Peer passenger norms and pressure: Experimental effects on simulated driving among teenage males

Abstract

Objective Serious crashes are more likely when teenage drivers have teenage passengers. One likely source of this increased risk is social influences on driving performance. This driving simulator study experimentally tested the effects of peer influence (i.e., risk-accepting compared to risk-averse peer norms reinforced by pressure) on the driving risk behavior (i.e., risky driving behavior and inattention to hazards) of male teenagers. It was hypothesized that peer presence would result in greater driving risk behavior (i.e., increased driving risk and reduced latent hazard anticipation), and that the effect would be greater when the peer was risk-accepting. Methods Fifty-three 16- and 17-year-old male participants holding a provisional U.S., State of Michigan driver license were randomized to either a risk-accepting or risk-averse condition. Each participant operated a driving simulator while alone and separately with a confederate peer passenger. The simulator world included scenarios designed to elicit variation in driving risk behavior with a teen passenger present in the vehicle. Results Significant interactions of passenger presence (passenger present vs. alone) by risk condition (risk-accepting vs. risk-averse) were observed for variables measuring: failure to stop at yellow light intersections (Incident Rate Ratio (IRR)=2.16; 95% Confidence Interval [95CI]=1.06, 4.43); higher probability of overtaking (IRR=10.17; 95CI=1.43, 73.35); shorter left turn latency (IRR=0.43; 95CI=0.31,0.60); and, failure to stop at an intersection with an occluded stop sign (IRR=7.90; 95CI=2.06,30.35). In all cases, greater risky driving by participants was more likely with a risk-accepting passenger versus a risk-averse passenger present and a risk-accepting passenger present versus driving alone. Conclusions Exposure of male teenagers to a risk-accepting confederate peer passenger who applied peer influence increased simulated risky driving behavior compared with exposure to a risk-averse confederate peer passenger or driving alone. These results are consistent with the contention that variability in teenage risky driving is in part explained by social influences.

Global brain dynamics during social exclusion predict subsequent behavioral conformity

“…The combined sample had a mean of 1.91% change toward the confederate’s attitude and a standard deviation of 24.65%. Consistent with behavioral reports using these data ( Bingham et al , 2016 ), these behavioral results indicate a bias toward conformity to the confederate’s attitude, but the large amount of variance confirms its value for understanding individual differences in susceptibility to social influence for risky behaviors.…”

“…This research was part of a five-study investigation of the effects of peer influence on teen driving ( Falk et al , 2014 ; Simons-Morton et al , 2014 ; Cascio et al , 2015b ; Bingham et al , 2016 ; Schmälzle et al , 2017 ). A previous study in this research program found an association between neural activation during exclusion and risk taking ( Falk et al , 2014 ), and prior reports on the driving simulator data reported here noted substantial individual variability in susceptibility to influence ( Bingham et al , 2016 ). In this analysis, we extend these results by examining a measure of global functional connectivity during exclusion vs inclusion and investigate its predictive relationship with individual differences in conformity ( Figure 1 ).…”

Beyond stereotypes of adolescent risk taking: Placing the adolescent brain in developmental context

“…In particular, greater risk taking in the presence of peers is consistent with a group polarization effect of peer influence, such that when drivers think peer passengers expect them to drive aggressively, they are more likely to do so. However, when peers are not expected to hold these preferences, adolescent drivers are no more likely to drive in a risky manner ( Bingham et al, 2016 , Simons-Morton et al, 2014 ).…”

Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

“…In the first area, researchers in the past have investigated various factors that are contributing to crashes involved with teen drivers, such as speeding [4,5], distracted driving [6,7], driver age and gender [8,9], risky behavior [10], impaired driving [11], night-time driving [12], and peer passengers [13,14]. In the second field, many researchers have developed prediction models on crash severity, which can be generally divided into two groups: parametric models and nonparametric (or machine learning) methods.…”