveel M. Anger motivates costly punishment of unfair behavior. Motiv. Emot. 2014. 38: 578–588.
Spitzer M., Fischbacher U., Herrnberger B., Grön G., Fehr E. The Neural Signature of Social Norm Compliance. Neu-ron. 2007. 56 (1): 185–196.
Steinbeis N., Bernhardt B., Singer T. Impulse Control and Underlying Functions of the Left DLPFC Mediate Age–Related and Age–Independent Individual Differences in Strategic Social Behavior. Neuron. 2012. 73: 1040–1051.
Strobel A., Zimmermann J., Schmitz A., Reuter M., Lis S., Windmann S., Kirsch P. Beyond revenge: Neural and ge-netic bases of altruistic punishment. NeuroImage. 2011. 54 (1): 671–680.
Sun L., Tan P., Cheng Y., Chen J. and Qu Ch. The effect of al-truistic tendency on fairness in third–party punishment. Frontiers in Psychology. 2015. 6: 820.
Tricomi E., Rangel A., Camerer C.F., O’Doherty J.P. Neural evidence for inequality–averse social preferences. Na-ture. 2010. 463: 1089–1091.
Turillo C., Folger R., Lavelle J., Umphress E., Gee J. Is virtue its own reward? Self–sacrificial decisions for the sake of fairness. Organizational Behavior and Human Decision Processes. 2002. 89: 839–865.
Keywords: social norms, social punishment, third-party punishment, dorsolateral prefrontal cortex, dorsome-dial prefrontal cortex, temporo-parietal junction, transcranial magnetic stimulation (TMS), transcranial direct current stimulation, functional magnetic resonance imaging (fMRI).
Modern neuroimaging studies begin to explore neurobiological mechanisms of social norms enforcement. Several regions of frontal lobes and temporo-parieto-occipital cortex play a key role in decision making of social punishment of fairness’ norm violation. The cutting–edge methods of brain stimulation allow to change frequency and intensity of social punishment in different economic tasks (games). The analysis of modern studies show that brain mechanisms of decision making to punish non–cooperative individual requires further investigation with brain stimulation methods to differentiate the role of frontal and temporo-parieto-occipital regions and clarify its interaction.
1 Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation.
2 Laboratory for Experimental and Behavioral Economics, National Research University Higher School of Economics, Moscow, Russian Federation.
3 Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation.
e-mail: [email protected], 2 [email protected], [email protected]
O. Zinchenko1, A. Belyanin2, V. Klucharev3
Neurobiological Mechanisms of Fairness-Related Social Norm Enforcement: a Review of Interdisciplinary Studies
Attachment C. Article “Commentary: The Emerging Neuroscience of Third-Party
Punishment».
More than a decade of neuroimaging research has established that several distinct
brain networks are consistently recruited during social punishment, i.e., the
propensity of cooperative individuals to spend some of their resources penalizing
norm violators. Studies in behavioral economics have shown that social
punishment can explain why genetically unrelated individuals are often able to
maintain high levels of socially beneficial cooperation. Recently, Krueger and
Hoffman (2016) reviewed and summarized the roles of three brain networks that
are activated during TPP: the salience network (SN), the default mode network
(DMN), and the central executive network (CEN). First, they suggested that the SN
(the insula, amygdala, and dorsal anterior cingulate) detects and generates an
aversive experience that initiates TPP. Second, the authors argued that the DMN
(the medial prefrontal cortex, posterior cingulate cortex, and TPJ) integrates the
perceived harm and inference of intentions into an assessment of blame. Finally,
they proposed that the CEN (the dorsolateral prefrontal cortex and posterior
parietal cortex) converts the blame signal into a specific punishment decision.
These recent findings raise intriguing and testable questions for future research,
e.g., in the use non-invasive brain stimulation to further verify fMRI findings. We
speculate that an enhancement of TPJ activity, along with the simultaneous
suppression of DLPFC activity, should enhance an antagonistic CEN/DMN
interaction and lead to increased TPP. The aforementioned behavioral effect should
be associated with changes in the functional connectivity between the TPJ and
DLPFC. A combined non-invasive brain stimulation-neuroimaging approach could
further uncover the complex intrinsic network dynamics in the brain, which
underlies TPP.
GENERAL COMMENTARYpublished: 24 October 2017
doi: 10.3389/fnhum.2017.00512
Frontiers in Human Neuroscience | www.frontiersin.org 1 October 2017 | Volume 11 | Article 512
Edited by:
Xiaolin Zhou,
Peking University, China
Reviewed by:
Hongbo Yu,
University of Oxford, United Kingdom
Matthew Ginther,
Court of Federal Claims, United States
*Correspondence:
Oksana Zinchenko
Received: 10 July 2017
Accepted: 09 October 2017
Published: 24 October 2017
Citation:
Zinchenko O and Klucharev V (2017)
Commentary: The Emerging
Neuroscience of Third-Party
Punishment.
Front. Hum. Neurosci. 11:512.
doi: 10.3389/fnhum.2017.00512
Commentary: The EmergingNeuroscience of Third-PartyPunishment
Oksana Zinchenko 1* and Vasily Klucharev 1, 2
1Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia,2Department of Psychology, National Research University Higher School of Economics, Moscow, Russia
Keywords: third-party punishment, default mode network, central-executive network, transcranial direct current
stimulation, temporoparietal junction, dorsolateral prefrontal cortex, functional connectivity, social norms
A commentary on
The Emerging Neuroscience of Third-Party Punishment
by Krueger, F., and Hoffman, M. (2016). Trends Neurosci. 39, 499–501. doi: 10.1016/j.tins.2016.06.004
More than a decade of neuroimaging research has established that several distinct brain networksare consistently recruited during social punishment, i.e., the propensity of cooperative individualsto spend some of their resources penalizing norm violators. Studies in behavioral economics haveshown that social punishment can explain why genetically unrelated individuals are often ableto maintain high levels of socially beneficial cooperation (Fehr and Gächter, 2002; de Quervainet al., 2004; Gureck et al., 2006). In particular, social norms can be reinforced by parties thatare directly affected by norm violators (“second parties” punishment—SPP) and parties that arefinancially unaffected (“third parties” —TPP) (Fehr and Fischbacher, 2004). Importantly, normviolations often do not hurt other people directly. Thus, third-party sanctions are particularlyeffective at reinforcing group norms that regulate human behavior (Bendor and Swistak, 2001; Fehrand Fischbacher, 2004).
Pioneering behavioral studies have showed that strong emotions trigger the willingness topunish norm violators (Hirshleifer, 1987; Frank, 1988; Fehr and Gächter, 2002); in particular, TPPis motivated by both empathy toward the victim and anger toward the norm violator (Batson et al.,2007; Pedersen, 2012). Recently, neuroimaging studies have demonstrated a critical role of executive(the dorsolateral prefrontal cortex, DLPFC) and mentalizing (the temporoparietal junction, TPJ)brain regions in TPP (Baumgartner et al., 2012; Bellucci et al., 2016). Thus, neuroscience studiescould help to further develop psychological theories of TPP by clarifying the specific neurocognitivemechanisms triggering punishment decisions in various social contexts.
Recently, Krueger and Hoffman (2016) reviewed and summarized the roles of three brainnetworks that are activated during TPP: the salience network (SN), the default mode network(DMN), and the central executive network (CEN). First, they suggested that the SN (the insula,amygdala, and dorsal anterior cingulate) detects and generates an aversive experience that initiatesTPP. Second, the authors argued that the DMN (the medial prefrontal cortex, posterior cingulatecortex, and TPJ) integrates the perceived harm and inference of intentions into an assessment ofblame. Finally, they proposed that the CEN (the dorsolateral prefrontal cortex and posterior parietalcortex) converts the blame signal into a specific punishment decision.
Interestingly, these three networks partially overlap with those underlying the detection of normviolations in other social contexts. There is a growing cognitive neuroscience literature on a neuralmechanism that detects when individual behavior or beliefs differ from those of others (for reviews,
Zinchenko and Klucharev Commentary: The Emerging Neuroscience of Third-Party Punishment
see Izuma, 2013; Klucharev and Shestakova, 2015). A numberof neuroimaging studies have demonstrated that the activityof the SN, DMN, and CEN encodes perceived deviationsfrom group norms (Klucharev et al., 2009; Berns et al., 2010;Campbell-Meiklejohn et al., 2010; Izuma and Adolphs, 2013). Inparticular, the insula, dorsal anterior cingulate, medial prefrontalcortex, posterior cingulate cortex, and DLPFC have all beenimplicated in norm monitoring. Interestingly, many of thesestudies also reported norm-monitoring activity in the ventralstriatum (Klucharev et al., 2009; Crockett et al., 2013; Xiang et al.,2013), which is a key region implicated in reward valuation.Despite the fact that the ventral striatum was not mentioned byKrueger and Hoffman (2016), recent studies have also implicatedthis region in TPP (Strobel et al., 2011; Hu et al., 2015), whichfurther indicates that these two lines of research (detection ofnorm violations and TPP) share common neural mechanismsand should be further integrated.
