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. Author manuscript; available in PMC: 2026 Mar 20.
Published before final editing as: J Stud Alcohol Drugs. 2026 Jan 14:10.15288/jsad.25-00105. doi: 10.15288/jsad.25-00105

Risk Perception and Susceptibility to Peer Influence Predict Substance Use in Early Adolescence: Findings From the ABCD Study

Dakota Kliamovich a,b,†, Scott A Jones a,b, Mizan Gaillard b,c, Amanda C Del Giacco a,b, Arturo Lopez Flores a,b, Bonnie J Nagel a,b,c
PMCID: PMC13001071  NIHMSID: NIHMS2140733  PMID: 41532693

Abstract

Objective:

Experimentation with alcohol and other substances during the early adolescent period is associated with a myriad of potentially deleterious health outcomes. The present analysis utilized data from the Adolescent Brain and Cognitive DevelopmentSM Study (ABCD Study®) to investigate the relationships between risk perception, susceptibility to peer influence, and substance use in early adolescence.

Method:

Participants completed a Social Influence Task (SIT) in which they were asked to rate the riskiness of various hypothetical scenarios. They were then presented with a risk rating that had ostensibly been provided by their peers and were asked to rate each scenario a second time. Mixed effects logistic regression models were used to evaluate the relationship between risk perception (initial risk rating), susceptibility to peer influence (risk rating change), and future substance use.

Results:

Higher initial risk perception was associated with lower odds of future substance use. Greater susceptibility to peer influence in the positive direction (i.e., being more swayed by peers to say that a scenario was more risky than the participant had originally decided) was associated with lower odds of future substance use as well.

Conclusions:

These findings highlight the potential importance of leveraging positive peer influence as a means for affecting substance use outcomes.

Keywords: Adolescence, substance use, risk perception, peer influence

Introduction

Adolescence is a time of remarkable physical, cognitive, behavioral, and social maturation (Dahl et al., 2018; Luciana, 2013). Not only do youth become increasingly sensitive to social evaluation during adolescence (Crone & Dahl, 2012), but they also develop skills for navigating social pressures while establishing their own sense of identity (Guyer et al., 2016). One of the hallmarks of this transition is the shift from family to peers as the primary source of social support (Crone & Dahl, 2012; Forbes & Dahl, 2010; Nelson et al., 2005). As adolescents gain more autonomy from their family, they correspondingly spend a greater amount of time around same-aged peers. It has been demonstrated that adolescents tend to gravitate toward peers who share similar perspectives and experiences (selection effects), and that they become increasingly similar to those peers over time (socialization effects) (Brechwald & Prinstein, 2011). Therefore, the characteristics of an adolescent’s peer group play a crucial role in shaping their core beliefs, values, and behaviors into adulthood.

The process of socialization is often discussed in the context of escalating risky behavior during adolescence. While there are healthy forms of risk-taking behavior that facilitate exploration and increased autonomy during adolescence (Duell & Steinberg, 2019), there remains a significant increase in risky behaviors that are likely to have adverse consequences (Romer et al., 2017), including experimentation with alcohol and other substances, unsafe or reckless driving, involvement in violence and physical fighting, and participation in unprotected sexual behaviors (IOM & NRC, 2011). Importantly, adolescents are more inclined to participate in risky behaviors when they believe they are being observed by their peers (Chein et al., 2011), even when the negative consequences are clear (Smith et al., 2014). This preference for a risky choice over a safe alternative in the presence of peers is a phenomenon that appears to be amplified during adolescence (Albert et al., 2013; Chein et al., 2011). Notably, it’s not just the presence of peers or actual peer group behaviors that influence adolescent risk perception and risk taking – perceived peer norms are also predictive of adolescent behavior (Andrews et al., 2008; Trucco et al., 2011).

