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Risk Tolerance, and the Impact of Central Executive Abilities on Dual-Task Performance
by
David Alexander Canella
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Mechanical and Industrial Engineering University of Toronto
© Copyright by David Alexander Canella 2013
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Risk Tolerance, and the Impact of Central Executive Abilities on
Dual-Task Performance
David Alexander Canella
Master of Applied Science
Mechanical and Industrial Engineering University of Toronto
2013
Abstract
Multiple Resource Theory (Wickens, 1980) has evolved over the past three decades into a four
dimensional multiple resource model. Separately, central executive functioning has been
investigated. Other research has examined the relationship between risk taking and behaviour.
The research in this thesis aimed to address questions arising out of these theoretical
approaches. An experiment was carried out to explore the impact of executive abilities, risk
perception, and risk-taking behaviour on multitasking performance. Using a novel methodology
it was found that executive functioning, and the way that information is presented, were each
significantly related to task performance and eye gaze in a dual-task setting. Statistically
significant relationships were also found between independently developed instruments of risk
perception and of risky driving behaviour. The implications of these findings for theories of
attentional resources, executive functions, and mental workload are discussed.
iii
Acknowledgments
I wish to acknowledge the dedicated support of my family and friends as well as the guidance
provided by my supervisor, Professor Mark Chignell, and my lab mates. I would not have been
able to complete this work without the patient encouragement of my fiancée, Inês Ribeiro, my
parents, Louis and Piera, and my brother, Daniel. I am also deeply grateful for the feedback
provided by members of the Interactive Media Lab, especially the thoughtful insights of Ryan
Kealey and Sachi Mizobuchi. Last, but certainly not least, I remain indebted to Mark for having
welcomed me into the Master’s program, for his unabashed optimism, and for his perceptive
feedback. Thank you, everyone.
For mom and dad.
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Table of Contents Acknowledgments ......................................................................................................................... iii
Table of Contents ............................................................................................................................ iv
List of Tables .................................................................................................................................vii
List of Figures .............................................................................................................................. viii
List of Appendices .......................................................................................................................... ix
Chapter 1: Introduction .................................................................................................................... 1
1.1 Motivation............................................................................................................................ 1
1.2 Research Questions & Scope ............................................................................................... 2
1.3 Thesis Overview .................................................................................................................. 3
Chapter 2: Literature Review........................................................................................................... 4
2.1 Multiple Resource Theory (MRT) ....................................................................................... 4
2.1.1 Beyond Resources: Multiple Resource Theory and the Central Executive ............. 7
2.2 The Central Executive ......................................................................................................... 9
2.2.1 Shifting, Updating, and Inhibition ......................................................................... 10
2.2.2 Complex Cognitive Tasks ..................................................................................... 12
2.3 The Central Executive, Multiple Resource Theory, and Driver Distraction: Towards Unification ......................................................................................................................... 14
2.4 Risk Tolerance ................................................................................................................... 15
2.4.1 Research on Risk and Driving ............................................................................... 16
2.4.2 Risk Scale Specifics ............................................................................................... 19
2.5 Summary ............................................................................................................................ 20
Chapter 3: Methodology ................................................................................................................ 21
3.1 Online Survey .................................................................................................................... 21
3.1.1 Demographic Information and Driving History .................................................... 21
3.1.2 The Perception of Risk .......................................................................................... 22
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3.1.3 Risky Behaviour While Driving ............................................................................ 22
3.2 Experiment ......................................................................................................................... 23
3.2.1 Measures of Cognitive Abilities ............................................................................ 23
3.2.2 Dual-task Scenario ................................................................................................. 26
3.2.3 Debriefing and Compensation ............................................................................... 32
3.3 Apparatus ........................................................................................................................... 32
Chapter 4: Results .......................................................................................................................... 34
4.1 Risk Tolerance ................................................................................................................... 35
4.1.1 The DOSPERT-DBQ Relationship ....................................................................... 35
4.2 Primary Pedal Tracking Task Performance ....................................................................... 37
4.2.1 Sample Demographics ........................................................................................... 37
4.2.2 Pedal Tracking Task Accuracy .............................................................................. 38
4.3 Secondary Vowel Monitoring Task Performance ............................................................. 39
4.3.1 Accuracy & Presentation Style .............................................................................. 39
4.3.2 Accuracy, Risk, and Central Executive Abilities .................................................. 39
4.4 Effects of Cognitive Ability and Presentation Style on Eye Gaze .................................... 40
Chapter 5: Discussion .................................................................................................................... 43
5.1 The Relationship between Measures of Risk Tolerance.................................................... 43
5.2 Task Performance and the Presentation of Information .................................................... 45
5.3 Risk Tolerance, Central Executive Functions, and Driving Performance ......................... 47
5.4 Presentation Style, Central Executive Functions, and Eye Gaze....................................... 47
Chapter 6: Conclusion ................................................................................................................... 50
6.1 Contributions ..................................................................................................................... 50
6.2 Limitations ......................................................................................................................... 51
6.3 Future Research ................................................................................................................. 52
6.4 Concluding Statement ........................................................................................................ 54
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Works Cited ................................................................................................................................... 55
Copyright Acknowledgements ...................................................................................................... 78
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List of Tables Table 1: A sample ordering of the first 12 experimental conditions of the list-monitoring
task. ........................................................................................................................................ 30
Table 2: The relationship between summed DBQ violation scores and mean DOSPERT subdomain scores. .................................................................................................................. 37
Table 3: Correlations between Executive Abilities and Mean Monitor One Dwell Proportions across Presentation Styles. ................................................................................. 41
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List of Figures Figure 1: Wickens four-dimentional multiple resource model depicted graphically. (Redrawn
from Wickens, Hollands, Parasuraman, & Banbury, 2013) ................................................. 6
Figure 2: Miyake et al. (2000) three factor model and associated tasks (Redrawn from Miyake, et al., 2000). ........................................................................................................................ 11
Figure 3: a) A recent replication of Miyake et al.'s (2000) original three factor model using new data by Friedman, Miyake, Robinson, and Hewitt (2011). b) A revised representation of executive functioning from the same sample (Friedman, Miyake, Robinson, & Hewitt, 2011). (Redrawn from Miyake & Friedman, 2012) ........................................................... 13
Figure 4: Mizobuchi-Chignell model of cognitive task demands and individual abilities (S - Shifting, U - Updating, and I - Inhibition) as related to experienced workload. (Mizobuchi S., Chignell, Suzuki, Koga, & Nawa, 2012) ....................................................................... 15
Figure 5: Inhibition Stroop Task................................................................................................. 24
Figure 6: Updating Colour Monitoring Task .............................................................................. 26
Figure 7: Pedal-Tracking Task details (left); List-Monitoring Task visual condition visualizations (right). .......................................................................................................... 27
Figure 8: Pedal Tracking Task bumper size calculations ........................................................... 29
Figure 9: The Dual-Task Scenario Apparatus. ........................................................................... 33
Figure 10: A) (left) A scattergram of DOSPERT Ethical subdomain scores by Total DBQ Violation scores; B) (right) A scattergram of DOSPERT Health & Safety subdomain scores by Total DBQ Violation scores. .............................................................................. 37
Figure 11: Mean out of bounds error scores across presentation styles. .................................... 38
Figure 12: Main effects of presentation style on the mean proportion of time spent dwelling on Monitor One. ...................................................................................................................... 40
Figure 13: The relationship between the mean proportion of time spent gazing at Monitor One and updating ability by presentation style. ......................................................................... 42
Figure 14: The relationship between the mean proportion of time spent gazing at Monitor One and inhibition ability by presentation style. ........................................................................ 42
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List of Appendices Appendix A : Survey Items Presented During the Online Portion of Data Collection ................. 65
Appendix B : Call for Participation Document ............................................................................. 71
Appendix C : Client Information Sheet and Informed Consent Form for the Study: Investigating the Effects of Cognitive Ability and Interface Modality Preferences on Dual-Task Performance .......................................................................................................... 72
Appendix D : Correlations Between DOSPERT and DBQ Total and Subscale Scores ................ 74
Appendix E : Relationship between Ethical and Health & Safety DOSPERT Subdomains and DBQ Violation scores ...................................................................................................... 75
Appendix F : Speculation as to the Nature of the Relationship Between Risk Measures and Secondary Task Accuracy ...................................................................................................... 76
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Chapter 1: Introduction
1.1 Motivation
Driving-related accidents are a leading cause of death and injury. While there are many causal
factors that lead to accidents, distraction of the driver is an important factor and its role appears
to be increasing as more technologies are used in vehicles. Not only have studies found that a
large percentage of accidents are caused by distraction (E.g., ~20% Wickens, Hollands,
Parasuraman, Banbury, 2013), but due to the fact that many of these rely on self-reported data
volunteered by drivers in police reports (Dingus, Hanowski, & Klauer, 2011), distracted driving
is likely also responsible for an immeasurable number of unreported close calls and near misses
in daily life. One naturalistic driving study (Dingus, et al., 2006) found that 78% of crashes and
65% of near crashes observed were due to inattention. Further, of the four types of inattention
investigated (secondary task distraction, drowsiness, driving-related inattention to the forward
roadway – e.g., Blind spot checking – and nonspecific eye-glance away from the forward
roadway), secondary task distraction was found to have the largest effect (Dingus, et al., 2006).
From another perspective, in a world of social media and round-the-clock interconnectedness,
driving itself is an inconvenience that costs vehicle operators precious time during which they
might otherwise perform important non-driving tasks. Figures such as those cited by the
National Highway Traffic Safety Administration, that in 2008 11% of drivers were estimated to
be using a cell phone at any given daylight moment (National Highway Traffic Safety
Administration, 2009) speak to the fact that individuals want to remain connected while
travelling from point A to point B, and frequently do so even when driving.
Given the concerns about the disruptive effects of in-vehicle technologies, research-based
guidance is needed to determine how interfaces and interactions can be designed so as to
minimize distraction. Drivers are required to multitask as a condition of driving (E.g., checking
blindspots, maintaining following distances, maintaining appropriate speeds, signaling turns,
checking for pedestrians and road debris, etc.), they occasionally multitask when performing
non-driving tasks (E.g., checking messages while engaging in conversation with a passenger),
and they further divide scarce mental resources when attempting to do all these things at once
(E.g., driving in traffic while checking phone messages and participating in a conversation).
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This thesis sought to investigate the effects of central executive functions, risk perception, self-
reported risky behaviour, and different ways of presenting information on measures of multi-
tasking performance. Research of this type is in its third year at the University of Toronto
Interactive Media Lab under the supervision of Professor Mark Chignell and lead investigator
Sachi Mizobuchi1. Funding was provided by the Toyota Infotechnology Center Company
Limited. Past work has investigated the role of central executive functions using driver
simulator studies (Mizobuchi S. , Chignell, Suzuki, Kogo, & Nawa, 2011). The experiment
reported in this thesis used a similar multi-tasking methodology to that utilized earlier by
Mizobuchi, Chignell, Suzuki, Koga and Nawa (2012).
1.2 Research Questions & Scope This dissertation focused on how task and individual characteristics affect multitasking
performance in a driving-related environment. The individual characteristics of interest were
risk tolerance and cognitive abilities. The presentation styles were modality (auditory vs. visual)
and timing (sequential vs. simultaneous, in the visual domain). Specific questions posed in this
research were:
• What is the relationship between primary driving-related task performance, secondary
infotainment system-synonymous task performance, and auditory, visual (one-item-at-a-
time) sequential, and visual (all-items-at-once) simultaneous methods of information
presentation?
• How, if at all, is performance on either a primary driving-related task or a secondary
infotainment system-synonymous task related to shifting, updating, and inhibition
cognitive abilities?
• How is eye gaze related to auditory, visual sequential, and visual simultaneous methods
of information presentation in a driving-related context?
• How do the shifting, updating and inhibition central executive abilities affect eye gaze
in a driving-related laboratory experiment?
1 Since Sachi Mizobuchi was the lead investigator for the project her name appeared on ethics protocol documents (e.g., consent forms).
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In choosing measuring instruments for assessing factors related to riskiness we found two
distinct inventories (the DBQ and the DOSPERT) that had been used frequently in previous
studies. This led to the following additional question:
• How might the Domain Specific Risk Tolerance Scale and the Driver Behaviour
Questionnaire be related?
1.3 Thesis Overview The following chapter reviews relevant literature on multiple resource theory, executive
functions, driver distraction, and risk perception and behaviour. Particular focus is paid to
relevant cognitive models, measures of risk tolerance, and applicable past research. The
experimental methodology used in this thesis, which includes both an online survey and an
experiment, is described in Chapter Three. The overall research design combines measures of
individual characteristics - risk scales and cognitive ability measures - with an experimental
dual-task scenario that tracks individual task performance as well as eye gaze. The dual task
comprised a primary pedal tracking task and a secondary list monitoring task, under a variety of
different conditions. Chapter Four presents the results of the statistical analyses that were carried
out. Those results are then summarized and discussed in Chapter Five. Finally, Chapter Six
outlines the contributions of this work, some of its limitations, and opportunities for future
research.
