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Effects of distraction and experience on situation awareness and simulated driving
Transportation Research Part F 10 (2007) 321–329 www.elsevier.com/locate/trf
Effects of distraction and experience on situation awareness and simulated driving Steven J. Kass a
a,*
, Kerstan S. Cole b, Claudia J. Stanny
a
University of West Florida, Department of Psychology, 11000 University Parkway, Pensacola, FL 32514, United States b Texas Tech University, Department of Psychology, Lubbock, TX 79409-2051, United States Received 29 May 2006; received in revised form 6 December 2006; accepted 8 December 2006
Abstract This study examined the impact of cell phone conversation on situation awareness and performance of novice and experienced drivers. Driving performance and situation awareness among novice drivers ages 14–16 (n = 25) and experienced drivers ages 21–52 (n = 26) were assessed using a driving simulator. Performance was measured by the number of driving infractions committed: speeding, collisions, pedestrians struck, stop signs missed, and centerline and road edge crossings. Situation awareness was assessed through a query method and through participants’ performance on a direction-following task. Cognitive distractions were induced through simulated hands-free cell phone conversations. The results indicated that novice drivers committed more driving infractions and were less situationally aware than their experienced counterparts. However, the two groups suffered similar decrements in performance during the cell phone condition. This study provides evidence of the detrimental effects of cell phone use for both novice and experienced drivers. These findings have implications for supporting driving legislation that limits the use of cell phones (including hands-free) in motor vehicles, regardless of the driver’s experience level. 2006 Elsevier Ltd. All rights reserved. Keywords: Distracted driving; Situation awareness; Attention; Experience level
1. Introduction Many studies have shown that talking on a cell phone while driving significantly influences driver performance. Furthermore, consumers are purchasing cell phones at increasing rates. As the number of cell phone users increases, the potential health risks also increase not only for those who choose to converse while driving, but also for passengers, pedestrians, and other drivers (Ferguson, 2003; Lam, 2002; Peters & Peters, 2002). In an analysis of nearly 700 cell phone-related accidents, Redelmeier and Tibshirani (1997) concluded that
*
Corresponding author. Tel.: +1 850 474 2107; fax: +1 850 857 6060. E-mail address: [email protected] (S.J. Kass).
1369-8478/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2006.12.002
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talking on a cell phone increased the probability of a collision between 3 and 6.5 times. They also suggested that these distraction effects are comparable to a blood–alcohol-content above the legal limit. In fact, Strayer, Drews, and Crouch (2006) found that in a driving simulation task, cell phone users showed greater impairments as measured by increased number of rear-end collisions and time required to regain speed following braking than drivers who were legally drunk (i.e., blood–alcohol-content of 0.08). Additionally, Strayer and Johnston (2001) reported that drivers engaged in cell phone conversations missed twice as many traffic signals and had slower reaction times. Consiglio, Driscoll, Witte, and Berg (2003) also found that cell phone use (hand-held or hands-free) slowed drivers’ braking reactions compared to when they drove without distraction or when listening to music on the radio. 1.1. Role of situation awareness in driving Research on situation awareness (SA) is often traced back to military aviation studies, but SA is crucial to the performance of any dynamic complex task, including driving in heavy traffic (Endsley, 1995). SA involves identifying relevant environmental stimuli or cues, integrating that information into the operator’s knowledge base to form a mental model or representation of the situation, and using that representation to project the occurrence of events in the near future (see Dominguez, 1994; Endsley, 1990; Kass, Herschler, & Companion, 1991). As drivers move through the environment, they must identify the relevant information in rapidly changing traffic patterns (e.g., distance to other vehicles, closing speed) and be prepared to react to events that may occur (e.g., car backing out of driveway, stop sign) to avoid accidents. To achieve SA, individuals must rely on perception and pattern recognition abilities (Durso & Gronlund, 1999; Kass et al., 1991), attention and working memory (Gugerty, 1997; Wickens & Hollands, 2000), as well as long-term memory (e.g., Endsley, 1995). Therefore, cognitive distractions that tax a driver’s attention or memory load may adversely impact SA. Recently, researchers (e.g., Beede & Kass, 2006; Garcia-Larrea, Perchet, Perrin, & Amendo, 2001; McKnight & McKnight, 1993; Recarte & Nunes, 2003) have provided empirical evidence that driving performance suffers as a result of such cognitive distractions as cell phone use. These distractions may become particularly important safety issues when motorists are navigating through changing traffic patterns while attempting to maintain SA. Attention and hazard detection, aspects of SA, are known to be adversely affected by the cognitive distractions of cell phone conversation (Strayer & Johnston, 2001). 1.2. Experience level and situation awareness Research on risk exposure of younger drivers indicates that they are more likely to speed, pull into smaller gaps in traffic, and glance away from the road for longer intervals than experienced drivers (see Ferguson, 2003; Strayer & Drews, 2004; Underwood, Crundall, & Chapman, 2002). Crundall and Underwood (1998) investigated the differences in spatial strategy between novice and experienced drivers under different road conditions by examining participants’ visual attention. Experienced drivers employed a more flexible form of spatial strategy, such as searching for alternative routes, while novice drivers had rigid spatial strategies and usually focused on the one possible strategy that their visual search allowed. Lacking experience, novice drivers may not have learned to cope with the cognitive load imposed by complex road conditions while simultaneously attending to the overall demands of the driving task. This may result in a loss of SA and an inability to avoid collisions resulting in injury or death. The current experiment was designed to test the hypothesis that cell phone conversations disrupt SA and impair driving performance by preventing drivers from attending to situation-relevant stimuli such as speed limit postings, stop signs, pedestrians, and other traffic. These failings should manifest themselves in terms of increased driving infractions and an inability to take on an additional task (direction following). Prior research suggests that practice effects (e.g., Ferguson, 2003; Strayer & Drews, 2004), mental models (e.g., Langham, Hole, & Edwards, 2002), and flexible spatial strategies (e.g., Crundall & Underwood, 1998) developed through experience enhance operators’ performance. Thus, experience level was expected to mitigate some of the decrements in driving performance and SA associated with cell phone conversations.