Susceptibility to social influence—the ways in which individuals adapt their behavior to meet the demands of the social environment—has been shown to affect a host of risk-taking behaviors during adolescence, including alcohol and substance use (Nesi et al., 2017; Van Ryzin et al., 2012), driving recklessly or speeding (Chein et al., 2011; Gardner & Steinberg, 2005), and sexual behavior (Baumgartner et al., 2011). Despite the short-term benefits of either garnering praise from peers or avoiding social rejection, a greater willingness to conform to negative peer norms (either real or perceived) is a major risk for deleterious health outcomes (DiIorio et al., 2001; Prinstein et al., 2001; Urberg et al., 1997). For example, experimentation with alcohol and other substances during the early adolescent period is associated with a greater likelihood of developing addictive use patterns in adulthood (Grant et al., 2006; McCambridge et al., 2011; Weissman et al., 2015). Even just sipping alcohol by the 6th grade has been shown to be associated with increased odds of engaging in other risky behaviors upon entering high school (Jackson et al., 2015). Given the sharp uptick in substance use experimentation and initiation during the adolescent period, it is crucial to examine how risk perception and susceptibility to peer influence regarding risk evaluation are associated with early use patterns, as this may lend important insights for improving prevention strategies.

This investigation capitalized on data collected from the Social Influence Task (SIT), administered as part of the year 2 neurocognitive battery in the Adolescent Brain and Cognitive DevelopmentSM Study (ABCD Study®), to explore the relationships between risk perception, social influence, and substance use. In this paradigm, participants were asked to rate the riskiness of various hypothetical scenarios on a visual sliding scale. They were then shown how other youth their age had supposedly rated each of the same scenarios and were asked to rate each one a second time. Here, we examined the degree to which risk perception (average initial risk rating) and susceptibility to peer influence (average rating change) predicted future substance use. Based on prior literature, we hypothesized that higher initial risk perception would be associated with lower odds of substance use (Grevenstein et al., 2015; Lipari et al., 2017). We also hypothesized that greater susceptibility to negative peer influence (i.e., being swayed by peers to say that a scenario is less risky than they had originally decided) would be associated with greater odds of substance use, whereas greater susceptibility to positive peer influence would be associated with lower odds of substance use (Chung et al., 2020; Trucco et al., 2011).

Method

Participants and Procedures

The ABCD Study®

The ABCD Study® is a longitudinal, multi-site project that aims to characterize psychological and neurobiological development across adolescence (Feldstein Ewing et al., 2018; Garavan et al., 2018; Volkow et al., 2018). At baseline, the cohort included 11,876 youth aged 9-10 years, recruited from 21 research sites across the United States. These participants are followed prospectively for a decade with annual assessments of physical and mental health (Barch et al., 2018; Uban et al., 2018), neurocognitive functioning (Luciana et al., 2018), substance use (Lisdahl et al., 2018), and cultural and environmental factors (Zucker et al., 2018). Caregivers provided voluntary informed consent to participate and youth provided assent. Study procedures were approved by each data collection site’s respective Institutional Review Board. The analyses reported herein utilized data from ABCD Data Release 6.0 [NBDC DOI (10.82525/jy7n-g441)], which included data for the full ABCD Study® cohort from baseline through the year 5 follow-up visit, as well as data for approximately 75% of the cohort at year 6 due to staggered enrollment.

The Social Influence Task (SIT)

The SIT is a neurocognitive task that aims to assess risk perception and susceptibility to peer influence regarding risk-taking behavior. This task was adapted from the risk perception paradigm originally published by Knoll and colleagues (Knoll et al., 2015, 2017) and was programmed by Millisecond. The SIT was administered to the full cohort of the ABCD Study® at the year 2 follow-up visit (mean age=12.06 years), which served as the primary analytic time point for this report. The task was also administered to a small subset of participants at year 4 before being discontinued; however, these data were used only for supplemental test-retest reliability analyses.

Briefly, participants were instructed to consider a variety of hypothetical scenarios (e.g., “Driving very fast”, “Ice-skating on a half-frozen lake”) and rate the level of risk associated with that scenario on a visual slider bar (Figure 1). After making their initial risk rating, they were presented with an artificial risk rating of the same scenario that had ostensibly been provided by their peers. The peer ratings differed from the participant’s initial risk rating by either ±2 or ±4 points on the rating scale. Participants were then asked to rate the scenario a second time. Details regarding timing parameters of the SIT, trial scenarios, and the condition assignment algorithm are provided in Supplemental Methods.

Figure 1. Sample scenario presented in the Social Influence Task (SIT).

Figure 1.

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A) Participants were presented with the initial risk scenario. B) They were asked to rate how risky they perceived that scenario to be on the orange slider bar. C) Participants were then presented with a risk rating that had supposedly been provided by their peers (“People your age rated:”) that was either 4 points lower, 2 points lower, 2 points higher, or 4 points higher than their initial risk rating. D) They were then asked to rate the scenario a second time.