However, amygdala activity was reported only in SPP andTPP studies (Buckholtz et al., 2008; Yu et al., 2015; Gintheret al., 2016). This can be explained by the financial losses andharms associated with this paradigm. TPJ activity also seemsto be specific to the context of TPP (Baumgartner et al., 2012,2014). A recent quantitative review suggested that the TPJconsists of functionally and spatially distinct neuroanatomicalsub-regions specializing in different cognitive processes (Schurzet al., 2017). It has been hypothesized that the TPJ supports theprocessing of social contexts that require the representation of(a) the social context (stimuli) and (b) the context provided byattention, memory, and language (Carter and Huettel, 2013).These convergent processes constitute a theory of mind. Thisability to make inferences about other people’s mental states,which is associated with the TPJ, is critical to the ability to blamethem for violations of complex context-dependent social norms.Thus, to uncover the neural mechanisms of TPP, it is essential toclarify the neurocomputational mechanism that allows the TPJ(as a part of the DMN) to link norm-violation detection (SN) tospecific punishments (CEN).
Interestingly, TPJ activity during TPP is paralleled by an initialdeactivation of the DLPFC (Buckholtz et al., 2008). This indicatesfunctionally opposed neural activity in these two regions. TheDLPFC demonstrates a biphasic neural activity—following initialdeactivation, it increases activity—when subjects make thefinal decision to punish “based on assessed responsibility andblameworthiness” (Buckholtz et al., 2008, p. 935). Thus, it isimportant to explain the “antagonistic” relationship betweenthe DMN (TPJ) and CEN (DLPFC). Many recent studies haveevaluated functional and effective connectivity during SPP (Yuet al., 2015) and TPP (Treadway et al., 2014; Bellucci et al., 2016).They demonstrated that the lateral regions of the prefrontalcortex receive an input from the TPJ during SPP (Yu et al.,
2015), while the dorsomedial prefrontal cortex plays the role ofa hub, coordinating DLPFC and TPJ activity during the decisionstage of TPP (Bellucci et al., 2016). Neuroimaging studies havedemonstrated that the temporoparietal-medial-prefrontal circuitsuppresses the amygdala during evaluations of unintentionalharm (Treadway et al., 2014; Yu et al., 2015) in both SPP andTPP or boosts amygdala activity and strengthens its connectivity
with the lateral prefrontal regions (during TPP) when a harmis intentional (Treadway et al., 2014). This suggests that thetemporoparietal-medial-prefrontal circuit gates the emotionalresponses to norm violations and regulates subsequent reactivepunishment.
These recent findings raise intriguing and testable questionsfor future research, e.g., in the use non-invasive brain stimulationto further verify fMRI findings. There is evidence suggestingthat transcranial current stimulation could effectively modulatewithin- and between-network interactions. For example,transcranial alternating current stimulation induced oscillatorydesynchronization between the medial frontal and parietalcortices and, therefore, affected value-based decisions but notclosely matched perceptual decisions (Polanía et al., 2015).Simultaneous anodal transcranial direct current stimulationof the DLPFC, together with cathodal stimulation of thesupraorbital region, led to changes in the default mode networkand frontal-parietal networks (Keeser et al., 2011) and increasedsynchrony within the focused attention network (Peña-Gómezet al., 2012). According to Buckholtz et al. (2008), the CENexerts an inhibitory influence over the DMN in order to programdecisions about an appropriate punishment. Thus, a personcould use a simultaneous application of transcranial direct oralternating current stimulation to the TPJ and DLPFC in orderto modulate an antagonistic CEN/DMN interaction during TPP.We speculate that an enhancement of TPJ activity, along with thesimultaneous suppression of DLPFC activity, should enhancean antagonistic CEN/DMN interaction and lead to increasedTPP. The aforementioned behavioral effect should be associatedwith changes in the functional connectivity between the TPJand DLPFC. A combined non-invasive brain stimulation-neuroimaging approach could further uncover the complexintrinsic network dynamics in the brain, which underlies TPP.
AUTHOR CONTRIBUTIONS
All authors listed have made substantial, direct and intellectualcontribution to the work, and approved it for publication.
FUNDING
The study has been funded by the Russian Academic ExcellenceProject “5-100.”
REFERENCES
Batson, C. D., Kennedy, C. L., Nord, L. A., Stocks, E. L., Fleming, D. Y. A.,Marzette,
C. M., et al. (2007). Anger at unfairness: is it moral outrage? Eur. J. Soc. Psychol.
37, 1272–1285. doi: 10.1002/ejsp.434
Baumgartner, T., Götte, L., Gügler, R., and Fehr, E. (2012). The mentalizing
network orchestrates the impact of parochial altruism on social norm
enforcement. Hum. Brain Mapp. 33, 1452–1469. doi: 10.1002/hbm.21298
Baumgartner, T., Schiller, B., Rieskamp, J., Gianotti, L. R. R., and Knoch, D.
(2014). Diminishing parochialism in intergroup conflict by disrupting the
Frontiers in Human Neuroscience | www.frontiersin.org 2 October 2017 | Volume 11 | Article 512
Zinchenko and Klucharev Commentary: The Emerging Neuroscience of Third-Party Punishment
right temporo-parietal junction. Soc. Cogn. Affect. Neurosci. 9, 653–660.
doi: 10.1093/scan/nst023
Bellucci, G., Chernyak, S., Hoffman, M., Deshpande, G., Dal Monte, O., Knutson,
K., et al. (2016). Effective connectivity of brain regions underlying third–party
punishment: functional MRI and Granger causality evidence. Soc. Neurosci. 10,
1–11. doi: 10.1080/17470919.2016.1153518
Bendor, J., and Swistak, P. (2001). The evolution of norms. Am. J. Sociol. 106,
1493–1545. doi: 10.1086/321298
Berns, G. S., Capra, C. M., Moore, S., and Noussair, C. (2010). Neural mechanisms
of the influence of popularity on adolescent ratings of music. Neuroimage 49,
2687. doi: 10.1016/j.neuroimage.2009.10.070
Buckholtz, J., Asplund, C. L., Dux, P. E., Zald, D. H., Gore, J. C., Jones, O. D., et al.
(2008). The neural correlates of third-party punishment. Neuron 60, 930–940.
doi: 10.1016/j.neuron.2008.10.016
Campbell-Meiklejohn, D. K., Bach, D. R., Roepstorff, A., Dolan, R. J., and Frith, C.
D. (2010). How the opinion of others affects our valuation of objects. Curr. Biol.
20, 1165–1170. doi: 10.1016/j.cub.2010.04.055
Carter, R. M., and Huettel, S. A. (2013). A nexus model of the
temporal-parietal junction. Trends Cogn. Sci. (Regul. Ed). 17, 328–336.
doi: 10.1016/j.tics.2013.05.007
Crockett, M., Apergis-Schoute, A., Herrmann, B., Lieberman, M., Müller,
U., Robbins, T., et al. (2013). Serotonin modulates striatal responses
to fairness and retaliation in humans. J. Neurosci. 33, 3505–3513.
doi: 10.1523/JNEUROSCI.2761-12.2013
de Quervain, D. J. F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U.,
Buck, A., et al. (2004). The neural basis of altruistic punishment. Science 305,
1254–1258. doi: 10.1126/science.1100735
Fehr, E., and Fischbacher, U. (2004). Third-party punishment and social
norms. Evol. Hum. Behav. 25, 63–87. doi: 10.1016/S1090-5138(04)
00005-4
Fehr, E., and Gächter, S. (2002). Altruistic punishment in humans. Nature 415,
137–140. doi: 10.1038/415137a
Frank, R. (1988). Passions within Reason: The Strategic Role of the Emotions. New
York, NY: Norton.
Ginther, M. R., Bonnie, R. J., Hoffman, M. B., Shen, F. X., Simons,
K. W., Jones, O. D., et al. (2016). Parsing the behavioral and brain
mechanisms of third-party punishment. J. Neurosci. 36, 9420–9434.
doi: 10.1523/JNEUROSCI.4499-15.2016
Gureck, O., Irlenbusch, B., and Rockenbach, B. (2006). The competitive advantage
of sanctioning institutions. Science 312, 108–111 doi: 10.1126/science.
1123633
Hirshleifer, J. (1987). “On the emotions as guarantors of threats and promises,” in
The Latest on the Best, ed J. Dupre (Cambridge, MA: MIT Press), 307–326.
Hu, Y., Strang, S., and Weber, B. (2015). Helping or punishing strangers:
neural correlates of altruistic decisions as third-party and of its relation
to empathic concern. Front. Behav. Neurosci. 9:24. doi: 10.3389/fnbeh.2015.
00024
Izuma, K. (2013). The neural basis of social influence and attitude change. Curr.
Opin. Neurobiol. 23, 456–462. doi: 10.1016/j.conb.2013.03.009
Izuma, K., and Adolphs, R. (2013). Social manipulation of preference in the human
brain. Neuron 78, 563–573. doi: 10.1016/j.neuron.2013.03.023
Keeser, D., Meindl, T., Bor, J., Palm, U., Pogarell, O., Mulert, C., et al.
