Social Neuroscience

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How does social information affect charitablegiving?: Empathic concern promotes support forunderdog recipient
Yoshimatsu Saito, Atsushi Ueshima, Shigehito Tanida & Tatsuya Kameda
To cite this article: Yoshimatsu Saito, Atsushi Ueshima, Shigehito Tanida & Tatsuya Kameda(2019): How does social information affect charitable giving?: Empathic concern promotes support
for underdog recipient, Social Neuroscience, DOI: 10.1080/17470919.2019.1599421
To link to this article: https://doi.org/10.1080/17470919.2019.1599421© 2019 The Author(s). Published by Informa
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SOCIAL NEUROSCIENCE
https://doi.org/10.1080/17470919.2019.1599421

How does social information aﬀect charitable giving?: Empathic concern
promotes support for underdog recipient
Yoshimatsu Saitoa,b, Atsushi Ueshimaa,b, Shigehito Tanidac and Tatsuya Kamedaa,d,eaDepartment of Social Psychology, The University of Tokyo, Tokyo, Japan; bJapan Society for the Promotion of Science, Tokyo, Japan;
Faculty of Psychology and Sociology, Taisho University, Tokyo, Japan; dBrain Science Institute, Tamagawa University, Tokyo, Japan; eCenter
for Experimental Research in Social Sciences, Hokkaido University, Hokkaido, Japanc

ABSTRACTARTICLE HISTORY

Charitable giving represents a unique cooperative characteristic of humans. In today’s environment with social media, our charitable decisions seem to be inﬂuenced by social informationsuch as a project’s popularity. Here we report three experiments that examined people’s reactions to social information about a charitable endeavor and their psychophysiological underpinnings. Participants were ﬁrst solicited to make donations to either the Africa or Syria project of
UNICEF. Then participants were provided an opportunity to learn social information (i.e., howmuch each project had raised from previous participants) and change their decision if desired.
Contrary to expectation, participants who learned that their initial preferences were consistent
with the majority of previous participants’ choices exhibited a sizable tendency to switch to theless popular project in their ﬁnal choices. This anti-conformity pattern was robust across the threeexperiments. Eye-tracking data (gaze bias and pupil dilation) indicated that these “Changers”
were more physiologically aroused and formed more diﬀerential valuations between the twocharity projects, compared to “Keepers” who retained their initial preferences after viewing thesocial information. These results suggest that social information about relative popularity may
evoke empathic concern for the worse-oﬀ target, in line with the human tendency to avoid
unequal distributions.

Received 24 January 2018
Revised 15 February 2019
Published online 2 April
2019

Introduction
Charitable giving is a widespread and unique characteristic of human cooperation, by which we help not only
familiar but also unknown or unrelated others who areunlikely to reciprocate (Bowles & Gintis, 2011; Trivers &Coultas, 1971). According to the World Giving Index(Charities Aid Foundation, 2018), one ﬁfth of the population in around 100 developing and developed countries donated some money to charity projects in 2017.
Despite the ubiquity and importance of these activities,
our understanding about the psychophysiological processes underlying charitable giving remains insuﬃcient.
In our daily lives, people often encounter calls for charitable giving at religious centers, retail establishments, andmore recently on the Internet. For example, on “crowdfunding” websites (e.g., StartSomeGood, Razoo), potential
donors can access information about numerous ongoingcharity projects. Without specialized knowledge and skills,
as well as suﬃcient time and motivation, it is too demanding for an individual to evaluate all of the projects in detailand choose the “best” one in terms of merit, need, andother key dimensions. In such cases, a typical heuristic is to

KEYWORDS

Inequity aversion;
anti-conformity; eye-tracking;pupil dilation; gaze bias

use social information about which projects others havesupported (Festinger, 1954).
However, most previous research on the use of social
information in charitable giving has focused on situations
in which a single target is highlighted, and potential donors
are informed about others’ support for the project(Andreoni, 1998; Dellavigna, List, & Malmendier, 2012;
Shang & Croson, 2009; Soetevent, 2005; Vesterlund, 2003).
Although important in their own right, ﬁndings from theseprevious studies may not be applicable to the rapidlyspreading phenomenon of charitable giving on theInternet, where donation calls for many projects are listed
simultaneously. Here we address this situation more
directly, exploring how social information (i.e., a project’s
relative popularity among preceding donors as often seenon crowdfunding sites) aﬀects real charity decisions.
How might people use social information when theychoose among several viable options? We will discuss
two potential psychological forces in charitable givingthat may have opposite impacts on people’s choices.
Social psychological research has repeatedly shownthat the preceding majority exerts strong social inﬂuence

CONTACT Tatsuya Kameda
tatsuyakameda@gmail.com
Supplemental data for this article can be accessed here.
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

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Y. SAITO ET AL.

