Seven Myths about Cognitive Distraction and Driving

Seven Myths about Cognitive Distraction and Driving Richard A. Young, Ph.D. Research Professor Department of Psychiatry and Cognitive Neuroscience [email protected]

Detroit, Michigan, USA

Introduction • The focus of the ICTTP 2012 conference is the interaction between theory and practice. • Today’s symposium on “Driver attention and distraction” is highly relevant to this focus. – The U.S. Department of Transportation has made driver distraction a major initiative (see distraction.gov, DOT1 and NTSB2 hearings, proposed NHTSA driver distraction guidelines3 and docket4).

• While visual-manual distraction has been extensively researched, cognitive distraction has not been, leading to these 7 “myths” (or “false beliefs”).

1http://www.c-spanvideo.org/event/186640

2http://www.ntsb.gov/news/events/2012/attentive_driving/index.html 3http://www.gpo.gov/fdsys/pkg/FR-2012-02-24/pdf/2012-4017.pdf.

4http://www.regulations.gov/#!docketDetail;rpp=100;so=DESC;sb=docId;po=0;D=NHTSA-2010-0053.

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Myth 1: Cell phone conversations increase crash risk 4-fold relative to baseline driving Hand-Held Crash Control in car in car # on phone 129 30 Person-time (min)a 1290 1290 Rate (callers per min) 0.100 0.023 Rate Ratio (95% CL)

4.30 (2.89, 6.4)

Hands-Free

HH + HF

Crash Control in car in car 41 7 410 410 0.100 0.017

Crash Control in car in car 170 37 1700 1700 0.100 0.022

5.86 (2.63, 13.1) 4.59 (3.22, 6.56)

• MYTH 1: “The risk of a collision when using a cellular telephone was four times higher than the risk when a cellular telephone was not being used.”

– “units that allowed the hands to be free … offered no safety advantage over hand-held units.” 8/30/2012

- Raw data and quotes from Redelmeier & Tibshirani (1997) ICTTP 2012 Young - 7 Myths

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Fact 1: Cell phone conversations have a crash risk near that of baseline driving • People are in the car only 20% of control window time.1 • People call at a 10x higher rate when in the car.1 • From this information, we can estimate the in-car and out-of-car times and calls in the control windows as follows: Hand-Held Crash Control Control in car in car out of car # on phone 129 21.4 8.6 Person-time (min)a 1290 258 1032 Rate (callers per min) 0.100 0.083 0.008 Rate Ratio (95% CL)

1.20 (0.75, 1.94)

Hands-Free

HH + HF

Crash Control Control Crash Control Control in car in car out of car in car in car out of car 41 5 2 170 26.4 10.6 410 82 328 1700 340 1360 0.100 0.061 0.0061 0.100 0.078 0.0078

1.64 (0.63, 4.28)

1.29 (0.84,1.97)

• FACT 1: Counting only people in the car, the estimated cell phone conversation crash risk is near that of baseline driving in passenger vehicles for both hands-free and hand-held phones singly or in 1Young (2011, 2012a,b,c) combination. 8/30/2012 ICTTP 2012 Young - 7 Myths 4

Myth 1 vs. Fact 1: Passenger Cars

Rate Ratio

Cell Phone Conversation & Passenger Cars FACT 1: Counting BAD only time in 10 the car 5.05 shows the relative risk 3 of cell phone 2 conversation 1 is near baseline 0.5 0.5 HH HF HH+HF HH HF HH+HF driving in GOOD passenger Myth 1 Fact 1 cars. Rate Ratio 4.30 5.86 4.59 1.20 1.64 1.29 8/30/2012

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- Young (2012a,b,c)

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Myth 2: Cell phone conversations decrease crash risk relative to baseline driving MYTH 2: Two large naturalistic studies of heavy vehicles claimed: 1. “talking/listening on a hands-free phone significantly decreased the odds of involvement in a safety-critical event.”1 Hickman (2010, p. xiii) [Table 3: OR = 0.65 (95% CL 0.56-0.76)]

2. "talking or listening on a hands-free phone…provided a significant protective effect (OR = 0.4)." Olson (2009, p. xxii) 3. "talking/listening on a hand-held phone... was shown to be protective for fleets without a fleet cell phone policy.“ Hickman (2010, p. 48) [Table 29: OR = 0.78 (95% CL 0.62-0.98)]

4. FMCSA (2010, p. 2) describing Hickman et al. (2010): “Talking/listening on a hand-held or hands-free cell phone had a protective effect.”2 1“Safety-critical

event” refers to crashes, near crashes, and crash-relevant conflicts in this study. quote incorrectly implies that Hickman et al. (2010) claimed a protective effect for conversation on hand-held phones in general; in fact, Hickman et al. (2010) claimed a protective effect for hand-held phones only “for fleets without a fleet cell phone policy” (quote 3 above).

