Reproduced with permission in the format post in course management system via Copyright
Clearance Center.
Warning Concerning Copyright Restrictions
The Copyright Law of the United States (Title 17, United States Code) governs the making of
photocopies or other reproductions of copyrighted materials.
Under certain conditions specified in the law, libraries and archives are authorized to furnish a
photocopy or other reproduction. One of these specified conditions is that the photocopy or
reproduction is not to be used for any purpose other than private study, scholarship, or research. If
electronic transmission of reserve material is used for purposes in excess of what constitutes “fair
use,” that user may be liable for copyright infringement.
Hanging Out With the Right Crowd: Peer Influence on Risk-Taking
Behavior in Adolescence
Jorien Van Hoorn, Eveline A. Crone, and Linda Van Leijenhorst
Leiden University
Peer influence plays a key role in the increase of risk-taking behavior during adolescence. However, its underlying processes
are not fully understood. This study examined the effects of social norms, conveyed through peer advice, on
risk-taking behavior in 15- to 17-year-old adolescents (N = 76). Participants played a card-guessing task alone and with
online peer advice. Results showed that risk-taking increased in the presence of peers. The results further showed that
adolescents took into account the uncertainty associated with gambles, as well as the social norms conveyed by peers.
Our findings suggest that peers are most influential in uncertain situations and demonstrate the value of a social norms
approach in examining the processes underlying peer effects.
If all your friends jumped off a cliff, then would
you? Parents who worry about the negative influence
of peers frequently pose this question to their
adolescent son or daughter and expect the answer
to be “no.” It is not surprising that parents are concerned
about the influence of friends on their
child’s engagement in risk-taking behaviors. The
rates of these behaviors, such as substance abuse,
risky driving, or gambling, increase in adolescence
(Boyer, 2006). In addition, risk-taking behavior is
more likely to occur when in the presence of peers
than when alone (Albert, Chein, & Steinberg, 2013;
Dishion & Tipsord, 2011).
A large body of literature has consistently
demonstrated that peers increase risk-taking behavior
in the laboratory (Chein, Albert, O’Brien,
Uckert, & Steinberg, 2011; Gardner & Steinberg,
2005; Haddad, Harrison, Norman, & Lau, 2014;
Knoll, Magis-Weinberg, Speekenbrink, & Blakemore,
2015; Munoz Centifanti, Modecki, MacLellan,
& Gowling, 2016; Simons-Morton et al., 2014;
Smith, Chein, & Steinberg, 2014; but see Lourenco
et al., 2015) and in daily life (Simons-Morton et al.,
2011). Even though these results suggest that peer
influence can be considered a risk factor in adolescence,
it may also promote cautious behavior
(Brown, Bakken, Ameringer, & Mahon, 2008). The
process underlying these peer effects on risk-taking
behavior is not yet fully understood.
This study employed a social norms perspective
to examine the positive and negative effects of peer
advice on gambling behavior. Social norms can be
defined as expectations about appropriate behavior
endorsed by a group (reviewed in McDonald &
Crandall, 2015). Through social norms, peers can
potentially encourage risky as well as risk-averse
behavior. Using this novel approach in an experimental
task, we set out to investigate how social
norms conveyed through different types of peer
advice relate to risk-taking behavior during adolescence.
Peer Effects: The Underlying Process
One hypothesis about the process underlying peer
effects is that peer presence negatively influences
adolescents’ cognitive control functions by increasing
impulsivity during decision making (O’Brien,
Albert, Chein, & Steinberg, 2011; Weigard, Chein,
Albert, Smith, & Steinberg, 2014). Delay discounting
is one form of impulsivity and can be
described as the tendency to exhibit impatience
when given a choice between an immediate small
reward versus a larger but delayed reward (Romer,
2010). Recent experimental studies investigating
delay discounting showed that the presence of
peers increased young adults’ (age 18–22 years)
preference for immediate rewards over larger
delayed rewards (O’Brien et al., 2011; Weigard
et al., 2014). Another study showed that, after
© 2016 The Authors
Journal of Research on Adolescence © 2016 Society for Research on Adolescence
DOI: 10.1111/jora.12265
The authors would like to thank Sanne van Rijn for help with
the data collection and programming of the experiment.
This research is funded by a NWO Research Talent Grant
(406-11-019 Crone & Van Hoorn).
Requests for reprints should be sent to Jorien Van Hoorn,
Department of Developmental and Educational Psychology,
Leiden University, Wassenaarseweg 52 2333 AK Leiden, The
Netherlands. E-mail:
viewing impulsive decisions of age-matched peers,
young adults (age 18–25 years) had a preference
for smaller, earlier payments as well (Gilman, Curran,
Calderon, Stoeckel, & Evins, 2014).
A second hypothesis states that peer presence
either “primes” the social–emotional system for
reward opportunities or influences both the reward
and control systems (Albert et al., 2013; Chein
et al., 2011; Smith, Steinberg, Strang, & Chein,
2015). In line with this second hypothesis, the presence
of peers may increase the subjective value of
rewards, for example by making rewards more
arousing, and thereby also increase the preference
for a risky choice over a safe alternative (Albert
et al., 2013). These aspects of adolescent risk-taking
have been well captured in developmental dual
process and imbalance models (Galvaan, 2010; Somerville,
Jones, & Casey, 2010; Steinberg et al., 2008),
which propose that adolescents show heightened
social–emotional sensitivity in early adolescence
and protract the development of cognitive control
in late adolescence. Peer effects could then be a
factor that tips the balance to less control and more
reward sensitivity, leading to risk-taking behavior.
