Alertness Maintaining Tasks: a Fatigue Countermeasure During Vehicle Automation?

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Alertness Maintaining Tasks: A Fatigue Countermeasure During Vehicle Automation?

A dissertation submitted to the

Division of Research and Advanced Studies of the University of Cincinnati

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

in the Department of Psychology of the College of Arts and Sciences

2014

Catherine E. Neubauer

M.A. University of Cincinnati, 2011 B.S. University of Central Florida, 2008

Committee Chair: Peter Chiu, Ph.D. ABSTRACT

Driver fatigue is a leading cause of vehicular accidents (Lee, 2006). Additionally, development of newer technology such as vehicle automation offers a potential countermeasure to driver fatigue. As vehicle operation becomes increasingly automated, driver fatigue appears to be a pressing safety issue. A number of countermeasures have been evaluated in the attempt to alleviate driver fatigue. In the present context trivia games have been suggested as a fatigue countermeasure but like cell phone use, they may prove distracting. The present study investigated the effects of two especially relevant workload factors on driver performance: automated driving and secondary media usage. Vehicle automation is a relatively new trend among automakers that can potentially alleviate the adverse effects of fatigue and in turn regulate workload, however recent studies have suggested that automation may result in a dangerous state of underload in which effort is withdrawn from the driving task (Desmond, Hancock & Monette,

1998; Funke et al., 2005). A manipulation of full and partial vehicle automation was used to induce fatigue during simulated driving. Participants were also assigned to one of three media device conditions (control, cell phone or trivia). Subjective state response, vehicle control and reaction time to a sudden event were recorded. As predicted, the media devices did help minimize the loss of task engagement and elevated distress produced by vehicle automation. We also extended findings that the media devices helped improve concurrent driver performance, with control driving shown to be associated with the worst vehicle control. However, media usage was not associated with faster response time to subsequent “sudden events”, suggesting that such devices may not enhance alertness during unpredictable events.

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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 mentors, Gerry Matthews and Peter Chiu, for their guidance and support during this time. I would also like to thank my family who has always supported me throughout my life and also my research assistants, Jessica Bailey, Laqueena

Mitchell and Erin Roy for their help with this project.

TABLE OF CONTENTS

TABLE OF CONTENTS………………………………………………………………………..v

LIST OF TABLES……………………………………………………………………………..viii

LIST OF FIGURES………………...………………………………………………………...... ix

Introduction……………………………………………………………………………………....1

Background of Fatigue within the Transportation Setting……………………....………………...4

Cognitive Aspects of Driver Performance……………………….………………………………..7

Attentional Models…………………………...……………………………………………7

Automated Vehicle Systems……………………………………………………………………..10

Workload and Vehicle Automation………………..………………………………….....10

Subjective State and Vehicle Automation………….……….…………………………...12

Secondary Alertness Maintaining Tasks………...……………………………………………….13

The Interaction between Stress, Fatigue and Driving……………………………………………16

Active vs Passive Fatigue……………..…………………………………………………16

The Transactional Model of Driver Stress……………………………………………….17

The Dundee Stress State Questionnaire………………………………………………….18

Personality within the Transportation Context…………………………….…………………….20

The Driver Stress Inventory……………………………………………………………...20

Aims and Objectives………………………...…………………………………………………...22

Method…………………………….…………………………………………………………….24

Participants……………………………………………………………………………………….24

Experimental Design and Simulator Tasks…………….………………………………………...25

Questionnaires………………………………………………………………………………..…..25

Cell Phone Usage Questionnaire…………………………………………………...……25

The Driver Stress Inventory…………………………………………………………..….26

The Dundee Stress State Questionnaire………………………………………………….26

The Driving Simulator……………..………………………………………………………...…..27

Cellular Telephones and Bluetooth Device……………………………..……………………….28

Driving Tasks, Automation and Secondary Media Conditions……………….…………………29

Practice Drive………………………………………………………………………...…..30

Main Drive…………...…………………………………………………………….…….30

Automation Conditions…………………………………………………………………..30

Secondary Media Conditions…………………………………………………………….31

Performance Assessment………………………...…………………………………..…..32

Procedure……………………………………………………………………………………..….34

Results…………………………….……………………………………………………………..37

Baseline Analyses………………………………………………………………………………..38

Task-induced Effects of Automation and Secondary Media on Subjective Stress State………...38

Perceived Mental Workload………………………….………………………………………….42

Predictors of Subjective State………………………………...………………………………….43

Correlations………………...…………………………………………………………….43

Regression…………..…………………………………………………………..………..44

Driver Performance Measures ……………...………………………………………………...…46

Vehicle control………………………………………………………………………...…46

Response times…………………………………………………………………………...49

Crash rates………………………………………………………………………………..51

Discussion………………………………………………………………………………..52

General Discussion……………………………………………………...…..…………………..53

Overview of Findings…………..…………………………….………………………………….53

Theoretical implications…………………………………………………………………………57

Vehicle Automation and Secondary Media………………….…………………………..58

Practical Applications……………………………………...…………………………………….61

The Role of Stress Vulnerability and Individual Differences……………………………………66

Limitations……………………………………………………………………………………….68

Summary and Overall Conclusions……..……………………………………………………….69

REFERENCES………………………………………………………………………………….73

APPENDIX A: Frequency of Cell Phone Use Questionnaire………………………………..82

APPENDIX B: Driver Stress Inventory…………….………………………………………...83

APPENDIX C: Pre-task DSSQ………………………………………………………………...88

APPENDIX D: Post-task DSSQ…………….. ………………………………………………..92

APPENDIX E: Complete List of Trivia Questions………….………………………………..99

APPENDIX F: Script for Cell Phone Conversation……………………...…………………112

APPENDIX G: Informed Consent Form……….……………………………………………115

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LIST OF TABLES

1. Standardized mean pre and post task scores of the DSSQ for automation and secondary media conditions.

2. Mean overall workload scores and standard deviations for all experimental conditions.

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

4. Standard deviation of lateral position for automation and secondary media conditions.

5. Mean response times for automation and secondary media conditions.

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LIST OF FIGURES

1. Experimental setup using a System Technologies, Inc., STISIM Drive, build 2.08.10, a

Westinghouse 42-inch LCD monitor and Logitech MOMO racing force feedback wheel.

2. a) Participant phone, LG Rumor 2 b) experimenter phone, LG LX 101 c) JABRA Bluetooth headpiece. Photos for phones and Bluetooth headpiece were obtained via http://cgi.iwirelesshome.com/phones/ and http://www.jabra.com/headsets-and-speakerphones/all- products/bluetooth respectively.

3. Screen shot of the sudden event.

4. Pre to post-drive changes in subjective state for the control, trivia and cell phone conditions.

Error bars are standard errors.

5. Standard deviation of lateral position for non-automation and partial automation groups. Error bars are standard errors.

6. Standard deviation of lateral position for the control, cell phone and trivia groups. Error bars are standard errors.

7. Response times for steering and braking between the non-automated, partial and total automation groups. Error bars are standard errors.

8. Response times for steering and braking by control, cell phone and trivia groups. Error bars are standard error

Introduction

The United States is home to the largest transportation sector in the world. Recently, there was an estimated 254.5 million registered passenger vehicles on the road, with a steady increase since 1960 (Department of Transportation, 2007). With the increase in the number of drivers on the road also come numerous traffic accidents. A number of factors can contribute to the cause of an accident, mainly (1) the environment (e.g., roadway, scenery, and weather), (2) the individual

(e.g., driver and other road users) and (3) the vehicle (e.g., industrial design) (Shinar, 2007;

Evans, 2004). Unfortunately, the individual or “human factor” tends to play the largest role in most accidents. Individual factors can refer to the driver’s emotional or physiological state (i.e., stress or drowsiness) and the driver’s strategies for managing task workload (Saxby et al., 2013).

Two such factors gaining much attention in the press are driver fatigue and distraction. Driver fatigue is a highly cited cause of roadway accidents, yet the number of fatigued drivers on the road is still fairly high (Lee, 2006). Estimates of the contribution of fatigue to accidents differ, but one US study estimated that there are 56,000 fatigue-related road crashes annually in the

USA, resulting in 1,550 fatalities (NCSDR/NHTSA, 1998). Additionally, The National Highway

Traffic and Safety Administration (NHTSA) (2013) estimates that 3,331 people were killed in crashes involving distracted drivers in 2011. Despite the dangers of fatigue and distraction, little is known about how they may interact.

In response to recent driving concerns such as driver fatigue, traffic congestion and even distraction, more aspects of the driving task are becoming automated and may eventually support driverless vehicles. Examples of automated systems include adaptive cruise control, hazard detection and lane monitoring/correcting systems. These systems attempt to reduce driver workload and distraction, but may actually result in poorer performance by decreasing task

1 engagement and situation awareness and shifting the driver’s attention to personal discomfort and stress symptoms during vehicle automation (Desmond, Hancock & Monette, 1998; Hancock

& Verwey, 1997; Neubauer et al., 2012a; Stanton & Young, 2005). Passive fatigue, in particular, may exacerbate the detrimental effects of automation, because this form of fatigue is associated with loss of alertness and task disengagement (Saxby et al., 2013). Such issues may be especially salient in future driverless vehicles, if the driver is to re-engage control of the vehicle. The design of automation may influence its interaction with fatigue. Previous studies have shown harmful effects on driver alertness of full vehicle automation, whether imposed externally (Saxby et al.,

2013), or selected voluntarily by the driver (Neubauer et al., 2012a). By contrast, it has been found that partial vehicle automation may help maintain alertness by requiring that the driver remain involved in some aspects of the drive without decreasing engagement and withdrawal of effort (Desmond, Hancock & Monette, 1998; Funke et al., 2005).

Furthermore, the dangers of distraction from in-car media are increasing as manufacturers improve connectivity and functionality of systems. If fatigued drivers become increasingly distractible media use might exacerbate a loss of alertness (Neubauer et al., 2012b). A different perspective comes from the literature on countermeasures to fatigue. Throughout the years various methods to counteract the effects of fatigue while driving have been evaluated, either through the research and development of fatigue countermeasures or assessment of anecdotal methods used by drivers. In the latter case, drivers may recognize their fatigue symptoms and take steps that attempt to help them remain engaged. A secondary task may increase the cognitive load of drivers and help maintain task engagement, which is sometimes diminished when fatigued. For example, Verwey and Zaidel (1999) found that, under certain conditions, performing a secondary task that was attentionally demanding increased task engagement and

2 alertness. Furthermore, Gershon et al. (2009) found that an interactive cognitive task (ICT) called

“Trivia” helped improve driver performance and mental state. It was found that drivers who engaged in 20 minutes of trivia exhibited better vehicle control, had higher ratings of motivation and less subjective sleepiness compared to a control. In addition, many drivers anecdotally report that using a cell phone helps maintain alertness while driving during long periods of time

(Atchley, Chan & Gregerson, 2013). In fact, Atchley et al. (2013) found that drivers showed better lane keeping and higher EEG related alertness levels when engaged in a secondary verbal task. Although the dangers of cell phone use are well documented (Drews et al., 2009; Strayer &

Johnston, 2001), such usage may prove effective as a motivating secondary task in the face of boredom or fatigue. In light of the findings of Gershon et al. (2009) and Atchley et al. (2013), the current study employed a variant of the Gershon et al. (2009) “Trivia” game as well as an in depth cell phone conversation in the attempt to increase task motivation and cognitive load during vehicle automation.

The following introduction will provide a brief overview of past and present research conducted on fatigue-induced driving impairments, focusing specifically on cognitive mechanisms underlying the fatigue state. Next, the benefits and dangers of vehicle automation will also be addressed, focusing specifically on the interaction between vehicle automation and driver performance and subjective state. Research on the potential benefits of secondary media devices within the transportation setting will also be presented. In addition, subjective measures of stress, fatigue and personality will be addressed, focusing specifically on emotional state and personality traits that relate to stress vulnerability while driving. Finally, the specific aims and hypotheses of the current study will be presented.

Background of Fatigue within the Transportation Setting

As previously mentioned, the “human factor” is one of the leading contributors to traffic accidents and fatal crashes. One “human factor” of particular interest is driver fatigue. In recent years, the National Highway Traffic and Safety Administration (NHTSA) reported that driver fatigue accounts for approximately 1.2 – 3.6% of all crashes (Knipling & Wang, 1994; 1995).

Driver fatigue is especially dangerous because the actual number of fatigue related crashes might be much higher than is typically reported. A number of factors may contribute to the under- reporting of driver fatigue. First, a widely acceptable definition for driver fatigue has not been available yet despite many years of research. More specifically, fatigue can result from both physiological (i.e., sleep loss) as well as psychological factors (i.e., stress). Additionally, fatigue may be multidimensional, with symptoms including physical/muscular fatigue as well as mental fatigue (Williamson et al., 2011). Second, it has also been suggested that drivers may underreport their level of fatigue following an accident in order to avoid any legal or insurance related issues

(Wijesuriya et al., 2007).

The dangers of driver fatigue are multifaceted and include a wide range of performance impairments including, but certainly not limited to, poorer vehicle control and reduced arousal

(Feyer & Williamson, 2001). Furthermore, the risks of driving while fatigued have been compared to the risks associated with driving while under the influence of alcohol (Fletcher et al., 2005). Although dangerous and highly prevalent among all drivers, fatigue may be most prominent among professional, long-distance truck drivers. In fact, the risk for being involved in a fatigue related crash is about 20 times higher for professional drivers (Hitchcock & Matthews,

2005). A number of factors put these individuals at heightened risk, such as irregular scheduling and the unexpected increase in sleep debt, as well as physical demands such as cargo loading that

4 increase physical/muscular fatigue (Lal & Craig, 2001). Although certain guidelines are put in place to combat these factors, many professional drivers are still fatigued during their job.

A number of survey-based studies have attempted to catalogue fatigue related risks within this population. For example, a survey involving long-distance truck drivers (N= 593) found that

47.1% of those drivers reported falling asleep at least once in their career (Lee, 2006).

Additionally, a survey among commercial light and short haul truck drivers by Friswell and

Williamson (2008) found that 71.1% of drivers believed that fatigue negatively impacted their driving performance, seen as a slowing of reaction time, poorer vehicle control and a decrease in situation awareness. More specifically, 91% of those drivers reported that fatigue was the cause of at least one potentially dangerous event such as a nodding off at the wheel, colliding with something or even running off the road. These findings suggest that the nature of the task and working conditions conflict with the natural human circadian cycle, which can result in higher levels of fatigue and reduced alertness in such drivers (Fournier, Montreuil & Brun, 2007).

When discussing fatigue, it is important to distinguish this particular state from another similar state, driver sleepiness. Many times, the effects of sleep and fatigue on driving are used interchangeably, but the terms are not synonymous. Sleepiness signifies a difficulty in remaining awake, is physiological in nature and is subject to natural circadian influences and sleep debt.

Here, sleepiness tends to disappear following sleep but not rest (Phillip et al., 2005b).

Conversely, fatigue refers to a “disinclination to continue performing the task at hand”, is more subjective in nature and tends to disappear following a period of rest (Brown, 1994). Generally speaking, fatigue can stem from a number of different sources such as the driver’s emotional state or varying task demands (Saxby et al., 2013). Perhaps more importantly, task demands can elicit qualitatively different forms of driver fatigue. For example, active fatigue can stem from

5 high task demands such as driving during heavy traffic, while passive fatigue results from low workload situations, such as driving along a monotonous roadway for an extended period of time

(Desmond & Hancock, 2001; Saxby et al., 2013). Moreover, when fatigued or sleepy, drivers have difficulty regulating information processing resources and tend to under-mobilize effort

(Desmond & Matthews, 1997). At the extreme ends of the spectrum, sleepiness can actually result in falling asleep during a task, while fatigue can result in a gradual withdrawal of attention and performance efficiency (Phillip et al., 2005b; Sagberg, 1999). Within the transportation setting, it appears that the main difference between sleepiness and fatigue is their cause (i.e., sleep loss vs. task demands) and countermeasure (i.e., sleep vs. rest) respectively (Phillip et al.,

2005a).

Throughout the years, a number of methods have been evaluated in the attempt to counter fatigue while driving. Common efforts include listening to the radio, drinking caffeinated beverages, or rolling down the window for example. These methods may benefit drivers for a short period of time, but will not completely counteract the dangers of being fatigued or drowsy.

One of the most effective countermeasures to fatigue is simply taking a nap. Naps have been found to decrease sleepiness and performance impairments (Wylie et al., 1996). Although obviously effective, most drivers don’t choose this particular countermeasure due to scheduling pressures for professional drivers or simply because they are undesirable or inconvenient while driving (Drory, 1985; Wylie et al., 1996). As a performance based task, driving is somewhat repetitive and requires mental effort and sustained attention, which tends to diminish over time

(Warm et al., 2008). In fact, the repetitive nature of driving can be fatiguing even for relatively alert drivers (McCartt, Rohrbaugh, Hammer & Fuller, 2000). It is essential that countermeasures be put in place in order to combat against the harmful effects of fatigue on driving. Currently,

6 legislation focuses on regulating the number of hours driven for professional drivers (Feyer &

Williamson, 2001). However, creating schedules that foster rest taking may be more effective

(Guppy & Guppy, 2003). Finally, differentiating between qualitatively different forms of fatigue and assessing the cognitive mechanisms that underlie this state may help guide the development of the most effective countermeasures (May & Baldwin, 2009).

Cognitive Aspects of Driver Performance

Although road accidents can certainly stem from a number of factors including the environment and the individual, it also appears that certain cognitive factors such as driver attention are a key component to safe driving (Amado & Ulupinar, 2005). Inadequate driver attention has been cited to cause up to 50% of all roadway accidents and can include general inattention, improper lookout and excessive speed (McKnight & McKnight, 1993; Strayer &

Johnston, 2001). Additionally, driver attention also tends to be moderated by fatigue, where fatigued drivers have difficulties sustaining attention during a task (Desmond & Matthews,

1997). Furthermore, dividing attention among multiple tasks is also somewhat difficult for fatigued drivers (Hosking, Young & Regan, 2009). This is particularly dangerous during driving situations where fatigued drivers find themselves in a number of driving scenarios. For example, driving along a monotonous roadway or concurrently engaging in a secondary task (e.g., using a cell phone) may be especially dangerous to fatigued drivers. As previously stated, task underload, when combined with decreased motivation and high task demands, may lead to a misappropriation of cognitive resources and in turn performance impairments (Oron-Gilad,

Ronen & Shinar, 2008).

