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

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workload, but recent studies suggest that automation might provoke dangerous states of underload in which effort is withdrawn from the driving task.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.

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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.

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.

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

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

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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

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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.

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.

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 phone use on the road but those efforts typically focus on restricting peripheral factors like manual phone operation (e.g., dialing), usually allowing drivers to utilize hands free technologies (Strayer, Drews & Johnston, 2003). Despite the potential benefits of these legislative efforts, they are many times quite difficult to implement (Eby, Vivoda & St.

Louis, 2006).

Although cell phones offer potential benefits such as reporting traffic accidents or emergencies, there are far too many dangerous consequences when they are used concurrently with driving (Brookhuis, De Vries & De Waard, 1991). Additionally, stress and fatigue may enhance distractibility thereby increasing accident risk (Saxby & Matthews, 2007). It also appears that drivers may differ in their decision to engage in cell phone use, where some

1 individuals choose to respond more or less frequently while driving, a potential contributor to accident involvement. By contrast, automated driving systems, an emerging trend among automakers, can potentially reduce the number of accidents on the road. These automated driving systems take over some of the control functions of the driver, potentially reducing driver workload and distraction.

The introduction of this paper will provide a brief overview of past and present research conducted on cell phone usage and driving, focusing specifically on performance decrements as well as attentional models of dual-task performance. Next, subjective measures of stress and fatigue will be addressed. Relevant personality traits will also be covered, focusing specifically on driver stress vulnerability measures that may be useful in identifying those individuals who are at particular risk during dual-task driving. In addition, the potential benefits as well as dangers of vehicle automation will be introduced in relation to cell phones and driving. Finally, the specific aims and hypotheses of this study will be presented.

Research on Cell-Phone (Dual-Task) Induced Performance Deficits

It has been estimated that as much as one quarter of automobile accidents result from driving while performing distracting activities (Strayer & Johnston, 2001), with effects that can negatively impact visual processing of the roadway and also motor response (Hosking, Young &

Regan, 2009). These same authors define driver distraction as “the diversion of attention away from activities critical for safe driving toward a competing activity”. There is a substantial amount of research showing that driving ability may be hindered by factors that cause distraction, notably cell phones. In recent years, there has been a substantial increase in cellular phone use. As of 2010, the Cellular Telecommunications and Internet Association (CTIA) estimated that there are approximately 300 million cell phone subscribers in the United States

(CITA, 2010), of which, a substantial number of those individuals most surely use their cell phone while driving. Strayer et al. (2001) suggest that the number of individuals driving while operating a cell phone is increasing, with an estimated 60% of driving time spent while concurrently on a cell phone.

Although driver distraction has existed since the beginning of automobile transportation, notably through common distractions such as eating, conversing with a passenger or tuning a radio, it appears that cell phone usage is more dangerous given the fact that it may include multiple types of distraction such as cognitive, auditory and visual distraction (McCartt, Hellinga

& Bratiman, 2006). Strayer, Drews and Crouch (2006) argue that these types of ‘newer’ distractions are more dangerous because they are more cognitively demanding and occur over a longer interval. In one of the earliest studies involving driver distraction and in-vehicle communication, Brown, Tickner and Simmonds (1969) found that using a telephone while driving impaired the perception of gap size in traffic, whereby drivers showed an increase in their acceptance of inaccessible gap sizes, suggesting that decision making skills are more sensitive to these types of perception decrements.

There are a number of dangerous consequences found to be associated with driving while using a cell phone. It has been suggested that using a cell phone may significantly decrease the driver’s situation awareness, while increasing their perceived mental workload (Drews et al.,

2008). It has also been argued that the risks associated with concurrently driving while using a cell phone are comparable to having a blood alcohol level of .08% blood alcohol concentration, a level similar to or exceeding legal limits (Strayer, Drews & Crouch, 2006; Strayer & Johnston,

2001). In more recent studies, it was found that drivers had slower reaction times to braking lights and poorer vehicle control when placed in a dual-task condition, of which the impairments

3 were equal for both handheld and hands free cell phones (Drews et al., 2009; Strayer, Drews &

Crouch, 2006; Strayer & Johnston, 2001). Likewise, Strayer and Drews (2004) found that drivers who used cell phones were more likely to miss crucial aspects of the traffic scene, with performance impairments such as slower response times to an emergency situation (e.g., a car or child suddenly coming in front of the vehicle), greater following distance and a greater amount of time needed to regain speed following braking. In addition, these authors also found that drivers conversing on a cell phone were twice as likely to be involved in a rear-end collision.

Interestingly enough, although cell phone use appears to be an unsafe act during driving, it has been suggested that drivers may be aware of their potentially dangerous driving behaviors but continue to engage in them anyway. During a naturalistic observation, Strayer and Drews

(2004) found an increase in following distance for drivers engaged in cell phone use, which may be an attempt on the driver’s part to compensate for their self-perceived dangerous behaviors, although the compensation afforded may not be enough to counteract the potential performance impairments caused.

The Asynchronous Nature of Text Messaging. Text messaging, a relatively new option for drivers, is especially prevalent among youthful drivers, and may contribute to vehicular fatalities. According to the CTIA, text-message use is increasing, with over two trillion text messages being sent in 2010 (CTIA, 2010). Because text messaging requires the user to engage in manual entry for a longer period of time than it takes to simply dial a phone number, Drews et al. (2009) argue that text messaging is much more dangerous while driving. It appears that the demands of texting while driving force the driver to juggle visual (e.g., reading a message), manual (e.g., responding) and various cognitive demands (e.g., attention), which may be more difficult than when simply conversing on a cell phone (Hosking, Young & Regan, 2009). Similar

4 to talking on a cell phone, research has revealed that text messaging may also negatively impact driver performance. In a recent study, it was found that drivers who engage in text messaging during driving show similar if not worse performance impairments to talking on a cell phone, impairments such as an increase in driver reaction times to braking events as well as following distance and an increased chance of being involved in a traffic accident (Drews et al., 2009). In another recent study, Hosking, Young and Regan (2009) found that the amount of time that drivers spend texting and not focusing on the roadway significantly impaired driver performance with effects such as increased compensatory steering and lane excursion deviations. The findings of the previous studies suggest that impaired visual attention that increases the amount of time spent with the driver’s eyes not on the road is one of the major contributors to dangerous driving habits and accident risk.

Alternatively, it may be that driver performance is only minimally affected during text messaging, compared to conversing on a cell phone. Because drivers can potentially choose a safe time to respond to a text message (e.g. during straight road sections or sparse traffic), the impairment may only be minimal, compared to drivers talking on a cell phone who may feel pressured into responding at a synchronized rate as the person on the other line (Drews et al.,

2009). Furthermore, it was found that text messagers many times significantly increase their following distance to a vehicle in front of them, which suggests that these drivers are potentially aware of the risks they are taking which may also reduce their chances of getting into an accident

(Drews et al., 2009; Strayer, Drews & Johnston, 2003), although this compensatory strategy is most likely applied inadequately.

Cognitive Mechanisms Underlying Deficits in Driving Performance

Although cell-phone induced performance deficits may stem from a number of factors including, but certainly not limited to, psychomotor (response based) and visual factors, it appears that cognitive factors such as attention are at the forefront of safe driving (Amado &

Ulupinar, 2005). As previously mentioned, driver inattention can account for up to 50% of transportation accidents (Strayer & Johnston, 2001), which is particularly dangerous during dual- task driving. Attention related errors, which may be more important to driver performance than vehicle control, include improper lookout, excessive speed and inattention (McKnight &

McKnight, 1993; Brookhuis, De Vries & De Waard, 1991). This type of driving behavior is especially dangerous when there is a reduction in attention directed toward the primary (i.e., driving) task. According to Hosking, Young and Regan (2009), performance degradation during a secondary task depends on several factors, which include task demands of the primary

(driving) and secondary (cell phone use) task and how attention is divided between the two.

Attentional Models. When discussing cognitive mechanisms for driver impairment, it may be useful to describe some models that enhance the understanding of attention during dual- task performance. Some states have banned the use of handheld cell phones, due to the dangerous consequences of “peripheral interference” while driving. The peripheral interference hypothesis argues that cell-phone induced performance impairments stem from peripheral factors like manually operating a phone (e.g., holding a phone, dialing), a proposal which is in direct contrast to attention based theories, which argue that any kind of interference that diverts attention away from the primary task is dangerous (Strayer & Johnston, 2001). Within the transportation context, the effects of cell phone usage appear to correspond to sharing models of attention. According to Drews et al. (2009), drivers appear to divide their attention between both

6 a primary and secondary task, although self-assessed priority (e.g. which task is primary or secondary) may differ according to task demands. Furthermore, drivers involved in text messaging appear to engage in attentional switching, where the majority of attention is distributed between one, not both tasks. With this type of attentional switching, it is particularly dangerous when attention is allocated to the secondary task, as it has been shown that reaction times to braking are significantly higher when operating a cell phone while driving (Drews et al.,

2009).

Additionally, performance during dual tasks may largely depend on availability and allocation of attentional resources. Within performance based settings, the term resources can represent a type of reservoir of ‘energy’ available for processing task demands (Matthews &

Desmond, 2002). First proposed by Wickens (1984), multiple resource theory argues that cognitive resources are limited and can be voluntarily allocated across task demands. Within this framework, there are multiple sources of attentional energy. The extent of dual-task interference may then be predicted from analysis of the specific resources required by each task, and the extent to which they overlap. By contrast, the theory of unitary resources argues that there is only a single reservoir of resources that must attempt to meet all task demands, which can especially overload attention during performance of dual tasks (Wickens, 2002). Multiple resource theory allows for different attentional modalities to be drawn upon (i.e. visual processing and auditory processing) at the same time during dual-tasking without severe consequences to performance. Here, dual task performance seems to suffer when two simultaneously occurring tasks require the same mode of processing. For example, a dual task that requires a driver to maintain vehicle control while reading a text message would draw upon

7 the same modes of visual processing and may thus interfere with driver performance. Similarly, texting and steering might both draw on the same resource required for manual response.

According to Briem and Hedman (1995), having a limited capacity does not mean that dual-tasks cannot be carried out simultaneously, but it does suggest that some form of interference will ensue when processing information is delivered in the same sensory modalities.

During cell phone conversations, the driver may draw upon various auditory and verbal resources, which may have no negative drawbacks during driving, where visual and spatial resources are necessary (Strayer & Drews, 2007). Here, there is little to no overlap between resources, which should protect against performance decrements, but this is not necessarily seen in dual-task driving. According to Strayer and Drews (2007), multiple resource theory does not apply to this context due to an attentional bottleneck. When drivers attend to the cell phone conversation, attention is temporarily blocked and cannot be used for processing the roadway environment, ensuring a sort of serial processing or sequential switching between tasks (i.e., processing the roadway and the cell phone conversation) (Strayer & Drews, 2007; Strayer &

Johnston, 2001).

Consequently, Drews et al. (2009) and Strayer, Drews and Johnston (2003) argue that drivers conversing on a cell phone are only able to process approximately fifty percent of their surrounding information, a crucial aspect of driving, suggesting that a form of inattentional blindness results from this secondary task associated with driving (Strayer, Drews & Johnston,

2003; Strayer & Drews, 2007). In a simulator study, Strayer and Drews (2007) found that, even when drivers were looking at roadway objects, cell phone use disrupted the formation of a lasting memory, which supports an inattention hypothesis. These performance impairments seem to suggest that drivers fail to “see” critical traffic signals because their attention shifts from external

8 to internal cognitive demands associated with the cell phone conversation (Strayer & Drews,

2004).

Stress and Fatigue-based Influences in Driving

In addition to attentional overload, it also appears that certain psychological states such as stress and fatigue can induce driving impairments, although fatigue effects appear to be better understood. Stress and fatigue may combine with high workload conditions to elevate risk of crashes and road fatalities (Hitchcock & Matthews, 2005). Within the driving context, fatigue can result from physical aspects of driving (e.g. time of day, roadway environment) to psychological factors (e.g. driver attention, stress) (Wijesuriya et al., 2007). Fatigue can be characterized by subjective tiredness or by a loss of task motivation (Brown, 1997), which can lead to performance impairments, especially for professional drivers performing long-duration drives. Moreover, fatigue can be difficult to define due to the overlap of fatigue with other symptoms such as stress (Saxby et al., 2007; Matthews & Desmond, 1998). It appears that fatigue is best defined as a broad term for a variety of subjective states.

Active and Passive Fatigue. Additionally, it may be useful to conceptualize fatigue based on Desmond and Hancock’s theory of active and passive fatigue (2001). Active fatigue can result from high task demands, while passive fatigue can be initiated during periods of underload, whereby these two extremes can result in a reduction in effortful compensation (Hancock &

Warm, 1989). Moreover, it also appears that different performance decrements may result due to the nature of the fatigue-induction. To better understand the effects of active and passive fatigue,

Saxby et al. (2007) subjected participants to one of three conditions; normal vehicle driving, active fatigue, induced through wind gusts, and passive fatigue, induced through total vehicle automation. Results of this study found that active fatigue manipulation elevated workload,

9 distress and worry in drivers, as measured by the DSSQ (Matthews et al., 2002), while passive fatigue resulted in a decrease in task engagement, consistent with Desmond and Hancock’s theory of passive fatigue (2001), providing further support for the utility of multidimensional models of subjective states.

Fatigue Models. It appears that fatigue responses can drain attentional resources, whereby increased task demands lead to even higher performance decrements (Desmond &

Matthews, 1997). Resource theories argue that attention is limited so that fatigue induced deficits may occur when task demands outweigh attentional resource availability (Matthews and

Desmond, 2002). Here, it is essential that drivers effectively allocate resources across task demands, an especially challenging goal during dual-task performance. Driver fatigue can also be understood in relation to dynamic models of stress, which emphasize operator adaptation

(Hancock & Warm, 1989). According to this model, drivers exert less effort during task underload and more during task overload, so that both conditions can threaten safety, especially with the addition of stress. Within this framework, moderate levels of demand support effective adaptation, and appear most suitable for safe driving. In contrast to resource theory, the Hancock and Warm (1989) model thus states that fatigue may be dangerous in underload conditions.

Matthews and Desmond (2002) explored the effects of fatigue induction on performance impairment. Here, drivers were subjected to either single or dual task performance (fatigue induction) as well as workload manipulation (curved vs. straight road driving). According to adaptation models, drivers will under mobilize effort during undemanding tasks, while resource theories support the notion that high task demand will result in driver impairment. Generally,

Desmond and Matthews (1997) found that during fatigued driving, vehicle control was most pronouncedly affected during straight road driving sections (low workload), suggesting a

10 decrease in motivation and effort, which supports the Hancock and Warm (1989) adaption model. Driving on curved sections was not affected by fatigue, suggesting that fatigued drivers are able to maintain compensatory effort when workload is high. In addition, it was also found that a reduction in active coping and increased cognitive interference resulted during the fatigue induction, suggesting that task complacency (during straight road driving) is associated with under mobilization of effort. Overall, these results seem to support adaptational models of driving, where relative task difficulty (curved roads) allows drivers to adapt to increased task demands, compared to when task demands are relatively easy (straight road), suggesting that here, fatigued drivers fail to adequately apply effort.

The Transactional Model of Driver Stress. As previously mentioned, fatigue states are best conceptualized as a general term for a variety of subjective states, including but not limited to stress. In addition to fatigue, stress may also result during dual-task driving, with potentially significant performance decrements. Stress and fatigue responses during driving depend on both person and environmental factors. Examples of environmental factors likely to produce fatigue are increased length of the drive and an under stimulating roadway environment (Thiffault &

Bergeron, 2003), while person factors such as driver stress and fatigue predispose certain drivers to transient states of subjective response (Desmond & Matthews, 2009). The transactional model of driver stress best conceptualizes this unique interaction (Lazarus, 1999). Within this framework, stress is a dynamic process that results from the interaction between an individual and external task demands. There are two key aspects of this model, driver appraisal and coping.