on subsequent choices (Asch, 1951; Sherif & Murphy,1936). People often consider the majority view to reﬂect
some physical and/or social reality (“informational inﬂuence”: Deutsch & Gerard, 1955). They also conform to themajority position to avoid possible embarrassment orsocial sanction for holding a divergent position (“normative inﬂuence”; see Cialdini & Goldstein, 2004; Kameda,
Wisdom, Toyokawa, & Inukai, 2012 for review). According
to these observations, people may simply follow the
majority view, supporting the already-popular project incharitable donations as well (see Salganik, Dodds, & Watts,
2006 for examples in consumer choices).
However, in charitable giving, it is also conceivable thatpeople may decide to intentionally deviate from the majority choice to help the less-popular “underdog” among the
projects. Previous research showed that people havea robust tendency to react negatively to inequitable distribution between themselves and others (e.g., Fehr &Schmidt, 1999). It is also known that such an inequity aversion extends to a third-party situation, where peoplechoose distributions strictly for others unrelated to themselves (Hsu, Anen, & Quartz, 2008; Kameda et al., 2016;
Zhong, Chark, Hsu, & Chew, 2016). Given that people areoften concerned with the most unfortunate other in social
distribution (Kameda et al., 2016), empathy for another’s
misfortune might drive people to support the less popular
project in charitable giving.
In reality, majority inﬂuence and inequity aversion may
not be mutually exclusive when an individual makesa charitable decision. When learning social information,
people may experience psychological conﬂicts about
whether to follow the implicit norm endorsed by the majority or to deviate from the majority in order to reduce theinequity against the underdog. Thus, the question of how
people use social information for choosing charity recipients and how they may resolve the potential conﬂicts is
empirically open. Here, we report a series of experiments
using behavioral and eye-tracking measures to address thisquestion. In Experiment 1, we ﬁrst asked participants tomake donation decisions individually and then provided
social information about the choices of preceding donors.
Upon the receipt of this social information, participants
could keep or change their initial choice for their ﬁnal
decision. Donations were later sent to the recipients ofparticipants’ ﬁnal decisions through UNICEF. Experiment 2
was designed to replicate the key behavioral ﬁnding inExperiment 1 using a larger sample with suﬃcient statisticalpower. Experiment 3 examined psychophysiological processes associated with the behavioral pattern using an eye
tracker. In particular, we measured eye movement (Mullet &Stewart, 2016; Orquin & Mueller Loose, 2013) and pupil
dilation (Bradley, Miccoli, Erscrig & Lang, 2008; Murphy,
O’Connell, O’Sullivan, Robertson, & Balsters, 2014) to shedlight on the psychological conﬂicts that participants mightexperience after learning the social information.

Experiment 1
MethodParticipants
One hundred and thirty-two (77 male, 55 female;
Mage ± SDage = 20.9 ± 0.6) students at the Universityof Tokyo and Taisho University in Japan participated in
the study. The study was approved by the ethical committee of the Department of Social Psychology of the
University of Tokyo. All participants gave written
informed consent.
Procedure
Students who had just completed other psychologicalexperiments were approached by an experimenter. The
experimenter explained that his laboratory was cooperating with charity projects of the Japan Committee
for UNICEF, and solicited them to take part in a shortsurvey as volunteers without compensation. If the students agreed, they were placed into private cubicles
with a computer terminal. All the instructions thereafter
were given on the monitor.
Participants were ﬁrst presented with images anddescriptions of the ongoing UNICEF charity projects for
Africa and Syria (see Supplementary Figure 1) on the computer screen. They were asked to subjectively rate eachproject’s deservingness of support on a 7-point scale(Figure 1(a)). Participants were then informed that theexperimenter’s laboratory would match any donation for
the UNICEF projects on a one to one basis, and were asked
if they would be willing to make a donation to one of the
projects. It was emphasized that the decision to donate
was completely voluntary and that they could leave thelaboratory at this point.
Participants who decided to stay were asked tochoose either the Africa project or the Syria projectfor donation and then indicate how much money they
were willing to contribute, from 10 yen to 1000 yen in
10 yen increments (Figure 1(b)).
After the individual decision, participants were provided
with social information showing the sum of money
donated to each charity project by preceding anonymous
participants. Here we had two conditions for social information. In the Majority condition, the charity project thatthe participant chose had raised more money than theother project (Figure 1(c) left). In the Minority condition,
the project chosen by the participant had raised lessmoney than the other project (Figure 1(c) right). We also
had a Control condition in which no social information was

SOCIAL NEUROSCIENCE

3

Figure 1. Task ﬂow in Experiment 1. (a) Participants were ﬁrst asked to rate each project’s deservingness of support on a 7-point
scale. (b) Participants who decided to donate were individually asked to choose either the Africa project or the Syria project for
donation and indicate how much money they were willing to contribute (from 10 yen to 1000 yen in 10 yen increments). (c) Afterthe individual decision, participants were provided with social information showing the amount of money donated to each charity
project by preceding anonymous participants. In the Majority condition, the display indicated that the charity project chosen by theparticipant had raised more money than the other project (left). In the Minority condition, the display indicated that the project
chosen by the participant had raised less money than the other project (right). The accumulated amounts were ﬁxed across the two
conditions and for all participants. A participant’s initial decision was displayed at the bottom of the screen (this example illustrates
a case in which the participant initially decided to donate 100 yen to the Africa project). (d) Then participants were asked to conﬁrm
their decisions, deciding whether to keep or change their initial decisions about which project to support and how much to donate.

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Y. SAITO ET AL.

available to participants. Because we did not want to use
deception, we arranged social information to reﬂect participants’ actual donations in particular sessions. Speciﬁcally,
we ran the Control condition before the Majority andMinority conditions, and then selected several sessions sothat the sum of the donations for Africa and Syria matched
the patterns as displayed in Figure 1(c). Accordingly, theﬁgures participants actually saw during the experiment
had a caption such as “the sum of donations from participants in sessions on February 12th – 14th.”
Participants were then asked to conﬁrm their decisions (Figure 1(d)). Here participants were able to either
maintain or change their initial decisions about which
project to support and how much to donate. Participants
then answered a short questionnaire, put the donatedmoney in a box, were thanked and left the laboratory.
After the entire experiment was completed, we madedonations to the respective UNICEF projects according
to the above procedure (18,590 yen [= 180 US dollars] for
Africa and 19,160 yen [= 190 US dollars] for Syria).