2This

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Fact 2: Cell phone conversations have a crash risk near that of baseline driving

• An overall “crude” odds ratio calculated after summing across all strata might not be valid if there is heterogeneity between strata. • There is strong heterogeneity between the 3 heavy vehicle types in Hickman et al. (2010) for hands-free talk/listen (p < 0.0000001). Stratum Method 1 crude TTT Buses crude >2-axle crude All crude 2 pooled All

OR [95% CI] Table 0.58 0.47 0.73 32 1.34 1.05 1.72 31 1.38 0.92 2.06 30 0.65 0.56 0.76 3 1.01 0.55 1.87

1Tractor-Trailer/Tanker 2Episheet

meta-analysis (using the “random effects” model, recommended as “generally more appropriate” than the fixedeffects model (Fleiss, 1993, p. 123). 8/30/2012

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• The “crude” odds ratio of 0.65 (red circle) is hence likely invalid, and separate analyses of each vehicle type are indicated (Fleiss, 1993).3 • If an overall odds ratio estimate across strongly heterogeneous strata is required, a simple metaanalysis2 finds a pooled OR of 1.01 (95% C.I. 0.55-1.87) (black circle). 7

Fact 2: Cell phone conversations have a crash risk near that of baseline driving HF Cell Phone Conversation & Heavy Vehicle Safety-Related Event Risk

Odds Ratio

BAD

2.0 1.6

FACT 2

1.2 1.0 0.8 0.6

GOOD

0.4 0.4

Odds Ratio 8/30/2012

MYTH 2 TTT

Buses

>2-axle

crude

pooled

0.58

1.34

1.38

0.65

1.01

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FACT 2: Pooling across strata shows the relative risk of cell phone conversation is near baseline driving in heavy vehicles.

8

Fact 2 Caveats

• Hickman et al. (2010) classified cell phone conversation as a secondary task, not a cognitive distraction,(1, Section 1.4) which they define as “daydreaming” or “lost in thought”, clearly not the case when on the phone. – But 14 other studies do classify cell phone conversation as a cognitive distraction.(see 1,Sections 2,3,4) Hence, my claim that “protective effect” from cell phone conversation is a myth about cognitive distraction seems reasonable, even though it disagrees with the definition of cognitive distraction used by Hickman et al. (2010).

• Young & Schreiner (2009) found a crude rate ratio of 0.67 for hands-free personal calls in passenger vehicles using the OnStar device. – This cohort study had 91 million calls and 2,037 airbag-deployment crashes in 3 million passenger vehicles over 30 months. – This 0.67 crude estimate is near the Hickman et al. (2010) crude estimate of 0.63 for hands-free cell phone calls in heavy vehicles. – But the OnStar study noted the potential for bias arising from differences in baseline crash rates or other demographic factors in different strata (p. 198), and was careful not to claim a “protective effect” of hands-free conversation.

• Bias from many factors is likely present in any crude estimate of relative crash risk which does not take heterogeneity into account. – e.g., heterogeneity between crashes, near crashes, and crash-relevant conflicts; vehicle types; driving exposure; driving experience; risk-prone vs. risk-averse drivers; 1see Young (2012d, Appendix A) age; gender; education; etc. 9

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Transition from Real-World Safety to Basic Research Effects • The first 2 “Myths” and “Facts” dealt with real-world safety. • The last 5 deal more with basic research claims about cognitive distraction, rather than real-world safety claims. • Also, all the following myths refer to the “message” – the conversational content – rather than the “medium “(hands-free vs. hand-held”). 8/30/2012

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Myth 3: Emotional conversations always produce more cognitive distraction than neutral conversations • More driving errors are made in the dual task Worse simulator condition when spider-phobic drivers discussed spiders (fear).1 • MYTH 3: “The more emotionally involving the conversation, the greater its potential for distraction.”1 8/30/2012

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Dual Task1 Phobic

1Briggs

Normal

et al. (2011)

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Fact 3: Angry speech produces less cognitive distraction than neutral speech • FACT 3: Briggs et al. (2011) studied only fear, and incorrectly generalized to other emotions. • Response times (RTs) are a useful measure of cognitive distraction while driving (Young, 2012e). • Angry speech produces faster brake RTs (less cognitive distraction) to visual events than neutral speech (arousal effect?).