Driver appraisal reflects the personal significance of the external environment, while driver coping shapes decision-making and emotion-regulation. Here, the impact of external stressors is moderated by individual difference factors that can bias cognitive appraisal and coping. In stress-

11 vulnerable drivers, these cognitive processes may result in adverse subjective responses and also potentially dangerous performance decrements.

One key aspect of this model suggests that driver fatigue results from a withdrawal of effort and a reduction in task-focused coping, whereby drivers may exert minimal effort to maintain driving safety, especially during apparently easy tasks (Desmond & Matthews, 2009;

Matthews, 2002). According to this model, overall attention can be lost due to a shift in attention from external stimuli and events to internal, cognitive ones (Matthews, 2002). Driver performance can become impaired when external and internal factors combine to produce dangerous appraisal and coping mechanisms. For example, highly stressful situations can result in a form of emotion-focused coping, which results in cognitive distraction, diversion of attention from the external environment to self-related processing, and potentially dangerous driving behavior. In addition, aggressive drivers may appraise other drivers negatively and engage in confrontive coping, which is associated with more errors and violations while driving

(Matthews, 2002).

The Dundee Stress State Questionnaire. As previously mentioned, it is useful to conceptualize driver stress and fatigue as multidimensional states. Matthews et al. (2002) offer a three-dimensional model of subjective stress and fatigue, as differentiated by the Dundee Stress

State Questionnaire (DSSQ). The DSSQ is a 96-item measure designed to assess three broad dimensions of subjective stress states, which has been validated in a number of simulator and field studies as a reliable predictor of driver stress (e.g. Matthews & Desmond, 2002). Utilizing factor analysis, the DSSQ yields ten primary factors including mood (energy, tension and hedonic tone), motivation and six cognitive factors (self-focus, self-esteem, concentration,

12 confidence and control and cognitive interference). Additionally, these ten correlated primary factors yield three higher order secondary factors of task engagement, distress and worry.

Task engagement is characterized by feelings of low energy/tiredness and motivation, while distress can refer to feelings of high tension and unpleasant mood. Lastly, worry can result in subjective ratings of low self-confidence, where task-induced performance worries may intrude upon an individual’s thought processes during driving. Scores on the DSSQ are based on standardized units. The questionnaire provides a means for systematically evaluating the profile of multidimensional state change in both simulated and real driving, by comparing pre-drive and post-drive scores on the various factors (Matthews, 2002). Transient state responses, as assessed by the DSSQ, may be influenced both by environmental stressors, and personality factors, such as stable vulnerabilities to driver stress. The next section will discuss stress vulnerability factors as they pertain to individual differences in subjective and behavioral response, exploring the implications of such variables on cell phone usage and driving.

Personality Variables, Risky Driving and Accident Involvement

Although it appears that cognitive factors such as attention and subjective states such as stress and fatigue can influence cell-phone induced performance impairments, little is known, however, about the personality factors which may predict cell phone use. According to

Rakauskas, Gugerty and Ward (2004), drivers who use their cell phones have more dangerous driving habits such as more speed violations and impaired driving and may have certain attitudes and personality traits that predispose them to greater accident risk. In addition, there may be certain self-selecting factors that lead to accident risk, such as engaging in risky behaviors or being in a particular emotional state which can lead to performance impairment and increased frequency of cell phone use (Strayer, Drews & Crouch, 2006).

There have been a number of studies which suggest that individual differences contribute, at least in part, to performance impairment and risky behaviors. In a study conducted by Strayer et al. (2003), drivers reported no perceived increase in driving difficulty while using a cell phone, but did report that other drivers showed marked performance decrements, suggesting that drivers using cell phones may not adequately gauge their own potentially dangerous driving behaviors.

Additionally, research suggests that people who choose to drive while using a cell phone may be more likely to engage in other risky driving behaviors such as speeding (Strayer and Johnston,

2001). It is important to identify those individuals who are prone to dangerous driving behaviors as well as individual vulnerabilities to the negative effects of driver stress and fatigue in order to implement appropriate countermeasures.

The Driver Stress Inventory. Research using the Driver Stress Inventory (DSI:

Matthews, Desmond, Joyner, Carcary & Gilliland, 1997) demonstrates that personality measures specific to driving are reliable predictors of performance and subjective stress response.

Identifying individuals prone to driver stress and fatigue is of great importance in order to develop various countermeasures for these particular individuals (Iverson & Rundmo, 2002).

Matthews et al. (1997) developed a questionnaire that reliably predicts an individual’s vulnerability to driver stress. The Driver Stress Inventory (DSI) discriminates five personality traits that reliably pertain to the driving experience and various safety criteria: Aggression,

Dislike of Driving, Fatigue Proneness, Hazard Monitoring, and Thrill Seeking (Matthews et al.,

1997). Aggression and Dislike of Driving are traits that make individuals prone to negative feelings of increased anger or hostility and anxiety respectively. Individuals scoring high in

Fatigue Proneness are most vulnerable to feelings of high fatigue or tiredness while driving, while Hazard Monitoring relates to active engagement with the task during driving and higher

14 risk awareness. Of particular note, Thrill Seeking is characterized by a neglect of safety and higher risk acceptance, and so it may predict a range of potentially other dangerous driving behaviors such as cell phone use. There is also evidence that these five DSI factors are predictive of performance decrement, but more specifically to unsafe acts committed while driving. For example, Matthews (2002) reports that Aggression and Thrill Seeking are predictive of self- reported accident involvement, as well as more convictions for speeding.

Aspects of personality may induce an individual to commit certain acts while driving but it also appears that these traits may govern emotional responses in drivers. More specifically, the transactional model of driver stress suggests that these personality traits, as identified by the DSI, should relate to reliable patterns of cognitive appraisal and coping, which are enhanced by the environment to which a driver is exposed (Matthews, 2002). For example, a driver scoring high in aggression would tend to appraise other drivers behavior negatively, especially in congested traffic, leading to confrontive coping, which is expressed through hostile behavior towards other drivers. A key aspect to this process is that feelings of anger or hostility are concomitants rather than direct causes of engaging in dangerous driving behaviors (Matthews et al., 1996). Similarly, thrill seeking is characterized by especially high instances of confrontive coping, whereby drivers relieve their emotions through risk taking. Dislike of driving is associated with emotion- focused coping, whereby drivers appraise their own driving as incompetent and can exacerbate potentially negative aspects of driver stress (Matthews et al., 1996; Desmond & Matthews,

2009). Perhaps more dangerously, fatigue proneness most reliably predicts a loss of task engagement through a reduction in task-focused coping during driving. In a simulator study,

Matthews and Desmond (1998) showed that fatigue proneness is directly related to the change in state induced by a fatiguing drive. By contrast, hazard monitoring relates to somewhat higher

15 levels of task engagement, an essential aspect of safe driving (Matthews, 2002). Here, drivers may engage in task-focused coping, which is associated with safety enhancing behaviors

(Matthews et al., 1996).

These various cognitive mechanisms may significantly contribute to dangerous or risky behaviors while driving such as frequent overtaking (e.g. high aggression, confrontive coping) or increased use of cell phones (e.g. thrill seeking, confrontive coping) (Desmond & Matthews,

2009). For example, a driver scoring high in Thrill Seeking enjoys the feelings of danger and may attempt to relieve those feelings by engaging in high risk-taking, which can be associated with cell phone use. Furthermore, a fatiguing drive with many task demands may result in a reduction in effort in the primary task, especially in those highly fatigue prone drivers, where an increase in cell phone use can be seen as an attempt on the drivers part to remain engaged in the secondary task.

The next section will explore vehicle automation and its effects on factors such as driver performance, operator workload and subjective state. In addition, it will address the implications that automation may have on cell phone use.

Automated Systems and Cell Phones

In addition to advances in vehicle technology such as cellular telephones and GPS, which may assist the driver in emergency situations, there have also been advances in automated vehicle systems, which may assist the driver in counteracting fatigue (Hancock & Verwey,

1997). The on-going development of automated vehicle systems promises to remove some of the task load that contributes to fatigue, thereby regulating driver workload (Hancock &

Parasuraman, 1992). Examples of such automated systems include automated highway system

(AHS) and adaptive intelligent cruise control (AICC) (Desmond & Hancock, 2001). AHS, or

16 smart roads, include technologies that support driverless vehicles, while AICC allows control to be passed back between the driver and automated system, allowing the vehicle to slow or speed up when needed. It appears that automated systems may promote safer driving by reducing the workload placed upon drivers and in turn decreasing subjective ratings of stress and fatigue. By contrast, it is also possible that these technological advances may increase the negative effects of these psychological factors due to decreased driver situation awareness and perceived control

(Young & Stanton, 2007, 2005).

The Benefits and Dangers of Vehicle Automation. Although the benefits of vehicle automation may be widespread, there are also some negative consequences to be considered.

First, automation may generate complacency about safety and increase willingness to engage in dangerous and distracting activities such as operating a cell phone. In addition, prolonged automation use may reduce situation awareness, whereby reaction times may increase in response to unexpected events in the roadway, for example (Saxby et al., 2008; Young &

Stanton, 2002, 2005). Continuous automation use may be particularly dangerous when drivers quickly need to take back manual control of the vehicle. To better understand this switch from automated to manual control, Desmond, Hancock and Monette (1998) measured how well drivers were able to recover after automation failed. They found that drivers in the manual drive condition were better at recovery than drivers in the automated condition, suggesting that full vehicle automation may be dangerous. Partial automation may avoid these hazards. Funke et al.

(2007) found a decrease in subjective workload ratings and distress when drivers remained somewhat involved in the driving task (e.g., controlling lane position), while controlling other aspects (e.g. speed), which may help guard against the driver over-relying on the system.

Workload and Vehicle Automation. It is important to explore the effects of automation on workload, as it appears that high workload relates to fatigue (Friswell & Williamson, 2008).

Moreover, the level of workload placed upon drivers can also moderate fatigue effects, where automated systems can potentially help manage such states. As previously stated, it is important that automated systems regulate workload, not simply reduce it, because an extreme reduction in workload (e.g. underload) may be just as dangerous to drivers as overload (Young & Stanton,

2007). Underload is particularly dangerous for the already fatigued driver, where drivers may cope with low task demands by withdrawing effort, resulting in a state of passive fatigue

(Desmond & Matthews, 2009; Matthews & Desmond, 2002). According to Stanton and Young

(2005), fatigue may result from a paucity of varied, environmental stimuli.

It has also been suggested that automation use slows reaction times for the human operator (De Waard et al., 1999). To further explore the dangers of vehicle automation, Saxby et al. (2008) tested reaction times to a sudden event (e.g. brake and steering), a car unexpectedly pulling in front of drivers. Using the same fatigue manipulations as Saxby et al. (2007), the

Saxby et al. (2008) study found that drivers in the passive fatigue condition (e.g. full-automation use) were slower to respond to an emergency event, providing further evidence for the dangers of vehicle automation. Furthermore, and similar to Saxby et al. (2008), Neubauer et al. (2011) found that automation availability related to somewhat slower to response times to emergency events, providing further support that automated environments result in decreased subjective alertness (Hancock & Verwey, 1997).

Automation and Subjective States. It does appear that automation use may increase subjective ratings of stress and fatigue, but these emotional responses may also influence the decision to engage in automation in the first place. In a simulator study, Neubauer et al. (2011)

18 allowed drivers the option of automation use and found that drivers choosing to engage in automated systems were lower in task engagement (measured by the DSSQ) prior to the drive and showed marked declines in task engagement following automation use, suggesting that driver-controlled automated systems do not protect against the adverse effects of fatigue.

Desmond et al. (1998) also found that prolonged automation use resulted in increased tension and lower task engagement, suggesting that automation may induce these stressful reactions in drivers.

It also appears that fatigue may interact with automation use, which may be interpreted as a low workload task. Desmond and Matthews (1997) found that vehicle control decreased as a result of fatigue in low workload conditions, suggesting a decrease in effort regulation during underload task demands. Task demands that require little to no effort (i.e., automation) may be perceived by the driver as undemanding and not requiring adequate attention, giving further support to dynamic adaptation models (Hancock & Warm, 1989). Although such systems can be beneficial to drivers, there are valid concerns that continuous automation use may result in drivers entrusting too much responsibility, while relinquishing control, to automated systems

(Endsley, 1996), resulting in a decrease in task engagement and situation awareness (Young &

Stanton, 2007). More specifically, these results suggest that fatigued drivers may be at a particular risk when task demands are low (i.e., during vehicle automation), whereby secondary tasks such as using a cell phone may actually benefit the driver by requiring them to remain somewhat engaged in a secondary task. During this period of driving, extra task demands may counteract the effects of withdrawal of effort, thereby increasing drivers’ engagement with the task (Desmond & Matthews, 1997).

Aims

The current study aimed to investigate the effects of type of cell phone usage (spoken response or texting) and automation on subjective state response, frequency of phone use, and driver performance. A driving simulator was used to manipulate type of usage and automation experimentally, during high workload driving situations. Drivers were required to respond to incoming text messages either through speech or texting only, or were given a free choice of response modality, to investigate their spontaneous preferences. Some messages were obligatory; for others, response was optional. Automation may be considered to be a fatigue manipulation: the study tested whether different modes of phone usage might moderate the fatigue response. It also investigated whether automation has an impact on the decision to engage in cell phone and text message use. It is important also to explore the effects of automation and cell phone use on driver performance as future systems can potentially improve driver safety by eliminating driver error due to secondary tasks for example (Thorpe et al., 1997). Performance was assessed both during concurrent driving and phone usage (in the non-automated condition only), and during response to an emergency event taking place towards the end of the drive (in both automation conditions). The study also explored individual differences in willingness to respond to phone messages, and in performance deficits associated with phone usage.

Specific Hypotheses

The central hypotheses of this research are as follows:

1. Because cell phone usage requires a significant portion of driver attention, it was assumed that response to the same messages via cell phone call back or text message will significantly impair driver performance. It was hypothesized that the impairment would be greater in the text message condition. Although both types of activities are distracting to drivers, text messaging

20 requires drivers to call upon the same mode of attentional resources (i.e., visual processing) during driving, so it is expected that this type of behavior will most negatively affect driver performance. It was expected that texting would adversely affect both vehicle control, indexed by higher standard deviation of lateral position, and response to the emergency effect.

2. Participants in the Automated conditions will respond more frequently to the optional messages presented, as compared to those drivers in the Non-Automated group. Total vehicle automation is likely to decrease task engagement, so it is expected that cell phone use will increase in an attempt to remain engaged in the primary task.

3. The ‘Thrill Seeking’ and ‘Dislike of Driving’ personality dimensions will correlate with frequency of response to the optional messages presented. A fatiguing drive is likely to increase boredom and task disengagement in Thrill Seekers, and anxiety in those scoring high in Dislike of Driving. Thrill-seekers may then seek to mitigate boredom by showing especially high frequencies of response to optional text-messages. Similarly, those high in Dislike may seek distraction from their anxieties by allocating attention to the phone task.

4. To the extent that drivers high in ‘Dislike of Driving’ and ‘Thrill Seeking’ are more readily distracted by the phone, we expect to see especially high performance deficits for drivers with high scores on these traits.

5. Post-task distress and worry will be highest in the text message condition and post-task engagement will be lowest in the cell phone condition. Text messaging requires a great deal of effort, which may induce a state of overload for drivers in this condition, but also evoke greater involvement in the task (especially in automated conditions).