Results
Distribution of donations
More than 90% of the students approached by the experimenter agreed to participate in the survey. Among 132
participants in the survey, 71 participants (54%) agreed to
donate either to the Africa or to the Syria project; theremaining 61 (46%) participants left the laboratory after
they ﬁnished rating the deservingness of each project.
Figure 2(a) displays distribution of donations. The
average initial donation was 130 yen (participants who
did not agree to donate and left the laboratory were

included as 0 yen contributors). The right skewness of
distribution of the donated amounts parallels theresults of previous studies on charitable giving (e.g.,Hare, Camerer, Knoepﬂe, O’Doherty, & Rangel, 2010).
Among the 71 participants who agreed to donate, theproportion of donors to the Africa project (54%) in the initialdecision was not statistically distinguishable from that of
the Syria project (46%), p = .635 by binomial test. There wasalso no signiﬁcant diﬀerence in the donated amounts
between the two projects (Figure 2(b): MAfrica = 214.2 yen
and MSyria = 273.0 yen), Welch’s t(62.84) = 1.346, p = .183,
95% CI = [−28.511, 146.15].

How did social information aﬀect participants’choices?
Figure 3 displays proportions of participants who kept
or changed their initial decisions after the social information. The proportions were signiﬁcantly diﬀerent
between the two conditions (p = .028 by Fisher’s
exact test). While none of the 22 participants (0%) in
the Minority condition changed their initial minority
position, 6 out of 28 participants (21%) in the Majority
condition changed their initial majority position to support the “less popular” minority project (the switchingratio was slightly higher among female participants[27%] than among male participants [15%]). Given
that participants in the Control condition who were
not provided social information rarely changed theirinitial choices (1 out of 21 participants: 5%), the lesspopular (minority) project among the preceding donors
exerted more gravity in participants’ ﬁnal choices,
whether it was for Africa or Syria (p = .648 by Fisher’s
exact test), than the majority project.

Figure 2. (a) Frequency of donations in Experiment 1. Participants who did not agree to donate were plotted as 0 contributors. (b)Mean donation amounts for the two charities among 71 participants who agreed to contribute to the UNICEF projects. Error bars
indicate standard error.

SOCIAL NEUROSCIENCE

5

Figure 3. Proportions of participants who kept or changed their initial decisions after viewing the social information in Experiment
1. While none of the 22 participants (0%) in the Minority condition changed their initial minority position (shown in blue), 6 out of
the 28 participants (21%) in the Majority condition changed their initial majority position to the minority position (red). This patternheld whether the initial choice was the Africa project or the Syria project.

Using a multiple logistic regression model, we examined the eﬀects of several predictors of participants’
eventual choices in the Majority condition (i.e., whether
to keep or change their initial choices) after learning thesocial information. The absolute diﬀerences of thedeservingness ratings [see Figure 1(a)] between thetwo charity projects (Mkeeper = 0.73, Mchanger = 0.50)
had no signiﬁcant eﬀect, β = 0.875, SE = 0.958,
Z = 0.913, p = .361. Thus, it was not the case that only
those who equally rated the deservingness of the twocharity projects changed their initial choices. Likewise,
neither the initial donation amounts (Mkeeper = 245.5
yen, Mchanger = 285.0 yen: β = −0.002, SE = 0.003,
Z = −0.799, p = .424) nor the response time in the initialdecision (Mkeeper = 8.97 s, Mchanger = 9.11 s: β = 0.07,
SE = 0.109, Z = 0.642, p = .521) had a signiﬁcant eﬀect onparticipants’ eventual choices. Taken together, theseresults indicated that participants’ decisions to keep orchange their choices in the majority condition were notdetermined by diﬀerences in their initial commitment tothe chosen project. The mean response time for making
ﬁnal decisions after learning the social information was
also statistically indistinguishable between Keepers(M = 22.07 s) and Changers (M = 17.07 s), Welch’st (3.46) = −1.296, p = .275, 95% CI = [−16.414, 6.407].

Discussion
We observed a signiﬁcant asymmetry between the
Majority and Minority conditions in participants’ choices
after viewing social information. When participants
believed that their own choices were consistent withthe majority’s choice, 21% of them changed theirchoices to the less popular project, but all of the participants in the Minority condition retained their minority
choice. Considering the robust phenomenon of majority-inﬂuence in social decision making (Hastie &Kameda, 2005; Kameda, Tsukasaki, Hastie, & Berg,2011), the anti-conformity pattern here is the oppositeof the typical social inﬂuence observed in many attitudinal judgments (Cialdini & Goldstein, 2004; Salganik
et al., 2006).

We reason that this seemingly counterintuitive pattern may be due to inequity aversion (Fehr & Schmidt,
1999). Inequity aversion refers to the robust tendency
to react negatively to unequal distribution of resources
between recipients, and is known to operate in allocation between self and other (Fehr & Schmidt, 1999), as
well as allocation strictly among others (Hsu et al., 2008;
Kameda et al., 2016; Zhong et al., 2016). Notice that, inthe Majority condition, changing one’s position to theless popular, minority position would be expected todecrease the inequity in donations (i.e., diﬀerence incumulative amounts) between the two projects. On
the other hand, in the Minority condition, changing
one’s initial position toward the majority project
would increase the inequity. Consistent with this reasoning, 4 out of 6 participants (67%) who changed theirinitial choices in the Majority condition explicitly mentioned empathic concern toward the less-popular project as the reason for the change in the post-session
questionnaire, whereas it was mentioned by only 1 outof 22 participants (5%) who kept their initial choices,p = .003, by Fisher’s exact test (see Supplementary
Table 1 for details). Furthermore, the mean rating of
the usefulness of social information was marginallylower in the Minority condition (M = 2.54) than in the
Majority condition (M = 3.43), Welch’s t (47.844) = 1.726,
p = .091, 95% CI = [−0.145, 1.912].
Although these patterns are potentially important,the sample size of Experiment 1 may have been too
small to achieve suﬃcient statistical power. We thus
conducted Experiment 2 to see if the key behavioral
ﬁndings from Experiment 1 were replicable witha larger sample size. There was a high dropout rate
for participants (46%) in Experiment 1, reﬂecting therequirement for participants to volunteer to donate
their own money following the recruitment. Given
that the high dropout rate severely limited the size ofthe eﬀective sample for which the majority/minority
manipulation was applicable, we decided to employ
a vignette design for Experiment 2, where participants
stated their intentions for charitable giving ina hypothetical scenario.