– Angry speech (red bars) had faster behavioral RTs than neutral speech (blue bars) at all test sites. – Paired t-tests comparing the intrasubject differences between angry and neutral speech (green bars) showed statistical significance. 8/30/2012

- Raw data from Seaman et al. (2008), Hsieh et al. (2009b, 2010) ICTTP 2012 Young - 7 Myths

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Myth 4: Cognitive distraction is a property of the mind, and cannot be directly measured in the brain • Many real-world studies find little or no evidence of cognitive distraction. • In the 100-car “naturalistic” driving study only 1 out of 69 crashes (1.4%) was attributed to cognitive distraction.1 – There were 0 crashes with a cause of “looked but did not see.”(1, Table 5-15) – Naturalistic driving studies in heavy trucks2 have a similarly negligible prevalence of “cognitive distraction.”

• Note that all these studies used an “operational” definition of cognitive distraction which did not include cell phone conversation.(3, Appendix A, para. 1.4) • Because these investigators believe there is no valid way to measure cognitive distraction in naturalistic studies, they have no data to calculate odds ratios for cognitive distraction, and so it is not even listed as a causative factor in crashes. – However, early on-site crash investigation studies (before any cell phones existed) find that 10-12% of crashes are caused by “looked-but-did-not-see” errors,4 which is a form of cognitive distraction by most definitions.3

• MYTH 4: Cognitive distraction is something in the mind, and so cannot be directly measured. 1Dingus

et al. (2006). et al. (2009), Hickman et al. (2010). 3Young (2012d). 4Wang et al. (1996), Treat et al. (1979), Treat (1980), NASS (2008). 2Olson

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Fact 4: Cognitive distraction has been directly measured in the brain • With no hands-free phone No Conversation Conversation conversation (left), the amount of brain activity in the right superior parietal lobe predicts the behavioral brake response time (RT) to an event light during simulated driving. • FACT 4: During hands-free phone conversation (right), the behavioral RT increases by about 200 ms, and the association with RT in this area disappears - a direct measure of cognitive distraction in the brain. A) Brain responses at 200 ms during primary task (response to lights while viewing driving video). B) Brain responses to primary plus secondary task (conversation) at 200 ms. The strength of the regression relationship between the brain activity and reaction time at this time interval is decreased in the conversation condition (B), particularly in the right superior parietal lobe, which other studies have shown is important for voluntary orienting of attention (Corbetta & Shulman, - Bowyer et al. (2009), Hsieh et al. (2009a) 2002).

Right superior parietal lobe

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Myth 5: Cognitive distraction increases response time to visual events in the periphery more than in central vision, thereby reducing the “useful field of view.” Myth 5: “The conversational task led to large reductions in the functional field of view.”1

“Useful field of view” is like “tunnel vision”4,5

Fig. 1. Conceptual illustration2 of variation in the “useful” or “functional” field of view.3

Fig. 2. Conceptual illustration of “tunnel vision.”6

1 Atchley

& Dressel (2004). 2http://www.positscience.com/science/how-training-works/UFOV. 3The term “functional field of view “ is synonymous with the term “useful field of view.” 4 see Williams (1985, 1995), Olsson & Burns (2000), Martens & Van Winsum(2000), Ball & Owsley (1993). 5tunnel vision refers to “a hypothesized perceptual narrowing of the visual field where the visual sensitivity reduction is greatest in the periphery.” - Victor et al. (2009, p. 141 footnote). 6http://www.arrivealive.co.za/pages.aspx?i=1371 8/30/2012

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Fact 5: Cognitive distraction effects are not specific to peripheral vision

• Cognitive distraction causes an equal increase in response time to central and peripheral visual events,1 or even auditory and tactile events.2 • Fact 5: “Tunnel vision” or an apparent reduction in functional or useful field of view can arise simply from an overall decline in responsiveness across the entire field of view, with nothing to do with any specific peripheral vision effect (for mechanism see Fig. to right from Young, 2012d) 1Young

2Merat

& Angell (2003), Merat et al. (2006), Engström & Mårdh (2007), Victor et al. (2009). & Jamson (2008), Young (2012f).

