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DOI: 10.1177/0956797616645673
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Research Article
Social media are immensely popular among adolescents:
Nearly 90% of American teens report being active users,
and young people have continually outpaced other age
groups in adopting new media (Lenhart, 2015). Given
this prevalence, it is unsurprising that parents, educators,
and the popular press have expressed concerns about
the effects of social media on social-skill development
and interpersonal interactions. Frequently, these con-
cerns manifest themselves in questions about the effect
of social media on the developing brain. Nonetheless,
few studies have examined neural mechanisms underly-
ing any kind of social-media use (Choudhury & McKinney,
2013; Mills, 2014).
The neural correlates of social-media use are particularly
important to understand in the context of adolescence, and
not only because adolescents are enthusiastic users. Ado-
lescence is especially important for social cognitive devel-
opment; it is theorized to be a sensitive period during
which young people are uniquely attuned to the complexi-
ties of interpersonal relationships (Baird, 2012; Blakemore
& Mills, 2014). Subcortical regions functionally associated
with emotion processing and reward undergo considerable
changes and reorganization during puberty (Brenhouse &
Andersen, 2011; Sisk & Foster, 2004). The dopaminergic
system and related regions in the striatum are implicated in
645673PSSXXX10.1177/0956797616645673Sherman et al.Effects of Peer Influence on Responses to Social Media
research-article2016
Corresponding Author:
Lauren E. Sherman, Department of Psychology, UCLA, 1285 Franz
Hall, Box 951563, Los Angeles, CA 90095-1563
E-mail: lsherman@ucla.edu
The Power of the Like in Adolescence:
Effects of Peer Influence on Neural and
Behavioral Responses to Social Media
Lauren E. Sherman1,2,3, Ashley A. Payton4,
Leanna M. Hernandez2,4, Patricia M. Greenfield1,3,
and Mirella Dapretto2,5
1Department of Psychology, University of California, Los Angeles; 2Ahmanson-Lovelace Brain Mapping Center,
University of California, Los Angeles; 3Children’s Digital Media Center @ Los Angeles, University of California,
Los Angeles, and California State University, Los Angeles; 4Interdepartmental Neuroscience Program,
University of California, Los Angeles; and 5Department of Psychiatry and Biobehavioral Sciences,
University of California, Los Angeles
Abstract
We investigated a unique way in which adolescent peer influence occurs on social media. We developed a novel
functional MRI (fMRI) paradigm to simulate Instagram, a popular social photo-sharing tool, and measured adolescents’
behavioral and neural responses to likes, a quantifiable form of social endorsement and potential source of peer
influence. Adolescents underwent fMRI while viewing photos ostensibly submitted to Instagram. They were more
likely to like photos depicted with many likes than photos with few likes; this finding showed the influence of virtual
peer endorsement and held for both neutral photos and photos of risky behaviors (e.g., drinking, smoking). Viewing
photos with many (compared with few) likes was associated with greater activity in neural regions implicated in
reward processing, social cognition, imitation, and attention. Furthermore, when adolescents viewed risky photos (as
opposed to neutral photos), activation in the cognitive-control network decreased. These findings highlight possible
mechanisms underlying peer influence during adolescence.
Keywords
adolescent development, social cognition, social influences, risk taking, neuroimaging, open materials
Received 9/12/15; Revision accepted 3/31/16
Psychological Science OnlineFirst, published on May 31, 2016 as doi:10.1177/0956797616645673
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2 Sherman et al.
potential mechanisms underlying two common features of
adolescence: escalation in risk-taking behaviors and
increased desire to spend time with and earn the approval
of peers (Steinberg, 2008). For example, when adolescents
completed a risky driving task alone or in the presence of
peers, the presence of peers was associated with increases
in both risk taking and activity in the nucleus accumbens
(NAcc), a hub of reward circuitry (Chein, Albert, O’Brien,
Uckert, & Steinberg, 2011). Smith, Chein, and Steinberg
(2014) replicated these behavioral effects when peers were
virtually connected, demonstrating that peer influence also
occurs online (see also Cohen & Prinstein, 2006).