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2. Method 2.1. Participants Two groups of participants were recruited for this study and classified as either novice (not yet licensed) or experienced drivers. The novice group consisted of 24 students (12 girls, 12 boys) recruited from a high school driver’s education class in northwest Florida. These students had completed 10 weeks of class instruction. Parents or legal guardians granted informed consent for their children to participate. The student drivers’ ages ranged from 14 to 16 years (14.68, SD = 0.56). The experienced driver group consisted of 25 adults (12 men, 13 women) ages 21–52 years (M = 29.0, SD = 8.94) recruited from the local community. These participants ranged in driving experience from 6 to 34 years (M = 13.69, SD = 8.22). Because the driving simulator may seem similar to a video game, we asked the participants to identify how may hours per week they played video games (on the following scale: 0 h, 1–5 h, 6–10 h, etc.). The median response for the younger, novice group of drivers was that they played video games for 6–10 h/week whereas the median response for the experienced group was 0 h/week. Neither group was engaged in extensive video game play, though a slight advantage may be suggested for the novice group when using the simulator. 2.2. Apparatus The STISIM-Drive simulator software, developed by Systems Technology Inc., was used to measure and document participants’ driving behaviors. The software simulates a vehicle with an automatic transmission and receives inputs from a game controller steering wheel, accelerator, and brake pedal. A 61 cm (24 in.) LCD monitor mounted on a desktop displayed the city roadway scenes. Additionally, STISIM models speed-sensitive steering and provides finely detailed, dynamic representations of various driving tasks and objects (e.g., buildings, pedestrians, vehicles, signs and signal lights) providing the researchers with full control in customizing scenarios. Though the experimental configuration is a desktop computer simulation, the high speed, detailed graphics (i.e., texture, shading, and lighting) and experimenter-configured vehicle control dynamics (i.e., steering, braking) are identical to those used by higher-end simulator systems. Furthermore, the software provides visual and auditory feedback and records such driver performance data as the number of stop signs missed, pedestrians struck, speeding occurrences, off-road excursions, and centerline crossings, as well as the capability to playback a participant’s completed scenario. 3. Materials 3.1. SA questions At varying intervals during the simulation, the participants’ SA was assessed using a query method (Endsley, 1988a, 1988b) in which the experimenters stopped the simulator and asked the participant relevant questions about the scene. In the current experiment, participants were asked a total of nine SA questions that pertained to the task of driving (e.g., ‘‘what is the speed limit?’’ ‘‘how many cars backed out in front of you?’’). All participants received the same nine questions, but in random order, and all were relevant to driving awareness. The questions were presented in random order so that the difficulty of a particular question was not related to the difficulty of a particular portion of the driving course. 3.2. Simulated cellular phone conversations Because recent research indicates that the detrimental effects of hand-held cell phones and hands-free cell phones are similar (Strayer & Johnston, 2001), all participants in the distracter condition wore headphones with a mouth-piece and were instructed to ‘‘answer the hands-free cell phone when it rings by saying hello clearly into the mouth piece.’’ Previously recorded questions (wav files) were used in this condition to simulate the cell phone conversation (Beede & Kass, 2006). Questions were presented at irregular intervals to allow time for responses and to simulate a naturalistic setting. These questions were purposely developed to produce
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cognitive distractions and required participants to visualize their answers (e.g., asking for directions to a particular place, visualizing the number of windows in their home, and computing simple arithmetic problems). These demanding questions were combined with other questions and statements to create a naturally flowing conversation. In order to ensure that participants would actively engage in the phone conversation, they were told that their responses were being recorded (they actually were not). 3.3. Procedure Participants were randomly assigned to one of two conditions: the hands-free cell phone distracter condition or the no distracter condition. Participants were instructed to pay attention to environmental stimuli such as cross-traffic and pedestrians and to obey all traffic laws. In order to assess SA in a naturalistic manner and to simulate real-world driving behaviors, experimenters implemented a direction-following task in a 5-min practice scenario and also in the experimental scenario. During the practice scenario, participants were given a typed set of directions and attempted to follow those directions to the destination. After participants completed the practice scenario, experimenters gave participants a different set of typed directions and asked them to read over the directions before driving. For example, participants were instructed as follows: ‘‘At the second light, take a left at the gas station.’’ Participants were allowed to refer back to the instructions at any time. If a turn was missed, the error was recorded and the driver continued to follow the directions. The street scenes presented by the simulation followed the driver (regardless of actual turns made), so that all drivers saw the same scenes even if they did not make the same turns. As a result, drivers did not need to retrace a route to correct for missed turns and were not instructed to do so. The driver could miss one turn without missing any of the remaining turns in the route (five turns total). During three irregularly spaced intervals, the simulator paused and the experimenter asked three SA questions chosen at random from the list of nine and asked participants to answer these questions to the best of their ability. The experimental driving scenarios lasted approximately 11–12 min. 3.4. Design and analysis The experiment consisted of a 2 (novices vs. experienced) · 2 (cell phone vs. no distracter) between subjects factorial design. The dependent variables measured were the numbers of various driving infractions and scores on the two measures of situation awareness. Driving infraction data collected by the simulator included: number of collisions, number of pedestrians hit, number of speeding violations (8 kph or 5 mph over speed limit), number of stop signs missed, and errors of lane maintenance (i.e., centerline crossings and road-edge excursions). Situation awareness was measured by assessing the total number of SA questions participants answered correctly and by the number of turns missed while following the driving directions. The driving conditions consisted of a hands-free cellular phone condition and a no distracter control condition. 4. Results 4.1. Driving infractions To control for experiment-wide error rate associated with conducting multiple analyses of variance (ANOVA) on the 6 different driving infraction variables, a multivariate ANOVA (MANOVA) was performed. The dependent variables analyzed included the number of collisions, pedestrians hit, speeding violations, stop signs missed, centerline crossings, and road-edge excursions. Fig. 1 presents the means for the total number of infractions made by novices and experienced drivers across conditions. The MANOVA indicated significant main effects of experience level (F(6, 42) = 16.20, p < 0.001, partial x2 = 0.48) and cell phone use (F(6, 42) = 9.60, p < 0.001, partial x2 = 0.34). No significant interaction was found (F(6, 42) = 1.54, p = 0.19, partial x2 = 0.03), suggesting that when drivers use a cell phone they suffer the same amount of performance decrement regardless of experience level. Because MANOVA main effects of experience level and cell phone use were found, the univariate analyses of the six dependent variables were examined. These analyses revealed that novice drivers were involved in
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Fig. 1. Driving infractions.