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Myth 6: Drivers with increased gaze time to the forward roadway during a cognitively loading task are engaged in unsafe driving behavior. • Gaze concentration1(looking at forward roadway a higher % of task time) increases with the cognitive load of a secondary task.2 • But gaze concentration improves lane-keeping in simulated3 or realworld4 driving, particularly during car-following.5 – The few exceptions find little or no effect,6 or a negative effect7 (but the latter study did not control for confounding of its lane-keeping metric by task time).8

• Myth 6: Gaze concentration still results in unsafe driving behavior because it is associated with impaired visual detection.9

1Gaze

concentration (myth 6) should not be confused with tunnel vision (myth 5) (Victor et al., 2009, p. 141 footnote) & Nunes (2000), Victor et al. (2005), Harbluk et al. (2007). 3Engström et al. (2005), Victor et al. (2005), Jamson & Merat (2005), Horrey & Simons (2007), Merat & Jamson (2008), He(2012). 4Brookhuis et al. (1991), Peng et al. (in press). 5Liang & Lee (2010), Mühlbacher & Krüger (2011), Merat (2012). 6 Alm & Nilsson (1995), Lamble et al. (1999), Horrey & Wickens (2006). 7 Horrey et al. (2009). 8 Young (2012). Attachment 1: Additional concerns on NHTSA visual-manual driver distraction guidelines . http://www.regulations.gov/#!documentDetail;D=NHTSA-2010-0053-0124 9Recarte and Nunes (2003). 8/30/2012 ICTTP 2012 Young - 7 Myths 2Recarte

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Fact 6: Drivers with gaze concentration to the forward roadway are engaged in safe driving behavior. • Many studies show that driver safety increases to the extent that a driver’s eyes are on the road (e.g. Klauer et al., 2006). • 70% of crashes come from directions visible through the front windshield so gaze concentration should help reduce crashes.1,2 • Fact 6: More gaze concentration Frequency of impact angles for crashes3 means better event detection: – Auditory-vocal tasks with more gaze concentration have only slight, nonsignificant decrements in event detection vs. a “just driving” baseline.4 – Visual-manual tasks with less gaze concentration have large, statistically significant decrements in event detection compared to baseline.4

1Young

(2012d). gaze concentration in real-world driving, drivers tend to slow down (Angell et al., 2006), and likely reduce lane changes, thereby avoiding a safety penalty for reducing speedometer or mirror glance time. 3http://www.crashtest.com/explanations/nhtsa/usncap.htm. 4Angell et al. (2006; 2010 Fig. 4), Angell (2007). 2During

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Myth 7: Short visual-manual tasks have less cognitive distraction than long visual-manual tasks • Short tasks have lower workload demand:1

– fewer lane and speed deviations, eyes off road, steps;1 – lower self-ratings of driver frustration, workload;1 – higher self-ratings of situation awareness.1

• Myth 7: Lower workload demand means short tasks use fewer mental resources, and hence have more “spare mental capacity”, less cognitive distraction, and better event detection than long tasks.

– The CAMP-DWM study called short tasks with poor event detection “paradoxical” and investigated whether they were a methodological artifact.2 1Angell

et al. (2002), Young and Angell (2003), Young et al. (2012e). 2Angell et al. (2006, p. 8-6) 8/30/2012

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Fact 7: Many short visual-manual tasks have more cognitive distraction than long visual-manual tasks Principal component analyses of driver performance metrics1,2 show that the event detection/cognitive distraction dimension is orthogonal to the driver workload dimension: cognitive distraction has no association with task time. • FACT 7: A newly-discovered class of short tasks have poor event detection.

Bad Event Detection Score

- 5 out of 13 (38%) CAMP-DWM3 visual-manual tasks were short, yet had poor event detection for a single real event during actual driving.2 o This same result is found even with multiple simulated events randomly presented during 79 tasks,1 so this result is not an artifact.2

Good 8/30/2012

Bad Driver Workload Score2

- Short tasks in this class also have long mean single glance durations,2 which predict realworld crash and near-crash events better than total eyes-off-road time.4 - Hence, the safety implications of Fact 7 bear further investigation. 1Young

& Angell (2003). 2Young et al. (2012e). 20 Angell et al. (2006). 4Liang et al. (2012).