Studies that employed a video driving game
have shown that both passive (friends observing
performance; Chein et al., 2011) and active (friends
calling out advice; Gardner & Steinberg, 2005) peer
influence resulted in riskier driving in adolescents
(age 13–18 years) but not in adults. The impact of
active feedback is generally larger than passive
observation (Munoz Centifanti et al., 2016), but this
seems to be dependent on task specifics (e.g., Haddad
et al., 2014). Taken together, there is evidence
from experimental studies showing that adolescents
are sensitive to both passive peer presence
and active peer influence when taking risks.
A Social Norms Perspective on Peer Effects
Another useful framework for understanding peer
influence on risk-taking behavior is the social norms
perspective (Bandura, 1986; Cialdini & Trost, 1998;
Van Hoorn, Van Dijk, Meuwese, Rieffe, & Crone,
2016). Social norms specify which social behaviors
are accepted in the peer context and whether such
behaviors will elicit approval from peers (Berger,
2008; McDonald & Crandall, 2015). These norms
may not always encourage an increase in risk-taking,
but may instead also promote a decrease in
risk-taking behavior (Brown et al., 2008).
In general, adults are more likely to act according
to social norms when a situation is novel,
ambiguous, or uncertain (Cialdini & Trost, 1998).
Given that social acceptance is important during
adolescence (Sebastian, Viding, Williams, & Blakemore,
2010), individuals may be especially susceptible
to social norms during this time—and even
more so in situations of uncertainty. One previous
study showed increased risk-taking in 15- to 17­
year-olds as a result of peer presence in a probabilistic
gambling task (PGT), but only for gambles with
a lower gain–loss probability (Smith et al., 2014).
Naturalistic studies that employed the social
norms perspective have shown that there is variability
in adolescent risky driving outcomes with
peer passengers that may be dependent on how
these peers behave (Simons-Morton et al., 2011,
2014). The effect of peer presence on teenage males’
(age 16–18 years) simulated driving behavior was
investigated by comparing driving alone to driving
in the presence of a risk-accepting peer and a riskaverse
peer (Simons-Morton et al., 2014). Evidence
for a general effect of peer presence was found,
which is consistent with prior studies showing that
driving with a peer leads to more risky driving
(e.g., Allen & Brown, 2008; Pradhan et al., 2014).
However, driving with a risk-accepting peer
increased risky driving more than driving in the
presence of a risk-averse peer. These findings show
that social norms influence risk-taking behavior,
and sensitivity to these norms may explain variability
in risk-taking behavior. However, to date, it
is unknown how social norms conveyed by peer
advice and uncertainty interact in risky decision
making during adolescence.
The Present Study
In this study, we tested the effects of peer advice
on risk-taking behavior under varying uncertainty
conditions. This novel approach combining social
norms with experimental methods allowed us to
manipulate different advice types that either
enhanced or reduced risk-taking while we varied
the uncertainty associated with the outcomes of the
risk. We tested the hypothesis that adolescents are
specifically sensitive to peer advice when outcomes
are uncertain. For this purpose, we designed a
card-guessing task to investigate risk-taking behavior,
referred to as Guess Gambling Game (GGG)
(similar to Critchley, Mathias, & Dolan, 2001; Delgado,
Miller, Inati, & Phelps, 2005; Smith et al.,
On each trial, the participant was shown a playing
card and was asked to guess whether a subsequently
drawn card would have a higher or lower
value than the current card. Then, participants bet
a variable number of poker chips on whether they
guessed correctly. Risk-taking behavior was operationalized
in this task as the number of chips bet.
The GGG was played alone and in the presence of
anonymous online peers. Participants were told
that the online peer watched their decision and
would give them advice on how many chips to bet.
This peer advice was experimentally controlled to
be low bet advice (bet 1 or 2 chips), medium bet
advice (bet 4 or 6 chips), or high bet advice (bet 8 or
9 chips). Because the task consisted of a guess and
a gamble, we were able to disentangle the effects
of peers on guessing behavior (i.e., the ability to
make a rational choice in line with the card probability)
and gambling behavior (i.e., risk-taking
Our first analysis tested the hypothesis that
guessing behavior would show a dichotomous pattern
in both the alone and peer advice conditions,
in which participants would select higher for cards
1–4, lower for cards 6–9, and would have no preference
for card 5. In this card condition, we expected
a 50% probability of higher (Critchley et al., 2001).
This pattern is in accordance with previous work
that illustrates that adolescents, like children and
adults, can make accurate decisions about probabilities
(Reyna & Farley, 2006; Van Duijvenvoorde &
Crone, 2013; Van Leijenhorst et al., 2010).
Second, we examined the influence of the type
of peer advice on gambling behavior. Although we
expected to find a general increase in betting
behavior with peers present (Munoz Centifanti
et al., 2016; O’Brien et al., 2011; Smith et al., 2014;
Weigard et al., 2014), based on the social norms
conveyed in peer advice we predicted a differentiated
pattern (Simons-Morton et al., 2014). In line
with social norms theory, we hypothesized that
participants would place their bets in accordance
with the advice expressed by the online peer (i.e.,
low bet, medium bet, or high bet) and that these
effects would be largest in the most uncertain situation
(Cialdini & Trost, 1998; Smith et al., 2014).
In the current study, we collected data from
adolescents aged 15–17 years for several reasons.
First, we wanted a comparable sample to previous
studies of interest. Smith et al. (2014) studied 15- to
17-year-olds and studies from the social norms perspective
used 16- to 18-year-olds because of the
U.S. legal driving age (Simons-Morton et al., 2011,
2014). Across the literature, there is some inconsistency
with regard to the definitions of adolescence.