significantly more collisions with other vehicles (Mnov = 2.12, SD = 0.97 vs. Mexp = 1.12, SD = 0.95; F(1, 47) = 15.28, p < 0.001, partial x2 = 0.12), drove through significantly more stop signs (Mnov = 1.52, SD = 0.87 vs. Mexp = 0.27, SD = 0.45; F(1, 47) = 42.60, p < 0.001, partial x2 = 0.29), and crossed the centerline significantly more often (Mnov = 1.64, SD = 1.75 vs. Mexp = 0.62, SD = 0.85; F(1, 47) = 6.66, p < 0.05, partial x2 = 0.05). No significant effects of experience were found for the number of pedestrians struck (Mnov = 1.08, SD = 1.00 vs. Mexp = 0.69, SD = 0.84; F(1, 47) = 2.07, p > 0.05, partial x2 = 0.01), speeding violations (Mnov = 4.60, SD = 2.27 vs. Mexp = 3.81, SD = 1.88; F(1, 47) = 1.68, p > 0.05, partial x2 = 0.01), or road-edge excursions (Mnov = 0.56, SD = 0.82 vs. Mexp = 0.31, SD = 0.62; F(1, 47) = 1.41, p > 0.05, partial x2 = 0.00). Not surprisingly, these results suggest that experienced drivers had a clear performance advantage in the driving simulator environment even when they reported less experience with video games. The impact of the cell phone conversation on driving performance was elucidated by the following univariate results. Drivers in the cell phone condition were involved in significantly more collisions with other vehicles (Mcell = 2.04, SD = 0.89 vs. Mctrl = 1.19, SD = 1.10; F(1, 47) = 10.39, p < 0.01, partial x2 = 0.09), struck more pedestrians (Mcell = 1.28, SD = 0.98 vs. Mctrl = 0.50, SD = 0.70; F(1, 47) = 10.14, p < 0.01, partial x2 = 0.08), exceeded the posted speed limits more frequently (Mcell = 5.24, S.D = 1.94 vs. Mctrl = 3.19, SD = 1.74; F(1, 47) = 15.16, p < 0.001, partial x2 = 0.12), and drove through more stop signs (Mcell = 1.12, SD = 1.01 vs. Mctrl = 0.65, SD = 0.80; F(1, 47) = 4.43, p < 0.05, partial x2 = 0.03). Cell phone use did not significantly impact the number of times drivers crossed the centerline (Mcell = 1.36, SD = 1.75 vs. Mctrl = 0.88, SD = 1.07; F(1, 47) = 1.16, p > 0.05, partial x2 = 0.00), or the number of times they drove off the road (Mcell = 0.52, SD = 0.65 vs. Mctrl = 0.35, SD = 0.80; F(1, 47) = 0.59, p > 0.05, partial x2 = 0.00). 4.2. SA questions Fig. 2 presents the total number of SA questions answered correctly as a function of driver experience and distraction condition. The experienced group of drivers (M = 7.54, SD = 1.42) correctly answered significantly more questions than did the novice drivers (M = 4.76, SD = 2.18; F(1, 47) = 44.90, p < 0.001, partial x2 = 0.46). The effect of the cell phone conversation was evident in that drivers who were engaged in conversation (M = 4.88, SD = 1.83) answered significantly fewer questions correctly than did those in the control condition (M = 7.42, SD = 2.00; F(1, 47) = 36.80, p < 0.001, partial x2 = 0.41). The experience level by distracter interaction was not significant (F(1, 47) = 0.00, p > 0.05, partial x2 = 0.00).
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Fig. 2. SA questions.
4.3. Turns missed The ability to follow a set of driving directions was also used as an indicator of situation awareness (see Fig. 3). Novices were less able to follow directions and missed an average of 2.40 turns (SD = 1.73) whereas experienced drivers missed an average of just over one turn during the driving scenario (M = 1.11, SD = 1.11; F(1, 47) = 16.32, p < 0.001, partial x2 = 0.23). The distraction of the cell phone also reduced drivers’ ability to follow directions (F(1, 47) = 43.57, p < 0.001, partial x2 = 0.45). Drivers not distracted by the cell phone missed, on average, less than one turn (M = 0.77, SD = 0.95) whereas drivers engaged in conversation missed 2.76 turns (SD = 1.45). Further, the ANOVA indicated a small, but significant interaction of experience and distracter conditions (F(1, 47) = 4.19, p < 0.05, partial x2 = 0.06). Novices missed an average of 1.08 turns (SD = 1.16) in the control condition and an average of 3.62 turns (SD = 1.19) in the cell phone condition.
Fig. 3. Missed turns.
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Alternatively, experienced drivers had little difficulty in the control condition, missing an average of only 0.50 turns (SD = 0.65), but again, those in the cell phone condition made more than three times as many errors (M = 1.83, SD = 1.11). A Tukey HSD revealed that experienced drivers in the cell phone condition missed significantly more turns than when they were not talking on a cell phone. Thus, while the effect of the cell phone was slightly greater for novice drivers, experience did not fully mitigate the detrimental effects of cell phone use on SA in terms of the ability to follow a printed set of driving directions. 5. Discussion This study examined the distracting effects of cell phone conversations on SA while driving and whether drivers with greater levels of experience would be less impaired. In the simulated drive, novices were involved in more collisions, drove through more stop signs, and crossed the centerline more often, but did not differ from experienced drivers on the number of pedestrians struck, speed limits obeyed, or road departures. Cell phone usage impaired novice drivers’ ability to attend to many relevant environmental stimuli, such as other vehicles (collisions), pedestrians, and speed limit and stop signs, but did not alter horizontal lane maintenance behaviors (crossing road edge or centerline). This is consistent with a recent meta-analysis by Horrey and Wickens (2006) that showed that the impact of cell phone use on lane-keeping performance was much smaller than its impact on response time to critical road stimuli. Horrey and Wickens suggested that lane keeping may be a relatively automatic skill requiring fewer cognitive resources. Participants’ scores on the SA measures showed that engagement in conversation decreased drivers’ ability to cope with the simultaneous demands of driving and following directions. Essentially, when participants talked on the cell phone they were unable to maintain the same level of SA (as assessed by either a query method or direction-following task) as other drivers. One explanation for this finding is that the cell phone conversation and the act of driving taxed the same cognitive resources. For example, the cognitive distractions produced by the cell phone conversations may have interfered with the driver’s ability to navigate through the simulated scene. While the addition of the cell phone conversation was designed to increase the driver’s cognitive workload, future research may address the driver’s perceptions of increased workload. That is, researchers may wish to assess whether drivers continue to engage in distracting tasks because they believe that these tasks do not significantly increase their workload levels, and therefore do not impact their ability to drive. Moreover, the results demonstrated that both novice and experienced drivers suffered decrements in performance when talking on a cell phone. Even experienced drivers could not cope