1. Background

Adolescents in the US have higher crash-involvement rates than adult drivers (Bingham & Shope, 2005; National Highway Traffic Safety Administration, 2012), and die more often from injuries received in motor vehicle crashes than from any other cause (Centers for Disease Control and Prevention, 2014). Adolescents’ greater risk is attributable to multiple factors, including their status as novice drivers and their stage of development. Their inexperience as novice drivers results in risk taking due to ignorance of common driving hazards and/or lack of essential driving skills (Williams, 2006). As a result of their developmental stage, teens relative to adults are less mature emotionally, psychologically, and neurologically (Bingham, Shope, Zakrajsek, & Raghunathan, 2008; Keating, 2007), more prone to inattention (Lee et al., 2009), and have a heightened propensity for risk taking (Dunlop & Romer, 2010; Romer, 2010; Williams, 2003). Their crashes may be a product of lacking an appreciation of potential consequences of risk taking or purposefully taking risks (Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011a; Williams, 2003). Factors associated with increased fatal crash risk for adolescents include being male (Williams, 2003) and the presence of young passengers (Curry, Mirman, Kallan, Winston, & Durbin, 2012; Ouimet, Simons-Morton, & Zador, 2010). Fatal crash data indicate that adolescent drivers are more likely to crash when similar-aged passengers are present (in the absence of an adult passenger), an association that is especially true for male adolescent drivers (L.-H. Chen, Baker, Braver, & Li, 2000; Doherty, Andrey, & MacGregor, 1998; Ouimet et al., 2010). Passenger sex has also been found to moderate adolescent driving risk, with male passenger presence associated with higher-risk driving for both male and female adolescents compared to a female passenger or driving alone (Ouimet et al., 2010; Shepherd, Lane, Tapscott, & Gentile, 2011; Simons-Morton, 2005). These associations suggest that the social influences of peers might affect adolescent driving (Shope & Bingham, 2008).

Peers can have powerful influences on adolescent health risk behavior. While there are various forms of social influence, social norms and peer pressure are two types that have been found to motivate adolescent involvement in health risk behaviors (Simons-Morton & Chen, 2006), and may also influence risky driving (Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011b). Having friends who are risky drivers has been associated with adolescent risky driving behavior, suggesting that norms and/or pressure may influence risky teen driving (Simons-Morton, Ouimet, & Chen, 2012; Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011b). Recent experimental research supports the contention that the mere presence of a peer as a passenger or simply as an observer can increase risky driving (Chein, Albert, O'Brien, Uckert, & Steinberg, 2010; Shepherd et al., 2011; Simons-Morton et al., 2014). These data are consistent with the idea that reward salience is greater in the presence of adolescent peers, increasing the propensity to engage or accept higher than usual levels of risk (Steinberg, 2008).

Although the results of these studies are persuasive, evidence is lacking that performance on computerized driving games (Shepherd et al., 2011) or while in an functional magnetic resonance imaging (fMRI) scanner (Chein et al., 2010) is a valid predictor of everyday driving. In contrast, driving simulation has been shown to be an externally valid predictor of real-world driving (Fisher, Pradhan, Pollatsek, & Knodler, 2008), and provides a safe method for investigating peer influence on adolescent risky driving. Recent research using full-cab high-fidelity visually-immersive simulators provides evidence that social norms influence adolescents’ driving behavior. One such experiment demonstrated poorer hazard detection by male adolescents in the presence of a peer passenger (Ouimet et al., 2013). In another experiment, novice male adolescent drivers exhibited more high-risk driving behavior following a social norms manipulation leading them to believe that their same-sex passenger was risk-accepting compared to those perceiving their passenger as risk-averse (Simons-Morton et al., 2014). Although these studies are informative, the roles of social norms and peer pressure remain incompletely understood and neither previous simulated driving study directly manipulated peer pressure. Given the significance of these social influences in shaping adolescent behavior, and the need to reduce injury from crashes involving adolescent drivers, this area remains important for further study.

The Theory of Normative Social Behavior (TNSB) (Rimal, 2005) is a conceptual model positing that injunctive norms, defined as the perceptions of the behavioral expectations of salient others (e.g., peers, parents, teachers), influence behavior. Based on TNSB, it is logical to posit that in the presence of a salient peer who is perceived as preferring higher risk driving (e.g., norms favoring risky driving), an adolescent would be more likely to drive in a riskier manner than he would if driving alone or with a peer who prefers low-risk driving. In this way, injunctive norms influence volitional behavioral choices. However, evidence from available research is less clear regarding the influence of injunctive norms on unintentional behaviors, such as failure to attend to roadway hazards due to lack of skill or ability, or on learned risky behavior patterns that have achieved automaticity.

The study of social influences on risky driving is complicated by the susceptibility of novice teenagers to a variety of driving errors, which can be categorized as lapses, mistakes, and violations (A. J. McKnight & McKnight, 2000; Parker, Reason, Manstead, & Stradling, 1995; see Reason, 1990). Lapses are minor deviations from intentions, and typically contribute little to driver risk. Mistakes are the unintended failure to adequately perform an essential skill, and can arise from improper skill execution (e.g., due to distraction) or from the absence of essential skills. Violations result from a choice to disregard rules and regulations related to safety (Parker et al., 1995) and could be influenced by social influences to accept greater risk. Mistakes and violations are the most common contributors to crash risk. Violations may occur less frequently, but are most likely in response to an external factor, such as peer influence. Therefore, one would anticipate that peer influence in the form of risky driving norms and pressure would primarily influence violations, whereas mistakes would be less susceptible to peer influence, instead resulting in novice teen drivers due to their inexperience resulting in underdeveloped skills, and greater susceptibility to distraction, or inattention (Klauer et al., 2014).

The purpose of this research was to experimentally test the effects of passenger presence and social influence, in the form of injunctive norms and pressure, on the driving risk behavior (i.e., increased driving risk and reduced latent hazard anticipation) of male adolescent novices in a simulated driving task. It was hypothesized that: 1) peer passenger presence would result in greater driving risk behavior compared to driving alone; 2) social influences would moderate the association between passenger presence and driving risk behavior such that the effect of passenger presence would be even greater when accompanied by social influences that were risk-accepting compared to social influences that were risk averse; and 3) social influences would be greater in risk conditions consistent with intentional violation of safety-related roadway rules than in conditions consistent with mistakes or lapses due to the lack of skill or it's application.