(2011). Prefrontal transcranial direct current stimulation changes connectivity
of resting-state networks during fMRI. J. Neurosci. 31, 15284–15293.
doi: 10.1523/JNEUROSCI.0542-11.2011
Klucharev, V., Hytönen, K., Rijpkema, M., Smidts, A., and Fernández, G. (2009).
Reinforcement learning signal predicts social conformity. Neuron 61, 140–151.
doi: 10.1016/j.neuron.2008.11.027
Klucharev, V., and Shestakova, A. (2015). “Social influence and persuasion
and message propagation,” in Brain Mapping: An Encyclopedic
Reference, ed A. W. Toga (San Diego, CA: Academic Press), 251–258.
doi: 10.1016/B978-0-12-397025-1.00189-5
Krueger, F., and Hoffman, M. (2016). The emerging neuroscience of third-party
punishment. Trends Neurosci. 39, 499–501. doi: 10.1016/j.tins.2016.06.004
Peña-Gómez, C., Sala-Lonch, R., Junqué, C., Clemente, I. C., Vidal, D., Bargalló,
N., et al. (2012). Modulation of large-scale brain networks by transcranial direct
current stimulation evidenced by resting-state functional MRI. Brain Stimul. 5,
252–263. doi: 10.1016/j.brs.2011.08.006
Pedersen, E. J. (2012). The Roles of Empathy and Anger in the Regulation of
Third-Party Punishment. Open Access Theses. 377. Available online at: http://
scholarlyrepository.miami.edu/oa_theses/377
Polanía, R., Moisa, M., Opitz, A., Grueschow, M., and Ruff, C. C. (2015). The
precision of value-based choices depends causally on fronto-parietal phase
coupling. Nat. Commun. 6:8090. doi: 10.1038/ncomms9090
Schurz, M., Tholen, M. G., Perner, J., Mars, R. B., and Sallet, J. (2017). Specifying
the brain anatomy underlying temporo-parietal junction activations for theory
of mind: a review using probabilistic atlases from different imaging modalities.
Hum. Brain Mapp. 38, 4788–4805. doi: 10.1002/hbm.23675
Strobel, A., Zimmermann, J., Schmitz, A., Reuter, M., Lis, S., Windmann, S., et al.
(2011). Beyond revenge: neural and genetic bases of altruistic punishment.
Neuroimage 54, 671–680. doi: 10.1016/j.neuroimage.2010.07.051
Treadway, M. T., Buckholtz, J. W., Martin, J. W., Jan, K., Asplund, C. L., Ginther,
M. R., et al. (2014). Corticolimbic gating of emotion-driven punishment. Nat.
Neurosci. 17, 1270–1275. doi: 10.1038/nn.3781
Xiang, T., Lohrenz, T., and Montague, P. R. (2013). Computational substrates
of norms and their violations during social exchange. J. Neurosci. 33,
1099a−1108a. doi: 10.1523/JNEUROSCI.1642-12.2013
Yu, H., Li, J., and Zhou, X. (2015). Neural substrates of intention–
consequence integration and its impact on reactive punishment
in interpersonal transgression. J. Neurosci. 35, 4917–4925.
doi: 10.1523/JNEUROSCI.3536-14.2015
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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Frontiers in Human Neuroscience | www.frontiersin.org 3 October 2017 | Volume 11 | Article 512
Attachment D. Article “The role of the temporoparietal and prefrontal cortices in
third-party punishment: a tDCS study”.
Recent studies have demonstrated that the right dorsolateral prefrontal cortex
(rDLPFC) and the right temporoparietal junction (rTPJ) are causally involved in
social norm compliance. Here, we tested the hypothesis that a third party’s
decision to punish norm violations depends on the activity of the entire
rDLPFC/rTPJ network. We used transcranial direct current stimulation (tDCS) to
independently or jointly modulate rTPJ and rDLPFC activity during the third-party
dictator game. We found a significant effect of anodal tDCS of the rTPJ, which
decreased the third-party punishment of moderately unfair splits. Joint stimulation
of the rTPJ (by anodal tDCS) and rDLPFC (by cathodal tDCS) produced a
marginal effect on third-party punishment.
Zinchenko, O., Belianin, A., Klucharev, V. The role of the temporoparietal and prefrontal cortices in
third-party punishment: a tDCS study // Psychology. Journal of the Higher School of Economics.
2019. (in print).
The role of the temporoparietal and prefrontal cortices in third-party
punishment: a tDCS study
tDCS study of social punishment
Zinchenko Oksana, junior research fellow, Institute of Cognitive Neuroscience, Centre
for Cognition and Decision Making, National Research University Higher School of
Economics, National Research University Higher School of Economics, 20
Myasnitskaya Ulitsa, Moscow, 109316, Russia, [email protected].
Research interests: cooperation, social norms, social punishment.
Belianin Alexis, PhD, National Research University Higher School of Economics;
ICEF and Laboratory for Experimental and Behavioral Economics, [email protected]
Klucharev Vasily, Cand.of Science, professor, Institute of Cognitive Neuroscience,
Centre for Cognition and Decision Making, National Research University Higher
School of Economics, [email protected]
Abstract
Recent studies have demonstrated that the right dorsolateral prefrontal cortex
(rDLPFC) and the right temporoparietal junction (rTPJ) are causally involved in social
norm compliance. Here, we tested the hypothesis that a third party’s decision to punish
norm violations depends on the activity of the entire rDLPFC/rTPJ network. We used
transcranial direct current stimulation (tDCS) to independently or jointly modulate
rTPJ and rDLPFC activity during the third-party dictator game. We found a significant
effect of anodal tDCS of the rTPJ, which decreased the third-party punishment of
2
moderately unfair splits. Joint stimulation of the rTPJ (by anodal tDCS) and rDLPFC
(by cathodal tDCS) produced a marginal effect on third-party punishment.
Keywords: dorsolateral prefrontal cortex, transcranial direct current stimulation,
temporoparietal junction, third-party punishment, social norms.
Introduction
Human societies crucially depend on social norms that often regulate appropriate
actions in various situations and can be reinforced by “second” parties that are directly
affected by the norm violators and “third” parties that are not directly affected (Fehr
and Fischbacher, 2004). Since norm violations often do not directly hurt other people,
third-party sanctions are especially critical in reinforcing social norms (Bendor and
Swistak, 2001; Fehr and Fischbacher, 2004). More than a decade of neuroimaging
research has established that several distinct brain networks are consistently recruited
during social punishment; that is, cooperative individuals’ propensity to spend part of
their resources to penalize norm violators (Krueger and Hoffman, 2016). Here, we
further investigate the neural underpinnings of third parties’ punishment of a fairness
norm violation.
The social norm of fair distribution implies a rejection of the distribution of goods that
violates the equality principle (Elster, 1989; Kahneman et al., 1986). The norm of
fairness is often investigated using economic games, allowing different distributions of
financial transfers between players. Importantly, behavioral studies have robustly
demonstrated that many players (including third parties) in economic games not only
prefer fair distributions to unequal ones (Guth et al., 1982; Engel, 2010), but they also
tend to spend personal resources to punish unfair distributions (norm violations) on
their own accord (Fehr and Fischbacher, 2004; Ruff et al., 2013).
3
Functional magnetic resonance imaging (fMRI) and brain stimulation studies have
suggested that the right dorsolateral prefrontal cortex (rDLPFC) controls selfish
impulses (Strang et al., 2015) and responds to inequity (Fliessbach et al., 2012), where
individual differences in sanction-induced norm compliance correlate with rDLPFC
activity (Spitzer et al., 2007). Brüne and colleagues (2012) showed that inhibitory
rTMS of the rDLPFC increased third-party punishment during the dictator game,
which suggests that the rDLPFC associated third parties’ emotional responses to
observed unfairness of dictators. In contrast, rTMS of the rDLPFC resulted in
decreased third-party punishment when participants where shown criminal scenarios
ranging from simple theft to murder (Buckholtz et al., 2015). Inconsistencies in effects
of rTMS on rDLPFC require further work to clarify the role of the rDLPFC on third-
party punishment. Like Brüne and colleagues (2012), the current project utilized the
third-party dictator game but in contrast to their experiment manipulations, we aimed
to apply excitatory anodal tDCS on the rDLPFC, and predict that the opposed effects
would ensue and therefore decrease third-party punishment.
Another neuroanatomical structure that has been found to play a critical role in third
parties’ punishment decisions is the right temporoparietal junction (rTPJ)
(Baumgartner et al., 2012; Baumgartner et al., 2014). Importantly, the ability to make
inferences about other people’s mental states is associated with TPJ activation, that is
crucial for the ability to blame others for violations of complex context-dependent
social norms. Increased rTPJ activity has been associated with reduced punishment of
defecting in-group members during the prisoner’s dilemma game (Baumgartner et al.,
2012). Here, we hypothesized that excitatory anodal tDCS of the rTPJ should reduce
third-party punishment during the third-party dictator game.