Risk perception was defined as each participant’s average initial risk rating. Susceptibility to peer influence was defined as the degree to which participants changed their risk rating in response to each of the four artificial peer rating conditions (‘−4’, ‘−2’, ‘+2’, and ‘+4’). Rating changes were averaged within each condition. Then, to mitigate issues with multicollinearity during modeling, weighted averages representing susceptibility to negative peer influence (i.e., average rating change in the ‘−4’ and ‘−2’ conditions, weighted by the number of trials of each condition) and positive peer influence (i.e., average rating change in the ‘+2’ and ‘+4’ conditions, weighted by the number of trials of each condition) were calculated. Greater susceptibility to peer influence was indexed by more positive rating change values in the positive conditions, and more negative rating change values in the negative conditions. Lower susceptibility to peer influence was indexed by rating change values that were closer to zero.

Substance Use

Details regarding the substance use interview for the ABCD Study® have been published previously (Lisdahl et al., 2018; Sullivan et al., 2022) and are summarized in Supplemental Methods. For the purposes of this analysis, substance use was broadly operationalized as any report of use of any substance, even low-level experimentation (e.g., sipping alcohol, puffing/trying nicotine or cannabis). Importantly, levels of regular substance use in early adolescence are low (Sullivan et al., 2022), and a continuous measure of use (i.e., total or maximum lifetime quantity) would be highly zero-inflated. Additionally, there is substantial evidence to suggest that even low-level substance use during early and mid-adolescence is associated with heightened risk for addictive use patterns in adulthood (Jackson et al., 2015; Piehler et al., 2012). To determine how risk perception and susceptibility to peer influence predict future substance use, we differentiated between substance use that was reported up through year 2 (contemporaneous with the administration of the SIT; included as a covariate) and any substance use reported thereafter (i.e., years 3-6; the outcome variable of interest).

Sociodemographic Characteristics & Additional Measures

Age at year 2 and sex assigned at birth were assessed via self-report and included as developmental covariates in this analysis. Other characteristics, including annual household income, highest level of parental education, race, and ethnicity are reported to assess generalizability.

Several additional measures were included in supplementary analyses to evaluate the robustness of the primary findings. Briefly, self-report on the Resistance to Peer Influence (RPI) scale was used to assess construct validity of the SIT (Steinberg & Monahan, 2007), and a measure of peer disapproval of alcohol use derived from the Peer Tolerance of Use measure was used to evaluate the predictive value of risk perception and susceptibility to peer influence above and beyond perceived peer norms (Johnston et al., 2015). Further details regarding both of these measures are provided in Supplemental Methods.

Statistical Analysis

Statistical analyses were conducted using R version 4.4.2 (R Core Team, 2024). Preliminary associations between task behavior and developmental covariates were examined using the native R stats package. Mixed effects logistic regression models, implemented in the R package glmmTMB (Brooks et al., 2017), were used to investigate the degree to which risk perception and susceptibility to peer influence related to future substance use. Nested random intercepts for participant, family, and data collection site were used to account for the hierarchical clustering of observations in the ABCD Study® (Barr et al., 2013; Heeringa & Berglund, 2020; Snijders & Bosker, 2012).

Model Building and Evaluation

The model building process followed a step-wise approach, starting from an intercept-only model that included the full random effects structure predicting future substance use. Successive models incorporated additional variables as fixed effects. All continuous variables were centered and scaled prior to analysis.

  • Model 1: Intercept-only model.

  • Model 2: Added risk perception (average initial risk rating).

  • Model 3: Added susceptibility to peer influence (average rating change in positive and negative conditions)

  • Model 4: Added developmental covariates (age, sex assigned at birth, and prior substance use through year 2)

Finally, interactions of covariates (age, sex, and prior substance use) with the primary predictors of interest (risk perception and susceptibility to peer influence) were evaluated one at a time. Interactions that resulted in significant model improvement were retained in the final model. Likelihood ratio tests (LRTs) were used to compare incrementally more complex models against simpler versions for statistically significant improvements.

Missing Data

A total of 10227 participants had SIT data at year 2. However, because the artificial peer rating condition for each trial of the SIT was constrained by the participant’s initial risk rating, such that certain conditions could not be administered if they would fall outside the 0-10 rating scale (e.g., a ‘+4’ rating could not be assigned if the participant’s initial risk rating was a 7 out of 10), some participants did not experience all four peer influence conditions. Although the task algorithm attempted to reassign conditions when possible (see Supplemental Methods), this design led to incomplete condition coverage for youth with consistently high or consistently high low initial risk perception. The analyses presented here used a complete-case approach and only participants with complete SIT summary data were included (N=9841).