2. Methods

2.1. Participants

Eligible participants included 16- and 17-year-old male drivers from Southeast Michigan, US who met the following inclusion criteria: had held a Level 2 Michigan driver license (i.e., independent driving with restrictions) for at least 4-8 months; drove twice or more weekly; had normal or corrected-to-normal vision using contact lenses; and passed a sensitivity to simulator sickness survey. These criteria were measured during recruitment as part of a telephone-based screening. Eyeglasses were disallowed due to interference with the eye tracking system. Of the original 75 eligible adolescents, 17 participated in pilot testing. Of the 58 remaining, five were unable to complete the experimental protocol, four due to simulator sickness, and one due to a simulator malfunction, resulting in a total sample of 53 (M age=16.9; SD=0.34). The majority of participants were in 11^th grade (77%), and white (77%) (see Table 1). Each participant was compensated $50. The University of Michigan Institutional Review Board approved the study procedures. Participants were told that the study purpose was to examine the physiology of driving.

Table 1

Sample demographic characteristics (N = 53)

Note. The numbers may not add to the total due to missing values. Chi square tests revealed no significant differences for all variables except grade (Χ² = 16.4, p < .001) but the results should be used with caution because many cell sizes are less than 5. Of 75 recruited, 4 were lost due to simulation sickness and 1 due to technical problems during the drives, and 17 participated in pilot testing.

2.2. Procedures

A crossover mixed randomized experimental design was used. The two counterbalanced within-subjects driving trials were passenger presence (solo drive and passenger drive), and a between-subjects driving risk behavior condition represented passenger type (risk-accepting or risk-averse) (see Figure 1). At the experimental session, participants completed a pre-drive survey, and then joined a confederate posing as a second, similar-aged participant in the driving simulator lab. The experimental manipulation included specific injunctive norms and peer pressure to modify driving behavior. Injunctive norms were manipulated by the confederate excusing his late arrival to the appointment followed by a video rating task. In the risk-averse condition the late-arriving confederate said, “Sorry I was a little late getting here. I tend to drive slower, plus I hit every yellow light.” In the risk-accepting condition the confederate said, “Sorry I was a little late getting here. Normally I drive way faster, but I hit like every red light.”

Figure 1 Study design and flowchart

Two drives followed a practice drive, (passenger present first, or solo first, counterbalanced at random, each lasting 10-15 minutes). Before the passenger drive, the confederate and participant watched two short videos together of driving on a freeway showing low-risk and high-risk driving. Video views were from the front right-side passenger seat and the order was counterbalanced. The low-risk video showed a vehicle being driven carefully. The high-risk video displayed a vehicle being raced at high speed through traffic. After watching each video, the participant and then the confederate were asked two questions on a 10-point scale: 1) “How similar is your driving to the driver in the video?” (1=not at all similar to 10=highly similar); and 2) “How likely would you be to ride with the person in this video?” (1=not at all likely to 10=highly likely). Relative to the participant, the confederate's response was less risky in the risk-averse risk condition and more risky in the risk-accepting condition.

Before the solo drive, participants were asked to do a word puzzle for five minutes where they identified as many words as possible in a square field of letters in rows and columns. This task provided separation similar in length to that between the introduction to the confederate and the solo drive so that any lingering effects of the coaching/practice drive and the passenger drive could dissipate. Both drives represented realistic, ecologically valid drives containing prescripted scenarios eliciting driver responses. These responses were measured so that differences in driving risk behavior could be compared between the experimental conditions. The scenarios minimized the chance of crashes, loss of control, or events that might induce hyper-alertness in order to avoid reduced internal validity.

In both risk conditions, prior to the passenger drive the participant and confederate were told to imagine that they were going to a concert by their favorite artist, and would start and the doors would close in 20 minutes. They were also told that driving to the concert venue took 16 minutes following the correct route, and that the passenger would navigate using a map. The confederate and participant together were told that they had been assigned to different groups; the participant to the driver group, and the confederate to the passenger group. The experimental drives were then completed.

In the passenger drive, the confederate gave directions (e.g., turn left at the stop sign, continue straight at the light) while seated in the front right-side passenger seat. While giving directions, the confederate delivered the second experimental manipulation. Without prescribing behaviors (e.g., Drive faster! Hurry! Slow down!) peer pressure was imposed by statements reflecting a modest desire to go faster (risk-accepting condition) or proceed slower (risk-averse condition) (e.g., noting that the speed limit is 70mph [risk accepting]; pointing out a reduced speed zone [risk averse]). The participant and confederate were told to focus on driving and giving directions, respectively. Verbal comments and directions were given at the same points in every passenger drive. The participant drove alone in the solo drive and was guided by construction barrels (not present in the passenger drive) to guide navigation by blocking exits from the intended course. To minimize bias, the confederate remained blind to the study condition until immediately prior to entering the simulator lab. The experimenter was blind until hearing the confederate's script. A post-drive survey and debriefing tested the deception and experimental manipulation.

There were two versions of the simulated world. Both versions had four segments (i.e., residential, rural, urban, and freeway) ordered differently and populated with their own details (e.g., buildings and landscape) to alter appearance; however, both versions had identical roadway types, driving scenarios, and driving risk behavior measures. The versions were counterbalanced to reduce learning effects. Teen assent and parental consent were obtained for each participant.

2.3. Driving Simulator

A fixed-base high-fidelity simulator located in a dedicated lab space was used for this study. The simulator comprised a full vehicle cab (Nissan Versa) surrounded by three forward screens and one rear screen. The forward screens were projected at a resolution of 1400 X 1050 pixels each and the rear screen at 1024 X 768 pixels each, providing a 120-degree forward field of view and a 40-degree rear field of view. The simulator ran RTI's (Realtime Technologies, Inc., Royal Oak, MI, US) SimCreator software. The simulator system included steering feedback, road vibration, a virtual LCD instrument cluster and side-view mirrors, and simulated audio. A four-camera remote-mounted eye-tracking system manufactured by Smart Eye AB, Sweden was integrated into the driving simulator system and used to measure participants’ eye movements and gaze location. The driving simulator recorded vehicle and driving performance data, up to six synchronized channels of video and two channels of audio at 30 Hz. Eye tracker data were recorded at 60 Hz.

2.4. Measures

2.4.1. Dependent Measures

The measures for this study are detailed in Table 2. The dependent measures of driving risk behavior were classified either as risky driving behaviors or hazard anticipation. These two groups of variables were measured using data from the driving simulator and/or the eye-tracking system. Risky driving behavior represented risks that resulted from violations due to the participant's driving decisions (e.g., continue through an intersection on a yellow light), and included time in red, failed to stop, left turn latency, and overtaking. Of these measures, two related to decision making involving stop signal dilemmas at four-way intersections (i.e., time in red and failed to stop [see below]); one evaluated left turn decisions (i.e., left turn latency); and one related to passing a lead vehicle (i.e., overtaking). These four risky driving scenarios were selected because they are common driver behaviors and relevant to real-world crash risk at red light intersections (Retting, Ulmer, & Williams, 1999) left-hand turn intersections (Bingham & Ehsani, 2012; X. Wang & Abdel-Aty, 2008) and while overtaking on two-way rural roads (Khorashadi, Niemeier, Shankar, & Mannering, 2005; Nordfjærn, Jørgensen, & Rundmo, 2010) (see Table 2).