Recently, Krueger and Hoffman (2016) argued that during third-party punishment, the
TPJ integrates the inference of intentions into an assessment of blame. The
DLPFCconverts the blame signal into a specific punishment decision. Thus, the
4
DLPFC plays an executive role, while the TPJ drives processes associated with blame
and initiates punishment. Although these mechanistic actions are neurobiologically
plausible, the exact interaction between the rTPJ and rDLPFC for the function of third-
party punishment remain unclear, because it is unknown whether the rDLPFC entrains
the rTPJ or that rTPJ activates independently (for a detailed discussion, see Zinchenko
and Klucharev, 2017). It has also been shown that, TPJ activity during third-party
punishment is paralleled by an initial deactivation of the DLPFC which indicates
functionally opposed neural activity in these two regions (Buckholtz et al., 2008). The
DLPFC demonstrates biphasic neural activity—after the initial deactivation, it later
increases in activity—when subjects make the final decision to punish “based on
assessed responsibility and blameworthiness” (Buckholtz et al., 2008, p. 935). Overall,
Buckholtz and colleagues (2008) suggested that this pattern of reciprocal activation
could reflect a crucial mentalizing process before an appropriate punishment is
determined and a decision is made. Therefore, it would be important to further study
the functional interaction of the rTPJ and rDLPFC during third-party punishment
through the use of joint stimulation of the rDLPFC and rTPJ in a reciprocal manner.
In the current study, we further investigated the role of the DLPFC and TPJ in third-
party punishment with an overarching aim of understanding neural resource activation
plays a role in social norm reinforcement. Our motivation was based on previous
studies (Baumgartner et al., 2012; Brüne et al., 2012) and sought to replicate their
results, but by uniquely testing the opposed effect of independent excitatory anodal
tDCS of the rTPJ or rDLPFC and predict that decreased third-party punishment of
unfair splits would be resultant (Hypothesis I, Study 1) compared to sham stimulation.
On the other hand, based on the seminal fMRI study (Buckholtz et al., 2008), we
hypothesized that a joint anodal tDCS of the rTPJ and cathodal tDCS of the rDLPFC
of third parties might make third-party punishment of unfair splits stronger comparing
to sham stimulation (Hypothesis II, Study 2). Therefore, in Study 1, we stimulated the
5
rDLPFC and rTPJ independently, while in Study 2, we jointly stimulated the rDLPFC
and rTPJ in a reciprocal, antagonistic manner. Importantly, according to the theory of
inequity aversion, individuals dislike outcomes that are perceived as inequitable (Fehr
and Schmidt, 1999). Therefore, we expected to find the strongest effect of tDCS on
third-party punishment in trials with a payoff structure, where sanctioners (third
parties) were able to establish the equality between all players (Hypothesis III).
Overall, we used tDCS to further investigate the role of the rDLPFC and rTPJ in third
parties’ punishment of a fairness norm violation. According to our hypotheses, an
independent or joint stimulation of the rDLPFC and rTPJ could lead to different
behavioral effects.
Methods
Subjects
Twenty-three healthy, right-handed subjects (mean age = 21.5 years, range = 18–27
years, 7 males) participated in Study 1. Twenty-one healthy, right-handed subjects
(mean age = 22.79 years, range = 18–27 years, 10 males) participated in Study 2. Each
subject participated in only one of the two studies. All subjects gave written informed
consent to participate in the study. Subjects (n = 5) who did not punish at least once or
demonstrated only antisocial punishment in fair trials (20∶20 split condition) were
excluded from the analysis, resulting in 20 (n = 20, Study 1) and 19 (n = 19, Study 2)
subjects respectively. The studies conformed to the Declaration of Helsinki, and the
experimental protocol was approved by the university ethics committee. The sample
size was based on the previous study of Brüne and colleagues (2012), which included
20 subjects.
Procedure
6
Each subject participated in three sessions of the dictator game that were separated by
7±2 days. Next, tDCS was applied for 15 minutes. The third-party dictator game lasted
approximately 20–25 minutes. A structured debriefing after each session revealed that
the subjects believed the instructions and their behavior were comparable to those in
“real-life” situations.
Study design
Dictator game with third-party punishment
The subjects participated in multiple rounds of a preprogrammed dictator game as
sanctioners (third parties). In the instructions, the dictator distributed 40 experimental
monetary units (MUs; 1 MU ≈ 0.26 Russian rubles, or 0.004 U.S. dollars) between
herself and the recipient.
Figure 1 demonstrates the details of the trial structure. To make the game more social,
in each trial the participants first observed pictures of two individuals (a dictator and a
recipient). The genders of the dictators and recipients were counterbalanced across
subjects. Participants (sanctioners) were able to punish dictators using a budget of 20
MUs. The budget was renewed for each round, and all points not invested in
punishment were converted into a monetary payoff and paid to the participant after the
experiment. To avoid demand effects, the instructions described the task using neutral
language, such as “You will be able to deduct the first player’s earnings.” Sanctioners
could use 0–18 MUs out of their 20 MUs budget to punish the dictator, and were able
to use an even number of MUs, such as 2, 4, 6, etc. These MUs were multiplied by two
and deducted from the dictator’s budget. For example, if the sanctioner used 10 MUs
to punish the dictator, 20 MUs (2 × 10 MUs) were deducted from dictator’s budget.
7
Figure 1. Trial structure of the dictator game with third-party punishment. At the
beginning of each trial, a dictator (Player 1) received 40 monetary units (MUs; Stage
I) to choose whether to give some MUs to the recipient (Player 2; Stage II). Next, the
subject (Player 3, sanctioner) received 20 MUs (Stage III) to choose how much (if
any) to spend on punishing the dictator (Stage IV), in which every MU spent by the
sanctioner reduced the dictator’s payoff by 2 MUs.
The photos of dictators and recipients were preselected from 300 photos of young
adults. The images were retrieved from the Internet from open access sources, such as
popular social media without being logged in. For ethical reasons, it was carefully
ensured that the photos stayed anonymous—no personal information was stored.
Similar to the study of Brüne and colleagues (2012), we pretested stimuli: photos were
evaluated for attractiveness, trustworthiness, and cooperativeness on a seven-point
Likert-type scale by 17 subjects (10 females) prior to the study. We calculated the
8
average rating of each measure (attractiveness, trustworthiness, and cooperativeness)
for each photo. Similar to the previous rTMS study (Brune et al. 2012), only photos
with a mean average rating between 2.5 and 5.5 points were used in the current study.
If the average rating for at least one measure was higher or lower than this range, the
photo was excluded and never used in the study.
The following information was emphasized to the participants: (1) their partners were
real people participating in the game at the same time, located in different rooms; (2)
the partners varied in each round; and (3) both the participants and their partners
would be paid real money, as all points that had not been invested during the game
would be paid out at the end of the study. Although the subjects believed that they
were playing an “online” game, they were, in fact, playing with prerecorded human
players (dictators and recipients) who had played the same game before against other
human opponents (see Brüne et al., 2012, for the same approach). Therefore, each
session consisted of 48 trials per split condition, with shares of 0:40 (n = 2), 15:25 (n =
1), 20:20 (n = 26), 25:15 (n = 4); 30:10 (n = 6), 35:5 (n = 3) and 40:0 (n = 6). The trials
were randomized in each session. All the subjects were native Russians recruited via
email. The number of trials in each split condition was defined based on a behavioral
pilot study (n = 178).
tDCS
The tDCS is a noninvasive brain stimulation technique that can modulate activity in
specific regions of the cortex (Nitsche and Paulus 2001, Nitsche et al. 2003; Paulus
2011). During tDCS, weak electrical currents are applied to the scalp surface from the
anode to cathode: anodal tDCS typically depolarizes (excites) and cathodal tDCS
typically hyperpolarizes (inhibits) neurons. In the current study, a direct current was
induced using two saline-soaked surface sponge electrodes (active electrode area = 25
9
cm2) and delivered by a battery-driven, constant current StarStim 8 stimulator
(Neuroelectrics). The stimulation intensity was set at 1.5 mA and lasted 15 minutes,
with ramping up and ramping down time equal to 30 seconds. Impedances were kept
below 10 kOhm.
After 15 minutes of tDCS, participants immediately participated in the dictator game
as a third-party. Importantly, several methodological studies demonstrated that even
tDCS (1 mA) delivered for a short time (5–13 minutes) induced long-lasting changes
of cerebral excitability: up to 90 minutes after the end of stimulation (Nitsche and
Paulus, 2001) for anodal tDCS and up to one hour for the cathodal tDCS (Nitsche et
al., 2003). Therefore, a 15-minute tDCS in our study should modulate cortical
excitability during the entire dictator game.
The stimulation point for the rDLPFC was defined using the MNI coordinates reported
by Spitzer et al. (2007) for rDLPFC activity (x = 52, y = 28, z = 14), which showed
both stronger fMRI activation for punishment condition minus baseline condition as
well as a correlation of brain activity with the transfer difference between punishment
and baseline conditions (see Ruff et al., 2013 for a similar approach). To further clarify
the optimal electrode position, we simulated tDCS using SimNIBS software, version
2.1.1. (see Figure 2; www.simnibs.de/start; Thielscher, Antunes and Saturnino, 2015).