Of the 9841 participants with complete SIT data at year 2, there was minimal missing data with regard to substance use through year 4 (baseline: 0%, year 1: 2.1%, year 2: 0.3%, year 3: 6.6%, year 4: 13.4%, year 5: 20.4%, year 6: 52.2%). For participants whose year 6 data was not included in ABCD Data Release 6.0, future substance use was based only on data through year 5, except for where indicated in sensitivity analyses.

Sensitivity Analyses

Several additional sensitivity analyses were performed to aid interpretation of the primary findings. First, to address potential bias due to missingness in certain conditions of the SIT, all models were re-estimated using multiple imputation (implemented via mice) rather than restricting to complete cases (van Buuren & Groothuis-Oudshoorn, 2011). Second, to account for the possibility that prior substance use could influence results beyond statistical adjustment, analyses were repeated restricting the sample to only participants without any reported use prior to year 2. Third, because not all participants had data for year 6 in ABCD Data Release 6.0, analyses were repeated using only data through year 5 for all participants. Fourth, to test whether the findings were specific to alcohol use or more generalizable across substances, analyses were repeated using alcohol use alone as the outcome of interest. Finally, to evaluate whether risk perception and susceptibility to peer influence had predictive value above and beyond perceived peer norms regarding substance use, analyses were repeated with an added measure of peer disapproval of alcohol use included in modeling.

Results

Sample Characteristics

Sociodemographic characteristics for participants included in this analysis are presented in Table 1. There were statistically significant differences in the prevalence of substance use prior to year 2 between male and female adolescents, with a greater proportion of male adolescents having already engaged in substance use by this time point (32%) compared to female adolescents (28%; χ2=27.53, p<0.001). Conversely, a significantly greater proportion of female adolescents reported substance use after year 2 (41%) compared to male adolescents (37%; χ2=18.71, p<0.001). Participants included in the analytic sample (N=9841) differed from those who were excluded (N=1132) with respect to several key characteristics, including data collection site, racial identity, parental education, household income, and substance use (Supplemental Table A).

Table 1.

Key sociodemographic characteristics for all participants included in the present analysis, stratified by sex assigned at birth.

Characteristic N Male
N = 5,167a 		Female
N = 4,674a 		p-valueb
Age (years) 9,841 12.07 (0.67) 12.05 (0.67) 0.051
Research site 9,841 0.085
 Children’s Hospital of Los Angeles 144 (2.8%) 148 (3.2%)
 Florida International University 266 (5.1%) 232 (5.0%)
 Laureate Institute for Brain Research 333 (6.4%) 314 (6.7%)
 Medical University of South Carolina 148 (2.9%) 157 (3.4%)
 Oregon Health & Science University 239 (4.6%) 249 (5.3%)
 SRI International 138 (2.7%) 122 (2.6%)
 University of California, Los Angeles 186 (3.6%) 171 (3.7%)
 University of California, San Diego 328 (6.3%) 310 (6.6%)
 University of Colorado Boulder 271 (5.2%) 231 (4.9%)
 University of Florida 180 (3.5%) 160 (3.4%)
 University of Maryland, Baltimore 249 (4.8%) 239 (5.1%)
 University of Michigan 301 (5.8%) 301 (6.4%)
 University of Minnesota 291 (5.6%) 235 (5.0%)
 University of Pittsburgh Medical Center 184 (3.6%) 147 (3.1%)
 University of Rochester 149 (2.9%) 114 (2.4%)
 University of Utah 493 (9.5%) 362 (7.7%)
 University of Vermont 258 (5.0%) 217 (4.6%)
 University of Wisconsin, Milwaukee 180 (3.5%) 154 (3.3%)
 Virginia Commonwealth University 218 (4.2%) 242 (5.2%)
 Washington University in St. Louis 334 (6.5%) 321 (6.9%)
 Yale University 277 (5.4%) 248 (5.3%)
Racial identity 9,841 0.045
 American Indian/Alaska Native 18 (0.3%) 27 (0.6%)
 Asian 108 (2.1%) 97 (2.1%)
 Black/African American 719 (14%) 717 (15%)
 More than one race 556 (11%) 527 (11%)
 Native Hawaiian or Other Pacific Islander 8 (0.2%) 6 (0.1%)
 Other (unknown or not reported) 180 (3.5%) 195 (4.2%)
 White 3,578 (69%) 3,105 (66%)
Parental education 9,788 0.306
 Up to high school (no diploma) 206 (4.0%) 212 (4.6%)
 High school diploma/GED 450 (8.8%) 401 (8.6%)
 Some college 1,625 (32%) 1,443 (31%)
 Bachelor’s degree 1,140 (22%) 975 (21%)
 Graduate school or professional degree 1,721 (33%) 1,615 (35%)
Household income 9,801 0.172
 Less than $50,000 1,126 (22%) 1,015 (22%)
 Between $50,000 and $100,000 1,264 (25%) 1,162 (25%)
 Greater than $100,000 2,377 (46%) 2,095 (45%)
 Donť know 209 (4.1%) 178 (3.8%)
 Decline to answer 175 (3.4%) 200 (4.3%)
Substance use before Year 2 9,841
 Any substance use 1,676 (32%) 1,288 (28%) <0.001
 Alcohol 1,623 (31%) 1,250 (27%) <0.001
 Marijuana 33 (0.6%) 10 (0.2%) 0.001
 Tobacco/nicotine 112 (2.2%) 68 (1.5%) 0.008
 Other substances 33 (0.6%) 27 (0.6%) 0.698
Substance use after Year 2 9,841
 Any substance use 1,920 (37%) 1,937 (41%) <0.001
 Alcohol 1,690 (33%) 1,716 (37%) <0.001
 Marijuana 573 (11%) 761 (16%) <0.001
 Tobacco/nicotine 654 (13%) 729 (16%) <0.001
 Other substances 414 (8.0%) 468 (10%) <0.001
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a