Attentional Models. In discussing cognitive mechanisms of driver fatigue, it may be useful to outline several models that illustrate the effects of fatigue on attention. Attention based

7 theories argue that anything, whether task (e.g., dual-tasks) or person-oriented (e.g., stress), that diverts attention away from the primary task is inherently dangerous (Strayer & Johnston, 2001).

In this context, these models of attention sharing attempt to explain how drivers are capable of distributing attention between tasks, focusing specifically on how drivers divide their attention between a primary and secondary task. Performance impairments may occur when drivers prioritize the secondary task rather than the primary task of driving (Drews et al., 2009).

Furthermore, depending upon task demands, drivers tend to engage in attentional switching, where attention is primarily focused on one but not both tasks. This is particularly dangerous as fatigue results in a difficultly in effectively allocating attention across task demands (Desmond &

Matthews, 1997).

Furthermore, fatigue effects may largely depend upon the availability of information processing resources. The term ‘resource’ represents a type of ‘energy’ or ‘cognitive fuel’ that is available for cognitive processing of task demands (Matthews & Desmond, 2002). Resource theories pose several assumptions: (1) humans have a limited attentional capacity and (2) those resources can be strategically allocated across different task demands, suggesting a voluntary component of task performance (Wickens, 1984). Because attention is limited, performance impairments may occur when task demands outweigh attentional resource availability (Matthews

& Desmond, 2002). Resources may be best conceptualized as an “attentional gas tank”, where task demands slowly deplete the reservoir or fuel needed to perform tasks. Additionally, it also appears that fatigue can drain attentional resources akin to driving uphill, where high task demands can lead to even greater performance impairments (Desmond & Matthews, 1997).

Driver fatigue may also be understood in relation to dynamic models of stress and sustained attention (Hancock & Warm, 1989). These models emphasize operator adaptation,

8 where moderate levels of task demand support effective operator adaptation and safe driving behaviors. Here, moderate task demands seem to be a key component to optimal performance.

These models also argue that drivers engage in different coping strategies based on task demands. Within this framework, task underload tends to result in less effort while task overload results in more effort. Task overload as well as underload can inhibit effective coping. Both conditions can impact safety especially with the addition of other negative emotional states such as stress or fatigue. Here, fatigue may be more dangerous in underload conditions, in contrast to predictions made by resource theories (Hancock & Warm, 1989). Being in a state of underload does not necessarily refer to a lack of cognitive resources needed to perform a task but rather the lack of the ability to regulate the amount of resources needed to do so (Desmond & Matthews,

1997).

In order to better understand these two contrasting theories within the transportation sector, Matthews and Desmond (2002) explored the qualitative effects of fatigue on driving performance. They subjected drivers to either a single or dual-task (fatigue manipulation) driving condition and assigned drivers to either a straight or curved road section of driving (workload manipulation). According to resource theories, performance should become impaired during high task demands (i.e., curved road, dual-task driving), while adaptation models support the notion that undemanding tasks (i.e., straight road driving) will result in drivers under mobilizing effort.

The results showed that vehicle control was more impaired during straight road sections of driving, suggesting that low workload results in a decrease in effort, providing support for

Hancock and Warm’s (1989) theory of dynamic adaption. Interestingly enough, curved road driving was not affected by the fatigue manipulation (i.e., single vs. dual-task driving), suggesting that drivers are able to maintain effort even when workload is high. The results seem

9 to suggest that adaptation models of driver attention best conceptualize the interaction between task demands and fatigue while driving. Furthermore, with the recent advent of automobile advances, the effects of passive fatigue are many times exacerbated when drivers engage in automated driving systems. Here, it is crucial to gauge the potentially dangerous interaction between fatigue and vehicle automation in order to implement intelligence design and effective legislation. The following section will address automated systems and their effects on driver performance, workload and subjective state, with implications for secondary tasks as an alertness-maintaining device.

Automated Vehicle Systems

In addition to in-vehicle fatigue alerting devices (i.e., visual monitoring; see Dinges &

Grace, 1998), there have also been advances in automated vehicle systems, which attempt to reduce the negative consequences of fatigue through workload regulation (Hancock & Verwey,

1997). Examples of automated systems include adaptive intelligence cruise control (AICC) and automated highway systems (AHS) (Desmond & Hancock, 2001). AICC involves a laser or type of radar that detects when the vehicle needs to slow down or speed up when needed, allowing control to pass back and forth between the vehicle and the driver, while AHS or smart roads include technologies that support driverless vehicles. Automated systems attempt to reduce task demands, which can contribute to fatigue, essentially regulating workload to an optimally moderate level (Hancock & Parasuraman, 1992). Additionally, such technology may also reduce the negative effects of stress that are commonly experienced while driving.

Workload and Vehicle Automation. As previously mentioned, one goal of an automated system is to regulate workload. Workload regulation is critical because the level of workload placed upon drivers can moderate fatigue effects. In fact, Friswell and Williamson (2008) argue

10 that high workload tasks can lead directly to fatigue and performance impairments. It is important to note that the goal of an automated system should be to regulate workload, not simply reduce it, as an extreme reduction in workload (e.g., task underload) can be just as dangerous to driver performance (Young & Stanton, 2007). Although potentially useful in counteracting the hazardous effects of stress and fatigue, the dangers of vehicle automation should also be considered. First, driverless vehicles may create an “out of the loop” performance problem, which reflects the consequences of relieving individuals of additional components of the task. Being “out of the loop” generates performance complacency about safety, reflects a decrease in situation awareness and is especially dangerous if drivers suddenly need to regain control of the vehicle (Desmond et al., 1998). This may prove especially difficult following a lengthy automated drive. In fact, Desmond et al. (1998) found that drivers in automated conditions had a more difficult time at vehicle recovery, compared to manual drivers, suggesting that full vehicle automation may negatively impact situation awareness.

In addition to decreasing effortful task coping, underload, resulting from an extreme form of workload reduction (i.e., total vehicle automation), may decrease performance in a number of other areas, including but not limited to reaction time (DeWaard et al., 1999). For example,

Neubauer et al. (2012a) gave drivers the choice of engaging in total vehicle automation for 5- minute blocks throughout a 35-minute simulated drive and found that automation availability resulted in slower reaction times to an emergency event. Similarly, Saxby et al. (2013) found that reaction time to an unexpected event increased significantly for drivers engaging in automated driving (e.g., speed, braking and steering controlled) and found that drivers who had previously been placed in an automated driving scenario (e.g., full automation use for the majority of the drive) were slower to respond to the sudden event. These studies provide evidence that fully

11 automated environments may decrease alertness and increase reaction time among drivers

(Hancock & Verwey, 1997).

Subjective State and Vehicle Automation. In addition, it has also been found that automation use can moderate emotional responses. For example, a simulator study conducted by

Desmond et al. (1998) found that prolonged automated use elicited a decrease in task engagement and an increase in distress. Additionally, Neubauer et al. (2012a) gave drivers the choice to engage in vehicle automation and found that having automation as an option was not beneficial to drivers. More specifically, they found that the task engagement predicted use of automation in the first place where drivers choosing to use automated systems had lower levels of pre-task engagement (as measured by the Dundee Stress State Questionnaire, Matthews et al.,

2002) prior to driving. Drivers also showed marked declines in task engagement following automation use, suggesting that driver-controlled automated systems may actually induce these stressful reactions in drivers.

Furthermore, full vehicle automation may free up cognitive resources which can induce negative emotional states such as task-relevant and task-irrelevant worry (Neubauer et al.,

2012a). For example, Funke et al. (2007) subjected drivers to a partially automated drive (e.g., speed but not steering controlled) and found a decrease in distress and subjective ratings of workload (compared to a manual and lead-vehicle following condition), suggesting that maintaining control of at least some aspects of an automated task decreases the negative emotional consequences of vehicle automation. This implies that the loss of control, found during automated driving, must be accounted for so as to buffer against excessive trust, complacency and task-irrelevant stress and worry. Findings such as these suggest that secondary tasks such as talking on a cell phone, having a passenger conversation or playing a game may

12 actually be beneficial to drivers engaging in automated systems. It would appear that the addition of a stimulating secondary task might increase the cognitive load that is diminished during vehicle automation, thereby countering boredom and the withdrawal of effort (Desmond &

Matthews, 1997).

Secondary Alertness Maintaining Tasks

If the above statements are confirmed, one might expect that underload, combined with a decrease in motivation might cause a misappropriation of resources needed to effectively perform a task (Oron-Gilad, Ronen & Shinar, 2008). If so, then a motivating secondary task might increase engagement with the primary task of driving, decrease fatigue and increase performance efficiency (Gershon et al., 2009). For example, Verwey and Zaidel (1999) found that, under certain conditions, performing a secondary task that was attentionally demanding increased task engagement and alertness. Additionally, Oron-Gilad et al. (2008) found that cognitive tasks, especially tasks involving long-term memory, helped maintain driver alertness during a lengthy drive. Drivers may also implement their own strategies to remain motivated.

Surveys conducted by Maycock (1997) found that drivers also tend to utilize “mental games” to challenge themselves when fatigued.

However, several considerations should be taken into account when implementing a secondary task to combat fatigue. First, the task itself should not overtly tax the driver, in other words, demands of the secondary task should not outweigh the demands of the primary task, so as not to increase driver distraction. However, the task should require enough attention to elicit and maintain a somewhat high level of alertness (Drory, 1985). Additionally, Desmond and

Matthews (1997) argue that fatigued drivers can improve their performance when they are highly motivated, which suggests that driver interest and motivation should be taken into consideration

13 as well. In fact, Gershon et al. (2009) found that an interactive cognitive task (ICT) called

“Trivia” helped improve driver performance and mental state. Their secondary task was an auditory-motor task, designed within the principles of a common knowledge game known as

“Trivia”. Here, multiple-choice questions from several categories were presented to drivers through speakers in the rear of the car. Drivers were allowed to choose from one of five categories (e.g., food, sports, movies, current events and general knowledge) and were required to indicate their response by pressing one of four buttons placed next to the steering wheel.

Giving drivers a choice of different categories to choose from may increase their own personal involvement, interest and control with the task. Additionally, questions were grouped into three categories of difficulty (i.e., easy, moderate and hard). The task was designed so that early questions in each category were easy and got harder as the driver progressed to the next phase

(based upon an independent evaluation of students). Drivers were subjected to a 140-minute simulated drive, in one of two driving scenarios, a drive with ICT activation and a drive without.

Within the ICT condition, the trivia game was activated during two, 20-minute periods (during

60-80 minutes and 100-120 minutes). Results of this study found that drivers who engaged in 20 minutes of trivia exhibited better vehicle control, higher ratings of motivation and less subjective sleepiness compared to a control. In light of these findings, the current study employed a variation of the Gershon et al. (2009) “Trivia” game in the attempt to increase task motivation and cognitive load during vehicle automation.

Furthermore, many drivers anecdotally report that using a cell phone helps maintain alertness while driving during long periods of time (Atchley et al., 2013). Cell phone use is widely popular, where an estimated that 81% of drivers use a cell phone while driving (Wogalter

& Mayhorn, 2005). Additionally, that figure may be even higher for college drivers, where an

14 estimated 99% of college students talk on a cell phone while driving (Nelson, Atchley & Little,

2009). The evidence of cell phone induced performance impairments is somewhat equivocal.

However, the dangers of cell phone use are well documented and include a fourfold increase in the chances of being involved in a car accident (Redelmeier and Tibshirabi, 1997), slower reaction times and an increase in likelihood of missing crucial elements of the driving environment (Strayer and Johnston, 2001). One factor behind these impairments is that cell phone use requires visual attention, especially when text messaging. In fact, it has been argued that text messaging may be a more dangerous form of cell phone use (compared to talking).

The demands of text messaging requires drivers to juggle manual (e.g., responding), visual (e.g., reading messages) and various cognitive demands (e.g., attention) while driving, which may prove more dangerous than simply talking on a cell phone (Hosking et al., 2009). In a simulator study, Drews et al. (2009) found that drivers who engage in text messaging while driving showed marked performance impairments in the form of decreased reaction times and an increase in the chances of being involved in a traffic accident. Hosking et al. (2009) also found that text messaging results in an increase in compensatory steering and lane excursion deviations.

More recently, Neubauer et al. (2012b) found that drivers placed in a text message condition exhibited poorer vehicle control, compared to drivers in a hands-free cell phone condition or control. The findings of the previous studies seem to suggest that text messaging may increase the accident risk of drivers. Conversely, it appears that talking on a cell phone may not impact performance to the same extent and so the current study utilized this particular form of cell phone use (see Neubauer et al., 2012b).

One aim of the current study was to evaluate the effects of these two interactive cognitive tasks (i.e., trivia game and hands-free cell phone conversation) on subjective state and

15 vehicle performance. More specifically, the current study evaluated these effects within an automated driving scenario, which can exacerbate the effects of fatigue. The specific goals for this study were to compare performance and subjective state effects of the ICT’s and to find whether these effects are uni- or multidimensional (i.e., performance as well as subjective state effects). Additionally, subjective state can moderate the effects of certain driving tasks on performance. The next section will outline the interactive relationship between stress, fatigue and driving.

The Interaction between Stress, Fatigue and Driving

Driver stress and fatigue are well known to enhance the risk of getting into a crash or fatal accident (Hitchcock & Matthews, 2005). As previously mentioned, fatigue can stem from a number of sources within the driving context, which can include time of day and traffic environment (Wijesuriya et al., 2007). In addition, fatigue can also have a psychological component that may enhance accident risk (Evans, 2004; Shinar, 2007). Furthermore, identifying the cause of fatigue can be somewhat difficult given its dualistic nature. For example, it has been argued that fatigue represents an umbrella term for a wide variety of subjective states, which can overlap with other symptoms such as stress (Matthews & Desmond, 1998; Saxby et al., 2013).

This overlap makes fatigue somewhat difficult to define. Traditionally, fatigue may be best characterized by a decrease or loss in task motivation, which suggests that a subjective component exists between fatigue and task performance (Brown, 1997). Identifying the cause of fatigue is especially important because professional drivers such as long-haul truck drivers may be particularly susceptible to such subjective states.

Active vs Passive Fatigue. Even though fatigue is somewhat difficult to define, it may be best to conceptualize fatigue based upon Desmond and Hancock’s theory of active and passive

16 fatigue (2001). Active and passive fatigue states are qualitatively different in nature. Active fatigue is elicited during high workload tasks, while passive fatigue is a result of task underload.

These two extreme forms of fatigue are particularly dangerous because they interfere with the driver’s ability to apply effortful compensation in response to task demands (Hancock & Warm,

1989). Because they are qualitatively different in nature, it appears that active and passive fatigue can result in different types of performance impairments. For example, Saxby et al. (2013) explored the differences between active and passive fatigue in a driving simulator study. They subjected participants to one of three driving conditions; normal vehicle driving, passive fatigue

(induced through total vehicle automation) and active fatigue (induced through high workload wind gusts). The results of this study found that the passive fatigue manipulation caused a decrease in task engagement, while the active fatigue manipulation resulted in higher levels of distress and worry following the drive, consistent with Desmond and Hancock’s (2001) theory.

The results of the previous study also provide support for the applicability of multidimensional models of subject states.

The Transactional Model of Driver Stress. As previously mentioned, fatigue may be best conceptualized as an umbrella term for a wide variety of subjective states such as stress. In general, stress can result from the interaction between task demands and individual stress vulnerability. Task demands can include fatigue-inducing environmental factors such as time on task as well as a monotonous roadway environment (Thiffault & Bergeron, 2003). Individual stress vulnerability refers to specific personality dimensions (see DSI: Matthews et al., 1997) that predispose drivers to transient states such as anxiety, worry and even distress while driving

(Desmond & Matthews, 2009). The transactional model of driver stress best conceptualizes this unique interaction (Lazarus, 1999). This model proposes two key aspects: 1) driver appraisal and

2) coping style. More specifically, driver appraisal refers to a driver’s assessment of task demands. Coping style guides decision-making and emotion-regulation while driving. Here, stress is seen as a dynamic process that results from the interaction between the environment and the individual.

One key aspect of this model is that individual difference factors can bias cognitive appraisal and coping style. In stress vulnerable drivers, these cognitive processes may result in potentially dangerous subjective responses and performance impairments. Additionally, this model suggests that during apparently easy tasks, drivers may exert minimal effort to maintain safe driving practices when fatigued, due to a withdrawal of effort and a reduction in task- focused coping (Desmond & Matthews, 2009; Matthews, 2002). Overall attention can be lost due to a shift in attention from external events to more internal, cognitive ones (Matthews, 2002).

Driving becomes dangerous when these factors combine to produce mal-adaptive cognitive appraisal and coping mechanisms. For example, emotion-focused coping is a type of cognitive distraction and results in a diversion of attention away from the primary task towards self-related processing and negative thoughts (Matthews, 2002). Typically, this particular coping style is elicited by highly stressful situations. Another mal-adaptive coping style is known as confrontive coping and is typically seen in drivers high in aggression. These drivers tend to appraise other drivers negatively and react by engaging in intentional errors and violations while driving

(Matthews, 2002). Evidence such as this suggests that the interaction between the individual and the specific environment in which they find themselves can produce dangerous driving behaviors. These behaviors can divert attention away from the task, toward self-referent negative affect, producing stress and cognitive distraction.

The Dundee Stress State Questionnaire. In order to best conceptualize the multidimensionality of subjective states, Matthews et al. (2002) developed The Dundee Stress

State Questionnaire (DSSQ: Matthews et al., 2002), a three-dimensional model of subjective stress. The DSSQ is a 96-item measure, designed to assess transient states of subjective stress that are specific to performance based settings. Additionally, the DSSQ has been validated in a number of studies to be a reliable predictor of driver stress (Matthews & Desmond, 2002). The

DSSQ utilizes factor analysis to yield ten primary factors of mood (energy, tension and hedonic tone), motivation and cognitive factors (e.g., self-focus, self-esteem, concentration, confidence and control and cognitive interference). These ten factors are then grouped into three higher order secondary factors of task engagement, distress and worry.

Task engagement refers to feelings of energy/tiredness and motivation, while distress is characterized by feelings of high tension and unpleasant mood. Worry is somewhat different and may reflect a relatively stable emotional state. More specifically, worry is related to feelings of low self-confidence and task irrelevant thought processes. This is especially dangerous because task-induced performance worries may intrude upon an individual’s thought processes during driving. Scores on the DSSQ are based on standardized units, which provide 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 these three factors (Matthews, 2002).

These state changes may be influenced by the task (i.e., environmental factors) but also by personality factors such as stable vulnerabilities to driver stress. The next section will explore research on individual differences in terms of stress vulnerability, focusing specifically on the implications of such factors on secondary media usage and driving.