10
Figure 2. Simulations of electric current distributions (E-field, V/m) for different
tDCS protocols.
A: Simulations of the tDCS protocols used in the current study (F8 electrode for
rDLPFC stimulation, CP6 electrode for rTPJ stimulation), B: Simulations of the
alternative tDCS protocol, where F8 electrode is replaced with F4 (F4 electrode for
rDLPFC stimulation, CP6 electrode for rTPJ stimulation).
Overall, the results of the simulation indicated that the F8 electrode position was an
adequate target for the rDLPFC stimulation. To stimulate the rTPJ, the target electrode
was located over CP6 region (Santiesteban et al., 2012; Sellaro et al., 2015). For the
sham stimulation, the intensity and position of the electrodes were the same as during a
real stimulation, but the stimulator was only turned on for 30 seconds. The positions of
the electrodes for the sham stimulation in Study 1 and Study 2 were randomized and
counterbalanced, as was the order of the stimulation sessions (see Figure 3).
11
Figure 3. Transcranial direct current stimulation (tDCS) set-ups for Study 1 and
Study 2. Study 1: Condition 1.1—anodal tDCS of the rDLPFC; Condition 1.2—
anodal tDCS of the rTPJ; Condition 1.3—sham condition. In all conditions of Study 1,
the cathodal electrode was placed over vertex. Study 2: Condition 2.1—simultaneous
anodal tDCS of the rDLPFC and cathodal tDCS of the rTPJ; Condition 2.2—
simultaneous cathodal tDCS of the rDLPFC and anodal tDCS of the rTPJ; Condition
2.3—sham.
In Study 1, we applied the anodal tDCS of the rDLPFC and rTPJ independently, as
follows: (1) rDLPFCa condition (Condition 1.1)—anodal tDCS of the rDLPFC; (2)
12
rTPJa condition (Condition 1.2)—anodal tDCS of the rTPJ; and (3) sham condition
(Condition 1.3). In all conditions of Study 1, the cathodal electrode was placed over
the vertex (Cz electrode position). We expected (Hypothesis I) to decrease third-party
punishment in Conditions 1.1 and 1.2 as compared with the control (Condition 1.3).
In Study 2, we simultaneously modulated rDLPFC and rTPJ activity, as follows: (1)
rDLPFCa/rTPJc condition (Condition 2.1) — simultaneous anodal tDCS of the
rDLPFC and cathodal tDCS of the rTPJ; (2) rDLPFCc/rTPJa condition (Condition 2.2)
— simultaneous cathodal tDCS of the rDLPFC and anodal tDCS of the rTPJ, and (3)
sham condition (Condition 2.3), which was the same as in Study 1. Following the
findings of Buckholtz and colleagues (2008), which demonstrated a reciprocal
activation of the rDLPFC and rTPJ, we expected (Hypothesis II) to increase third-party
punishment in Condition 2.2 as compared with Conditions 1.2 and 2.3.
There is evidence suggesting that tDCS could effectively modulate within-network and
between-network interactions. For example, the simultaneous anodal tDCS of the
DLPFC, together with cathodal tDCS of the supraorbital region, led to changes in the
default mode network (Keeser et al., 2011; Peña-Gómez et al., 2012). Here, we used a
simultaneous application of tDCS to the rTPJ and rDLPFC to modulate their
interaction during third-party punishment. Thus, we developed a mixed design that
allows exogenous modulation of between-network interaction.
Statistical analysis
For each experimental condition, we calculated a sum of MUs, which were used to
punish the dictator, to estimate the punishment level, or total investment in punishment
(see Brune et al., 2012 for the same approach). According to the payoff matrix of the
dictator game, our participants would experience advantageous inequity (when they
receive more than others) or disadvantageous inequity (when they receive less than
others) after all splits except for the 20:20 split. Importantly, only when the dictator
13
chose a 30:10 split were participants able to restore equality by spending 10 of their
own MUs on punishing the dictator. Interestingly, only in the case where participants
observed moderately unfair 30:10 splits, the conflict between material (selfish) and
moral (prosocial) costs was minimal, since participants either did not punish or
punished extremely little if the material costs were high. Therefore, we could expect
that the strongest effect of tDCS would be observed in the 30:10 split condition, when
participants were able to restore equality and protect their own material interests
(Hypothesis III).
To test Hypothesis III, we aggregated split conditions of the game into three trial
types: FS-trials—fair splits (20:20 and 25:15 splits), US_equal-trials—unfair splits
(30:10 splits), where third-party punishment was able to establish equality between all
players, and US_inequal-trials (35:5 and 40:0 splits), where participants were unable
to establish such equality. Due to a very low number of observations, 0:40, 15:25 split
conditions were not included in the main analysis. However, Table 1 provides
descriptive statistics for all split conditions.
14
Table 1. The mean and standard deviations of punishment level in Study 1 and Study 2.
Condition/
Types of splits
0:40 +
15:25
(not
included
into main
analysis)
20:20+25:15
[FS-trials]
30:10
[US_equal-
trials]
35:5+40:0
[US_inequal-
trials]
Study 1
Condition 1.1.
Mean 0.5 22 48.60 118.30
SD 1.43 10.05 19.22 45.17
Condition 1.2.
Mean 2.7 20.40 46.20 112
SD 6.53 10.59 17.96 43.61
Condition 1.3.
Mean 1.5 21.30 49.50 119.30
SD 3.55 10.02 16.78 39.70
Study 2
Condition 2.1.
Mean 0.21 13.79 37.05 112.53
SD 0.92 11.25 20.56 33.65
15
Condition 2.2.
Mean 1.16 13.37 40.52 109.37
SD 4.18 10.67 19.43 32.19
Condition 2.3.
Mean 2.11 11.26 34.21 111.37
SD 8.23 8.92 20.36 28.86
Since the punishment levels were not normally distributed, behavioral results were
analyzed using the Wilcoxon signed-rank test and the Friedman test, and p-values <.05
were considered significant. To correct for multiple comparisons, the false discovery
rate (FDR) correction at 10% level using the Benjamini-Hochberg procedure (1995)
was computed to compare the effects of three types of stimulation (Conditions 1.1, 1.2,
and 1.3) and three types of splits (FS-trials, US_equal-trials, and US_inequal-trials) in
Study 1. To compute the FDR correction, p-values obtained in the statistical analysis
were ranked from the lowest to the highest and then compared to FDR-corrected alpha
levels (Benjamini-Hochberg critical value). Only p-values not exceeding FDR-
corrected alpha levels were considered significant. To control individual differences in
third-party punishment, we normalized punishment levels: for each trial type, the
punishment level was divided by the punishment level in the sham condition and
multiplied by 100%. Between-group differences were further evaluated using the
Kruskal–Wallis H test, which was applied to normalized data.
Results
Study 1: Independent modulation of the rDLPFC and rTPJ
16
In total, in the sham condition (Condition 1.3), participants spent only 1.5 (±3.6) MUs
for the punishment of generous 0:40 and 15:25 splits, 21.3 (±10.0) MUs—for the
punishment of fair FS-trials, while for unfair US_equal-trials they used 49.5 (±16.8)
MUs and for US_inequal-trials, 119.3 (±39.7) MUs. Due to a very low number of
observations, 0:40, 15:25 split conditions were not included into further analyses.
The lowered punishment level of FS-trials compared with US_equal-trials and
US_inequal-trials was observed in all experimental tDCS conditions. Table 1
represents the mean and standard deviations of punishment level of third-party
punishment for each. As expected, the participants punished unfair splits much more
strongly than they did fair splits.
rDLPFCa condition. We observed a trend of a stronger third-party punishment in the
rDLPFCa condition (Condition 1.1) than in the rTPJa condition (Condition 1.2): Z = -
2.177; p = 0.029, which did not survive FDR correction (see Table 2 for FDR-
corrected alpha levels).
Table 2. The false discovery rate computation (Study 1).
Study 1
Condition
p-values obtained
in the statistical
analysis Rank
FDR-corrected
alpha levels
(Benjamini-
Hochberg critical
values)
Condition 1.2–
Condition 1.3
(30:30)
0.006*
1 0.011
17
Condition 1.1–
Condition 1.2
(35:5+40:0)
0.029
2 0.022
Condition 1.2–
Condition 1.3
(35:5+40:0)
0.045
3 0.033
Condition 1.1–
Condition 1.2
(20:20+25:15)
0.347
4 0.044
Condition 1.1–
Condition 1.2
(30:10)
0.450
5 0.055
Condition 1.1–
Condition 1.3
(20:20+25:15)
0.459
6 0.066
Condition 1.1–
Condition 1.3
(30:10)
0.649
7 0.077
Condition 1.1–
Condition 1.3
(35:5+40:0)
0.678
8 0.088
Condition 1.2–
Condition 1.3
(20:20+25:15)
1.000
9 0.100
18
rTPJa condition. We found that the third-party punishment in US_equal-trials (30:10
splits) in the rTPJa condition (Condition 1.2) was significantly smaller than it was in
the sham condition (Condition 1.3): Z = -2.746, p = 0.006 (see Table 1 and Table 3 for
details). We also observed a trend of a smaller third-party punishment in US_inequal-
trials (35:5 and 40:0 splits) in the rTPJa condition (Condition 1.2) compared with the
sham condition (Condition 1.3): Z = -2.006, p = 0.045, which did not survive FDR
correction (see Table 2 for FDR-corrected alpha levels).