Less is known about how features unique to social
media contribute to peer influence. For example, digital
and in-person communication differ significantly in
their affordance for quantifiable interactions. Whereas
in-person communication is necessarily qualitative and
involves subjective interpretation, many online environ-
ments allow for feedback that is purely quantitative. For
example, a feature of most social media tools is the abil-
ity to like an image, text, or other piece of information,
allowing for a simple, straightforward measure of peers’
endorsement. For adolescents, who are particularly
attuned to peer opinion, this quantifiable social endorse-
ment may serve as a powerful motivator. Furthermore,
quantifiable social endorsement provides a unique
research opportunity: Although it is a form of interaction
that occurs in the real world, it is simple enough to be
experimentally manipulated.
The present study is, to our knowledge, the first to
replicate social media interaction in the MRI scan-
ner; however, important earlier work using behavioral
and functional MRI (fMRI) methods has demonstrated
how peer endorsement biases values (e.g., Campbell-
Meiklejohn, Bach, Roepstorff, Dolan, & Frith, 2010; Izuma
& Adolphs, 2013; Klucharev, Hytönen, Rijpkema, Smidts,
& Fernández, 2009). In these studies, adults rated stimuli,
then learned how other people rated the same stimuli,
and finally rated the stimuli a second time. Participants
changed their ratings to conform to those of peers or
experts and showed greater NAcc activation during trials
on which they agreed with these individuals than during
trials on which they did not agree. Our study differs from
previous work in that adolescents viewed content posted
on social media simultaneously with information about
its popularity—much as content is typically experienced
online. We thus tested whether initial impressions were
colored by the content’s popularity and explored the
overall effects of positive peer opinion on brain responses.
Specifically, we investigated the neural correlates of
viewing photos with many or few likes to assess the role of
quantifiable social endorsement in peer influence. We
recruited adolescents to participate in an “internal social
network” that simulated Instagram, a popular photo-sharing
tool. Participants submitted their own Instagram photos,
and they believed that all photos would be seen and liked
by peers. We tested the possibility that the number of likes
appearing under each photo would affect participants’
responses. We hypothesized that participants would tend to
like photos liked by more peers and refrain from liking less
popular photos. We also hypothesized that neural responses
to popular and unpopular photos would differ. Given previ-
ous research suggesting that peer presence heightens NAcc
response (Chein et al., 2011), we predicted that viewing
other people’s photos that had a greater number of likes
would similarly elicit greater NAcc activation. Evidence link-
ing NAcc response to social evaluation (Meshi, Morawetz, &
Heekeren, 2013) and sharing information about the self
(Tamir & Mitchell, 2012), as well as the well-documented
role of the NAcc in reward and reinforcement in general,
suggests that viewing one’s own popular photos would also
elicit greater NAcc activity.
Peer influence is very important during adolescence; it
is a means by which adolescents learn how to behave
appropriately in their sociocultural environment. How-
ever, peer pressure can be maladaptive when it reinforces
dangerous behaviors, such as drunk driving or drug use.
Furthermore, young people frequently post content
online depicting risky behaviors, and this may affect their
peers’ tendency to engage in such behaviors (Huang
etal., 2014). Thus, we also investigated whether quantifi-
able social endorsement specifically influenced responses
to risky behaviors by including photos depicting these
behaviors. Well-established theories of adolescent risk
taking suggest that the NAcc interacts with neural regions
implicated in cognitive control during risky decision
making (Casey, 2015; Steinberg, 2008). Accordingly, we
directly compared adolescents’ neural activity as they
viewed risky images and neutral images to examine
whether exposure to risky content online would influ-
ence activity in cognitive-control regions, regardless of
the supposed popularity of the photos.
Method
Participants and fMRI paradigm
Thirty-four typically developing adolescents (18 female;
age range = 13–18 years) participated in the present
study. Two of these 34 participants were excluded from
fMRI data analysis, 1 because of scan-console malfunc-
tion and 1 because of excessive motion. The sample size
reflects the maximum number of participants that we
were able to recruit given available funding, as well as
timing constraints imposed by an institutional upgrade of
the MRI magnet. Participants completed written consent
in accordance with the institutional review board at the
University of California, Los Angeles.