In particular, those aged 18+ are alternately called
(late) adolescents, youths, or young adults. To
avoid confusion, our sample did not include 18­
year-olds. Fifteen-year-olds were included in this
study for practical reasons as well, given that we
included participants from two consecutive school
years, which included 15- to 16-year-olds and 16­
to 17-year-olds. Second, this age group is specifi­
cally interesting because neuroimaging work has
shown that adolescent risk-taking behavior peaks
around the age of 15–17, when the brain is particularly
sensitive to rewards (e.g., Braams et al., 2015).
The specific age range allowed us to test hypotheses
about this age group and explore individual
differences in terms of gender. A meta-analysis
suggests higher rates of gambling behavior in
males relative to females over the age range of 10–
21 years old (Byrnes, Miller, & Schafer, 1999).
Moreover, some literature points to enhanced sensitivity
to peer influence in males relative to
females either across all of the adolescent period
(Steinberg & Monahan, 2007) or most pronounced
in 13- to 15-year-olds (Sumter, Bokhorst, Steinberg,
& Westenberg, 2009). Therefore, we expected males
to be more influenced by the online peers than
The sample consisted of 76 adolescents between
the ages of 15 and 17 years (M = 15.9,
SD = 6 months, range 15.0–17.1), including 44
males (58%) and 32 females (42%). Six additional
participants from the original sample (N = 82) had
to be excluded due to incomplete data. Both parental
consent and participant’s consent for minors
were obtained from all participants. All adolescents
for whom we obtained informed consent participated
in the study. Participants were recruited
from several consecutive years in a school that teaches
secondary vocational education (Dutch school
system: VMBO). We did not collect information
regarding parental income or parental education
level. However, participants were mostly Caucasian
and the school was located in a middle-class
neighborhood in the Netherlands (Knol, 2012).
To obtain an estimate of general intellectual ability,
participants completed Raven’s Standard Progressive
Matrices (SPM) (Raven, Raven, & Court,
1998). Raven’s SPM consists of 60 items, categorized
in five sets (A through E) of 12 items each.
Each item consists of a 2 9 3 or 3 9 3 matrix figure
in which one cell is empty. Either six (sets A
and B) or eight (sets C through E) pieces are displayed
below the figure from which the participant
has to select the one piece that completes the figure.
The different sets and items within a set
increase in difficulty. Based on the number of correct
items, estimated IQ scores were obtained using
international norms (Raven et al., 1998). Due to
missing data (N = 3), we included the IQ scores
from N = 73 participants in the final sample. All IQ
scores from the final sample fell within the average
to above average range, M(SD) = 108.78 (9.92); 85–
125. There was no significant difference in IQ for
the two genders (Mfemale (SD) = 107.17 (9.12) and
Mmale (SD) = 109.84 (10.37), t(71) = 1.13, p = .264).
We designed a computerized task with playing
cards, the Guess Gambling Game, that incorporated
two types of decision making: guessing
behavior and gambling behavior. Trials started
with one playing card that was presented face up,
from a deck of cards ranging between hearts 1
(ace) to hearts 9. Subsequently, the second card
was presented with the reverse side up, such that
the value of this card was unknown. Participants
were asked to guess whether the second card
would be higher or lower than the first card.
After this guess, participants placed a bet ranging
from 1 to 9 chips and they found out whether
they guessed correctly. If the gamble was correct,
the number of chips bet was doubled and added
up to the number of remaining chips to provide a
final score for that trial [e.g., a bet of 8 chips following
a correct guess resulted in a score of 17 (8
chips 9 2 added to 1 remaining chip)]. However,
when the guess was incorrect, the participant lost
the chips that were placed in the bet, but kept the
chips that were not bet in the trial [e.g., a bet of 8
chips following an incorrect guess resulted in a
score of 1 (the chip that was not bet)].
Each trial was played with a new deck of playing
cards and a new stack of 9 poker chips, such
that each trial was unrelated to previous trials. The
experiment consisted of 160 trials: card 5 was
shown 32 times and all other cards were shown 16
times each. Participants were not informed of how
many times each card would be shown. Participants
first played Guess Gambling Game alone in
a block of 40 trials. The next three blocks of 40 trials
(120 in total) were played with an online peer,
indicated by a messenger symbol in the corner of
the screen. These peers were 50% female (60 trials),
indicated by a pink messenger symbol, and 50%
male (60 trials), indicated by a blue messenger
symbol. We chose not to counterbalance the order
of alone and peer advice because prior studies
have shown that there can be carry over effects
(Van Hoorn et al., 2016), and we aimed to create a
pure baseline before introducing peer influence.
Note that we did not examine possible effects of
the gender of the peer, because there were too few
trials to draw valid conclusions; instead we controlled
for possible peer gender effects by counterbalancing
male and female peers.
The fictitious online peer watched the performance
on the entire trial and gave participants
advice on how many chips to bet, indicated by a
number next to the messenger symbol (Figure 1).
We manipulated three types of betting advice: low
bet (bet 1 or 2 chips), medium bet (bet 4 or 6
chips), or high bet (bet 8 or 9 chips). To maintain
credibility of the advice given by peers, the advice
for card 1 and 9 was always to bet 9 chips. Low,
medium, and high advice was each randomly provided
32 times during the trials in which cards 2 to
8 were presented (1/3 of 96 trials).
To control for possible button press effects, half
of the participants used their left index finger to
guess higher and their right index finger to guess
lower and the buttons were reversed for the other
half of the participants.