Mean(SD); n(%)

b

Wilcoxon rank sum test; Fisher’s exact test for count data simulated with p-value (based on 2000 replicates); Pearson’s χ2 test

Reliability and Validity of the Social Influence Task (SIT)

Test-retest reliability estimates among the subset of participants who completed the SIT at both year 2 and year 4 were modest, consistent with expected developmental changes across the two-year interval (Supplemental Table B). Supplemental analyses showed that task behavior at year 4 was also significantly associated with self-report on the RPI at year 4, such that higher self-reported resistance to peer influence was correlated with higher initial risk rating and lower susceptibility to both positive and negative peer influence (Supplemental Table C).

Associations Between SIT Behavior and Developmental Factors

Average initial risk rating was modestly positively correlated with average rating change in each of the task conditions (Pearson’s r’s: 0.101-0.177). In general, participants initially rated the task scenarios as moderately risky (across all conditions; M=6.76, SD=1.22), with initial ratings being marginally higher than what has been reported in prior studies (M=5.57, SD=0.99 for 12-14 year-olds) (Knoll et al., 2015).

In simple univariate models, older adolescents were found to have lower initial risk ratings (Figure 2B; r=−0.083, 95% CI: [−0.102, −0.063], p<0.001) and were less susceptible to both positive (Figure 2E; r=−0.13, 95% CI: [−0.15, −0.111], p<0.001) and negative (Figure 2E; r=0.093, 95% CI: [0.074, 0.113], p<0.001) peer influence relative to younger adolescents. Female adolescents had higher initial risk ratings compared to male adolescents (Figure 2C; b=0.095, SE=0.02, d=0.095, 95% CI: [0.056, 0.135], p<0.001) and were less susceptible to both positive (Figure 2F; b=−0.076, SE=0.02, d=−0.076, 95% CI: [−0.116, −0.037], p<0.001) and negative (Figure 2F; b=0.171, SE=0.02, d=0.171, 95% CI: [0.132, 0.211], p<0.001) peer influence.

Figure 2. Risk perception, susceptibility to peer influence, and relationships with demographic covariates.

Figure 2.

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A) Histogram of average initial risk rating for all participants included in the sample. B) Scatterplot displays the relationship between average initial risk rating and participant age. C) Boxplots compare average initial risk rating by sex assigned at birth. D) Histogram of average rating change in the negative (‘−4’ and ‘−2’) versus positive (‘+2’ and ‘+4’) peer rating conditions. Rating change values that are closer to zero represent lower susceptibility to peer influence. E) Scatterplots display the relationship between average rating change in negative vs. positive conditions and participant age. F) Boxplots compare average rating change in the negative vs. positive conditions by sex assigned at birth.