Table 2

Simulator and survey measures

Hazard anticipation measured errors (mistakes or lapses) that related to the omission of safe driving behaviors involving the anticipation of latent hazards (e.g., hazards that are possible given the circumstances, such as the view of a pedestrian crossing being blocked by a parked delivery van). These included intersection scanning, latent hazard scanning, and occluded stop sign scenarios. In all, there were scenarios at 8 intersections with Traversing, 10 with Turning, and 6 with Latent Hazard Anticipation scenarios. These were based on scenarios that were previously successfully used in driving simulator studies to measure hazard anticipation skills in drivers (Pradhan et al., 2005; Pradhan, Pollatsek, Knodler, & Fisher, 2009; Pradhan, Fisher, & Pollatsek, 2006). Failure to look, see, and react to potential hazards may be considered a driving mistake or lapse in skill, attention, or judgment (see Table 2).

2.4.2. Independent Measures

Independent measures for this study were passenger presence (solo drive or passenger drive) and risk condition (risk-accepting or risk-averse passenger) (see Table 2).

2.4.3. Randomization and Manipulation Checks

The measures used in this study to conduct checks of randomization and effectiveness of the experimental manipulation were assessed by two on-line surveys: the first was administered 7-10 days in advance of the simulator experiment (advance survey) as part of an fMRI session (results of the fMRI research are not included in this paper); the second was completed immediately following the simulator experiment (post-drive survey). The constructs measured to check randomization were selected because prior research indicated they are associated with risky driving (Arnett, 1992; Chein et al., 2010; Dunlop & Romer, 2010; Keating & Halpern-Felscher, 2008; Steinberg, 2011), and because differences in these measures remaining after randomization could bias the results. The post-drive survey was developed to test the success of the experimental manipulation of social norms. See Table 2 for measures descriptions and Table 3 for descriptive statistics.

Table 3

Survey measures by study condition (n = 53)

Differences are statistically significant.^aHigher scores represent greater resistance, approval, and driving risk perceptions.^bStandardized Cronbach's alpha coefficients are for the current study.^cIndependent t-tests were used to conduct the mean comparisons.

Measures administered in the advance survey assessed Resistance to Peer Pressure (Steinberg & Monahan, 2007), Friends’ Approval of Risky Driving (Chawla, Neighbors, Logan, Lewis, & Fossos, 2009), Friends’ Risk Behavior (Simons-Morton et al., 2012), Friends’ Risky Driving (Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011a), and Driving Risk Perceptions (Hartos & Simons-Morton, 2006; Simons-Morton et al., 2012). Measures given in the post-drive survey included Passenger Risk Acceptance (adapted from Ouimet et al., 2013), Identification with Passenger (Developed for this study), Perceived Passenger Approval of Risky Driving (Developed for this study), and Passenger Pressure (Developed for this study) (Table 2).

2.5. Data Analysis

Driving behavior was compared for passenger presence (i.e., solo vs. passenger present) and risk condition (risk-averse vs. risk-accepting passenger). In preliminary analyses, risk-averse and risk-accepting groups were compared on the advanced-survey covariates to test adequacy of random assignment and on measures of participants’ perceptions of the confederate to confirm the experimental manipulation of injunctive norms. Possible carry-over effects of the drive order (solo first vs. passenger present first) were tested within each risk condition, and none were identified.

The primary driving performance comparisons were to examine main and interaction effects of the risk condition and passenger presence to test study hypotheses for risky driving behaviors and hazard anticipation. Generalized linear mixed models were estimated using three distributions: a binomial distribution for Failed to Stop, Intersection Scanning (Turning and Traversing), Latent Hazard Scanning, and Occluded Stop Sign; a binary distribution for Overtaking, and a Poisson distribution for Left Turn Latency. A mixed model was used for Time in Red. As an additional precaution against carry-over effects, all models testing study hypotheses were adjusted for the order in which the drives were completed. SAS (version 9.2) PROC GLIMMIX and PROC MIXED were used for all hypothesis-testing models.

3. Results

3.1. Test of Randomization

Randomization was tested by comparing participants from the two risk conditions (risk-averse vs. risk-accepting) on months of licensure and covariates measured in the advance survey. There was no significant difference (t(51)= −0.24, p = .81) in the mean number of months of licensure between the risk-averse (M = 7.98 and SD = 4.13) and the risk-accepting (M = 7.76 and SD = 2.00) groups. Furthermore, no between-group differences were found for any of the advance survey covariates. These results indicate that randomization was successful in forming experimental groups that did not differ in a variety of important potential confounding characteristics (Table 3).

3.2. Experimental Manipulation Check

Participants’ perceptions of the passenger measured post drive were compared by risk condition to check that the manipulation was effective. Compared to the risk-averse condition, the passenger in the risk-accepting condition was perceived as more risk-accepting, and applied more peer pressure to drive in a risky manner, and approved more of participants’ risky driving). No differences were found in identification with the confederate (Table 3). These results indicate that the experimental manipulation was successful in altering injunctive norms and perceived pressure, while not altering the participants’ identification with the confederate (a potential confounder). These results were supported by participants’ subjective impressions of the confederate shared during a debriefing session.

3.3. Effects of Risk Condition and Passenger Presence

Figure 2 illustrates significant interactions of risk condition and passenger presence. In general, when driving with a passenger, participants in the risk-accepting condition drove in a manner that was riskier and less cautious compared to those in the risk-averse condition (see Table 4 for results). This included having a higher probability of Failing to Stop (IRR = 2.16, 95% CI [1.06, 4.43], p = .04) (Figure 2.1), shorter Left Turn Latency (IRR = 0.43, 95% CI [0.31, 0.60], p < .001) (Figure 2.2) and higher probability of Overtaking (IRR = 10.17, 95% CI [1.43, 73.35], p = .02) (Figure 2.3). Those in the risk accepting condition had a lower probability of stopping at the Occluded Stop Sign (IRR = 7.90, 95% CI [2.06, 30.35], p < .001) (Figure 2.4) compared to the risk-averse condition with the passenger present (due to the strong interaction of risk condition and passenger presence the main effects for Overtaking and Occluded Stop Sign cannot be interpreted). Additionally, differences in patterns of change between solo and passenger driving trials were apparent, with the probability of Failing to Stop and of Overtaking being greater in the passenger than in the solo drive for participants in the risk-accepting condition, and the opposite in the risk-averse condition (Figures 2.1 and 2.3). The probabilities for the left turn latency and occluded stop sign scenarios showed similar patterns to Failing to Stop and Overtaking, except that the two conditions crossed over between the two trials. This indicates that the probability of detection and latency were greater for the risk-averse compared to the risk-accepting condition during the solo drive, but higher for the risk-averse and lower for the risk-accepting condition during the passenger drive (see Figures 2.2 and 2.4).