The study was conducted in a quiet classroom in
which the task was individually administered to
participants using a laptop computer. The experimenter
provided standardized verbal instructions
about the procedures and was present at all times
to provide help with the instructions. In addition,
the task was preceded by an extensive written
instruction and practice trials. Participants completed
three different elements during the study:
first the Guess Gambling Game, then Raven’s SPM,
and finally the RPI questionnaire. Participants were
told that their final score on the GGG was calculated
by resolving the outcomes of 4 randomly
selected trials at the end of the gambling task. All
trials had the same probability to be included in
the final score, and therefore, each trial was equally
important. Participants could choose between two
possible rewards: a small amount of money related
to their final score (unbeknownst to the participants,
the final score always corresponded to a 3
Euro reward) or a lottery ticket for a bigger reward
(an iPod or 2 cinema tickets). No differences in
gambling behavior or peer effects were found
between participants that chose the immediate or
delayed reward. Participants were debriefed about
FIGURE 1 Example of a trial with peer advice in the Guess Gambling Game. Card 2 of hearts is shown while a male peer is watching,
as indicated by a blue messenger symbol in the upper corner. The participant guesses that the second card will be higher than
the first card. Following the guess, the online peer gives advice to place a bet of 9 chips, indicated with a number below the messenger
symbol. The participant decides to follow the advice of the peer and places a bet of 9 chips. This is a correct guess, and therefore
the score for this trial is 9 9 2 = 18 points.
the peer manipulation and goals of the study after
all data had been collected.
Guessing Behavior
First, we examined whether participants’ guessing
behavior was related to the actual probability of a
higher card being drawn and whether this was
influenced by peer presence (i.e., whether they
were playing alone or with peer advice). We
expected to find a dichotomous pattern in which
participants select higher for cards 1 to 4 and lower
for cards 6 to 9, whereas 50% probability was
expected for card 5. We submitted the percentages
of higher guesses to a 2 (condition: alone, peer
advice) 9 9 (cards: 1 to 9) 9 2 (gender: male,
female) repeated-measures analysis of variance
(ANOVA). Figure 2 shows the mean (SE) percentage
of higher guesses per card.
This analysis resulted in a main effect of Card (F
(8, 592) = 1160.92, p < .001, g2
p = .940), which
shows that participants’ guesses were influenced
by the probabilities associated with the different
cards. Post hoc analyses (Bonferroni-corrected; for
all comparisons see Table S1 in the Supporting
Information) revealed that the percentage of higher
guesses was highest for cards 1–2 and slightly
lower for cards 3–4, but guesses for these cards
were still in the high range (above 90%). As
expected, card 5 was associated with approximately
50% higher guesses, which was significantly
less than for cards 1–4 and significantly higher
than for cards 6–9. Finally, card 9 was associated
with the lowest percentage of higher guesses, and
although cards 6–8 were associated with slightly
more higher guesses, percentages were in the low
range (below 10%). Taken together, guesses followed
the expected dichotomous pattern.
The ANOVA also revealed a main effect of Peer
2 presence, F(1, 74) = 4.55, p = .036, gp = .058, quali­
fied by a Card 9 Peer presence interaction, F(8,
2 592) = 2.40, p = .015, gp = .031, showing that the
effect of the presence of an online peer varied as a
function of card condition. Post hoc analyses (Bonferroni-corrected)
revealed that for card 5 (p = .044)
and card 8 (p = .002), participants guessed that the
next card would be higher more often in the alone
compared to the peer advice condition.
The interaction between gender and peer presence
was not significant, indicating that the effect
FIGURE 2 Means (SE) for the percentage of guesses that the next card drawn will be higher for each card condition and peer condition.
Alone trials are displayed in patterned bars and peer advice trials are displayed in black bars.
of peers on guesses was similar for males and
females. Lastly, there was an interaction between
card and gender, F(8,592) = 3.81, p < .001,
2 gp = .049. The differences between the genders
were specific to card 2 (males > females, p = .044),
card 6 (females > males, p = .017), and card 8 (females
> males, p = .043). In these three conditions,
males tended to follow the probabilities associated
with the cards more than females, respectively, a
higher % of higher guesses for card 2 and a lower
% of higher guesses for cards 6 and 8.
Peer Advice and Gambling Behavior
Next, we tested whether the type of advice given
by the online peer influenced the number of chips
bet by participants. In these analyses, we included
only rational trials (i.e., trials in which participants
guessed higher for cards 1–4 and lower for cards
6–9) because this is a more conservative test that
reduces noise in the data. This selection led to the
removal of 4.42% of the data (see the Supporting
Information for the results from analyses including
all trials). Cards with equal probabilities were combined
into five card conditions (card conditions
1&9, 2&8, 3&7, 4&6, and 5). For this analysis, card
condition 1&9 was left out, because for those cards,
the peer advice was always to bet 9 chips. Therefore,
in the analyses presented below, we included
four card conditions.
A repeated-measures ANOVA was performed
with advice (4; alone, low, medium, high advice)
and card condition (4; cards 2&8, 3&7, 4&6, and 5)
as within-subject factors and gender (2; male,
female) as between subjects factor. This analysis
yielded two main effects for advice (F(3,
2 222) = 45.76, p < .001, gp = .380) and card condition
2 (F(3, 222) = 245.70, p < .001, gp = .769). These
effects were qualified by an Advice 9 Card condition
interaction, F(9, 666) = 2.32, p = .014,
2 gp = .030). Means (SE) for number of chips bet per
card condition are displayed in Figure 3.
Post hoc analyses (Bonferroni-corrected) were
performed to examine how advice influenced bets
for card conditions (for all post hoc comparisons,
see Table S2 in the Supporting Information). In
card conditions 2&8 and 3&7, there were signifi­
cant differences between playing alone and low
advice (p = .001 and p = .016, respectively), such
that participants placed higher bets for the low
advice condition than for the alone condition.