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Closing Remarks • Is this analysis of 7 “myths” itself biased? TOPIC MYTH FACT ∆ • The net change in possible safety 1 4x RR -1 0 +1 implications 2 protective effect +1 0 -1 going from 7 3 emotion worse -1 +1 +2 “myths” to 7 4 can't measure mind 0 -1 -1 “facts” is 0, 5 peripheral vision -1 -2 -1 indicating no systematic bias. 6 gaze concentration -1 +1 +2 7 short tasks +1 -1 -2 SUM: 0 • Hopefully this presentation will bring better focus to future studies of cognitive distraction and driving safety. 8/30/2012

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Acknowledgements • I thank my Wayne State University colleagues Associate Professor Li Hsieh, Research Associate Dr. Sean Seaman, and Research Assistant Amanda Zeidan, for their collaboration and support • I also thank Barbara Wendling, Bryan Reimer, Michael Posner and 2 anonymous reviewers for helpful comments. 8/30/2012

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Hsieh, L., Seaman, S., Sullivan, J., Bowyer, S., Moran, J., Angell, L., Young, R. (2009b). Effects of emotional speech tone of cell phone conversations on driving: ERP, lab and on-road driving studies. Paper presented at the Cognitive Neuroscience Society, San Francisco, CA. Hsieh, L., Seaman, S., Jiang, Q., Bowyer, S., Moran, J., & Young, R. (2010). Neural basis of emotional modulation on of simulated driving performance: An fMRI multitasking study. Cognitive Neuroscience Society, Montreal, Quebec, Canada. Jamson, A. H., & Merat, N. (2005). Surrogate in-vehicle information systems and driver behaviour: Effects of visual and cognitive load in simulated rural driving. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 79-96. Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., & Ramsey, D.J. (2006). The impact of driver inattention on nearcrash/crash risk: An analysis using the 100-car naturalistic driving study data (Report No. DOT HS 810 594), National Highway Traffic Safety Administration, Washington, DC. http://www.nhtsa.gov/DOT/NHTSA/NRD/Multimedia/PDFs/ Crash%20Avoidance/Driver%20Distraction/810594.pdf. Accessed Jan 24, 2011. Lamble, D., Kauranen, T., Laakso, M., & Summala, H. (1999). Cognitive Load and detection threshold in car following situations: safety implications for using mobile (cellular) telephones while driving. Accident Analysis and Prevention, 31, 617-623. Liang, Y., & Lee, J. D. (2010). Combining cognitive and visual distraction: Less than the sum of its parts. Accident Analysis & Prevention, 42(3), 881-890. doi: 10.1016/j.aap.2009.05.001 Martens, M. H., & Van Winsum, W. (2000). Measuring distraction: the peripheral detection task. Soesterberg, The Netherlands: TNO Human Factors. http://www-nrd.nhtsa.dot.gov/departments/Human%20Factors/driver-distraction/PDF/34.PDF. Merat, N., Johansson, E., Engström, J., Chin, E., Nathan, F., & Victor, T. (2006). Specification of a secondary task to be used in safety assessment of IVIS. AIDE deliverable 2.2.3, IST-1-507674-IP, European Commission. http://www.aideeu.org/pdf/sp2_deliv_new/aide_d2_2_3.pdf. Merat, N., & Jamson, A. H. (2008). The effect of stimulus modality on signal detection: implications for assessing the safety of in-vehicle technology. Human Factors, 50(1), 145-158. Merat, N. (2012). Enhanced lane keeping during verbal distraction with a lead car. Presented at ICTTP, 2012. Mühlbacher, D., & Krüger, H.-P. (2011). The effect of car-following on lateral guidance during cognitive load – A study conducted in the multi-driver simulation. Paper presented at the 2nd International Conference on Driver Distraction and Inattention. Göteborg, Sweden. NASS (2008), National motor vehicle crash causation survey. http://www.fmcsa.dot.gov/facts-research/researchtechnology/report/. 8/30/2012