Figure 2 Interactions between risk condition and passenger presence

Table 4

Effects of risk condition and passenger presence on binomial and count outcome variables

RR = incident risk ratio; 95% CI = 95% confidence interval.

The main effects of risk condition (risk-accepting vs. risk-averse) and passenger presence (solo vs. passenger) were tested while controlling for the order in which the drives occurred (shown in Table 4). Significant main effects of passenger presence was found for the occluded stop sign (IRR = 0.25, 95% CI [0.410, 0.63], p = .03) and intersection scanning (traversing) (IRR = 0.62, 95% CI [0.40, 0.96], p = .03), indicating that when a passenger was present participants were less likely to detect and respond to the occluded stop sign and to scan for hazards when traversing straight through the intersection (Table 4). No significant main or interaction effects were found for intersection scanning (turning), latent hazard scanning (Table 4) and percent time in red (Table 5).

Table 5

Associations of risk condition and passenger presence with a continuous outcome variable

95% CI = 95% confidence interval.

4. Discussion

This study systematically tested the effect of male peer passengers on risk-taking and hazard anticipation in a driving simulator. Three hypotheses were tested: 1) peer passenger presence would result in greater driving risk behavior (i.e., increased driving risk and reduced latent hazard anticipation) compared to driving alone; 2) social influences would moderate the association between passenger presence and driving risk condition such that the effect of passenger presence would be even greater when accompanied by social influences that were risk-accepting compared to social influences that were risk averse; and 3) social influences would be greater in risk conditions consistent with intentional violation of safety-related roadway rules than in conditions consistent with mistakes or lapses due to the lack of skill or it's application. The results indicated that among recently licensed male teenagers, risk-accepting social influences (i.e., injunctive norms and peer pressure) in the presence of a peer passenger increased risk-taking compared to driving alone or with a risk-averse peer passenger. In addition, the results indicate a trend toward reduced driving risk behavior when the passenger was risk averse. The results also demonstrated some evidence that mere passenger/peer presence increased driving risk behavior, similar to previous findings (Chein et al., 2010; Simons-Morton et al., 2014). This effect would be expected to be larger in the presence of actual friends or more than one peer than in this study. Generalization of these results to real-world driving sheds light on the role of social influences on the driving risk behavior of male teenage drivers, and further establishes the role of these influences as contributors to the observed association between peer-passenger presence and fatal crash involvement of teen drivers (L.-H. Chen et al., 2000; Williams, 2003). Further, the reduced driving risk behavior in the risk-averse condition provides some evidence that peer passengers can have a risk-dampening influence on male teen drivers. This has been found in prior naturalistic observation research, but with female rather than male passengers (Simons-Morton, 2005).

Although the evidence that social influences can lead to greater risk taking while driving was fairly consistent, the effects of social influences on latent hazard anticipation were less evident. Of the four measures of latent hazard anticipation, only one showed any effect of social influences. Detection of an occluded stop sign showed a main effect of social influences (i.e., risk condition) and a significant interaction with passenger presence in which response to the stop sign was less likely when the passenger exerted risk-accepting peer influences. However, the interpretation of this effect is not clear, as this measure is a hybrid task that could be associated with greater risk-taking or poorer latent hazard anticipation. This scenario was positioned at a four-way residential intersection, and the sign was blocked from view by foliage until the participant was near the intersection. Having a passenger could increase interference with the driving task, such as inattention or distraction, resulting in failure to detect and therefore stop for the sign. However, the presence of a risk-accepting passenger could also lead the participant to intentionally opt for the higher risk behavior of continuing through the intersection without stopping. This latter scenario is made more likely by the slow vehicle speed (i.e., speed limit was 25 mph), which might have allowed last minute checking for traffic before either braking or entering the intersection. Given that the participants were significantly more likely to continue through the intersection when a risk-accepting passenger was present, versus a risk-averse passenger, and that this was accompanied by a significant moderating effect of social influences in the presence of a passenger, it can be concluded that the effect was more likely due to greater risk taking; however, the main effect of passenger presence suggests there may have also been some other effect associated with the mere presence of a passenger (e.g., inattention, distraction, cognitive load). Overall, these results generally support prior studies, demonstrating the effects of peer presence in promoting risky driving behaviors among teens (Ouimet et al., 2013; Pradhan et al., 2014; Shepherd et al., 2011; Simons-Morton et al., 2014; White & Caird, 2010), and generalization of these results to real-world driving further establishes social influences in the form of injunctive norms reinforced by peer pressure as a potential contributor to the observed association between peer-passenger presence and fatal crash involvement of teen drivers (L.-H. Chen et al., 2000; Ouimet et al., 2010; Williams, 2003).

Although the results of this study are compelling, other studies have supported different conclusions. A naturalistic study of teen driving found no evidence that teenage passengers increased teen driver involvement in crash/near crash situations or risky driving (Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011a). The differences between this naturalistic study and the results of the current research may be attributable to several factors. Notably, the current study provided separate evaluations of risk due to choice and risk due to insufficient, unapplied or poorly executed skill, particularly with respect to driving behavior in the presence of passengers and resistance to peer influence, while the previous study did not make this distinction. Second, the current study experimentally controlled the risk level of injunctive norms by manipulating passenger type (i.e., risk-accepting vs. risk-averse), while the previous study did not. Finally, risk appears to vary according to individual differences among drivers, possibly in characteristics such as reward salience or sensitivity to social exclusion or social rewards (Chein et al., 2010; Falk et al., 2014). A previous study by this group also supported this conclusion, showing that teens who were more sensitive to social exclusion based on brain scans were also more likely to drive in a riskier manner in the simulator when a passenger was present versus driving alone, while controlling for passenger norms (Falk et al., 2014). Thus, it may be the case that peer passengers tend to promote riskier teenage driving by increasing reward sensitivity, consistent with broader accounts of increased reward salience in teens (Albert & Steinberg, 2011), but that this influence is moderated by individual differences in sensitivity to potential social rewards or punishments. The studies also support a link between social influences and active engagement in risky driving behavior, but do not provide much information regarding the potential for peers to increase the risk of teenage drivers by reducing attention to hazards. This is a topic for future research.