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Y. SAITO ET AL.

Experiment 2
MethodParticipants
The sample size of Experiment 2 was determined bya power analysis using the eﬀect size obtained from
Experiment 1. Because we found a moderate eﬀect size
(Cohen’s w = 0.319) for the Keeper/Changer frequencies in
the Majority, Minority and Control conditions of Experiment
1, the minimum sample size to detect the eﬀect with 80%
power (using a signiﬁcance level of 0.05) was 95.
Ninety-nine (35 male, 64 female; Mage ± SDage= 19.71 ± 1.52) students at the University of Tokyo andTaisho University in Japan participated in the study. The
study was approved by the ethical committee of the
Department of Social Psychology of the University ofTokyo. All participants gave written informed consent.
Procedure
Student volunteers participated in the experiment individually via Qualtrics survey software (Qualtrics Labs, Provo,
US). The basic design and procedure of Experiment 2 wereidentical to those of Experiment 1, except that all participants were asked about their intentions for charitable giving in a hypothetical scenario (i.e., “if you donated 100 yen[comparable to the average donation in Experiment 1] toeither the Africa or the Syria project, which project would
you choose?”) and rated the deservingness of each project(see Figure 1(a)) at both the beginning and the end of theexperiment. Social information provided to participants
after their initial choices in the Majority and Minority conditions was identical to that used in Experiment 1.
Participants were explicitly told that the social information
showed the sum of money donated to each charity projectby anonymous participants in a preceding related study.
Results
How did social information aﬀect participants’choices?
Figure 4 displays proportions of participants who kept
or changed their initial decisions after viewing thesocial information in the Majority and Minority conditions. While only 2 out of 33 participants (6%) in the
Minority condition changed their initial minority position, 18 out of 34 participants (53%) in the Majority
condition changed their initial majority position to support the “less popular” project (p < .0001 by Fisher’s
exact test). The switching ratio was slightly higher
among female participants (57%) than among maleparticipants (45%). Only 1 out of 32 participants (3%)
in the Control condition changed their initial decisions.
The proportions were signiﬁcantly diﬀerent between
the three conditions (χ2(2) = 31.278, p < .0001,
Cohen’s w = 0.562). These results replicated the choicepatterns in Experiment 1.
As in Experiment 1, the initial decision (i.e., Africa or
Syria) did not aﬀect participants’ choices about whether
to keep or change their initial decisions after learning thesocial information in the Majority condition (Fisher’s exact
test, p = .46). Likewise, a multiple logistic regression analysis
revealed that neither response times for making initialdecisions (Mkeeper = 18.44 s, Mchanger = 17.43 s: β = −0.007,
SE = 0.031, Z = −0.229, p = .819) nor the absolute diﬀerences of the initial deservingness ratings between the twocharity projects (Mkeeper = 0.75, Mchanger = 0.78: β = −0.012,
SE = 0.374, Z = −0.033, p = .974) predicted participants’
eventual decisions in the Majority condition.

Subjective responses after learning the social
information
As in Experiment 1, the modal subjective reason (10 out of
18, or: 56%) that Changers in the Majority condition provided for their ﬁnal decisions was empathic concern
toward the less-popular project, whereas this response
was not given by any of the 16 Keepers (Fisher’s exact
test, p = .0003; Supplementary Table 1). Likewise, after
learning the social information, Changers in the Majority
condition tended to diﬀerentiate deservingness ratings
between the two charity projects (M = 0.78) more than
Keepers (M = 0), Welch’s t (31.021) = 1.985, p = .056, 95%
CI = [−0.021, 1.577]). The mean rating of the usefulness ofsocial information was also marginally lower in the Minority
condition (M = 3.30) than in the Majority condition

Figure 4. Proportions of participants who kept or changed their initial decisions after the social information in Experiment 2. While
only 2 out of the 33 participants (6%) in the Minority condition changed their initial minority position (shown in blue), 18 out of the
34 participants (53%) in the Majority condition changed their initial majority position to the minority position (red). This pattern wasconsisted with the results of Experiment 1 (see Figure 3).

SOCIAL NEUROSCIENCE(M = 4.12), Welch’s t (63.34) = 1.69, p = .096, 95%
CI = [−0.149, 1.778]). Response times for making ﬁnal
choices after learning the social information were statistically indistinguishable between Keepers (M = 23.55 s) andChangers (M = 28.03 s), Welch’s t (27.304) = 0.976, p = .338,
95% CI = [−4.937, 13.908]. These results were consistent
with the results in Experiment 1.

Discussion
The key results of Experiment 1 were all replicated inExperiment 2 with suﬃcient sample size (as determinedby a power analysis using the eﬀect size from
Experiment 1). Although hypothetical-response experiments have obvious limitations compared to behavioral
experiments (but see Hainmueller, Hangartner, &Yamamoto, 2015), participants’ intentions about whether
to keep or change initial choices after learning the social
information and their reasoning for the ﬁnal decisions werehighly coherent between the two experiments. The overall
results indicated that participants who endorsed the minority position after viewing the social information may havebeen concerned with the inequity between the two projects and more committed to their ﬁnal choices than those
who endorsed the majority position.
Previous research using behavioral games has shownthat emotion-related brain regions such as the anterior
insula, amygdala, and anterior cingulate cortex areassociated with participants’ aversion to inequity
between self and other (Haruno & Frith, 2010; Yu,
Calder, & Mobbs, 2014; see also Gu, Hof, Friston, &Fan, 2013 for review). Activity in the anterior insula isalso modulated by the degree of distributive inequity
among others (Hsu et al., 2008; Zhong et al., 2016).
Given these ﬁndings, emotions associated with inequity
aversion may underlie the common behavioral patterndisplayed in Experiments 1 and 2.
To examine this possibility more directly, we conducted Experiment 3. In Experiment 3, we focused on
the Majority condition only, in which participants were
informed that their initial positions were consistent
with the majority view of the preceding participants.
Our purpose here was to replicate the key behavioral
pattern from Experiments 1 and 2 (the intentionalswitching to the minority position in the Majority condition) and to shed light on the psychophysiologicalprocesses that underlie this behavior.