Chapter 2

Method

Participants

As part of a research requirement, a total of 180 fully licensed drivers, from the

University of Cincinnati Introductory Psychology research pool (48 males, 132 females) were recruited for this study. Participants ranged in age from 18-30 (M = 19.59 years, SD = 2.64). All participants were required to show a valid driver’s license and to wear corrective lenses as noted on their driver’s license by restriction B. In addition, participants were excluded from participating in the study if they did not have a valid driver’s license, had a history of epilepsy or if they were taking any psychoactive medication (e.g. for the treatment of depression or anxiety).

Individuals with a history of epilepsy may react negatively (i.e., have seizures) to the simulation setup, so they were excluded from the present study.

Experimental Design and Simulator Tasks

This study employed a 2 (Non-Automation vs. Automation) x 3 (Cell phone vs. Text-

Message vs. Free-choice) between-subjects design, resulting in a total of 6 conditions with 30 participants in each condition. Between subjects factors for this study included automation condition (Non-Automated or Automated) and cell phone response condition (Cell phone, Text- message or Free-choice). Dependent variables for this study included subjective ratings of stress and fatigue and objective driver performance measures. Subjective state was measured using the

Dundee Stress State Questionnaire (DSSQ) (Matthews et al., 1999), while the Driver Stress

Inventory (DSI) was used to assess driver stress vulnerability (DSI: Matthews et al., 1997).

Finally, driver performance was assessed via performance logs of the driving simulator, discussed in further detail below.

Questionnaires

Participants completed a total of four questionnaires. Questionnaires were administered pre- and post-task and were used to assess changes in subjective state.

Cell Phone Usage Questionnaire. An unpublished questionnaire (see Appendix A) was first given to participants in order to assess frequency and ease of cell phone use. In addition, this questionnaire was used to group and then counterbalance participants across all six conditions in order to control for regular cell phone use.

The Dundee Stress State Questionnaire (DSSQ). Subjective stress states were measured before and after the simulated drive using the Dundee Stress State Questionnaire (DSSQ:

Matthews et al., 1999). The pre-task DSSQ is a 96-item measure (see Appendix B), which assesses 11 dimensions of mood, motivation and cognition in performance-based settings. The mood dimensions include ratings of energetic and tense arousal as well as pleasantness of mood, while the motivation dimension consists of ratings of inherent task motivation. The cognition assessment is somewhat more detailed, with dimensions measuring self-focused attention, self- esteem, concentration, confidence and control, task-relevant cognitive interference (task-induced worry) and task-irrelevant cognitive interference (self-worry).

These 11 dimensions are then ordered into three higher-order scales. Scales are grouped into three clusters associated with task engagement (e.g., energy, task motivation), distress (e.g., tension, low confidence) and worry (e.g., self-esteem, task-related thoughts) symptoms.

Following the drive, participants completed the post-task DSSQ (Appendix C), which assesses the same 11 dimensions of mood, with the addition of an embedded version of the NASA Task

Load Index (NASA-TLX; Hart & Staveland, 1998). The NASA-TLX is a measure of subjective workload, which is based on task demands. The post-task DSSQ also includes a measure of

23 coping style in response to task-demands. Scores on the DSSQ are standardized, expressed as state changes in SD units.

The Driver Stress Inventory (DSI). The Driver Stress Inventory (DSI: Matthews et. al.,

1997) (see Appendix D) was used to assess participant’s attitudes or feelings towards driving.

More specifically, the DSI is used to assess participants’ stable predispositions to driver stress.

The DSI is separated into two main components. First, 12 items record driver history, which include questions used to assess typical driving habits such as annual miles driven, number of days driven per week, number of traffic accidents etc. Second, the DSI also assesses driver stress vulnerability, based on five dimensions of driver stress: Aggression, Dislike of Driving, Hazard

Monitoring, Thrill Seeking and Fatigue Proneness. These five scores are treated as continuous variables.

The Driver Fatigue Questionnaire (DFQ). The Driver Fatigue Questionnaire (DFQ)

(unpublished questionnaire) (see appendix E) was administered pre and post task to measure various aspects of fatigue that are specific to driving. The DFQ is a 90- item measure, which is used to assess seven dimensions of subjective fatigue. The seven dimensions include muscular fatigue, sleepiness/low energy, boredom, confusion, performance worries, comfort seeking and self-arousal.

The Driving Simulator

All groups participated in a 35 minute drive on a Systems Technology, Inc., STISIM

Model 400 simulator, Build 2.09.01, equipped with a 38” NEC XM3760 monitor. The simulator is programmable to create a variety of driving situations, which can produce stress and fatigue in the driver. The simulator is also equipped with a 42” LCD screen, which was used to display the roadway and other traffic environment to participants.

Participants drove via a Logitech MOMO Racing Force Feedback Wheel, which provides realistic controls such as gas and brake pedals and an adjustable car seat. In addition, the steering component is capable of 360 degree steering with speed sensitive “steering feel” provided by a computer controlled torque motor. The simulator conveys an immersive aspect of the driving situation that may elicit characteristic driving behaviors related to cell phone use. The experimental setup is illustrated in Figure 1.

Figure 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.

Cellular Telephones

Two cellular telephones were used in this study in order to create a realistic, dual-task driving environment. The participant was presented with an LG Rumor 2 cellular telephone, while the experimenter used an LG LX101 cellular phone. The experimenter and participant had different phones in order to recreate participants’ natural driving experiences, which include

25 being able to respond to text messages using a typical and familiar keyboard layout. Because there are typically two types of cellular phone display layouts, the standard keypad and the

QWERTY keypad, participants were provided with a cell phone that has both options (e.g. LG

Rumor 2). QWERTY keypads are laid out like a computer, while standard keypads have the layout typical of a basic phone: four rows with three buttons in each. In the absence of this response option, participants’ decisions to respond via text message or phone conversation, in the free choice condition, could potentially be confounded by phone familiarity. Both cellular phones are shown in Figure 2.

a) b)

Figure 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/.

Driving Tasks and Automation

In order to familiarize themselves with the driving simulator and cell phones, all participants completed a 3-minute practice drive, followed by the 35-minute main drive to which they had been assigned. The 35-minute drive provided the opportunity for primary performance measures to be collected, the standard deviation of 14 successive lateral positions, which was obtained while participants were engaging in cell phone use.

Practice Drive. Prior to the main drive, all participants performed a 3-minute practice drive. This practice drive allowed participants to familiarize themselves not only to the driving simulator, but also to the cell phone they were assigned. The practice drive required that participants drive straight ahead, without turning down any side streets. The roadway consisted of two lanes, with occasional oncoming traffic. Participants were told to adhere to traffic signals and speed limits (40 minimum, 45 maximum). In addition to the drive, participants were also required to respond to one test, text message during this time. According to their response condition, participants were required to respond via cell phone call back, text message or either

(i.e., free-choice).

Main Drive. Following the practice drive, all participants began the 35-minute main drive to which they had been assigned. All drive conditions consisted of the same roadway makeup, a two lane highway, with some hills and road curvature as well as occasional oncoming traffic.

More specifically, the simulation was set up to create varied scenery, which transitioned from rural (e.g. small town) to city driving, similar to Neubauer et al. (2011) and Saxby et al. (2008,

2007). Participants were instructed to adhere to all traffic signals and stop signs, as well as maintain proper speed, which ranged from rural scenery speed (40 minimum, 50 maximum) and city driving (50 minimum, 60 maximum).

In the Non-automated drive, participants maintained manual control (e.g. steering, acceleration and speed) throughout the entire drive, while participants in the Automation group were required to drive with full-vehicle automation. In the Automation condition, steering, acceleration and speed were automatically controlled for the first 25 minutes of the drive. To ensure task engagement did not drop too rapidly in the Automation condition, participants were required to monitor the screen for a divided attention event. Every eight minutes, the

27 experimenter manually implemented a divided attention event, a red diamond in the upper left or right hand corner of the screen would switch from a diamond to a downward facing triangle, indicating “automation failure”. Participants were instructed to press their corresponding turn signals when they detected this change, similar to Saxby et al. (2007). In addition, participants were told that automation might fail completely at some point during the drive, and that they would be required to take over manual control of the vehicle. In actuality and unbeknownst to the participant, the experimenter turned off automation 25 minutes into the drive, requiring participants in the Automation group to drive manually for 10 minutes.

Performance Assessment. Performance was based on the participants’ SD of lateral position, or how well they were able to stay within their lane while driving. The standard deviation of several successive lateral positions was obtained, with a higher lateral position indicating poorer vehicle control. In the Non-Automated group, the standard deviation of lateral position was calculated 14 successive times, from approximately 0 to 30 minutes. Each lateral position was calculated from the time of participants’ text message receipt to the time of their response.

Towards the end of the drive (at approximately 26 minutes), the experimenter also inserted a sudden event into the scenario, a van suddenly appearing on the side of the road and then pulling out in front of the driver, similar to Neubauer et al. (2011) and Saxby et al. (2008).

This event was triggered as participants were responding to their 13th text message, again either by calling the experimenter back (CP group), by texting the experimenter back (TM group) or by either (FC group), in order to assess how well they were able to respond to a sudden, potentially dangerous event while driving and using a cell phone. Mean response times were collected for each participant, measuring how fast they were able to brake and steer away from the van, as

28 well as mean crash rates among groups. The van was programmed to pull to the side of the road following either a collision with the participant or if they were able to stop in time. Figure 3 shows a screenshot of the sudden event.

Figure 3. Screen shot of the sudden event. At approximately 26 minutes, a van suddenly pulled out in front of the driver.

Procedure

The location for all participants was in a laboratory setting, a room without windows to prevent any potential distraction. When participants first entered they were greeted and provided with a written informed consent document (see Appendix F), which explained the procedures of the experiment as well as any potential risks associated with participating. Potential risks included simulator sickness, a form of nausea, which can result from viewing a simulation.

Participants were told that they could withdraw from the experiment at any time if they felt sick or uncomfortable. Each participant was also provided with a copy of the informed consent

29 document for his or her records. After consent was obtained, participants were randomly assigned to one of six experimental conditions. Participants then completed a series of questionnaires. First, participants completed the cell phone usage questionnaire, the Driver Stress

Inventory (DSI), the Driver Fatigue Questionnaire (DFQ) and the pre-task Dundee Stress State

Questionnaire (DSSQ).

The current study employed a 2 (Non-Automation vs. Automation) x 3 (Cell phone, Text-

Message, Free-Choice) between-subjects design. Participants were randomly assigned to either the Automation (A) condition or the Non- Automated Condition (NA). Next, within the A or NA condition, participants were then assigned to the cell phone (CP), text-message (TM) or free- choice (FC) condition. Participants in the A condition were required to use automated cruise control throughout the majority of the drive (i.e., steering, braking and acceleration controlled), while participants in the NA condition were required to drive manually. Within the CP condition, participants were given a wireless Motorola HS850 Bluetooth headset and were required to respond to 14 randomized text messages (see Appendix G), while participants in the TM condition were asked to respond to those same 14 messages via text message. Participants in the

FC condition were given the option of responding either way, via cell phone callback or text message.

Two kinds of messages were delivered. Half were marked URGENT; participants were told that they must respond to these as soon as they reasonably can. The other half of the text messages were marked OPTIONAL for participants to respond to only if they feel it is safe to do so. Measurement of frequency of response to optional messages indexes willingness to engage in texting or cell phone use. The kinds of questions asked were comprised of neutral conversation starters, which avoided potentially controversial topics that may have made participants

30 uncomfortable, resulting in their declining the option of responding to OPTIONAL messages.

During the drive, the experimenter was in a separate room, in order to avoid a passenger-like conversation.

Following the questionnaires, participants were then asked to have a seat in the driving chair. The driving seat is adjustable so that participants could comfortably reach the brake and gas pedals. The experimenter also adjusted the height of the steering wheel in order to re-create a more naturalistic, comfortable driving scenario. Prior to the main drive, participants completed a three-minute practice drive in order to acquaint themselves with the simulator and also the cell phone. They were told to drive straight ahead and not make any right or left hand turns and also to adhere to any traffic signals, stop signs and speed limit signs. At approximately one minute, the experimenter sent 1 test text message. Participants were then instructed to respond according to the cell phone condition to which they were assigned (CP, TM or FC). At this time, participants were given the opportunity to ask any questions. Following the practice drive, participants then completed the 35-minute main drive to which they had been assigned. Prior to driving, participants were instructed how to drive as well as how to respond to the experimenter’s text messages.

Those participants in the Non-Automated group were given the same instructions as the practice drive (i.e., drive straight ahead, adhere to traffic signals). Participants in the Automated group were told that the drive would be fully automated (i.e., steering, braking, acceleration controlled) and that they would need to monitor for “automation failure”. Finally, the Automated group was informed that at some point during the drive automation might fail and that they would need to regain control of the vehicle, although in actuality the experimenter turned off automation at 25 minutes. Individuals in both the A and NA conditions received the same 14 text

31 messages, throughout the 35-minute drive. The simulator logged indices of the variability of lateral position of the vehicle, providing a measure of vehicle control; a higher lateral position indicates poorer vehicle control. Messages were general knowledge questions requiring an answer of several words or more.

Following the drive, participants were asked to complete the post-task Short-Form DSSQ to assess their change in mood. Finally, participants completed the post-task DFQ to assess changes in fatigue state resulting from the simulated drive. After completion of the DFQ participants were debriefed. Finally, participants were given the opportunity to ask questions, and thanked for their participation.

Chapter 3

Results

Data Analysis Overview

The data analysis was divided into four main sections in order to test the hypotheses presented. First, various ANOVAs tested for task-induced effects of the automated drive as well as cell phone response type on subjective state. Here, standardized DSSQ scores were measured in order to assess the effects of both the drive and secondary task demands on subjective state.

Second, frequency of cell phone use was examined in order to see whether it was associated with subjective state or stress vulnerability, measuring frequency of response to optional text messages within the automation conditions and cell phone conditions. Next, correlational and regression analyses were performed in order to identify any predictors, such as driver stress vulnerability, on state response. For this analysis, correlations and multiple regressions between the five DSI variables Aggression, Dislike of Driving, Hazard Monitoring, Thrill Seeking and

Fatigue Proneness and three DSSQ factors of task engagement, distress and worry were performed. Lastly, the effects of automation manipulation as well as cell phone response type on driver performance and alertness during the entire drive and following an emergency event, measuring vehicle control and response time to a sudden event were tested.

Task-induced Effects of Automation and Cell Phone Use on Subjective Stress State

State was assessed using the three DSSQ factors of task engagement, distress and worry.

The DSSQ was given to participants before and after the drive to assess any changes from pre to post-drive subjective state. Table 1 shows the standardized pre and post task z-scores for the entire sample and for both automation and cell phone conditions.

Table 1.Standardized mean pre and post task scores of the DSSQ for automation and cell phone conditions. Standard deviations are in parenthesis.

Task Distress Worry

Engagement

Automation Cell Pre Post Pre Post Pre Post

Phone

CP .39 .06 -.27 -.03 .32 .81

(.84) (1.04) (.94) (.98) (.97) (1.02)

TM .26 -.19 -.42 .48 1.08 -.21

(.69) (.98) (.76) (.92) (1.07) (1.13)

FC .28 -.15 -.39 .32 .212 .08

(.80) (.78) (.72) (.87) (.85) (1.12)

NA CP .40 .14 -.37 .23 .11 -.01

(.61) (.80) (.71) (.78) (1.07) (1.21)

TM .58 .06 -.66 .35 .25 .34

(.70) (.90) (.70) (.96) (.71) (.95)

FC .07 .11 -.40 .47 .02 .092

(.76) (.79) (.88) (1.10) (1.08) (1.11)

Total Mean .33 .01 .42 .30 .33 .18

(.73) (.88) (.79) (.94) (.96) (1.10)

In order to assess the effects of automation and cell phone response type on subjective state, a 2 x 3 x 2 (automation condition x cell phone condition x pre-post) ANOVA was run.