Prospective Associations With Substance Use

Model fit statistics and LRTs comparing each successively more complex model with simpler versions are presented in Table 2. The best fitting model was the final model, which included fixed effects for initial risk perception, susceptibility to peer influence, and developmental covariates (Table 3 & Figure 3). None of the interactions of the developmental covariates with the primary predictors of interest were statistically significant. Model diagnostics (residuals, dispersion, zero-inflation) indicated acceptable model fit (Supplemental Figure A).

Table 2.

Model fit statistics (AIC, BIC, and log-likelihood) and χ2 likelihood ratio tests comparing each successively more complex model against its nested/simpler version.

Model df AICa BICb Log-likelihood χ2 p-value
Intercept-only 3 12819.87 12841.45 −6406.94
Adding initial risk perception 4 12726.95 12755.73 −6359.48 94.92 <0.001
Adding susceptibility to peer influence 6 12635.46 12678.63 −6311.73 95.49 <0.001
Adding developmental covariates 9 11796.38 11861.13 −5889.19 845.08 <0.001
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a

Akaike Information Criterion

b

Bayesian Information Criterion

Table 3.

Results of multivariate logistic regression predicting future substance use (year 3 through year 6) from risk perception, susceptibility to peer influence, age, sex assigned at birth, and prior substance use (baseline through year 2).

Substance Usea
Variable No Yes b SE Wald χ2 p-value
Initial risk rating Mean (SD) 6.9 (1.2) 6.6 (1.3) −0.159 0.027 −5.96 <0.001
Negative peer influence Mean (SD) −1.0 (0.9) −0.9 (0.8) −0.050 0.039 −1.30 0.195
Positive peer influence Mean (SD) 1.3 (0.9) 1.1 (0.9) −0.206 0.038 −5.35 <0.001
Age (years) Mean (SD) 12.0 (0.7) 12.2 (0.7) 0.212 0.026 8.07 <0.001
Sex assigned at birth Male 3247 (54.3) 1920 (49.8)
Female 2737 (45.7) 1937 (50.2) 0.318 0.051 6.21 <0.001
Substance use before Year 2 No 4877 (81.5) 2000 (51.9)
Yes 1107 (18.5) 1857 (48.1) 1.466 0.062 23.70 <0.001
Family Variance = 0.593; SD = 0.77
Site Variance = 0.102; SD = 0.319
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a

Mean(SD); n(%)

Figure 3. Odds of future substance use as a function of risk perception, susceptibility to peer influence, and demographic covariates.

Figure 3.

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Forest plot displays addjusted odds ratios and 95% confidence intervals for future substance use, derived from the primary mixed effects logistic regresison model.

Higher risk perception was associated with lower odds of future substance use (adj. OR=0.85, 95% CI: [0.81, 0.90], p<0.001). Greater susceptibility to positive peer influence was also associated significantly lower odds of future substance use (adj. OR=0.81, 95% CI: [0.75, 0.88], p<0.001). However, susceptibility to negative peer influence was not associated with future substance use (p=0.195). Finally, female sex at birth (adj. OR=1.37, 95% CI: [1.24, 1.52], p<0.001), older age (adj. OR=1.24, 95% CI: [1.17, 1.30], p<0.001), and prior substance use (adj. OR=4.33, 95% CI: [3.84, 4.89], p<0.001) were all associated with higher odds of substance use in subsequent years.

Sensitivity Analyses

Findings were consistent across sensitivity analyses. Analyses based on multiple imputation produced estimates that closely mirrored complete-case models (Supplemental Tables D & E), despite missing SIT data being primarily driven by initial risk ratings near the extremes of the rating scale (Supplemental Figure B). Restricting the sample to participants without prior substance use also did not alter the pattern of results (Supplemental Tables F & G), nor did limiting the follow-up period to year 5 (Supplemental Tables H & I). Substituting alcohol use as the outcome of interest also produced similar results (Supplemental Tables J & K), which is consistent with alcohol use being the primary form of substance use reported in the sample (Table 1). Finally, similar effects were observed even when adding a measure of perceived peer disapproval of alcohol use to the model, suggesting that risk perception and susceptibility to peer influence impact risk for later substance use above and beyond perceived peer norms (Supplemental Tables L & M).