with the cognitive demands of cell phone conversations. Only on the direction-following task was the performance decrement caused by cell phone use slightly greater for novices than for experienced drivers. When conversing on a cell phone, experienced drivers’ overall performance (in terms of driving infractions) was equivalent to that of non-distracted novice drivers. The finding that experience does not mitigate the distracting effects of cell phone conversation has important implications for traffic safety issues. Experienced drivers continue to talk and drive, perhaps because they are unaware of the potential risks involved when talking on a cell phone. Essentially, drivers may be unaware that they are inattentive to various environmental stimuli due to the cognitive distraction induced by the cell phone (see Lesch & Hancock, 2004). Consequently, cell phone-distracted drivers may be both situationally unaware and perhaps overconfident about their performance. Some caution should be taken when generalizing from the current results. The lack of motion cues and the artificiality of the situation may have affected driving behavior. For instance, although participants were instructed to obey all traffic laws, they suffered no repercussions from their violations or from collisions. On the other hand, for each type of driving infraction (excluding speeding violations) experienced drivers in the control group committed an average of less than one infraction suggesting that they took the instructions seriously. Although the artificial environment of the simulator was an obvious limitation of the study, Horrey and Wickens (2006) found that the effects of cell phone use on driving performance have been, on average, greater in field studies than in simulator studies. Interpretation of the results concerning the effects of experience should also be tempered by the fact that novice drivers in the current study were much younger than their experienced counterparts. Because of the age difference between groups, one cannot conclude that experience alone, and not age, was the only
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contributing factor in the performance differences. The novice group, consisting of 14–16-year old high school students, could not be expected to possess the same level of cognitive ability as the experienced group with an average age of 29. Ideally, the two groups would be matched for age, but the difficulty of recruiting novice drivers from the local area within the same age range of the experienced group made that sampling strategy a very remote possibility. Future researchers may wish to address this issue by controlling for the factors of age and cognitive ability in studies of situation awareness in distracted driving. While many states in the US have enacted laws against hand-held cell phone use while driving, it remains unknown whether these laws have decreased cell phone use or traffic accidents. Perhaps the best approach to preventing cell phone use among drivers is to address the issue through training rather than legislation. Studies such as the current one can be used to discourage cell phone use among young drivers. This study demonstrated that safety is compromised even after drivers gain years of experience, and that this decreased performance is a normal result of overtaxing one’s attention resources rather than an indictment of personal driving skill. Hopefully, drivers’ education programs will help young drivers make the right choice about whether to talk on the cell phone while they drive and maintain that behavior throughout their lifetimes. Acknowledgements The authors wish to express their appreciation to Dr. Ben Lawson and the Naval Aerospace Medical Research Laboratory for the use of their driving simulator equipment. References Beede, K. E., & Kass, S. J. (2006). Engrossed in conversation: The impact of cell phones on simulated driving performance. Accident Analysis and Prevention, 38, 415–421. Consiglio, W., Driscoll, P., Witte, M., & Berg, W. P. (2003). Effect of cellular telephone conversations and other potential interference on reaction time in a braking response. Accident Analysis and Prevention, 35, 495–500. Crundall, D. E., & Underwood, G. (1998). Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics, 48, 448–458. Dominguez, C. (1994). Can SA be defined?. In M. Vidulich, C. Dominguez, E. Vogl, & G. McMillan (Eds.), Situation awareness: Papers and annotated bibliography (pp. 5–15). AL/CF-TR-1994-0085. Armstrong Laboratory. Durso, F. T., & Gronlund, S. D. (1999). Situation awareness. In F. T. Durso, R. S. Nickerson, R. W. Schvaneveldt, S. T. Dumais, D. S. Lindsay, & M. T. H. Chi (Eds.), Handbook of applied cognition (pp. 283–314). New York: Wiley. Endsley, M. (1988a). Design and evaluation for situation awareness enhancement. In Proceedings of the human factors society 32nd annual meeting (pp. 97–101). Santa Monica, CA: Human Factors Society. Endsley, M. (1988b). Situation Awareness Global Assessment Technique (SAGAT). In Proceedings of the national aerospace and electronics conference (pp. 41–45). Human Factors and Ergonomics Society. Endsley, M. (1990). Predictive validity of an objective measure of situation awareness. Proceedings of the Human Factors Society, 34, 41–45. Endsley, M. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64. Ferguson, S. (2003). Other high risk factors for young drivers—how graduated licensing does, doesn’t, or could address them. Journal of Safety Research, 34, 71–77. Garcia-Larrea, L., Perchet, C., Perrin, F., & Amendo, E. (2001). Interference of cellular phone conversations with visuomotor tasks: An ERP study. Journal of Psychophysiology, 15, 14–21. Gugerty, L. J. (1997). Situation awareness during driving: Explicit and implicit knowledge in dynamic spatial memory. Journal of Experimental Psychology: Applied, 3, 42–66. Horrey, W. J., & Wickens, C. D. (2006). Examining the impact of cell phone conversations on driving using meet-analytic techniques. Human Factors, 48, 196–205. Kass, S. J., Herschler, D. A., & Companion, M. A. (1991). Training situational awareness through pattern recognition in a battlefield environment. Military Psychology, 3, 105–112. Lam, L. T. (2002). Distractions and the risk of car-crash injury: The effect of driver’s age. Journal of Safety Research, 33, 411–419. Langham, M., Hole, G., & Edwards, J. (2002). An analysis of ‘‘looked but failed to see’’ accidents involving parked police vehicles. Ergonomics, 45, 167–185. Lesch, M. F., & Hancock, P. A. (2004). Driving performance during concurrent cell-phone use: Are drivers aware of their performance decrements?. Accident Analysis and Prevention 36, 471–480. McKnight, A. J., & McKnight, A. S. (1993). The effect of cellular phone use upon driver attention. Accident Analysis and Prevention, 25, 259–265. Peters, G. A., & Peters, B. J. (2002). The distracted driver. Journal of the Royal Society for the Promotion of Health, 121, 23–28.