Experiment 3
In Experiment 3, while participants made decisions weused an eye tracker to measure their gaze pattern andpupil diameter.

7

Gaze pattern reveals how participants allocate attention among choice options. Previous research has shownthat participants direct their gaze more selectively to theeventually-chosen option in many decision contexts,
such as when choosing favorite faces (Shimojo, Simion,
Shimojo, & Scheier, 2003), selecting a route in a maze
(Wiener, Hölscher, Büchner, & Konieczny, 2012), or judging oldness of photographs (Glaholt & Reingold, 2009).
Gaze bias is considered to reﬂect diﬀerential valuations
about choice alternatives, i.e., how distinct the chosen
option is from the other option on the decision-related
criterion (Fiedler & Glöckner, 2012; Glaholt & Reingold,
2011; Mullet & Stewart, 2016; Orquin & Mueller Loose,
2013; Schotter, Berry, McKenzie, & Rayner, 2010).
Pupil dilation (increase in diameter) has been shown
to reﬂect physiological arousal. Previous research indicates that pupil dilation is correlated with skinconductance change when viewing emotionally arousing images (Bradley et al., 2008), and neural activationin the anterior insula, anterior cingulate cortex (Paulus
et al., 2015) and the locus coeruleus-norepinephrine
system (LC-NE: Murphy et al., 2014; see Eckstein,
Guerra-Carrillo, Miller Singley, & Bunge, 2017 forreview). The LC-NE system plays a role in arousal
mechanisms (Sara & Bouret, 2012). We thus comparedpupil dilation between Keepers and Changers to see
how emotional arousal may underlie their decisions
after viewing social information.

MethodParticipants
One hundred and forty-nine participants (85 male, 64
female; Mage ± SDage = 19.4 ± 1.3) were recruited fromthe subject pool at Hokkaido University in Japan. The
study was approved by the Institutional Review Boardof the Center for Experimental Research in Social
Sciences at Hokkaido University. All participants gave
written informed consent.
Procedure
Like Experiment 1, Experiment 3 examined participants’actual charitable decisions. Students who had just ﬁnished other psychological experiments were solicited by
an experimenter to take part in a short survey concerning the UNICEF charity projects for Africa and Syria asvolunteers and potential donors. After the experiment
was complete, we matched participants’ donations andcontributed the money to the respective projects
through UNICEF (22,520 yen [= 220 US dollars] for
Africa and 17,300 yen [= 170 US dollars] for Syria).
The procedure of Experiment 3 was identical to thatof Experiment 1, except that all participants were8

Y. SAITO ET AL.

Figure 5. Proportions of participants who kept or changed their initial decisions after the social information in Experiment 3.
Twenty-eight out of 83 participants (34%) changed their initial majority position to support the less popular project. Note that all
participants in Experiment 3 were assigned to the Majority condition.

assigned to the Majority condition, and an eye tracker
was used. Participants’ eye movements and pupil sizes
were recorded at 60 Hz using an EyeTribe eye tracker(The Eye Tribe, Copenhagen, Denmark). All stimuli were
presented on a 12-inch Microsoft Surface Pro 3 screen
at 1280 × 740 pixel resolution. Participants were seatedwith their head resting on a chin rest 60 cm from thescreen, and were asked not to move their head fromthe chin rest to ensure accurate measurements.
Calibration was performed using a nine-point calibration procedure by Eye Tribe UI Version 0.9.56 (The Eye
Tribe, Copenhagen, Denmark).

Stimuli
To simplify eye tracking, we used images of the twocharity projects without text and kept the room illuminated at 300 lux during the experiment. To avoid diﬀerences in extraneous stimuli features (e.g., color orcontrast), the images and background were set in grayscale with equal average luminance over all pixels. Eachstimulus screen was preceded by a ﬁxation cross lastingfor 10 seconds to measure a baseline for each participant’s pupil size. The positions of the two charity projects on the stimulus screens were counterbalanced
across participants.
Data preparation for eye-tracking
We followed the Tobii User’s Manual Version 3.2 toanalyze raw gaze data (Tobii Technology AB,
Stockholm, Sweden). We used the moving-averagemethod to reduce noise, and a window-size parameterof 3. In order to detect ﬁxations, saccades, and blinks,
we used the default setting of “I-VT ﬁxation ﬁlter”
described in the Tobii manual. Four participants were
excluded from the analysis because of excessive head
movement, leaving us with 145 participants in total (83male, 62 female; Mage ± SDage = 19.4 ± 1.3).
Results and discussion
Behavioral choices
Distribution of donations. We ﬁrst examined basic
statistics from Experiment 3 to assure comparability
with Experiment 1. In Experiment 3, 83 (57%) of 145

participants in the survey agreed to donate either tothe Africa or the Syria project, and the average donation (including those who did not agree to donate as 0
contributors) was 116 yen. The distribution of donations
in Experiment 3 (Supplementary Figure 2) was not statistically diﬀerent from that of Experiment 1, k = 0.069,
p = .882 by Kolmogorov-Smirnov test. Participants’
initial support of the two projects was also similar tothat in Experiment 1, with 59% for Africa and 41% forSyria (Fisher’s exact test, p = .517). Thus, the two data
sets were comparable in terms of basic features.