Discussion

This is the first publication, to our knowledge, to describe participant behavior on the Social Influence Task (SIT) from the ABCD Study® in relation to substance use. The results from this analysis demonstrated that adolescents do indeed adjust their perception of risk in response to an artificial peer rating, suggesting that they are influenced by perceived peer consensus (Knoll et al., 2015, 2017; Welborn et al., 2016). Importantly, perceived risk and the willingness to conform to peer norms have both been linked to substance use during adolescence (Grevenstein et al., 2015; Martz et al., 2022; Schuler et al., 2019). Consistent with our initial hypotheses, we found that higher risk perception was associated with lower odds of future substance use. Indeed, several large cross-sectional studies have highlighted the importance of this relationship (Grevenstein et al., 2015; Lipari et al., 2017; Nawi et al., 2021). Results from the National Survey on Drug Use and Health have shown that perceived harm from substance use coincides with less use among youth between the ages of 12 and 17 years (Lipari et al., 2017), and relative risk perception is one of the strongest predictors of adolescent substance use (Grevenstein et al., 2020). Recent findings from the ABCD Study® have also shown that youth who report higher levels of peer substance use express greater curiosity to try substances (Martz et al., 2022), underscoring the critical role of the peer group in risk perception and attitudes toward substance use. Notably, changes in risk perception have also been shown to predict changes in substance use behavior (Grevenstein et al., 2015), suggesting that interventions aimed at increasing risk perception may be particularly relevant for affecting youth substance use.

Contrary to our initial hypotheses, we did not find greater susceptibility to peer influence in the negative direction (i.e., being swayed by peers to say that a situation is less risky) to be associated with greater odds of substance use. We did find, however, that greater susceptibility to peer influence in the positive direction was associated with lower odds of substance use. This finding is important because peer influence has predominantly negative connotations when it comes to substance use and other risk-taking behavior during adolescence. While it is true that most youth who begin experimenting with alcohol, tobacco, and marijuana do so in a social context (Dishion & Owen, 2002), and there is a wealth of literature supporting the notion that deviant peer influence increases risk-taking behavior in this group (Albert et al., 2013; Chein et al., 2011; Dishion & Owen, 2002; Gardner & Steinberg, 2005; Grevenstein et al., 2015, 2020; Keyzers et al., 2020; Schuler et al., 2019; Smith et al., 2014; Urberg et al., 1997; Van Ryzin et al., 2012; Yurasek et al., 2019), our findings add to a growing body of evidence highlighting the protective effects of positive peer influence (Beard & Wolff, 2022; Chung et al., 2020; Kim-Spoon et al., 2019). For example, positive peer pressure has been associated with lower likelihood of substance use (Kim-Spoon et al., 2019), and some researchers have suggested that observing peers’ safe choices increases the subjective value of those safe choices (Chung et al., 2020). Indeed, the process of evaluating and adapting one’s behavior to match perceived group norms can be part of a healthy developmental process that facilitates prosocial behavior (Allen & Antonishak, 2008; Brechwald & Prinstein, 2011; Choukas-Bradley et al., 2015; Eisenberg & Morris, 2004). Therefore, positive peer influence may create a buffer against risky behaviors for adolescents who are susceptible to such influence (Carlo et al., 2011; Telzer et al., 2017, 2018), challenging the strong bias in the literature that treats susceptibility to peer influence as purely maladaptive (Telzer et al., 2022).

Our analyses also highlight key sex- and age-specific differences in adolescents’ risk perception and susceptibility to peer influence. Specifically, female adolescents exhibited higher initial risk perception and were less susceptible to both positive and negative peer influence compared to male adolescents. These findings complement other reports from the ABCD Study® demonstrating that male adolescents display greater curiosity around substances and report higher levels of peer substance use (Martz et al., 2022). These differences may reflect female adolescents’ heightened sensitivity to interpersonal consequences of risky behavior (Michael & Ben-Zur, 2007; Rose & Rudolph, 2006), whereas male adolescents tend to experience stronger pressure to conform to stereotypical gender norms that glorify risky, delinquent, or violent behavior (Kiesner et al., 2010; Kleiser & Mayeux, 2021; Widman et al., 2016). Still, we did not observe sex-specific associations between these constructs and later substance use. We also found that both initial risk perception and susceptibility to peer influence were lower among older adolescents, which parallels developmental trends in social conformity (Foulkes et al., 2018; Knoll et al., 2017; Steinberg & Monahan, 2007). Although there is an initial re-orientation of social behavior during adolescence, where youth become more attentive to the opinions of their peers, they are simultaneously beginning a process of individuation. With the self-assurance and confidence in their own identity that comes with age, adolescents’ need to change their behavior to fit in with others declines. Importantly, though, we also show that older age is associated with higher odds of substance use, so intervening on peer influence may be more effective during early adolescence for affecting substance use outcomes, before substance use initiation becomes more prevalent (Sullivan et al., 2022).