The Theory of Normative Social Behavior posits that human behavior is influenced by social norms that are demonstrated in society broadly and perceived in the expectations of others. In this study, risk-accepting social influences affected volitional choices by participants to continue through rather than stop at an intersection with a yellow light, overtake a slightly slower lead vehicle rather than allow more time for it either to accelerate or to leave the road, and make a left turn through heavy traffic without waiting long for a larger gap or safer opportunity to turn. Although these behaviors are not violations in the legal sense, they do increase crash risk. There was less evidence that social influences affected hazard anticipation, consistent with a failure to execute a skill-based behavior, in this case to detect hazards (Reason, 1990). There are two explanations of the poor anticipation of hazards observed in this study that should be examined in future studies. One explanation is that good hazard anticipation skills (e.g., where and when to focus attention) have not yet developed in novice teens, and therefore could not be applied fairly to the situations presented in the driving simulation (i.e., a floor effect would minimize the potential to detect differences). A second explanation is that the presence of a peer passenger could interfere with the task of detecting hazards through mechanisms such as inattention, distraction, or increased cognitive load.

Risky driving due to the commission of violations could be addressed by adapting interventions that change norms and attitudes of drivers and passengers (Carins & Rundle-Thiele, 2014; Janssen, Mathijssen, van Bon-Martens, van Oers, & Garretsen, 2013; Lumpkins et al., 2015) to address the driving context, \by actual or perceived likely legal enforcement of traffic violations (Quackenbush, 2011), or parental management of risky driving behavior (Simons-Morton, Ouimet, Zhang, Klauer, Lee, Wang, Chen, Albert, & Dingus, 2011b). The effectiveness of changes in social norms and strong enforcement is evident in changes in safety belt use when there was no law compared to secondary, and to primary enforcement plus public campaigns to alter attitudes and expectations (Beck & Shults, 2009; Shults & Beck, 2012); however, despite big increases, teenagers have among lowest use rates (Williams & Tefft, 2014). The high effectiveness of red-light cameras in reducing red-light running is another example (Retting, Ferguson, & Farmer, 2008; Retting, Ferguson, & Hakkert, 2003; Shin & Washington, 2007); however, the effects of this approach has not been studied in young drivers, per se. Mistakes in driving, however, might best be reduced by promoting the early development and use of appropriate higher-order driving skills, such as latent hazard anticipation. For example, hazard anticipation training shows promise as a potentially effective approach to addressing deficits in attention, scanning, and anticipation of roadway hazards, especially in less experienced drivers (Fisher et al., 2008; Pradhan et al., 2006; 2009). Additional research is needed to determine the persistence of these training effects on driver behavior and performance.

4.1. Strengths and Limitations

Strengths of this research include high experimental control of potential confounding and target variables, an effective confederate passenger deception, and external validity provided by a high-fidelity simulator to assess risky driving. However, generalization of the study findings is limited by the inclusion of only male teenage novice drivers. Likewise, study participants were closely clustered around average rates of resistance to peer influence for this age group (Steinberg & Monahan, 2007), so it is unclear to what extent these findings would generalize to participants with weaker resistance to peer influences. Although we are confident that the pre-drive manipulation of injunctive norms was effective, given the expected group differences around risk acceptance and post-drive measures of the experimental manipulation, further dissection of the effective components of the experimental manipulation should be examined in future studies. For example, although the use of an age-peer confederate passenger allowed substantial experimental control, it may have provided participants with an artificial experience compared to the influence of actual friends. Also, the confederate exerted minimal pressure within the driving environment, which suggests that the results may underestimate the effect of peer influence in the real world.

4.2. Summary and Conclusions

The present study provided a systematic test of social influences on male teenage driving risk behavior. The moderation by social influences of the association between simulated risky driving and passenger presence supports the contention that social influences affect driving risk behavior. This is consistent with research on other behaviors and with social norms theory, which posits that all other things being equal people conform to the norms of others by modifying their behavior (Cialdini, 2009; Rimal, 2005). In addition, skill-based risk-reducing behaviors, which require training in order to be effective, can be enhanced by social influences that promote the application of safety skills to reduce and prevent crashes. Hence, the results are consistent with policies that set limits on teenage passengers while novice teen drivers gain experience and skills essential for safe driving, and support the use of evidence-based training of higher order safety skills such as latent hazard anticipation.

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Introduction

This study examined the influence of male peer passengers on risk-taking and hazard anticipation among novice teenage drivers within a simulated driving environment. We hypothesized that: 1) the presence of a peer passenger would be associated with riskier driving behaviors and reduced hazard anticipation compared to driving alone; 2) social influences would moderate the relationship between passenger presence and driving behaviors, with riskier behaviors more pronounced in the presence of a risk-accepting passenger compared to a risk-averse passenger; and 3) the effects of social influence would be more evident in driving situations conducive to intentional violations of traffic regulations compared to those involving skill-based errors or lapses in attention.

Findings

Our findings revealed that among recently licensed male teenagers, the presence of a risk-accepting passenger amplified risk-taking behaviors compared to driving alone or with a risk-averse passenger. Participants in the risk-accepting passenger condition exhibited a greater likelihood of engaging in behaviors such as running red lights, executing left turns with reduced latency, and overtaking vehicles compared to those in the risk-averse passenger condition. Conversely, the presence of a risk-averse passenger demonstrated a trend toward reduced risky driving behaviors. These results underscore the significant role of social influence, specifically injunctive norms and peer pressure, in shaping the driving behaviors of male adolescents, lending further support to the observed association between peer passenger presence and elevated crash risk in this population (Chen et al., 2000; Williams, 2003). Furthermore, the observed reduction in risky driving behaviors in the risk-averse passenger condition provides preliminary evidence that peer passengers can exert a mitigating influence on risk-taking among male teen drivers, aligning with previous naturalistic observations, although these have primarily focused on female passenger influences (Simons-Morton, 2005).

