The Effects of Different Types of Cell Phone Use, Automation and Personality on Driver Performance and Subjective State in Simulated Driving
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The Effects of Different Types of Cell Phone Use, Automation and Personality on Driver Performance and Subjective State in Simulated Driving A thesis submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the degree of MASTER OF ARTS in the Department of Psychology of the College of Arts and Sciences 2011 By Catherine E. Neubauer B.S. University of Central Florida, 2008 Committee Chair: Gerald Matthews, Ph.D. ABSTRACT Driver distraction is a leading cause of vehicular accidents (Strayer & Johnston, 2001). There are numerous types of driver distraction, but one type in particular, cell phone use, seems to be exceptionally dangerous to drivers. These ‘newer’, technology-based distractions are more dangerous because they are more cognitively demanding, requiring the driver to manage multiple visual, manual and auditory demands while attempting to remain engaged in the primary task of driving. Additionally, there may be differences in driver performance depending on the type of cell phone usage such as calling back and text messaging. These issues may be conceptualized within models of driver workload. The present study investigated the effects of two relevant workload factors on driver performance: type of phone usage and automation of driving systems. Automation is an emerging trend among automakers that can potentially assist drivers by reducing workload, but recent studies suggest that automation might provoke dangerous states of underload in which effort is withdrawn from the driving task. There may also be individual differences in response to distraction that are linked to personality factors. As predicted from the workload model, the present study found that there are differential effects of talking on a cell phone versus texting, with text messaging shown to be associated with worse vehicle control. Individuals in the text messaging group also had the highest levels of distress following the drive. Drivers given a choice of response options tended to favor texting over talking, illustrating drivers’ lack of insight into the safety issues. Automation did not produce clear signs of underload, such as large-magnitude loss of task engagement, suggesting there may be some benefits to phone use during automated driving. In sum, results demonstrate that talking and texting on a cell phone have differing impacts on driver safety, as well as providing further evidence to the benefits as well as dangers associated with vehicle automation. ii iii ACKNOWLEDGEMENTS I would first like to thank my committee members for their very helpful input on this project. I would especially like to thank my mentor, Gerry Matthews, for his constant guidance and support throughout my time at the University of Cincinnati. In addition, I would also like to thank my research assistants, Mandy Reber, Karen Shull and Jeremy Grove for their help with this project. Finally, I would like to express my gratitude to the Department of Psychology, who provided me with the opportunity to fund this project through the Seeman-Frakes fund. iv DEDICATION I would like to dedicate this work to my family for their constant patience, love and support. In particular, I would like to mention my two grandmothers, Rita Wassenberg and Irma Neubauer for their continued support throughout my life. v TABLE OF CONTENTS TABLE OF CONTENTS………………………………………………………………………..v LIST OF TABLES……………………………………………………………………………..viii LIST OF FIGURES………………...……………………………………………………….......ix CHAPTER 1: Introduction……………………………………………………………………...1 Study Overview…………………………………………………………………………………...1 Research on cell phone (dual-task) induced performance deficits………………………………..2 The asynchronous nature of text messaging………………………………………………4 Cognitive mechanisms underlying deficits in driving performance………………………………6 Attentional models………………………………………………………………………...6 Stress and fatigue-based influences in driving…………………………………………………….9 Active and passive fatigue………………………………………………………………...9 Fatigue models…………………………………………………………………………...10 The transactional model of driver stress…………………………………………………11 The Dundee Stress State Questionnaire………………………………………………….12 Personality variables, risky driving and accident involvement………………………………….13 The Driver Stress Inventory……………………………………………………………...14 Automated systems and cell phones……………………………………………………………..16 The benefits and dangers of vehicle automation…………………………………………17 Workload and vehicle automation……………………………………………………….18 Automation and subjective states………………………………………………………...18 Aims……………………………………………………………………………………………...20 vi Specific hypotheses………………………………………………………………………………20 CHAPTER 2: Method………………………………………………………………………….22 Participants……………………………………………………………………………………….22 Experimental design and simulator tasks………………………………………………………...22 Questionnaires………………………………………………………………………………..