There are several limitations of the current study that warrant consideration. First, we cannot confirm whether participants fully believed the artificial peer ratings, as no exit questionnaire was administered, though similar paradigms have been used to probe risk perception and peer influence in prior research (Knoll et al., 2015, 2017; Welborn et al., 2016). A more important consideration might be related to the ecological validity of the task. In the SIT, there were no social consequences for failing to align oneself with the artificial peer rating, whereas in real-life situations involving risk assessment and peer influence, adolescents are acutely aware of how their choices can impact their interpersonal relationships and overall social standing. Additionally, the primary analysis in this report only included participants with complete summary data for the SIT, meaning that they received every trial condition type (‘−4’, ‘−2’, ‘+2’, and ‘+4’). Due to the design of the task, participants whose initial risk ratings consistently fell near the extremes of the visual rating scale are disproportionately likely to be missing certain conditions, and therefore be excluded from this sample. Although the sensitivity analysis performed with imputed data may help to mitigate this concern, it should still be noted that this data is not missing at random. Future work should explore adaptive task designs to more accurately capture patterns across the full distribution of baseline risk perception. Finally, the participants included in this analysis differed from the remainder of the ABCD Study® cohort with respect to several key sociodemographic variables, which limits the generalizability of the reported findings.

In sum, our work highlights the potential importance of leveraging positive peer influence as a means for affecting substance use outcomes. Our findings also complement a growing body of literature from the ABCD Study® demonstrating that adolescent substance use risk is multifactorial—spanning genetic liabilities, neurocognitive traits, and environmental factors—by illustrating a potentially protective role for peer influence (Choi et al., 2025). Importantly, the ABCD Study® is poised to clarify whether these associations between risk perception, susceptibility to social influence, and substance use persist into adulthood, and to trace individual pathways of risk versus resilience for related outcomes.

Supplementary Material

1

Public Health Significance Statement.

Adolescents’ perceptions of risk and sensitivity to peer influence play an important role in the early stages of substance use. This study found that youth who viewed risky behaviors as more dangerous, and those more strongly influenced by peers promoting safe choices, were less likely to engage in substance use in the future. These findings suggest that prevention programs leveraging positive peer influence may help reduce early substance use.

Funding Statement

Data used in the preparation of this article were obtained from the Adolescent Brain and Cognitive Development™ (ABCD) Study, held in the NIH Brain Development Cohorts Data Sharing Platform. This is a multi-site, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under the following award numbers: U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at Federal Partners - ABCD Study. ABCD Consortium investigators designed and implemented the ABCD Study® and/or provided data, but did not necessarily participate in the analysis or writing of this report. The content reflects the views of the authors and does not necessarily represent the official views of the National Institutes of Health or ABCD Consortium investigators. Research reported in this publication was also supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR002371, and used computational infrastructure supported by the Office of Research Infrastructure Programs, Office of the Director, of the National Institutes of Health under Award Number S10OD034224.

Data & Code Availability Statement

The ABCD data repository grows and changes over time. The data used in this report came from the ABCD Data Release 6.0 and can be found at NBDC DOI (10.82525/jy7n-g441). Qualified researchers can request access to ABCD Study® data from the NBDC Data Access Committee. Data analysis code is available via Open Science Framework [link to be added upon publication] or upon request from the primary author, Dakota Kliamovich (kliamovi@ohsu.edu).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2140733-supplement-1.pdf (675.2KB, pdf)

Data Availability Statement

The ABCD data repository grows and changes over time. The data used in this report came from the ABCD Data Release 6.0 and can be found at NBDC DOI (10.82525/jy7n-g441). Qualified researchers can request access to ABCD Study® data from the NBDC Data Access Committee. Data analysis code is available via Open Science Framework [link to be added upon publication] or upon request from the primary author, Dakota Kliamovich (kliamovi@ohsu.edu).

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