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Chapter 2: Literature Review
Several areas of research are relevant to assessing the effects that methods of presenting
information (Presentation Style), cognitive abilities, and risk tolerance have on dual task
performance. Wickens’ Multiple Resource Theory (Wickens C. D., 1980; 2008; Wickens,
Hollands, Parasuraman, & Banbury, 2013) and Miyake et al.’s conceptualization of central
executive functioning (Miyake, et al., 2000; Miyake & Friedman, 2012) will be used as
theoretical foundations for explaining performance differences between individuals and task
settings. Theories of risk perception and behaviour will also be summarized in the remainder of
this chapter. This chapter will synthesize relevant results and discussion from past research and
will discuss the roles that central executive functions and risk tolerance may play in multitasking
environments.
2.1 Multiple Resource Theory (MRT)
Early theories of human attention assumed that there was a bottleneck in information processing
leading to only a limited quantity of information being processed at any given time (Craik,
1948; Broadbent, 1958; Welford, 1967). Craik (1948) proposed that time-lags observed while
processing information may be explained by “the building up of some single ‘computing’
process which then discharges down the motor nerves….” Welford (1967) expanded on this
notion by suggesting that ‘mental load’ was related to the assumption that decision-making
takes time, and that if a given time requirement exceeds the amount of time available, then
responses are delayed or omitted.
Moray (1967) suggested that the human brain is a “limited capacity central processor whose
organization can be flexibly altered by internal self-programming” and which is capable of
processing multiple tasks simultaneously. Kahneman (1973) in his book on attention and effort
described this capacity limitation as being variable, with the capacity dependent on level of
arousal. Kahneman’s approach can be thought of as a limited “resource model” (Wickens C. D.,
2002) where task performance is dependent on interactions between an allocation policy that
determines which tasks to prioritize and task difficulty, which is responsible for dictating the
amount of effort supplied. Kahneman believed that task interference occurs both at a structural
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level when tasks require access to the same mechanisms, and at a capacity level when combined
demands exceed the total capacity available (Kahneman, 1973, p. 11).
Later theorists addressed the observation that some ‘automatic’ processes could be performed
quite well while demanding few resources (Fitts & Posner, 1967, p. 14; Schneider & Shiffrin,
1977). Norman and Bobrow (1975) identified a task-resource continuum where every task was
found to fall somewhere between being either fully resource-limited, indicating at one extreme
that maximal performance on a single task was attainable only through the commitment of all
resources, to fully data-limited, which indicates at the other extreme that task performance
depends on the quality of data available and so is independent of resources. Multitasking
performance was thought to be dependent on the extent to which component tasks were resource
or data-limited. Regarding automaticity, Norman and Bobrow (1975) argued that practicing a
task brings about increases in performance and a decrease in conscious processing requirements,
which can be interpreted as shifting a given task closer towards the data-limited end of the
continuum.
There are a number of factors that could explain performance changes across different dual-task
scenarios. Two exemplary works cited by Wickens (2002) include the research of Treisman and
Davies (1973), who found that all else being equal, dual-task performance for two visual tasks
was worse than performance with one visual and one audio task, and that of Parkes and
Coleman (1990), who found that both driving performance and instruction comprehension were
improved when instructions were read out loud to the driver rather than when the driver was
forced to read them. Based on his analysis of findings such as these, Wickens developed
Multiple Resource Theory and created what would become the four-dimensional multiple
resource model (Wickens C. D., 1980; 2002; 2008).
The four dimensions of the multiple resource model are: Stages (Perception, Cognition, and
Responding); Perceptual Modalities (Audio and Visual); Visual Channels (Focal and Ambient);
and Processing Codes (Spatial and Verbal). Figure 1 depicts the model graphically with each
solid line indicating where resources are split. The basic principle underlying MRT is that, due
to a greater availability of resources, dual-task performance will be superior when each task
involves different levels across the aforementioned dimensions. (Wickens C. D., 2008)
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Conversely, when tasks share dimension levels, fewer sets of different resources are accessed
and performance will be worse.
Figure 1: Wickens four-dimentional multiple resource model depicted graphically.
(Redrawn from Wickens, Hollands, Parasuraman, & Banbury, 2013)
The Stages dimension refers to the three steps in information processing that occur as an
individual interacts with the world. First the information is perceived, then the meaning is
processed (Cognition), and finally an appropriate response is formulated (Responding). Even
though the perception and cognition stages are distinct entities, they are thought to share the
same pool of resources (Wickens C. D., 2002). The second dimension concerns the Modality
through which information is presented. Figure 1 shows how audio and visual modalities are
believed to draw upon separate pools of resources. However, two caveats exist in that (1) while
the original model accounted for only audio and visual modalities, evidence suggests that other
modalities, such as tactile input, have their own distinct resource pools (Wickens, Hollands,
Parasuraman, & Banbury, 2013), and (2) it is possible that modality-sharing tasks also conflict
on a physical level such as when the sound from one audio task masks information from a
second audio task, or when two visual tasks that present information in very different physical
locations are attempted simultaneously (Wickens C. D., 2002).
The third dimension of the multiple resource model, which was added to the original three-
dimension model as a result of additional research (Leibowitz & Post, 1982; Previc, 1998;
Wickens C. D., 2008), is Visual Channels, which is related to the way that visual information is
processed in the brain. The dimension is comprised of focal versus ambient vision. Focal vision
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occupies the centre of our field of view, involving mostly foveal vision, and is used primarily to
collect detailed information. In contrast, ambient vision relies mostly on peripheral vision that
can pick up far fewer details, but which is highly adept at identifying motion and changes in
lighting (Wickens, Hollands, Parasuraman, & Banbury, 2013). The final dimension of the
multiple resource model involves spatial versus verbal Processing Codes. Unlike perceptual
modalities and visual channels, which play a role only at the perceptual stage (Figure 1),
processing codes are thought to have separate resources both at the perceptual-cognition stages
and at the response stage. This means that the visual presentation of a map, for example, will
draw upon different resources than the processing of a visually presented verbal list of
directions. Also, responding to one task manually draws on different resources than responding
verbally.
The four dimensions of the multiple resource model provide a theoretical basis upon which to
investigate multitasking in a driving context. In particular, the model describes how mental
workload might be affected by task modalities and related contextual factors. In questioning the
role that presentation style has on the performance of a spatial-visual-manual primary driving
task paired with a verbal, mixed-modality, manual secondary task (as studied in this
experiment), MRT provides a prediction that performance across both tasks will be best in
auditory secondary task conditions. However, beyond these predictions, this thesis seeks to
quantify the effects of additional individual characteristics on task performance. It is
hypothesized that levels of central executive (CE) ability and risk tolerance will have an effect
on performance above and beyond those explicable by presentation style and traditional multiple
resource theory.
2.1.1 Beyond Resources: Multiple Resource Theory and the Central Executive
Wickens et al. (2013) provided an updated perspective on the role that MRT plays in
multitasking, focusing on four constructs: Effort (Resource Demands), a ‘Multiplicity
Requirement’, the Central Executive (which serves to allocate resources), and the degree of
Task Confusability. Their conceptualization asserts that multitasking performance is worse than
individual task performance due to a combination of both the resource requirements of each
component task and the extent to which tasks share similarities at the multiple resource and
(higher) cognitive levels. Increased resource requirements and greater similarities between tasks
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result in larger performance decrements, although this process is mediated by the central
executive through the selective allocation of resources between tasks.
According to Wickens et al., effort requirements vary between almost fully automatic at one
extreme and immensely effortful at the other, with one’s position in this continuum in a
particular task context being based on the extent of one’s experience with the task(s) and its
(their) intrinsic simplicity. Multiplicity reflects the contributions of the Multiple Resource
Model, where larger requirements are believed to exist when perceptual modalities, information
codes, stages of processing, or visual channels are shared between tasks. The allocation of
resources by the Central Executive is said to be either graded, meaning dynamically adjusted
according to individual strategy, or all-or-none in cases where an ongoing task is abandoned so
that a shorter interrupting task might be completed (Wickens, Hollands, Parasuraman, &
Banbury, 2013).
The final concept described is Task Confusability with respect to the sharing of processing
routines and material between tasks. One example of this type of confusion includes the finding
that performing two mental arithmetic or two spelling tasks (i.e., two tasks that presumably
share similar processing routines) at the same time leads to worse performance than that
observed when simultaneously attempting one arithmetic and one spelling task (Hirst & Kalmar,
1987). Wickens et al. acknowledge that this is similar in nature to the Multiple Resource Model
where shared similarities lead to performance decrements, but assert nonetheless that
confusability operates at a higher cognitive level than can be explained by shared resources.
Specifically, Wickens et al. (2013) cite Navon’s (1984; Navon & Miller, 1987) work on
“Outcome Conflict” as an explanatory mechanism. This theory states that information processed
for one task interferes with the information processing of another.
While the combination of Effort, Multiplicity, and the Central Executive’s selective allocation
of resources is quite parsimonious, questions arise concerning the nature of confusability. What
are the underlying mechanisms? Is confusion a process in and of itself or is it instead the
outcome of something else? The section that follows will look at another conceptualization of
complex task performance using the Central Executive Functions of Shifting, Updating, and
Inhibition (Miyake, et al., 2000). From this perspective, an inability to correctly update
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information in working memory, shift between mental sets, or otherwise inhibit a prepotent
response might contribute to confusion.
2.2 The Central Executive
Miyake et al. (2000) define executive functions as “general purpose control mechanisms that
modulate the operation of various cognitive subprocesses and thereby regulate the dynamics of
human cognition.” The study of this aspect of human information processing was originally
motivated by studies of people with cognitive impairments, including their performance on
complex cognitive tasks (Miyake, et al., 2000). One famous early example was Phineas Gage,
an individual whose behaviour and personality is said to have changed significantly following
an accident where a metal bar (railroad tamping iron) passed through his skull and part of his
frontal lobe (Harlow, 1848; Macmillan, 2000). Analysis of case studies such as these, as well as
laboratory experiments using complex cognitive tasks, have motivated theories concerning the
frontal lobes’ role in cognition (E.g., Shallice & Burgess, 1991), and led to models such as
Baddeley’s multi-component model of working memory (Baddeley & Hitch, 1974; Baddeley A.
D., 1986; 2007) and Norman and Shallice’s (1986; Shallice, 1988) depiction of the Supervisory
Attentional System (SAS), upon which Miyake et al.’s work builds.
In the multi-component model of working memory Baddeley postulated that two processing
systems exist, one for speech-based verbal information (the articulatory or phonological loop)
and one for visuo-spatial data (the visuo-spatial sketchpad). Moreover, he hypothesized the
existence of a central executive whose role was to regulate other cognitive processes (Baddeley
A. D., 1986; Baddeley A. , 2007). Norman and Shallice (1986) argued that a Supervisory
Attentional System (SAS) exists, which serves as a high level controller of cognitive processes
that is used to solve complex problems that lower-level, more automatic systems cannot resolve.
Specifically, the SAS was thought to operate by way of either promoting or inhibiting the
activation of schemas in situations where no one schema was adequate (Norman & Shallice,
1986). Baddeley (1986; 2007) proposed that the SAS was a close match to his conceptualization
of the central executive.
Since the creation of these theories a number of different functions were proposed that were
thought to operate within the central executive and contribute to high-levels of cognitive
functioning. However there has been much debate concerning whether these functions are
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unitary, meaning that they are all merely different names ascribed to one underlying process, or
diverse, which suggests that each contributes in its own way to central executive functioning
(Teuber, 1972). In their research Miyake et al. (2000) describe how the field initially favoured a
unitary model, but that conflicting findings existed. Of particular interest, Miyake et al.’s (2000)
review of a number of studies found that different tasks thought to involve executive functions
(e.g., Wisconsin Card Sort and Tower of Hanoi tasks) were consistently not significantly
correlated (r < .40). The review also found a tendency for Exploratory Factor Analysis (EFA) to
identify the existence of multiple separable factors rather than a single unitary one.
2.2.1 Shifting, Updating, and Inhibition
The first goal of Miyake et al.’s (2000) work was to assess the extent to which one or more
mechanisms underlie central executive functions. Of the many functions that have been
proposed, the three that they chose for their investigation were Shifting, Updating, and
Inhibition. These three were chosen because the research literature had identified them as being
associated with a number of simple and complex tasks (Baddeley A. D., 1996; Logan, 1985;
Lyon & Krasnegor, 1996; Rabbitt, 1997; Smith & Jonides, 1999). In the paragraphs that follow
these three central executive functions are described as conceptualized by Miyake et al. in their
study.