Personality within the Transportation Context

Although cognitive factors such as driver inattention and subjective states such as stress and fatigue can enhance distractibility and reduce effortful compensation, little is known, however, about the personality factors that may moderate the effects of secondary media while driving manually or even during vehicle automation. Several studies have suggested that individual differences may contribute to performance impairment and risky driving behaviors.

For example, Strayer, Drews and Crouch (2006) argue that certain self-selecting factors can enhance accident risk, such as engaging in risky behaviors (i.e., speeding, texting on a cellular phone) or being in a particular emotional state (i.e., anxious or aggressive). Moreover,

Rakauskas, Gugerty and Ward (2004) argue that drivers who choose to use their cell phone may possess certain attitudes and personality traits that predispose them to greater accident risk.

Additionally, drivers engaging in potentially dangerous behaviors such as cell phone usage may not be aware of their increased accident risk. In a simulator study, Strayer et al. (2003) found that drivers using a cell phone reported that other drivers showed marked performance decrements but reported no perceived increase in driving difficulty for themselves. It is of great importance to identify individuals who are prone to dangerous or risky driving behaviors as well as those drivers who are particularly susceptible to negative subjective states in order to implement effective countermeasures.

The Driver Stress Inventory (DSI). Understanding individual differences in terms of driver stress vulnerability is of great importance in order to develop appropriate countermeasures for such individuals (Iverson & Rundmo, 2002). In order to identify such risk factors, Matthews et al. (1997) developed a personality inventory that readily predicts an individual’s vulnerability to driver stress. The Driver Stress Inventory (DSI) discriminates between five different

20 personality traits that are specific to driving and safety criteria. These include Aggression,

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

1997). Research using the DSI has found that these dimensions reliably predict both performance and subjective stress response within the driving context.

The personality dimensions Aggression and Dislike of Driving are traits that make individuals particularly susceptible to feelings of anger, hostility and anxiety while driving.

Individuals scoring high in Hazard Monitoring are characterized by higher risk awareness, while

Fatigue Proneness relates to fatigue vulnerability or tiredness while driving. Finally, Thrill

Seeking is best characterized by higher risk acceptance and voluntary neglect of safety and so may predict a range of other dangerous driving behaviors. Additionally, evidence suggests that these five factors are reliable predictors of performance impairment but also more specifically, to unsafe acts voluntarily committed while driving. For example, the personality dimensions

Aggression and Thrill Seeking have been found to be highly predictive of self-reported accident involvement as well as a larger proportion of convictions for driving violations (i.e., speeding)

(Matthews, 2002).

Aspects of these personality dimensions may induce certain driving behaviors but it also appears that these traits can govern emotional responses in drivers. As previously mentioned, the transactional model of driver stress illustrates how the environment and personality (assessed via the DSI) interact to produce reliable patterns of cognitive appraisal and coping (Matthews,

2002). For example a congested environment may produce a pattern of confrontive coping in drivers scoring high in aggression, which is typically expressed as hostility towards other drivers.

A key aspect of 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). Additionally,

21 high levels of confrontive coping characterize the Thrill Seeking dimension. In this type of scenario, drivers relieve their emotions through risk taking. Furthermore, drivers scoring high in

Dislike of Driving tend to cope with task demands through emotion-focused coping. Emotion- focused coping is somewhat maladaptive, as drivers tend to appraise their own performance as incompetent, thereby enhancing negative feelings of driver stress (Desmond & Matthews, 2009;

Matthews et al., 1996). Furthermore, Fatigue Proneness most reliably predicts a loss of task engagement and a reduction in task-focused coping, and is directly related to a change in state that can be induced through a fatiguing drive (Matthews & Desmond, 1998). By contrast, Hazard

Monitoring results in one of the most beneficial forms of task coping. Here, drivers tend to have somewhat higher levels of task engagement and engage in task-focused coping, focusing specifically on safety enhancing behaviors (Matthews, 2002; Matthews et al., 1996).

Understanding these cognitive mechanisms can significantly enhance our understanding of certain emotional vulnerabilities and potentially dangerous driving behaviors. In addition to contributing to dangerous or risky behaviors such as high aggression or confrontive coping, these mechanisms may also help identify which individuals can benefit from certain in-vehicle devices and fatigue countermeasures. For example, a fatiguing drive may result in a reduction in effortful processing of the primary task especially for drivers high in fatigue proneness. Here, secondary tasks such as a game of trivia or even talking on a cell phone may help these individuals remain engaged while driving manually or during vehicle automation.

Aims and Objectives

The current study aimed to extend the findings of Gershon et al. (2009) and identify whether the secondary media tasks outlined above helped to counter the effects of fatigue while driving. More specifically, the current study evaluated these effects within a fatiguing automated

22 driving scenario, with two levels of automation (partial and total). The current study assessed the effects of these two media devices (trivia game and hands-free cell phone conversation) on subjective state and vehicle performance. The first aim was to investigate whether a game of trivia or cell phone use impacted subjective responses produced by automation. On the basis of the Gershon et al. (2009) and Atchley et al. (2013) studies, it was expected that the media devices would enhance task engagement, while decreasing distress and worry, compared to the control. Media use should also increase workload. The second aim was to test how the two types of media usage affected performance (e.g., vehicle control and reaction time) of the fatigued driver. Previous studies (Neubauer et al., 2012b; Saxby et al., 2013) showed that prior exposure to total automation slowed responses to an emergency event (imminent collision with another vehicle). If media use increases engagement, then it should reduce the automation-induced slowing, relative to the control condition. Finally, this study explored individual differences associated with stress and fatigue vulnerability that can result from the simulated drive.

Specific hypotheses were as follows:

H1. It was hypothesized that the secondary media devices would help improve vehicle control, assessed via the standard deviation of lateral position. Because it was proposed that the secondary tasks may help maintain subjective task engagement, it was assumed that participation in the media conditions (either trivia or cell phone use) would significantly improve driver performance, compared to the control.

H2. It was also expected that the media tasks would help enhance alertness especially in the total and partial vehicle automation conditions. Alone, total and/or partial vehicle automation are likely to decrease alertness, so it is expected that the secondary media will likely keep drivers in the loop and therefore minimize performance impairment.

H3. The personality dimensions “Dislike of Driving” and “Fatigue Proneness” will correlate with positive emotional outcomes (i.e., high task engagement, low distress and worry) following media participation.

H4. Post-task Distress and Worry will be highest and post-task engagement will be lowest in the control conditions. Automation produces a state of passive fatigue and effort withdrawal, so it was expected that the secondary media would increase task engagement by increasing cognitive demand.

H5. High task demands are associated with elevated workload, so it was expected that the high task demands associated with media use, would increase workload.

Method

All participants completed a 45-minute simulated drive. The driving simulator is built around a programmable SystemsTech STISIM driving simulator, equipped with a 42” LCD screen and realistic driving controls. The simulator conveys an immersive aspect of the driving environment that is capable of resembling automated and non-automated driving scenarios.

Participants

A total of 180 fully licensed drivers (71 males, 109 females) were recruited for this study.

Participants were students from the University of Cincinnati Introductory Psychology research pool and were required to complete a mandatory research assignment. Participants ranged in age from 18-30 years (M = 20 years, SD = 3.5). All participants were required to provide a valid driver’s license and had normal or corrected-to-normal vision as noted on their driver’s license by a restriction B. Additionally, participants were excluded from the study if they met any of the following criteria: had a history of epilepsy, were taking any psychoactive medication (e.g., for the treatment of anxiety or depression), English was not the primary language, or their age was

24 below 18 or over 40. Individuals with a history of epilepsy or individuals taking psychoactive medications may react negatively (i.e., have seizures, become nauseous) to the simulation, and so those individuals were excluded from the present study. The experiment was conducted under conditions approved by the University of Cincinnati Institutional Review Board.

Experimental Design and Simulator Tasks

This study employed a 3 (Non-Automation vs. Total Automation vs. Partial Automation) x 3 (Control vs. Trivia vs. Cell Phone), between subjects design, resulting in a total of 9 conditions with 20 participants in each condition. Between subjects factors included automation condition (Non-Automation, Total Automation, and Partial Automation) and secondary media condition (Control, Trivia and Cell Phone). Dependent variables for the current study included driver performance measures and subjective ratings of stress and fatigue. Driver performance was assessed via reaction time to an emergency event and lane keeping (discussed in further detail below). Subjective state was measured using a series of pre and post-task questionnaires

(discussed in the following section).

Questionnaires

In the current study, participants completed a total of 4 questionnaires. The Cell Phone

Usage Questionnaire and The Driver Stress Inventory (DSI) were administered prior to the drive in order to assess regular cell phone usage and driving personality respectively. Additionally, the

Dundee Stress State Questionnaire (DSSQ: Matthews et al., 2002) was administered pre and post-task to assess changes in the driver’s 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

25 questionnaire was used to group and counterbalance participants while controlling for regular cell phone use.

The Driver Stress Inventory (DSI). Participants completed the DSI (DSI: Matthews et. al., 1997) (see Appendix B) prior to the drive to assess any driving related personality factors that may influence fatigue proneness. 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 Dundee Stress State Questionnaire (DSSQ). The Dundee Stress State Questionnaire

(DSSQ: Matthews et al., 2002) was administered prior to and after the simulated drive, to evaluate effects of the task on stress and workload. The pre and post-task questionnaire differ slightly. The pre-task DSSQ is a 96-item measure (see Appendix C), designed to assess multidimensional task-induced stress. The survey 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

26 motivation), distress (e.g., tension, low confidence) and worry (e.g., self-esteem, task-related thoughts) symptoms. Upon completion of the drive, participants completed the post-task DSSQ

(Appendix D), which assesses the same mood dimensions but includes an embedded version of the NASA-Task Load Index (NASA-TLX: Hart & Staveland, 1988). The NASA-TLX is a measure of subjective workload and is based on task difficulty. The post-task DSSQ also includes a measure of coping style in response to task-demands. Scores on the DSSQ are standardized, expressed as state changes in SD units.

The Driving Simulator

All participants completed a 45-minute drive on a Systems Technology, Inc., STISIM

Model 400 simulator, version 2.08.10, equipped with a 38” NEC XM3760 monitor. The driving simulator is capable of a wide range of programming capabilities, which can mimic realistic driving scenarios that are intended to elicit stress and fatigue in the driver. The simulation was displayed via a 42” Westinghouse LCD flat screen television. A Logitech MOMO Racing Force

Feedback Wheel was used to evoke realistic driving feedback via 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. Figure 1 illustrates the experimental setup.

Figure 1. Experimental setup using a System Technologies, Inc., STISIM Drive, build 2.08.10, a

Westinghouse 42-inch LCD monitor and Logitech MOMO racing force feedback wheel.

Cellular Telephones and Blue Tooth Device

In the cell phone condition, two cellular telephones were used in order to create a realistic, dual-task driving environment. The participant was given an LG Rumor 2 cellular telephone, while the experimenter used an LG LX101 cellular phone. Participants were also provided with a JABRA Bluetooth headpiece. A Bluetooth headpiece was given so that participants could engage in a hands-free phone conversation while driving. Both cellular phones and Bluetooth headpiece are shown in Figure 2.

28 a) b) c)

Figure 2. a) Participant phone, LG Rumor 2 b) experimenter phone, LG LX 101 c) JABRA

Bluetooth headpiece. Photos for phones and Bluetooth headpiece were obtained via http://cgi.iwirelesshome.com/phones/ and http://www.jabra.com/headsets-and-speakerphones/all- products/bluetooth respectively.

Driving Tasks, Automation and Secondary Media Conditions

Participants were first exposed to a 3-minute practice drive in order to acquaint themselves with the driving simulator. Following practice, participants completed a 45-minute simulated drive. The drive consisted of a non-automation, total automation or partial automation drive. Participants were also instructed to engage in a secondary media condition while driving, which consisted of a control, trivia or cell phone condition. Additionally, the simulator logged data at a speed of 30 frames per second. During the drive, successive performance measures were obtained which consisted of the standard deviation of lateral position (SDLP) and reaction time to an emergency event. SDLP is a widely used index of vehicle control (see O’Hanlon,

1984; O’Hanlon et al., 1982). Additionally, SDLP was analyzed during the time that drivers were engaged in their respective secondary media conditions, while reaction time was assessed after automation use had ceased.

Practice Drive. Prior to the 45-minute main drive, participants completed a 3-minute practice drive. They were given the opportunity to practice using the controls and gas and brake pedals. The drive consisted of a multi-varied scenario, which included straight and curved road sections of both rural (suburban) and urban (city) driving. The roadway was a two-lane highway that consisted of occasional oncoming traffic, intersection stops with pedestrian crossings and stop signs. Participants were asked to adhere to speed limit signs (40 minimum, 55 maximum), stop signs and red lights. They were also asked to refrain from turning down any side roads or stopping at any stores or other shops along the way. In addition to the practice drive, participants in the cell phone condition were required to answer 1 test-phone call from the experimenter. This test-phone call allowed participants to familiarize themselves with the cell phone and Bluetooth device prior to the main drive.

Main Drive. Following practice, participants completed the 45-minuted simulated drive to which they had been assigned. All simulated drives consisted of the same driving scenario, which consisted of a multi-varied, two-lane highway. More specifically, the drive consisted of occasional oncoming traffic, pedestrian crossings and intersection stops. The environment transitioned from rural (small town) to city (urban) driving approximately every 5 minutes, similar to Neubauer et al. (2012a) and Saxby et al. (2013). Similar to the practice drive, participants were again instructed to refrain from making any gratuitous stops and were also asked not to turn down any side streets as well as adhering to the speed limit signs, which ranged from rural scenery speed (40 minimum, 50 maximum) and city driving (50 minimum, 60 maximum), stop signs and red lights.

Automation Conditions. Participants were first assigned to the non-automation (NA), total automation (TA) or partial automation (PA) group. In the non-automated drive, participants

30 were instructed to maintain manual control of the vehicle throughout the entire drive.

Conversely, participants in the total and partial automation drives were instructed to engage in some form of automated driving. In the TA condition the steering, braking and speed were all controlled for the first 40 minutes of the drive. Similar to Funke et al. (2005), participants in the

PA condition were required to engage in partial vehicle automation, with speed but not steering controlled. To ensure that participants did not fall asleep in both automation conditions and to buffer against an extreme form of passive fatigue, drivers were instructed to engage in a “divided attention” event. The divided attention event required participants to monitor the screen and indicate when the event had taken place. The screen consisted of two gray squares, which outlined two red diamonds positioned at the upper left and right hand corner of the screen.

Approximately every 10 minutes, the experimenter triggered the “divided attention” event, which consisted of the red diamond changing into a downward facing triangle indicating

“automation failure”. In the event of automation failure, participants were instructed to press their corresponding turn signal when they detected the event, similar to Neubauer et al. (2012a) and Saxby et al. (2013). Participants were also told that automation may turn off towards the end of the drive and that they would need to take over full control of the vehicle. In reality, the experimenter turned the automation feature off 40 minutes into the drive, requiring participants in the total and partial automation conditions to drive manually for the remaining five minutes of the drive.

Secondary Media Conditions. Next, within each automation condition, participants were then assigned to the control (CT), trivia (TR) or cell phone (CP) conditions. In both the TR and

CP conditions, effects of the secondary media were assessed during the same two, 10-minute periods (5-15 minutes and 30-40 minutes). Within the TR condition, participants were required

31 to engage in a game of trivia, similar to Gershon et al. (2009). Participants were given the opportunity to select a question from one of five categories: food, movies, sports, current events and general knowledge (see Appendix E). In order to mimic an in-vehicle media device, the experimenter asked questions in a neutral tone while sitting out of view of the participant.

Participants were instructed to select a question from one of the five categories and were given as much time as they needed to answer each question. Questions were designed to become more difficult as the game progressed. Following a response, participants were given feedback in the form of a “correct” or “incorrect” response from the experimenter. Numbers of correct, incorrect or passing responses were recorded. Participants in the CP condition were given a wireless

JABRA Bluetooth headset and told that they would be participating in two, 10-minute cell phone conversations. In order to re-create a more naturalistic dual-task driving environment, the experimenter initiated the cell phone conversations in a separate room. The first conversation consisted of neutral conversation starters, such as “What is your college major?” and “Do you have any fun or interesting hobbies?” while the second conversation included a “close-call” scenario. Close-call stories were used to increase personal engagement and prompt an in-depth cell phone conversation with the experimenter (see Appendix F).

Performance Assessment. The primary measure of driver performance was assessed via the participants’ SDLP, a well-known measure of lane maintenance, where a higher lateral position indicates poorer vehicle control. The timing of the performance assessment coincided with the timing of the secondary media assessment (i.e., from 5-15 minutes and 30-40 minutes), yielding 2, 10-minute phases of performance assessment. SDLP was only analyzed in the non- automation and partial automation conditions, as participants in the total automation condition were not in control of their steering for the majority of the drive. In order to account for

32 familiarization with the media conditions, data from the middle 5 minutes of each phase were used (i.e., minutes 8-12 and 33-37). Therefore, the final performance assessment consisted of two phases of 5 successive, minute long periods.

Additionally, towards the end of the drive and when automation had ceased in all conditions, the experimenter manually initiated a “sudden event” into the driving scenario (at approximately 42 minutes). The “sudden event” consisted of a van suddenly appearing on the side of the road, after about 3 seconds it promptly pulled out in front of the driver similar to

Neubauer et al. (2012a; 2012b) and Saxby et al. (2013). This event was intended to assess the driver’s situation awareness and reaction time. The performance assessment consisted of mean response times for braking (how fast participants applied their brake) and steering (how fast participants were able to adjust their steering wheel). The van was programmed to pull to the side of the road 30 seconds after activation. A screen shot of the sudden event is illustrated in

Figure 3.

Figure 3. Screen shot of the sudden event.

Procedure

The experiment took place in a windowless laboratory setting, to prevent any outside distraction. Participants were first greeted by the experimenter and presented with a written informed consent document (see Appendix G). The experimenter kept one signed copy of the document and the participant was provided with a copy for their personal records. The informed consent document outlined the procedures, benefits and any potential risks associated with the study. Risks were kept at a minimum and were unlikely but included, simulator sickness, a form of nausea, which can arise from viewing any type of simulated display. Participants were also told that they could stop the experiment at any time if such an event occurred. After consent was obtained, participants completed a series of questionnaires and were randomly assigned to one of nine experimental conditions. The first questionnaire was the Cell Phone Usage Questionnaire.

Next, the pre-task Dundee Stress State Questionnaire (DSSQ) and finally the Driver Stress

Inventory (DSI) were administered via an electronic computer survey.