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Effects of Peer Influence on Responses to Social Media 3
During recruitment, participants were informed that
they would be involved in a study examining the brain’s
responses during social-media use. Participants were
asked to submit photos from their own accounts on Ins-
tagram, a popular social-media tool used for sharing pho-
tos on mobile devices and the Internet. They were told
that all of these photos would be combined to form an
internal social network, that every participant would see
a feed of these photos in the MRI scanner, and that the
photos would appear as they did on Instagram. In reality,
participants saw only some of their own photos while in
the MRI scanner; all other stimuli were selected by the
study team from among publicly available images on Ins-
tagram. During the laboratory visit, each participant was
instructed that approximately 50 other adolescents had
already viewed the feed of Instagram photos. This step
was taken to establish the size of the audience, and to
standardize how many likes would be regarded as many
or few, irrespective of the size of a given participant’s
own social network. Participants were told that they
could see how many times each photo was liked by pre-
vious participants and that the feed would be updated
after their visit to reflect any new likes they contributed.
In reality, the number of likes displayed under each
image was assigned by the study team, as described later
in this section.
The social-media task was presented to participants in
the scanner using magnet-compatible 3-D goggles
(VisuaStim; Resonance Technology, Inc., Northridge, CA)
with a resolution of 800 × 640 pixels. The task mimicked
the experience of browsing Instagram on a smartphone:
Participants viewed a feed of photos, each of which was
accompanied by text indicating how many other people
had already liked the image. Photos were displayed one
at a time on a white background accompanied by two
on-screen buttons prompting the participant to choose
“♥Like” to like the image or “→Next” to move on to the
next image without liking it (Fig. 1). Images were pre-
sented for 3,000 ms, with an interstimulus interval that
varied between 1,000 and 11,000 ms.
Participants saw 148 unique photos. These included
42 risky images and 66 neutral, nonrisky images. Risky
photos depicted alcohol, cigarettes, marijuana, smoking
paraphernalia, rude gestures, or adolescents (male and
female) wearing provocative or skimpy clothing. Neutral
photos depicted typical images (e.g., pictures of friends,
food, and possessions) found on the social-media pro-
files of adolescents. Participants also saw 40 of the images
they had submitted from their own Instagram accounts.
Across participants, all neutral and risky images were
assigned both a popular value of 23 to 45 likes and an
unpopular value of 0 to 22 likes. Two versions of the
imaging paradigm were created: In Version 1, half of the
photos in each category (risky, neutral) were displayed
with a high number of likes and half were displayed with
a low number of likes. In Version 2, the displayed popu-
larity was opposite that in Version 1 (i.e., if a photo was
displayed with many likes in Version 1, it was displayed
with few likes in Version 2). Thus, half of the participants
saw Version 1 of each image and half saw Version 2 of
each image; this allowed us to hold the content and the
aesthetic quality of the images constant while manipulat-
ing popularity.
To assign likes to participants’ own images, author
L. E. Sherman divided the 40 photos into groups on the
basis of content (e.g., a people group or an objects group,
depending on the participant). Then, each of the groups
of photos was randomly split into two halves; one half
was assigned many likes, and the other half was assigned
few likes. Thus, the content of the popular and unpopular
images was similar. Half of each participant’s own photos
appeared with 23 to 45 likes, and the other half appeared
with 0 to 22 likes. Note that likes were not distributed
continuously and evenly across the spectrum of 0 to 45.
We did not expect neural and behavioral responses to
vary linearly as the number of likes increased; instead, we
hypothesized that participants would display qualitatively
different responses to popular images than to unpopular
images. Thus, we used a bimodal distribution of likes in
which the majority was clustered between 30 and 45 likes
(popular photos) or between 0 and 15 likes (unpopular
photos). We chose to use a bimodal distribution to clearly
differentiate popular and unpopular images. Of the
148 photos displayed during the scan, only 8 were
depicted with intermediate values of 23 to 29 likes and 8
were depicted with 16 to 22 likes; these 16 images were
included to avoid any suspicion among participants that
might be caused by the obviously bimodal distribution.
In light of our experimental manipulation, our categorical
analyses reflect the difference between popular and
unpopular images.
During the scan, participants were asked to view the
images as they appeared and to decide whether they per-
sonally liked each image using the criteria they would
normally use when deciding to like pictures on Insta-
gram. Participants selected “♥Like” or “→Next” by press-
ing one of two buttons on a button box.
Data acquisition and analyses
Neuroimaging data were collected using a 3-T MRI scan-
ner (Trio; Siemens Healthcare, Erlangen, Germany). The
social-media paradigm was presented during a functional
echoplanar, T2*-weighted gradient-echo scan lasting
11min and 44 s (repetition time = 2,000 ms, echo time =
28 ms, flip angle = 90°, matrix size = 64 × 64, 34 axial
slices, field of view = 192 mm, 4-mm slices with a 1-mm
interslice gap). Button-press data were recorded in E-Prime
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4 Sherman et al.