Referencing the work of Monsell (1996), Miyake et al. define Shifting as the function of
consciously switching between mental sets or tasks. They also note the association between
shifting and the frontal lobes, among other regions of the brain (Moulden, et al., 1998).
Crucially, shifting in the central executive sense should not be confused with less cognitive
forms of shifting such as shifting attention between multiple perceptual stimuli. For example,
Posner and Raichle (1994) found that different areas of the brain are responsible for the “visual
orientation network” (parietal lobes and midbrain) as compared with the Executive Attention
Network (frontal lobes including the anterior cingulate). Shifting tasks used in the Miyake study
include the plus-minus task (Jersild, 1927), number-letter task (Rogers & Monsell, 1995), and
local-global task (Miyake, et al., 2000; Navon D. , 1977), all of which look at shifting between
different types of information. This conceptualization of shifting appears quite similar to that
used by Wickens, Hollands, Parasuraman, and Banbury (2013) in their description of task
switching.
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Updating (Morris & Jones, 1990) involves the monitoring and processing of information
relevant to a target task and then replacing outdated information in working memory with more
recent data. This may involve ‘temporal tagging,’ which Jonides and Smith (1997) describe as a
mechanism that allows the brain to keep track of how long ago information was presented.
Updating has been described as a dynamic process that involves maintaining information in
short-term working memory and not a long-term memory process that passively stores
information for later use (Jonides & Smith, 1997). Tasks that were associated with the updating
function and used by Miyake et al. included the keep-track task (Yntema, 1963), letter memory
task (Morris & Jones, 1990), and tone monitoring task (Miyake et al., 2000, based on the Mental
Counters task of Larson, Merritt, & Williams, 1988).
Finally, Inhibition is defined by Miyake et al. (2000) as the ability to consciously inhibit
“dominant, automatic, or prepotent responses” and by Logan (1994) as “an internally generated
act of control.” This is clearly distinguishable from other, non-deliberate, forms of inhibition
that occur either due to neural activations or in conditioning contexts (Miyake, et al., 2000).
Single tasks that were known to be associated with Inhibition include the Stroop task (Stroop,
1935), Antisaccade task (Hallett, 1978), and Stop-signal task (Logan, 1994).
Figure 2: Miyake et al. (2000) three factor model and associated tasks (Redrawn from Miyake, et al., 2000).
When the results from the nine tasks administered to 137 college students by Miyake et al. were
analyzed using confirmatory factor analysis with one, two, three, and no factor models, the
results indicated that three factor model predictions, which included Shifting, Updating, and
Inhibition as separate functions, did not significantly differ from observed results. Figure 2
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shows the extent of these findings using standardized factor loadings (underlined numbers)
estimated using the Maximum Likelihood method. In the figure, small straight arrows represent
error terms while curved arrows show the correlations between factors. All correlations and
factor loadings were reported as being significant (Miyake, et al., 2000). Miyake et al.
concluded that while there exists some commonality (unity) between the three functions, the
three are distinguishable nonetheless (diversity).
2.2.2 Complex Cognitive Tasks
The second goal of Miyake et al.’s work was to identify the extent to which Shifting, Updating,
and Inhibition contributed to an assortment of complex cognitive tasks. They found that each
does so differently, with the Wisconsin Card Sort Task (Berg, 1948) being primarily affected by
Shifting, the Tower of Hanoi Task (Humes, Welsh, Retzlaff, & Cookson, 1997) by Inhibition,
Random Number Generation (Miyake, et al., 2000; Towse & Neil, 1998) by a combination of
Updating and Inhibition, and Operational Span (Miyake, et al., 2000; Turner & Engle, 1989) by
Updating ability. Finally, on a dual task that combined the Maze Tracing Speed Test (Ekstrom,
French, Harman, & Dermen, 1976) with a word generation task, no impact from any factor was
found. This led Miyake et al. to hypothesize that the coordination of multiple tasks may
represent a central executive function that is distinct from Shifting, Updating, and Inhibition,
although they caution that this is an area where future work is needed. This hypothesized fourth
central executive function that is specialized for multi-tasking is especially interesting given
Wickens et al.’s (2013) theorizing that the central executive is responsible for the allocation of
resources between tasks. It may be the case that the dual-task executive function serves this
purpose as a resource manager.
Ultimately, Miyake et al. conclude that their results confirmed Teuber’s (1972; Duncan,
Johnson, Swales, & Freer, 1997) position that there exists both a unity and diversity of
functions, as well as other findings supportive of the concept of a “family resemblance” between
distinct central executive functions (Duncan, Johnson, Swales, & Freer, 1997; Engle, Kane, &
Tuholski, 1999; Kimberg & Farah, 1993). However, they note that investigations of central
executive functions often involve contributions from non-executive components (the “Task-
Impurity Problem”) that can make the attribution of behaviour to specific executive processes
difficult (Burgess, 1997; Miyake, et al., 2000). They also postulate the existence of a different
13
inhibitory process, one that is not deliberately engaged, but which operates to inhibit the
updating of irrelevant information and prevents the activation of an irrelevant mental set
(Miyake, et al., 2000). Further discussions of Miyake’s work and that of other researchers
studying executive functioning have been provided by Jurado and Rosselli (2007), and Banich
(2009).
Recently Miyake and Friedman (2012) published an update of their view of executive functions.
In this updated view, higher-level executive functions such as planning still involve executive
functions such as shifting, updating, and inhibition, which themselves can be decomposed into
lower-level functions such as “monitoring, item addition, active maintenance, and item deletion
[in the case of] updating” (Miyake & Friedman, 2012).
Figure 3: a) A recent replication of Miyake et al.'s (2000) original three factor model using new data by Friedman, Miyake, Robinson, and Hewitt (2011). b) A revised representation of executive functioning from the same sample
(Friedman, Miyake, Robinson, & Hewitt, 2011). (Redrawn from Miyake & Friedman, 2012)
Results from a series of longitudinal genetic (twin) studies informed a series of revisions to the
three factor model (Shifting, Updating, and Inhibition) as shown in Figure 3. Each executive
14
function was decomposed into shared and unique components which, once completed, led to the
finding that almost no unique variance was explained by Inhibition (Figure 3b) (Miyake &
Friedman, 2012).2 As before, in the figure short straight arrows represent error terms and
underlined numbers, factor loadings. While admitting that the model remains under
development, and that other executive functions such as “dual-tasking” have yet to be explored,
Miyake and Friedman (2012) believe that the “Common Executive Function” reflects the ability
to focus on task goals and goal-related information, and serves as a guide for lower-level
processing. This is an ability that has been linked to response inhibition (Munakata, et al., 2011).
Regarding shifting and updating-unique components, Miyake and Friedman (2012) believe the
shifting-specific component involves the ability to switch to new task-related mental sets with
ease, while the updating-specific component is associated with either the control of information
as it enters working memory or the retrieval of items from long-term memory. Finally, in citing
current and future research directions, Miyake and Friedman discuss their attempts at examining
how individual differences in executive functions relate to a variety of psychological
phenomena.
2.3 The Central Executive, Multiple Resource Theory, and Driver Distraction: Towards Unification
Mizobuchi et al. (2012) hypothesized that individuals vary in their abilities as they relate to the
three central executive functions identified by Miyake et al. (2000), and that these different
levels of ability affect experienced workloads and task performance (Figure 4). Mizobuchi et
al.’s results showed that task performance could be related to a certain extent to levels of
abilities. Following a literature review no other research was found that investigated driver
distraction from the perspective of both Wickens’ Multiple Resource Theory and Miyake et al.’s
central executive functions, although Wickens (2008) did mention that the central executive
appears to be a factor in how resources are allocated between tasks.
2 Descriptions of tasks shown in Figure 3 are available from Friedman et al. (2008).
15
Figure 4: Mizobuchi-Chignell model of cognitive task demands and individual abilities (S - Shifting, U - Updating, and I - Inhibition) as related to experienced workload. (Mizobuchi S., Chignell, Suzuki, Koga, & Nawa, 2012)
Wickens et al. and Miyake et al. both hypothesize the existence of multitasking-specific
functions (the central executive as a resource allocator, Wickens, Hollands, Parasuraman, &
Banbury, 2013; and the coordination of multiple tasks fourth executive function, Miyake et al.,
2012). It seems likely that Miyake et al.’s central executive functions may play a role at the
stages of processing level in the multiple resource model. Thus, the Wickens Multiple Resource
Model may describe some of the processes that Miyake et al. (2000; 2012) mention as
contributing to the task impurity problem (i.e., limited resources dedicated to perceptual
modalities, information codes, visual channels, and stages of processing account for a portion of
the variance that is inexplicable using the Miyake three factor model). Meanwhile, executive
functions such as Shifting, Updating, and Inhibition (or the in-development Shifting-specific,
Updating-specific, and Common Executive Functions) determine how information is processed
and manipulated in working memory. The Mizobuchi model (Mizobuchi et al., 2012) adds to
this by further explaining individual differences in performance, although the extent to which
central executive abilities consistently differ between individuals has been questioned in the past
(Rabbitt, 1997, p. 12). With this in mind, research reported in this thesis will show the extent to
which central executive abilities and information presentation styles affect individual dual-task
performance and eye gaze tendencies.
2.4 Risk Tolerance Having reviewed theories concerning the effects that the presentation of information has on
multitasking performance, and the role of central executive in complex tasks, the final
theoretical area of interest involves risk tolerance. Here, risk tolerance relates to the combination
16
of an individual’s perception of risk and the extent to which they engage in risky behaviour. It is
hypothesized that these measures are related to both multitasking performance and eye gaze
tendencies.
2.4.1 Research on Risk and Driving
Risk research is multifaceted. Some studies investigate drivers’ perceptions of risk while others
look at absolute risk through driving history. Attitudes towards driving are also frequently
studied. In the majority of cases self-report data is collected via questionnaires, although in
some cases participant analysis of photographs or videos of driving scenarios are used
(Hergovich, Arendasy, Sommer, & Bognar, 2007). Census data and government databases have
also proven useful (Begg, Brookland, Hope, Langley, & Broughton, 2003).
Given that the purpose of incorporating risk measures in this study was to identify drivers’
subjective attitudes towards taking risks while driving, and to see if those with high risk
tolerance display different dual-task performance and eye gaze tendencies than others, it was
decided to assess both risk-taking behaviour and perceptions of risk. Further, since risk
tolerance was to be measured along with demographic data using an online survey, relatively
short questionnaires were preferred.
Several measures were identified that met our criteria and which have seen repeated citation and
validation. These include the DOSPERT (Domain Specific Risk Taking) scale (Weber, Blais, &
Betz, 2002; Blais & Weber, 2006), Zuckermann’s Sensation Seeking Scale (SSS) (Zuckerman,
Kolin, Price, & Zoob, 1964; Zuckerman, 1994), and Reason’s Manchester Driver Behaviour
Questionnaire (DBQ) (Reason, Manstead, Stradling, Baxter, & Campbell, 1990; Parker, Reason,
Manstead, & Stradling, 1995; Reimer, et al., 2005). Of these the DOSPERT (Blais & Weber,
2006) and an Americanized version of the DBQ (Reimer, et al., 2005) were chosen as our
measures.
Of relevance to the scales mentioned above, foundational theories cited by authors of the scales
include the expected utility framework and prospect theory (Kahneman & Tversky, 1979;
Tversky & Kahneman, 1992), the risk-return framework of risky choice (Sarin & Weber, 1993;
Weber E. U., 1997; 1999), the theory of reasoned action (Fishbein & Ajzen, 1975; Ajzen &
Fishbein, 1977), the theory of planned behaviour (Ajzen I. , 1985; Ajzen I. , 1991), work on the
17
sensation seeking personality trait defined by Zuckerman (Zuckerman, Kolin, Price, & Zoob,
1964; Zuckerman, 1994), Rasmussen’s description of skill, rule, and knowledge-based
behaviour (Rasmussen, 1980), and research into the classification of human error (Reason J. T.,
1988; 1990).
Early theorizing on the nature of decision making in risky situations focused on expected utility
theory wherein the decisions individuals made were thought to be based on a comparison
between the values of potential outcomes and the probability with which each might occur. [See
von Neumann & Morgenstern (1944) for a detailed review]. Kahneman and Tversky (1979)
furthered this line of thinking with Prospect Theory, which distinguished between two phases
when choosing between options. In an editing phase prospective choices were analyzed and
simplified into easily comparable forms, while in the evaluation phase prospects were compared
and the one with the highest value selected. Another important aspect of this framework was
risk aversion (the preference of a certain gain over a risky outcome), and risk seeking
(preference for the risky outcome).
Unlike expected utility, which was a function of value and probabilities, the risk-return
framework described choices as resulting from the analysis of trade-offs between expected
benefits and perceived risk associated with each option (Weber, Blais, & Betz, 2002).