Participants were randomly assigned to one of nine experimental conditions. First, they were assigned to a non-automation (NA), total automation (TA), or partial automation (PA) driving condition. Within those conditions, participants were then randomly assigned to one of three secondary media conditions, which included the control (CT), trivia (TR) or cell phone

(CP) condition. Drivers in the NA condition were simply instructed to maintain full control of the vehicle (as outlined above) and adhere to all traffic signals and rules of the road. Participants in the TA and PA driving conditions were instructed to engage in total vehicle automation or cruise control respectively (also outlined above) for the majority of the drive (i.e., 40 minutes).

Next, participants were given instructions according to their secondary media condition. Drivers in the CT condition were not instructed to engage in any type of secondary media while driving.

Within the TR condition, drivers were told that they would be playing a game of trivia for two, ten-minute periods during the drive. They were instructed to choose a question from one of five categories and were given as much time as they needed to answer each question (outlined above). Finally, drivers in the CP condition were told that they would be using a cell phone during two, ten-minute periods as well. They were provided with a JABRA Bluetooth headpiece, were instructed on how to use the device and were given a test phone call during the practice drive. The cell phone conversation consisted of an informal/introductory conversation followed by an in-depth conversation relating to the participants experience with a “close call” event.

Measurement of lane control was assessed during this time in order to understand how secondary media devices may influence driver performance. During the cell phone conversation, the experimenter was in a separate room in order to avoid passenger-like conversations, which may moderate driver performance (Strayer & Johnston, 2001).

After participants had given their consent and been assigned to their respective experimental conditions, they were asked to have a seat in the driving simulator chair.

Participants were given the opportunity to adjust the car seat and the height of the steering wheel to match their comfortable driving preference. Next, participants completed a 3-minute practice drive in order to acquaint themselves with the specified task, driving simulator and the cell phone

(if applicable). During the practice, they were told to adhere to all traffic signals and signs, including speed limit signs, stop signs and red lights. They were also told to drive straight ahead and avoid making any extraneous stops. Participants in the CP group received a practice call at approximately 1:30 to ensure that they were able to use the Bluetooth device. Following practice, participants were given the opportunity to ask any questions. Finally, participants completed the

45-minute drive to which they had been assigned. Prior to the start of the drive, participants were given instructions and the opportunity to ask any final questions.

Participants in the CT condition were given the same instructions as the practice drive.

Conversely, participants in both the TA and PA condition were told that the drive would be either fully (i.e., steering, braking and speed controlled) or partially automated (i.e., braking and speed controlled) for the majority of the drive. Automated drivers were also told that they would need to monitor for “automation failure” and indicate when the appropriate signal occurred (see above). Lastly, automated drivers were told that at some point during the end of the drive the automation feature may turn off and that they would need to regain full control of the vehicle.

Unbeknownst to the participant, the experimenter manually ended the automation feature 40 minutes into the drive. Additionally, participants in the TR condition were required to engage in a game of “trivia” during two, 10-minute periods throughout the drive (i.e., 5-10 minutes and 30-

40 minutes respectively). During this time participants were given the opportunity to select a question from the category of their choice, were given as much time as they needed to answer the question and were able to have the question repeated if needed. Participants were given feedback in the form of a “correct” or “incorrect” response from the experimenter. Furthermore, participants in the CP condition were also required to engage in the same two, 10-minute periods of secondary media, this time with a cell phone. The experimenter initiated two phone calls at the start of each period. Conversations consisted of a general introduction followed by the recollection of a more in depth “close call” experience of the participant. During this time, the simulator logged indices of the variability of lateral position, which indicates vehicle control.

Upon completion of the drive, participants were asked to complete a series of post-drive surveys. First, they were asked to complete the post-task DSSQ. The post-task DSSQ assesses

36 any changes in mood that occurred during the task. Additionally, this measure assesses the driver’s workload. Participants were then thanked for their participation, debriefed and given the opportunity to ask any questions about the study.

Results

The analysis for the current study was divided into four main sections in order to test the hypotheses presented. First, several 3 (automation condition) x 3 (secondary media condition)

ANOVAs were performed to determine the effects of the automation manipulation and secondary media conditions on subjective ratings of stress and fatigue. For this analysis standardized DSSQ scores of task engagement, distress and worry were measured in order to assess the effects of the drive and secondary media conditions on subjective state. Second, workload analyses were conducted to determine the effects of the automation and media conditions on perceived mental workload. Next, correlational and regression analyses were performed in order to identify predictors of driver stress vulnerability on subjective state response. Here, bivariate correlations and multiple regression were performed between the five

DSI dimensions of Aggression, Dislike of Driving, Hazard Monitoring, Thrill Seeking and

Fatigue Proneness as well as the three DSSQ factors of task engagement, distress and worry.

Finally, several performance analyses were conducted which included performing several 3

(automation condition) x 3 (secondary media condition) ANOVAs to determine the effects of the automation manipulation and secondary media conditions on vehicle control and alertness.

Vehicle control was indexed via the standard deviation of lateral position, while alertness was assessed via response time to a sudden event.

Baseline Analyses

Preliminary analyses were first conducted to ensure the absence of any pre-existing group differences. A series of 3 (automation) x 3 (media condition) between subjects ANOVAs were conducted using number of traffic accidents and years of driving experience as dependent variables. Results revealed no significant differences between conditions for number of traffic accidents and years of experience, p > .05 for all conditions.

Task-induced Effects of Automation and Secondary Media on Subjective Stress State

Task-induced stress state was assessed using the three DSSQ factors of task engagement, distress and worry. The standardized mean pre and post-task z-scores for the DSSQ factors of task engagement, distress and worry for both the automation and secondary media conditions are presented in Table 1. A positive z score on a post-task measure indicates that scores increased as a result of the task, while a negative post-task z score indicates a reduction in stress. Increased distress and worry and decreased task engagement indicate adverse state changes in stress.

Table 1.

Standardized mean pre and post task scores of the DSSQ for automation and secondary media conditions. Standard deviations are in parenthesis.

Task Distress Worry Engagement Automation Secondary Pre Post Pre Post Pre Post Condition Media Non-Automation Control .27 -.85 -.70 -.03 .54 .43 (.49) (.66) (.63) (.73) (1.36) (1.21) Trivia .24 -.12 -.33 .55 .42 .39 (.66) (.97) (.74) (1.01) (.95) (1.05) Cell Phone .45 -.07 -.44 -.03 .11 .33 (.55) (.96) (.63) (.79) (.85) (1.20) Total Automation Control .43 -.74 -.84 -.13 -.52 -.58 (.70) (.87) (.63) (.82) (.48) (.81) Trivia .09 -.33 -.33 .30 .40 .52 (.67) (.82) (.61) (.85) (.93) (1.08) Cell Phone .19 -.03 .01 .07 .34 .46 (.52) (.74) (.97) (.84) (1.42) (1.50) Partial Automation Control .53 -.37 -.55 .07 .38 .4786 (.95) (1.21) (.84) (1.10) (.84) (.97) Trivia .48 -.02 -.32 .30 .52 .07 (.59) (.83) (.70) (.91) (1.04) (1.20) Cell Phone .30 .03 -.52 -.23 -.20 -.32 (.63) (.82) (.76) (.89) (.83) (.74) Total Mean .33 -.28 -.45 .10 .22 .20 (.65) (.92) (.75) (.90) (1.04) (1.15)

The DSSQ was administered pre and post-task to index changes in stress state. In order to test the expectation that secondary media would increase task engagement while decreasing distress and worry, several ANOVA’s were run. Pre-drive scores were first analyzed in order to determine whether any pre-existing differences in subjective state existed between groups. This was accomplished using a 3 x 3 x 3 ANOVA (automation x secondary media x scales). In addition to the expected main effect of scales, F(2, 170) = 57.95, p < .001, partial η2 = .41, the only other significant effect was the scales x media condition interaction, F(4, 342) = 3.13, p <

.05, partial η2 = .04. In order to further investigate this effect, a series of 3 (automation condition) x 3 (secondary media condition) x2 (pre-post) ANOVA’s were run. ‘Automation Condition’ and

‘Secondary Media Condition’ were between subjects factors contrasting the non-automation, total automation and partial automation conditions as well as the three secondary media conditions (i.e., control, trivia and cell phone). ‘Pre-post’ was a within subjects factor contrasting pre and post-task state.

For task engagement and distress the pre-post x media condition interaction was significant, F(2, 171) = 18.60, p < .001, partial η2 = .18 and F(2, 171) = 5.91, p < .01, partial η2 =

.07 respectively. The analyses for worry revealed no significant main effects or interactions, p

>.05 in all cases. Figure 4 illustrates the interactive effect of the media conditions on pre and post-task engagement and distress. Additionally, the ANOVA also reflects that the automation manipulation did not elicit a significant effect on subjective stress (i.e., all main effects and interactions involving the automation conditions were not significant). Figure 4 illustrates the change in state from pre to post-task for task engagement, distress and worry as a function of secondary media condition, with data collapsed across automation condition.

1.5

0.5 Control 0 Trivia -0.5 Cell Phone

-1 State Change State(z scores) -1.5

-2 Worry Engagement Distress

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

Qualitative inspection of the figure suggests that all groups, on average, showed relative increases in distress and relative decreases in task engagement. The figure also suggests that worry remained relatively stable throughout the task. More specifically, the figure shows that, on average, engagement was lowest for drivers in the control group and highest among drivers in the trivia and cell phone groups, while distress was highest among drivers in the control and trivia groups and lowest among drivers in the cell phone group. The figure also shows that the temporal decline in task engagement was most pronounced in the control group.

Perceived Mental Workload

Table 2 shows overall mean workload scores and standard deviations for all conditions.

Table 2.

Mean overall workload scores and standard deviations for all experimental conditions.

Standard deviations are in parenthesis.

Automation Condition Secondary Media Workload Non-Automation Control 27.70 (8.46) Trivia 31.65 (6.48) Cell Phone 30.05 (7.09) Total Automation Control 22.05 (9.67) Trivia 25.05 (7.24) Cell Phone 28.40 (8.77) Partial Automation Control 31.85 (6.81) Trivia 26.85 (5.62) Cell Phone 27.80 (9.00) Total Mean 27.93 (8.16)

Additionally, 3 (automation condition) x 3 (media condition) between subjects ANOVA was conducted to determine any differences in perceived mental workload. A significant main effect was found for automation, F(2, 171) = 5.91, p < .01, partial η2 = .07 but not media condition, p > .05. Additionally, the automation x media condition interaction was significant,

F(4, 171) = 3.18, p < .05, partial η2 = .07. The significant automation x media condition interaction was further broken down using a series of one-way ANOVAs. Using ‘Automation’ as a between subjects factor, the analyses revealed a significant main effect for both the control and trivia condition, F(2, 59) = 6.87, p < .01 and F(2, 59) = 5.54, p < .01, respectively, but not the cell phone condition.

Additionally, Posthoc Sidak tests were run to further explore this interaction. The Sidak test is a variation of the Bonferroni test, which is slightly more powerful as it allows for additional control for experimental error with multiple comparisons and is used when needed in

42 each subsequent analysis (De Muth, 2006). They revealed that workload was significantly lower in the total automation condition, compared to the non-automation and partial automation conditions, p < .05. Workload did not significantly differ between the non-automation and partial automation conditions, p > .05.

Predictors of Subjective State

The next section explores predictors of subjective stress state. More specifically, bivariate correlations and multiple regression were performed between the five DSI factors as well as the three DSSQ factors in order to identify any predictors of driver stress vulnerability and test the expectation that the personality dimensions ‘Fatigue Proneness’ and ‘Dislike of Driving’ would relate to positive emotional outcomes (e.g., high task engagement, low distress and worry).

Correlations. Bivariate correlations between subjective state and driver stress vulnerability are pooled across all task conditions and shown in Table 3. Correlations were performed between the five DSI scores Aggression, Dislike of Driving, Hazard Monitoring,

Thrill Seeking and Fatigue Proneness as well as the three pre and post-task DSSQ factors task engagement, distress and worry.

Table 3.

Correlations between the DSI factors and pre and post-task DSSQ scores.

Pre Post Pre Post Pre Post Worry Worry Engagement Engagement Distress Distress Aggression .295** .247** .003 -.077 .202** .175* Dislike of Driving .252** .281** -.331** -.175* .423** .385** Hazard Monitoring .056 -.003 .168* .241 -.042 .050 Thrill Seeking .158* .062 -.014 -.021 -.022 -.052 Fatigue Prone .323** .326** -.119 -.058 .241** .211** Note. * p < .05 ** p < .01

Table 2 shows that some of the DSI variables correlated with all three pre and post-task

DSSQ factors. More specifically, aggression was most strongly associated with high levels of worry and distress both before and after the drive. Dislike of driving was associated with feelings of worry, distress and task engagement. Furthermore, this trait significantly correlated with higher feelings of worry, both pre (r = .252, p < .01) and post-drive (r = .281, p < .01). Dislike of driving was also associated with higher distress pre (r = .423, p < .01) and post-drive (r = .385, p

< .01) and lower levels of engagement pre (r = -.331, p < .01) and post-drive (r = -.175, p < .05).

Hazard Monitoring correlated with high levels of pre-task engagement (r = .168, p < .05), while

Thrill Seeking was associated with feelings of pre-task worry (r = .158, p < .05). Finally, Fatigue

Proneness significantly correlated with high levels of pre and post-task worry and distress.

Regression. Additionally, regression analyses assessed whether the five DSI dimensions predicted post-drive state. Multiple regression was run using four steps, with each of the three post-task DSSQ variables as the criterion. Effect coding was used for both the automation and secondary media conditions (Pedhazur, 1997). For the automation conditions two vectors were created (autocode 1 and autocode 2). Within the first vector (autocode 1) the non-automation condition was labeled -1, the partial automation condition was labeled 1 and the total automation condition was labeled 0. Within the second autocode vector (autocode 2), the non-automation condition was labeled -1, the partial automation condition was labeled 0 and the total automation condition was labeled 1. Two more vectors were created for the three secondary media conditions. In the first vector (mediacode1), the control condition was coded -1, the trivia condition was coded 1 and the cell phone condition 0. In the second vector (mediacode2), the control condition was coded -1, the trivia condition 0 and the cell phone condition 1. Finally, another four vectors corresponding to the four interaction terms between the automation and

44 secondary media vectors were created. Predictors were entered in four steps for each regression equation. The ‘autocode’ vector was first entered followed by the two ‘mediacode’ vectors as the second step. The third step consisted of the interaction terms. Lastly, the five DSI variables were entered as the fourth step.

The first regression used post-task engagement as the dependent variable. It was found that the automation conditions were not predictive of post-task engagement, p < .05, but the media conditions were, F(4, 179) = 4.96 , p = .001 (ΔR2=.087). Additionally, the interaction terms between both automation and media conditions were significant, F(8, 179) = 2.66, p < .01

(ΔR2=.009). It was also found that the DSI factors predicted a significant portion of variance in post-task engagement, F(13, 179) = 3.52, p < .001 (ΔR2=.105). More specifically, it was found that dislike of driving and hazard monitoring were most predictive of post-task engagement regardless of the automation and media conditions, β = -.261, t(179) = -3.16, p < .01 and β =

.249, t(179) = 3.38, p = .001 respectively.

In the second regression, post-task distress was used as the dependent variable. The automation condition and interaction terms were not found to be significant, p > .05 in both cases. However, the media conditions were found to be predictive of post-task distress, F(4, 179)

= 2.54, p < .05 (ΔR2=.052). Additionally, it was also found that the DSI factors again predicted a significant portion of the variance, F(13, 179) = 3.65, p < .001 (ΔR2=.155). Here, dislike of driving was most predictive of post-task distress β = .393, t(179) = 4.78, p <.001.

The last regression used post-task worry as the dependent variable. Automation and media condition were not predictive of post-task worry states, p > .05 in both cases. However, the interaction term and the DSI variables were found to be predictive of post-task worry, F(8,

179) = 2.56, p < .05 (ΔR2=.087) and F(13, 179) = 3.52, p < .001 (ΔR2=.109) respectively. More

45 specifically, it was found that fatigue proneness significantly predicted post-task worry β = .167, t(179) = 2.01, p <.05.

Driver Performance Measures

Several performance measures were obtained to assess the effects of the automation manipulation and secondary media conditions on vehicle control and alertness. The standard deviation of lateral position (SDLP) was obtained to gauge vehicle control. SDLP was analyzed during two assessment phases, where each phase consisted of five successive performance points

(e.g., minutes 8-12 and minutes 33-37) (discussed in more detail below). Additionally, response time was also measured to index driver alertness. Finally, the total number of crashes within each group was also compared.

Vehicle Control. The standard deviation of lateral position was the primary performance measure used to index vehicle control (see O’Hanlon, 1984; O’Hanlon et al., 1982). SDLP was only analyzed in the non-automation and partial automation conditions as drivers in the total automation condition were not in control of their steering for the first 40 minutes of the drive.

Lateral position was analyzed during two performance assessment phases in order to test our hypothesis that secondary media would enhance vehicle control and alertness (e.g., faster response time). These phases coincided with the timing of secondary media (i.e., cell phone conversation or trivia game) during minutes 5-15 (phase one) and 30-40 (phase 2). More specifically, SDLP was taken from the middle 5 minutes of assessment, from minutes 8-12

(phase one) and 33-37 (phase two) so that drivers could acclimate themselves to the secondary media. Finally, a higher SDLP indicates poorer vehicle control. Data for the standard deviation of lateral position during phase 1 and 2 for the entire sample is presented in Table 4.

Table 4.

Standard deviation of lateral position for automation and secondary media conditions. Standard deviations are in parenthesis.