‘Pre-post’ was a within-subjects factor contrasting pre- and post-drive state. ‘Automation

Condition’ and ‘Cell Phone Condition’ were between subjects factors contrasting the automation and non-automation groups and three cell phone groups. For task engagement, the pre-post x cell phone interaction was significant, F(2, 174) = 3.25, p < .05, partial η2 = .04, as well as the pre- post x automation x cell phone interaction, F(2, 174) = 3.10, p < .05, partial η2 = .034. For distress, only the pre-post x cell phone interaction was significant, F(2, 174) = 7.27, p < .001, partial η2 = .08. The analyses for worry revealed a significant effect for the pre-post x automation condition interaction, F(1, 174) = 5.97, p < .05, partial η2 = .03.

Figure 4 illustrates the change from pre- to post-task for task engagement, distress and worry as a function of cell phone group, with data collapsed across automation condition. The figure illustrates the interactive effect of cell phone condition and pre-post on distress. On average, the figure shows that engagement and worry dropped in each group, while distress increased across groups. More specifically, the figure shows that, on average, distress and worry were lowest in the cell phone group, while engagement was lowest in the text message group.

The figure also suggests that the temporal decline in task engagement was most pronounced in the text message condition. However, the ANOVA also showed that the pre-post x cell phone interaction was further modified by automation, (i.e., as a significant three-way interaction).

Figure 5 shows changes in task engagement during the task separately for the automation and non-automation group and also the cell phone, text message and free-choice group. Generally, the figure shows that, on average, the cell phone and text message groups showed a reduction in task engagement in both the automated and non-automated group. For the free-choice group a

35 significant difference in change scores was evident. Here change scores for engagement were lowest in the automated group and highest in the non-automated group. The only other significant effect of the task factors on state was the interactive effect of pre-post and automation on worry. Here it was found that worry decreased by -.24 SD units in the automation condition and increased by .01 SD units in the non-automation. Complete pre and post task state graphs for all conditions can be seen in Appendix I.

Engagement Distress Worry

Figure 4. Pre to post-drive changes in subjective state for the cell phone, text message and free- choice conditions. Error bars are standard errors.

0.1

-0.1

-0.2 A -0.3 NA

-0.4

-0.5

-0.6 Cell Phone Text Message Free-Choice

Figure 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.

Influences on Frequency of Cell Phone Use

It was also of interest to explore the effects of automation condition and cell phone condition on the frequency with which drivers may choose to engage in more or less cell phone usage. An independent samples t-test revealed that drivers in the automated condition responded significantly more times, t(178) = 3.013, p < .01 to optional text messages (M = 4.03, SD = 2.49), compared to drivers in the non-automated condition (M = 2.92, SD = 2.46). Additionally, an

ANOVA revealed a significant difference in response of frequency between the three cell phone groups, F(2,179) = 3.59, p < .05. Post hoc Sidak tests revealed that drivers in the free-choice condition responded significantly more times to optional messages (M = 3.98, SD = 2.35) compared to the cell phone condition (M = 2.8, SD = 2.40), p < .05. Frequency of response did not significantly differ between the free-choice and text- message groups (M = 3.65, SD = 2.71), p > .05 or the text-message and cell phone groups, p > .05. Additionally, frequency of response

37 did not correlate with any of the five DSI factors or pre-drive states of task engagement, distress or worry.

Within the free choice group, 35 out of 60 drivers responded via text message throughout the entire drive (17 within the non-automated, 18 within the automated group), while only 5 out of 60 responded by calling back throughout the entire drive (3 within the non-automated, 2 within the automated group). Exactly 20 out of 60 drivers (10 within the non-automated, 10 within the automated) showed a somewhat mixed response by calling and texting back throughout the drive.

Predictors of Subjective State

Correlates of Subjective State. Bivariate correlations for the entire group between subjective state and driver stress vulnerability are shown in Table 2. Correlations were first performed between the five DSI scores (aggression, dislike of driving, hazard monitoring, thrill seeking and fatigue proneness) and the three pre and post-task DSSQ factors (task engagement, distress and worry) for all task conditions. Correlations were then performed between the five

DSI scores and three pre and post-task DSSQ factors within both automation groups and three cell phone groups. Correlations for both automation groups and three cell phone groups showed somewhat similar relationships and so those tables are displayed in Appendix H.

Table 2. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the entire sample.

Pre Post Pre Post Pre Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .215** .249** -.141 -.125 .337** .173*

Dislike of Driving .300** .238** -.249** -.070 .406** .389**

Hazard

Monitoring .050 .142 .144 .099 -.097 -.089

Thrill Seeking .075 -.035 -.039 -.124 .078 -.060

Fatigue Prone .235** .206** -.318** -.294** .424** .291**

Note. * p < .05 ** p < .01

Table 2 shows that, for the entire group, some of the DSI variables predicted all three pre and post-task DSSQ variables. Aggression was most strongly associated with feelings of worry and distress both before and after the drive. Dislike of driving correlated with feelings of high distress and worry both before and after the drive and low task engagement prior to the drive.

Fatigue proneness significantly correlated with lower levels of task engagement for both pre (r =

-.318, p < .01) and post-drive engagement levels (r = -.294, p < .01). This trait was also associated with higher distress pre (r = .424, p < .01) and post-drive (r = .291, p < .01) and worry pre (r = .235, p < .01) and post-drive (r = .206, p < .01).

Regressions. Regression analyses tested whether the five DSI variables predicted post- drive subjective state, assessed via the three post-task DSSQ variables. Multiple regressions were run with four steps, using each of the three post-task DSSQ variables as the criterion. First, effect coding was used for the automation and cell phone conditions. For the automation conditions,

39 one vector was created (autocode). Automation condition was labeled 1 and non-automation -1.

Two further vectors were also created for the three cell phone conditions. In the first vector

(cellcode1) cell phone condition was coded -1, text message condition 1 and free-choice condition 0. In the second vector (cellcode2) cell phone condition was coded -1, text message condition 0 and free-choice condition 1. Finally, after multiplying the autocode vector by the two cellcode vectors, another two vectors were created, which corresponded to the two interaction terms between the automation and cell phone vectors.

For each regression equation, predictors were entered in four steps. First, the autocode vector for the automation condition was entered, followed by the two cellcode vectors as the second step. The third step was entered using the two interaction terms. Last, the five DSI variables were entered as the fourth step.

In the first regression, post-task engagement was used as the dependent variable. It was found that automation and cell phone condition were not predictive of post-task engagement (p >

.05). Additionally, the interaction term between both the automation condition and cell phone condition was not found to be significant, F(5,179) = .734, p > .05. However, it was found that the DSI factors predicted a significant portion of variance in post-task engagement, F(10, 179) =

2.71, p < .01 (ΔR2=.117). More specifically, it was found that fatigue proneness was most predictive of post-task engagement regardless of automation or cell phone condition β = -.308, t(179) = -4.19, p < .001.

In the second regression, post-task distress was used as the dependent variable.

Consistent with the post-task engagement results, neither the automation nor cell phone conditions were predictive of post-task distress (p > .05). The interaction term was also non- significant, although it was found that the DSI factors again predicted a large proportion of the

40 variance, F(10, 179) = 4.56, p < .001 (ΔR2=.179). Here, dislike of driving was most predictive of post-task distress β = .305, t(179) = 3.89, p <.001.

In the third regression, post-task worry was used as the dependent variable. Automation and cell phone condition were not predictive of post-task worry states (p > .05). The interaction term was also not significant, but the DSI variables were again found to be predictive of post- task worry, F(10, 179) = 3.38, p < .01 (ΔR2=.145). More specifically, it was found that aggression and hazard monitoring significantly predicted post-task worry β = .237, t(179) = 3.09, p <.01 and β = .171, t(179) = 2.37, p < .05 respectively. Additionally, several more regressions were run, using the DSI variables fatigue proneness and dislike of driving as the criterion to further predict post-drive subjective state but none of those results were significant.

Performance

Several performance measures were obtained for drivers in the automated and non- automated and also the cell phone, text message and free choice groups. First, the standard deviation of lateral position was obtained to gauge vehicle control. Lateral position was analyzed

14 successive times throughout the entire drive from the time of receipt of the first text message

(approximately 1 minute 45 seconds) to the time participants sent their last text message

(approximately 30 minutes), in the non-automated group. Next, response times following an emergency event, a van suddenly appearing from the side of the road, were recorded followed by total number of crashes for the total drive between each group.

Vehicle Control. The primary performance measure used was the standard deviation of lateral position, which measures vehicle control of the driver. Standard deviation was only assessed within the non-automated group because drivers in the automated group were not in control of the vehicle for the majority of the time that those performance measures were

41 obtained. A higher lateral position indicates poorer vehicle control. Lateral position was analyzed during 14 successive minute and a half periods throughout the entire drive (from approximately

1:45 to approximately 30 minutes). The 14 periods of lateral position correspond to the 14 text message times, so for each participant lateral position was obtained from the time of receipt of each text message to the time of the sending of their reply.

An ANOVA was run, using cell phone condition as a between subjects factor and lateral position period as a within subjects factor. The results of the ANOVA revealed a significant main effect for cell phone condition, F(2, 84) = 5.07, p < .01, partial η2 = .11 and a significant interaction for lateral position period x cell phone condition, F(14, 592) = 1.91, p < .05, partial η2

= .04. Post hoc Sidak tests revealed a significant difference between the cell phone and text message groups and the cell phone and free choice groups, p < .05, but not the free choice and text message groups, p > .05. Inspection of the means indicate that drivers in the cell phone condition had significantly lower lateral positions throughout the drive, except for lateral position 13, compared to drivers in the text message and free-choice groups. Figure 6 illustrates the means for the SD of lateral position, for 14 periods of cell phone usage throughout the entire drive, within the non-automated condition, for the cell phone, text message and free-choice response groups, showing that drivers in the cell phone condition exhibited better vehicle control throughout the entire drive. Period 13 corresponds to the activation of the emergency event for all participants.

2.4

2.1

1.8

1.5

1.2 CP TM 0.9 FC 0.6

0.3

Figure 6. Standard deviation of 14 successive lateral positions for all three cell phone groups.

Error bars are standard errors.

Additionally, it was also of interest to explore how the response of drivers in the free choice conditions affected driver performance. Responses in the free choice condition (i.e., via cell phone call back or text message) were separated and then allocated to either the cell phone or text message group. An ANOVA revealed a significant main effect for cell phone condition,

F(1, 73) = 10.79, p < .01, partial η2 = .13 and a significant interaction for lateral position period x cell phone condition, F(6.25, 456) = 2.14, p < .05, partial η2 = .03 Figure 7 shows driver performance, reflecting either cell phone or text message responses in the free-choice group, again showing that drivers in the cell phone condition were better able to control the vehicle as indicated by their lower lateral positions. Again, period 13 corresponds to the activation of the emergency event for all participants.

2.4

2.1

1.8

1.5

1.2 CP 0.9 TM 0.6

0.3

Figure 7. Standard deviation of 14 successive lateral positions based on response via cell phone call back or text message. Error bars are standard errors.

Response Times. Towards the end of the drive, and while all participants were engaged in some type of cell phone use, an emergency event was activated, a van pulling in front of the driver. Mean response times (in seconds) for braking (how fast drivers were able to apply their brakes), steering (how fast drivers were able to swerve away from the van) and de-acceleration

(how fast drivers took their foot off the accelerator) were obtained for both the automated and non-automated groups as well as the three cell phone groups. Response times with a score of 0 were excluded, which indicated that participants did not respond to the event using the previously mentioned vehicle controls. For braking, 24 out of 180 responses were not included, for steering, a total of 18 out of 180 responses were excluded and for de-acceleration 10 out of

180 responses were not included. For braking, the percentages excluded within each of the six cells of the design ranged from 3.3% (in the NA: TM condition) to 33% (in three conditions).

For the steering response, the percentages excluded within each of the six cells of the design ranged from 3.3% (in two conditions) to 33% (in one condition). Finally, the percentages 44 excluded within each of the six cells of the design for the de-acceleration response ranged from

3.3% (in three conditions) to 10% (in one condition). Thus, percentages of data excluded were fairly similar across groups for the three performance measures. Including these data may have potentially reduced the mean response time for one group.

Independent samples t-tests revealed a non-significant difference between the automation and non-automation groups for the response times for steering, t(159) = -.332, p > .05, braking, t

(153) = -.121, p > .05 and de-acceleration, t(167) = -.138, p > .05. Even though responses did not significantly differ, the non-automated group showed somewhat faster response times for steering and de-acceleration but not for braking, compared to the automated group. Figure 8 shows the mean response times within the automated and non-automated conditions.

2.5

1.5 A NA 1

0.5

0 steer acc brakes

Figure 8. Response times for steering, de-acceleration and braking between the automated and non-automated groups. Error bars are standard errors.

For the cell phones groups an ANOVA was run and revealed no significant differences between the cell phone, text message and free-choice groups for the response times for steering,

F(2,160) = .806, p > .05, braking, F(2,154) = 1.808, p > .05 and de-acceleration, F(2,168) =

.840, p > .05. Although not significant, the cell phone group showed faster response times for steering and braking, compared to the text message and free-choice groups. Figure 9 shows the mean response times for the three cell phones groups.

2.5

CP 1.5 TM

1 FC

0.5

0 steer acc brakes

Figure 9. Response times for steering, de-acceleration and braking by cell phone, text message and free-choice groups. Error bars are standard errors.

Crash Rates. Lastly, the total number of crashes for the entire drive was compared between the non-automation and cell phone groups. Crash rates between the automation and non- automation group were only analyzed on a qualitative basis. This was due to the fact that only 10 minutes of participant controlled driving occurred within the automated group, compared to a total of 35 minutes in the non-automated group. The automation group had a total 70 crashes (M

= .78, SD = .83), while the non-automation group had a total of 331 (M = 2.31, SD = 2.57). For the automation group, 73% of crashes occurred while drivers were using a cell phone, while 21% of crashes occurred while using a cell phone in the non-automated group. Additionally, an

ANOVA revealed a non-significant difference, F(2,179) = 1.13, p > .05 between the number of crashes for the cell phone (M = 1.23, SD = 1.49), text message (M = 1.78, SD = 2.05) and free 46 choice groups (M = 1.54, SD = 2.05). For drivers in the cell phone group, 74% of all crashes occurred while calling back on their cell phone, 34% of all crashes in the text message group occurred during text messaging and 41% of all crashes occurred while either calling or texting back in the free-choice group. Table 3 shows the proportion of crashes for all three cell phone conditions, while drivers were and were not using their cell phones for drivers in the non- automated group.

Table 3. Proportion of crashes for each cell phone group.

Cell Phone Text Message Free-Choice

Proportion of crashes while using a cell phone 74 34 41

Proportion of crashes while not using a cell phone 26 66 59

It was also found that 51% of drivers crashed into the van, but the frequency of crashes was similar amongst groups. Here it was found that 46 out of 90 drivers in the automated group and 47 out of 90 drivers in the non-automated group crashed into the van, while 35 out of 60 drivers in the cell phone group, 33 out of 60 drivers in the text message group and 25 out of 60 drivers in the free-choice group crashed into the van.

Lastly, it was also of interest to identify those individuals who crashed into the van but then still chose to respond to the last and optional message. Drivers who crashed into the van while using their cell phone should be aware that secondary tasks such as using a cell phone are dangerous while driving. Of those drivers who crashed into the van, 32% still chose to respond to

47 the last and optional text message, furthering their potential for more crashes and other potentially dangerous consequences of concurrently using a cell phone while driving.

Chapter 4

Discussion

Overview of Findings

The current study explored the effects of vehicle automation and different types of cell phone use during simulated driving on subjective ratings of stress and fatigue as well as driver performance. It was also of interest to identify any pre-existing factors, such as driver stress vulnerability, that might prompt drivers to engage in more or less cell phone use while driving.