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Recarte, M. A., & Nunes, L. M. (2003). Mental workload while driving: Effects on visual search, discrimination, and decision-making. Journal of Experimental Psychology: Applied, 9, 119–137. Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular-telephone calls and motor vehicle collisions. The New England Journal of Medicine, 226, 453–458. Strayer, D. L., & Drews, F. A. (2004). Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers. Human Factors, 46, 640–649. Strayer, D. L., Drews, F. A., & Crouch, D. J. (2006). A comparison of the cell-phone driver and the drunk driver. Human Factors, 48, 381–391. Strayer, D. L., & Johnston, W. (2001). Driven to distraction: Dual–task studies of simulated driving and conversing on a cellular phone. Psychological Science, 12, 462–466. Underwood, G., Crundall, D., & Chapman, P. (2002). Selective searching while driving: The role of experience in hazard detection and general surveillance. Ergonomics, 45, 1–12. Wickens, C. D., & Hollands, J. G. (2000). Engineering psychology and human performance (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Effects of distraction and experience on situation awareness and simulated driving Steven J. Kass a
a,*
, Kerstan S. Cole b, Claudia J. Stanny
a
University of West Florida, Department of Psychology, 11000 University Parkway, Pensacola, FL 32514, United States b Texas Tech University, Department of Psychology, Lubbock, TX 79409-2051, United States Received 29 May 2006; received in revised form 6 December 2006; accepted 8 December 2006
Abstract This study examined the impact of cell phone conversation on situation awareness and performance of novice and experienced drivers. Driving performance and situation awareness among novice drivers ages 14–16 (n = 25) and experienced drivers ages 21–52 (n = 26) were assessed using a driving simulator. Performance was measured by the number of driving infractions committed: speeding, collisions, pedestrians struck, stop signs missed, and centerline and road edge crossings. Situation awareness was assessed through a query method and through participants’ performance on a direction-following task. Cognitive distractions were induced through simulated hands-free cell phone conversations. The results indicated that novice drivers committed more driving infractions and were less situationally aware than their experienced counterparts. However, the two groups suffered similar decrements in performance during the cell phone condition. This study provides evidence of the detrimental effects of cell phone use for both novice and experienced drivers. These findings have implications for supporting driving legislation that limits the use of cell phones (including hands-free) in motor vehicles, regardless of the driver’s experience level. 2006 Elsevier Ltd. All rights reserved. Keywords: Distracted driving; Situation awareness; Attention; Experience level
1. Introduction Many studies have shown that talking on a cell phone while driving significantly influences driver performance. Furthermore, consumers are purchasing cell phones at increasing rates. As the number of cell phone users increases, the potential health risks also increase not only for those who choose to converse while driving, but also for passengers, pedestrians, and other drivers (Ferguson, 2003; Lam, 2002; Peters & Peters, 2002). In an analysis of nearly 700 cell phone-related accidents, Redelmeier and Tibshirani (1997) concluded that
*
Corresponding author. Tel.: +1 850 474 2107; fax: +1 850 857 6060. E-mail address: [email protected] (S.J. Kass).
1369-8478/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2006.12.002
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talking on a cell phone increased the probability of a collision between 3 and 6.5 times. They also suggested that these distraction effects are comparable to a blood–alcohol-content above the legal limit. In fact, Strayer, Drews, and Crouch (2006) found that in a driving simulation task, cell phone users showed greater impairments as measured by increased number of rear-end collisions and time required to regain speed following braking than drivers who were legally drunk (i.e., blood–alcohol-content of 0.08). Additionally, Strayer and Johnston (2001) reported that drivers engaged in cell phone conversations missed twice as many traffic signals and had slower reaction times. Consiglio, Driscoll, Witte, and Berg (2003) also found that cell phone use (hand-held or hands-free) slowed drivers’ braking reactions compared to when they drove without distraction or when listening to music on the radio. 1.1. Role of situation awareness in driving Research on situation awareness (SA) is often traced back to military aviation studies, but SA is crucial to the performance of any dynamic complex task, including driving in heavy traffic (Endsley, 1995). SA involves identifying relevant environmental stimuli or cues, integrating that information into the operator’s knowledge base to form a mental model or representation of the situation, and using that representation to project the occurrence of events in the near future (see Dominguez, 1994; Endsley, 1990; Kass, Herschler, & Companion, 1991). As drivers move through the environment, they must identify the relevant information in rapidly changing traffic patterns (e.g., distance to other vehicles, closing speed) and be prepared to react to events that may occur (e.g., car backing out of driveway, stop sign) to avoid accidents. To achieve SA, individuals must rely on perception and pattern recognition abilities (Durso & Gronlund, 1999; Kass et al., 1991), attention and working memory (Gugerty, 1997; Wickens & Hollands, 2000), as well as long-term memory (e.g., Endsley, 1995). Therefore, cognitive distractions that tax a driver’s attention or memory load may adversely impact SA. Recently, researchers (e.g., Beede & Kass, 2006; Garcia-Larrea, Perchet, Perrin, & Amendo, 2001; McKnight & McKnight, 1993; Recarte & Nunes, 2003) have provided empirical evidence that driving performance suffers as a result of such cognitive distractions as cell phone use. These distractions may become particularly important safety issues when motorists are navigating through changing traffic patterns while attempting to maintain SA. Attention and hazard detection, aspects of SA, are known to be adversely affected by the cognitive distractions of cell phone conversation (Strayer & Johnston, 2001). 1.2. Experience level and situation awareness Research on risk exposure of younger drivers indicates that they are more likely to speed, pull into smaller gaps in traffic, and glance away from the road for longer intervals than experienced drivers (see Ferguson, 2003; Strayer & Drews, 2004; Underwood, Crundall, & Chapman, 2002). Crundall and Underwood (1998) investigated the differences in spatial strategy between novice and experienced drivers under different road conditions by examining participants’ visual attention. Experienced drivers employed a more flexible form of spatial strategy, such as searching for alternative routes, while novice drivers had rigid spatial strategies and usually focused on the one possible strategy that their visual search allowed. Lacking experience, novice drivers may not have learned to cope with the cognitive load imposed by complex road conditions while simultaneously attending to the overall demands of the driving task. This may result in a loss of SA and an inability to avoid collisions resulting in injury or death. The current experiment was designed to test the hypothesis that cell phone conversations disrupt SA and impair driving performance by preventing drivers from attending to situation-relevant stimuli such as speed limit postings, stop signs, pedestrians, and other traffic. These failings should manifest themselves in terms of increased driving infractions and an inability to take on an additional task (direction following). Prior research suggests that practice effects (e.g., Ferguson, 2003; Strayer & Drews, 2004), mental models (e.g., Langham, Hole, & Edwards, 2002), and flexible spatial strategies (e.g., Crundall & Underwood, 1998) developed through experience enhance operators’ performance. Thus, experience level was expected to mitigate some of the decrements in driving performance and SA associated with cell phone conversations.