Eﬀects of social information on participants’choices. Figure 5 displays proportions of participants
who kept or changed their initial decisions after being
informed that their choices were consistent with the
majority view among preceding participants. Similar toExperiments 1 and 2, 28 out of 83 participants (34%)
changed their initial majority position to support theless popular (minority) project in their ﬁnal choices (theswitching ratio was higher among female participants[47%] than among male participants [22%], p = .02 by
Fisher’s exact test). This pattern was commonly
observed whether the participant’s initial choice was
Africa or Syria (p = .105 by Fisher’s exact test).
A multiple logistic regression analysis also conﬁrmedthat participants’ eventual choices after learning thesocial information were not predicted by the absolute
diﬀerences in the initial deservingness between the twocharity projects (Mkeeper = 0.62, Mchanger = 0.46:
β = 0.147, SE = 0.288, Z = 0.509, p = .611), donation
amounts (Mkeeper = 195.64 yen; Mchanger = 216.79 yen:β = −0.001, SE = 0.002, Z = −0.388, p = .698), or
response times for initial decisions (Mkeeper= 8.23 s; Mchanger = 9.20 s: β = −0.014, SE = 0.04,
Z = −0.348, p = .728). Response times for making ﬁnal
decisions were also statistically indistinguishable
between Keepers (M = 14.00 s) and Changers(M = 13.57 s), Welch’s t (58.007) = −0.212, p = .833,
95% CI = [−4.523, 3.656].
Experiment 3 successfully replicated the key behavioral ﬁnding from Experiments 1 and 2, along with theother basic features. Having established the behavioral
consistency between the three experiments, we next

SOCIAL NEUROSCIENCE

9

investigated the psychophysiological processes underlying the anti-conformity pattern using the eye-tracking
data. In particular, we compared Keepers and Changers
in their psychophysiological responses to see how emotional arousal and commitment may underlie their decisions in charitable giving.

p = .883) were not signiﬁcant. These results indicatethat the selective gaze bias to the eventually-chosen
option was evident among Changers but not among
Keepers, indicating that relative evaluation of the two
options in ﬁnal choices may have been more distinct inChangers than Keepers.

Eye-tracking results
Gaze pattern. Figure 6 displays temporal changes in
the average proportions of gaze ﬁxation on the eventually-chosen option (as compared to the other option)
among Keepers who retained their majority position
and Changers who switched to the minority position(see Supplementary Figure 3 for the deﬁnition of screen
areas). As described above, the average decision time
was statistically indistinguishable between Keepers andChangers. Because there were large individual variations in decision times, we divided the decision timeused by each participant to reach his/her ﬁnal decision
into quartiles, and calculated the proportion of gazeﬁxed on the eventually-chosen option in each quartile(see also Supplementary Figure 4 for another analysis).
We analyzed the gaze data using a linear mixed
model (LMM) as implemented in the R package ‘lme4ʹ
(Bates, Mächler, Bolker, & Walker, 2015) and R version
3.4.2 (R Core Team, 2017) with ﬁxed eﬀects of group(Keeper = 0, Changer = 1), time (the former versus thelatter half), and their interaction, and random eﬀect ofparticipant on the intercept. The LMM revealed
a signiﬁcant interaction eﬀect between group andtime (β = 0.232, SE = 0.096, Z = 2.427, p = .017), but
the eﬀects of group (β = −0.049, SE = 0.088, Z = −0.553,
p = .581) and time (β = −0.009, SE = 0.058, Z = −0.148,

Pupil dilation. We deﬁned pupil dilation by calculating
the ratio of pupil diameter at each time point to thebaseline. For each participant, the baseline was set as
the average pupil diameter over a time window of thelast 8 seconds on the preceding ﬁxation-cross screen(the ﬁrst 2 seconds were discarded to remove carryovereﬀects from the earlier screen). Because participants
made self-paced decisions in the current study, we
chose the ﬁnal 2 seconds just prior to conﬁrming theﬁnal choice as the time window for analysis.
Figure 7 displays mean pupil dilation for Keepers andChangers. For the analysis, we divided the 2 seconds
prior to the ﬁnal choice into halves, computing mean
pupil dilation during each second for each participant.
We analyzed the pupil dilation data using a linear
mixed model (LMM) with ﬁxed eﬀects of group(Keeper = 0, Changer = 1), time (the former versus thelatter half), and their interaction, and random eﬀect ofparticipant on the intercept. The model indicateda signiﬁcant eﬀect of time, β = 0.044, SE = 0.021,
Z = 2.081, p = .042, and the interaction, β = 0.065,
SE = 0.026, Z = 2.461, p = .014. The eﬀect of group
was not signiﬁcant, β = 0.013, SE = 0.022, Z = 0.609,
p = .544. Pupil dilation prior to conﬁrming the ﬁnal
choice was higher among Changers, suggesting thatthey were more aroused than Keepers.

Figure 6. Temporal changes in the average proportions of gaze ﬁxation on the eventually-chosen option among Keepers (those who
retained the majority position) and Changers (those who switched to the minority position) after learning that their individual
choices were consistent with the majority view among preceding participants. We divided decision time (from onset of the choicescreen to participant’s decision; see also the caption of Supplementary Figure 3) used by each participant to reach his/her ﬁnal
decision into quartiles. Error bars indicate standard error.

10

Y. SAITO ET AL.

Figure 7. Time course of pupil dilation during ﬁnal 2 seconds just prior to conﬁrmation of ﬁnal choices among Keepers andChangers. The Y axis represents pupil dilation as a ratio of the baseline. The dotted line represents average pupil dilation among
Keepers (those who retained the majority position), and the solid line represents that of Changers (those who switched to theminority position). Shaded regions represent standard error.