However, in conditions 4&6 and 5, there were no
significant differences between alone and low
advice (both p’s > .05). Furthermore, participants
bet more chips for card conditions 3&7 (p = .001),
4&6 (p < .001), and 5 (p < .001), but not for 2&8
(p > .05) when medium advice was given compared
to low advice. The contrast of medium versus
high advice revealed that only for card
conditions 3&7 and 5, participants placed more
chips following high advice compared to medium
advice (p’s = .002). In these categories, participants
bet more chips when they received high advice
from peers than when they received medium
Taken together, in all card conditions, the number
of chips bet increased when high advice was
given compared to when low advice was given
FIGURE 3 Mean number (SE) of chips bet for alone trials in patterned bar, low bet advice in black bar, medium bet advice in gray
bar, and high bet advice in white bar for each card condition separately. *Indicates significant difference at p < .05 level (Bonferronicorrected).
(card condition 2&8, p = .038; other ps < .001). This
increase in bets was larger when higher uncertainty
was associated with the outcome, from a 16%
increase in the most certain 2&8 condition, to a
20% increase in card conditions 3&7 and 4&6, and
a 30% increase in card 5. The difference in increase
between card condition 5 and the other card conditions
was significant (ps < .05), whereas the other
comparisons between card conditions showed no
significant differences (ps > .05).
Finally, there was an interaction effect of gender
and card condition, F(3, 222) = 2.89, p = .036,
2 gp = .038. Further analyses indicated that this effect
was specific to card condition 2&8 (p = .009). Males
bet more chips than females in this condition,
Mmales (SE) = 8.09 (.17), Mfemales (SE) = 7.40(.19).
There were no gender differences in the other card
conditions (all ps > .05). There was no Gender
9 Advice interaction (p > .05).
Reaction Times and Gambling Behavior
Lastly, we tested whether the type of advice given
by the online peer influenced reaction times (RTs).
We submitted average RTs to a repeated-measures
ANOVA, with advice (4; alone, low, medium, high)
and card condition (4; cards 2&8, 3&7, 4&6, and 5)
as within-subject factors and gender (2; male,
female) as between subjects factor. This analysis
yielded main effects for advice (F(3, 222) = 29.51,
2 p < .001, gp = .285) and card condition (F(3,
2 222) = 11.27, p < .001, gp = .132). These main
effects were further qualified by an interaction
effect of advice and card condition (F(9, 666) = 9.20
2 (p < .001), gp = .111). In addition, we found a main
effect for gender (F(1, 74) = 8.31, p = .005,
2 gp = .101). Overall, males (Mmales(SE) = 921.08 ms
(50.41)) responded faster than females (Mfemales
(SE) = 1144.98 ms (59.11)). Mean RTs (SE) for each
card condition separately are shown in Figure 4.
Post hoc analyses (Bonferroni-corrected) for the
Advice 9 Card condition interaction showed that
RTs did not differ for card conditions 2&8, 4&6,
and 5 when playing alone compared to when low
advice was given (all ps > .05). Only in card condition
3&7 was the RT for low advice shorter than
for alone (p = .024). When we compared low
advice versus medium advice, for card conditions
2&8, 3&7, and 4&6 RTs were shorter for low than
for medium advice (ps < .001), but there was no
difference in card condition 5 (p > .05). For card
conditions 3&7 and 4&6, but not card conditions
2&8 and 5 (ps > .05), RTs during high advice were
longer than for the medium advice (card condition
3&7, p = .044; card condition 4&6, p = .002).
For all reaction time comparisons, see Table S2 in
the Supporting Information.
The aim of the present study was to examine the
effects of peer advice on risk-taking behavior from
FIGURE 4 Average reaction times in MS (SE) for alone trials in patterned bar, low bet advice in black bar, medium bet advice in
gray bar, and high bet advice in white bar for each card condition separately. *Indicates significant difference at p < .05 level (Bonferroni-corrected).
a social norms perspective. This was investigated
with a card-guessing task, the Guess Gambling
Game (GGG), in which participants received advice
from online peers about their decisions. Before
playing with peer advice, participants played some
trials alone, without peer advice. The GGG
included two types of decisions: a guess (is the
next card higher or lower?) and a gamble (betting
chips). Our key finding is that the effects of peer
influence on gambling behavior were dependent
on the uncertainty associated with the cards, as
well as on the social norms conveyed by online
peer advice.
The results of this study revealed that guesses
showed a dichotomous pattern, which followed the
outcome probabilities associated with the cards in
both the alone and peer advice conditions. Consistent
with prior studies, participants most often
selected higher for cards 1 to 4 and lower for cards 6
to 9, while choices for card 5 showed a 50% probability
(Critchley et al., 2001; Smith et al., 2014). The
similarity between the guessing patterns when
playing alone and with peer advice supports the
notion that the presence of peers does not alter
adolescents’ ability to reason about card probabilities
or expected value (Reyna & Farley, 2006; Van
Duijvenvoorde & Crone, 2013; Van Leijenhorst
et al., 2010).
As expected, gambling behavior was influenced
by general peer presence. Participants placed
higher bets when they played in the presence of
online peers than when they played alone. These
findings corroborate previous work showing effects
of peer influence in information-limited contexts
such as driving (Chein et al., 2011, age 13–16;
Gardner & Steinberg, 2005, age 14–18; Munoz Centifanti
et al., 2016, age 16–20) and information-rich
contexts such as wheel of fortune tasks (Haddad
et al., 2014, age 11–18; Smith et al., 2014, age 15–
17). The current study extends this previous work,
by showing that different types of advice yield a
nuanced pattern of risk-taking behavior in interaction
with varying uncertainty in 15- to 17-yearolds.
Peer Influence on Risk-Taking Behavior:
Uncertainty and Social Norms
In the GGG, we used several card conditions, ranging
from decisions with a highly uncertain outcome
(card 5) to decisions with highly certain outcomes
(e.g., card condition 2&8). In all card conditions,
participants placed higher bets when they played
with peer advice compared to when they played
alone, and on average the number of chips bet
decreased as uncertainty about the outcomes
increased. Importantly, participants’ decisions were
influenced by the advice given by online peers.