Psychological risk-return models (Weber E. U., 1997; 1999) defined perceived risk as a
subjective variable that differed across individuals and contexts. This distinction explained
situations where individuals either perceived risk differently across different domains, or
otherwise tolerated different levels of risk across domains. However, additional work by Weber
(1999) has shown that perceived-risk attitudes (individuals’ willingness to select an option with
a given level of risk) are consistent across groups and situations when risk perceptions are
controlled for. Therefore, it appears as though most domain-related differences are due to
changing perceptions of risk and not of general risk-related attitudes (Weber E. U., 1999).
With respect to the relationship between attitudes and behaviour, major psychological theories
of interest are the theory of reasoned action (Ajzen & Fishbein, 1977) and the more recent
theory of planned behaviour (Ajzen I. , 1985; Ajzen I. , 1991). Here behaviour is determined by
the extent to which personal attitudes towards the given behaviour, subjective norms, and
18
perceived behavioural control over the situation influence behavioural intentions (Ajzen I. ,
1991).
Weber, Blais and Betz (2002) cite the risk-return model as being a theoretical basis for the
DOSPERT instrument that they developed. Other work cited includes that of Byrnes, Miller, &
Shafer (1999) who conducted a meta-analysis of risk taking experiments for the purposes of
identifying gender differences across different contexts and task-types. It was Byrnes, Miller,
and Shafer’s work, among others, that led Weber, Blais and Betz to identify the Financial,
Health & Safety, Recreation, Ethics, and Social domains. Conversely, the Sensation Seeking
Scale is based on the sensation seeking personality trait, which was defined by Zuckerman
(1994) as “a trait defined by the seeking of varied, novel, complex, and intense sensations and
experiences, and the willingness to take physical, social, legal, and financial risks for the sake of
such experience.” Finally, the DBQ was created based on the distinction between errors, “the
failure of planned actions to achieve intended consequences,” and violations, “deliberate
(though not necessarily reprehensible) deviations from those practices believed necessary to
maintain the safe operation of a potentially hazardous system.” (Reason, Manstead, Stradling,
Baxter, & Campbell, 1990)
While researchers had previously examined the relationship between violations and accidents
[Parker, Reason, Manstead and Stradling (1995) mention the works of Biecheler-Fretel and
Moget-Monseur (1984) who looked at the infringement of traffic rules, and Peck, McBride, and
Coppin (1971) who compared the number of traffic offence convictions with the number of
accidents individuals were involved in, as examples], Reason (1988; 1990) went a step further
with his typology of errors that was based on Rasmussen’s (1980) work. Rasmussen (1980, pp.
108-110) distinguished between skill-based behaviour related to the perception of information
and automated actions, rule-based behaviour determined by “skilled actions or routines …
controlled by stored rules”, and knowledge-based behaviour related to intelligent problem
solving. Citing Rasmussen’s work, Reason (1990) defined three types of errors: Slips and lapses
(failures of planned actions due to execution and/or memory storage failures associated with
skill-based behaviour), and mistakes (failures due to plans themselves being flawed), which
were further split into rule-based and knowledge-based sub-types. Reason et al.’s (1990) initial
50-item questionnaire investigated slips, lapses, mistakes, unintended violations, and deliberate
19
violations on British roadways, and was later revised into a shorter 24-item Americanized
version (Reimer, et al., 2005).
Weber, Blais, and Betz (2002) investigated the relationship between the Sensation-Seeking
Scale Version V (Zuckerman, 1994) and an early version of the DOSPERT. They found that
sensation seeking was correlated with risky behaviour, risk perceptions and expected benefits.
Specifically, they found moderate positive correlations between the risk-behaviour scale
subdomains and sensation seeking subscales and concluded that sensation seeking influences
risk taking by affecting the perception of risks and benefits. Similarly, Rimmo and Aberg (1999)
compared types of driving errors, as defined using Reason et al.’s typology, with Sensation
Seeking scores for 700 Swedish drivers, and found that high Thrill and Adventure Seeking
(TAS) and Disinhibition (Dis) sensation seeking subscale scores were positively associated with
self-reported violation-type behaviour.3
2.4.2 Risk Scale Specifics
The DOSPERT Scale (Weber, Blais, & Betz, 2002; Blais & Weber, 2006) is a validated self-
report questionnaire that measures Risk-Taking behavioural intentions (the chances that one
might engage in risky behaviour) and risk-perceptions (judging how risky each activity is) for
the same items. The scale references multiple domains in accordance with past findings that
willingness to take risks appears to vary in different contexts (Blais & Weber, 2006). While the
driving context is not specifically assessed, elements of driving risk overlap with some of the
DOSPERT’s domains. One potential alternative to the DOSPERT is the Vienna Risk Taking
Test – Traffic, which is not a pen-and-paper test and so was deemed impractical for use in a
short online survey. Therefore, as will be described in the methodology section below, the risk-
perception subscale was chosen to be one of the two scales presented to participants in this
study.
In contrast to the DOSPERT, Reason’s Manchester Driver Behaviour Questionnaire (Reason,
Manstead, Stradling, Baxter, & Campbell, 1990; Parker, Reason, Manstead, & Stradling, 1995)
3 While TAS scores were associated only with Violations, Dis scores were found to be positively related to mistakes and errors as well. Rimmo and Aberg (1999) speculate that this occurs because the Dis subscale measures impulsive behaviour rather than deliberate actions.
20
records self-reported frequencies of risk-taking behaviour while driving (See de Winter and
Dodou (2010) for a meta-analysis of 174 studies using the DBQ.) In this way the scale serves as
a better measure of driving-related risk-taking behavioural intentions than the more general
DOSPERT Behaviour subscale, and so was used in its place. On the bases of the analysis of risk
perceptions and historic driving behaviour, it was hypothesized that (a) differences exist
between those with different patterns of scores, and (b) that those with generally higher scores
(risk takers) will display different performance than those with lower scores. Finally, a focused
literature search for publications that used a combination of the DOSPERT and DBQ in a
driving context returned no results. Therefore, to the best of our knowledge the use of the
DOSPERT and DBQ in this thesis represents a novel combination of risk measures, which
allows for a first look at how the two scales may relate.
2.5 Summary The preceding literature review summarized relevant research concerning Multiple Resource
Theory, Central Executive Functioning and Measures of Risk Tolerance. Also, theories
concerning the ways in which mental resources are allocated and how the central executive
functions were highlighted. Current findings suggest that separate resource pools exist that are
utilized in the execution of tasks. Further, elements of the central executive support both the
allocation of resources in multi-tasking scenarios and serve to guide high-level processing by
way of updating, shifting, and inhibition functions. Research on risk taking was also reviewed
and validated measures of risky behaviour and risk-perceptions were identified. Based on this
foundation, this thesis will investigate how different measures of risk are related, and will
examine multi-tasking in an experimental context to provide further insights in the operation of
executive functions and mental resources.
21
Chapter 3: Methodology
In this experiment, data collection occurred in two distinct parts. The first part was an online
survey created using the SurveyMonkey web service. The second part involved individual
experimental sessions. This study was carried out in accordance with an ethics protocol that was
approved by the University of Toronto Social Sciences, Humanities, and Education Research
Ethics Board (Protocol Reference Number: 28332). In the sections that follow, the processes
and procedures involved in this investigation will be detailed.
3.1 Online Survey
The survey portion of this experiment involved the collection of three types of data:
• Demographic information and driving history;
• Self-reported risk perceptions; and,
• Self-reported historical risk taking while driving.
Each of these sections will be described in turn, with all survey items available for reference in
Appendix A. Recruitment was accomplished through a combination of announcements in
undergraduate engineering classes, posters placed on university bulletin boards within
engineering department buildings, posters placed in campus parking lots next to pay stations,
and an advertisement on the Craigslist online listing service (Appendix B contains a copy of the
Call for Participation document). Upon survey completion participants were invited to submit a
contact e-mail address in order to be entered into a draw for an Apple iPad Mini. The winner
was chosen by random draw at the end of December 2012 and the winning participant received
the prize shortly thereafter.
3.1.1 Demographic Information and Driving History
The purpose of this first portion of the survey was to collect descriptive information so as to
identify the subsets of the general population represented in the sample and to identify those
who met inclusion criteria for the on-campus follow-up session. Demographic data collected
included Sex, Age, Native Language, and Level of Education.
22
Additionally, driving history was sampled using questions concerning type of licensure, the
number of years spent driving on public roads, driving frequency, times ticketed, collision
history (both as driver and as a passenger), and whether or not one was required to drive for
work purposes. Following this, the remainder of the online survey consisted of the Domain-
specific Risk-taking Perception scale (DOSPERT) and the Driver Behaviour Questionnaire
(DBQ).
3.1.2 The Perception of Risk
The original 40-item DOSPERT scale was created by Weber, Blais, and Betz (2002) as a means
of investigating risk-taking perceptions, actions, and expected returns across five common
domains (Ethical, Financial, Health & Safety, Recreational, and Social). The scale measured
perceived-risk attitudes, which Weber et al. defined as “the willingness to engage in a risky
activity as a function of its perceived riskiness.” This scale was validated in a number of follow-
up studies (Weber, Blais, & Betz, 2002; Hanoch, Johnson, & Wilke, 2006) and later revised into
its current shorter form (Blais & Weber, 2006).
For the purposes of this study only the perception subscale of the recently revised 30-item
DOSPERT was applied. This subscale was included so as to quantify participants’ “gut level
assessment” of the risk involved in a variety of situations and behaviours (Blais & Weber,
2006). A seven point Likert scale was presented in a single browser window with check boxes
representing each of the seven possible responses, which ranged from “1 - Not at all Risky” to
“7 - Extremely Risky.” Since the main DOSPERT subdomains were not directly related to an
automotive context the DOSPERT’s perception subscale was used as a means of assessing risk
perceptions across a variety of general domains. Risk-related factors were also assessed with a
second scale, the Driver Behaviour Questionnaire (DBQ), which assessed driving-specific risky
behaviour.
3.1.3 Risky Behaviour While Driving
The Driver Behaviour Questionnaire is a 24-item inventory (with six point Likert scale
responses) originally created for use in the United Kingdom by Parker, Reason, Stradling, and
Manstead (1995) in order that individuals might report how often they engage in risky behaviour
while driving. In this research I used the Americanized version of the DBQ, adapted by Reimer
23
et al. (2005) to match the North American driving environment. The DBQ complemented the
DOSPERT risk perception subscale and created a fuller picture of each participant’s risk
tolerance (a combination of risk perception and behaviour). This scale was presented in its own
section of the web survey with checkboxes representing each possible answer, which ranged
from “0 - Never” to “5 - Nearly all the time.”
3.2 Experiment The experiment was carried out in an eye-tracking laboratory within the Institute of
Biomaterials and Biomedical Engineering at the University of Toronto. Data collection was
completed for a maximum of four participants a day, in individual one and a half to two hour
sessions. The sample was restricted to those survey respondents who indicated an interest in
attending the on-campus portion of the investigation and to those who met inclusion criteria.
Those criteria included being between 18 and 35 years of age, having a valid driver’s license,
having no difficulty hearing and understanding auditory English instructions, being able to
easily distinguish between different colours (red, blue, purple, green, yellow, orange, brown and
gray), having no difficulty manipulating a pedal with their right foot, and having corrected to
normal vision so long as contacts could be worn (eye glasses interfered with the eye-tracking
system).
Those who met the inclusion criteria and who accepted the invitation to return were asked to
read an information sheet and provide informed consent (Appendix C). After doing so, each
individual was asked to complete a series of tasks targeted towards measuring cognitive abilities
and assessing performance in a dual task setting. These tasks are detailed below.
3.2.1 Measures of Cognitive Abilities
Upon arrival, and following a briefing, participants were presented with a series of three tasks
designed to measure their Inhibition, Shifting, and Updating central executive abilities. These
were the Stroop Test (Stroop, 1935), Wisconsin Card Sort Test (Berg, 1948), and Colour
Monitoring Test (Mizobuchi S. , Chignell, Suzuki, Koga, & Nawa, 2012), respectively. All three
tasks were presented on a Fujitsu laptop in a random order, to control for possible order effects.
24
3.2.1.1 Stroop Task
The version of the Stroop task (Stroop, 1935; Mizobuchi S. , Chignell, Suzuki, Koga, & Nawa,
2012) used in this study involved the presentation of the names of six colours (“black,” “white,”
“yellow,” “orange,” “purple,” and “green”) in one of the same six font colours. There were 36
potential word-font colour combinations and stimuli were presented individually and in random
order. Simultaneous to the presentation of each coloured word, three response options appeared
in black ink on the bottom of the screen. These words were also colour names. The goal of the
experiment was to respond to the font colour of the word that appeared in the centre of the
screen. Responses were recorded by key press using keyboard arrow keys, with each arrow
corresponding to the location of the desired response on the screen. For example (Figure 5), the
word “White” might appear in black font colour on the centre of the screen along with three
potential responses: “Green,” ”Black,” “White”. In this case the correct answer would be to
press the centre arrow key because Black was the colour corresponding to the target word’s font
colour.