Table 3. Effect of unilateral transcranial direct current stimulation on third-party
punishment (Study 1).
Split 20:0+25:5 30:10 35:5+40:0
Rank (1.90; 1.88; 2.23) (2.30; 1.58; 2.13) (2.20; 1.60; 2.20)
χ2 1.937 7.508 5.408
df 2 2 2
p 0.380 0.023* 0.067
Notes: Friedman test for 3 samples. * Significant at the level of p = 0.05.
Condition 1.2–Condition
1.3
Split 20:0+25:5 30:10
35:5+40:0
Z 0.000 –2.746
-2.006
p 1 0.006* 0.045
Condition 1.1–Condition
1.3
Split 20:0+25:5 30:10
35:5+40:0
19
Z -0.741 –0.456
-0.415
p 0.459 0.649 0.678
Condition 1.1–Condition
1.2
Split 20:0+25:5 30:10
35:5+40:0
Z -0.940 –0.755
-2.177
p 0.347 0.450 0.029
Notes: Wilcoxon signed-rank test for sums of punishment points. * Significant at the
level of p = 0.05.
We found no other significant effects of tDCS on third-party punishment. Therefore,
Hypotheses I and III were partly supported: anodal tDCS of the rTPJ significantly
decreased third-party punishment, but only in US_equal-trials (unfair 30:10 splits),
where third-party punishment was able to establish equality between all players.
Study 2: Simultaneous modulation of the rDLPFC and rTPJ
Similar to Study 1, the participants in Conditions 2.1, 2.2, and 2.3 punished unfair
splits (US_equal-trials and US_inequal-trials) more strongly than they did fair splits.
We found no significant effects of tDCS in Study 2. Interestingly, the third-party
punishment for 30:10 splits in the rDLPFCc/rTPJa condition (Condition 2.2) tended to
be higher than that in the sham condition (Condition 2.3), Z = -1.917, p = 0.055
20
(uncorrected; see Table 4 for details). Therefore, our results did not support
Hypothesis II.
Table 4. Effect of reciprocal transcranial direct current stimulation on third-party
punishment (Study 2).
Split 20:20+25:5 30:10 35:5+40:0
Rank (1.71; 2.16; 2.13) (1.66; 2.24; 2.11) (1.92; 2.08; 2.00)
χ2 2.984 5.115 0.269
df 2 2 2
p 0.225 0.077 0.874
Notes: Friedman test for 3 samples.
Condition 2.2–Condition
2.3
Split 20:0+25:5 30:10
35:5+40:0
Z -0.999 –1.917
-0.028
p 0.318 0.055 0.977
Condition 2.1–Condition
2.3
Split
20:0+25:5
30:10
35:5+40:0
Z -1.307 –1.401
-0.087
21
p 0.191 0.161 0.930
Condition 2.1–Condition
2.2
Split
20:0+25:5
30:10
35:5+40:0
Z -0.140 –1.337 -0.570
p 0.888 0.181 0.569
Between-group analysis
A Kruskal–Wallis H test showed that the normalized punishment levels for 30:10
splits differed in Study 1 and Study 2: χ2 = 4.481, p = 0.034, for 30:10 splits
(Condition 1.2 versus Condition 2.2; see Table 5). Third-party punishment for 30:10
splits was significantly lower in Study 1 than in Study 2. This could indicate an
opposite effect of the anodal tDCS of the rTPJ (rTPJa condition) compared with the
anodal tDCS of the rTPJ when paralleled with cathodal tDCS of rDLPFC.
Table 5. Comparison of transcranial direct current stimulation effects on third-party
punishment in Study 1 and Study 2 (Condition 1.2–Condition 2.2 [Normalized Data]).
Split 25/15 30/10 35/5 40/0
Rank (23.28; 16.55) (16.25; 23.95) (23.10; 16.74) (20.23; 19.76)
χ2 3.420 4.481 3.105 0.016
df 1 1 1 1
p 0.064 0.034* 0.078 0.898
22
Inequity aversion model
To assess the effect of stimulation on third-party punishment from theoretical
viewpoint, we used a modified inequity aversion model (Fehr and Schmidt, 1999)
extended to third parties (Svedsater and Johannsson, 2005). The model assumes that
each player in the game generally dislikes unfair outcomes reducing their utility. First,
consider the utility function of sanctioner, who observes the dictator game between
dictator and recipient, but who has no punishment option. The sanctioner receives an
endowment and feels unhappy whenever any other players receive either more (with
parameter α) or less than she does (with parameter β). The sanctioner also experiences
moral loss when the payoffs of the dictator and recipient are unequal (with parameter
γ). In terms of inequity aversion model of Fehr and Schmidt (1999), the resulting
utility is:
U3N = w3 − α max(X1 − w3,0) − α max(X2 − w3,0) − β max(w3− X1,0) − β max(w3 −
X2,0) − γ|X1 − X2|. (Eq. 1)
Here, X1 and X2 are MUs collected by dictator and sanctioner, respectively, X1 + X2 =
40 (MUs); w3 = 20 is the endowment of sanctioner, and α, β, and γ are parameters of
inequity aversion. Only if both other players receive exactly as much as the third
player (and hence, their respective payoffs are equal) the third player experiences no
utility loss, receiving just w3.
Fehr and Schmidt’s (1999) canonical two-player inequity aversion model typically
assumes that α > 1 > β > 0; this captures the envy of each player who dislikes being
treated unfairly more than (s)he dislikes being unfair towards another player. In our
study, dictator’s decision does not materially affect the sanctioner, who may want to
punish the former player only for violations of ethical standards, but not because of
personal material losses. Hence, moral loss of the sanctioner can be assumed to be
23
larger than the cost of punishment; that is, 1 > α > β > 0. Furthermore, in our
application, we may separate two feelings of the sanctioner, as follows:
(1) The sanctioner’s discomfort/disapproval of unfair actions of the dictator, which
she may compensate for by the third-party punishment. The level of this discomfort is
proportional to the extent of unfairness, and its strength is captured by parameter γ (we
assume that γ > 1); and
(2) The costs of punishment, which consist of two elements, namely the monetary cost
of punishment and the sanctioner’s wellbeing relative to that of the other players.
Inequity-averse sanctioners are concerned about fairness of the terminal distribution;
hence, these feelings are proportional to the realized differences between the revenues
of Players 3 and 1 and 3 and 2, taken with strengths α and β, respectively. We assume
that the sanctioner does not distinguish between her residual income relative to Players
1 and 2’s terminal incomes; hence, parameters α and β of the sanctioner are the same
when applied to Players 1 and 2.
In total, the sanctioner’s utility in the case of punishment is
U3P=w3 − x3 − α max((X1 − kx3 − (w3 − x3), 0)) − α max((X2 − (w3 − x3), 0)) − β
max((w3 − x3 − (X1 − kx3), 0)) − β max((w3− x3) − X2,0)) − γ|X1 − kx3 − X2|,
(Eq. 2)
where k (=2) is the punishment efficiency parameter — the number of MUs taken
from Player 1 if that player is punished by x3 (x3 ≤ w = 18). This utility function is
maximized with respect to x3 (punishment size), and it reaches a maximum when all
terms involving x3 are brought to 0, that is, when the shares of Players 1 and 2 are
exactly equal. A rational Player 3 (sanctioner) with these preferences will punish if
Eq.2 > Eq.1.
24
Proposition: A unique equilibrium punishment strategy of Player 3 with utilities given
by Eq.1 and Eq.2 is
x3 = 0 if X1<20,
x3 = X1 g / (g − 1) − 20(g +1) / (g − 1) if 20<X1 and x3<18, where g = α+β+2γ,
x3 = 18 if x3>= 18.
Equilibrium punishment strategy and model predictions are the following: the more
unfair the split the sanctioner observes, the stronger the punishment she assigns to
Player 1, until the maximum number of MUs allowed, 18.
Positive punishment should take place whenever Eq. 1< Eq. 2. By construction, X1 >
w3 whenever w3 > X2, hence Eq.1 equals either
U3Na
= w3 − α max(X1 − w3,0) − β max(w3 − X2,0) − γ(X1 − X2) (Eq.3)
or
U3Nb
=w3 − α max(X2 − w3,0) − β max(w3 − X1,0) − γ(X2 − X1) (Eq.4)
provided X1 ≠ X2 (otherwise, there is no inequity and no reason to punish at all). When
punishment takes place, Eq. 2 becomes either
U3Pa
=w3 − x3 − α max((X1− kx3− (w3 − x3), 0)) − β max((w3 − x3) − X2,0)) − γ(X1 − kx3
− X2) (Eq.5)
or
U3Pb
=w3 − x3 − α max((X2− (w3 − x3), 0)) − β max((w3 − x3 − (X1 − kx3), 0)) − γ(X2 −
X1+kx3) (Eq.6)
25
given the definition of X1 = 40 − X2, and k, it is straightforward to see that X1 − kx3 −
(w3 − x3) and X2 − (w3 − x3) cannot be both > 0.