Phase Phase 1 2 Automation Secondary LP 1 LP LP 3 LP LP LP 6 LP 7 LP 8 LP 9 LP 10 Condition Media 2 4 5 Non- Control 1.16 1.03 .90 1.03 1.03 .89 .89 .85 .88 .91 Automation (.43) (.43) (.21) (.25) (.27) (.41) (.30) (.34) (.38) (.40) Trivia .97 .87 .85 .97 1.03 .74 .71 .76 .76 .73 (.27) (.21) (.23) (.29) (.37) (.23) (.16) (.22) (.16) (.20) Cell .1.03 .84 .83 .89 .95 .71 .71 .66 .75 .74 Phone (.31) (.14) (.24) (.29) (.16) (.17) (.23) (.17) (.21) (.20)

Partial Control .94 1.01 1.09 1.17 1.05 .97 1.09 1.13 1.03 1.02 Automation (.31) (.41) (.34) (.42) (.32) (.66) (.77) (.66) (.44) (.43) Trivia .64 .74 .74 .85 .79 .62 .64 .65 .61 .71 (.15) (.21) (.18) (.26) (.22) (.26) (.21) (.28) (.16) (.23) Cell .67 .72 .77 .83 .83 .65 .69 .76 .71 .65 Phone (.14) (.20) (.20) (.19) (.21) (.18) (.25) (.20) (.27) (.24)

Total .90 .87 .86 .96 .95 .76 .79 .80 .79 .79 Mean (.34) (.31) (.26) (.31) (.28) (.38) (.40) (.38) (.31) (.32)

An ANOVA was run, using automation and secondary media condition as between subjects factors and lateral position period as a within subjects factor. Results revealed a significant main effect for both automation condition, F(10, 105) = 6.28, p < .001, partial η2 =

.37 and secondary media condition, F(20, 212) = 1.81, p < .05, partial η2 = .15. Figure 5 illustrates the means of the SDLP for drivers in the non-automation and partial automation conditions, showing that drivers in the partial automation condition show a slightly lower SDLP during phase one but a slightly higher SDLP during portions of phase two.

1.2

0.8

0.6

Position Non-Automation 0.4 Partial Automation 0.2

Standard DeviationLateralof Phase 1 (min 8-12) Phase 2 (min 33-37) 0 8 9 10 11 12 33 34 35 36 37 Lateral Position Points (minutes into drive)

Figure 5. Standard deviation of lateral position for non-automation and partial automation groups. Error bars are standard errors.

Additionally, post hoc Sidak tests revealed a significant difference in SDLP between the cell phone and control and trivia and control groups, p < .05, but not between the trivia and cell phone groups, p > .05. Upon further inspection of the means it appears that drivers in the cell phone and trivia conditions had significantly lower SDLP throughout the entire drive compared to drivers in the control condition (with one exception). For lateral position point five, the means between the control and trivia did not significantly differ, p > .05 but did differ for the control and cell phone condition, p < .05. Figure 6 illustrates the means for the SDLP during both performance phases, for the cell phone, trivia and control conditions, again showing that drivers in the cell phone and trivia conditions maintained significantly better vehicle control throughout the entire drive, compared to drivers in the control, perhaps illustrating that secondary tasks help maintain vehicle control.

1.4

1.2

0.8

0.6 Position Control 0.4 Cell Phone 0.2 Trivia

Standard DeviationLateralof Phase 1 (min 8-12) Phase 2 (min 33-37) 0 8 9 10 11 12 33 34 35 36 37 Lateral Position Points (minutes into drive)

Figure 6. Standard deviation of lateral position for the control, cell phone and trivia groups.

Error bars are standard errors.

Response Times. Additionally, mean response times to an emergency event were recorded to index the driver’s alertness. Towards the end of the drive, and when automation and media usage had ceased in all conditions, an emergency event was activated. At approximately

42 minutes, the experimenter triggered the sudden event which was a van suddenly pulling out in front of the driver. Mean response times (in seconds) were obtained for several reflexive responses, which included steering (how fast drivers adjusted their steering wheel) and braking

(how fast drivers applied their brakes). These measures were obtained in all conditions.

Response times with a score of 0 were excluded as they indicated a non-response. For braking,

26 out of 180 responses were not included and for steering, a total of 37 out of 180 responses were excluded. Mean response times to the sudden event for the entire sample are presented in

Table 5.

Mean response times for automation and secondary media conditions. Standard deviations are in parenthesis.

Steering Braking Automation Condition Secondary Media Non-Automation Control 1.45 (.68) 1.56 (.45) Trivia 1.51 (1.38) 1.63 (.51) Cell Phone 1.74 (1.17) 1.61 (.40) Total Automation Control 1.21 (.93) 1.93 (.41) Trivia 2.23 (1.51) 2.09 (.84) Cell Phone 1.84 (.94) 1.98 (.96) Partial Automation Control 1.70 (1.08) 1.88 (.51) Trivia 1.87 (.97) 2.26 (1.11) Cell Phone 1.27 (1.05) 1.99 (.70) Total Mean 1.64 (1.09) 1.87 (.71)

An ANOVA revealed a significant main effect for automation condition, F(6, 240) =

2.66, p < .05, partial η2 = .06 but not secondary media condition, F(4, 242) = 2.90, p > .05, partial η2 = .05. Further inspection revealed that the automation manipulation had a significant effect on the response time of braking F(2, 121) = 5.40, p < .01, partial η2 = .08, but not steering, p > .05, revealing that drivers in the non-automation group were slightly faster at braking, compared to drivers in the total automation and partial automation conditions. Figure 7 shows the mean response times for drivers in the non-automation, partial automation and total automation conditions. Even though response times for steering did not significantly differ,

Figure 7 also illustrates that drivers in the non-automation group were slightly faster to respond compared to drivers in the total and partial automation conditions. Additionally, Figure 8 illustrates the non-significant effect of the secondary media on response time, but does reveal that drivers in the control condition were slightly faster at steering and braking.

2.5

1.5 Non_Automation 1 Partial Automation Total Automation

0.5 Response Time Response Time (seconds)

0 Steer Brake

Figure 7. Response times for steering and braking between the non-automated, partial and total automation groups. Error bars are standard errors.

2.5

1.5 Control Cell Phone 1 Trivia

0.5 Response Time Response Time (seconds)

0 Steer Brake

Figure 8. Response times for steering and braking by control, cell phone and trivia groups. Error bars are standard errors.

Crash Rates. Finally, the total number of crashes for the entire drive was compared between all groups. An independent samples t-test revealed a significant difference, t(118) =

2.04, p < .05 between the non-automation groups 27 total crashes (M = .63, SD = .84) and the partial automation groups 9 total crashes (M = .30 SD = .94). Crash rates were not obtained for

51 drivers in the total automation condition, as they were not in control of the steering, braking or speed of the vehicle for the majority of the drive. Additionally, an ANOVA revealed a significant main effect of secondary media condition F(2, 177) = 4.46, p < .05, partial η2 = .05 on crash rates. More specifically, it was found that drivers in the control group had a total of 16 crashes (M = .43 SD = .96), while drivers in the trivia group had a total of 17 crashes (M = .45

SD = .85). Drivers in the cell phone condition had the lowest number of total crashes, with only 5 crashes throughout the entire drive (M = .08 SD = .28).

It was also found that 66% of drivers crashed into the van during the “sudden event”, but the frequency of crashes was similar among each group. For example, 49 out of 60 drivers in the non-automation group crashed into the van while 37 and 32 out of 60 drivers in the total and partial automation conditions crashed into the van. Additionally, 39 out of 60 drivers in the control, 42 out of 60 drivers in the trivia and 37 out of 60 drivers in the cell phone condition crashed into the van.

Discussion. Gershon et al. (2009) suggested that use of an in-car trivia game might serve as a counter-measure to fatigue, although such an intervention also has the potential for distraction. The present findings partially support this suggestion, in that the trivia condition did indeed elevate task engagement during fatiguing drives. Furthermore, the trivia game offered no advantage over cell phone use; here, the game produced higher levels of distress. Additionally, changes in distress may follow changes in workload (Matthews et al., 2002), but in this case, the two responses were not closely coupled, here workload of trivia and cell phone conditions was similar. Total automation tended to reduce workload, as expected, but workload was maintained across automation conditions in the cell phone condition. This form of workload may be benign to the extent that it contributes to higher task engagement.

To the extent that trivia is comparable to phone use, it may be unwise to advocate this manipulation as a countermeasure to fatigue, given the well-known risks of distraction (Strayer

& Johnston, 2001). However, consistent with Neubauer et al.'s (2012b) conclusions, both phone use and trivia might be effective in maintaining driver engagement during fully automated driving. In fact, results of the current study support recent findings that a secondary verbal task such as trivia and phone use may help enhance alertness during fatiguing drives (Atchley et al.,

2013), suggesting that concurrent tasks may improve performance for fatigued drivers. Further work is necessary to determine how best to configure secondary media use so as to support behavioral readiness as well as subjective engagement, as the driver transitions back to full control.

Additionally, performance benefits were mixed, being confined to vehicle control, and not alertness. However, media use did not significantly impair later alertness, but the results do suggest dissociation between media use effects on steering and alertness. Both phone use and trivia might be effective in maintaining driver engagement during fully automated driving as seen by improved lane maintenance. Results also revealed that prior exposure to full vehicle automation slows braking response to emergency events, although there was no effect here on steering response.

General Discussion

Overview of Findings

The current study explored the effects of vehicle automation and secondary media devices on subjective state, workload and performance during a simulated driving task. Our primary aim was to test whether use of media during automated periods of driving counters driver fatigue, both during the drive, and after full control of the vehicle is restored. Indeed, the

53 findings partially support the claim that secondary media devices may help counteract some of the effects of fatigue while driving (Atchley et al., 2013; Gershon et al., 2009). Specifically, both media conditions elevated task engagement; however, media use did not counteract the slowed braking to an emergency event produced by automation. Furthermore, these results help identify the effects that the two media devices used (i.e., cell phone usage versus trivia game) can have on multidimensional subjective state as well as performance. It was also of interest to identify any pre-existing individual differences that may influence driver stress vulnerability.

The effects of the automation manipulation and secondary media devices on subjective state were first investigated. Both automated and control drives produced large-magnitude declines in task engagement. We expected greater fatigue with total automation, but drive duration may have been sufficiently long to elicit fatigue in the other conditions also. However, drivers in the trivia and cell phone condition exhibited the highest levels of post-task engagement, supporting H4. Secondary tasks that are relevant and personally engaging may diffuse boredom and increase task engagement (Gershon et al., 2009; Matthews et al., 2010); the cell phone conversation or a game of trivia appeared equally effective in countering fatigue. In addition, post-drive distress also increased among all groups. In this case, cell phone use was effective in reducing distress, but the trivia game was not, in partial support of H4. The evaluative nature of a game may limit its efficacy as a stress countermeasure. One source of stress during fatiguing drives may be the driver’s awareness of their own feelings of discomfort, fatigue and anxiety (Neubauer et al., 2012a). The cell phone conversation here asked about memories of a different personal event, and so may have been more effective than the trivial game in providing a buffer against the self-awareness of discomfort. Additionally, changes in distress may follow changes in workload (Matthews et al., 2002), but in this case, the two

54 responses were not closely coupled. Workload of trivia and cell phone conditions was similar.

Total automation tended to reduce workload, as expected, but workload was maintained across automation conditions in the cell phone condition. This form of workload may be benign to the extent that it contributes to higher task engagement. Finally, worry remained relatively stable and was not significantly impacted by the automation or media conditions, illustrated through non- significant main effects and interactions in all cases. Increases in worry, as seen in field studies

(Desmond & Matthews, 2009), may not be evident over the relatively short duration driving task seen here.

The expectations for vehicle performance were partially confirmed. The hypothesis that secondary media devices (i.e., cell phone use and a trivia game) would increase vehicle control

(H1) was supported. An under-stimulating environment can impair effective allocation of processing resources; indeed, adding a secondary task may improve vehicle control when workload is low (Matthews, Sparkes & Bygrave, 1996). Here, NASA-TLX means were towards the lower end of the scale. In the current study, a significant main effect for automation condition was found on vehicle control. More specifically it was found that drivers in the partial automation condition showed a slightly lower SDLP during phase 1 but a slightly higher SDLP during phase 2, compared to the non-automation condition, suggesting that vehicle automation may interact with driver performance to produce variable effects on vehicle control. Once again,

SDLP was not recorded in the total automation condition, as drivers were not in control of their steering during the performance assessment phase. Additionally, the significant main effect for media condition in the present study indicated that drivers in the cell phone and trivia conditions exhibited better vehicle control throughout the entire drive, compared to the control, supporting previous studies (Atchley et al., 2013; Gershon et al., 2009). Additional tasks demands such as

55 these may help enhance vehicle control during partially automated driving, even prior to the onset of fatigue later in the drive. However, beneficial effects of media use may be confined to low-workload driving.

We also investigated the effects of the automation manipulation and media conditions on driver alertness, indexed via reaction time an emergency event. The expectations for H2 were not confirmed. We replicated previous findings that prior exposure to full vehicle automation slows braking response to emergency events (Saxby et al., 2013), although there was no effect here on steering response. Differences in reaction time to the emergency event were found for the automation conditions but not according to expectations. More specifically, a main effect of automation condition was found for the response time of steering but not braking, where drivers in the non-automation condition were slightly faster to respond, compared to drivers in the total and partial automation conditions. We did not replicate Neubauer et al.'s (2012b) finding that cell phone use during automated driving enhanced subsequent response speed. However, the earlier study used more frequent use of the phone, which may have impacted alertness more strongly.

Consequently, media use in the current study did not significantly impair later alertness, although the results do suggest dissociation between media use effects on steering and alertness, as in previous driver distraction studies (Strayer & Johnston, 2001). The lack of an effect for media conditions suggests that response time may be a more sensitive index of driver performance when engaging in automated driving or media devices. Although it was found that secondary media helped improve vehicle control, this type of activity may not be beneficial to situation awareness following emergency situations and may result in a disruption of attention.

Additionally, the results of the current study provide further support for the usage of personality questionnaires within performance-based settings. Consistent with the transactional

56 model of driver stress (Desmond & Matthews, 2009), all five DSI scales related to some post- task subjective states. However, these dimensions did not predict driving performance and so the hypothesis relating to individual differences (H3) was not confirmed. In partial support of H3, it was found that some of the DSI variables were associated with all three DSSQ factors. For example, drivers scoring high in Fatigue Proneness were associated with feelings of high engagement. Drivers high in fatigue proneness are especially vulnerable to passive fatigue and so requiring some effort during the partial automation condition and having secondary media tasks may have helped counter this adverse state during vehicle automation, however these tasks did not help counter the effects of distress. Conversely, drivers scoring high in dislike of driving were associated with especially high levels of worry and distress and low levels of engagement post-drive, which suggests that additional task demands and vehicle automation did not help alleviate feelings of distress and fatigue (as measured by lower task engagement) in these drivers.

The next sections will address theoretical as well as practical implications for the current study, focusing on theories of attention that are especially relevant within this context.

Additionally, safety and design implications for fatigue countermeasures and vehicle automation will be addressed. Lastly, the role of individual differences as a fatigue moderator will be discussed.

Theoretical Implications

As previous stated one of the largest contributors to transportation related crashes and accidents is the “human factor”. Although many of these factors have been studied, such as emotional state and decision-making, one such factor, driver fatigue, is of particular interest to human factors researchers. Driver fatigue is an important issue to study because the effects of

57 fatigue can account for thousands of road fatalities each year (Knipling & Wang, 1994; 1995). In addition to road fatalities, the effects of fatigue are further related to work and health related costs that result after such an incident. Additionally, vehicle automation can enhance a state of passive fatigue, which can result in effort withdrawal and performance impairments (Neubauer et al., 2012a; Saxby et al., 2013). In a performance based setting, driving requires not only psychomotor and visual resources but also a great deal of cognitive processing and attention, which can be very difficult when stress or fatigue disrupt attentional processing or when adding a secondary task demand, such as using a cell phone.

Vehicle Automation and Secondary Media. Driver fatigue, which can include physical fatigue due to a lack of sleep and mental fatigue, which can stem from task demands, appears to account for approximately 1.2 – 3.6% of all vehicle accidents (Knipling & Wang, 1994; 1995).

Although driver fatigue is not a new issue among transportation specialists, it has been argued that fatigue may become more dangerous with the advent of some types of vehicular technology such as vehicle automation (Young & Stanton, 2007). Furthermore, many studies have suggested that secondary tasks may benefit drivers by helping them remain engaged during automated driving (Atchley et al., 2013; Gershon et al., 2009). According to Gershon et al. (2009), secondary tasks increase the cognitive load that is sometimes lost when task demands are low

(i.e., during vehicle automation), an argument that was supported in the current study. For example, cell phone use appeared to buffer against the extreme loss in engagement and increase in distress as well as maintaining vehicle control. Furthermore, playing a game of trivia was found to help combat against the loss of fatigue (e.g., higher task engagement) but did not protect against an increase in distress, suggesting that differential effects exists between these two types of media.

Hancock and Warm’s theory of dynamic adaptation (1989) may best explain this finding.

Their theory argues that operator adaptation is optimal when task demands are relatively moderate. When task demands are too high (which may be evident in the trivia condition), effortful compensation is more difficult to execute, which can result in a reduction in task-related coping. In the trivia condition, participants may have felt extra pressure to perform well, given that they were required to answer a trivia question. On the other hand, when task demands are too low so as to insufficiently engage attention (i.e., task underload), performance may also be impaired, due to a failure in applying sufficient effort (Matthews & Desmond, 2002). Here, subjective stress responses may be sensitive to a loss in operator adaptation, which may be a precursor to performance impairment (Hancock & Warn, 1989).

In the present context, the theory would suggest that overload resulting from the game of trivia should both enhance distress and impair performance. Conversely, previous studies (Saxby et al., 2013) have found that underload, resulting from the automation manipulation, more specifically the total automation manipulation, should also result in lower task engagement and performance impairment. The expectations relating to overload were partially supported, in that the trivia game produced more adverse effects on distress than did cell phone use (H1 partially supported), but did not impair vehicle control. The evaluative nature of the trivia condition (i.e., requiring participants to answer potentially unfamiliar questions) may have placed too many demands on the driver, requiring too much effort and resulting in task overload, while the task demands placed on drivers in the cell phone group (i.e., simply requiring them to engage in a casual conversation) may not require as much effort, which may have allowed drivers in this group to adapt more effectively.

Additionally, although automation did not produce a significant effect on subjective state, it did provide partial support for the expectations relating to underload. For example, drivers in the partial automation were better at vehicle control during phase 1 of the drive but were slightly worse during the second phase 2, compared to the control. Additionally, drivers in the non- automation conditions were slightly faster to apply their brakes and steer away from the van following the sudden event. Although partial automation helped buffer against an extreme loss in task engagement, (as seen by a variable increase/decrease in vehicle control), it did not help enhance alertness. Here drivers in the non-automated conditions exhibited better situation awareness (indexed via reaction time), suggesting that relieving drivers of task demands may have resulted in a decrease in effortful compensation, while requiring drivers to maintain full control of the vehicle throughout the entire drive allowed drivers in this group to adapt more effectively.