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References 4 Olsson, S., & Burns, P. C. (2000). Measuring driver visual distraction with a peripheral detection task. http://wwwnrd.nhtsa.dot.gov/departments/nrd-13/driver-distraction/PDF/6.PDF Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations: Final report (FMCSA-RRR-09-042). Washington, DC: Department of Transportation. Retrieved from http://www.distraction.gov/research/PDF-Files/Driver-Distraction-Commercial-Vehicle-Operations.pdf. Peng, Y., Boyle, L. N., & Hallmark, S. L. (in press). Driver's lane keeping ability with eyes off road: Insights from a naturalistic study. Accident Analysis & Prevention. doi: 10.1016/j.aap.2012.06.013. Recarte, M. A., & Nunes, L. M. (2000). Effects of verbal and spatial-imagery tasks on eye fixations while driving. Journal of Experimental Psychology: Applied, 6(1), 31-43. doi: 10.1037/1076-898x.6.1.31 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(2), 119-137. Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular-telephone calls and motor vehicle collisions. New England Journal of Medicine, 336(7), 453-458. Rothman, K. (2002). Epidemiology: An Introduction. New York, NY: Oxford University Press. Rothman, K., Greenland, S., & Lash, T. (2008). Modern Epidemiology 3rd Ed. Philadelphia, PA: Lippincott Williams & Wilkins. Seaman, S., Hsieh, L., & Young, R. (2008). The effect of emotional conversation on visual detection during simulated driving: A behavioral study. Paper presented at the Cognitive Neuroscience Society, San Francisco, CA. Treat, J. R. (1980). A study of precrash factors involved in traffic accidents. HSRI Research Review [Vol. 10(6) May-June, 11(1) July-Aug, pp. 1-35]. Ann Arbor, MI USA: University of Michigan Highway Safety Research Institute Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., . . . Castellan, N. J. (1979). Tri-level Study of the Causes of Traffic Accidents (DOT HS-805 099). Washington, D.C.: US. Department of Transportation, National Highway Traffic Safety Administration Retrieved from http://deepblue.lib.umich.edu/bitstream/2027.42/64993/1/43120.pdf. Victor, T. W., Harbluk, J. L., & Engström, J. A. (2005). Sensitivity of eye-movement measures to in-vehicle task difficulty. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 167-190. doi: 10.1016/j.trf.2005.04.014. Victor, T. W., Engström, J., & Harbluk, J. L. (2009). Distraction assessment methods based on visual behavior and event detection. In M. A. Regan, J. D. Lee & K. L. Young (Eds.), Driver Distraction: Theory, Effects, and Mitigation (pp. 135-165). Boca Raton, FL: CRC Press. 8/30/2012

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References 5 Wang, J., Knipling, R. R., & Goodman, M. J. (1996). The role of driver inattention in crashes: New statistics from the 1995 crashworthiness data system. 40th Annual Proceedings: Association for the Advancement of Automotive Medicine, 377-392. Williams, L. J. (1985). Tunnel vision induced by a foveal load manipulation. Human Factors: The Journal of the Human Factors and Ergonomics Society 27(2): 221-227. Williams, L. J. (1995). Peripheral target recognition and visual field narrowing in aviators and nonaviators. The International Journal of Aviation Psychology, 5(2), 215-232. doi: 10.1207/s15327108ijap0502_6. Young, R.A. & Angell, L.S. (2003). The dimensions of driver performance during secondary manual tasks. Driving Assessment 2003: Second International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Park City, Utah, July, http://drivingassessment.uiowa.edu/DA2003/pdf/25_Youngformat.pdf. Young, R. A., & Schreiner, C. (2009). Real-world personal conversations using a hands-free embedded wireless device while driving: Effect on airbag-deployment crash rates. Risk Analysis, 29(2), 187-204. doi: 10.1111/j.1539-6924.2008.01146.x. Young, R. A. (2011). Driving consistency errors overestimate crash risk from cellular conversation in two case-crossover studies. Proc. of the Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 298-305. Young, R. A. (2012a). Cell phone use and crash risk: Evidence for positive bias. Epidemiology, 23(1), 116-118. http://journals.lww.com/epidem/Fulltext/2012/01000/Cell_Phone_Use_and_Crash_Risk___Evidence_for.17.aspx. Young, R. A. (2012b). Cell phone use and crash risk [letter]. Epidemiology, 23(4), 649-650. http://journals.lww.com/epidem/Fulltext/2012/07000/Cell_Phone_Use_and_Crash_Risk.25.aspx. Young, R.A. (2012c). The author replies [letter], Epidemiology 23(5):774-775. http://journals.lww.com/epidem/Fulltext/2012/09000/The_author_replies.28.aspx. Young, R. A. (2012d). Cognitive distraction while driving: A critical review of definitions and prevalence in crashes. Society for Automotive Engineers, SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 5(1). doi: 10.4271/2012-01-0967. Young, R. A. (2012e). Event detection: The second dimension of driver performance for visual-manual tasks (SAE Paper #201201-0964). Paper presented at the Society for Automotive Engineers, Detroit, MI. Young, R., Seaman, S., & Hsieh, L. (2012f). Measuring cognitive distraction on the road and in the lab with Wayne State Detection Response Task. Paper presented at the Transportation Research Board 2012 Annual Meeting, Washington, D.C. 8/30/2012

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