Overall, the results of this study provide further support to those authors who claim that distracted driving is dangerous, but more specifically that a particular kind of distracted driving, cell phone use, disrupts driver performance (Drews et al., 2009; Drews & Crouch, 2006; Strayer

& Johnston, 2001). Furthermore, these results enhance the understanding of the dangerous effects of talking on a cell phone versus text messaging while driving and how those types of cell phone use elicit changes in subjective state.

Expectations for driver performance were generally confirmed. The hypothesis that cell phone use (via call back, text message or both) would impair vehicle control (H1) was supported.

In support of Drews et al.’s (2009) claim, text messaging appears to draw upon more visual and cognitive demands, for example, than talking on a cell phone, and thus can be concluded to be more dangerous to drivers. Moreover, it was found that drivers in the free-choice group chose to respond most frequently by engaging in text messaging rather than calling back, so responses within the free-choice group were then collapsed to reflect either text message or cell phone call back responses, again showing that poorer vehicle control can be attributed to text messaging.

Furthermore, it was found that drivers in the free-choice group responded most frequently to the optional messages, compared to drivers in the cell phone and text message groups. As previously

49 mentioned, drivers in the free-choice group chose to typically respond by texting. Here, texting was found to be associated with poorer vehicle control, yet drivers are choosing this response option more often than calling back when given the choice.

Differences in reaction time to the emergency event between the automation and non- automation group as well as the cell phone, text message and free-choice groups were not significant. The lack of significance to the sudden event may be due to the fact that all groups were engaged in a secondary task (cell phone use). Although it was found that text messaging contributes to a disruption of vehicle control, all types of distracted driving, which include talking on a cell phone, can result in performance decrements. Including a condition with no cell phone use may be necessary to reveal a significant difference in reaction time to this sudden event, which has been observed in past studies (Neubauer et al., 2011; Saxby et al., 2008).

The effects of cell phone use on subjective state, in the automation and non-automation group, were also investigated. As expected, engagement dropped more rapidly following automated driving than in the non-automation condition. However, the interactive effect of cell phone use, automation and the “pre-post” factor showed an effect of automation only in the free- choice condition, within which there was no loss of engagement in the non-automation condition. Increasing control over task demands tends to increase task engagement (Matthews et al., 2010) so it may be that control over both cell phone response modality and the vehicle is sufficient to maintain engagement during a relatively short drive. In addition, post-drive worry was lower in the automation group, which suggests that vehicle automation can potentially alleviate this adverse state change when paired with secondary task demands. Past studies

(Neubauer et al., 2011) have suggested that automation use increases the potential awareness a driver may have of their own levels of fatigue, which may in turn increase stress levels, but no

50 effect of automation on distress was found in the current study. Possibly, placing extra task demands on the driver (i.e., cell phone use) requires the driver to shift their attention from internal thoughts of distress, when engaged in automated driving, to those of the secondary external task demands, which may provide a buffer against the self-awareness of discomfort.

Distress was affected by cell phone condition, with cell phone use eliciting the smallest increase in distress from pre- to post-task, partially supporting H5. Distress is sensitive to task workload

(Matthews et al., 2002), and so this finding is consistent with texting imposing greater workload than vocal response (Drews et al., 2009).

In support of H2, drivers in the automated group responded more frequently to the optional text messages, compared to drivers in the non-automated group. Increased cell phone use may be seen as an attempt on the driver’s part to remain somewhat engaged in the primary task. A minimal task load may encourage more cell phone use, although it does not necessarily alleviate fatigue (measured by lower task engagement) during automated driving. On the other hand, optional response was also higher in the free-choice condition relative to the cell phone and text message conditions. As just discussed, this condition elicited more distress than the cell phone condition, implying that workload was higher. Thus, relatively high levels of optional response may be found in both lower and higher workload conditions, suggesting that response frequency is not necessarily reflective of workload. Decisions on whether or not to respond to optional messages may depend on multiple factors.

DSI scales were related to post-task subjective state as in previous studies, consistent with the transactional model of driver stress (Desmond & Matthews, 2009). However, the trait factors assessed in this study did not predict either frequency of phone use or driving

51 performance, so that the hypotheses relating to individual differences (H3 and H4) were not confirmed.

The following sections will address the theoretical and practical implications of the findings of this study, focusing specifically on theories of attention relevant to driving, and design and safety implications for vehicle automation. Finally, the role that individual differences have in moderating the effects of a fatiguing drive will then be discussed.

Theoretical Implications

As previously discussed, distracted driving is extremely important to study because the effects of distracted driving can account for thousands of road fatalities, which can amount to billions of dollars in work and health related costs. Additionally, drivers’ stress and fatigue can also contribute to vehicular fatalities, especially when drivers are engaged in secondary tasks.

The act of driving requires a great deal of cognitive, psychomotor and visual resources and can be difficult when stress and fatigue contribute to attentional impairment, but even more so when adding a secondary task demand, such as using a cell phone.

Talking on a Cell Phone versus Texting. Distracted driving, which can include tuning a radio, talking with a passenger, using a cell phone or even applying makeup, appears to account for up to one quarter of vehicular accidents (Strayer & Johnston, 2001). Although distractions have been ubiquitous since the dawn of automobile transportation, it has been argued that the effects of cell phones are especially dangerous given the fact that a multitude of cognitive, auditory and visual distractions typically ensue (McCartt, Hellinga & Bratiman, 2006).

Furthermore, there does seem to be a difference between the effects of talking on a cell phone and of texting on driver state and performance. According to Drews et al. (2009), text messaging requires that drivers engage in much longer periods of manual phone entry, which requires

52 longer periods of the driver’s visual attention as well, compared to simply talking on a cell phone.

Hancock and Warm’s theory of dynamic adaptation (1989) suggests that an operator’s adaptations to task demands are best when those task demands are relatively moderate. When task demands are too high (which may be evident in the text message group), effortful compensation is impaired, whereby a reduction in task-related coping can be seen. Conversely, when task demands are insufficient to adequately engage attention (i.e., underload), performance may also be impaired due to failure to commit sufficient effort (Matthews & Desmond, 2002).

Hancock and Warm (1989) also suggest that subjective expressions of stress may be more sensitive to incipient loss of adaptation, providing a warning of impending performance breakdown.

In the present study, the theory then suggests that overload resulting from texting should both enhance distress and impair performance. Conversely, underload associated with automation should elicit the combination of low task engagement and performance impairment, as found in previous studies (Saxby et al., 2008). The predictions related to overload were largely supported, in that texting produced more adverse effects on driver stress than did cell phone use

(H5 partially supported). Although all forms of cell phone usage are potentially harmful, texting produced the largest impact on performance. The secondary task demands of texting back responses while driving may require too much effort and can result in overload, while the secondary task demands placed on drivers in the cell phone group may not require as much effort on the driver’s part, which may have allowed drivers in this group to adapt more effectively.

However, although Hancock and Warm’s theory of dynamic adaptation (1989) can possibly account for the negative effects of text messaging, it fails to account for the effects in

53 the free-choice group. Here some drivers switched back and forth between text messaging and calling, so it would seem that task demands were relatively moderate in comparison to the text message group, but it was found that driver performance (e.g., maintaining vehicle control) was equal if not worse at times to the text message group. The free-choice group tended to respond more frequently to optional text messages, which may have contributed to performance impairment. Giving drivers greater freedom of choice in adapting to task demands does not necessarily lead to more effective adaptation to task demands. In addition, drivers in the free- choice group showed a trend towards poorer alertness in response to the emergency event (e.g., slower response times for steering and braking) than drivers in the text message group.

The detrimental effects of underload resulting from automation predicted by the Hancock and Warm (1989) theory were not obtained in any straightforward way, in that the expected effect of automation on engagement was found only in the free-choice condition. Furthermore, worry was actually lower in automated than in non-automated conditions, a result that was not obtained in previous studies of vehicle automation (Neubauer et al., in press; Saxby et al., 2008).

There was also no effect of automation on subsequent performance. One broad possibility is that during automated driving the additional workload imposed by all forms of cell phone use actually helps the driver to regulate the potentially adverse effects of monotony and underload. It is notable that engagement levels during automated driving remained somewhat higher than what has been found in past studies (Neubauer et al., in press; Saxby et al., 2007). There may be a general tendency for repeated phone use, of whatever kind, to counteract loss of engagement to some degree during automated driving.

The results are broadly consistent with resource theory, to the extent that texting is more resource-demanding than is vocal response using the phone. However, the findings are also

54 somewhat problematic for multiple resource models of attention during dual-task performance.

Multiple resources models suggest that dual-task performance should not be negatively affected if attention draws upon different modes or attentional processes (Wickens, 2002). Within this context, a cell phone conversation, a primarily auditory-verbal task, should not interfere with driving, a primarily visual-spatial task, but this result was not found in this study, where all types of cell phone use negatively affected driver performance. These results would suggest that some sort of overlap or attentional bottleneck exists between processing of the roadway and the cell phone conversation, where an increase in attentional switching can be seen. The next section will discuss the practical implications of this study, focusing on safety concerns, informed design and the utility of subjective questionnaires in developing appropriate countermeasures.

Practical Implications for Safety and Intervention

The findings of the current study have several practical applications. First, these findings provide further support to those authors who have identified the multiple dangers of distracted driving, more specifically driving while using a cell phone. Engaging in a secondary task while driving relies on many cognitive, visual and psychomotor resources, with effects that may interfere with the driver’s engagement with the primary task. Furthermore, texting while driving relies more heavily on multiple demands of cognitive attention, visual attention, manual handling etc., compared to talking on a cell phone. Distracted driving is always dangerous but more so when the driver’s cognitive resources are already taxed by task demands. Drivers should be aware that using a cell phone while driving is dangerous but more so when the driver engages in text messaging. Data from this study revealed that both types of distracted driving cause performance impairments such as a decrease in vehicle control, but not for reaction time to the sudden event, suggesting that on the whole this type of behavior (e.g., text messaging) most

55 negatively affects driving over a longer interval. Strayer and Drews (2004) suggested that all types of distracted driving adversely affect the driver’s ability to make safe, split second reactions, but this study did not substantiate any differential effect of cell phone use and texting on alertness.

The present findings can potentially influence the development of new legislative efforts to protect against the negative effects of distracted driving. Currently, only a handful of states outlaw the use of handheld cell phones. Furthermore, there is not one state that bans the use of all types of cell phones, where hands free cell phones are typically allowed. Prior research has suggested that driving impairments are equal for both hands-free as well as handheld cell phone use (Strayer, Drews & Johnston, 2003), so this type of legislation is most likely ineffective.

Additionally, it does appear that lawmakers are more aware of the dangers of text messaging because over 30 states have outlawed this type of cell phone use but more legislation banning the use of text messaging is needed. The present findings reinforce the dangers of texting.

Second, the results also provide several safety implications in regard to recent advances in automated systems. Such systems are useful in potentially counteracting the negative effects of driver fatigue and workload (Hancock & Verwey, 1997; Hancock & Parasuraman, 1992), but relying too heavily on this kind of technology may decrease driver situation awareness (Young &

Stanton, 2007), which may lead to even greater decrements in performance. The data provide support to those authors who stress the need for secondary tasks when utilizing vehicle automation (Funke et al., 2007). These authors found that requiring drivers to control one aspect of their driving (e.g., lane position), while automatically controlling another (e.g., speed), decreased subjective workload and distress ratings. Full vehicle automation appears to interfere with the driver’s active engagement with the task, which is dangerous if the driver suddenly

56 needs to regain control of the vehicle. Conversely, requiring the driver to remain somewhat engaged in the task (e.g., monitoring for automation failure, engaging in some cell phone use) may help engagement levels remain somewhat higher than they normally would be in this type of automated situation.

One of the intended purposes of automated systems is to relieve some of the workload that can contribute to fatigue, an aim which has not necessarily been supported in past studies

(Neubauer et al., 2011). By giving drivers the option of engaging in short periods of automation, these authors found that the availability of such systems can actually exacerbate fatigue levels (as measured by task disengagement). In order to counteract this effect, it may be useful for automated systems to be coupled with a secondary task; to keep operators somewhat engaged in the primary task at hand, so as to reduce the effects of passive fatigue that are many times seen in automated situations (Saxby et al., 2007). It seems that adding another task such as requiring drivers to use their cell phones for example may decrease the possibility for a loss in task engagement. In the present study, participants were required to engage in a secondary task (using a cell phone and monitoring for automation failure). Although the findings of this study revealed that cell phone use did not necessarily curb against a loss in task engagement, it could be argued that this secondary task helped engagement levels to remain somewhat higher during vehicle automation, compared to past studies (Neubauer et al., 2011; Saxby et al., 2008). Additionally, it was also found that drivers in the automated group responded significantly more times to the optional messages presented, compared to drivers in the non-automated group, which can be seen as the driver’s active attempt to remain engaged in the task, although drivers in the automated group may have simply had nothing else to do during the automated drive and thus chose to engage in more cell phone use.

Although the findings of the present study seem to suggest that secondary tasks help counteract the adverse effects of state change in automated situations, pairing automation with a secondary task may not help driver situation awareness in emergency situations. Although not significant, drivers in the automation group were somewhat slower to respond to the emergency event, for steering and de-acceleration but not braking, than drivers in the non-automated group.

Thus, prolonged use of automation may be associated with a decrease in situation awareness and driver alertness, which can be dangerous when drivers need to suddenly regain control of the vehicle. Supporting the driver’s transition from the underload of automation to allocating full attention to the traffic environment, as control over the vehicle is re-established, remains a significant human factors issue.

Although results of the current and past studies of vehicle automation suggest that such technology should be used with caution, there are some benefits that automated systems offer. In the current study, automation was found to protect against some adverse state effects of a fatiguing drive. Worry levels were somewhat lower in the automation groups, compared with those drivers who were required to remain in full control of the vehicle during the entire drive.

The findings of this study suggest that vehicle automation benefits drivers who are engaged in a secondary task, providing further support to adaptation models, although caution should be afforded when drivers are expected to suddenly regain control after prolonged automation use.

Third, the results of this study provide further evidence for the need for in-vehicle countermeasures to driver distraction. For example, developing a system that allows drivers to verbally state commands, such as operating an MP3 device or answering a call, without having to remove their hands from the steering wheel could be beneficial. Additionally, such systems may benefit drivers with the development of a voice commanded text-messaging system that

58 assists the driver in reading text messages aloud while driving. Past research has suggested that listening to books on tape did not significantly impair driver performance, so similar results should be obtained with this type of technology (Strayer & Johnston, 2001). These types of innovations seem to offer promising safety solutions for driver distraction that can be contributable to cell phone usage, but more empirical research is needed to test for these effects during driving.

Lastly, results of this study provide further support for the use of subjective questionnaire measures in identifying changes in driver state, such as driver stress and fatigue. Stress and fatigue states are important to identify so that appropriate countermeasures can be put into effect.

According to Hancock and Warm (1989), subjective state change tends to occur prior to decrements in performance so it is important to identify those symptoms that concur along with driver fatigue and stress. It is well known that stress and fatigue drain attentional resources and can produce numerous performance decrements during driving (Desmond & Matthews, 1997), so identifying those individuals who are particularly vulnerable to such states is necessary for selection of professional drivers, who are required to perform long, monotonous drives as part of their job. Utilizing measures such as the DSI (Matthews et al., 1997), researchers can identify those individuals who are highly susceptible to feelings of increased tiredness (e.g., fatigue proneness) and anxiety (e.g., dislike of driving) while driving.