Historically the Stroop task has been found to be associated with the central executive inhibition
ability (Miyake, et al., 2000). The rationale behind this is rooted in the observation that
individuals completing the task must inhibit responding according to colour name (word
meaning) and instead must respond with the font colour.
Figure 5: Inhibition Stroop Task.
3.2.1.2 Wisconsin Card Sort Test (WCST)
The Wisconsin Card Sort Test (Berg, 1948; Mueller, 2012; Mizobuchi S. , Chignell, Suzuki,
Koga, & Nawa, 2012) involved presenting participants with four face up playing cards that
varied according to three characteristics: Colour (red, green, blue, and yellow), shape (circle,
star, cross, and square), and number of items (one, two, three, or four). Participants were then
given one additional card at a time and asked to sort it into the appropriate category without ever
25
being explicitly told which sorting rule to apply. This meant that trial and error was initially
required, although all responses were followed by a clear visual indication of correctness
(“correct” or “incorrect”). The classification rule was changed after every ten correct decisions
under a given rule. For instance, if one was correctly sorting by colour for ten trials, then after
ten correct decisions the rule would switch to sorting by number or shape. The test ended either
when eight different rules had been correctly replied or following 128 trials, whichever came
first.
In this task a perseverative error was defined as the continued application of an inappropriate
rule following an indication that it was no longer correct. This measure provides insight into
participants’ shifting abilities, since increased numbers of perseverative errors indicate an
inability to mentally shift to a new rule. The link between shifting ability and the WCST was
confirmed through latent variable analysis by Miyake et al. (2000) and this has been used in past
research in driving distraction as a measure of shifting ability (Mizobuchi S. , Chignell, Suzuki,
Koga, & Nawa, 2012).
3.2.1.3 Colour Monitoring Task
During the colour monitoring task (adapted from Mizobuchi S. , Chignell, Suzuki, Koga, &
Nawa, 2012) participants were shown blue and yellow circles approximately 8 cm in diameter
one-at-a-time on a white background (Figure 6). The order of appearance was randomized, with
a new circle appearing every 2500 ms and staying visible for only 500 ms. Participants were
tasked with indicating whenever a given colour appeared for the third time. This meant that
participants had to track the number of times a given colour appeared and repeatedly update this
information in working memory. For example, if the presentation order was “Yellow, Blue,
Blue, Yellow, Yellow” then the participant should respond to the third occurrence of Yellow
(Figure 6). Further, the number of times each colour was presented was automatically reset to
zero whenever a key was incorrectly pressed so that the effects of lapses and errors on
performance might be minimized. Participants were briefed on these details and given time to
practice prior to data collection.
The colour monitoring task as used in this thesis represents a colour-based variation of the N-
Back task. The N-Back task (Kirchner, 1985) involves tracking the number of times that a
specific stimulus has been presented, represented here by circles of different colours, and
26
responding when the Nth appearance occurs. In this task, the number of times that a given
stimulus has been presented must be constantly updated in working memory each time the
stimulus appears. As was the case in past driver distraction research, updating ability was
quantified using response accuracy (Mizobuchi S. , Chignell, Suzuki, Koga, & Nawa, 2012).
Figure 6: Updating Colour Monitoring Task
3.2.2 Dual-task Scenario
Following the assessment of executive abilities, participants switched to a desktop workstation
and began the Dual-task portion of the study. From this point onward eye-gaze was recorded
using a two-camera Remote Eye-Gaze Estimation (REGT) system (VISION 2020-RB, EI-MAR
Inc.) developed by Guestrin and Eizenman (2007). The two tasks that participants were asked to
perform simultaneously were a Pedal-tracking task associated with a widescreen monitor
directly in front of the participant and a List-monitoring task where information was displayed
on another monitor that was offset to the right hand side and below eye level. This was done so
as to imitate an in-vehicle environment where driving task-related information is presented
directly in front of the vehicle operator and where infotainment system-related tasks require
glances towards the centre console. Pedal-tracking task inputs were made through the
manipulation of a Logitech Driving Force GT Gaming Wheel pedal controller. Participants
interacted with the List-monitoring task using a standard Dell computer keyboard.
27
Figure 7: Pedal-Tracking Task details (left); List-Monitoring Task visual condition visualizations (right).
3.2.2.1 Primary Pedal-tracking Task
The pedal-tracking task that was chosen was created by Mizobuchi et al. (2012), who based
their design on the work of Uno and Nakamura (2010). The task was originally envisioned as a
means of quantifying individuals’ directional control of a vehicle along its forward path and was
found to be sensitive to increases in driver workload (Uno & Nakamura, 2010 as cited in
Mizobuchi, Chignell, Suzuki, Koga, & Nawa, 2012). In the version of the task created by
Mizobuchi et al., interface changes were made so that the task more closely mimicked the task
of driving some distance behind a lead car on the highway (Figure 7). A fixed yellow frame was
created along with a variable blue target frame. The blue frame was designed to increase or
decrease in size according to user foot pedal inputs. This creates a changing stimulus that is
somewhat analogous to the apparent size of the rear bumper of a vehicle that one is following
while driving. When the accelerator is pressed, one’s own vehicle will get closer to the car in
front causing that car’s bumper (blue target rectangle) to appear to grow in size. When the
accelerator is released the car ahead will pull away making the bumper appear to shrink. The
role of the fixed yellow frame was to identify the boundaries of an ideal following distance (not
too close and not too far behind).
The goal of the task was to modulate the foot pedal in such a way so that the blue rectangle
never grew or shrank beyond the yellow target area. If the pedal was pressed or released for too
long then the blue rectangle would either grow to be larger than the yellow target area or
otherwise shrink to be smaller, respectively. Whenever the blue rectangle exited the bounds of
the yellow target frame it would change colour (from blue to red) to indicate that corrections
28
were required. In order that this task might be more realistic, the size of the blue (bumper)
rectangle was made to vary subtly in addition to changing as a result of foot pedal modulations.
This was done so as to model the variability in speed of the vehicle one is attempting to follow.
Such variations adhered to equations (1-3) as illustrated in Figure 8. Dependent variables
included frequency of pedal modulations, eye-gaze duration on Monitor One, accuracy as
measured by time spent within the target area, and the standard deviations of throttle inputs.
𝑆(𝑛) = ∑ �𝐺𝑖 × sin�2𝜋 �𝑛∆𝑡𝑊𝑖
+ 𝑃𝑖���4𝑖=1 (1)
𝐿(𝑛) = �𝑇−∆𝑡𝑇� × 𝐿(𝑛 − 1) + 𝑘(∆𝑡
𝑇)(𝐻𝑃) (2)
𝑎(𝑛) = 𝑆(𝑛) − 𝐿(𝑛) (3a)
𝑣(𝑗) = 𝑣0 + ∑ (𝑎(𝑛))∆𝑡𝑗𝑛=0 (3b)
𝐷(𝑘) = 𝐷0 + ∑ (𝑣(𝑚)𝑘𝑚=0 )∆𝑡 (3c)
Where: • D(k) is the following distance at any given time, t = kΔt as measured in intervals of Δt and
expressed as a rectangle with a side length proportional to that of the screen (varying between 0% and 100%);
• D0 is the initial midpoint in the target range, 50% of screen size; • v0 is the initial rate of change between the simulated bumper and the target area, i.e., 0 percent per
second; • Δt is a constant equal to 0.1 seconds that represents the time interval between samples; • S(n) is the fluctuation signal representing acceleration by the vehicle being followed;
o G represents the amplitude of a randomly generated sine wave (4 total); G1 = 0.5; G2 + G3 + G4 = 0.5 (values generated through randomization);
o W represents the period of each sine wave; W1 = 1/fmin, where fmin was set to 0.2 Hz; W4 = 1/fmax, where fmax was set to 0.77 Hz ; W2 = W1 + (W4 – W1)/3; W3 = W1 + 2(W4 – W1)/3;
o P refers to the phase shifting of each sine wave; P1 = 0; P2-4 = random number between 0 and 1, representing a 0 to 2π range of
potential phase shifts; o The signal is scaled such that the average acceleration attributable to the fluctuating
signal is equal to zero; • L(n) represents the user’s acceleration, as controlled by the throttle pedal;
o T is a time constant representing the responsiveness of the pedal, set to 10Δt; o k is a sensitivity constant (gain), set to 1; o HP is the throttle value inputted by participant, P, scaled from -1 to 4;
• (S(n) – L(n)) is the net instantaneous acceleration of the simulated bumper towards or away from the centre of the screen.
29
Figure 8: Pedal Tracking Task bumper size calculations
At all times the pedal-tracking task was accompanied by the list-monitoring task. In total there
were 16 experimental conditions each comprising eight trials. While list-monitoring task
requirements changed between conditions, the pedal-tracking task always remained the same.
For this reason, the presentation order of secondary task conditions was randomized to control
for learning effects. Also, participants were instructed that the two tasks were of equal
importance and that attention should be split equally between the two.
3.2.2.2 Secondary List-monitoring Task
For this task, participants were asked to monitor the number of vowels in a sequence of
randomly generated letters presented in list form (E.g., “BAAAB”). At the end of the list, as
indicated by a change in stimulus features, different keyboard keys were to be pressed
depending on whether the total number of vowels presented was odd (NumPad1) or even
(NumPad2). Cues to respond took the form of either a change in final letter appearance (from
black letters to a white letter outlined in black) for visual sequential conditions or a change in
voice (from female to male) for audio conditions. There was no response cue in visual
simultaneous conditions because the entire list appeared at the same time. In all cases a
maximum of 7500 ms was allotted for participants to respond after which a “skipped” response
was recorded.
Before each trial, participants were cued using a distinct auditory beep to select either an audio
or visual presentation modality. In audio conditions, lists of letters would be read out
sequentially by a computerized voice through computer speakers. When the visual modality was
selected, capital letters in 24-point font size were presented either one-at-a-time in a sequential
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list (appearing for 1500 ms and separated by an interval of 1000 ms) or otherwise all-at-once
(simultaneously) depending on the experimental condition. In simultaneous conditions, response
times were recorded starting when the list first appeared. Trials across all conditions were
spaced 2000 ms apart, as measured from the moment feedback was provided for the previous
trial to the starting tone for the next trial. Visual list presentations were displayed in the centre of
a plain white window on a secondary monitor offset to the right (Figure 7). In all cases
selections had to be entered within a 3000 ms time window using either the “A” keyboard key
for audio or the “Z” key for visual.4 These keys were chosen during pilot testing so that the left
hand would always be associated with modality selection and the right hand with task responses
to avoid confusion. Following modality selection there was a 300 ms delay before the
presentation of the first stimulus.
The 16 within-subjects experimental conditions associated with the list-monitoring task
involved variations in list length (4 letter lists, 12 item lists, or variable list lengths), letters used
(“AB” vs. “AIUCFM”), modality (Audio or Visual), and presentation style (Sequential or
Simultaneous). These conditions were split into two groupings, the first of which contained 12
randomly-ordered conditions with different combinations of list length (4 vs. 12), modality
(Audio vs. Visual), letters used (“AB” vs. “AIUCFM”), and presentation style (Sequential vs.
Simultaneous). Table 1 shows a sample ordering for this first group.
Table 1: A sample ordering of the first 12 experimental conditions of the list-monitoring task.
4 Due to the way in which experimental software was programmed participants were always asked to select a modality regardless of condition. During the first 12 conditions participants were instructed which modality to select. After, participants were allowed to choose whichever modality they preferred for the final four conditions.
31
The second grouping was comprised of four conditions presented in random order. The
difference between the first and second groupings lies in list lengths and modality selections.
Whereas the first grouping varied between four and 12 letter lists, and had fixed modality
selections, the second group of conditions had variable list lengths and allowed participants to
select whichever modality they preferred. This was done so as to allow final list length to act as
a dependent performance measure with longer final lengths indicating superior performance,
and so that modality preferences might be objectively quantified. The second group of four
conditions always followed the first group of 12. Also, following each condition in the second
grouping participants were asked to indicate on a scale from zero (100% Audio preference) to
100 (100% Visual preference) which modality they subjectively preferred. This was recorded on
paper by having each participant place an “X” on a line with markings at zero, the midpoint, and
100.
For the second (final) grouping of conditions, all participants began with four letter lists that
then increased or decreased in length as a function of the ratio of correct responses to errors. If
two or more responses were correct in the previous three trials then the list length increased by
one letter. If one response was correct then the list remained the same length, and if zero were
correct then the list decreased in length by one letter. Regardless of list length, letters were
always presented in random order with no fixed frequency of occurrence (I.e., “BBBB” was as
likely to occur as “ABAB”). In all cases conditions were randomly ordered for each participant.