Participants placed higher bets when they were
given high advice compared to low advice. Risktaking
with a high bet advice compared to a low
bet advice increased with uncertainty of the
gambles, from a small rise (16%) in the relatively
certain condition to a substantial rise (30%) in the
most uncertain condition. These findings suggest
that for decisions with a relatively certain outcome,
the presence of peers rather than the type of advice
is the most important factor influencing decision
making, whereas for decisions with a relatively
uncertain outcome, the type of peer advice is the
most important factor.
These findings are in agreement with social
learning theory (Bandura, 1986). Similar to behavior
in the domain of risky driving (Simons-Morton
et al., 2011, 2014), gambling behavior varied
according to different social norms. Moreover, in
line with our hypothesis, peer norms were most
influential in the highly uncertain situation (Cialdini
& Trost, 1998; Smith et al., 2014). Learning
from social norms in peer influence seems to play
an important role in the variability seen in risk-taking
behavior during adolescence. In general, adolescents
tend to overestimate the degree to which
their peers take risks and consequently adapt their
behavior to that flawed perception (Prinstein &
Wang, 2005). In the current study, however, social
norms were made explicit by the advice of the
online peers. Adolescents may have been inclined
to conform to these norms because they wanted to
be accepted by their peers.
Overall, analyses of the reaction times showed
that peer presence did not simply facilitate the
decision-making process. Under high uncertainty
(card 5), participants made their decisions equally
quickly when online peers provided them with
advice and when they played alone. Interestingly,
medium and high advice in card condition 2&8
facilitated the decision-making process (i.e., shorter
reaction times), but low advice in the 2&8 condition
resulted in longer reaction times, suggesting
additional decision-making conflict. One alternative
interpretation of these results may be that longer
reaction times in these conditions are the result of
confusion or disbelief in the task. However, we
suggest that this seemingly contradictory effect of
peer advice on reaction times is due to the nature
of the advice. The advice to bet 1 or 2 chips is not
rational in the relatively certain card condition,
given that the probability of a favorable card is relatively
high. We interpret this contradiction in
reaction times as participants taking more time to
think about their response upon encountering
“irrational” advice.
Our results suggest that impulsivity alone cannot
explain the effects of peer presence on reaction
times (also see Krajbich, Bartling, Hare, & Fehr,
2015). Moreover, these findings are different from
the findings from studies that focused on delay discounting
(Gilman et al., 2014; O’Brien et al., 2011;
Weigard et al., 2014) which did find an increase in
impulsivity in the presence of peers; therefore,
future studies should examine the role of impulsivity
and the facilitating versus hindering effects of
peers on reaction times in more detail (for a recent
discussion on impulsivity, see Steinberg & Chein,
Gender Differences in Risk-Taking Behavior
A secondary goal of this study was to explore
whether there were gender differences in susceptibility
to peer influence on risk-taking behavior.
Although subtle, the gender effect in guessing
behavior seems to imply that males tended to
guess more in line with expected value than
females. In terms of gambling behavior, males
showed more risk-taking behavior than females but
only in relatively certain decisions. These risks in
the relatively certain condition can be considered
as an adaptive form of risk-taking behavior
because the benefits (i.e., double chips) associated
with this decision are far more likely to occur than
the potential costs (Byrnes et al., 1999). These
results fit with previous work on gender differences,
showing that males are generally less riskaverse
and more sensitive to peer influence than
females (Byrnes et al., 1999; Steinberg & Monahan,
2007; Sumter et al., 2009). The “gender gap” in
risk-taking behavior seems to vary with the type of
risk-taking, age (i.e., decrease over development),
and task context but is commonly found in gambling
tasks (Byrnes et al., 1999).
The age range of participants included in the present
study was relatively narrow (15- to 17-yearolds).
Although this sample is very comparable to
the age range that was previously studied, is of
specific interest in terms of brain development, and
gave us the opportunity to explore individual differences
in gender, it limits our ability to directly
compare adolescents to children and/or adults.
Given that the broader adolescent peer influence
literature has included a larger age range (11- to
22-year-olds) and shows consistent effects of peer
influence, we speculate that our findings may generalize
to younger and older adolescent populations.
Based on the literature, we expect that peer
effects would be augmented in adolescents
compared to adults (Gardner & Steinberg, 2005).
Developmental comparisons would be a relevant
extension of the present study and should be
addressed in future research, such that we can test
whether adolescents are more sensitive to social
norms than children or adults.
Another possible limitation of this study is that
the task order may have influenced the bets placed
and reaction times between alone and peer advice
trials, as all participants first played alone and then
with peer advice. This order may have resulted in
practice or learning effects, and therefore the
results should be replicated with a counterbalanced
design. Moreover, even though none of the participants
reported disbelief in the online peer manipulation,
this belief was not directly assessed.
Finally, the social situation provided in this
experiment is less complex than social relationships
in real life. A different anonymous online peer gave
advice on every trial, such that there was no relationship
involved between the participant and the
peer, and each decision was equally important. We
used anonymous peers in this task to control for
possibly confounding assumptions about behaviors
or beliefs of friends. However, to increase ecological
validity, future research could address the
effects of the opinions of real friends or include
peer characteristics such as social status or likeability
in a school environment (see e.g., Burnett Heyes
et al., 2015; Welborn et al., 2016). Another interesting
direction for future research would be to vary
aspects of this task, for example, to investigate realworld
situations with larger rewards or to examine
social versus monetary reward.