Individual Differences, Stress Vulnerability and Cell Phone Use

Although task demands may elicit various changes in subjective state, there also appear to be some fairly stable predispositions that can influence driver state independent of task demands. Certain personality factors can interact with situational task demands that result in various coping strategies as well as changes in subjective stress states, which mediate the effects

59 of the task. Contrary to H3 and H4, frequency of response to text messages was not found to be associated with the personality dimensions ‘Thrill Seeking’ or ‘Dislike of Driving’. Although, there were no significant findings between driver stress vulnerability and frequency of cell phone use, there were some findings similar to past studies (Neubauer et al., 2011; Desmond &

Matthews, 2009; Matthews & Desmond, 1998), which found that the DSI scales were a reliable predictor of state response.

Past research has suggested that these predispositions to driver stress (as measured by the

DSI) relate to reliable patterns of coping and appraisal, which can result in task-induced changes in subjective state (Matthews, 2002). How the individual interprets the situational stressor greatly affects their subjective state and performance outcomes. For example, Matthews and Desmond

(1998) found that after a fatiguing drive, the DSI scale fatigue proneness was directly related to the change in subjective fatigue states. Results of the current study mirrored the findings of past studies of simulated driving (Neubauer et al., 2011; Matthews & Desmond, 1998). In this study, the DSI scale fatigue proneness was found to be most predictive of task-induced declines in task engagement, but the effects were similar in all conditions. It appears fatigue proneness predicts a decrease in task engagement regardless of automated or non-automated driving or cell phone usage. Additionally, drivers high in dislike of driving showed greater levels of subjective stress in response to the drive. Regardless of secondary task demands or a fatiguing drive, feelings of increased anxiety are synonymous with the trait dislike of driving, which are shown to be most predictive of distress following the drive. Drivers scoring high in this trait may already be aware of the potential anxiety that can ensue during driving before they do so, and so coupling a secondary task during driving may not be adequate compensation for such readily accessible feelings. A different finding was evident for worry. Here, aggression and hazard monitoring

60 predicted worry states following the drive, regardless of the fatigue manipulation (e.g., automated drive) or cell phone usage.

Past research has shown that drivers scoring high in fatigue proneness are especially vulnerable to passive fatigue (Desmond and Matthews, 2009), which can be elicited during automated driving (Saxby et al., 2007). Indeed past findings suggest that individuals who are prone to such states of fatigue should not over-rely on automation as a fatigue countermeasure

(Neubauer et al., 2011). Thrill-seeking has also been implicated in fatigue vulnerability

(Thiffault & Bergeron, 2003). However, the present findings show that these personality factors are not predictive of individual differences in cell phone use during potentially fatiguing drives.

Future research might aim to identify traits associated with preferences for regulating fatigue through conversation. It is possible too that individual differences depend more on other relevant traits such as sociability and conscientiousness than on traits related to fatigue vulnerability or to fatigue management. Identifying measures of individual differences and vulnerabilities that influence performance and safety in operational environments remains an important goal for research (Szalma, 2009).

Summary and Overall Conclusions

To summarize, the findings of this study offer several theoretical and practical implications. First, these results further the understanding of the dangers of distracted driving, supporting interventions that can be implemented in public safety, legislation and technology development (Drews et al., 2009). Distracted driving can include several different types of behaviors that divert the driver’s attention away from the primary task of driving. Although all types of distracted driving are dangerous, it appears that using a cell phone is more dangerous, than, say, tuning a radio, given the length of time required to engage in such a task and the

61 multitude of cognitive demands necessary (Strayer, Drews & Crouch, 2006). Furthermore, although texting appears to be more dangerous than talking on a cell phone, when given a free choice, more drivers choose to engage in it rather than calling back on their cell phone, demonstrating drivers’ lack of awareness of the safety issue.

It also appears that both automation and the various kinds of cell phone use can result in different kinds of subjective state response. For example, talking on a cell phone, rather than texting, appears to curb against the potentially harmful increases in distress. Additionally, these various kinds of cell phone use can impact driver performance in different ways, consistent with previous research (Strayer et al., 2006). Drivers talking on a cell phone were found to have better vehicle control throughout the drive, whereas those drivers in the text message and free choice groups appear to be impacted most negatively during this secondary task.

Second, the analyses of the subjective and performance data suggest that automated systems may offer potential benefits as well as dangers to drivers engaged in secondary tasks.

Consistent with previous studies (Funke et al., 2007; Saxby et al., 2007), the data suggest that automation should be used sparingly during periods of underload, where passive fatigue may ensue, resulting in a loss of situation awareness as shown by a slowing of reaction time to an emergency event. Although some caution should be shown with automated systems regarding driver performance during secondary tasks, it does appear that, under these circumstances, automation may help to alleviate worry. Furthermore, even though engagement levels were lower for drivers using automation, it does appear that coupling such technology with a secondary task such as cell phone use prevented levels from dropping as drastically as in past studies (Neubauer et al., 2011; Saxby et al., 2007).

Lastly, utilizing subjective questionnaire measures is of great importance in identifying those individuals who are particularly susceptible to fatigue and anxiety while driving. As measured by the DSI, fatigue proneness and dislike of driving were found to be the greatest predictors of low task engagement and distress respectively. Similar to past studies (Neubauer et al., 2011; Matthews & Desmond, 1998; Matthews et al., 1996), these individual differences seem to be most predictive of stress states even after controlling for cell phone and automation use.

Broadly speaking, fatigue-proneness tends to decrease task-engagement irrespective of the fatigue manipulation and secondary task demands, providing further support that this trait is associated with feelings of increased fatigue and low task engagement. Other DSI scales are also predictive of such task-induced state changes. It appears that dislike of driving also tends to increase subjective distress irrespective of the kind of drive or type of cell phone conversation.

This information can be made extremely useful in the public and private sector in identifying those individuals who are especially susceptible to such reactions following a fatiguing drive.

However, identifying those individuals who are most likely to respond to phone messages requires further research.

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APPENDIX A

Frequency of Cell Phone Use Questionnaire

Participant is to fill out this page

1. Would you characterize yourself as a frequent cell phone user? (i.e. What percentage of your time is spent using a cell phone?)

______Frequent (over 40% of the time)

______Intermediate (1 to 40% of the time)

______Not at all (0 % of the time)

2. How many hours a day would you say that you typically use your cell phone?

______Over 4 Hours a day

______1- 4 Hours a day

______0 Hours a day

3. Would you characterize yourself as a frequent text messager? (i.e. What percentage of your time is spent text messaging?)

______Frequent (over 40% of the time)

______Intermediate (1 to 40% of the time)

______Not at all (0 % of the time)

4. Do you typically respond to the text messages you receive while driving? If yes, how do you typically respond? Please circle one

Via Text Message Via Call Back

5. Would you consider yourself an expert texter?

Yes No

6. Would you characterize yourself as a safe driver while you text?

Yes No

7. Would you characterize others as safe drivers while they text?

Yes No

APPENDIX B

Pre-Task DSSQ

STATE QUESTIONNAIRE

General Instructions. This questionnaire is concerned with your feelings and thoughts at the moment. We would like to build up a detailed picture of your current state of mind, so there are quite a few questions, divided into four sections. Please answer every question, even if you find it difficult. Answer, as honestly as you can, what is true of you. Please do not choose a reply just because it seems like the 'right thing to say'. Your answers will be kept entirely confidential. Also, be sure to answer according to how you feel AT THE MOMENT. Don't just put down how you usually feel. You should try and work quite quickly: there is no need to think very hard about the answers. The first answer you think of is usually the best.

Before you start, please provide some general information about yourself.

Age...... (years) Sex. M F (Circle one) Occupation...... If student, state your course...... Date today...... Time of day now......

First, there is a list of words which describe people's moods or feelings. Please indicate how well each word describes how you feel AT THE MOMENT. For each word, circle the answer from 1 to 4 which best describes your mood. 1. MOOD STATE Slightly Definitely Definitely Slightly Not Not 1. Happy 1 2 3 4 2. Dissatisfied 1 2 3 4 3. Energetic 1 2 3 4 4. Relaxed 1 2 3 4 5. Alert 1 2 3 4 6. Nervous 1 2 3 4 7. Passive 1 2 3 4 8. Cheerful 1 2 3 4 9. Tense 1 2 3 4 10. Jittery 1 2 3 4 11. Sluggish 1 2 3 4 12. Sorry 1 2 3 4 13. Composed 1 2 3 4 14. Depressed 1 2 3 4 15. Restful 1 2 3 4 16. Vigorous 1 2 3 4 17. Anxious 1 2 3 4

18. Satisfied 1 2 3 4 19. Unenterprising 1 2 3 4 20. Sad 1 2 3 4 21. Calm 1 2 3 4 22. Active 1 2 3 4 23. Contented 1 2 3 4 24. Tired 1 2 3 4 25. Impatient 1 2 3 4 26. Annoyed 1 2 3 4 27. Angry 1 2 3 4 28. Irritated 1 2 3 4 29. Grouchy 1 2 3 4

Please answer some questions about your attitude to the task you are about to do. Rate your agreement with the following statements by circling one of the following answers:

Extremely = 4 Very much = 3 Somewhat = 2 A little bit = 1 Not at all = 0

2. MOTIVATION 1. I expect the content of the task will be interesting 0 1 2 3 4 2. The only reason to do the task is to get an external reward (e.g. payment) 0 1 2 3 4 3. I would rather spend the time doing the task on something else 0 1 2 3 4 4. I am concerned about not doing as well as I can 0 1 2 3 4 5. I want to perform better than most people do 0 1 2 3 4 6. I will become fed up with the task 0 1 2 3 4 7. I am eager to do well 0 1 2 3 4 8. I would be disappointed if I failed to do well on the task 0 1 2 3 4 9. I am committed to attaining my performance goals 0 1 2 3 4 10. Doing the task is worthwhile 0 1 2 3 4 11. I expect to find the task boring 0 1 2 3 4 12. I feel apathetic about my performance 0 1 2 3 4 13. I want to succeed on the task 0 1 2 3 4 14. The task will bring out my competitive drives 0 1 2 3 4 15. I am motivated to do the task 0 1 2 3 4

In this section, we are concerned with your thoughts about yourself: how your mind is working, how confident you feel, and how well you expect to perform on the task. Below are some statements which may describe your style of thought RIGHT NOW. Read each one carefully and indicate how true each statement is of your thoughts AT THE MOMENT. To answer, circle one of the following answers:

Extremely = 4 Very much = 3 Somewhat = 2 A little bit = 1 Not at all = 0

3. THINKING STYLE 1. I'm trying to figure myself out. 0 1 2 3 4 2. I'm very aware of myself. 0 1 2 3 4 3. I'm reflecting about myself. 0 1 2 3 4 4. I'm daydreaming about myself. 0 1 2 3 4 5. I'm thinking deeply about myself. 0 1 2 3 4 6. I'm attending to my inner feelings. 0 1 2 3 4 7. I'm examining my motives. 0 1 2 3 4 8. I feel that I'm off somewhere watching myself. 0 1 2 3 4 9. I feel confident about my abilities. 0 1 2 3 4 10. I am worried about whether I am regarded as a success or failure. 0 1 2 3 4 11. I feel self-conscious. 0 1 2 3 4 12. I feel as smart as others. 0 1 2 3 4 13. I am worried about what other people think of me. 0 1 2 3 4 14. I feel confident that I understand things. 0 1 2 3 4 15. I feel inferior to others at this moment. 0 1 2 3 4 16. I feel concerned about the impression I am making. 0 1 2 3 4 17. I feel that I have less scholastic ability right now than others. 0 1 2 3 4 18. I am worried about looking foolish. 0 1 2 3 4 19. My attention is directed towards things other than the task. 0 1 2 3 4 20. I am finding physical sensations such as muscular tension distracting. 0 1 2 3 4 21. I expect my performance will be impaired by thoughts irrelevant to the task. 0 1 2 3 4 22. I have too much to think about to be able to concentrate on the task. 0 1 2 3 4 23. My thinking is generally clear and sharp. 0 1 2 3 4 24. I will find it hard to maintain my concentration for more than a short time. 0 1 2 3 4 25. My mind is wandering a great deal. 0 1 2 3 4 26. My thoughts are confused and difficult to control. 0 1 2 3 4 27. I expect to perform proficiently on this task. 0 1 2 3 4 28. Generally, I feel in control of things. 0 1 2 3 4 29. I can handle any difficulties I encounter 0 1 2 3 4 30. I consider myself skillful at the task 0 1 2 3 4

This set of questions concerns the kinds of thoughts that go through people's heads at particular times, for example while they are doing some task or activity. Below is a list of thoughts, some of which you might have had recently. Please indicate roughly how often you had each thought DURING THE LAST TEN MINUTES or so, by circling a number from the list below.

1= Never 2= Once 3= A few times 4= Often 5= Very often

4. THINKING CONTENT 1. I thought about how I should work more carefully. 1 2 3 4 5 2. I thought about how much time I had left. 1 2 3 4 5 3. I thought about how others have done on this task. 1 2 3 4 5 4. I thought about the difficulty of the problems. 1 2 3 4 5 5. I thought about my level of ability. 1 2 3 4 5 6. I thought about the purpose of the experiment. 1 2 3 4 5 7. I thought about how I would feel if I were told how I performed. 1 2 3 4 5 8. I thought about how often I get confused. 1 2 3 4 5 9. I thought about members of my family. 1 2 3 4 5 10. I thought about something that made me feel guilty. 1 2 3 4 5 11. I thought about personal worries. 1 2 3 4 5 12. I thought about something that made me feel angry. 1 2 3 4 5 13. I thought about something that happened earlier today. 1 2 3 4 5 14. I thought about something that happened in the recent past 1 2 3 4 5 (last few days, but not today). 1 2 3 4 5 15. I thought about something that happened in the distant past 1 2 3 4 5 16. I thought about something that might happen in the future. 1 2 3 4 5

APPENDIX C

Post-Task DSSQ

STATE QUESTIONNAIRE

General Instructions

This questionnaire is concerned with your feelings and thoughts while you were performing the task. We would like to build up a detailed picture of your current state of mind, so there are quite a few questions, divided into four sections. Please answer every question, even if you find it difficult. Answer, as honestly as you can, what is true of you. Please do not choose a reply just because it seems like the 'right thing to say'. Your answers will be kept entirely confidential. Also, be sure to answer according to how you felt WHILE PERFORMING THE TASK. Don't just put down how you usually feel. You should try and work quite quickly: there is no need to think very hard about the answers. The first answer you think of is usually the best.

First, there is a list of words which describe people's moods or feelings. Please indicate how well each word describes how you felt WHILE PERFORMING THE TASK. For each word, circle the answer from 1 to 4 which best describes your mood.