Dispositional empathy and attention to images incharitable giving
So far we have shown that, after viewing social information
indicating that they belonged to the majority (Figure 1(c)left), Changers who switched to the minority position
exhibited more physiological arousal (Figure 7) and (re-)
evaluated the two projects more distinctly (Figure 6) than
Keepers who retained the majority position. These results
may also indicate that, compared to Keepers, Changers
were not only emotionally more reactive to the social
information, but also more empathic and sensitive toothers’ misfortune in general. To address this point, weanalyzed participants’ dispositional empathy and voluntaryattention to images in charity, which are associated withaﬀective neural responses in charitable giving (Genevsky,
Vastfjall, Slovic, & Knutson, 2013).
Empathic concern. We measured dispositional empathy
using the Interpersonal Reactivity Index (Davis, 1983), in
which the participant is asked about tendencies to experience feelings of sympathy and compassion for unfortunate
others in daily life on a 7-point scale (e.g., “I often havetender, concerned feelings for people less fortunate thanme”). The empathic concern score was higher among
Changers (Mchanger = 5.12) than Keepers (Mkeeper = 4.67),
Welch’s t(64.477) = 2.589, p < .05 (Bonferroni corrected; see
Supplementary Figure 5), 95% CI = [0.102, 0.793].
Attention to images. We also examined the degree towhich eventual Keepers and Changers viewed the images
while making initial choices before receiving social information (see Supplementary Figure 6 for the deﬁnition of
screen areas). For each participant, we computed the
proportion of ﬁxation duration in the picture areas overthat in the entire screen area. The proportions of picture
viewing had a marginally signiﬁcant eﬀect on participants’
eventual choices, if added as a predictor in the multiple
logistic regression analysis (β = 1.811, SE = 1.085,
Z = 1.668, p = .095) along with the aforementioned predictors (i.e., the absolute diﬀerences in the initial deservingness between the two charity projects, β = −0.018,
SE = 0.296, Z = −0.062, p = .95; donation amounts,
β = 0.001, SE = 0.002, Z = 0.424, p = .672; and response
times for initial decisions, β = 0.002, SE = 0.042, Z = 0.051,
p = .959).
We also examined participants’ gaze alternation
between the two images during initial decision-making.
For each participant, we counted the frequency of ﬁxations on the two pictures consecutively without ﬁxating
on other screen areas in between (e.g., an Africa-SyriaAfrica ﬁxation sequence counts as two alternations). Gazealternation between the two images predicted participants’ eventual choices signiﬁcantly (multiple logistic
regression analysis, β = 0.487, SE = 0.219, Z = 2.217,
p = .027) along with the aforementioned predictors (i.e.,
the absolute diﬀerences in the initial deservingness
between the two charity projects, β = 0.024, SE = 0.3,
Z = 0.081, p = .935; donation amounts, β = 0.0004,
SE = 0.002, Z = 0.25, p = .803; and response times for initialdecisions, β = −0.044, SE = 0.051, Z = −0.869, p = .385).
Because its correlation with the proportion of picture
viewing was high, r = 0.482, p < .0001, we decided not
to include the two predictors simultaneously to avoid
a multicollinearity in the model. These results indicate
that, compared to eventual Keepers, eventual Changers
had a greater tendency to focus on concrete images ofthe victims of famine or war when they made initialdecisions for charitable giving.

SOCIAL NEUROSCIENCE

General discussion
Charitable giving is a signiﬁcant sector of the modern
economy, and it critically depends on each individual’s
willingness to help distant, unrelated others without
expecting direct or immediate reciprocity from the recipients (Bowles & Gintis, 2011; Trivers & Coultas, 1971).
For example, according to recent statistics (Giving USA,2017), 72% of the $390 billion in donations in the US in
2016 came from individuals, while only 5% came from
corporations. In this study, we examined how individual
charitable giving decisions may be aﬀected by social
information. More speciﬁcally, we focused on how
a “majority heuristic” (Festinger, 1954; Henrich & Boyd,
1998; Kameda & Nakanishi, 2002, 2003) may operate incharity situations, in which students who do not necessarily have the specialized knowledge or skills to evaluate the “true” qualities of various ongoing charity
projects, make real donations from their own funds.
Contrary to the robust majority-inﬂuence found inmany attitudinal (Cialdini & Goldstein, 2004; Kameda
et al., 2012) and consumer decisions (Salganik et al.,2006), Experiment 1 revealed that no participant in
the Minority condition conformed to the majority position, while 21% of the participants in the Majority condition switched to the less popular, minority position.
This switching from the majority to the minority position (along with an almost complete absence of switching from the minority to the majority position) was
replicated in Experiment 2, conﬁrming the robustnessof this phenomenon.
Experiment 3 was designed to shed light on psychophysiological processes that underlie the switching behavior using an eye tracker. Previous research showed thatemotion-related brain regions such as the anterior insula
and amygdala were associated with the degree of
inequity aversion in behavioral choices (Haruno & Frith,
2010; Hsu et al., 2008; Yu et al., 2014; Zhong et al., 2016).
We thus examined participants’ gaze pattern and pupil
dilation during decision making to see how emotionalarousal and commitment might relate to switching behavior. The results conﬁrmed that Keepers, who retained the
majority position, and Changers, who switched to theminority position, were markedly diﬀerent on both ofthese measures.
Speciﬁcally, while Changers showed a typical gazebias, directing their attention to the eventually-chosen
option (Glaholt & Reingold, 2009; Wiener et al., 2012),
the gaze pattern among Keepers was divided between
the two options until the end (Figure 6). Given thatgaze bias reﬂects diﬀerentiation between options on
the decision-related criterion (Fiedler & Glöckner, 2012;
Glaholt & Reingold, 2011; Mullet & Stewart, 2016;11