While the findings regarding the influence of social factors on risk-taking behaviors were robust, the effects on hazard anticipation were less conclusive. Of the four hazard anticipation measures employed, only one, the detection of an occluded stop sign, demonstrated sensitivity to social influence. The presence of a risk-accepting passenger was associated with a decreased likelihood of stopping at the occluded stop sign compared to a risk-averse passenger. However, the interpretation of this finding is not straightforward. The occluded stop sign scenario represents a complex task that could be influenced by both risk propensity and hazard anticipation skills. The presence of a passenger could introduce distractions or increase cognitive load, potentially impairing hazard detection. Conversely, a risk-accepting passenger could encourage the driver to deliberately disregard the stop sign, opting for a riskier behavior. Given the observed increase in the likelihood of proceeding through the intersection in the presence of a risk-accepting passenger, coupled with the significant interaction effect between passenger presence and risk condition, it is plausible that the observed effect is primarily driven by heightened risk-taking. However, the main effect of passenger presence suggests that other factors, such as distraction or inattention, may also be at play. Collectively, these findings align with prior research demonstrating the role of peer passengers in promoting risky driving behaviors among adolescents (Ouimet et al., 2013; Pradhan et al., 2014; Shepherd et al., 2011; Simons-Morton et al., 2014; White & Caird, 2010). The generalization of these findings to real-world driving strengthens the argument for social influence, particularly injunctive norms reinforced by peer pressure, as a contributing factor to the association between peer passenger presence and elevated crash risk among teen drivers (Chen et al., 2000; Ouimet et al., 2010; Williams, 2003).

It is important to note that while our findings offer compelling evidence, other studies have yielded contrasting results. For instance, a naturalistic study by Simons-Morton et al. (2011a) did not find an association between teenage passengers and an increased risk of crash/near-crash events or risky driving behaviors. Several factors could contribute to these discrepancies. Firstly, our study explicitly distinguished between risks arising from intentional choices and those stemming from skill deficits or lapses in attention, providing a more nuanced understanding of driving behavior in the presence of passengers. Secondly, unlike the previous study, we experimentally manipulated passenger risk levels (risk-accepting vs. risk-averse), allowing for a more controlled assessment of social influence. Finally, individual differences in factors such as reward sensitivity or susceptibility to social influence, which can moderate the impact of peer passengers on driving behavior, may account for some of the observed differences across studies (Chein et al., 2010; Falk et al., 2014). Indeed, research has shown that adolescents with heightened sensitivity to social exclusion, as measured by brain imaging techniques, exhibit riskier driving behaviors in the presence of a passenger compared to driving alone, even when controlling for passenger norms (Falk et al., 2014). This suggests that while peer passengers may generally increase risk-taking propensities in teenagers, possibly by amplifying reward sensitivity, the magnitude of this effect is likely influenced by individual differences in social sensitivity. Taken together, these studies highlight a link between social influence and active engagement in risky driving behaviors, but provide limited insight into the potential for peers to indirectly increase risk by impairing hazard attention, an area warranting further investigation.

The Theory of Normative Social Behavior (TNSB) posits that social norms exert a powerful influence on human behavior (Rimal, 2005). Our findings support this theory, demonstrating that risk-accepting social influences affect volitional decisions made by drivers, such as proceeding through yellow lights, overtaking vehicles in potentially unsafe situations, and making left turns with limited gaps in traffic. These behaviors, while not necessarily illegal, heighten the risk of crashes. In contrast, we found less evidence for the influence of social factors on hazard anticipation, suggesting that skill-based behaviors, which require deliberate training for effective execution, may be less susceptible to social influence.

The observed limitations in hazard anticipation among participants in this study could be attributed to two potential explanations that warrant further exploration in future research. One possibility is that novice teen drivers have yet to develop proficient hazard anticipation skills, such as effective visual scanning and attention allocation, resulting in a floor effect that masks potential differences across conditions. Alternatively, the presence of a peer passenger could introduce distractions or increase cognitive load, interfering with the driver's ability to effectively detect hazards.

Addressing Risky Driving Behaviors

Addressing risky driving behaviors resulting from intentional violations could involve adapting existing interventions to the specific driving context. These interventions, often focused on modifying social norms and attitudes toward risky driving (Carins & Rundle-Thiele, 2014; Janssen et al., 2013; Lumpkins et al., 2015), could be tailored to emphasize the potential for legal consequences and leverage parental involvement in managing teen driving behaviors (Quackenbush, 2011; Simons-Morton et al., 2011b). The effectiveness of such approaches is exemplified by the marked increase in seatbelt use following the implementation of primary enforcement laws, coupled with public awareness campaigns aimed at shifting social norms (Beck & Shults, 2009; Shults & Beck, 2012). Similarly, red-light cameras have proven highly effective in curbing red-light running behaviors (Retting et al., 2003, 2008; Shin & Washington, 2007), although their specific impact on young drivers remains unexplored.

In contrast to violations, mitigating driving errors, often stemming from skill deficits, may necessitate a focus on fostering the early development and application of higher-order driving skills, such as hazard anticipation. Hazard anticipation training, for instance, has shown promise in addressing attentional limitations, improving visual scanning strategies, and enhancing hazard anticipation abilities, particularly among novice drivers (Fisher et al., 2008; Pradhan et al., 2006, 2009). However, further research is needed to determine the long-term efficacy and sustainability of such training programs.

Strengths and Limitations

This study benefited from several strengths, including rigorous experimental control over potential confounding variables, a convincing confederate passenger deception, and the use of a high-fidelity driving simulator, enhancing the ecological validity of the findings. However, it is important to acknowledge certain limitations. The inclusion of only male novice teenage drivers restricts the generalizability of the findings to other demographic groups. Additionally, the participants' relatively homogeneous levels of resistance to peer influence, clustering around the average for this age group (Steinberg & Monahan, 2007), limit our ability to extrapolate these findings to individuals with varying levels of susceptibility to peer pressure.

While we are confident in the effectiveness of the pre-drive manipulation of injunctive norms, further research is warranted to disentangle the specific components contributing to its efficacy. For instance, the use of an age-peer confederate, while offering substantial experimental control, may not fully capture the dynamics of real-world peer influence, particularly among close friends. Moreover, the relatively subtle peer pressure exerted by the confederate within the driving environment suggests that the observed effects may underestimate the true impact of peer influence in naturalistic settings.

Summary and Conclusions

This study offers valuable insights into the role of social influences on risky driving behaviors among male teenage drivers. The findings support the notion that the presence of a peer passenger, particularly one who endorses risk-taking behaviors, can significantly amplify risk-taking on the road. Conversely, passengers who model risk-averse behaviors may contribute to a reduction in risky driving. These findings underscore the importance of policies that restrict teenage passengers during the initial stages of driving, allowing novice drivers to gain experience and develop essential driving skills in a less hazardous environment. Moreover, our results lend support to the implementation of evidence-based training programs aimed at fostering higher-order driving skills, such as hazard anticipation, as a means of mitigating crash risk in this vulnerable population.