This is the first experimental study that examined
peer influence on risk-taking behavior from a social
norms perspective. We showed that peers do not
alter adolescents’ ability to make a rational guess
in line with probabilities. Rather, our findings
implicate that peer effects on gambling behavior
were more nuanced, depending on both social
norms conveyed in peer advice and uncertainty
associated with the outcome. Together, these
results contribute to the understanding of the process
underlying peer influence on risk-taking
behavior. To gain a deeper understanding of this
complex process, future studies should move
beyond peer presence effects, to investigating what
it is exactly about these peers that results in
changes in behavior. In uncertain circumstances, it
does seem to make a difference what crowd an
adolescent hangs out with. This has important
implications for interventions, for example, by
informing the design of a peer intervention in
which we can use peer advice to promote more
cautious behavior that in turn may lead to reduced
health-risk behaviors in adolescence.
Albert, D., Chein, J., & Steinberg, L. (2013). The teenage
brain: Peer influences on adolescent decision making.
Current Directions in Psychological Science, 22, 114–120.
Allen, J. P., & Brown, B. B. (2008). Adolescents, peers,
and motor vehicles: The perfect storm? American Journal
of Preventive Medicine, 35(3 Suppl.), S289–S293.
Bandura, A. (1986). Social foundations of thought and action:
A social cognitive theory. Englewood Cliffs, NJ: PrenticeHall.
Berger, J. (2008). Identity signaling, social influence, and
social contagion. In M. J. Prinstein & K. A. Dodge
(Eds.), Understanding peer influence in children and adolescents
(pp. 181–199). New York, NY: Guilford Press.
Boyer, T. (2006). The development of risk-taking: A multi-perspective
review. Developmental Review, 26, 291–
345. doi:10.1016/j.dr.2006.05.002
Braams, B. R., van Duijvenvoorde, A. C. K., Peper, J. S.,
& Crone, E. A. (2015). Longitudinal changes in adolescent
risk-taking: A comprehensive study of neural
responses to rewards, pubertal development and risk
taking behavior. Journal of Neuroscience, 35(18), 7226–
7238. doi:10.1523/JNEUROSCI.4764-14.2015
Brown, B. B., Bakken, J. P., Ameringer, S. W., & Mahon,
M. D. (2008). A comprehensive conceptualization of
the peer influence process in adolescence. In M. J.
Prinstein & K. A. Dodge (Eds.), Understanding peer
influence in children and adolescents (pp. 17–44). New
York, NY: Guilford Press.
Burnett Heyes, S., Jih, Y.-R., Block, P., Hiu, C.-F., Holmes,
E. A., & Lau, J. Y. F. (2015). Relationship reciprocation
modulates resource allocation in adolescent social networks:
Developmental effects. Child Development, 86,
1489–1506. doi:10.1111/cdev.12396
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999).
Gender differences in risk taking: A meta-analysis.
Psychological Bulletin, 125, 367–383. doi:10.1037/0033­
Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg,
L. (2011). Peers increase adolescent risk taking by
enhancing activity in the brain’s reward circuitry.
Developmental Science, 14, F1–F10. doi:10.1111/j.1467­
Cialdini, R. B., & Trost, M. R. (1998). Social influence:
Social norms, conformity, and compliance. In D. Gilbert, S.
Fiske, & G. Lindzey (Eds.), Handbook of social psychology
(pp. 151–192). New York, NY: McGraw-Hill.
Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2001).
Neural activity in the human brain relating to uncertainty
and arousal during anticipation. Neuron, 29,
537–545. doi:10.1016/S0896-6273(01)00225-2
Delgado, M. R., Miller, M. M., Inati, S., & Phelps, E. A.
(2005). An fMRI study of reward-related probability
learning. NeuroImage, 24, 862–873. doi:10.1016/j.neuroimage.2004.10.002
Dishion, T. J., & Tipsord, J. M. (2011). Peer contagion in
child and adolescent social and emotional development.
Annual Review of Psychology, 62, 189–214.
Galvaan, A. (2010). Adolescent development of the reward
system. Frontiers in Human Neuroscience, 4, 1–9.
Gardner, M., & Steinberg, L. (2005). Peer influence on
risk taking, risk preference, and risky decision making
in adolescence and adulthood: An experimental study.
Developmental Psychology, 41, 625–635. doi:10.1037/
Gilman, J. M., Curran, M. T., Calderon, V., Stoeckel, L.
E., & Evins, A. E. (2014). Impulsive social influence
increases impulsive choices on a temporal discounting
task in young adults. PLoS One, 9, 1–8. doi:10.1371/
Haddad, A. D., Harrison, F., Norman, T., & Lau, J. Y. F.
(2014). Adolescent and adult risk-taking in virtual
social context. Frontiers in Psychology, 18, 1–7.
Knol, F. (2012). Statusontwikkeling van wijken in Nederland
1998-2010 (Report 2012-26). Retrieved from Sociaal en
Cultureel Planbureau Den Haag: http://
Knoll, L. J., Magis-Weinberg, L., Speekenbrink, M., &
Blakemore, S.-J. (2015). Social influence on risk perception
in adolescence. Psychological Science, 26, 583–592.
Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015).
Rethinking fast and slow based on a critique of reaction-time
reverse inference. Nature Communications, 6,
1–9. doi:10.1038/ncomms8455
Lourenco, F. S., Decker, J. H., Pedersen, G. A., Dellarco,
D. V., Casey, B. J., & Hartly, C. A. (2015). Consider the
source: Adolescents and adults similarly follow older
adult advice more than peer advice. PLoS One, 10, 1–
15. doi:10.1371/journal.pone.0128047
McDonald, R. I., & Crandall, C. S. (2015). Social norms
and social influence. Current Opinion in Behavioral
Sciences, 3, 147–151. doi:10.1016/j.cobeha.2015.04.006
Munoz Centifanti, L. C., Modecki, K. L., MacLellan, S., &
Gowling, H. (2016). Driving under the influence of risky
peers: An experimental study of adolescent risk-taking.