(Version 2.0; Psychology Software Tools, Sharpsburg, PA)
and converted to IBM SPSS Statistics format for analysis.
Binomial tests were used to determine whether participants
conformed to peers’ responses more often than would be
predicted by chance. fMRI data were preprocessed and
analyzed using the Analysis of Functional NeuroImages
(AFNI; Version 16.0.00) software environment (Cox, 1996)
and the Functional MRI of the Brain software library (FSL;
Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012).
Preprocessing for each individual’s data included image
realignment to correct for head motion, normalization to
the standard stereotactic space of the Montreal Neurologi-
cal Institute’s (MNI) 152-brain template, and spatial
smoothing using a 5-mm full-width, half-maximum
Gaussian kernel to increase signal-to-noise ratio.
For each participant, linear contrasts were calculated
for several planned comparisons. Specifically, we mod-
eled three linear contrasts comparing popular photos
(many likes) and unpopular photos (few likes) in all
three categories (i.e., neutral photos, risky photos, and
participants’ photos). In addition to modeling the six
types of stimuli at the first level, we included several
other parameters. These included the participant’s but-
ton-press choice and reaction time for each trial and the
luminosity of each image as determined using Adobe
Photoshop. Group-level random-effects analyses were
then conducted across all participants. At the group level,
a prethreshold binary mask consisting of all regions
exhibiting significant activity for any type of photo, com-
pared with a fixation cross on a white background, was
used to restrict our analyses to regions displaying signifi-
cant task-related activity. Specifically, we first individually
contrasted the six types of stimuli (e.g., neutral photos
with many likes, neutral photos with few likes, risky pho-
tos with many likes) > fixation and then added the maps
of each of these individual contrasts (thresholded at
z> 1.7, corrected for multiple comparisons at p < .05)
together. The final mask covered a considerable portion
of the cortex and subcortex. Along with all of our group
contrast maps, it is available for download at NeuroVault
(http://neurovault.org/collections/RYSBTTMN/). We per-
formed contrasts examining the effect of popularity
(many likes > few likes and the reverse) for neutral pho-
tos, risky photos, and participants’ photos. We also com-
pared all neutral photos ostensibly submitted by peers
with all risky photos ostensibly submitted by peers.
To test our a priori hypothesis that popular photos
would elicit significantly greater activation in the bilateral
Fig. 1. Two examples of stimuli presented during the imaging paradigm. Participants saw innocuous photos of adolescents or everyday objects
(e.g., the coffee drinks on the left) or images of objects related to risky behavior (e.g., the marijuana cigarette on the right) or adolescents engaging
in risky behaviors. Images appeared as they would have in the Instagram app on a smartphone in the year 2014: The number of likes was displayed
underneath each photo next to a heart icon, and the Instagram menu buttons were displayed beneath the likes. Finally, there were two buttons
allowing participants to like an image (“♥Like”) or to move on without liking the image (“→Next”).
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Effects of Peer Influence on Responses to Social Media 5
NAcc than unpopular photos would, we used a small-
volume-correction approach. Our functional regions of
interest (ROIs), derived from an independent sample of
participants completing a monetary-incentive-delay task
(Tamir & Mitchell, 2012), consisted of two 8-mm spheres
in the left and right NAcc (MNI coordinates: x = 10, y = 6,
z = −4, and x = −8, y = 4, z = −6, respectively). AFNI’s
3dClustSim was used to determine that a contiguous clus-
ter of 53 or more voxels was necessary to meet statistical
criteria within these ROIs. To examine whether the many
likes > few likes contrast differed significantly as a func-
tion of type of photo (neutral, risky, participant), we
extracted parameter estimates (regression coefficients)
from the bilateral ROIs for each contrast of interest and
performed paired-samples t tests using IBM SPSS.