Orquin & Mueller Loose, 2013; Schotter et al., 2010),
Keepers seem to have remained ambivalent in theirﬁnal decisions between the two charity projects, andthus did not take any action to change their decision.
Time course of pupil dilation, which is related to neuralactivation in the locus coeruleus-norepinephrine system(Eckstein et al., 2017; Murphy et al., 2014), revealed
a similar pattern (Figure 7). Pupil dilation prior to conﬁrming the ﬁnal choice was higher among Changers,
suggesting that they were more physiologically aroused
than Keepers during decision making after learning thesocial information (Bradley et al., 2008; Paulus et al.,2015; Sara & Bouret, 2012). Such arousal may havemade Changers more likely to form diﬀerential valuations between the two charity projects in their ﬁnal
choices (as inferred from subjective responses inExperiment 2) than Keepers (Kempf, 1999; Mano, 1997).
Interestingly, Changers directed more attention to
concrete images as well, alternating their gazes more
often between the two images when making initialdecisions as compared to Keepers. Their dispositional
empathic-concern scores (Davis, 1983) were also higher
than those of Keepers. Taken together, these results
suggest that empathic concerns about others’ misfortune play a key role for the support of the “underdog”
in charitable giving. The inequity in donated amounts
between the two charity projects may elevate emotional arousal for some participants enough to switch
to the less popular, minority position (Changers), whileothers might remain ambivalent about their ﬁnal
choices, failing to resolve the psychological conﬂicts(Keepers).
As far as we know, this is the ﬁrst study to report theunderdog eﬀect in charitable giving (especially theintentional switching from the majority to the minority
position in response to social information). Given its
novelty, however, it is important to consider possible
limitations.
This study employed a charity situation based on theongoing UNICEF projects for Africa and Syria. Obviously,
the aim of these UNICEF projects is to help people suﬀering from large-scale violence, epidemics, famine, and natural disasters. It is likely that emotions play a large role insuch cases. As demonstrated in the “identiﬁable victim
eﬀect” (Kogut & Ritov, 2005), the tangible and vivid
images of another’s misfortune may well trigger empathic
concern for the least fortunate, prompting donors toattend to inequity among recipients in cumulative
amounts. However, this condition may not hold forother charitable causes. For example, in donations toreligious organizations, people are likely to be committed
to their own religions or in-groups (Everett, Faber, &Crockett, 2015). Also, when donating to scientiﬁc research,12

Y. SAITO ET AL.

people may be less emotional and tend to support
a “socially signiﬁcant” research project, following theimplicit majority norm. In such cases, responses to social
information seem likely to depend on the type of charitable activity and the social context. The underdog eﬀect
as observed here may be most relevant to cases where
donors can decide as neutral third parties, and emotionalfeatures associated with inequity are used to signal need.
Future research addressing the boundary conditions forthe underdog eﬀect will be important for a better understanding of the impacts of social information in charitablegiving.
Some cautions are also due in the interpretations of thepupil-dilation results in this study. Across the three experiments, the modal subjective reason that Changers provided
for their ﬁnal choices was sympathy for the less-supported“underdog” (Supplementary Table 1). We believe that ouremotion-based interpretation of the underdog eﬀect isgenerally in line with these subjective responses as well asother behavioral responses (e.g., greater focus on picture
images among Changers). However, we must acknowledge
that the pupil-dilation results per se are by no means conclusive for this interpretation (see Hess & Polt, 1964;
Murphy, Vandekerckhove, & Nieuwenhuis, 2014; Urai,
Braun & Doner, 2017 for other interpretations of the pupil
signal). Future research addressing the involvement ofemotion-related brain regions, such as anterior insula,amygdala, and anterior cingulate cortex, will be highly
important to understand the neural underpinnings of theunderdog eﬀect more directly (e.g., Genevsky et al., 2013;
Haruno & Frith, 2010; Hsu et al., 2008; Yu et al., 2014; Zhong
et al., 2016).
It is also important to see how robust this phenomenon is across diﬀerent cultures. The three experimentsin this study were run at two diﬀerent locations inJapan (Experiments 1 & 2 in Tokyo and Experiment 3
in Hokkaido). Some argue that the culture of Hokkaido,
which was a frontier area until settlement started about
150 years ago, is individualistic, valuing autonomy andself-reliance more, as compared to the older regions ofJapan including Tokyo (Kitayama, Ishii, Imada,
Takemura, & Ramaswamy, 2006). Although we observed
no such regional diﬀerences here, examining sociocultural boundaries is critical to understanding how(possibly diﬀerent) emotions may come into play incharitable giving. Humans are certainly equipped with
empathic concerns for others’ misfortune (De Waal,
2010; Hsu et al., 2008; Kameda et al., 2016). However,
the activation and expression of those altruistic capacities in helping may diﬀer deeply between societies
and cultures (Henrich et al., 2001), which constitutes
a key dimension to understanding humans asa cooperative species (Bowles & Gintis, 2011).

Acknowledgments
We thank the faculty and staﬀ of the Center for Experimental
Research in Social Sciences at Hokkaido University, who kindly
allowed us to use their laboratory resources. We also thankthe Japan Committee for UNICEF for permission to use thepictures of the Africa and Syria charity projects in this article.
This research was conducted independently and received noﬁnancial support from UNICEF.

Disclosure statement
The authors declared no conﬂicts of interest with respect totheir authorship or the publication of this article.

Funding
This work was supported by Japan Society for the Promotionof Science Grant-in-Aid for Scientiﬁc Research JP16H06324 &JP25118004 and Japan Science and Technology Agency
CREST Grant JPMJCR17A4 (17941861) to T.K. Support fromCiSHub at the University of Tokyo is also appreciated.

ORCID
Yoshimatsu Saito

http://orcid.org/0000-0003-2626-391X

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