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How Peer Pressure and Social Norms Affect Teen Boys' Driving in Simulations

1. Introduction

Teenage drivers in the US are more likely to be in car crashes than adults and car crashes are the leading cause of death for this age group. This is because teens are new drivers and are still developing, leading to risky driving behaviors such as ignoring hazards and lacking driving skills. Teenagers are also less mature than adults emotionally and mentally, making them more likely to be distracted and take risks without considering the consequences. Having male passengers in the car further increases the risk of crashes for teen drivers.

Research suggests that peers can strongly influence teenagers' risky behavior, particularly through social norms (what they think their peers expect of them) and peer pressure. Studies have shown that teenagers whose friends engage in risky driving are more likely to do so themselves. Experiments using computerized driving games and brain imaging have confirmed that having peers present can increase risky driving in teenagers. However, it's unclear if these findings translate to real-world driving.

Driving simulators offer a safe way to study this issue. Studies using simulators have shown that social norms can impact how teenagers drive, such as male teens being less aware of hazards with a peer passenger present. Another study found that novice male drivers took more risks when they believed their male passenger was risk-taking.

Building on these findings, this study explores the Theory of Normative Social Behavior (TNSB), which suggests that people's behavior is influenced by what they perceive to be the expectations of important individuals in their lives, like friends and family. The study aimed to examine how a male passenger and their perceived social norms (risk-accepting vs. risk-averse) would affect a male teen's driving risk behavior in a driving simulator.

2. Methods

2.1. Participants

The study involved 53 male teenagers (16-17 years old) from Southeast Michigan who had held a driver's license allowing independent driving with restrictions for at least 4-8 months. They had to drive at least twice a week, have normal vision (with contact lenses if needed), and pass a simulator sickness test. Eyeglasses were not allowed because they interfered with the eye-tracking system. Each participant received $50 for their participation.

2.2. Procedures

The study used a mixed randomized experimental design. Participants completed two driving trials: one driving alone and one driving with a male confederate posing as a participant. The confederate was assigned to either a "risk-accepting" or "risk-averse" condition, which was the between-subjects factor.

Participants first completed a pre-drive survey. In the "risk-averse" condition, the confederate arrived late and apologized, mentioning they drive slowly and stop at every yellow light. In the "risk-accepting" condition, the confederate apologized for being late, explaining they usually drive fast but had to stop at many red lights that day.

After a practice drive, participants completed two 10-15 minute drives (order was randomized: passenger first or solo first). Before the passenger drive, both watched two driving videos: one showing safe driving and the other showing risky driving. They rated how similar their driving was to each video and how likely they would be to ride with that driver. The confederate's responses were scripted to be either risk-averse or risk-accepting.

Before the solo drive, participants completed a word puzzle for five minutes to minimize the lingering effects of the passenger drive. Both drives included scenarios designed to elicit risky driving behaviors and hazard anticipation, but minimized the chance of crashes or loss of control.

In the passenger drive, the confederate navigated using a map and applied peer pressure through statements like mentioning the speed limit (risk-accepting) or pointing out reduced speed zones (risk-averse). During the solo drive, participants followed a designated route marked by construction barrels.

The confederate was unaware of the assigned risk condition until right before the experiment, and the experimenter was unaware until hearing the confederate's script. Participants completed a post-drive survey and debriefing to check the effectiveness of the deception and experimental manipulation.

2.3. Driving Simulator

The study used a high-fidelity driving simulator with a full vehicle cab, multiple screens, and a realistic driving experience including steering feedback and simulated sounds. An eye-tracking system recorded participants' eye movements.

2.4. Measures

2.4.1. Dependent Measures

The main measures were categorized as risky driving behaviors (e.g., running red lights, overtaking) and hazard anticipation (e.g., scanning intersections, noticing hidden stop signs). These were measured using data from the driving simulator and eye-tracking system.

2.4.2. Independent Measures

The independent measures were passenger presence (driving alone or with a passenger) and risk condition (risk-accepting or risk-averse passenger).

2.4.3. Randomization and Manipulation Checks

Surveys were conducted before and after the experiment to check for randomization and manipulation effectiveness. The initial survey measured traits associated with risky driving to ensure groups were similar. The post-drive survey assessed participants' perception of the confederate's risk-taking behavior, approval of risky driving, and pressure exerted.

2.5. Data Analysis

Data analysis compared driving behavior between driving conditions (solo vs. passenger) and risk conditions (risk-averse vs. risk-accepting). Statistical analyses were conducted to control for potential confounding factors and to test the study's hypotheses.

3. Results

Analysis showed no significant differences between the risk-averse and risk-accepting groups in terms of driving experience and other factors measured in the initial survey. This indicates that the random assignment of participants to the two groups was successful.

The post-drive survey showed that participants perceived the confederate in the risk-accepting condition as more accepting of risk and more likely to pressure them to drive more riskily compared to the risk-averse condition. This confirms that the experimental manipulation was successful in creating distinct risk profiles for the confederate.

The results showed a significant interaction between passenger presence and risk condition on several risky driving behaviors. Participants in the risk-accepting condition were more likely to:

• Run red lights

• Make faster left turns

• Overtake other vehicles

• Run a stop sign hidden by foliage

These risky behaviors were more pronounced when driving with the risk-accepting passenger compared to driving alone.

Interestingly, participants with a risk-averse passenger showed a trend towards less risky driving compared to driving alone.

However, there was less evidence that social influence impacted hazard anticipation. Only the detection of the hidden stop sign was affected, which could be interpreted as either increased risk-taking or poorer hazard anticipation.

4. Discussion

This study found that the presence of a male peer, particularly one who endorses risk-taking behaviors, can significantly increase risky driving among newly licensed male teenagers. The study also provides some evidence that a risk-averse passenger can actually discourage risky driving. These findings reinforce the idea that social norms and peer influence play a significant role in teen driving behavior and may contribute to the higher crash rates seen in this age group, especially when driving with peers.

The study's findings highlight the importance of addressing the influence of social norms in interventions aimed at reducing risky driving among teens. Programs that target and reshape norms around risky driving, potentially by leveraging the influence of peers, may prove more effective than traditional driver education programs.

While this study provides valuable insights, it is important to acknowledge its limitations. The study only included male participants, and further research is needed to determine if these findings hold true for female teen drivers. Also, the confederate passenger, while providing experimental control, may not perfectly reflect the dynamics of real-world friendships. Future research should explore these nuances further.

4.1. Strengths and Limitations

This study had several strengths:

• High experimental control: The study effectively manipulated social influence and controlled for other potential factors, strengthening the link between social influence and risky driving.