Although Hancock and Warm’s theory of dynamic adaptation (1989) seems to account for the negative effects of the trivia game on distress, it fails to account for the variable effects of the partial automation condition on vehicle performance. Here drivers exhibited slightly better vehicle control in the first half of the drive, but worse control in the second half, compared to the control. Drivers were required to steer while the speed and braking were controlled, so it would seem that task demands were relatively moderate in comparison to the non-automated group, but it was found that driver performance was actually worse than the non-automated group at times.

Here, it appears that relieving drivers of certain aspects of the driving task does not produce reliable patterns of performance improvement. The partial automation group did not have control of their brakes for the majority of the drive (i.e., first 40 minutes), which may have contributed to performance impairment following automation as the driver transitioned from automated to

60 manual control. In addition, drivers in the partial and even total automation groups showed a significant trend toward poorer alertness in response to the emergency event (e.g., significantly slower response times for braking and approaching significant response times for steering) than drivers in the non-automated group, suggesting that automation use did not help enhance alertness.

The results of this study are somewhat consistent with resource theory, to the extent that playing a game of trivia is more resource demanding than is simply engaging in a cell phone conversation. Here drivers had higher levels of distress, suggesting that workload was also higher for drivers in this group. However, the results are somewhat problematic for multiple resource models of attention. Multiple resource models suggest that performance should not be impaired when multiple tasks draw upon different modes of attention (Wickens, 2002). In this context, the demands of driving, a primarily visual-spatial task should not interfere with a trivia game, which is primarily an auditory-verbal task, but this result was not found in the current study, where trivia negatively affected distress. Multiple resource theory did account for the performance findings of the secondary media conditions, where it was found that vehicle control was not impaired for drivers in the cell phone and trivia conditions. The next section will discuss the practical applications that the findings of this study offer, focusing specifically on safety and design concerns and also the utility of subjective questionnaires in identifying individual difference vulnerabilities.

Practical Applications

In addition to theoretical implications, the findings of the present study also offer several practical applications that focus on safety and legislation. First, findings such as these provide further support to those authors who have advocated the use of secondary tasks to help maintain

61 task engagement (Atchley et al., 2013; Gershon et al., 2009). Additionally, these findings have also furthered the understanding of the relationship between secondary media and vehicle automation. Engaging in a secondary task while driving relies on many cognitive and visual resources and alone may prove dangerous if they interfere with the driver’s primary task

(Neubauer et al., 2012b). However, when these tasks are paired with vehicle automation, they may prove to be a stimulating secondary task that increases the cognitive load that is sometimes lost during automated driving.

Furthermore, it appears that playing a game of trivia may enhance stress through the evaluative nature of the task, which may in turn increase distress. In the current study, distress was highest for drivers in the trivia and control group, which suggests that secondary tasks that aim to counter distress during vehicle automation should not be evaluative in nature. Conversely, having a game of trivia did help counter the loss of fatigue (as measured by higher task engagement). Additionally, the cell phone conversation was seen to be beneficial for drivers in both cases. Here, task engagement was highest and distress was lowest for drivers engaging in a cell phone conversation. Additionally, these tasks were also found to help minimize performance impairment. Data from this study revealed that both types of media devices were beneficial to drivers in the form of an increase in vehicle control, but not for reaction time to a sudden event.

Drivers should be aware that these devices may help maintain long-term control but may not be beneficial to when they are required to make unexpected or sudden decisions. Data from the response time analysis suggests that secondary media may be a distraction when it comes to making sudden judgments, which supports previous studies (Strayer & Drews, 2004).

Additionally, the present findings can potentially influence the development of new legislation in the effort to protect against the harmful effects of fatigue while driving, especially

62 during automation. Currently, no law exists that attempts to regulate the use of automated systems. Furthermore, there is no agreed upon standard for automation use as a fatigue or distraction countermeasure. Although full automation use while driving is still within the future, many commercial, civilian and military personal use some form of automated driving or cruise control. Although potentially beneficial to drivers, the general population should be made aware of the limits of the system, so as not to cause over-reliance. Additionally, past studies have also suggested that all types of cell phone usage are dangerous to drivers. While some forms of cell phone use should be avoided (i.e., text messaging; see Neubauer et al., 2012b; Drews et al.,

2009), not all forms are harmful and may actually benefit drivers in certain settings. In the current study it was found that talking on a cell phone was actually beneficial to drivers in terms of subjective state change and performance (e.g., vehicle control) and so legislation should focus on decreasing usage of more dangerous aspects of cell phone use, while also educating drivers on the potential dangers of driver fatigue and vehicle automation.

Second, the results also provide several safety implications regarding the use of vehicle automation. Such technology is potentially useful in countering the harmful effects of driver fatigue and regulating workload (Hancock & Parasuraman, 1992; Hancock & Verwey, 1997), however, over-reliance on this type of technology may reduce situation awareness, a key component to safe driving (Young & Stanton, 2007). The data provide further support to those authors who stress the need to control some aspects of driving but not all (Funke et al., 2007). In fact, these authors found that requiring drivers to maintain some aspect of vehicle control, such as steering but not braking or speed decreased subjective workload and distress. It appears that full vehicle automation may interfere with the drivers active engagement with the primary task, which is particular dangerous during sudden events (Desmond et al., 1998; Neubauer et al.,

2012a; Saxby et al., 2013). In the present study, it appears that requiring the driver to remain somewhat engaged in the task, either through partial automation use or secondary media tasks

(e.g., game of trivia or cell phone conversation) helped keep engagement levels somewhat higher than in the control and in past studies using vehicle automation (see Funke et al., 2007; Neubauer et al., 2012a; Saxby et al., 2013). Additionally, it was also found that drivers in the cell phone group had the lowest levels of post-task distress, compared to drivers in the trivia and control condition. In the trivia condition, participants were required to answer several trivia questions in the presence of the experimenter who gave feedback in the form of a “correct” or “incorrect” response. It may be useful to include a trivia condition where a computer generates feedback so that the participant does not feel evaluated or judged by the experimenter.

Although the findings of the present study seem to suggest that secondary tasks help counteract the loss of task engagement (i.e., fatigue), pairing automation with secondary tasks may not assist the driver in regards to situation awareness during emergency situations. Drivers in the non-automated group were faster to brake, compared to drivers in the partial and total automation group. Additionally, drivers in the control group were also somewhat faster to brake and steer, compared to drivers in the trivia and cell phone condition. Thus, prolonged automation use, coupled with secondary tasks, may be associated with a decrease in driver alertness, which is particularly dangerous if automation suddenly fails or if drivers need to regain control of the vehicle. Transitioning from automation use to manual vehicle control requires adequate support of the system, a significant human factors issue.

Lastly, the findings of this study provide further support for the need for in-vehicle countermeasures to driver fatigue. For example, developing an in-vehicle system that provides different types of activities such as trivia or other moderately demanding games may help

64 maintain engagement levels, thereby reducing fatigue. Although cell phone usage benefited drivers in terms of subjective state (e.g., lower levels of distress, higher levels of engagement and better vehicle control), not all types of cell phone usage is safe. For example, Neubauer et al.

(2012b) found that text messaging enhanced distress, worry and diminished task engagement and decreased vehicle control, even when using automated systems. This suggests that if drivers do choose to use a cell phone they should talk instead of text messaging. Design specialists should also take into account the fact that drivers do not always comply with safety standards and attempt to implement a system that allows drivers to verbally state commands, such as operating a cell phone, MP3 device or even replying to messages and responding to games. This type of technology would allow the driver to engage in these types of activities without removing their hands from the steering wheel or averting their eyes from the road, which may enhance alertness and diminish driver fatigue. Past research has found that simply listening to books on tape did not significantly impact driver performance and so this type of technology may prove to be a promising safety solution to driver fatigue, while at the same time not enhancing driver distraction (Strayer & Johnston, 2001).

Finally, the results of this study provide further support for the utility of subjective measures in the assessment of changes in driver’s emotional state, such as driver stress and fatigue. It is important to accurately identify stress and fatigue states so as to implement appropriate countermeasures. In fact, some authors have even suggested that subjective state change is a precursor to performance decrements (Hancock & Warm, 1989). In addition to task demands, stress and fatigue can further drain attentional resources and so it is of great importance to identify those individuals who are prone to such states, especially for professional drivers. These drivers are required to perform long and monotonous drives, which can be

65 particularly dangerous if these individuals are prone to certain states such as fatigue proneness.

Results of the current study advocate utilizing measures such as the DSI (Matthews et al., 1997), which can help identify individuals who are particularly susceptible to feelings of high anxiety

(e.g., Dislike of Driving) and elevated tiredness (e.g., Fatigue Proneness) while driving. The next section will address the role that certain personality dimensions have on subjective state change.

The Role of Stress Vulnerability and Individual Differences

Although task demands may elicit qualitatively different changes in subjective state, it also appears that stable predispositions such as driver personality can impact state, independent of task demands. Specific personality dimensions may interact with task demands and elicit various coping strategies as well as changes in post-task subjective state. Contrary to H3, positive emotional outcomes (e.g., high task engagement, low distress and worry levels) were not found to be associated with the personality dimensions ‘Dislike of Driving’ or ‘Fatigue

Proneness’. Additionally, these personality dimensions did not significantly correlate with any performance measures. Although, there were no significant findings between positive emotional outcomes, performance and driver stress vulnerability factors, there were some findings that were similar to past studies (Desmond & Matthews, 2009; Matthews & Desmond, 1998;

Neubauer et al., 2012a; 2012b; Saxby et al., 2013), which found that the DSI scales were reliable predictors of the stress response following a simulated drive.

Past research has also found that stable predispositions to driver stress (measured by the

DSI) reliably relate to specific patterns of appraisal and coping, which can then elicit changes in subjective state (Matthews, 2002). The way in which an individual interprets the situation greatly affects their emotional state and in turn performance outcomes. For example, one study involving a simulated drive designed to elicit fatigue found that the DSI scale ‘fatigue proneness’

66 significantly corresponded to changes in fatigue states. Utilizing regression analyses, results of the current study support past findings of studies involving simulated driving (Matthews &

Desmond, 1998; Neubauer et al., 2012a; 2012b; Saxby et al., 2013). In this study, dislike of driving and hazard monitoring were found to be most predictive of post-task task engagement. It appears that these traits most strongly predict the change in task engagement regardless of the automation manipulation and secondary media. Furthermore, dislike of driving was also found to be most predictive of post-task distress. Irrespective of the task, feelings of anxiety and distress are synonymous with the personality dimension dislike of driving and those feelings are most predictive of distress post-task. In the past it has been suggested that secondary tasks may help buffer against the self-awareness of stress (e.g., high distress and worry and low task engagement) drivers scoring high in this trait are likely to possess, but was not supported in this study. In fact, drivers scoring high in dislike of driving were actually more distressed, worried and less engaged even after having participated in secondary media tasks. A different finding was evident for worry. Here, fatigue proneness predicted worry states following the drive, regardless of the fatigue manipulation (e.g., automated drive) or media device.

Furthermore, past research has shown that drivers scoring high on the fatigue proneness scale are especially vulnerable to feelings of passive fatigue (Desmond & Matthews, 2009), feelings that are elicited during automated driving tasks (Saxby et al., 2013). In fact, past studies have argued that such individuals should not regard automation as a potential fatigue countermeasure (Neubauer et al., 2012a; 2012b). A number of other DSI factors (i.e., Thrill

Seeking and Hazard Monitoring) have been linked to fatigue vulnerability and performance respectively (Thiffault & Bergeron, 2003). However, findings of the present study show that these factors are not predictive of either fatigue vulnerability or driver performance within this

67 study. Future research might aim to identify traits that are associated with preferences for regulating fatigue through the use of secondary media. On goal that still remains high is to identify measures of individual differences and stress vulnerability that may influence the performance and safety of human operators (Szalma, 2009).

Limitations

Finally, study limitations should be noted. First, simulator methods are highly capable of investigating a wide variety of human factors issues due to the ease with which various performance-based environments can be reproduced (Matthews et al., 2011). In the current study driver fatigue was the factor of interest; however, the experience of fatigue in simulated driving does not necessarily have a 1:1 correspondence to real life driving. An important issue to consider is whether the simulator exhibits ‘functional fidelity’, which refers to a simulators ability to produce individual responses that correspond to real world human behavior (see

Neubauer et al., 2010; Sanders, 1991).

Generally speaking, a fatigued driver should exhibit changes in subjective state and objective behavior that correspond to those changes seen in real driving. Simulator findings may not always generalize to the real world but, the patterns of subjective stress responses and performance changes found in simulator studies, and in the current study, many times correspond to findings of real-life driving (Desmond & Matthews, 2009; Matthews, 2002; Matthews et al.,

2011). Consequently, the ability to offer control within a simulated environment may be especially useful in assessing the specific psychological mechanisms that underlie real-life driving, which can include decision making, attention and coping strategies to name a few

(Matthews et al., 2011).

Additionally, the durations used in this study were relatively long (i.e., 45 minutes). In the current study, cell phone use was found to be beneficial to one aspect of performance (i.e., vehicle control) during a relatively long, automated drive, but we did not test for these effects during relatively short drives, which represent much of real-life driving. However, in defense of the present method, results of past studies using relatively short durations have found similar patterns of subjective response and performance change (Desmond & Matthews, 2009; Saxby et al., 2013).

Furthermore, we also assessed the driver’s situation awareness indexed via reaction time to a sudden event following termination of automation and media use. Consequently, we did not assess what would happen if the driver was engaging in cell phone use or the trivia game at the time during which automation ceased (e.g., when drivers transitioned from automated to manual control) as well as during the sudden event. In fact, Desmond et al. (1998) found that performance recovery was better when drivers engaged in manual control of their vehicle throughout the drive, compared to drivers who were required to drive manually following automation failure. It seems plausible that under these specific circumstances media usage, more specifically cell phone usage may impair performance. Further research is needed to understand the transition from automated to manual driving and response to sudden events when engaged in media use during these unpredictable and potentially hazardous events.

Summary and Overall Conclusions

To offer a final summary, the findings of this study offer several theoretical and practical applications. First, the results of this study further the understanding of the dangers of fatigued driving and the value of interventions that can be implemented in the public safety, design and technology sector. Although fatigue by itself is dangerous, the effects may be enhanced when

69 paired with vehicle automation, a particular issue for future driverless cars. The goal of vehicle automation is to reduce automobile accidents by regulating workload but it may actually decrease alertness. Although workload regulation is one goal of an automated system, it appears that some types of vehicle automation (i.e., total vehicle automation) may be dangerous to drivers. Total vehicle automation may create an “out of the loop” performance problem, which reflects an increase in the proportion of tasks controlled by the machine and not the human

(Desmond et al., 1998). Slowing of response to hazardous events following automated driving, as found here, indicates that such problems may persist following termination of automation. In cases such as these, it appears that requiring the driver to maintain some responsibility of the driving task may help maintain engagement (Funke et al., 2005).

It also appears that both automation and different types of secondary media can result in different types of subjective state response. For example, talking on a cell phone, rather than playing a game of trivia, appears to curb against the potentially harmful increases in distress following driving. However, both the trivia and cell phone tasks were found to help curb against an extreme loss in task engagement that is found during vehicle automation, where the temporal decline in task engagement was most pronounced in the control group and supports findings of past studies (Neubauer et al., 2012a; Saxby et al., 2013). On the other hand, neither the automation manipulation nor secondary media tasks significantly affected the state of worry.

Past studies have found that automation, when paired with secondary tasks, help alleviate worry but was not found in the current study (Neubauer et al., 2012a).

Additionally, these various kinds of media devices can impact driver performance in different ways. Here, drivers engaging in a phone conversation and a game of trivia were found to have better vehicle control but not response time, compared to drivers in the control.

Second, the analyses of the performance data also suggest that automated systems may offer several potential benefits as well as dangers to drivers. The data is consistent with several previous studies, which have advocated that automation should be used sparingly and not in full, especially during periods of low task demand, where passive fatigue may ensue (Funke et al.,

2007; Neubauer et al., 2012a; Saxby et al., 2013). In the current study partial vehicle automation helped maintain vehicle control during the first half of the drive (compared to the control) but not the second half. Additionally, drivers in the non-automation condition were slightly faster to brake following the emergency event, suggesting that automation may influence driver performance in a multitude of ways. Drivers should use caution when engaging in vehicle automation towards the end of a lengthy drive and also during periods where control is suddenly passed back to the driver.

Finally, utilizing subjective state measures is of great importance in identifying those individuals who are particularly vulnerable to stress (e.g., anxiety) and fatigue while driving. As measured by the DSI, dislike of driving was found to be the greatest predictor of high distress and low task engagement. Dislike of driving was also found to be strongly associated with feelings of increased worry, distress and low task engagement following the drive. Finally, fatigue proneness was found to be strongly associated with higher worry following the drive.