Results
To determine whether participants were significantly
more likely than chance to match the supposed opinions
of peers (i.e., to like popular images and to refrain from
liking unpopular images), we conducted a series of bino-
mial tests. Across all photos presented during the scan,
participants matched their peers significantly more fre-
quently than expected by chance (p < .00001). This effect
was also significant for each individual type of photo,
including neutral images ostensibly provided by peers
(p=.03), images depicting risk-taking behaviors ostensi-
bly provided by peers (p = .03), and the participants’ own
images (p < .00001). The effect was significantly larger for
participants’ own photos than for either neutral images,
χ2(1, N = 3,544) = 10.1, p = .001, or risky images,
χ2(1,N=2,736) = 6.6, p = .01.
Neural responses also differed according to the num-
ber of likes for neutral, risky, and participants’ own pho-
tos. Figure 2a depicts regions in which activity was
significantly greater when photos were depicted as hav-
ing garnered many versus few likes for neutral, risky, and
participants’ own photos. The regions of significantly
greater activity for many likes compared with few likes
differed by photo type. When participants viewed neutral
photos with many likes, they showed significantly greater
activity in the visual cortex extending to the precuneus
and in the cerebellum (see Table S1 in the Supplemental
Material available online). When participants viewed
risky photos with many likes (compared with risky pho-
tos with few likes), significantly greater activity was found
in one cluster in the left frontal cortex, extending from the
precentral gyrus through the middle frontal gyrus and
inferior frontal gyrus (Table S1). When participants viewed
their own photos, significantly greater activity in response
to photos with many likes (compared with photos with
few likes) was observed in several regions (Table S1).
These included areas implicated in social cognition, such
as the precuneus, medial prefrontal cortex, left temporal
pole, lateral occipital cortex, and hippocampus (Mars
et al., 2012; Zaki & Ochsner, 2009), as well as reward
learning and motivation, including the nucleus accum-
bens, caudate, putamen, thalamus, ventral tegmental
area, and brain stem (e.g., Haruno & Kawato, 2006; Schott
etal., 2008).1 Table S1 includes a complete list of regions.
For all three photo types, the reverse contrast (few likes>
many likes) yielded no significant activation in the whole
brain.
Neural responses also differed according to whether
the photo depicted risky behavior (Fig. 2b). When par-
ticipants viewed neutral images (compared with risky
images) ostensibly submitted by peers, significantly
greater activity was observed in bilateral occipital cortex,
medial prefrontal cortex, and the inferior frontal gyrus
(for a complete list of regions, see Table S2 in the Supple-
mental Material). When viewing risky images compared
with neutral images (i.e., the reverse contrast), partici-
pants demonstrated significantly less activation in a net-
work of regions implicated in cognitive control and
response inhibition (e.g., Blasi et al., 2006; Bressler &
Menon, 2010; Sherman et al., 2014), including dorsal
anterior cingulate cortex, bilateral prefrontal cortex, and
lateral parietal cortex (Table S2).2
In addition to whole-brain analyses, we conducted
ROI analyses on the basis of our a priori hypothesis that
photos depicted with many likes would elicit significantly
greater activation in the bilateral NAcc than would those
depicted with few likes. Consistent with our hypothesis,
there was greater activity in the left NAcc when partici-
pants viewed neutral images that had many likes than
when they viewed neutral images that had few likes. We
also observed greater bilateral NAcc activation when par-
ticipants viewed their own images for the many likes >
few likes contrast. For images depicting risk-taking
behavior, likes had no effect on brain response in the
NAcc ROI. In the right NAcc, activation was significantly
greater when participants viewed their own photos than
when viewing other people’s neutral images, t(31) = 2.34,
p = .026, or risky images, t(31) = 2.45, p = .02, but did not
differ significantly in the left NAcc (for all comparisons,
p>.10).
Discussion
The present study highlights a new and unique way in
which peer influence occurs on social media: through
quantifiable social endorsement. We found that the pop-
ularity of a photo had a significant effect on the way that
photo was perceived. Adolescents were more likely to
like a photo—even one portraying risky behaviors, such
as smoking marijuana or drinking alcohol—if that photo
had received more likes from peers. This effect was
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6 Sherman et al.