Due to the nature of this method, no changes to list length occurred during the first three (out of
eight) trials. Finally, subjective modality selection was enabled. Participants chose modality by
pressing one of two keys when cued to select modality. Given that conditions differed in terms
of letters used and presentation style (except when audio was selected), participants were
encouraged to try different modalities during the first three of eight trials.
A variety of additional decisions were made concerning experiment details. First, it was decided
that the first group of 12 conditions should use four and 12 letter lists, as subjective and
objective performance indicators seemed to indicate differences between the two during pilot
testing. Second, letter selection across all conditions where more than two distractors were
needed was made based on a detailed analysis of audio and visual English letter confusability.
Research indicated that the capital letters “A,” “I,” “U,” “C,” “F,” and “M” were least likely to
be confused in both the audio and visual domain (Hull, 1973; Conrad, 1964; Townsend, 1971;
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Gilmore, Hersh, Caramazza, & Griffin, 1979). Third, sequential and simultaneous presentation
styles, along with audio and visual modalities, were chosen to enable comparisons between
different modalities and presentation styles. Three combinations were of primary interest: Audio
Sequential; Visual Sequential; and Visual Simultaneous. Since audio is an inherently streaming
(sequential) medium, only sequential letter lists were presented in audio conditions. Finally,
across all conditions, dependent measures recorded during the list-monitoring task included
modality selection, reaction times, accuracy, list-length, gaze direction, and gaze duration.
3.2.3 Debriefing and Compensation
Following data collection, participants were asked to complete a brief exit questionnaire that
recorded subjective assessments of which tasks were thought to be most difficult and what
strategies (if any) were adopted. Open-ended questions that allowed for comments and feedback
were also included. This paper questionnaire was a part of the document that participants were
handed when asked to indicate subjective modality preferences.
Finally, participants were compensated for their time. The compensation structure adopted
involved advertising in recruitment materials that participants were eligible for up to $50 in
exchange for their time. Upon arrival, individuals were told they were guaranteed $30 for
participating and that the remaining $20 would be awarded based on their performance on both
the pedal-tracking and list-monitoring tasks. Specific emphasis was placed on task accuracy:
Keeping the blue rectangle in the centre of the yellow target frame, and correctly identifying the
number of vowels in a list as odd or even. This was done so as to promote equal attention to
both tasks throughout the experiment. In other words, the compensation scheme promoted a
common prioritization of tasks between individuals who might otherwise have had different
priorities. Upon study completion participants were given $50 regardless of their performance
and informed of study details if interested.
3.3 Apparatus Figure 9 depicts the apparatus used for the dual-task portion of this investigation. Please note the
monitor placement and the locations of eye-tracking equipment. Also, Logitech foot pedals were
located beneath the desk on a carpeted surface that prevented movement during
experimentation.
33
Figure 9: The Dual-Task Scenario Apparatus.
Regarding eye tracking, a two-camera Remote Eye-Gaze Estimation (REGT) system (VISION
2020-RB, EI-MAR Inc.) developed by Guestrin and Eizenman (2007) was used throughout the
on-campus portions of this study. This system allowed for the estimation of eye gaze without
requiring that participants wear a head-mounted apparatus. Also, the technology accommodated
free head movements which meant that individuals did not need to focus on trying to stay
perfectly still. The basic principle underlying this technology is that two cameras detect the
centres of the pupil and one or more reflections off the cornea (Guestrin & Eizenman, 2007).
Using this information software then mathematically calculates the coordinates of the point-of-
gaze (where the individual is looking) as it intersects a two-dimensional plane parallel to, and
running through, the primary monitor. Additional calculations can then be performed to identify
whether an individual is looking at a primary or secondary display. In this experiment, multiple
reflections were used, which were created by four infrared light sources. Finally, although
success has been seen using simple single-point calibration techniques (where a participant need
only look at one place for the system to calibrate) (Guestrin & Eizenman, 2007), a more
traditional multi-point calibration method was used in this study due to sample demographics
(Multipoint calibration is only a large issue for specific populations, such as infants, who have
difficulty sustaining attention).
34
Chapter 4: Results5
This chapter reports on the results of the online survey, and laboratory experiment, that were
carried out in this thesis. Multiple analyses, including analyses of variance, regression
modelling, and multilevel linear modelling were performed on pedal-tracking task performance
data (proportion of time spent within target boundaries, number of errors made across
conditions, etc.), list-monitoring task accuracy and reaction times, and eye tracking data.
Covariates and independent variables included central executive ability task performance scores
(correct reaction times and proportions of errors), and scores on risk measures. Of these, only
significant results are reported below.6
44 individuals (30 male) aged 18 to 34 (𝑋� = 25, 𝑆𝐷 = 4.5) completed the online survey. Of
these, all reported holding some form of driving license (29 – fully licensed, 14 – learner’s
permits, 1 – unspecified) and 34 were native English speakers. Level of education was generally
high, with 39 individuals reporting at least some post-secondary education (26 –
University/College, 13 – Graduate University/College), with five mentioning secondary school
as their highest level of education achieved.
Twenty individuals reported driving on a weekly basis and 18 on a monthly basis, while four
reported driving only once or twice a year (two people did not answer this question). 26
participants claimed to have driven for more than five years, while five reported one to two
years of experience and six reported less than one year of driving. Close to one quarter of the
sample reported being ticketed for driving infractions in the past with one person reporting
5 IBM’s SPSS software package (IBM Corp., 2012) was used under license, along with R (R Core Team, 2013) to perform all reported calculations. In all cases analysis focused on the first 12 experiment conditions where modality and list length were determined by experimenters, and not by participants. Sample sizes changed according to the type of data under review. Analysis covered data from the online survey (N = 44), the on campus follow-up (N = 22) and successfully recorded eye tracking data (N=20). 6 Not mentioned elsewhere, failures of convergence were found when attempting multilevel linear modelling. This was assumed to be the result of sample size constraints. Also, correct reaction times and proportions of errors were chosen to represent executive abilities because they have both been used in the past and were most sensitive to experimental differences.
35
being ticketed over five times. Of the 20 individuals who reported having been in a collision at
some point, 13 were drivers at the time and seven were passengers.
The following analyses are reported in this chapter. First, a comparison is made between the
DOSPERT Perception scale and the DBQ using the data collected with the online questionnaire.
Performance on the primary pedal tracking task and secondary list monitoring task in the
context of different presentation styles, levels of risk tolerance, and central executive abilities
are then examined. Finally, the effects of presentation style and central executive abilities on
gaze are reviewed.
In addition to fulfilling the requirements for this Masters thesis, some of the experimental results
not reported here were reported in a paper written with Dr. Sachi Mizobuchi and her colleagues
from the Toyota Infotechnology Center Company Limited. That paper reports significant
relationships between additional factors such as list-monitoring task list length (four vs. 12) and
number of distracting letters (“AB” vs “AIUCFM”) and task performance. Further, it was shown
that these variables interacted with presentation style. These results relate to those reported here
by showing how task characteristics are associated with performance. The article will be
published in the Proceedings of the 2013 Human Factors and Ergonomics Society International
Annual Meeting (Mizobuchi S. , et al., 2013).
4.1 Risk Tolerance Unlike central executive abilities, for which research exists concerning the relationship between
different functions (Miyake, et al., 2000), no study was found that investigated potential
connections between DOSPERT and DBQ risk measures. DOSPERT and DBQ total and
subdomain scores were compared to assess the degree to which the two were related.
4.1.1 The DOSPERT-DBQ Relationship
As noted earlier, the DOSPERT scale is comprised of five subdomains: Ethical, Financial,
Health & Safety, Recreational, and Social. Of these, the Financial subdomain can be further
divided into Investment and Gambling subcategories. Scoring of the DOSPERT involves taking
either the sum or mean of scores for items associated with each of the subdomains (Blais &
Weber, 2006). Higher scores on each subdomain reflect increased perceptions of risk in a given
subdomain, and a higher score across subdomains indicates a higher overall perception of risk.
36
Scoring for the DBQ involves taking the sum of all 24 item scores and treating this as a measure
of risky behaviour while driving (Reimer, et al., 2005). Here higher scores indicate that
participants reported conducting risky driving behaviours with greater frequency than those with
lower scores. However, scale items can also be broken down into three subtypes of risky
behaviour: Those due to errors (misjudgments and failures of observation that may lead to
others being harmed), lapses (where risk occurs due to absent-mindedness, and attention and
memory failures, but which are unlikely to affect anyone other than the person responsible), and
violations (where the risky option is deliberately chosen) (Reimer, et al., 2005; Parker, Reason,
Manstead, & Stradling, 1995). For this analysis, summed scores for each category were used
along with the total combined score, which was calculated by taking the sum of all three
category scores.
Given the exploratory nature of this analysis, two-tailed significance testing was used. Initial
results showed a significant negative correlation, r = -.30, p <.05, between total DOSPERT-
Perception and Total DBQ scores. Next, subscale items were compared both within and between
scales (see Appendix D for detailed results). Analysis showed highly significant correlations
between DBQ total scores and all DBQ subcategories, as well as similarly significant
correlations between subcategories themselves. Correlations between Error, Lapse, and
Violation scores ranged between r = .43 and r = .47 (p < .01) while correlations between
subcategories and total score ranged between r = .74 and r = .85 (p < .001). Only about a third of
the correlations between pairs of DOSPERT subdomains were statistically significant. Of these
Ethical scores were related to Investment (r = .37, p < .05) and Health & Safety scores (r = .57,
p < .001), Investment scores were related to Gambling (r = .31, p < .05) and Social (r = .42, p <
.01) scores, Health & Safety scores were significantly related to Recreational Scores (r = .33, p
< .05), and Recreational scores were correlated with Social scores (r = .32, p < .05).
While few DBQ categories were significantly related to DOSPERT subdomains, all of those
relationships that were statistically significant were inversely correlated (just like the
relationship between overall scale totals). Of these relationships, DBQ Violation scores were
inversely related to Ethical (r = -.31, p < .05) and Health & Safety scores (r = -.42, p < .01), as
were DBQ total scores (Ethical r = -.33, p < .05; Health & Safety r = -.37, p < .05). Since
violations are the only type of behaviour that involve the willful commission of undesirable
action, it makes sense that only DBQ violation subscale scores were significantly related to
37
DOSPERT subscales. Table 2 summarizes the findings obtained. Figure 10 shows scattergrams
of the relationships between DBQ violation scores and two of the DOSPERT scales.
Table 2: The relationship between summed DBQ violation scores and mean DOSPERT subdomain scores.
Mean DOSPERT
Ethical Scores
Mean DOSPERT Investment
Scores
Mean DOSPERT Gambling
Scores
Mean DOSPERT Health &
Safety Scores
Mean DOSPERT
Recreational Scores
Mean DOSPERT
Social Scores
Total DBQ Violation
Scores -.31* .09 -.10 -.42** -.04 -.15
Note: *p < .05; **p < .01.
Figure 10: A) (left) A scattergram of DOSPERT Ethical subdomain scores by Total DBQ Violation scores; B) (right) A scattergram of DOSPERT Health & Safety subdomain scores by Total DBQ Violation scores.
4.2 Primary Pedal Tracking Task Performance
Pedal tracking data analysis investigated performance differences across secondary task
presentation styles (Audio - one item at a time - Sequential, Visual Sequential, and Visual – all
items appear at once - Simultaneous). I will begin the discussion of the results with a review of
sample demographics.
4.2.1 Sample Demographics
22 individuals (10 Female) aged between 18 and 33 years old (𝑋� = 25, 𝑆𝐷 = 4.6) participated
in the experiment. Every individual reported holding some form of driving license (16 – fully
licensed, six – learner’s permits) and 19 were native English speakers (although all participants
38
spoke English fluently). Level of education was high, with ten individuals having attended at
least some University/College, nine having attended or completed graduate University/College,
and three being high school graduates.
Ten individuals reported driving on a weekly basis and nine on a monthly basis. Two reported
driving only once or twice a year. 15 participants claimed to have driven for more than five
years, with two reporting one to two years of experience and four claiming less than one year on
the road (data not provided for one participant). Only four individuals reported being ticketed
for driving infractions in the past with one person reporting being ticketed over five times. Ten
individuals reported having been in a collision at some point, eight while driving and two as
passengers.
4.2.2 Pedal Tracking Task Accuracy
Figure 11: Mean out of bounds error scores across presentation styles.