These individual differences have been found to be highly predictive of stress states in a number of studies, even after controlling for task demands such as media and automation usage

(Matthews & Desmond, 1998; Matthews et al., 1996; Neubauer et al., 2012a; 2012b). Generally speaking, dislike of driving tends to increase subjective distress irrespective of the drive or type of media task, providing further support that this trait is associated with feelings of stress and anxiety while driving. Other DSI scales are also highly predictive of such task-induced state

71 changes. For example, fatigue proneness is associated with feelings of increased fatigue (as measured by low task engagement) and worry. This information is extremely valuable within the public and private sector in helping identify those individuals who are prone to such reactions following a fatiguing drive. However, understanding the nature of additional secondary tasks on subjective state and vehicle performance 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

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?

very much          not at all 0 1 2 3 4 5 6 7 8 9 10

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 C

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

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 D

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 92

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

96 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 E

Complete List of Trivia Questions

Cuisine Questions

1. The rice dish ‘paella’ comes from what country? Answer: Spain 2. Deer meat is known by what name? Answer: Venison 3. Are humans omnivore, herbivore or carnivore? Answer: Omnivore 4. What food is used as the base of guacamole? Answer: Avocado 5. What is the sweet substance made by bees? Answer: Honey 6. Lures, reels, rods, hooks, baits and nets are common equipment used in what food gathering activity? Answer: Fishing 7. Foods rich in starch such as pasta and bread are often known by what word starting with the letter C? Answer: Carbohydrates 8. What is another name for maize? Answer: Corn 9. Fruit preserves made from citrus fruits, sugar and water are known as what? Answer: Marmalade 10. Dairy products are generally made from what common liquid? Answer: Milk 11. What is the popular food used to carve jack-o-lanterns during Halloween? Answer Pumpkins 12. Chiffon, marble and bundt are types of what? Answer: Cake 13. What breakfast cereal was Sonny the Cuckoo Bird “cuckoo for”? Answer: Cocoa Puffs 14. Black-eyed peas are not peas. What are they? Answer: Beans 15. What animal’s milk is used to make authentic Italian mozzarella cheese? Answer: The water buffalo’s 16. What nation produces two thirds of the world’s vanilla? Answer: Madagascar 17. What was the first commercially manufactured breakfast cereal? Answer: Shredded Wheat 18. What is the literal meaning of the Italian word linguine? Answer: Little Tongues 19. What fried street food popular with vegetarians is claimed by Egyptian as their invention to be a replacement for meat during Lent? Answer: Falafel 20. What salad made from mozzarella cheese, tomatoes and basil is also the name of the town in Tuscany where Michelangelo was born? Answer: Caprese 21. What beverage, well-known from a children’s book series, was created in 2010 for a newly opened theme park and despite the name, contains no alcohol or dairy? Answer: Butterbeer 22. What is the second largest day for U.S. food consumption, after Thanksgiving Day? Answer: Super Bowl Sunday 23. The origin of what snack can be traced to the shape of folded arms of children in prayer? Answer: Pretzels 24. What Chinese dipping sauce derives its name from a word meaning ‘seafood’ (though it contains none)? Answer: Hoisin Sauce 25. The ‘Lumper’ which was affected by a blight in the mid-19th century causing great hardship in a particular country was a variety of what staple? Answer: The potato 26. According to The Wall Street Journal, in March 2011 which drink overtook Pepsi to become the No. 2 carbonated soft drink in the US, perhaps reflecting weight consciousness? Answer: Diet Coke 27. The name of which Italian-American dish that consists of pasta and fresh vegetables takes its name from the Italian for ‘spring’? Answer: Pasta Primavera

28. Which crop, that was of great nutritional importance in pre-Columbian Andean civilizations, was called as the ‘mother of all grains’ by the Incas? Answer: Quinoa 29. What type of salad invented in the 1930’s and made from chopped ingredients has now become generic for every sort of chopped salad? Answer: Cobb Salad 30. What species of coffee plant constitutes nearly 75% of world’s coffee-bean production? Answer: Arabica 31. The name of which spice also known as Jamaica Pepper was coined by people who thought that it combined the flavor of cinnamon, nutmeg and cloves? Answer: Allspice 32. What is the Australian dark brown food paste made from yeast extract? Answer: Vegemite 33. Which drink composed of champagne or other sparkling wine and orange juice is traditionally served to guests at weddings? Answer: Mimosa 34. Brandy is made from which fruit? Answer: Apples 35. In the food industry, ‘affinage’ is the craft of maturing and aging what item? Answer: Cheese 36. What is the name given to a baked confection made with coconut and egg white or with a coarse almond paste usually made into cookies? Answer: A Macaroon 37. Which herb now commonly associated with pizza derives its name from the Greek for ‘joy of the mountain’? Answer: Oregano 38. Which American-Italian term refers to food prepared ‘hunter style’ with mushrooms onions and tomatoes? Answer: Cacciatore 39. From the Arabic root meaning ‘to grind’ what is the name given to a paste of ground sesame seeds? Answer: Tahini 40. The name of which distinctly Southern American food item is often attributed to hunters or fisherman who would quickly fry corn meal and feed it to their dogs to keep them quiet? Answer: Hushpuppies 41. Which fermented alcoholic beverage is made of honey, water, and yeast and is known as ‘honey wine’? Answer: Mead 42. Which Korean fermented side dish made of select vegetables with varied seasoning s enjoys an iconic status in that country’s cuisine? Answer: Kimchi 43. Which dish consisting of pureed or finely chopped olives, capers and olive oil is a popular food in the south of France where it is generally eaten as an hors d’oeuvre? Answer: Tapenade 44. What type of ice cream is chocolate, vanilla and strawberry flavors side by side in the same container typically with no packaging in between? Answer: Neapolitan 45. What is the popular Middle-Eastern appetizer made from eggplants? Answer: Baba Ghanoush 46. What is the liquid remaining after milk has been curdled and strained? Answer: Whey 47. A prune is a dried fruit of what tree? Answer: The plum 48. Which food item of Chinese origin is made by coagulating soymilk, and then by pressing the resulting curds into blocks? Answer: Tofu 49. Which traditional Mexican dish, a spicy soup made with tripe, is often thought of as a cure for a hangover? Answer: Menudo 50. Which Chinese dish named after a late Qing Dynasty official consists of diced marinated chicken stir-fried with roasted peanuts? Answer: Kung Pao Chicken 51. Which Japanese dish prepared from the meat of pufferfish is lethally poisonous? Answer: Fugu 52. Which Asian beverage is a mixture of iced or hot sweetened tea, milk, and chewy tapioca balls that are consumed along with the beverage through a wide straw? Answer: Bubble Tea 53. What restaurant style of stir-frying meats and vegetables over a large iron griddle actually originated in Taiwan, despite its name? Answer: Mongolian Barbeque

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54. The name of which extremely sweet cordial comes from the Japanese for green? Answer: Midori 55. What does the Scoville scale measure? Answer: The spicy heat of a chili pepper 56. What is the name given to the citrus-marinated seafood that originated in Latin American countries? Answer: Ceviche 57. What Asian dish that originated in Indonesia consists of chunks of dice-sized meat on skewers, which are grilled and served with various spicy seasoning? Answer: Satay 58. Which traditional Swiss communal dish is shared at a table in an earthenware pot over a small burner? Answer: A Fondue 59. Which ancient civilization has the earliest documented usage of chocolate as food/drink? Answer: The Mayan Civilization 60. Which fortified wine flavored with aromatic herbs and spices is used for many cocktails including the Martini and the Manhattan? Answer: Vermouth 61. Which drink made from mint, bourbon, sugar and water is traditionally served at the Kentucky Derby? Answer: the Mint Julep 62. What condiment that comes in pods is referred to as ‘The Queen of Spices’ in India? Answer: Cardamom 63. What is the popular food used to carve jack-o-lanterns during Halloween? Answer: Pumpkins

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Sports Questions

1. In which country was ex-England cricket captain Tony Greig born? Answer: South Africa 2. What sport is played by the LA Lakers? Answer: Basketball 3. Only two Americans have won the Formula 1 Motor Racing Championship. Name one. Answer: Phil Hill and Mario Andretti 4. Who did Cassius Clay beat to win his first world heavyweight boxing title? Answer: Sonny Liston 5. Who became the first black manager of a Premiership club when he took over at Chelsea in 1996? Answer: Ruud Gullit 6. In English cricket what do the letters TCCB stand for? Answer: Test and County Cricket Board 7. Jerk, clean & snatch are terms used in what activity? Answer: Weight Lifting 8. In which Sport does your team only have to travel 3.6 meters To Win? Answer: Tug of War 9. Who was the first footballer to be knighted? Answer: Sir Stanley Mathews 10. Which Formula 1 team was barred for two races for running underweight cars? Answer: BAR 11. In which country was former motor racing driver Ayrton Senna born? Answer: Brazil 12. How old is a filly when she officially becomes a mare? Answer: 4 13. Is Brian Lara left or right handed when batting? Answer: Left 14. What animal is on the top of the Calcutta Cup? Answer: Elephant 15. What do Sumo wrestlers throw into the ring prior to a match? Answer: Salt 16. In Billiards, how many points are scored for a cannon? Answer: 2 17. How many players are there in a netball team? Answer: Seven 18. What is the longest race in men's athletics? Answer: 50 Kilometer Walk 19. On April 30, 1993, a knife wielding Gunter Parche in Hamburg had a debilitating effect on the career of whom? Answer: Monica Seles 20. When ultra-marathoner Dean Karnazes ran across the entire U.S. in 2011, a blog headlined it by comparing him to what movie character? Answer: Forrest Gump 21. Commemorating the 50th anniversary of a certain event, in 2004 Britain released a 50-pence coin that showed a stop clock at 3:59.4. Who was being honored? Answer: Roger Bannister 22. What fast paced sport takes place in a court called the fronton? Answer: Jai alai/Pelota 23. After the summer Olympics and the Soccer World Cup, what sport features an event that is third largest in terms of television audience? Answer: Cricket (The World Cup) 24. What ‘intoxicating’ practice of the sporting world was started in 1967? Answer: Spraying Champagne 25. In 1924, what did Grandland Rice collectively call Harry Stuhldreher, Don Miller, Jim Crowlet and Elmer Layden? Answer: The Four Horsemen 26. What Asian country is the most populous country in the world never to have won an Olympic medal? Answer: Bangladesh 27. The website of what annual sporting event sells an artifact called the Green/Purple Friendship Bracelet? Answer: Wimbledon 28. Which European soccer club is named after the goddess of youth in Roman mythology whose Greek equivalent name is Hebe? Answer: Juventus 29. South Africa was ejected from the 1970 Davis Cup in part due to the campaigning of which male star to whom that country previously denied a visa? Answer: Arthur Ashe 30. The MacRobertson Shield is the premier tournament in what sport? Answer: Croquet 31. Stroking and Cranking are the two main types of delivery in what sport? Answer: Bowling

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32. The trainer Angelo Dundee who passed away in February 2012 and who worked with 16 world champions is best associated with which sports icon? Answer: Muhammad Ali 33. Who are the native people of Mexico who are renowned for their ultra long-distance running ability though they have not seen much success in international competitions? Answer: The Tarahumara 34. What are Wenlock and Mandeville, of relevance to the sports of 2012? Answer: The official mascots for the 2012 Summer Olympics 35. The national football team of which country is also known as ‘The Eagles of Carthage’? Answer: Tunisia 36. From 1928 until 2000, the obverse side of Olympic medals contained an image of which person seen holding a winners crown in her right hand? Answer: Nike, the Greek goddess of victory 37. Chinook, the first computer program to win a world champion title against humans in any game was developed in 1989 to play what? Answer: Checkers 38. The 4 Deserts race that was recognized by Time as the world’s leading endurance footrace takes place in Gobi, Atacama, Sahara and what other location? Answer: Antarctica 39. In 2011, a team of conservationists caught a jaguar in Brazil that was missing half an ear. It was given the nickname of which sportsperson? Answer: Holyfield (Evander Holyfield) 40. Which sportsperson is associated with the phrases “He can run, but he can’t hide” and “Everyone has a plan until they’ve been hit”? Answer: Joe Louis 41. Sculls and Eggbeater are two of the basic skills in what aesthetically pleasing sport that became an Olympic medal event in 1984? Answer: Synchronized swimming 42. The oche, a line that is 2.369 meters from the target is a term from what sport? Answer: Darts 43. The name of what winter sport comes from early racers moving their heads backwards and forwards to make their sliding vehicles go faster? Answer: Bobsledding 44. Setting a trend, who won the high jump event at the 1968 Mexico City Olympics? Answer: Dick Fosbury 45. In athletics, what is the maximum permissible wind speed for a result to be registered as a record? Answer: 2 meters per second 46. In which Olympic sport do participants wear an electrically conductive jacket called a lame’ to define the scoring areas? Answer: Fencing 47. What puzzle game, whose name means ‘single number’ was popularized by Nikoli in its native country in 1986 before gaining international popularity from 2005 onwards? Answer: Sudoku 48. Which quotable sportsman got his nickname from a friend who said that he resembled a Hindu holy man whenever he sat around waiting to bat, or while looking sad after a losing game? Answer: ‘Yogi’ Berra 49. In 1998, which popular author was about to purchase the Minnesota Vikings football team but had to abandon the deal because of his divorce settlement? Answer: Tom Clancy 50. Sergey Bubka of Ukraine is best-known for his record setting ways in which athletic event? Answer: The pole vault 51. The U.S. has won 2,301 medals at the Sumer Olympic Games, more than any other country, but with 253 medals in the Winter Olympic Games, has the second most. Which Scandinavian country is the leader? Answer: Norway 52. Which sports locale is know for its Amen Corner, ‘The Big Oak Tree’ and the Eisenhower tree? Answer: The Augusta National Golf Club 53. Dhyan Chand of India is regarded as the greatest player of all time in which Olympic sport? Answer: Hockey

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54. Which Swiss city situated on the shores of Lake Geneva is the headquarters of the International Olympic Committee? Answer: Lausanne 55. Duke Kahanamoku, an Olympic swimming champion during the years 1912 and 1924, is credited with popularizing which sport? Answer: Surfing 56. Which Italian form of bowling that starts when a ball called ‘pallino’ is thrown has been played since Roman times? Answer: Bocce 57. Senet is a grid game for two players and is thought to be the oldest board game in the world. In which country did it originate? Answer: Egypt 58. The restaurant Tavern on the Green in Central Park, is the finish line for what annual event? Answer: The New York City Marathon 59. Which iconic American woman’s soccer star scored more international goals in her career than any other player, male or female, in the history of the sport? Answer: Mia Hamm 60. In the world of sports, what is the ‘Gretzky T206 Honus Wagner’? Answer: A Baseball Card 61. According to the DVD interview of the movie Amadeus, Tom Hulce studies which sportsman’s mood swings for his portrayal of Mozart’s unpredictable genius? Answer: John McEnroe 62. Which game is played in seven-minute periods called ‘chukkas’? Answer: Polo 63. What is the maximum score that can be achieved in a single game of bowling? Answer: 300 64. Which matching card game in which the objective is to create melds of cards of the same rank and then go out by discarding them is named after the Spanish word for ‘basket’? Answer: Canasta 65. In U.S. college sports, what is the most common nickname/school mascot? Answer: Eagles 66. Which American football player was in the cast of the movie The Dirty Dozen and announced his retirement from the sport during the filming of the movie? Answer: Jim Brown 67. As of 2010, what is the only Olympic sport in which no professionals compete? Answer: Boxing 68. The golfer Vijay Singh hails from which country? Answer: Fiji 69. In the Tour de France, who is the ‘lanterne rouge’ (red lantern)? Answer: The last-place rider 70. Where is the international Tennis Hall of Fame? Answer: Newport, Rhode Island, USA 71. Which event traditionally starts at a place called Hopkinton and ends at Copley Square? Answer: The Boston Marathon

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Movie Questions

1. What character is actor Daniel Radcliffe better known as? Answer: Harry Potter 2. Which actress won an Oscar in 2005 for her role as June Carter Cash in Walk the Line? Answer: Reece Witherspoon 3. What was actor Tom Cruise’s first leading role? Answer: Risky Business 4. What actor plays Detective John McClane in the Die Hard series? Answer: Bruce Willis 5. What was the name of the 1988 film directed by Tim Burton in which a deceased couple hire a bio-exorcist with a black and white-striped suit to rid their home of its new owners? Answer: Beetlejuice 6. Johnny Depp starred in this tale of a cursed pirate ship, what is the title? Answer: Pirates of the Caribbean 7. This 1987 film starred Jennifer Grey and Patrick Swayze. Answer: Dirty Dancing 8. This film centers on a golf course with an obnoxious new member and a destructive gopher. Answer: Caddyshack 11. In what movie does Harrison Ford play the President of the United States who must save his family and other passengers aboard a hijacked plane? Answer: Air Force One 9. In what movie did Arnold Schwarzenegger say, “I’ll be back!”? Answer: Terminator 10. What movie features the line, “Frankly, my dear, I don’t give a damn!”? Answer: Gone with the Wind 11. What movie’s main character is Lara Croft? Answer: Tomb Raider 12. Who played Ethan Hunt in Mission Impossible? Answer: Tom Cruise 13. What movie features the character Wolverine? Answer: X-Men 14. What movie does Sandra Bullock play the role Gracie Hart? Answer: Miss Congeniality 15. What is the name of the main character in Independence Day? Answer: Will Smith 16. In what movie is the main character named Evan Nolan? Answer: Evan Almighty 17. This movie stars Haley Joel Osment as a little boy who can see dead people. Answer: The Sixth Sense 18. This movie stars a big green ogre who falls in love with a beautiful princess. Answer: Shrek 19. In this film, Nicholas Cage, as Benjamin Franklin Gates, decodes riddles and follows clues left by the country’s forefathers to find treasure. Answer: National Treasure 20. Set in 1912, this movie centers on a sinking ship. Answer: Titanic 21. Steve Martin fathers 12 kids in this comedy. Answer: Cheaper by the Dozen 22. “At night they fly, you better run, these winged things are not much fun” “In the jungle you must wait, until the dice read five or eight” Are clues from what 1995 movie game? Answer: Jumanji 23. Cheoah Dam in North Carolina was the real-life location of the ‘dive-scene’ in what 1993 movie in which the protagonist is wrongly accused of murder? Answer: The Fugitive 24. The villain in which multi-award winning film was based on three serial killers- one who skinned his victims, one who employed a fake handicap to lure women, and one who kept them in his basement? Answer: The Silence of the Lambs 25. What role does Peter Sellers, Alan Arkin, Roger Moore, Steve Martin play? Answer: Inspector Clouseau (The Pink Panther films) 26. Because many parents brought children to see it for its fantasy elements, theaters in Mexico placed warnings about graphic violence while exhibiting what 2006 Oscar-nominated film of Guillermo del Toro? Answer: Pans Labyrinth

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27. The unusual spelling of what 2006 Will Smith hit has its origins in what a homeless Chris Gardner (on whom the film is based) saw on a sign? Answer: The Pursuit of Happyness 28. What frightening sci-fi film character, also called a xenomorph, was designed by H.R. Giger from a lithograph titled Necronom IV? Answer: Alien 29. Cal Trask, Jim Stark and Jett Rink are the only film characters played by whom called “too young to die” in a hit 70’s song? Answer: James Dean 30. The fictional ’34 Bisgrove Street, Pacific County, California’ is the location of what 2003 gut- wrenching drama that features Ben Kingsley and Jennifer Connelly? Answer: House of Sand and Fog 31. The main setting in what 1994 classic is modeled after Hell’s Gate National Park in Kenya where crew members of the film spent time to study on the film’s setting and observe the animals? Answer: The Lion King 32. What eerie 1973 British film that tells the story of an upright Christian police officer investigating the disappearance of a young girl has been called by a film magazine as “The Citizen Kane of Horror Movies”? Answer: The Wicker Man 33. According to imdb.com, the title of what Michael Douglas 1984 hit that is set in South America refers to a step in the preparation of a gem for use in jewelry? Answer: Romancing the Stone 34. What ‘arty’ 2003 film had the lead actress netting a cool 25$ million, the highest ever earned by an actress for a role? Answer: Mona Lisa Smile 35. In June 2011, FBI arrested Boston mob boss Jame ‘Whitey’ Bulger near Los Angeles after a 16- year manhunt. He was the inspiration behind the character Frank Costello in which Oscar- winning film? Answer: The Departed 36. Estimating that an average inch of hair weighs 50 micrograms, animators of what 2010 movie said that the hair of the lead character weighs 10.4 lbs? Answer: Rapunzel from Tangled 37. Which actor, the epitome of American masculinity has the appropriate distinction of being the only one on every annual list of America’s favorite film stars? Answer: John Wayne 38. The authentic Nazi submarine used in Raiders of the Lost Ark was rented from the production of what 1981 epic war film that tells the fictional story of the crew of U-boat U-96? Answer: Das Boot 39. The 1973 Japanese film Lady Snowblood that is about a woman seeking vengeance upon her parents’ killers was the inspiration behind what 2000s 2 part-blockbuster? Answer: Kill Bill 40. The Penrose stairs, a staircase with four 90-degree turns forming a loop is called the ‘impossible staircase’ as it is not possible to create such an object in 3-dimensions. They appear in a stunt sequence in what 2010 hit movie? Answer: Inception 41. The title of what poignant 1971 drama film set in Texas refers to the fact of the town’s only cinema closing forever? Answer: The Last Picture Show 42. Which 1992 romantic-thriller that had 2 Oscar nominated songs in “I have Nothing” and “Run to you” has spawned the best-selling movie soundtrack of all time? Answer: The Bodyguard 43. Which 1969 classic with the character of Rooster Cogburn was remade recently with Jeff Bridges in the lead role? Answer: True Grit 44. Which actress who received a 2009 honorary Oscar schemed in How to Marry a Millionaire along with Marilyn Monroe and Betty Grable? Answer: Lauren Bacall

