Participants

Fifty-two healthy participants between 18 and 25 years (33 female; M = 20.33, SD = 2.05) were included in the study. The study was approved by the institutional review board of the University of Maryland – College Park, and performed in agreement with the latest revision of the Declaration of Helsinki (2013) regarding the treatment of human research participants. Written informed consent was obtained, and all participants received $10 plus their winnings from the gambling task for participation.

Procedure

All experimental sessions were performed in groups of four participants. Participants first met and read the instructions for the paradigm together in a group room. Subsequently, participants were accompanied to individual testing rooms and the “social BART” was implemented. Then, participants were given a questionnaire investigating risk-taking in real life43. At the end of the experiment, participants were debriefed and received their payment of $10 plus winnings on one randomly chosen trial.

Social Balloon Analogue Risk Task

We adapted the automatic version45 of the BART38. All stimuli were presented using Psychtoolbox implemented in Matlab 9.2 (MathWorks) running on a Windows 7 PC. As in the original task, participants were required to pump up a simulated balloon (0–128) pumps. While every pump increased potential earnings of participants, the likelihood of the balloon exploding also increased with each pump. Thus, on every trial, participants’ decisions on how much they pumped the balloon to potentially increase winnings, at the risk of losing everything if they pumped up the balloon too much, provided a measure of risky decision-making. Each balloon exploded at a number of pumps unknown to the participants (explosion points were taken from previous work)45. The sequence of explosion points was based on previous work38,45, and for each balloon the explosion was equally likely to occur on any given pump, apart from the constraint that within each sequence of ten trials the average explosion point was 64. All participants were presented with the same distribution of explosion points to limit unnecessary variability. Following procedures used previously45, and in order to minimize learning across the session, participants were told that choosing 64 pumps represented a typical choice in the task.

We implemented the task in a way that participants believed to be playing with three peers. Thus, the paradigm was implemented as a group experiment and four people were tested simultaneously while believing they were actually seeing the choices of the other participants (which, in reality were predefined choices). Groups of participants were randomly created from participants who signed up for the respective time slots; participants were unknown to one another and included both genders. The interaction with other group members was limited to reading the instructions for the experiment together in a group room. After reading the instructions, all participants were led to individual testing rooms. While they believed they were playing the task together with the rest of the group, participants were presented with computer generated information throughout the experiment. Thus, because participants were not able to influence each other during the experiment, we treated each participant as providing independent data in our statistical analyses. The experiment had two decision-making rounds, an individual and a team round. The team round provided a cover explanation to participants as to why they would see the choices of others so as to detract from the actual goal of studying peer susceptibility. Thus, participants were told that they would play a gambling task as a team. Furthermore, participants were told that an individual round preceded the team round so that “individual measures” on decision making could be collected. Critically, regarding the individual round, participants were explicitly told that they would see others’ choices because the two types of rounds were designed to match each other in terms of visual input and setup; additionally, they were told that they did not have to pay attention to others’ choices during the individual round, as they would not affect their own earnings in this round.

For the team round, participants were explicitly told that they should try to find a way to earn as much money as possible for the whole team. A fixed order of experimental conditions was implemented: the individual round always preceded the team round. This was done to avoid potential priming effects or other carry-over effects between rounds. For instance, participants might develop a strategy playing as a team that could influence their choices in the individual round. Because we were interested in quantifying peer susceptibility, only the choices in the individual round were analyzed. In other words, strategic decision-making was not the object of study. We confirmed the success of our instructions in a debriefing session at the end of the experiment, as all participants reported believing that choices of others were task irrelevant during the individual round.