1. MOOD STATE Slightly Definitely Definitely Slightly Not Not 1. Happy 1 2 3 4 2. Dissatisfied 1 2 3 4 3. Energetic 1 2 3 4 4. Relaxed 1 2 3 4 5. Alert 1 2 3 4 6. Nervous 1 2 3 4 7. Passive 1 2 3 4 8. Cheerful 1 2 3 4 9. Tense 1 2 3 4 10. Jittery 1 2 3 4 11. Sluggish 1 2 3 4 12. Sorry 1 2 3 4 13. Composed 1 2 3 4 14. Depressed 1 2 3 4 15. Restful 1 2 3 4 16. Vigorous 1 2 3 4 17. Anxious 1 2 3 4 18. Satisfied 1 2 3 4 19. Unenterprising 1 2 3 4 20. Sad 1 2 3 4 21. Calm 1 2 3 4 22. Active 1 2 3 4 76

23. Contented 1 2 3 4 24. Tired 1 2 3 4 25. Impatient 1 2 3 4 26. Annoyed 1 2 3 4 27. Angry 1 2 3 4 28. Irritated 1 2 3 4 29. Grouchy 1 2 3 4

Please answer the following questions about your attitude to the task you have just done. Rate your agreement with the following statements by circling one of the following answers:

Not at all = 0 A little bit = 1 Somewhat = 2 Very much = 3 Extremely = 4

2. MOTIVATION AND WORKLOAD 1. The content of the task was interesting 0 1 2 3 4 2. The only reason to do the task is to get an external reward (e.g. payment) 0 1 2 3 4 3. I would rather have spent the time doing the task on something else 0 1 2 3 4 4. I was concerned about not doing as well as I can 0 1 2 3 4 5. I wanted to perform better than most people do 0 1 2 3 4 6. I became fed up with the task 0 1 2 3 4 7. I was eager to do well 0 1 2 3 4 8. I would be disappointed if I failed to do well on this task 0 1 2 3 4 9. I was committed to attaining my performance goals 0 1 2 3 4 10. Doing the task was worthwhile 0 1 2 3 4 11. I found the task boring 0 1 2 3 4 12. I felt apathetic about my performance 0 1 2 3 4 13. I wanted to succeed on the task 0 1 2 3 4 14. The task brought out my competitive drives 0 1 2 3 4 15. I was motivated to do the task 0 1 2 3 4

16. Please rate the MENTAL DEMAND of the task: How much mental and perceptual activity was required? low high 1 2 3 4 5 6 7 8 9 10

17. Please rate the PHYSICAL DEMAND of the task: How much physical activity was required? low high 1 2 3 4 5 6 7 8 9 10

18. Please rate the TEMPORAL DEMAND of the task: How much time pressure did you feel due to the pace at

which the task elements occurred? low high 1 2 3 4 5 6 7 8 9 10

19. Please rate your PERFORMANCE: How successful do you think you were in accomplishing the goals of the task? low high 1 2 3 4 5 6 7 8 9 10

20. Please rate your EFFORT: How hard did you have to work (mentally and physically) to accomplish your level of performance? low high 1 2 3 4 5 6 7 8 9 10

21. Please rate your FRUSTRATION: How discouraged, irritated, stressed and annoyed did you feel during the task? low high 1 2 3 4 5 6 7 8 9 10

In this section, we are concerned with your thoughts about yourself: how your mind is working, how confident you feel, and how well you believed you performed on the task. Below are some statements which may describe your style of thought during task performance. Read each one carefully and indicate how true each statement was of your thoughts WHILE PERFORMING THE TASK. To answer circle one of the following answers:

Not at all = 0 A little bit = 1 Somewhat = 2 Very much = 3 Extremely = 4

3. THINKING STYLE 1. I tried to figure myself out. 0 1 2 3 4 2. I was very aware of myself. 0 1 2 3 4 3. I reflected about myself. 0 1 2 3 4 4. I daydreamed about myself. 0 1 2 3 4 5. I thought deeply about myself. 0 1 2 3 4 6. I attended to my inner feelings. 0 1 2 3 4 7. I examined my motives. 0 1 2 3 4 8. I felt that I was off somewhere watching myself. 0 1 2 3 4 9. I felt confident about my abilities. 0 1 2 3 4 10. I was worried about whether I am regarded as a success or failure. 0 1 2 3 4 11. I felt self-conscious. 0 1 2 3 4

12. I felt as smart as others. 0 1 2 3 4 13. I was worried about what other people think of me. 0 1 2 3 4 14. I felt confident that I understood things. 0 1 2 3 4 15. I felt inferior to others. 0 1 2 3 4 16. I felt concerned about the impression I was making. 0 1 2 3 4 17. I felt that I had less scholastic ability than others. 0 1 2 3 4 18. I was worried about looking foolish. 0 1 2 3 4 19. My attention was directed towards things other than the task. 0 1 2 3 4 20. I found physical sensations such as muscular tension distracting. 0 1 2 3 4 21. My performance was impaired by thoughts irrelevant to the task. 0 1 2 3 4 22. I had too much to think about to be able to concentrate on the task. 0 1 2 3 4 23. My thinking was generally clear and sharp. 0 1 2 3 4 24. I found it hard to maintain my concentration for more than a short time. 0 1 2 3 4 25. My mind wandered a great deal. 0 1 2 3 4 26. My thoughts were confused and difficult to control 0 1 2 3 4 27. I performed proficiently on this task. 0 1 2 3 4 28. Generally, I felt in control of things. 0 1 2 3 4 29. I was able to handle any difficulties I encountered 0 1 2 3 4 30. I consider myself skillful at the task 0 1 2 3 4

This set of questions concerns the kinds of thoughts that go through people's heads at particular times, for example while they are doing some task or activity. Below is a list of thoughts, some of which you might have had recently. Please indicate roughly how often you had each thought during THE LAST TEN MINUTES (while performing the task), by circling a number from the list below.

1= Never 2= Once 3= A few times 4= Often 5= Very often

4. THINKING CONTENT 1. I thought about how I should work more carefully. 1 2 3 4 5 2. I thought about how much time I had left. 1 2 3 4 5 3. I thought about how others have done on this task. 1 2 3 4 5 4. I thought about the difficulty of the problems. 1 2 3 4 5 5. I thought about my level of ability. 1 2 3 4 5 6. I thought about the purpose of the experiment. 1 2 3 4 5 7. I thought about how I would feel if I were told how I performed. 1 2 3 4 5 8. I thought about how often I get confused. 1 2 3 4 5 9. I thought about members of my family. 1 2 3 4 5 10. I thought about something that made me feel guilty. 1 2 3 4 5 11. I thought about personal worries. 1 2 3 4 5 12. I thought about something that made me feel angry. 1 2 3 4 5 13. I thought about something that happened earlier today. 1 2 3 4 5 14. I thought about something that happened in the recent past (last few days, 1 2 3 4 5 but not today). 15. I thought about something that happened in the distant past. 1 2 3 4 5 16. I thought about something that might happen in the future. 1 2 3 4 5

Next, please answer some questions about the task. Please indicate what you thought of the task while you were performing it. Please try to rate the task itself rather than your personal reactions to it. For each adjective or sentence circle the appropriate number, on the six point scales provided (where 0 = not at all to 5 = very much so).

Threatening 0 1 2 3 4 5 Enjoyable 0 1 2 3 4 5 Fearful 0 1 2 3 4 5 Exhilarating 0 1 2 3 4 5 Worrying 0 1 2 3 4 5 Informative 0 1 2 3 4 5 Frightening 0 1 2 3 4 5 Challenging 0 1 2 3 4 5 Terrifying 0 1 2 3 4 5 Stimulating 0 1 2 3 4 5 Hostile 0 1 2 3 4 5 Exciting 0 1 2 3 4 5

The task was a situation:

Which was likely to get out of control 0 1 2 3 4 5 In which you were unsure of how much influence you 0 1 2 3 4 5

80 have In which somebody else was to blame for difficulties 0 1 2 3 4 5 In which you had to hold back from doing what you really want 0 1 2 3 4 5 Which you could deal with effectively 0 1 2 3 4 5 In which efforts to change the situation tended to make it worse 0 1 2 3 4 5 In which other people made it difficult to deal with the problem 0 1 2 3 4 5 Which was just too much for you to cope with 0 1 2 3 4 5

Finally, think about how you dealt with any difficulties or problems which arose while you were performing the task. Below are listed some options for dealing with problems such as poor performance or negative reactions to doing the task. Please indicate how much you used each option, specifically as a deliberately chosen way of dealing with problems. To answer circle one of the following answers:

Not at all = 0 A little bit = 1 Somewhat = 2 Very much = 3 Extremely = 4

6. DEALING WITH PROBLEMS 1. I worked out a strategy for successful performance 0 1 2 3 4 2. I worried about what I would do next 0 1 2 3 4 3. I stayed detached or distanced from the situation 0 1 2 3 4 4. I decided to save my efforts for something more worthwhile 0 1 2 3 4 5. I blamed myself for not doing better 0 1 2 3 4 6. I became preoccupied with my problems 0 1 2 3 4 7. I concentrated hard on doing well 0 1 2 3 4 8. I focused my attention on the most important parts of the task 0 1 2 3 4 9. I acted as though the task wasn't important 0 1 2 3 4 10. I didn't take the task too seriously 0 1 2 3 4 11. I wished that I could change what was happening 0 1 2 3 4 12. I blamed myself for not knowing what to do 0 1 2 3 4 13. I worried about my inadequacies 0 1 2 3 4 14. I made every effort to achieve my goals 0 1 2 3 4 15. I blamed myself for becoming too emotional 0 1 2 3 4 16. I was single-minded and determined in my efforts to overcome any problems 0 1 2 3 4 17. I gave up the attempt to do well 0 1 2 3 4 18. I told myself it wasn't worth getting upset 0 1 2 3 4 19. I was careful to avoid mistakes 0 1 2 3 4 20. I did my best to follow the instructions for the task 0 1 2 3 4 21. I decided there was no point in trying to do well 0 1 2 3 4

APPENDIX D

Driver Stress Inventory (DSI) Office use only

Please check one box only unless otherwise indicated (do not write in boxes at right margin).

Section A 1. Please state your age in years: ______

2. Please state your gender: Male Female

3. What is your highest educational qualification?______

4. Please state your occupation:______

5. Please state the year when you obtained your full driving license: ____

6. About how often do you drive nowadays? Everyday 2-3 days a week About once a week Less often

7. Estimate roughly how many miles you personally have driven in the past year: Less than 5000 miles 5000-10,000 miles 10,000-15,000 miles 15,000-20,000 miles Over 20,000 miles

8. Do you drive to and from your place of work? Everyday Most days Occasionally Never

9. Please state which of these types of road you use frequently (check one or more boxes as appropriate): Freeways Other main roads Urban roads Country roads

10. During the last three years, how many minor road accidents have you been involved in? (A minor accident is one in which no-one required medical treatment, AND costs of damage to vehicles and property were less than $800).

Number of minor accidents ____ (if none, write 0)

11. During the last three years, how many major road accidents have you been involved in? (A major accident is one in which EITHER someone required medical treatment, OR costs of damage to vehicles and property were greater than $800, or both).

Number of major accidents ____ (if none, write 0)

12. During the last three years, have you ever been convicted for:

(a) Speeding Yes No

(b) Careless or dangerous Yes driving No

(d) Other moving violation Yes - please specify: No

Section B

Please answer the following questions on the basis of your usual or typical feelings about driving. Each question asks you to answer according to how strongly you agree with one or other of two alternative answers. Please read each of the two alternatives carefully before answering. To answer, mark the horizontal line at the point which expresses your answer most accurately. Be sure to answer all the questions, even if some of them don't seem to apply to you very well: guess as best you can if need be.

Example: Are you a confident driver?

The more confident you are, the closer to the 'very much' alternative you should mark your cross. If you are quite a confident driver you would mark it like this: not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10

1. Does it worry you to drive in bad weather? very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 2. I am disturbed by thoughts of having an accident or the car breaking down very rarely | | | | | | | | | very often 0 1 2 3 4 5 6 7 8 9 10 3. Do you lose your temper when another driver does something silly? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 4. Do you think you have enough experience and training to deal with risky situations on the road safely? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 5. I find myself worrying about my mistakes and the things I do badly when driving very rarely | | | | | | | | | very often 0 1 2 3 4 5 6 7 8 9 10 6. I would like to risk my life as a racing driver

84 not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 7. My driving would be worse than usual in an unfamiliar rental car not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 8. I sometimes like to frighten myself a little while driving very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 9. I get a real thrill out of driving fast very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 10. I make a point of carefully checking every side road I pass for emerging vehicles very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 11. Driving brings out the worst in people not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 12. Do you think it is worthwhile taking risks on the road? very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10

13. At times, I feel like I really dislike other drivers who cause problems for me very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10

14. Advice on driving from a passenger is generally: useful | | | | | | | | | unnecessary 0 1 2 3 4 5 6 7 8 9 10

15. I like to raise my adrenaline levels while driving not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 16. It's important to show other drivers that they can't take advantage of you not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 17. Do you feel confident in your ability to avoid an accident? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 18. Do you usually make an effort to look for potential hazards when driving? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 19. Other drivers are generally to blame for any difficulties I have on the road not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 20. I would enjoy driving a sports car on a road with no speed-limit very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 21. Do you find it difficult to control your temper when driving? very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 22. When driving on an unfamiliar road do you become more tense than usual? very much | | | | | | | | | not at all 85

0 1 2 3 4 5 6 7 8 9 10 23. I make a special effort to be alert even on roads I know well very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 24. I enjoy the sensation of accelerating rapidly not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 25. If I make a minor mistake when driving, I feel it's something I should be concerned about very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 26. I always keep an eye on parked cars in case somebody gets out of them, or there are pedestrians behind them not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 27. I feel more anxious than usual when I have a passenger in the car not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 28. I become annoyed if another car follows very close behind mine for some distance very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 29. I make an effort to see what's happening on the road a long way ahead of me not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10

30. I try very hard to look out for hazards even when it's not strictly necessary not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10

31. Are you usually patient during the rush hour? very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10

32. When you pass another vehicle do you feel in command of the situation? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10

33. When you pass another vehicle do you feel tense or nervous? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 34. Does it annoy you to drive behind a slow moving vehicle? very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 35. When you're in a hurry, other drivers usually get in your way not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 36. When I come to negotiate a difficult stretch of road, I am on the alert very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 37. Do you feel more anxious than usual when driving in heavy traffic? not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 38. I enjoy cornering at high speed

not at all | | | | | | | | | very much 0 1 2 3 4 5 6 7 8 9 10 39. Are you annoyed when the traffic lights change to red when you approach them?

very much | | | | | | | | | not at all 0 1 2 3 4 5 6 7 8 9 10 40. Does driving usually make you feel aggressive?

41. Think about how you feel when you have to drive for several hours, with few or no breaks from driving. How do your feelings change during the course of the drive?

a) More uncomfortable No change physically (e.g. | | | | | | | | | headache or muscle pains) 0 1 2 3 4 5 6 7 8 9 10

b) More drowsy or sleepy No change | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10

c) Maintain speed of reaction Reactions to other | | | | | | | | | traffic increasingly slow 0 1 2 3 4 5 6 7 8 9 10

d) Maintain attention Become increasingly to road-signs | | | | | | | | | inattentive to road- signs 0 1 2 3 4 5 6 7 8 9 10

e) Normal vision Your vision becomes | | | | | | | | | less clear 0 1 2 3 4 5 6 7 8 9 10 f) Increasingly difficult Normal judgement to judge your speed | | | | | | | | | of speed 0 1 2 3 4 5 6 7 8 9 10

g) Interest in driving does Increasingly bored not change | | | | | | | | | and fed-up 0 1 2 3 4 5 6 7 8 9 10

h) Passing becomes No change increasingly risky | | | | | | | | | and dangerous 0 1 2 3 4 5 6 7 8 9 10

------Office use only a) b) c) d) e) f) g) h)

APPENDIX E

Driver Fatigue Questionnaire (DFQ)

Here are some words, phrases, and statements, which might describe how you might be feeling RIGHT NOW. Indicate how well each describes how you are feeling RIGHT NOW.