Hence, we can limit attention to two possible cases:
X1 > X2, and choice between Eq.3 and Eq.5, and
X1 < X2, and choice between Eq.4 and Eq.6.
Consider them in turn:
Case 1: X1 > X2
Decision to punish takes place when U3Na<U3Pa, i.e.
w3 − α (X1 − w3) − β (w3 − X2) − γ(X1 − X2) <w3 − x3 − α (X1 − kx3 − (w3 − x3)) − β (w3
− x3) − X2) − γ(X1 − kx3 − X2)
=> 0<x3( α + β + 2γ − 1)
(Eq.7)
which holds true whenever α, β, γ are all positive and large enough. Hence in this case
player 3 should punish until the condition is satisfied, i.e. up to the tipping point when
Eq.7 becomes violated. From Eq.5, it is straightforward to see that utility increases in
x3 and is given by
x3 = X1 (α + β + 2γ)/(α + β + 2γ − 1) − (w3(α − β + 1)+ 40(β + γ)) / (α + β + 2γ − 1) =
X1 (α + β + 2γ) / (α+β+2γ − 1) − 20 (α+β+2γ+1) / (α+β+2γ − 1)
(Eq.8)
as stated in the proposition. If g =α+β+2γ>1, this strategy increases from X1=20 to the
maximum punishment allowed of 18 at a rate greater than 1, up to the point where X1
26
− 20 − x3 >0. This last condition is satisfied provided X1 − 20 − X1 γ / (γ − 1) − 20(γ
+1) / (γ − 1) = (40 − X1) / (γ − 1) >0, which is true for any value of X1.
Case 2: X1 < X2
Punishment takes place whenever
U3Na
< U3Pb
, i.e.
w3− α (X2 − w3) − β (w3 − X1) − γ(X2 − X1) < w3 − x3 − α (X2 − (w3 − x3)) − β (w3 − x3
− (X1 − kx3)) − γ(X2 − X1 + kx3)
=> 0<− x3( α+β+2γ+1)
(Eq.9)
which condition can never be true if the parameters are positive.
In sum, the inequity aversion model (Fehr and Schmidt, 1999; Svedsäter and
Johansson, 2005) predicts that third-party punishment (as given by Eq.8) increases
linearly if X1>20, up to the maximum allowed amount of 18, and is zero otherwise.
Model predictions
To elaborate on our results, we used a computational model: participants’ material
costs were defined by parameters α and β, where α represents disadvantageous
inequality and β represents advantageous inequality. The model predicts no third-party
punishment of equal (20:20) splits and a stronger third-party punishment of more
unfair splits.
The model implies that the third-party punishment decision depends on two key
components: material costs (underlined by rDLPFC activity) and moral costs
(underlined by rTPJ activity). Material costs of third-party punishment increase as the
sanctioner pays higher amounts to punish the unfair behavior of the dictator. In
27
contrast, for moral costs, the sanctioner is better off the more she pays to punish the
dictator’s unfair behavior. Both components are depicted in Figure 4, which also
shows the following hypothetical effect of stimulation as predicted by the model:
anodal tDCS of the rTPJ, where the activation of moral feelings increases the
parameter γ and changes the slope of the moral cost line, but only up to the point
where the material costs do not exceed the moral ones. This may suggest an optimum
equilibrium of material and moral costs for third-party punishment, where the further
increase of punishment increases the material costs, while a decrease of punishment
would increase the moral costs.
The discussion above suggests that anodal tDCS of the rTPJ could increase the
marginal utility of the moral costs, which could shift the utility function of such costs
(Figure 4, optimal punishment point X3 changes to X3*), and consequently, decrease
third-party punishment. This effect of anodal tDCS of the rTPJ was indeed observed in
Study 1 (Condition 1.2). In contrast, when paralleled with cathodal stimulation of the
rDLPFC, anodal tDCS of the rTPJ should increase the marginal utility of the moral
costs while decreasing the marginal utility of the material costs, consequently
increasing third-party punishment. Interestingly, we observed a trend of this tDCS
effect in Study 2 (Condition 2.2).
Importantly, in our version of the dictator game, third-party punishments of extremely
unfair splits are quite costly. Accordingly, the model implies a strong conflict between
material and moral costs when participants punish such splits. Interestingly, only in
the case where participants observe moderately unfair 30:10 splits, the conflict
between material and moral costs is minimal, since participants either do not punish or
punish extremely little if the material costs are high. Therefore, tDCS could have the
strongest effect on the third-party punishment of 30:10 splits, as increased moral costs
would not conflict with marginally affected material costs. Thus, the model could
explain our findings of the significant effect of tDCS on third-party punishment of
28
slightly unfair 30:10 splits. Importantly, the model suggests that only for moderate
(30:10) splits were the subjects able to maximize the utility of third-party punishment,
and at the same time, minimize all players’ inequity. Interestingly, third-party
punishment of 30:10 splits creates a Pareto optimal distribution of MUs (10:10:10),
where it is impossible to improve the income of one player without worsening the
incomes of the other players.
Figure 4. Hypothetical scenario of moral and material costs’ interaction during
third-party punishment. The intersection of lines representing material costs and
moral costs indicates an optimal punishment decision (x3), while x3*
represents an
optimal punishment decision when the moral costs are affected by anodal tDCS to the
rTPJ.
Discussion
Our study demonstrated that anodal tDCS to the rTPJ decreased third-party
punishment of moderately unfair splits during the dictator game. Our finding is
consistent with the recent TMS study (Baumgartner et al. 2014), which demonstrated
29
that an inhibition of the rTPJ decreased the parochial punishment of outgroup
members.
A previous study showed that anodal tDCS applied to the rTPJ led to less blame for
accidental harms during a moral judgment task (Sellaro et al., 2015). This suggests that
rTPJ is involved in processing the agent’s moral intentions. Recent meta-analysis
suggests that rTPJ showed significant activation when making one’s own moral
decisions (Garrigan, Adlam and Langdon, 2016). Thus, rTPJ activity in our study
could underlie the processing of the dictator’s mental state—her moral intentions.
Alternatively, it could reflect thinking about the consequences of the third-party’s own
decision and how harmful it would be for others. Thus, anodal stimulation of this area
could exaggerate the latter process and consequently diminish the punishment.
Overall, anodal tDCS of the rTPJ could affect the perceived degree of the moral norm
violation, and consequently decrease the assigned blame and punishment of the
dictator.
Our results only partly support Hypothesis I, since we did not find a significant effect
of anodal tDCS of the rDLPFC on third-party punishment. A previous study showed
that the suppression of rDLPFC by TMS leads to increased third-party punishment
(Brüne et al., 2012). Importantly though, in this previous study, third-party punishment
was combined with helping behavior—the recipients’ payoffs increased by the same
amount that was taken from the dictators’ budget by the sanctioners. A recent study
suggested that the rDLPFC is especially activated during the helping behavior of third
parties (Hu, Strang and Weber, 2015). Thus, one possible explanation for the
discrepancy with our results is that, in our paradigm, third-party punishment was not
associated with helping behavior.
Interestingly, anodal tDCS of the rTPJ significantly decreased third-party punishment
only in US_equal-trials, where third-party punishment was able to establish equality
30
between all players. In our study, third-party punishment of 30:10 splits created a
Pareto optimal distribution of MUs (10:10:10), where it was impossible to improve the
income of one player without worsening the incomes of the other players. . Our model
predicted a minimal conflict between the material and moral costs of third-party
punishment in moderately unfair (30:10) splits. Thus, only the punishment of 30:10
splits could maximize the utility of third-party punishment and minimize the inequity
of all players. Overall, we speculate that anodal tDCS of the rTPJ could increase the
marginal utility of moral costs, which could shift the utility function of the moral costs
and decrease third-party punishment.
In Study 2, we simultaneously applied cathodal tDCS to the rDLPFC and anodal tDCS
to the rTPJ. A previous fMRI study demonstrated that TPJ activity during third-party
punishment is paralleled by an initial deactivation of the DLPFC (Buckholtz et al.,
2008). We found only a trend of effect of the reciprocal stimulation protocol on third-
party punishment and failed to confirm Hypothesis II. A recent meta-analysis showed
that the excitatory effect of anodal tDCS is replicable in cognitive studies, while the
cathodal stimulation effect is not stable and rarely leads to inhibition (Jacobson,
Koslowsky and Lavidor, 2012). In our study, cathodal tDCS of the rDLPFC could
have led to a marginal effect of rDLPFCc/rTPJa stimulation. Overall, the trend of an
increment of third-party punishment after rDLPFCc/rTPJa stimulation in our study
could indicate an effect of tDCS on the interaction of the default mode network (TPJ)
and central executive network (rDLPFC). Additional studies are needed to investigate
the effects of the rDLPFCc/rTPJa tDCS protocol.