Journal of Research on Adolescence, 27, 207–222.
O’Brien, L., Albert, D., Chein, J., & Steinberg, L. (2011).
Adolescents prefer more immediate rewards when in
the presence of their peers. Journal of Research on Adolescence,
21, 747–753. doi:10.1111/j.1532­
Pradhan, A. K., Li, K., Bingham, C. R., Simons-Morton,
B. G., Ouimet, M. C., & Shope, J. T. (2014). Peer passenger
influences on male adolescent drivers’ visual
scanning behavior during simulated driving. Journal of
Adolescent Health, 54(Suppl.), S42–S49. doi:http://dx.­
Prinstein, J. J., & Wang, S. S. (2005). False consensus and
peer contagion: Examining discrepancies between perceptions
and actual reported levels of friends’ deviant
and health risk behaviors. Journal of Abnormal Child
Psychology, 33, 293–306. doi:10.1007/s10802-005-3566-4
Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for
Raven’s Progressive Matrices and Vocabulary Scales. Section
1: General overview. San Antonio, TX: Harcourt
Reyna, V. F., & Farley, F. (2006). Risk and rationality in
adolescent decision making. Psychological Science in the
Public Interest, 7, 44. doi:10.1145/1142680.1142682
Romer, D. (2010). Adolescent risk taking, impulsivity,
and brain development: Implications for prevention.
Developmental Psychobiology, 52, 263–276. doi:10.1002/
Sebastian, C., Viding, E., Williams, K. D., & Blakemore,
S.-J. (2010). Social brain development and the affective
consequences of ostracism in adolescence. Brain and
Cognition, 72, 134–145. doi:10.1016/j.bandc.2009.06.008
Simons-Morton, B. G., Bingham, C. R., Falk, E. B., Li, K.,
Pradhan, A. K., Ouimet, M. C., Almani, F., & Shope, J.
T. (2014). Experimental effects of injunctive norms on
simulated risky driving among teenage males. Health
Psychology, 33, 616–627. doi:10.1037/a0034837
Simons-Morton, B. G., Ouimet, M. C., Zhang, Z., Klauer,
S. E., Lee, S. E., Wang, J. … Dingus, T. A. (2011). The
effect of passengers and risk-taking friends on risky
driving and crashes/near crashes among novice teenagers.
Journal of Adolescent Health, 49, 587–593.
Smith, A. R., Chein, J., & Steinberg, L. (2014). Peers
increase adolescent risk taking even when the probabilities
of negative outcomes are known. Developmental
Psychology, 50, 1564–1568. doi:10.1037/a0035696
Smith, A. R., Steinberg, L., Strang, N., & Chein, J. (2015).
Age differences in the impact of peers on adolescents’
and adults’ neural response to reward. Developmental
Cognitive Neuroscience, 11, 75–82. doi:10.1016/
Somerville, L. H., Jones, R. M., & Casey, B. J. (2010). A
time of change: Behavioral and neural correlates of
adolescent sensitivity to appetitive and aversive environmental
cues. Brain and Cognition, 72, 124–133.
Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham,
S., & Woolard, J. (2008). Age differences in sensation
seeking and impulsivity as indexed by behavior
and self-report: Evidence for a dual systems model.
Developmental Psychology, 44, 1764–1778. doi:10.1037/
Steinberg, L., & Chein, J. M. (2015). Multiple accounts of
adolescent impulsivity. Proceedings of the National Academy
of Sciences, 112, 8807–8808. doi:10.1073/pnas.
Steinberg, L., & Monahan, K. C. (2007). Age differences
in resistance to peer influence. Developmental Psychology,
43, 1531–1543. doi:10.1037/0012-1649.43.6.1531
Sumter, S. R., Bokhorst, C. L., Steinberg, L., & Westenberg,
P. M. (2009). The developmental pattern of resistance
to peer influence in adolescence: Will the
teenager ever be able to resist? Journal of Adolescence,
32, 1009–1021. doi:10.1016/j.adolescence.2008.08.010
Van Duijvenvoorde, A. C. K., & Crone, E. A. (2013). The
teenage brain: A neuroeconomic approach to adolescent
decision making. Current Directions in Psychological
Science, 22, 108–113. doi:10.1177/0963721413475446
Van Hoorn, J., Van Dijk, E., Meuwese, R., Rieffe, C., &
Crone, E. A. (2016). Peer influence on prosocial behavior
in adolescence. Journal of Research on Adolescence, 26,
90–100. doi:10.1111/jora.12173
Van Leijenhorst, L., Gunther Moor, B., Op de Macks, Z.
A., Rombouts, S. A., Westenberg, P. M., & Crone, E. A.
(2010). Adolescent risky decision-making:
Neurocognitive development of reward and control
regions. NeuroImage, 51, 345–355. doi:10.1016/j.neuroimage.2010.02.038
Weigard, A., Chein, J., Albert, D., Smith, A., & Steinberg,
L. (2014). Effects of anonymous peer observation on
adolescents’ preference for immediate rewards. Developmental
Science, 17, 71–78. doi:10.1111/desc.12099
Welborn, B. L., Lieberman, M. D., Goldenberg, D.,
Fuligni, A. J., Galvaan, A., & Telzer, E. H. (2016). Neural
mechanisms of social influence in adolescence.
Social Cognitive and Affective Neuroscience, 11, 100–109.
Supporting Information
Additional Supporting Information may be found
online in the supporting information tab for this
Table S1. Mean differences in % guesses that the
second card will be higher for all card comparisons.
Table S2. Mean differences in chips bet and reaction
times for all combinations of advice types.

Place this order or similar order and get an amazing discount. USE Discount code “GET20” for 20% discount

Posted in Uncategorized