On each round, participants performed a series of trials, each of which involved making two decisions. On each trial, they first indicated how much they wanted to pump up the balloon (solo decision). After they disclosed their decision, they were presented with the decisions of the other team members (in reality, they were predefined decisions). They were informed that their own decision would be shown to the rest of the team. After seeing the decisions of the team members, participants were asked again how much they want to pump up the balloon (informed decision). Participants were told that they could change their initial response or that they could stick with their first response. Following the informed decision, participants viewed an animation of the balloon being pumped up and either exploding or not. In case the balloon did not explode, participants heard a cash register sound, and were presented with visual feedback indicating how many points were won (each point was converted to 32 cents). If the balloon exploded, participants heard a deflating balloon sound together with visual feedback that they did not gain any points. Figure 1 depicts the different stages of each trial.

The experiment included three conditions. In the high-risk condition, participants viewed decisions by other players that were high risk (a relatively high number of pumps was chosen). In the low-risk condition, participants viewed decisions by other players that were low risk (a relatively low number of pumps was chosen). In the neutral condition, participants viewed decisions by other players that were close to the “typical” number of pumps (which they were previously informed to be 64). For each participant, each round included 20 trials per condition (high-risk, low-risk, neutral). Riskiness of decision was categorized as follows: high-risk choices ranged between 75–128 pumps, low-risk choices ranged between 1–53 pumps, and neutral choices ranged between 54–74 pumps. Neutral trials were added to increase variance in the purported choices of others to improve the face validity of our task (i.e., create the appearance that others’ choices came from real participants). Our analyses focused on the difference between high-risk and low-risk trials which were our two key experimental conditions. However, for the sake of completeness, we report results of neutral trials as well. In addition, to increase face validity of our task, on each condition and on each trial, two out of the three choices seen by a participant were high risk, low risk, or neutral; the remaining choice seen was always neutral.

Questionnaire assessing real-life risk taking

To asses real-life risk taking, we used the revised version of the Cognitive Appraisal of Risky Events questionnaire CARE-R43 which consists of three different scales: 1) Past Frequency Scale, investigating the frequency of certain risky behaviors over the course of the previous six months; 2) Expected Benefits Scale, investigating how participants rate the likelihood of positive consequences associated with certain risky behaviors; and 3) Expected Risks Scale, investigating how participants rate the likelihood of negative consequences associated with certain risky behaviors. For those three scales, the questionnaire investigates risk taking in the domain of risky sexual behaviors, drug use, and heavy drinking. Participants rated on a scale of 1 (lowest frequency of 0 times over last 6 months/not at all likely to result in positive consequences/not at all likely to result in negative consequences) to 7 (highest frequency of 31+ times in the last 6 months/extremely likely to result in positive consequences/extremely likely to result in negative consequences). Example questions from the CARE-R are: Please circle the number of times that you engaged in each behavior over the past 6 months: (1) Had sex without protection against sexually transmitted diseases with someone I just met or do not know well; (2) Drank alcohol too quickly; (3) Tried/used drugs other than alcohol.

Data processing

For each participant, we determined the mean score (i.e., number of pumps) for the solo decisions, which served as a measure of individuals’ risky decision making before learning about others’ choices. Likewise, for each participants and condition (high-risk, low-risk, and neutral), we determined the mean score for the informed decision (i.e., after learning about choices of others). To quantify potential change in choice after being informed about the decisions of others, we calculated the difference between solo decision and informed decision (per condition). The mean value provided an indicator of the influence of others on a participant’s own decision, which we call the influence index. Data were preprocessed using Matlab 9.2 (MathWorks).