• Effective use of a confederate: The confederate successfully portrayed the intended risk profiles, adding realism to the experiment.

• External validity: The use of a high-fidelity driving simulator provided a safe and controlled environment that mimicked real-world driving scenarios.

The study also had limitations:

• Limited generalizability: The study only included male participants, limiting the generalizability of the findings to other populations.

• Artificial passenger interaction: The use of a confederate may not fully capture the complexities of real-world peer interactions.

• Limited pressure from the confederate: The confederate's influence was relatively subtle, suggesting that the study may underestimate the true impact of peer pressure on teen driving.

4.2. Summary and Conclusions

This study provides evidence that social influence, specifically through perceived peer norms, can significantly affect risky driving behavior in male teens. The findings underscore the importance of considering social factors when developing interventions to reduce risky driving. Policies that restrict teen passengers while novice drivers gain experience, along with targeted training programs that address both driving skills and social influences on driving behavior, are likely to be most effective in reducing teen driver crashes.

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Teen Passengers and Risky Driving: How Your Friends Influence the Way You Drive

Background

We all know that teens get in more car accidents than adults. In fact, car accidents are the #1 cause of death for teens in the US! This is because teens are new drivers and don't have a lot of experience. They might also take more risks because their brains aren't fully developed yet, making it harder to control impulses and think about consequences.

Teens are easily influenced by their friends, and this can lead to riskier driving. When friends are in the car, teen drivers (especially boys) are more likely to speed, run red lights, and make dangerous passes. This might be because they want to show off or because they feel pressured to do what their friends are doing.

This article talks about a study that looked at how teen passengers and social pressure affect the way teen boys drive. The study used a driving simulator to see how teen boys drove when they were alone compared to when they had a friend in the car. The "friend" was actually an actor trained to act like a certain type of passenger: either someone who likes to take risks or someone who is more cautious.

The Study

Who participated?

The study included 53 teen boys (ages 16-17) who had their driver's licenses for at least 4-8 months and drove at least twice a week.

What happened?

The teens each drove in a driving simulator twice: once alone and once with the actor passenger. The actor pretended to be either a "risky" passenger who liked to drive fast or a "cautious" passenger who preferred to follow the rules.

What did they find?

• Passengers make a difference: Teens in the study were more likely to take risks when driving with a passenger, especially if the passenger was the "risky" type.

• Risky friends, risky driving: Teens who drove with the "risky" passenger were more likely to run red lights, make dangerous passes, and make quick left turns in front of traffic.

• Cautious friends, safer driving: Teens who drove with the "cautious" passenger tended to drive more safely.

• Hazard awareness: Passengers didn't seem to affect how well teens spotted hazards on the road, except for one situation where a stop sign was hidden. This suggests that teen drivers might be more focused on showing off to their friends than paying attention to the road.

What Does This Mean?

This study shows that friends can have a big influence on how teens drive. When teens feel pressured by their friends to drive recklessly, they are more likely to take risks that could lead to an accident.

This study also suggests that teaching teens how to be safer drivers is important. This includes teaching them how to spot hazards on the road and how to resist peer pressure to drive recklessly.

Driving Safe with Friends

• Be a safe driver: Always wear your seatbelt, follow the speed limit, and don't drive under the influence of drugs or alcohol.

• Choose your passengers wisely: Ride with friends who respect the rules of the road and won't pressure you to drive recklessly.

• Speak up: If your driver is doing something dangerous, don't be afraid to speak up and ask them to stop. Your safety is more important than anything else.

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Teen Drivers: Do Friends Make Them Take More Risks?

Why is this important?

Did you know car crashes are the number one killer of teens in the US? Teens crash more often than adults because they are still learning to drive and make risky choices. They don't always understand the dangers on the road or have enough practice driving. On top of that, teens' brains aren't fully developed yet, so they can be impulsive, easily distracted, and more likely to take risks. Boys, in particular, are more likely to crash, especially when they have friends in the car. It seems like having friends around can make teens drive more dangerously! But why does this happen?

Scientists believe that friends can influence each other's behavior, especially when it comes to risky things. They can pressure each other or simply act in a way that makes risk-taking seem normal or cool. To understand how this works when teens are behind the wheel, researchers decided to study how teen boys drive in a realistic driving simulator.

How Did the Study Work?

Scientists invited teen boys who had their driver's licenses for a few months to participate. The boys tried driving in a special simulator that felt like a real car. There were two important parts to the experiment:

• Driving with a friend: The teens drove the simulator twice – once alone and once with a teen boy "friend" (who was actually helping with the study).

• Types of friends: The "friend" acted in one of two ways:

  • Risk-taker: This "friend" said things like, "I usually drive way faster" or "The speed limit is 70 mph," suggesting it's okay to speed.

  • Cautious: This "friend" said things like, "I tend to drive slower" or "This is a reduced speed zone," encouraging safer driving.

The scientists wanted to see if having a friend in the car, especially one who seemed to like taking risks, would make the teen boys drive more dangerously. They also wanted to understand if friends influence dangerous driving choices (like running a yellow light) more than mistakes caused by inexperience (like not looking for hazards).

What Did They Find?

The study found that teen boys did take more risks when driving with a friend who encouraged risky driving. Compared to driving alone or with a cautious friend, teens with a risk-taking friend were:

• More likely to run red lights.

• Quicker to make left turns in traffic (which can be dangerous).

• More likely to pass other cars in risky situations.

• Less likely to notice a hidden stop sign (maybe because they were distracted).

Interestingly, having a cautious friend seemed to make the teens a little less risky in some cases! This suggests that friends can influence teen driving in both good and bad ways.

However, friends didn't seem to affect how well the teens spotted hazards on the road. This could be because they are still learning those skills and need more practice.

So, What Does This Mean?

This study tells us that friends can definitely make teen boys take more risks when driving, especially when those friends encourage risky behavior. It's like having someone in the car egging them on! This helps explain why teens get into more accidents when they have friends in the car.

The good news is that friends can also encourage safer driving. This means it's important for teens to talk to their friends about driving safely and to avoid pressuring each other to do dangerous things on the road.

What Can We Do?

To keep teen drivers safe, we can:

• Set rules: Parents and governments can set rules that limit teen passengers, especially when drivers are first starting out. This gives them time to practice and build skills without the added pressure of friends.

• Teach safe driving: We need to give teen drivers the best training possible, including how to spot hazards and make good decisions under pressure.

• Change attitudes: Everyone can work together to change how people think about risky driving. It shouldn't be seen as cool or fun - it's dangerous and can have serious consequences.

By understanding how friends influence teen drivers, we can work to keep them safe and prevent accidents.

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