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2. Method 2.1. Participants Two groups of participants were recruited for this study and classified as either novice (not yet licensed) or experienced drivers. The novice group consisted of 24 students (12 girls, 12 boys) recruited from a high school driver’s education class in northwest Florida. These students had completed 10 weeks of class instruction. Parents or legal guardians granted informed consent for their children to participate. The student drivers’ ages ranged from 14 to 16 years (14.68, SD = 0.56). The experienced driver group consisted of 25 adults (12 men, 13 women) ages 21–52 years (M = 29.0, SD = 8.94) recruited from the local community. These participants ranged in driving experience from 6 to 34 years (M = 13.69, SD = 8.22). Because the driving simulator may seem similar to a video game, we asked the participants to identify how may hours per week they played video games (on the following scale: 0 h, 1–5 h, 6–10 h, etc.). The median response for the younger, novice group of drivers was that they played video games for 6–10 h/week whereas the median response for the experienced group was 0 h/week. Neither group was engaged in extensive video game play, though a slight advantage may be suggested for the novice group when using the simulator. 2.2. Apparatus The STISIM-Drive simulator software, developed by Systems Technology Inc., was used to measure and document participants’ driving behaviors. The software simulates a vehicle with an automatic transmission and receives inputs from a game controller steering wheel, accelerator, and brake pedal. A 61 cm (24 in.) LCD monitor mounted on a desktop displayed the city roadway scenes. Additionally, STISIM models speed-sensitive steering and provides finely detailed, dynamic representations of various driving tasks and objects (e.g., buildings, pedestrians, vehicles, signs and signal lights) providing the researchers with full control in customizing scenarios. Though the experimental configuration is a desktop computer simulation, the high speed, detailed graphics (i.e., texture, shading, and lighting) and experimenter-configured vehicle control dynamics (i.e., steering, braking) are identical to those used by higher-end simulator systems. Furthermore, the software provides visual and auditory feedback and records such driver performance data as the number of stop signs missed, pedestrians struck, speeding occurrences, off-road excursions, and centerline crossings, as well as the capability to playback a participant’s completed scenario. 3. Materials 3.1. SA questions At varying intervals during the simulation, the participants’ SA was assessed using a query method (Endsley, 1988a, 1988b) in which the experimenters stopped the simulator and asked the participant relevant questions about the scene. In the current experiment, participants were asked a total of nine SA questions that pertained to the task of driving (e.g., ‘‘what is the speed limit?’’ ‘‘how many cars backed out in front of you?’’). All participants received the same nine questions, but in random order, and all were relevant to driving awareness. The questions were presented in random order so that the difficulty of a particular question was not related to the difficulty of a particular portion of the driving course. 3.2. Simulated cellular phone conversations Because recent research indicates that the detrimental effects of hand-held cell phones and hands-free cell phones are similar (Strayer & Johnston, 2001), all participants in the distracter condition wore headphones with a mouth-piece and were instructed to ‘‘answer the hands-free cell phone when it rings by saying hello clearly into the mouth piece.’’ Previously recorded questions (wav files) were used in this condition to simulate the cell phone conversation (Beede & Kass, 2006). Questions were presented at irregular intervals to allow time for responses and to simulate a naturalistic setting. These questions were purposely developed to produce
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cognitive distractions and required participants to visualize their answers (e.g., asking for directions to a particular place, visualizing the number of windows in their home, and computing simple arithmetic problems). These demanding questions were combined with other questions and statements to create a naturally flowing conversation. In order to ensure that participants would actively engage in the phone conversation, they were told that their responses were being recorded (they actually were not). 3.3. Procedure Participants were randomly assigned to one of two conditions: the hands-free cell phone distracter condition or the no distracter condition. Participants were instructed to pay attention to environmental stimuli such as cross-traffic and pedestrians and to obey all traffic laws. In order to assess SA in a naturalistic manner and to simulate real-world driving behaviors, experimenters implemented a direction-following task in a 5-min practice scenario and also in the experimental scenario. During the practice scenario, participants were given a typed set of directions and attempted to follow those directions to the destination. After participants completed the practice scenario, experimenters gave participants a different set of typed directions and asked them to read over the directions before driving. For example, participants were instructed as follows: ‘‘At the second light, take a left at the gas station.’’ Participants were allowed to refer back to the instructions at any time. If a turn was missed, the error was recorded and the driver continued to follow the directions. The street scenes presented by the simulation followed the driver (regardless of actual turns made), so that all drivers saw the same scenes even if they did not make the same turns. As a result, drivers did not need to retrace a route to correct for missed turns and were not instructed to do so. The driver could miss one turn without missing any of the remaining turns in the route (five turns total). During three irregularly spaced intervals, the simulator paused and the experimenter asked three SA questions chosen at random from the list of nine and asked participants to answer these questions to the best of their ability. The experimental driving scenarios lasted approximately 11–12 min. 3.4. Design and analysis The experiment consisted of a 2 (novices vs. experienced) · 2 (cell phone vs. no distracter) between subjects factorial design. The dependent variables measured were the numbers of various driving infractions and scores on the two measures of situation awareness. Driving infraction data collected by the simulator included: number of collisions, number of pedestrians hit, number of speeding violations (8 kph or 5 mph over speed limit), number of stop signs missed, and errors of lane maintenance (i.e., centerline crossings and road-edge excursions). Situation awareness was measured by assessing the total number of SA questions participants answered correctly and by the number of turns missed while following the driving directions. The driving conditions consisted of a hands-free cellular phone condition and a no distracter control condition. 4. Results 4.1. Driving infractions To control for experiment-wide error rate associated with conducting multiple analyses of variance (ANOVA) on the 6 different driving infraction variables, a multivariate ANOVA (MANOVA) was performed. The dependent variables analyzed included the number of collisions, pedestrians hit, speeding violations, stop signs missed, centerline crossings, and road-edge excursions. Fig. 1 presents the means for the total number of infractions made by novices and experienced drivers across conditions. The MANOVA indicated significant main effects of experience level (F(6, 42) = 16.20, p < 0.001, partial x2 = 0.48) and cell phone use (F(6, 42) = 9.60, p < 0.001, partial x2 = 0.34). No significant interaction was found (F(6, 42) = 1.54, p = 0.19, partial x2 = 0.03), suggesting that when drivers use a cell phone they suffer the same amount of performance decrement regardless of experience level. Because MANOVA main effects of experience level and cell phone use were found, the univariate analyses of the six dependent variables were examined. These analyses revealed that novice drivers were involved in
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Fig. 1. Driving infractions.