PHYSICAL FATIGUE

Muscular fatigue Not at all Very much Physically tired…………………………………….. 0 1 2 3 4 5

Having tremors in my limbs………………………. 0 1 2 3 4 5

Legs and arms feel stiff……………………………. 0 1 2 3 4 5 Muscles ache………………………………………. 0 1 2 3 4 5 Shoulders are stiff………………………………….. 0 1 2 3 4 5 Body feels heavy all over…………………………… 0 1 2 3 4 5 Lower back is stiff and painful……………………. 0 1 2 3 4 5 Legs feel weak……………………………………… 0 1 2 3 4 5

Visual fatigue

Eyes feel gritty……………………………………… 0 1 2 3 4 5 Feeling of heaviness in my eyes……………………. 0 1 2 3 4 5 Eyes feel strained…………………………………… 0 1 2 3 4 5 Vision is blurred……………………………………. 0 1 2 3 4 5 Road appears to ‘swim’…………………………….. 0 1 2 3 4 5 Eyes hurt……………………………………………. 0 1 2 3 4 5 Eyes are burning……………………………………. 0 1 2 3 4 5

Sleepiness

Sleepy………………………………………………. 0 1 2 3 4 5 Drowsy……………………………………………… 0 1 2 3 4 5

Half-awake…………………………………………. 0 1 2 3 4 5 I find myself yawning……………………………… 0 1 2 3 4 5 I feel like I’m dozing off…………………………… 0 1 2 3 4 5 I feel like I’m about to nod off……………………… 0 1 2 3 4 5 I feel like I’m fighting myself to stay awake……….. 0 1 2 3 4 5

TIREDNESS-DEMOTIVATION

Low energy/activity

Worn out……………………………………………. 0 1 2 3 4 5 Exhausted…………………………………………… 0 1 2 3 4 5 Drained……………………………………………… 0 1 2 3 4 5 Weary………………………………………………… 0 1 2 3 4 5 Overtired…………………………………………….. 0 1 2 3 4 5 Spent…………………………………………………. 0 1 2 3 4 5 Wiped out……………………………………………. 0 1 2 3 4 5 Burned-out…………………………………………… 0 1 2 3 4 5

Boredom/ Demotivation

Bored………………………………………………… 0 1 2 3 4 5 Would rather be doing something else………………. 0 1 2 3 4 5 Find driving repetitive………………………………. 0 1 2 3 4 5 Don't want to do this ever again…………………….. 0 1 2 3 4 5 I feel brain-dead……………………………………… 0 1 2 3 4 5 Fed up with driving………………………………….. 0 1 2 3 4 5 I have lost all interest in doing this………………….. 0 1 2 3 4 5

It hurts to think………………………………………. 0 1 2 3 4 5

COGNITIVE-ATTENTIONAL

Confusion/distractibility

I feel like I can’t think clearly………………………. 0 1 2 3 4 5 I’m feeling confused…………………………………. 0 1 2 3 4 5 My thoughts are wandering………………………….. 0 1 2 3 4 5 I’m easily distracted…………………………………. 0 1 2 3 4 5 I’m having difficulty concentrating…………………. 0 1 2 3 4 5 Takes a lot of effort to think…………………………. 0 1 2 3 4 5 I catch myself daydreaming…………………………. 0 1 2 3 4 5 I’m thinking about personal issues………………….. 0 1 2 3 4 5 I’m distracted by thoughts of family or home………. 0 1 2 3 4 5 I’m thinking about money issues……………………. 0 1 2 3 4 5 I’m thinking about things that don’t really matter………………………………………………….. 0 1 2 3 4 5 I spend a lot of time focusing on little things I see and hear……...…………………………………………. 0 1 2 3 4 5

Performance worries

I feel like I’m losing awareness of what’s going on around me……………………………………….. 0 1 2 3 4 5 I don’t feel as safe as I usually do…………………... 0 1 2 3 4 5 I’m finding it hard to control my speed…………….. 0 1 2 3 4 5 My reactions are slowed……………………………... 0 1 2 3 4 5 I don’t trust my judgment if I have to pass………….. 0 1 2 3 4 5 I’m having a hard time attending to road-signs……... 0 1 2 3 4 5

I’m not using my mirrors as much as I should………. 0 1 2 3 4 5 I keep losing track of where I am on the road……….. 0 1 2 3 4 5 I’m not braking as soon as I should…………………. 0 1 2 3 4 5 I’m slow to make decisions…………………………. 0 1 2 3 4 5 I’m worried about my state of mind…………………. 0 1 2 3 4 5 I feel like I’m not alert to other drivers……………… 0 1 2 3 4 5 Other vehicles are appearing where I don’t expect them…………………………………………... 0 1 2 3 4 5 I’m worried about getting to my destination on time………………………………………………….. 0 1 2 3 4 5 I’m concerned about what other drivers may do……. 0 1 2 3 4 5

COPING/FATIGUE MANAGEMENT

Avoidance

I’m staying detached or distanced from the situation…………………………………………………0 1 2 3 4 5 I’m deciding to save my efforts for something more worthwhile……………………………………. 0 1 2 3 4 5 I’m not going to take the drive too seriously……… 0 1 2 3 4 5 I’m telling myself it isn't worth getting upset……….. 0 1 2 3 4 5 I don’t care if I get to my destination late…………… 0 1 2 3 4 5 I’m just waiting for this to be over………………….. 0 1 2 3 4 5 I’m not going to worry about problems that may never occur…………………………………….. 0 1 2 3 4 5 I’m just wishing I were home………………………... 0 1 2 3 4 5

Comfort-seeking Not at all Very much I just want to take things easy……………………… 0 1 2 3 4 5 I don’t feel like exerting myself…………………… 0 1 2 3 4 5 I need to rest and relax…………………………….. 0 1 2 3 4 5 I just want to stay comfortable……………………… 0 1 2 3 4 5 I want to avoid getting stressed about the drive……... 0 1 2 3 4 5 Making sure I feel OK is my priority right now…….. 0 1 2 3 4 5 I’m just trying to keep my cool about things……… 0 1 2 3 4 5 I want to take a break to sleep………………………. 0 1 2 3 4 5

Self-arousal

I feel like:

Hitting myself around the head to stay awake……… 0 1 2 3 4 5 Listening to the radio……………………………….. 0 1 2 3 4 5 Talking to somebody else…………………………… 0 1 2 3 4 5

Blasting myself in the face with cold air……………. 0 1 2 3 4 5 Drinking coffee or smoking…………………………. 0 1 2 3 4 5 Singing or talking to myself…………………………. 0 1 2 3 4 5 Speeding up or changing lanes…………………….. 0 1 2 3 4 5 Looking at the scenery……………………………… 0 1 2 3 4 5 Looking at what other drivers are doing…………….. 0 1 2 3 4 5

Sources – Task-induced Fatigue Scale (Matthews & Desmond, 1998), Multidimensional Fatigue Symptom Inventory (Stein & Jacobsen), FACES for insomnia (Shapiro et al., 2002)

93 APPENDIX F

Consent to Participate

University of Cincinnati Department of Psychology Consent to Participate in a Research Study Principle Investigator: Catherine Neubauer Email: [email protected], 954-258-2287

Title of Study: The Effects of Personality and Automation on the Decision to Engage in Text Messaging During Simulated Driving. Introduction: Before agreeing to participate in this research study, it is important that the following explanation of the proposed research be read and understood. It describes the purpose, procedures, benefits, risks, and discomforts of the research study and the precautions that will be undertaken to ensure safety. It also describes the alternatives available if one cannot participate in the study or chooses to withdraw. Failure to complete the study or opting to withdraw will in no way effect the course grade. The person in charge of this research study is Catherine Neubauer, of the University of Cincinnati Psychology Department. She is being guided in this research by Dr. Gerald Matthews.

Special Requirements: Participants in this study must be able to complete multiple questionnaires and the physiological task at hand. For these reasons, you may not participate in this study if any of the following applies to you: • Do not have a valid driver’s license (YOU MUST BRING YOUR DRIVERS LICENSE WITH YOU). • If you are younger than 18 or older than 40 years of age. Subjects under the age of 18 will not be included in this study. • Impaired vision (corrected vision is acceptable). • Currently taking psychoactive medication (e.g. drugs for the treatment of anxiety or depression). • Physically unable to perform the cognitive tasks (due to any difficulty in using a keyboard and mouse as well as steering, braking, and pressing the turn signal on the driving simulator). • History of epilepsy. • English is not the primary language.

Purpose: The purpose of this research study is to investigate subjective reactions to performing driving tasks as well as the relationship between personality and text messaging. You will be one of approximately 250 participants taking part in this research study.

Duration: Your participation in this study will last between one and two hours, in any case it will last no longer than two hours.

94 Procedures: You will first be asked to complete a total of three questionnaires, which will assess your feelings and attitudes towards driving and the driving task at hand. You will be randomly assigned to one of two conditions (automation option or control). Within your condition you will then be assigned to one of two conditions (cell phone conversation or text message). You will then be asked to complete the simulated driving task you have been assigned to. Prior to the main drive, you will be able to participate in a three minute practice drive to acquaint yourself with the task. After the driving task, you will be asked to complete two questionnaires to assess your feelings during the drive.

Risks/ Discomforts: There are no known major risks or discomforts expected with this study, which involves minimal risk. The tasks involved have been used in previous psychological studies, with no harmful effects being reported. Some minor yet harmless discomforts may occur due to completing questionnaires (e.g. eye strain, postural discomfort from sitting, etc.). There is a risk of developing nausea due to the driving simulator. If at any time that you feel nauseous during the experiment please notify the experimenter. You will be encouraged to contact your medical service provider should you feel nauseated. Emergency care will be provided for you if you become ill or are injured from participating in this experiment. If you believe that you have been injured as a result of this research, you should contact Catherine Neubauer (954) 258-2287, or Dr. Gerald Matthews (513) 556-0954. You may also contact the Chair of the Institutional Review Board- Social and Behavioral Sciences, at (513) 558- 5784.

Benefits: You are not likely to receive any direct benefits from this study. However, your contribution as a participant may contribute to our understanding of driver behavior and text messaging.

Alternatives: If you cannot participate or choose to withdraw from the study an alternative assignment is also available as described in the Psychology Department’s memo regarding the Research Participation Requirement.

Confidentiality: The confidentiality of your study records will be strictly maintained. Agents of the University of Cincinnati will be allowed to inspect sections of the research records related to this study. The data of this study will be published; however, no identifiers such as the participants name will be used. Participant’s identity will remain confidential unless disclosure is required by law.

Right to Withdraw or Refuse: You have the right to withdraw or refuse to continue participating in the study at any point of the experiment. You will not be penalized or lose any benefits associated with this study. If you refuse to participate or discontinue your participation, you will receive 1 hour credit for participation lasting 0-60 minutes and 2 hours for anything over 60 minutes. The investigator has the right to withdraw you from the study at any time for reasons related solely to you (for example not following directions according to the investigator) or for reasons affecting the entire study (for example, termination of the study).

Offer to Answer Questions: If you have any questions regarding the study, you may contact Catherine Neubauer at (954) 258-2287. If you wish to receive a summary of the results of this study, you must furnish a stamped, self-addressed envelope.

The University of Cincinnati Institutional Review Board- Social and Behavioral Sciences reviews all non-medical research projects that involve human participants to be sure the rights and welfare of participants are protected. If you have questions about your rights as a participant, you may contact the Chairperson of the University of Cincinnati Institutional Review Board- Social and Behavioral Sciences at (513) 558-5784. If you have questions or concerns about the study you may also call the UC Research Compliance Hotline at (800) 889-1547.

Legal Rights: Nothing in this consent form waives any legal right you may have nor does it release the investigator, the institution, or its agents from liability for negligence. I HAVE READ THE INFORMATION PROVIDED ABOVE. I VOLUNTARILY AGREE TO PARTICIPATE IN THIS STUDY. I WILL RECEIVE A COPY OF THIS SIGNED AND DATED CONSENT FORM FOR MY INFORMATION.

_____ Participant Name (please print) Date

Participant Signature Date

______

Signature of Person Obtaining Consent Date

96 APPENDIX G

List of Text Messages Sent

TEST URGENT 1. This is a test text message; please reply that you were able to receive this message.

URGENT 1. What is your full legal name?

URGENT 2. What city and state were you born in?

OPTIONAL 3. What is your college major and what kind of job do you hope to get?

URGENT 4. Name some places you’ve been or would like to visit.

OPTIONAL 5. What kinds of pets do you have or would like to have?

URGENT 6. What is your favorite time of year and why?

OPTIONAL 7. What is your favorite kind of music?

OPTIONAL 8. What are some of your favorite classes you are taking this quarter and why?

OPTIONAL 9. Who is your favorite actor/actress?

URGENT 10. What types of things do you like to do in your spare time?

URGENT 11. What types of foods do you like to eat?

OPTIONAL 12. What did you do today?

URGENT 13. Do you have any hobbies?

OPTIONAL 14. Do you have any interesting or fun plans that you’re looking forward to?

98 APPENDIX H

Complete List of Correlation Tables

Table H1. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the automation group.

Post Post Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .311** .352** -.251* -.144 .432** .241*

Dislike of Driving .275** .258* -.147 -.053 .328** .412**

Hazard Monitoring .031 .094 -.001 .091 -.031 -.129

Thrill Seeking .223* .141 -.061 -.157 .157 -.067

Fatigue Prone .262* .310** -.325** -.349** .493** .407**

Note. * p< .05 ** p < .01

Table H2. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the non-automation group.

Post Post Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .113 .158 -.020 -.090 .230* .113

Dislike of Driving .330** .215* -.364** -.095 .497* .365**

Hazard Monitoring .062 .194 .290** .120 -.167 -.048

Thrill Seeking -.077 -.209 -.014 -.080 -.010 -.049

Fatigue Prone .209* .100 -.312** -.237* .354** .175

Note. * p< .05 ** p < .01

99 Table H3. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the cell phone group.

Post Post Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .291* .427** -.192 -.078 .461** .398**

Dislike of Driving .315* .345** -.298* -.033 .528** .614**

Hazard Monitoring .100 .172 -.052 .047 .075 -.006

Thrill Seeking .170 .113 -.187 -.281* .142 -.045

Fatigue Prone .320* .320* -.355** -.316* .519** .507**

Note. * p< .05 ** p < .01

Table H4. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the txt-message group.

Post Post Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .171 .237 -.178 -.286* .309* .227

Dislike of Driving .366** .173 -.235 -.137 .167 .111

Hazard Monitoring -.029 .069 .206 .074 -.229 -.151

Thrill Seeking .108 -.027 -.004 -.062 .076 .123

Fatigue Prone .114 .095 -.245 -.198 .350** .141

Note. * p< .05 ** p < .01

100 Table H5. Correlations between the DSI factors and pre and post-task DSSQ subjective states for the free-choice group.

Post Post Post

Worry Worry Engagement Engagement Distress Distress

Aggressive .159 .063 -.084 -.048 .165 -.023

Dislike of Driving .261* .196 -.167 -.026 .511** .433**

Hazard Monitoring .080 .208 .274* .206 -.148 -.092

Thrill Seeking -.068 -.179 .006 -.068 -.013 -.163

Fatigue Prone .276* .202 -.337** -.428** .368** .281*

Note. * p< .05 ** p < .01

101 APPENDIX I

Complete Pre and Post Task State Graphs

Non‐ Automaon; ; Automaon; ; 0.3501 0.3106 Non‐ Automaon; ; 0.0995

Automation Automaon; ; Non-Automation ‐0.0949

Figure I1. Pre and post task engagement for automation and non-automation conditions. Error bars are standard errors.

Non‐ Automaon; ; Automaon; ; 0.3491 0.2583

Automation Non-Automation

Automaon; ; ‐0.3587 Non‐ Automaon; ; Distress‐0.4764 Post Distress

Figure I2. Pre and post task distress for automation and non-automation conditions. Error bars are standard errors.

102

Worry Post Worry

Figure I3. Pre and post task worry for automation and non-automation conditions. Error bars are standard errors.

TM; ; 0.4206 CP; ; 0.3974 CP TM FC; ; 0.1731 FC CP; ; 0.0978

FC; ; ‐0.0239 Engagement Post Engagement TM; ; ‐0.067

Figure I4. Pre and post task engagement for three cell phone conditions.

103 TM; ; 0.4163 FC; ; 0.3941

CP; ; 0.1008 CP TM FC

CP; ; ‐0.3175 FC; ; ‐0.3968 TM; ; ‐0.5384 Distress Post

Figure I5. Pre and post task distress for three cell phone conditions.

Worry Post Worry

Figure I6. Pre and post task worry for three cell phone conditions.

104