2.3 z4
c
a
b
z = 48 z = 14
z = 10 z = –10
z = 32 z = 18
z = 16 z = 6
z = 46 z = 2
–2.3 z–4
x = –2
x = –2
x = –50
x = –50
x = –4
z = –2
Participants’ Own Images
Many Likes > Few Likes
Neutral Images
Many Likes > Few Likes
Risky Images
Many Likes > Few Likes
Risky > Neutral Images
Region of Interest: Bilateral
Nucleus Accumbens
Risky < Neutral Images
Fig. 2. Neural responses to Instagram photos with many likes compared with photos with
few likes. The brain maps in (a) show neural regions with significant activity (z>2.3, clus-
ter corrected at p < .05) for the many likes > few likes contrast, for each of the three types of
photos. The brain maps in (b) show neural regions with significant activity (z > 2.3, cluster
corrected at p< .05) for the risky > neutral contrast and the risky < neutral contrast. Brain
images are shown by radiological convention (i.e., left side of the brain is on the right). The
brain map in (c) shows the location of the region of interest in the nucleus accumbens that
was identified using a monetary-incentive-delay task in an independent sample of young
adults (Tamir & Mitchell, 2012).
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Effects of Peer Influence on Responses to Social Media 7
especially strong for photos the participants themselves
had supplied. Adolescence is a period during which self-
presentation is particularly important, including on social
media; thus, this significantly greater effect may reflect
the relative importance of self-presentation versus pro-
viding feedback to others.
Neural responses also differed according to number of
likes. For all three types of photos, participants exhibited
greater brain activity for photos with more likes. The
regions of greater activity included areas implicated in
social cognition and social memories, including the pre-
cuneus, medial prefrontal cortex, and hippocampus
(Mars etal., 2012; Zaki & Ochsner, 2009), as well as the
inferior frontal gyrus, which is implicated in imitation
(Pfeifer, Iacoboni, Mazziotta, & Dapretto, 2008). When
participants viewed their own photographs or neutral
photographs ostensibly submitted by peers, greater activ-
ity in the visual cortex was observed in response to pho-
tos with many likes compared with photos with few likes,
even though we controlled for photos’ luminosity and
content. The increased activation suggests that partici-
pants may have scanned popular images with greater
care. Taken together, our imaging findings suggest that
adolescents perceive information online in a qualitatively
different way when they believe that this information is
valued more highly by peers. The exact nature of these
changes differs depending on the content depicted in the
photo.
Our ROI analysis suggests that the NAcc, an important
hub of the brain’s reward circuitry, is implicated in the
experience of receiving positive feedback on one’s own
images as well as viewing other people’s images that
have been endorsed by peers. The NAcc response, like
our behavioral effects, was particularly robust for partici-
pants’ own photos, suggesting that self-presentation can
be especially rewarding and a motivation for using social
networks (Manago, Graham, Greenfield, & Salimkhan,
2008). The popularity of risky photos (or lack thereof)
had no differential effect on NAcc response. However,
several participants in our adolescent sample reported no
experiences with drugs and alcohol; this lack of familiar-
ity may have contributed to the failure to detect a peer
effect in the NAcc when comparing popular and unpop-
ular risky images. Future research should examine the
effect of popularity on NAcc response to risky photos in
adolescents who report greater experience with drugs
and alcohol.
Although quantifiable social endorsement is a rela-
tively new phenomenon, we believe that the implications
of this experiment extend beyond the digital context.
Quantifiable social endorsement is a simple but nonethe-
less significant example of sociocultural learning; a like is
a social cue specific to adolescents’ cultural sphere, and
adolescents use this cue to learn how to navigate their
social world. Adolescents learn from quantifiable social
endorsement in multiple ways, as evidenced by partici-
pants’ differentiated neural responses to their own and
other people’s photos. Peers socialize one another to
norms in multiple modes, including modeling appropri-
ate behavior (behavioral display) and reinforcing appro-
priate behavior in other people (behavioral reinforcement;
Brown, Bakken, Ameringer, & Mahon, 2008). Social
media embody both modes of socialization: Adolescents
model appropriate behavior and interests through the
images they post (behavioral display) and reinforce
peers’ behavior through the provision of likes (behavioral
reinforcement). Unlike offline forms of peer influence,
however, quantifiable social endorsement is straightfor-
ward, unambiguous, and, as the name suggests, purely
quantitative.
Although the present study does not allow us to
directly compare in-person versus online peer influence,
our findings are in line with results from previous research
suggesting that the presence of peers heightens responses
in reward circuitry and leads to differences in behavioral
decision making (Chein et al., 2011). Furthermore, the
present inquiry is, to our knowledge, the first to docu-
ment that quantifiable social endorsement, a ubiquitous
feature of social media, produces these measurable neu-
ral and behavioral effects. Future research should build
on our findings to investigate how individual differences
in neural response map onto behavioral outcomes: Can
individual neural responses predict the degree of confor-
mity that adolescents will demonstrate?