significantly more collisions with other vehicles (Mnov = 2.12, SD = 0.97 vs. Mexp = 1.12, SD = 0.95; F(1, 47) = 15.28, p < 0.001, partial x2 = 0.12), drove through significantly more stop signs (Mnov = 1.52, SD = 0.87 vs. Mexp = 0.27, SD = 0.45; F(1, 47) = 42.60, p < 0.001, partial x2 = 0.29), and crossed the centerline significantly more often (Mnov = 1.64, SD = 1.75 vs. Mexp = 0.62, SD = 0.85; F(1, 47) = 6.66, p < 0.05, partial x2 = 0.05). No significant effects of experience were found for the number of pedestrians struck (Mnov = 1.08, SD = 1.00 vs. Mexp = 0.69, SD = 0.84; F(1, 47) = 2.07, p > 0.05, partial x2 = 0.01), speeding violations (Mnov = 4.60, SD = 2.27 vs. Mexp = 3.81, SD = 1.88; F(1, 47) = 1.68, p > 0.05, partial x2 = 0.01), or road-edge excursions (Mnov = 0.56, SD = 0.82 vs. Mexp = 0.31, SD = 0.62; F(1, 47) = 1.41, p > 0.05, partial x2 = 0.00). Not surprisingly, these results suggest that experienced drivers had a clear performance advantage in the driving simulator environment even when they reported less experience with video games. The impact of the cell phone conversation on driving performance was elucidated by the following univariate results. Drivers in the cell phone condition were involved in significantly more collisions with other vehicles (Mcell = 2.04, SD = 0.89 vs. Mctrl = 1.19, SD = 1.10; F(1, 47) = 10.39, p < 0.01, partial x2 = 0.09), struck more pedestrians (Mcell = 1.28, SD = 0.98 vs. Mctrl = 0.50, SD = 0.70; F(1, 47) = 10.14, p < 0.01, partial x2 = 0.08), exceeded the posted speed limits more frequently (Mcell = 5.24, S.D = 1.94 vs. Mctrl = 3.19, SD = 1.74; F(1, 47) = 15.16, p < 0.001, partial x2 = 0.12), and drove through more stop signs (Mcell = 1.12, SD = 1.01 vs. Mctrl = 0.65, SD = 0.80; F(1, 47) = 4.43, p < 0.05, partial x2 = 0.03). Cell phone use did not significantly impact the number of times drivers crossed the centerline (Mcell = 1.36, SD = 1.75 vs. Mctrl = 0.88, SD = 1.07; F(1, 47) = 1.16, p > 0.05, partial x2 = 0.00), or the number of times they drove off the road (Mcell = 0.52, SD = 0.65 vs. Mctrl = 0.35, SD = 0.80; F(1, 47) = 0.59, p > 0.05, partial x2 = 0.00). 4.2. SA questions Fig. 2 presents the total number of SA questions answered correctly as a function of driver experience and distraction condition. The experienced group of drivers (M = 7.54, SD = 1.42) correctly answered significantly more questions than did the novice drivers (M = 4.76, SD = 2.18; F(1, 47) = 44.90, p < 0.001, partial x2 = 0.46). The effect of the cell phone conversation was evident in that drivers who were engaged in conversation (M = 4.88, SD = 1.83) answered significantly fewer questions correctly than did those in the control condition (M = 7.42, SD = 2.00; F(1, 47) = 36.80, p < 0.001, partial x2 = 0.41). The experience level by distracter interaction was not significant (F(1, 47) = 0.00, p > 0.05, partial x2 = 0.00).
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Fig. 2. SA questions.
4.3. Turns missed The ability to follow a set of driving directions was also used as an indicator of situation awareness (see Fig. 3). Novices were less able to follow directions and missed an average of 2.40 turns (SD = 1.73) whereas experienced drivers missed an average of just over one turn during the driving scenario (M = 1.11, SD = 1.11; F(1, 47) = 16.32, p < 0.001, partial x2 = 0.23). The distraction of the cell phone also reduced drivers’ ability to follow directions (F(1, 47) = 43.57, p < 0.001, partial x2 = 0.45). Drivers not distracted by the cell phone missed, on average, less than one turn (M = 0.77, SD = 0.95) whereas drivers engaged in conversation missed 2.76 turns (SD = 1.45). Further, the ANOVA indicated a small, but significant interaction of experience and distracter conditions (F(1, 47) = 4.19, p < 0.05, partial x2 = 0.06). Novices missed an average of 1.08 turns (SD = 1.16) in the control condition and an average of 3.62 turns (SD = 1.19) in the cell phone condition.
Fig. 3. Missed turns.