The first measure of primary task accuracy analyzed was the percentage of time within each
session where the target rectangle was kept within frame boundaries. These scores demonstrated
a pronounced ceiling effect and did not exhibit significant differences across the experimental
factors. Next, the number of times at least one out-of-bounds error occurred was counted for
each participant across the four conditions (two list lengths and two sets of distractors)
39
associated with each of the three presentation styles. This led to a measure that varied between 0
and 4, and reflected the number of times that at least one error was made for any given
presentation style. A repeated measures analysis of variance found significant differences in
these scores, F(2,42) = 3.60, p < .05. Although Mauchly’s test of sphericity was of borderline
concern, χ2(2) = 5.82, p = .055, significance was retained with both Greenhouse-Geisser, ε =
.80, p < .05, and Huynh-Feldt, ε = .85, p < .05, corrected degrees of freedom. Planned
comparisons identified the difference as being due to a significant difference in error rates
between the visual sequential and visual simultaneous conditions (Figure 11, p < .05).
4.3 Secondary Vowel Monitoring Task Performance
Secondary vowel-monitoring task data was reviewed both from a presentation style, and a risk
and central executive ability perspective.
4.3.1 Accuracy & Presentation Style
Repeated measures analysis of variance found no significant differences in secondary task
accuracy across presentation styles. Participants were relatively accurate across all conditions
with the minimum secondary task accuracy being 79%.
4.3.2 Accuracy, Risk, and Central Executive Abilities
Based on the hypothesis that task performance might be influenced by risk tolerance and central
executive abilities, measures of these constructs were combined in a stepwise-entry regression
analysis that analyzed mean secondary task accuracy. Predictors included DOSPERT total and
summed subdomain scores, DBQ total and subscale scores, Stroop inhibition task accuracy,
colour monitoring updating task correct reaction times, and Wisconsin card sorting shifting task
number of perseverative errors. The resulting model significantly predicted accuracy, p < .005,
R2 = .59, using Wisconsin Card Sorting Task ability (Perseverative Errors, β = -.66, p < .005),
DOSPERT recreational subdomain scores, β =.53, p < .005, and DBQ Lapse scores, β =.38, p <
.05. Higher shifting ability, as evidenced by fewer numbers of perseverative errors on the
WCST, was significantly related to higher secondary task accuracy, along with greater
perceptions of risk in recreational settings and increased self-reported attention and memory
lapses while driving.
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4.4 Effects of Cognitive Ability and Presentation Style on Eye Gaze
Valid eye gaze data was collected from 20 of the 22 participants. The following section
summarizes the results of a one-way repeated measures analysis of variance that found
significant main effects of presentation style on primary monitor gaze durations, as well as
follow-up correlational analysis wherein measures of central executive updating, shifting, and
inhibition abilities were found to be significantly related to gaze patterns when certain
presentation styles were used.
The mean proportion of time spent dwelling on Monitor One (associated with the pedal tracking
task) was calculated across each of the three secondary task presentation styles. Data
visualizations revealed large differences in gaze patterns between the different presentation
styles as well as differences due to participant updating and inhibition abilities.
Figure 12: Main effects of presentation style on the mean
proportion of time spent dwelling on Monitor One.
A one-way repeated measures analysis of variance found significant differences in dwell
patterns across presentation styles, F(2,38) = 209.57, p < .001. Pairwise comparisons using a
Bonferroni correction confirmed that the proportion of time spent dwelling on monitor one was
significantly larger across audio conditions than across either of the visual conditions. Within
the visual conditions, significantly greater Monitor One dwell times occurred across visual
41
simultaneous conditions than when secondary task information was presented visually
sequentially (Figure 12)
Correlational analysis investigated the effects of updating, shifting, and inhibition ability
measures on mean Monitor One dwell proportions using mean colour monitoring updating task
accuracy as a measure of updating ability, percentage of responses that were perseverative errors
made during the Wisconsin card sorting task for shifting ability, and mean correct reaction time
for the Stroop task as an indication of inhibition ability. Relationships were analyzed across
audio sequential, visual sequential, and visual simultaneous experimental conditions. Table 3
contains the results of this analysis, the details of which will be summarized below.
Table 3: Correlations between Executive Abilities and Mean Monitor One Dwell Proportions across Presentation Styles.
Audio Sequential Presentation Style
Visual Sequential Presentation Style
Visual Simultaneous Presentation Style
Updating Ability .42 .28 .59** Inhibition Ability -.01 -.53* -.50* Shifting Ability -.09 -.45* -.20
Note: *p < .05; ** p < .01.
The significant relationship between shifting ability and mean monitor one dwell times in the
visual sequential condition was affected by outlier data. When the percentage of perseverative
errors for one participant were either removed or replaced with the series mean, the relationship
was no longer significant. Due to the small sample size, the results shown in Table 3 should be
viewed as preliminary findings that will need to be confirmed or refuted in future studies.
Figure 13 shows the relationships between updating ability and monitor one dwell time as
scattergrams with corresponding lines of best fit for each of the three presentation styles (shown
with blue, green, and red lines of best fit respectively). Figure 14 shows the corresponding
scattergrams and fitted lines for the relationship between inhibition ability and Monitor One
dwell time, also with three lines of best fit, one for each of the presentation styles.
42
Figure 13: The relationship between the mean proportion of time spent gazing at Monitor One and
updating ability by presentation style.
Figure 14: The relationship between the mean proportion of time spent gazing at Monitor One and
inhibition ability by presentation style.
43
Chapter 5: Discussion
In this thesis, results will be discussed in the context of the state of the art as presented in the
literature review. The discussion will be separated into four sections:
• The relationship between the DOSPERT and DBQ;
• The effects of different styles of information presentation on primary and secondary task
performance;
• The effects that measures of risk and central executive abilities had on performance
measures; and,
• The extent to which both presentation styles and measures of central executive functions
affected eye gaze.
5.1 The Relationship between Measures of Risk Tolerance The analysis of risk tolerance was based on a sample of young mostly-male (68% male)
English-speaking individuals who were generally highly-educated, and experienced drivers.
Most drove at least on a monthly basis and over half had more than five years of driving
experience. All participants completed the DOSPERT and DBQ scales in which higher scores
reflect increased perceptions of risk or higher frequencies of risky driving behaviour,
respectively. The finding that DBQ Errors, Lapses, and Violations subscales were significantly
intercorrelated matches results reported by Reimer et al. (2005) who also found positive
correlations between subscales (although they do not mention significance or effect size). With
respect to DOSPERT subdomain scores, findings of scale intercorrelations varying between |r| =
.02 and |r| = .57 are in line with findings from Blais and Weber’s (2006) validation study, which
found varying intercorrelations from r = .19 to r = .66. It appears then that observed results
match those from the literature fairly well.
In regards to the relationship between the DOSPERT Risk-Perception scale and the DBQ,
results indicated a negative correlation between total scores from both scales and mostly
negative subscale intercorrelations. Since high DOSPERT scores indicate increased perception
of risk while high DBQ scores reflect increased instances of risky driving behaviour, it makes
sense that those who perceive greater risk might act in a risky fashion less often (although
44
causality is not assumed). These results match those from Blais and Weber (2006) who found
that risk perception was significantly negatively related to scores on their similarly scored risk
taking scale (where higher scores indicate an increased self-reported likelihood of engaging in
risky behaviours).
Regarding subscales, only relationships between DOSPERT subdomains and the DBQ violation
subscale (and related DBQ total scores) were significant. A possible explanation for this is that
errors and lapses do not represent willful risk taking while violations do. To reiterate, errors are
said to be “failures of planned actions” (Reimer et al., 2005) that occur due to “misjudgments
and failures of observation” (Parker, Reason et al., 1995), while lapses are “absent-minded
behaviours” (Parker, Reason et al., 1995) that come about due to “attention and memory
failures” (Reimer et al., 2005). Violations on the other hand involve the willful commission of
risky driving behaviour (Parker, Reason et al., 1995; Reimer et al., 2005). It makes sense then
that the DBQ measure concerned with willful risk taking is significantly inversely related to
Ethical, and Health & Safety DOSPERT Subdomain scores (and that total DBQ scores, which
are partially composed of violations, show similar relations to the DOSPERT subdomains).
Those who self-report taking risks more often while driving have significantly lower perceptions
of risk in Ethical, and Health & Safety domains (although again causality cannot be assumed7).
Why might Ethical and Health & Safety subdomains be significantly correlated with Violation
scores when other scales weren’t? The present data are insufficient to address this question, but
some speculations relating to it are presented in Appendix E.
Given the significant relationship found between the DOSPERT and DBQ subscales it would be
interesting to conduct a follow-up study where the full DOSPERT scale (including risk-
perception, behaviour, and expected benefits) was completed alongside the DBQ and the
Sensation Seeking Scale. Both the DBQ and the DOSPERT have been found, separately, to
correlate with the SSS in the past (Rimmo & Aberg, 1999; Weber, Blais, & Betz, 2002), and so
7 It may be the case that high risk perception causes people to take fewer risks or it could be that people who take fewer risks have less fearful driving experiences and so perceive less risk. Due to the correlational nature of this analysis it is not possible for a conclusion to be drawn either way. This is an area worthy of confirmatory research.
45
a definitive study that focuses on inter-relationships between all three scales and driving
performance measures might be informative.
5.2 Task Performance and the Presentation of Information
As mentioned earlier in the literature review, Wickens’ Multiple Resource Model predicts that
performance in general will be superior when two tasks access separate resource pools. In this
study, both tasks involved manual responses, but used different processing codes (spatial for
pedal tracking and verbal for list monitoring). Since the primary task presented information
visually and since the secondary task modality varied by condition, then MRT would seem to
predict that superior performance should occur when the audio secondary task was paired with
the visual primary task. Further, task prioritization would likely be split equally since the
compensation strategy explicitly assigned each task equal weightings.
Thus a simple resource loading view of task performance was not confirmed in this study. There
were no significant differences in primary or secondary task accuracy when audio vs. visual
presentation of the secondary task was used. However, primary pedal tracking task accuracy
scores, as measured by the percentage of time where an acceptable following distance was
maintained, showed ceiling effects, as did secondary task accuracies in remembering whether
the number of vowels presented were odd or even. Thus, these results may reflect an under-
loading of mental resources (the majority of participants had near perfect scores most of the
time). Further research is needed to see when (with a visual primary task) the advantage of
switching to auditory presentation of a secondary task is outweighed by simultaneous visual
presentation of the secondary task and the ability to switch efficiently between the primary and
secondary tasks.
For the pedal tracking task, when accuracy was replaced with the number of out-of-bounds
errors, significant results across conditions with different information presentation styles were
observed. More people had at least one out-of-bounds violation (in the primary task) with
visual-sequential presentation of information (versus visual-simultaneous information
presentation). The only difference between visual-sequential and visual-simultaneous conditions
was that in one case secondary task information was presented all at once, providing participants
with an opportunity to process all task information at their own pace, whereas in sequential
presentation information was presented one item at a time, requiring that users process items at a
46
set pace. Results suggest that performance was significantly better when individuals had control
over the rate at which information was processed.
This finding seems to support the notion of Time Dependence8 which is conceptualized here as
a task characteristic related to the rate at which information can be processed. When a task is
highly time dependent, the nature of the task limits the rate at which information can be
processed. Conversely, when a task is not time dependent, information processing can occur at a
rate chosen by the participant. Degree of Time Dependence is a distinguishing characteristic of
Sequential vs. Simultaneous presentation styles since the sequential presentation of information
is necessarily time dependent while simultaneous presentation is not. This concept is important
as it predicts performance variations in tasks that would seem equivalent from an unmodified
MRT perspective.9
Depending on the task settings used, there may be a tradeoff between resource offloading and
simultaneous secondary task processing that affects the quality of primary and secondary task
performance. Simultaneous presentation of the secondary task can be beneficial when the
primary and secondary tasks are coordinated in such a way that visual distraction between the
tasks is not too damaging. On the other hand, auditory presentation of the secondary task avoids
visual distraction but raises the possibility of cognitive distraction if a significant working
memory load is created as the person holds items in memory until they can be processed.
One other factor that may have influenced the results was discussed by participants in the
feedback forms that they completed after the experiment. Comments made by participants
mentioned trying to use peripheral vision to monitor the primary task in visual secondary task
conditions, thus reducing the need to switch foveal vision from one monitor to the other.
8 Mizobuchi, Personal Communication, 2012 9 However, given that MRT is concerned primarily with mental resources and since presentation style effects are not likely to operate at a resource level, then the extent of this statement is limited to claiming that issues such as time dependence may be useful in the explanation and prediction of performance, and so may prove beneficial if used alongside accepted models such as MRT.
47
5.3 Risk Tolerance, Central Executive Functions, and Driving Performance
Regression analysis of the data collected in this thesis revealed significant models in which
certain measures of risk and central executive abilities were significantly related to secondary
list-monitoring task performance. Specifically, fewer numbers of perseverative errors
(increased shifting ability) were significantly related to higher task accuracy, as were increased
perceptions of risk in recreational domains and an increased incidence of attention and memory
lapses reported while driving.