Statistical analyses

The effect of experimental condition (high-risk, low-risk, neutral) was assessed via repeated-measures ANOVAs. Two ANOVAs were performed, one using the informed decisions as the dependent variable and one using the influence index as the dependent variable. We included gender as a between-subjects factor in the two ANOVAs to test for possible interactions. Greenhouse-Geisser corrections were used when the homogeneity of covariances assumption was violated (as determined by Mauchly tests of sphericity). Data from the risk-taking questionnaire were processed by computing average scores for each scale (Frequency, Expected Benefits, and Expected Risks) for each risk-taking domain: risky sexual behavior (regular partner, risky sexual behavior; new partner, risky sexual behavior), drug use, and heavy drinking, resulting in 12 scores per participant. Correlations between behavioral data and questionnaire data were calculated using Spearman correlations. To control for multiple comparisons, we report correlations for p < 0.004 (0.05/12). Data were analyzed using SPSS v. 20 (IBM) and Matlab 9.2 (MathWorks). The threshold for claiming that an effect was detected was 0.05. Stepwise regression was calculated using the stepwiselm function implemented in Matlab. The linear regression model was developed via stepwise forward linear regression. The process started with a constant (the intercept) and at each subsequent step, a predictor was added (from the predefined eight predictors described in the results section) based on the change in the value of the Akaike information criterion (AIC)44, while also considering removal of previously selected predictors based on the same criterion. After the intercept, the following steps were produced (adjusted R2 takes into account the number of predictors in the model):

Inclusion of predictor C t−1 (participants’ own choice on the previous trial; adjusted R2 = 0.051; Eq. 3):

$$choic{e}_{t} \sim 1+{C}_{t-1}$$ (3)

Inclusion of predictor O R , t (others’ choice was risky on that trial; adjusted R2 = 0.083; Eq. 4):

$$choic{e}_{t} \sim 1+{C}_{t-1}+{O}_{R,t}$$ (4)

Inclusion of predictor O S , t (others’ choice was safe, or non-risky, on that trial; adjusted R2 = 0.084; Eq. 5):

$$choic{e}_{t} \sim 1+{C}_{t-1}+{O}_{R,t}+{O}_{S,t}$$ (5)

Inclusion of the interaction between O R,t and C t−1 and exclusion of these two predictors as independent variables (adjusted R2 = 0.085; Eq. 6):

$$choic{e}_{t} \sim 1+{O}_{S,t}+{O}_{R,t}\times {C}_{t-1}$$ (6)

Inclusion of predictor W t−1 (outcome on previous trial, that is, win or loss; adjusted R2 = 0.086; Eq. 7):

$$choic{e}_{t} \sim 1+{O}_{S,t}+{O}_{R,t}\times {C}_{t-{1}}+{W}_{t-{1}}$$ (7)

Finally, the inclusion of the interaction between W t−1 and C t−1 and exclusion of these two predictors as independent variables (adjusted R2 = 0.094; Eq. 8 (same as Eq. 1)):

$$choic{e}_{t} \sim 1+{O}_{S,t}+{O}_{R,t}\times {C}_{t-{1}}+{W}_{t-{1}}\times {C}_{t-{1}}$$ (8)

The optimal model chosen had the smallest AIC which is essentially a trade-off between model size and model fit44. Effect sizes are reported as ƞ p 2 for the ANOVA and adjusted R2 for the regression analysis.

The analysis based on a continuous predictor of others’ choices employed 6 predictors. After the intercept, the following steps were produced:

Inclusion of predictor C t−1 (participants’ own choice on the previous trial; adjusted R2 = 0.051; Eq. 9):

$$choic{e}_{t} \sim {1}+{C}_{t-{1}}$$ (9)

Inclusion of predictor W t−1 (outcome on previous trial, that is, win or loss; adjusted R2 = 0.052; Eq. 10)

$$choic{e}_{t} \sim {1}+{C}_{t-{1}}+{W}_{t-{1}}$$ (10)

Inclusion of interaction W t−1 and C t−1 and exclusion of these two predictors as independent variables (adjusted R2 = 0.062; Eq. 11 (same as Eq. 2)):

$$choic{e}_{t} \sim {1}+{C}_{t-{1}}\times {W}_{t-{1}}$$ (11)

For completeness, we ran a separate model with just others’ choices. The results were as follows. For a model with only O t R2 = 0.026; for a model with only O t−1 the best fitting regression model was a constant (thus, O t−1 did not result in changes in model fit); for a model with the interaction of Ot and O t−1 the adjusted R2 = 0.031.

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon request.

Code availability

The code used for running the social BART and analyzing the data is available from the corresponding author upon request.