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Alternatively, experienced drivers had little difficulty in the control condition, missing an average of only 0.50 turns (SD = 0.65), but again, those in the cell phone condition made more than three times as many errors (M = 1.83, SD = 1.11). A Tukey HSD revealed that experienced drivers in the cell phone condition missed significantly more turns than when they were not talking on a cell phone. Thus, while the effect of the cell phone was slightly greater for novice drivers, experience did not fully mitigate the detrimental effects of cell phone use on SA in terms of the ability to follow a printed set of driving directions. 5. Discussion This study examined the distracting effects of cell phone conversations on SA while driving and whether drivers with greater levels of experience would be less impaired. In the simulated drive, novices were involved in more collisions, drove through more stop signs, and crossed the centerline more often, but did not differ from experienced drivers on the number of pedestrians struck, speed limits obeyed, or road departures. Cell phone usage impaired novice drivers’ ability to attend to many relevant environmental stimuli, such as other vehicles (collisions), pedestrians, and speed limit and stop signs, but did not alter horizontal lane maintenance behaviors (crossing road edge or centerline). This is consistent with a recent meta-analysis by Horrey and Wickens (2006) that showed that the impact of cell phone use on lane-keeping performance was much smaller than its impact on response time to critical road stimuli. Horrey and Wickens suggested that lane keeping may be a relatively automatic skill requiring fewer cognitive resources. Participants’ scores on the SA measures showed that engagement in conversation decreased drivers’ ability to cope with the simultaneous demands of driving and following directions. Essentially, when participants talked on the cell phone they were unable to maintain the same level of SA (as assessed by either a query method or direction-following task) as other drivers. One explanation for this finding is that the cell phone conversation and the act of driving taxed the same cognitive resources. For example, the cognitive distractions produced by the cell phone conversations may have interfered with the driver’s ability to navigate through the simulated scene. While the addition of the cell phone conversation was designed to increase the driver’s cognitive workload, future research may address the driver’s perceptions of increased workload. That is, researchers may wish to assess whether drivers continue to engage in distracting tasks because they believe that these tasks do not significantly increase their workload levels, and therefore do not impact their ability to drive. Moreover, the results demonstrated that both novice and experienced drivers suffered decrements in performance when talking on a cell phone. Even experienced drivers could not cope with the cognitive demands of cell phone conversations. Only on the direction-following task was the performance decrement caused by cell phone use slightly greater for novices than for experienced drivers. When conversing on a cell phone, experienced drivers’ overall performance (in terms of driving infractions) was equivalent to that of non-distracted novice drivers. The finding that experience does not mitigate the distracting effects of cell phone conversation has important implications for traffic safety issues. Experienced drivers continue to talk and drive, perhaps because they are unaware of the potential risks involved when talking on a cell phone. Essentially, drivers may be unaware that they are inattentive to various environmental stimuli due to the cognitive distraction induced by the cell phone (see Lesch & Hancock, 2004). Consequently, cell phone-distracted drivers may be both situationally unaware and perhaps overconfident about their performance. Some caution should be taken when generalizing from the current results. The lack of motion cues and the artificiality of the situation may have affected driving behavior. For instance, although participants were instructed to obey all traffic laws, they suffered no repercussions from their violations or from collisions. On the other hand, for each type of driving infraction (excluding speeding violations) experienced drivers in the control group committed an average of less than one infraction suggesting that they took the instructions seriously. Although the artificial environment of the simulator was an obvious limitation of the study, Horrey and Wickens (2006) found that the effects of cell phone use on driving performance have been, on average, greater in field studies than in simulator studies. Interpretation of the results concerning the effects of experience should also be tempered by the fact that novice drivers in the current study were much younger than their experienced counterparts. Because of the age difference between groups, one cannot conclude that experience alone, and not age, was the only
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contributing factor in the performance differences. The novice group, consisting of 14–16-year old high school students, could not be expected to possess the same level of cognitive ability as the experienced group with an average age of 29. Ideally, the two groups would be matched for age, but the difficulty of recruiting novice drivers from the local area within the same age range of the experienced group made that sampling strategy a very remote possibility. Future researchers may wish to address this issue by controlling for the factors of age and cognitive ability in studies of situation awareness in distracted driving. While many states in the US have enacted laws against hand-held cell phone use while driving, it remains unknown whether these laws have decreased cell phone use or traffic accidents. Perhaps the best approach to preventing cell phone use among drivers is to address the issue through training rather than legislation. Studies such as the current one can be used to discourage cell phone use among young drivers. This study demonstrated that safety is compromised even after drivers gain years of experience, and that this decreased performance is a normal result of overtaxing one’s attention resources rather than an indictment of personal driving skill. Hopefully, drivers’ education programs will help young drivers make the right choice about whether to talk on the cell phone while they drive and maintain that behavior throughout their lifetimes. Acknowledgements The authors wish to express their appreciation to Dr. Ben Lawson and the Naval Aerospace Medical Research Laboratory for the use of their driving simulator equipment. References Beede, K. E., & Kass, S. J. (2006). Engrossed in conversation: The impact of cell phones on simulated driving performance. Accident Analysis and Prevention, 38, 415–421. Consiglio, W., Driscoll, P., Witte, M., & Berg, W. P. (2003). Effect of cellular telephone conversations and other potential interference on reaction time in a braking response. Accident Analysis and Prevention, 35, 495–500. Crundall, D. E., & Underwood, G. (1998). Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics, 48, 448–458. Dominguez, C. (1994). Can SA be defined?. In M. Vidulich, C. Dominguez, E. Vogl, & G. McMillan (Eds.), Situation awareness: Papers and annotated bibliography (pp. 5–15). AL/CF-TR-1994-0085. Armstrong Laboratory. Durso, F. T., & Gronlund, S. D. (1999). Situation awareness. In F. T. Durso, R. S. Nickerson, R. W. Schvaneveldt, S. T. Dumais, D. S. Lindsay, & M. T. H. Chi (Eds.), Handbook of applied cognition (pp. 283–314). New York: Wiley. Endsley, M. (1988a). Design and evaluation for situation awareness enhancement. In Proceedings of the human factors society 32nd annual meeting (pp. 97–101). Santa Monica, CA: Human Factors Society. Endsley, M. (1988b). Situation Awareness Global Assessment Technique (SAGAT). In Proceedings of the national aerospace and electronics conference (pp. 41–45). Human Factors and Ergonomics Society. Endsley, M. (1990). Predictive validity of an objective measure of situation awareness. Proceedings of the Human Factors Society, 34, 41–45. Endsley, M. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64. Ferguson, S. (2003). Other high risk factors for young drivers—how graduated licensing does, doesn’t, or could address them. Journal of Safety Research, 34, 71–77. Garcia-Larrea, L., Perchet, C., Perrin, F., & Amendo, E. (2001). Interference of cellular phone conversations with visuomotor tasks: An ERP study. Journal of Psychophysiology, 15, 14–21. Gugerty, L. J. (1997). Situation awareness during driving: Explicit and implicit knowledge in dynamic spatial memory. Journal of Experimental Psychology: Applied, 3, 42–66. Horrey, W. J., & Wickens, C. D. (2006). Examining the impact of cell phone conversations on driving using meet-analytic techniques. Human Factors, 48, 196–205. Kass, S. J., Herschler, D. A., & Companion, M. A. (1991). Training situational awareness through pattern recognition in a battlefield environment. Military Psychology, 3, 105–112. Lam, L. T. (2002). Distractions and the risk of car-crash injury: The effect of driver’s age. Journal of Safety Research, 33, 411–419. Langham, M., Hole, G., & Edwards, J. (2002). An analysis of ‘‘looked but failed to see’’ accidents involving parked police vehicles. Ergonomics, 45, 167–185. Lesch, M. F., & Hancock, P. A. (2004). Driving performance during concurrent cell-phone use: Are drivers aware of their performance decrements?. Accident Analysis and Prevention 36, 471–480. McKnight, A. J., & McKnight, A. S. (1993). The effect of cellular phone use upon driver attention. Accident Analysis and Prevention, 25, 259–265. Peters, G. A., & Peters, B. J. (2002). The distracted driver. Journal of the Royal Society for the Promotion of Health, 121, 23–28.
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