To further probe the frontoparietal interactions during third-party punishment, follow-
up studies could combine brain stimulation and brain imaging techniques.
Electroencephalography coherence as a measure of functional cortical connectivity on
a centimeter scale (Srinivasan et al., 1998; Nunez and Srinivasan, 2006) could offer a
31
tool for studying TPJ/DLPFC synchronization during third-party punishment
decisions.
Conclusion
Our study demonstrates that anodal tDCS of the rTPJ decreases third-party punishment
of moderately unfair behavior when the participants have an opportunity to restore
equality in their social groups. We found only a small, insignificant trend of the effect
of simultaneous anodal tDCS of the rTPJ and cathodal tDCS of the rDLPFC on third-
party punishment. Overall, our findings support the critical role of the rTPJ in third-
party punishment.
Acknowledgement
We thank Maartje Elisa Dankbaar, MSc, for her assistance in stimulus preparation; Dr.
Matteo Feurra for his expertise in tDCS; and Dr. Alexei Zakharov, who provided the
original behavioral data of the dictator game. This study used the HSE Synchronous
Eye-tracking, Brain Signal Recording and Non-Invasive Brain Stimulation System.
Funding
The article was prepared within the framework of the HSE University Basic Research
Program and funded by the Russian Academic Excellence Project '5-100'.
32
References
Baumgartner, T., Götte, L., Gügler, R., Fehr, E. (2012). The mentalizing network
orchestrates the impact of parochial altruism on social norm enforcement. Human
Brain Mapping, 33, 1452-1469.
Baumgartner, T., Schiller, B., Rieskamp, J., Gianotti, L. R., & Knoch, D. (2014).
Diminishing parochialism in intergroup conflict by disrupting the right temporo-
parietal junction. Soc Cogn Affect Neurosci, 653-660. doi: 10.1093/scan/nst023.
Bendor J, Swistak P. (2001). The Evolution of Norms. American Journal of Sociology,
106, 1493–1545.
Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical Society,
Series B. 57 (1): 289–300.
Brüne, M., Scheele, D., Heinisch, C., Tas, C., Wischniewski, J., & Güntürkün, O.
(2012). Empathy moderates the effect of repetitive transcranial magnetic stimulation of
the right dorsolateral prefrontal cortex on costly punishment. PloS one, 7(9), e44747.
Buckholtz, J.W., Asplund, C.L., Dux, P.E., Zald, D.H., Gore, J.C., Jones, O.D.,
Marois, R. (2008). The neural correlates of third-party punishment. Neuron, 2008,
930-940. doi: 10.1016/j.neuron.2008.10.016.
Buckholtz, J.W., Martin, J.W., Treadway, M.T., Jan, K., Zald, D.H., Jones, O., Marois,
R. (2015). From Blame to Punishment: Disrupting Prefrontal Cortex Activity Reveals
Norm Enforcement Mechanisms. Neuron, 87(6), 1369-1380.
http://dx.doi.org/10.1016/j.neuron.2015.08.023
Elster, J. (1989). Social Norms and Economic Theory. The Journal of Economic
Perspectives,3(4), 89–117.
33
Engel, C. (2010). Dictator Games: A Meta Study. MPI Collective Goods Preprint No.
2010/07. Available at SSRN: https://ssrn.com/abstract=1568732
Fehr, E., Schmidt K. (1999). A Theory of Fairness, Competition and Cooperation.
Quarterly Journal of Economics, 114, 817–868.
Fehr, E., and Fischbacher, U. (2004). Third-party punishment and social norms. Evol.
Hum. Behav. 25, 63–87.
Fliessbach K., Phillipps C.B., Trautner P., Schnabel M., Elger C.E., Falk A., Weber B.
(2012). Neural responses to advantageous and disadvantageous inequity. Front Hum
Neurosci. 8(6), 165. doi: 10.3389/fnhum.2012.00165.
Garrigan, B., Adlam, A.L., Langdon, P.E. (2016). The neural correlates of moral
decision-making: A systematic review and meta-analysis of moral evaluations and
response decision judgements. Brain Cogn., 108, 88-97.
Guth, W., Schmittberger, R. and Schwarze, B. (1982). An experimental analysis of
ultimatum bargaining. Journal of Economic Behavior & Organization, 3 (4), 367-388.
Hu, Y., Strang, S., & Weber, B. (2015). Helping or punishing strangers: neural
correlates of altruistic decisions as third-party and of its relation to empathic
concern. Frontiers in Behavioral Neuroscience, 9, 24.
Jacobson, L., Koslowsky, M., Lavidor, M. (2012). tDCS polarity effects in motor and
cognitive domains: a meta-analytical review. Exp Brain Res, ;216(1), 1-10.
Kahneman, D., Knetsch, J. and Thaler, R. (1986). Fairness and the Assumptions of
Economics. Journal of Business, 59, 5285–5300.
Keeser, D., Meindl, T., Bor, J., Palm, U., Pogarell, O., Mulert, C., Brunelin, J., Möller,
H.J., Reiser, M., Padberg, F. (2011). Prefrontal transcranial direct current stimulation
changes connectivity of resting-state networks during fMRI. Journal of Neuroscience,
31, 15284–15293.
34
Krueger, F. Hoffman, M. (2016). The Emerging Neuroscience of Third-Party
Punishment. Trends in Neurosciences, 39, 8, 499-501.
Nitsche, M.A., Paulus, W. (2001). Sustained excitability elevations induced by
transcranial DC motor cortex stimulation in humans. Neurology, 57(10):1899-1901.
Nitsche, M.A., Nitsche, M.S., Klein, C.C., Tergau, F., Rothwell, J.C., Paulus, W.
(2003). Level of action of cathodal DC polarisation induced inhibition of the human
motor cortex. Clin Neurophysiol. (4):600-604.
Nunez, P.L., Srinivasan, R. (2006) Electric Fields of the Brain: The Neurophysics of
EEG. 2nd ed. New York: Oxford University Press.
Paulus, W. (2011). Transcranial electrical stimulation (tES - tDCS; tRNS, tACS)
methods. Neuropsychol Rehabil., 21(5), 602-617.
Peña-Gómez, C., Sala-Lonch, R., Junqué, C., Clemente, I. C., Vidal, D., Bargalló, N.,
Bartrés-Faz, D. (2012). Modulation of large-scale brain networks by transcranial direct
current stimulation evidenced by resting-state functional MRI. Brain Stimulation, 5(3),
252-263. doi:10.1016/j.brs.2011.08.006
Ruff, C.C., Ugazio, G., Fehr, E. (2013). Changing social norm compliance with
noninvasive brain stimulation. Science, 342(6157), 482-484.
Santiesteban, I., Banissy, M.J., Catmur, C., Bird, G. (2012). Enhancing social ability
by stimulating right temporoparietal junction. Curr Biol, 22(23), 2274-7. doi:
10.1016/j.cub.2012.10.018.
Sellaro, R., Güroǧlu, B., Nitsche, M.A., van den Wildenberg, W.P., Massaro, V.,
Durieux, J., Hommel, B., Colzato, L.S. (2015). Increasing the role of belief
information in moral judgments by stimulating the right temporoparietal junction.
Neuropsychologia, 77, 400-408.
35
Spitzer, M., Fischbacher, U., Herrnberger, B., Grön, G., Fehr, E. (2007). The Neural
Signature of Social Norm Compliance. Neuron, 56(1), 185–196.
Srinivasan, R., Nunez, P.L., Silberstein, R.B. (1998) Spatial filtering and neocortical
dynamics: estimates of EEG coherence. IEEE Transactions on Biomedical
Engineering, 45, 814–825.
Stallen, M., Rossi, F., Heijne, A., Smidts, A., De Dreu, C.K.W., Sanfey, A.G.
(2018). Neurobiological Mechanisms of Responding to Injustice. J. Neurosci, 38,
2944-2954.
Strang, S., Gross, J., Schuhmann, T., Riedl, A., Weber, B., Sack, A.T. (2015). Be nice
if you have to – the neurobiological roots of strategic fairness. Soc Cogn Affect
Neurosci, 10(6), 790-796.
Svedsäter, H., Johansson, L.-Ol. (2005). Beyond Egocentric Judgments of Fairness:
Advantageous, Disadvantageous, and Third-Party Inequality Aversion. IACM 18th
Annual Conference. Available at SSRN: https://ssrn.com/abstract=736225 or
https://dx.doi.org/10.2139/ssrn.736225
Thielscher, A., Antunes, A. Saturnino, G.B. (2015). Field modeling for transcranial
magnetic stimulation: a useful tool to understand the physiological effects of TMS?
IEEE EMBS 2015, Milano, Italy.
Zinchenko, O., Klucharev, V. (2017). Commentary: The Emerging Neuroscience of
Third-Party Punishment. Frontiers in Human Neuroscience, 11, 512.
http://doi.org/10.3389/fnhum.2017.00512