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45. What Oscar nominated 2010 movie was adapted from a 2009 book called The Accidental Billionaires? Answer: The Social Network 46. Which DC comics antihero with a scarred face who operates in the American West had a not so- successful 2010 film adaptation? Answer: Jonah Hex 47. Which English actor played the role of ‘Nearly Headless Nick’ in the first two Harry Potter movies? Answer: John Cleese 48. The 2009 biographical film The Last Station starring Christopher Plummer and Helen Mirren, both of whom were nominated for acting Oscars is about the last year in the life of which literary figure? Answer: Leo Tolstoy 49. What 1997 science fiction drama that deals with eugenics takes its title from a combination of the initial letters of the four DNA bases of Adenine, Cytosine, Guanine and Thymine? Answer: Gattaca 50. Which 2001 film is based on the story Super-Toys Last All Summer Long by Brian Aldiss that deals with the age of machines where child creation is controlled? Answer: A.I. Artificial Intelligence 51. What 2009 hit that rewarded a woman director opens with the quotation ‘The rush of battle is a potent and often lethal addiction, for war is a drug’? Answer: The Hurt Locker 52. Which unforgettable 80s film character is claimed to be partially based on an arbitrageur called Ivan Boesky who gave a speech on greed at the University of California, Berkeley in 1986? Answer: Gordon Gecko from Wall Street 53. Which 2006 adaptation of a Somerset Maugham novel set in China stars Naomi Watts and Edward Norton? Answer: The Painted Veil 54. In which film starring Cary Grant and Deborah Kerr does a prospective meeting on the top of the Empire State Building assume a lot of significance? Answer: An Affair to Remember 55. What 2008 film adaptation starred Meryl Streep, Philip Seymour Hoffman, Amy Adams, and Viola Davis, all of whom were nominated for acting Oscars? Answer: Doubt 56. Monet’s 1908 painting San Giorgio Maggiore at Dusk received attention in 1999 as it was the focus of the plot in which John McTiernan’s remake starring Pierce Brosnan and Rene Russo? Answer: The Thomas Crown Affair 57. The story of Paul Rusesabagina who saved hundreds of African lives in the 1990s has been chronicled in what Oscar-nominated movie of 2004? Answer: Hotel Rwanda 58. Mammy Two Shoes, a recurring character in MGM’s Tom and Jerry Cartoons was inspired by which African-American actress and singer? Answer: Hattie McDaniel 59. What ethnic 2002 romantic comedy is the highest- grossing film to never have been number one on the weekly North American box-office charts? Answer: My Big Fat Greek Wedding 60. Which fictional character, who first appeared in the 1940 Disney film Pinocchio, was appointed by the Blue Fairy to serve as the official conscience for Pinocchio? Answer: Jiminy Cricket 61. Mehran Karimi Nasseri, an Iranian refugee who lived in the departure lounge of the Charles de Gaulle Airport from 198 until 2006 may have been the inspiration behind what 2004 movie? Answer: The Terminal 62. Rowan Atkinson plays which British comic character described as a ‘child in a grown man’s body’? Answer: Mr. Bean 63. On which 1953 film poster are Burt Lancaster and Deborah Kerr sharing a passionate kiss on the beach? Answer: From Here to Eternity 64. In which 1995 film does Kevin Spacey portray the serial killer John Doe? Answer: Se7en

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65. What 2007 film starring Chris Cooper is based on the story of Robert Hanssen, an FBI agent convicted of spying for the Soviet Union? Answer: Breach 66. Which 1972 movie musical has the distinction of winning the most Oscars (8) without winning the Best Picture Award? Answer: Cabaret 67. What 1988 movie tells the true-life story of naturalist Dian Fossey and her work with gorillas? Answer: Gorillas in the Mist 68. Which 1985 film starring Tom Hanks, Rita Wilson, and John Candy has a cult following among many generations of Peace Corps Personnel? Answer: Volunteers 69. What Jordanian site is featured in the movie Indiana Jones and the Last Crusade as the location of the Holy Grail? Answer: Petra 70. What term that has been described as ‘remembering the future’ is also a glitch that occurs when the machines alter an aspect of the Matrix in the movie? Answer: Déjà vu 71. What chilling 1968 film is set almost entirely in the Bramford apartment building in New York City? Answer: Rosemary’s Baby 72. Anna Wintour, the editor of Vogue was the inspiration for the character of Miranda Priestly in which 2006 film? Answer: The Devil Wears Prada 73. What 2000 film tells the fictional story of a teenage journalist writing for Rolling Stone magazine while covering the rock band Stillwater? Answer: Almost Famous 74. In the movie Jaws, what is the name of the boat the trio use to hunt down the beast? Answer: Orca 75. What ominous sounding fictional island is the home of King Kong? Answer: Skull Island 76. What is the name of the unforgettable character played by Sigourney Weaver in Alien? Answer: Ellen Ripley

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Current Event Questions

1. Before making his democratic convention speech, President Barack Obama released one of his favorite recipes. What was it for? Answer: Ale 2. Which Arab country recently introduced a smoking ban? Answer: Lebanon 3. Which UK newspaper published photos of the naked prince Harry? Answer: The Sun 4. Which popular airline had massive delays recently? Answer: Lufthansa 5. Which famous musician received the highest French award, France’s Legion of Honour? Answer: Sir Paul McCartney 6. What is the new blockbuster by Simon West? Answer: The Expendables 2 7. What country was struck by an earthquake September 8th, 2012? Answer: China 8. What famous film festival started on September 7th, 2012? Answer: Toronto Film Festival 9. A book that was in possession of Elvis Presley has been recently auction for 59 thousand GBP. What book was it? Answer: The Bible 10. Which country is leading in the medal classification at the Paralympics? Answer: China 12. What’s the name of the biggest current music hit song from Korea? Answer: Gangnam Style 13. What famous Hollywood actor died recently at the age of 54? Answer: Michael Clarke Duncan 14. Spain recently lifted a TV ban on what? Answer: Bullfighting 15. Who won the 2012 Nobel Peace Prize? Answer: The European Union 16. In a recent scandal, a Syrian airplane that was headed to Damascus was forced to land in one of the nearby countries. Where did it land? Answer: Turkey 17. Hugo Chavez won the recent presidential elections in Venezuela. How many times has he won in total? Answer: 3 18. Which famous Formula 1 driver has recently announced his retirement from the sport? Answer: Michael Schumacher 19. What is the latest Felix Baumgartner stunt? Answer: Parachute jump from 30km 20. While on a visit to Norwegia, the king of Ghana had his property stolen. What was it? Answer: Jewelry 21. The space shuttle Endeavour is going to rest in which US city? Answer: Los Angeles 22. Which famous sportsman was stripped of all his titles due to a doping scandal? Answer: Lance Armstrong 23. What is the name of the European counterpart for the GPS system? Answer: Galileo

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General Knowledge Questions

1. Which singer joined Mel Gibson in the movie Mad Max: Beyond The Thunderdome? Answer: Tina Turner 2. Vodka, Galliano and orange juice are used to make which classic cocktail? Answer: Harvey Wallbanger 3. Which American State is nearest to the former Soviet Union? Answer: Alaska 4. On TV, who did the character Lurch work for? Answer: The Addams Family 5. Which children’s classic book was written by Anna Sewell? Answer: Black Beauty 6. How many tentacles does a squid have? Answer: Ten 7. Which reggae singing star died May 11th, 1981? Answer: Bob Marley 8. What is converted into alcohol during brewing? Answer: Sugar 9. Which river forms the eastern section of the border between England and Scotland? Answer: Tweed 10. Which Briton won an ice-skating Gold at the Lake Placid Olympics? Answer: Robin Cousins 11. Name the 2 families in Romeo and Juliet? Answer: Montague and Capulet 12. If cats are feline, what are sheep? Answer: Ovine 13. For which fruit is the US state of Georgia famous? Answer: Peach 14. Which is the financial center and main city of Switzerland? Answer: Zurich 15. In which city was Martin Luther King assassinated in 1968? Answer: Memphis, Tennessee 16. What is the word used to describe an animal/plant that is both male and female? Answer: Hermaphrodite 17. In which country did the Mau Mau uprising (1952-60) occur? Answer: Kenya 18. What does a numismatists study or collect? Answer: Coins 19. Who was Radio 1’s first female DJ? Answer: Anne Nightingale 20. Who captained Jules Verne’s submarine Nautilus? Answer: Captain Nemo 21. The llama belongs to the family of animals commonly called what? Answer: Camels 22. Which guitarist is known as Slowhand? Answer: Eric Clapton 23. In which 1979 film was the spaceship called Nostromo? Answer: Alien 24. What have been cooked in syrup and glazed to make the sweet Marrons Glaces? Answer: Sweet Chestnuts 25. The Shatt-el-Arab (River of Arabia) is the confluence of which two other rivers? Answer: Tigris and Euphrates 26. What is an infant whale commonly called? Answer: Calf 27. Which DJ had a UK top ten hit with the song snot rap? Answer: Kenny Everett 28. In which film did Roger Moore first play James Bond? Answer: Live and Let Die 29. How many gallons of beer are in a furkin? Answer: Nine 30. In literature, who was the best known pupil of Greyfriar’s School? Answer: Billy Bunter 31. What is the alternative common name for a Black Leopard? Answer: Panther 32. Who composed the Wedding March? Answer: Felix Mendelsshon 33. Which actor appeared in Papillion and the Great Escape and died in 1980? Answer: Steve McQueen 34. In which bay is Alcatraz? Answer: San Francisco Bay 35. Which Cornish village claims to be the birthplace of King Arthur? Answer: Tintagel 36. In which Dickens novel was Miss Havisham jilted on her wedding day? Answer: Great Expectations

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37. What is an otter’s home called? Answer: Holt 38. Who had a 1985 hit with Saving All My Love for You? Answer: Whitney Houston 39. In Roman mythology, Neptune is the equivalent to which Greek God? Answer: Poseidon 40. Which TV character said, ‘Live long and prosper’? Answer: Mr. Spock 41. Which playwright wrote the Seagull, Uncle Vanya, and the Cherry Orchard? Answer: Anton Chekov 42. What make of car was the time-machine in the film Back to the Future? Answer: De Lorean 43. In which US state would you find the city of Birmingham? Answer: Alabama 44. Complete the name of the American Football team: “Washington ………’? Answer: Redskins 45. In which war was the Battle of Bunker Hill fought? Answer: American War of Independence 46. Robin Hood and Friar Tuck appear in which well-known novel, by Sir Walter Scott? Answer: Ivanhoe 47. What is Canada’s national animal? Answer: The Beaver 48. Which is the smallest member of the flute family? Answer: Piccolo 49. Mace is one of the spices obtained from the tree Myristica Fragrams- what is the other spice? Answer: Nutmeg 50. A palmiped’s feet are more commonly called what? Answer: Webbed 51. What is the heaviest naturally occurring metal on Earth? Answer: Plutonium 52. How many legs do butterflies have? Answer: 6 53. What is the most populous country in the world? Answer: China 54. Which state is the biggest in the US? Answer: Alaska 55. Which is the largest country (by area) in the world? Answer: Russia 56. What is the common name for Aurora Borealis? Answer: Northern Lights 57. What is the most common non-contagious disease in the world? Answer: Tooth Decay 58. What was the last recorded album of The Beatles? Answer: Abbey Road 59. What instrument did Mile Davis, the jazz musician play? Answer: Trumpet 60. In which sport could you get into a headlock? Answer: Wrestling 61. In which country was golf first played? Answer: Scotland 62. What is the largest mammal in the world? Answer: Blue Whale 63. Where did Reggae music originate? Answer: Jamaica 64. Who was the creator of Jeeves and Wooster? Answer: P.G. Wodehouse 65. Who painted the ceiling of the Sistine Chapel? Answer: Michelangelo 66. Who was the writer of Alice’s Adventures in Wonderland? Answer: Lewis Carroll 67. After which famous person was the teddy bear named? Answer: Theodore Roosevelt 68. Which is the smallest ocean in the world? Answer: Arctic Ocean 69. What is the rhino’s horn made of? Answer: Hair 70. Who created Snoopy? Answer: Charles M. Schulz 71. Who was the first non-royal to appear on a UK postage stamp? Answer: William Shakespeare 72. What is the capital city of Afghanistan? Answer: Kabul

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APPENDIX F

Script for Cell Phone Conversation

First phase of cell phone conversations (5-15 minutes). (Start to dial at 5 minutes)

“Hi this is (your name), we’re going to start our first phone conversation. I’m just going to ask you a few questions about yourself. Feel free to be open and honest and take as much time as you need to think about our conversation. OK?”

First of all, what is your full legal name?

What city and state were you born in? Have you lived here/there all your life? (If they have lived elsewhere) Where else have you lived?

So, I’m assuming you’re a college student here at UC?

What is your college major? What kind of job do you hope to get? What made you choose that?

What are some of your favorite classes you are taking this semester? Why is that? Who is the professor for that class? Sometimes my professors annoy me, is there anything this professor does or doesn’t do that gets on your nerves? Is there anything you really like about the class?

What types of things do you like to do in your spare time? Do you find that they interfere with school?

In your spare time, do you like to go to the movies? Have you seen anything new recently? Do you have a favorite actor/actress? What were some movies they were in? Oh I didn’t see that movie/tv show, what was that about?

In your spare time do you like to go out to eat? What are some of your favorite foods? Are there some restaurants around here that you really like and can recommend?

Do you like to listen to music? What is your favorite kind of music? Who are some of your favorite artists right now? Oh yeah I love them…

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Other than going to school, do you have any hobbies or a part-time job? (If yes) Where are you working? What do you have to do there? (Comment on job- Oh that’s interesting, or I’ve been there etc.)

Have you noticed the weather changing recently? Do you like the colder weather? What is your favorite time of year? Why is that?

Do you have any pets? (If yes) That sounds really cute, I also have a little dog, a rat terrier named Boone. He is so funny and has the best personality. Do you think your (pet) has a personality? (If no) Would you like to get some type of pet eventually?

I love to travel. How about you? What are some of the places you’ve traveled to? What did you do when you were there? How long were you there for?

If you could go anywhere, where would you go? Why?

So other than coming here today, what else have you done? Do you have plans for the rest of the day? Do you have any interesting or fun plans that you’re looking forward to? (Comment on plans or if nothing say something like well that means you can rest up and study!)

“Ok that’s the end of our first cell phone conversation so go ahead and continue driving”

Second phase of cell phone conversations (30-40 minutes) (Start to dial at 30 minutes)

“Hi this is (your name) again, we’re now going to start our second phone conversation. I’m just going to ask you a few more questions about yourself. Feel free to be open and honest and take as much time as you need to think about our conversation. OK?”

1. Tell me about an incident in which you had a “close call” experience. [If they didn’t have a close call experience, we will adapt the questionnaire to ask about “someone close to you” having a close call.] 2. It sounds like a scary ordeal. Describe how you felt emotionally during the experience. 3. What were your feelings after the experience? 4. That is certainly understandable. How did you cope with what had just happened? 5. What factors do you think contributed to you surviving the experience? 6. If you could go back, what things would you have done differently? 7. What do you think could have been done to avoid the situation entirely?

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8. Did your views on life and death change after the incident? 9. Many young people feel invincible. Did you ever feel this way and if so, did these feelings change after the close call? 10. Many people who have such experiences describe having an “enhanced sense of living in the present.” Did your experience affect you in this way? 11. Some people find that their priorities or interests change after having a close call. Did your experience affect you in this way? 12. How were your relationships with others affected by your close call experience for better or worse? 13. I was once involved in a minor close call hiking accident when I sprained my ankle. Luckily I had two friends with me. One stayed and the other got help. I’m still a little afraid to hike to this day. Did your close call cause you to avoid ______(whatever the person experienced a close call with)? 14. Thank you for sharing your story with me. Is there anything else you’d like to say about your close call?

“OK that’s the end of our second phone conversation so once again go ahead and continue driving”

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APPENDIX G

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: Alertness Maintaining Tasks During Vehicle Automation in Simulated Driving Introduction: You are being asked to take part in a research study. Please read this paper carefully and ask questions about anything that you do not understand. 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. Purpose: The purpose of this research study is to investigate subjective reactions to performing driving tasks as well as the relationship between secondary tasks and vehicle automation. You will be one of approximately 180 participants taking part in this research study.

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.

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

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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 several experimental driving conditions. 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 during secondary tasks.

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 credit hour per 15 minutes of participation, resulting in a maximum of 8 credit hours. 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 UC Institutional Review Board reviews all research projects that involve human participants

116 to be sure the rights and welfare of participants are protected. If you have questions about your rights as a participant or complaints about the study, you may contact the UC IRB at (513) 558-5259. Or, you may call the UC Research Compliance Hotline at (800) 889-1547, or write to the IRB, 300 University Hall, ML 0567, 51 Goodman Drive, Cincinnati, OH 45221-0567, or email the IRB office at [email protected]. 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.

Agreement: I have read this information and have received answers to any questions I asked. I give my consent to participate in this research study. I will receive a copy of this signed and dated consent form to keep.

Participant Name (please print) Date

Participant Signature Date

Signature of Person Obtaining Consent Date

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