Sociocultural learning can be adaptive, in that it allows
adolescents to flexibly learn from their environment. In
the case of socialization to risky behavior, however, it can
also be maladaptive. Multiple theoretical models (Casey,
2015; Steinberg, 2008) posit that risk taking in adoles-
cence arises in part from heightened neural sensitivity to
reward combined with immature capacity for cognitive
control. In results that are in line with these models, we
found that a network implicated in cognitive control
(e.g., Seeley etal., 2007) was less active when partici-
pants viewed images depicting risky behavior (compared
with neutral images). Certainly, viewing photos online
does not, in itself, constitute a risk. It is therefore all the
more striking that when simply viewing photos of risky
behaviors ostensibly taken and posted by peers, adoles-
cents exhibited decreased activation of the cognitive con-
trol network, possibly reflecting a mechanism by which
peer behaviors disinhibit cognitive control in high-risk
scenarios, thereby increasing the likelihood of engaging
in risk taking. Future research should examine whether
this decreased activation occurs into adulthood as well,
or if this finding potentially reflects the immaturity of the
prefrontal cortex in adolescence. Likewise, future
research can shed light on whether the NAcc response to
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8 Sherman et al.
social reward shown in the present study is particularly
heightened in adolescence, in line with previous research
on monetary reward (Braams, van Duijvenvoorde, Peper,
& Crone, 2015).
Our findings and approach have implications not only
for social media researchers, but also for those studying
social cognition more broadly. Social media provide a
compelling opportunity to examine social interaction in
an ecologically valid context. Typically, in the confines of
an MRI scanner, social interaction is limited and artificial.
Because social media exist on a screen, however, they can
be effectively imported into the scanner environment.
Our study provides proof of concept for quantifiable
social endorsement, a ubiquitous form of online interac-
tion that is easily experimentally manipulated. Future
research can build on this foundation to examine how
neural responses to quantifiable social endorsement pre-
dict individual differences in a variety of behavioral and
psychological domains.
Action Editor
Eddie Harmon-Jones served as action editor for this article.
Author Contributions
L. E. Sherman developed the study concept, and L. E. Sherman,
M. Dapretto, and P. M. Greenfield contributed to the study
design. Data collection was performed by L. E. Sherman, A. A.
Payton, and L. M. Hernandez. L. E. Sherman and A. A. Payton
performed the data analysis and interpretation under the super-
vision of M. Dapretto and P. M. Greenfield. L. E. Sherman
drafted the manuscript, and M. Dapretto and P. M. Greenfield
provided important revisions. All the authors approved the final
version of the manuscript for submission.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Funding
This research was supported by Grants C06-RR012169 and
C06-RR015431 from the National Center for Research Resources,
by Grant S10-OD011939 from the Office of the Director of the
National Institutes of Health (NIH), by National Institute on
Drug Abuse National Research Service Award F31-DA038578-
01A1 (to L. E. Sherman), and by Brain Mapping Medical Research
Organization, Brain Mapping Support Foundation, Pierson-
Lovelace Foundation, The Ahmanson Foundation, Capital
Group Companies Charitable Foundation, William M. and Linda
R. Dietel Philanthropic Fund, and Northstar Fund. Authors are
solely responsible for the content, which may not represent the
official views of NIH.
Supplemental Material
Additional supporting information can be found at http://pss
.sagepub.com/content/by/supplemental-data
Open Practices
All materials have been made publicly available via Open Sci-
ence Framework and can be accessed at https://osf.io/atj4d. The
complete Open Practices Disclosure for this article can be found
at http://pss.sagepub.com/content/by/supplemental-data. This
article has received the badge for Open Materials. More informa-
tion about the Open Practices badges can be found at https://
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Notes
1. The first set of regions also resembled the map for the term
“social” on Neurosynth (http://neurosynth.org; a large-scale
database of neuroimaging studies that provides meta-analytic
reverse-inference analyses) as of January 2016 (Yarkoni etal.,
2011). The second set of regions also resembled the map for the
term “reward” on Neurosynth as of January 2016.
2. This set of regions also resembled the Neurosynth map for
the term “cognitive control” as of January 2016.
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