…..23 Cell Phone Usage Questionnaire…………………………………………………...……23 The Dundee Stress State Questionnaire………………………………………………….23 The Driver Stress Inventory…………………………………………………………..….24 The Driver Fatigue Questionnaire…………………………………………………...…..24 The driving simulator………………………………………………………………………...…..24 Cellular telephones……………………………………………………………………………….25 Driving tasks and automation……………………………………………………………………26 Practice drive………………………………………………………………………...…..27 Main drive………………………………………………………………………….…….27 Performance assessment……………………………………………………………..…..28 Procedure……………………………………………………………………………………..….29 CHAPTER 3: Results…………………………………………………………………………..33 Data analysis overview…………………………………………………………………………..33 Task-induced effects of automation and cell phone use on subjective stress state……………....33 Influences on frequency of cell phone use……………………………………………………….37 Predictors of subjective state…………………………………………………………………….37 Correlates of subjective state…………………………………………………………….37 Regressions……………………………………………………………………..………..39 vii Performance…………………………………………………………………………………...…40 Vehicle control………………………………………………………………………...…41 Response times…………………………………………………………………………...44 Crash rates………………………………………………………………………………..46 CHAPTER 4: Discussion………………………………………………...……………………..49 Overview of findings…………………………………………………………………………….49 Theoretical implications…………………………………………………………………………52 Talking on a cell phone versus texting…………………………………………………..52 Practical implications for safety and intervention……………………………………….55 Individual differences, stress vulnerability and cell phone use………………………………….59 Summary and overall conclusions……………………………………………………………….61 REFERENCES………………………………………………………………………………….64 APPENDIX A: Frequency of Cell Phone Use Questionnaire………………………………..70 APPENDIX B: Pre-task DSSQ………………………………………………………………...72 APPENDIX C: Post-task DSSQ…………….. ………………………………………………..76 APPENDIX D: DSI……………………………………………………………………………..83 APPENDIX E: DFQ……………………..……………………………………………………..88 APPENDIX F: Informed Consent Form……….……………………………………………..94 APPENDIX G: List of Text Messages…………………………………………………………97 APPENDIX H: Complete Correlation Tables………………………….……………………..99 APPENDIX I: Complete Pre and Post Task State Graphs…………………………………102 viii LIST OF TABLES 1. Standardized mean pre and post task scores of the DSSQ for automation and cell phone conditions. 2. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the entire sample. 3. Proportion of crashes for each cell phone group. ix LIST OF FIGURES 1. Experimental setup using a System Technologies, Inc., STISIM Drive, build 2.09.01, a Westinghouse 42-inch LCD monitor and Logitech MOMO racing force feedback wheel. 2. a) Participant phone, LG Rumor 2 with standard and QWERTY keyboard b) experimenter phone, LG LX 101. Photos for phones were obtained via http://cgi.iwirelesshome.com/phones/. 3. Screen shot of the sudden event. 4. Pre to post-drive changes in subjective state for the cell phone, text message and free-choice conditions. Error bars are standard errors. 5. Pre to post-drive changes in task engagement for the cell phone, text message and free-choice conditions and automation and non-automation condition. Error bars are standard errors. 6. Standard deviation of 14 successive lateral positions for all three cell phone groups. 7. Standard deviation of 14 successive lateral positions based on response via cell phone call back or text message. 8. Response times for steering, de-acceleration and braking between the automated and non- automated groups. 9. Response times for steering, de-acceleration and braking by cell phone, text message and free- choice groups. x Chapter 1 Introduction Study Overview According to the National Highway Traffic Safety Administration (NHTSA), over forty thousand vehicular accidents occur each year, which can amount to billions of dollars in societal costs including, but certainly not limited to, health related costs and lost hours of work (NHTSA, 2007). The truly devastating costs, however, are the physical injuries and deaths due to vehicular accidents, which appear to be linked, at least in part, to operating a cell phone while driving. Cell phone usage only appears to be increasing, with a dangerous link between cell phones and vehicular accidents. In a report by Redelmeier and Tibshirani (1997), it was found that prior to any accident, almost one quarter of drivers were found to be concurrently using a cell phone while driving, which is associated with a fourfold increase in the chance of being involved in an accident (Strayer & Johnston, 2001). Certain legislative efforts have been made in an attempt to limit the amount of cell