The finding that high shifting ability (lower numbers of perseverative errors) significantly
relates to superior accuracy supports the notion that the extent to which one is able to perform
on an extraneous verbal task while multitasking is related to cognitive ability. This makes sense
from a theoretical perspective since shifting ability is believed to be involved in switching
between mental sets and operations, just as might be required when switching from pedal-
tracking to list monitoring or vice versa.
Speculation concerning possible explanations regarding the positive relationship between
increased perceptions of risk in recreational settings and superior accuracy in the secondary task
is presented in Appendix F .The positive relationship between self-reported number of lapses
while driving and secondary task accuracy could be due to a number of factors and further
research is needed to examine whether this relationship also occurs in driving related contexts
with other types of secondary task.
5.4 Presentation Style, Central Executive Functions, and Eye Gaze
The final results presented in this thesis concerned the impact of presentation style effects and
central executive functions on eye gaze. The mean percentage of time spent dwelling on the
primary monitor was greatest for audio conditions, and significantly so. As expected, there was
no need to look away from the primary monitor when all the required information was presented
using audio. The proportion of time spent dwelling at the primary display was significantly
lower when information was presented on a second screen (across both sequential and
simultaneous conditions). The visual sequential conditions had the lowest percentage of forward
48
dwell, significantly less than for visual simultaneous conditions. Thus, patterns of eye gaze were
strongly influenced by task demands. When participants didn’t need to look at the secondary
monitor (due to the availability of auditory information) gaze tended to be focused forward on
the primary task. When visual task information was presented all at once, people could develop
their own sampling strategy. The worst situation in terms of visual distraction away from the
primary task was when people had to repeatedly scan back to the secondary monitor to see items
of visual information that were being presented sequentially. In this study, visual distraction by
the secondary task in the visual sequential condition harmed the primary task in terms of
causing a larger number of participants to go out of bounds.
With respect to the effects of central executive abilities and eye gaze, results indicated that the
effects of updating, shifting, and inhibition executive functions vary when information is
presented in different ways. Regarding updating, the finding that increased ability was
associated with increases in the proportion of time spent dwelling on Monitor One (significant
for the visual simultaneous condition, but with similar trends in audio and visual sequential
conditions) supports the notion that those with higher central executive abilities show different
eye gaze patterns than those with lower skill levels. The significant effect of updating ability on
gaze in the visual simultaneous conditions is hypothesized to occur because increased updating
ability allows for the faster updating of secondary task information in working memory. Thus an
individual with high updating ability would be able to process (count and record in working
memory) the number of vowels in a simultaneously-presented list more efficiently than others
and so would have to spend less time looking at Monitor Two. This might also explain why
sequential condition effects were non-significant, because the time dependent nature of
sequential information presentation meant that restrictions were placed on information
processing and thus higher updating ability may not have been an advantage in this condition.
The added requirement of having to wait for each letter to be presented, even if only for a short
time, meant that delays in processing occurred, longer or additional glances were needed, and
the relative advantage of higher updating ability was decreased.
The significant negative relationship between perseverative errors and Monitor One dwell times
suggests that increased shifting ability (fewer perseverative errors) is significantly related to
spending a larger proportion of time staring at Monitor One in a dual visual task scenario. Since
the visual sequential condition involved the presentation of one letter at a time in fixed intervals,
49
and since shifting ability is presumed to reflect individuals’ ability to switch efficiently between
task-related mental sets, then higher shifting ability should be associated with increased
processing efficiency when multiple switches are required and therefore a decreased need for
repeated switches between monitors. However, since this data was affected by an outlier then
the generalizability of this finding is questionable and should be revisited in future studies.
Regarding inhibition ability, significant effects were found in both visual sequential and visual
simultaneous conditions whereby decreases in inhibition ability (as indicated by increased
correct reaction times) were associated with a decrease in the mean proportion of time spent
dwelling on Monitor One. Individuals who were better at focusing on task-related information
might have become confused less often, might have needed to spend less time sampling
information for it to be usable and might have had less need to refer back to the secondary
monitor for clarification.10
10 From this one can speculate that Miyake’s inhibition function may be an underlying mechanism that helps moderate the concept of confusability put forward by Wickens in his updated multitasking model. Future work may show that Miyake’s conceptualization of central executive functions supplements Wickens’ multiple resource theory, especially with respect to the explanation of multitasking performance. Further, it seems likely that central executive functions may operate to varying degrees within all three stages of information processing in MRT.
50
Chapter 6: Conclusion
This chapter reviews the contributions made in this research, while also acknowledging
limitations of the research and listing possible areas for future research.
6.1 Contributions
1. A novel list monitoring task.
The secondary task used in the research for this thesis was intended to meet the following
criteria: involve updating ability; be presented either visually or through audio equivalently;
present information either one item at a time or all items at once; and to require simple manual
responding. In doing so, stimuli (i.e., letters from the English alphabet) that were easily
distinguishable, regardless of modality, were selected. This task is proposed as a potentially
useful addition to the catalogue of tasks that are used in multitasking research.
2. Establishing a relationship between the DOSPERT and the DBQ.
Relationships were found between subdomains within the DOSPERT risk-perception scale and
self-reported risky behaviour from the Driver Behaviour Questionnaire. These results
complement relationships reported to be found between the DOSPERT and the SSS, and the
DBQ and SSS, so that relationships amongst all three pairwise combinations of the three scales
have now been evaluated.
3. Elucidation of effects of information presentation style on task performance and on eye gaze.
Significant differences in primary task performance and eye gaze data were found across
modality (previously identified and reported in the literature) and time dependence. These
findings demonstrate that the distinction between sequential and simultaneous means of
presenting information is a factor that may affect multitasking performance and have
implications for MRT.
51
4. Demonstrating the impact of cognitive ability on visual attention in a dual-task scenario.
Higher levels of updating and inhibition abilities were both associated with increased attention
on the primary monitor. Also, it was found that this was the case only in visual simultaneous
conditions for updating ability, and for both visual sequential and visual simultaneous
conditions with respect to inhibition ability. Shifting ability was identified as being potentially
related to increased attention on the primary monitor in visual sequential conditions.
In addition to the four contributions noted above the discussion in this thesis also raises the
novel possibility that Wickens’ Multiple Resource Theory, and Miyake’s conceptualization of
central executive functioning may be complementary. Based on my review of the relevant
research literature I could not find prior work that has investigated driving-related multitasking
from the combined perspective of multiple resource theory and the updating, shifting, and
inhibition central executive functions. Perhaps this combination of the theory of central
executive functions and multiple resource theory may lead to a more holistic conceptualization
of multitasking that accounts for both the allocation of attentional resources as well as how that
allocation is controlled between multiple tasks, through processes of updating, shifting, and
inhibition.
The research reported in this thesis was part of a larger multi-year study on the effects of
cognitive and visual distraction on driving, and driving-related tasks. The work that I carried out
individually, as part of this broader research project, involved researching models of central
executive functioning, risk, and English letter confusability. I also identified risk tolerance
measures, selected letters for use in the list monitoring task, processed subsets of the data,
analyzed risk tolerance data, and analyzed eye gaze data. Other work such as experimental
design, pilot testing, and analysis of performance results was carried out collaboratively with my
supervisor Mark Chignell and with the lead scientist on the Toyota driver distraction project,
Sachi Mizobuchi. Various aspects of this research have been published in several conference
proceedings (Mizobuchi S. , Chignell, Canella, & Eizenman, 2013; Mizobuchi S. , et al., 2013).
6.2 Limitations Experimental research often involves navigating amongst various trade-offs, and this thesis was
no exception. First, performance in the primary and secondary tasks used in this thesis was
52
generally high. However, in spite of the high levels of performance, significant differences in
accuracy were observed. Furthermore, driving tasks in general tend to be fairly accurate most of
the time (the majority of drivers do not crash on a daily basis), and thus one might expect
driving-related tasks to be relatively accurate as well.
Previously developed risk instruments were used in the research. While this meant that
validation work had been done, there was no dedicated driving domain in the DOSPERT scale.
It is possible that different results may have been obtained if a scale measuring risk perception
specifically in the driving domain had been used. However, correlations between the DOSPERT
and DBQ (which is driving specific) were found, suggesting that the kinds of risk perception
assessed in the DOSPERT are in fact relevant to driving.
There were four experimenters of use in this study all of whom conducted data collection in
pairs and who alternated as session leaders. Despite the creation of a script, large amounts of
instruction were required due to the variety and complexity of tasks, and so it is possible that
differences in instruction delivery or interpersonal style could have affected results. However,
since conditions were randomized, such effects should have been minimized. Seen from another
perspective, this limitation may also have been an advantage, since the assignment of two
experimenters per session meant that potential experimenter fatigue was minimized.
Each participant took approximately two hours to complete all experimental conditions, and so
there existed the possibility of participant fatigue. However, conditions were randomized so as
to minimize such effects and participants were given the opportunity to take breaks as often as
desired.
6.3 Future Research The trade-off between visual and cognitive distraction in driving-related tasks performed in the
presence of secondary tasks is a complex topic for research and there are no doubt many
different experimental approaches that could be developed to investigate it. In this section I will
suggest areas for future research that follow relatively closely from the research reported in this
thesis.
Future research might clarify the relationship between shifting ability and eye gaze. I found a
significant relationship between shifting ability and eye gaze, but this relationship was heavily
53
influenced by the results of one participant, who could have been an outlier. Future research
might focus on evaluating this possible relationship between shifting ability and eye gaze using
a larger sample.
The findings obtained in this thesis may well have depended, at least to some extent, on the
unique properties of the secondary task that was used. It would be interesting to use a similar
experimental paradigm, but with a secondary task that had different properties, where the
distinction between simultaneous and sequential visual presentation of the task could still be
made.
The safety aspects associated with different levels of executive abilities could also be examined
in more detail in future research. One interesting question for future research is: How can
executive functioning and risk tolerance be used for cognitive profiling and as a way to
influence the creation of new interface designs?
The question as to the role that visual cue separation has on performance remains to be tested.
Larger separations between the presentation of pedal tracking and list monitoring task visual
information could be investigated alongside conditions where information for both tasks is co-
located on the same screen. Separation is achievable by physically distancing two monitors
while co-location is possible using occluding goggles. Such goggles synch information for each
task to different exposures and so allow for the illusion of two tasks appearing in the same place,
although with only one visible at any given time.
Investigation of executive functions other than shifting, updating, and inhibition, (e.g., planning,
or the ability to actively maintain an item in working memory) and their effects on tasks
commonly found in a driving context, along with further work concerning how the updating,
shifting, and inhibition executive functions fit into multiple resource theory, should enhance our
understanding of how individuals operate in a driving-related multitasking environment. It may
also be useful to do more research that clarifies the relationship between MRT and time
dependence.
54
6.4 Concluding Statement
There exist complex relationships between executive functioning, risk tolerance, and different
ways of presenting information in multitasking performance. Shifting, updating, and inhibition
executive abilities were all found to be related to either performance measures or eye gaze in
this study. Risk tolerance, as reflected by risk perception and self-reported risky behaviour on
roadways, was also related to performance. Further significant differences were found between
conditions with sequential and simultaneous presentation styles. This research has drawn
together a number of established, but traditionally independent, areas of research. Future
research that advances this integration should further clarify the role of risk and central
executive functions on performance in multitasking contexts.
55
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Appendix A: Survey Items Presented During the Online Portion of Data Collection Please answer the following questions to the best of your ability. If at any point you feel uncomfortable answering a question feel free to leave it blank and move on to the next one. If you ever require assistance or if you no longer wish to participate in the study, please let one of the researchers know so that they can accommodate your requests. 1. Sex:
Male
Female 2. What is your age?
3. Is English your native tongue?
Yes
No 4. Which of the following best describes the highest level of education that you have completed?
Did not attend school
Primary School
Secondary School (High School)
Some Undergraduate College/University
Undergraduate College/University
Some Graduate College/University
Graduate College/University Please answer the following questions to the best of your ability. If at any point you feel uncomfortable answering a question feel free to leave it blank and move on to the next one. If you ever require assistance or if you no longer wish to participate in the study, please let one of the researchers know so that they can accommodate your requests. 5. Have you ever driven a passenger vehicle of any type (E.g., automobile, motorcycle, truck, etc.)?
Yes
No 6. How many years has it been since you first drove on public roads?
Less than 1 Year
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1 to 2 Years
3 to 4 Years
5 to 10 Years
More than 10 Years 7. Do you have a valid driver's license?
Yes
No 8. If you answered "Yes" to question 7, what type of license is it?
Ontario G1
Ontario G2
Ontario Full G
Non-Ontarian Learner's Permit
Non-Ontarian Full License
Other (please specify) 9. Do you hold any licenses that allow you to operate motorized vehicles other than consumer vehicles such as cars and trucks (E.g., Planes, boats, helicopters, motorcycles, commercial vehicles, etc.)?
Yes
No 10. If you answered "Yes" to question 9, what licenses do you hold?
11. Approximately how often do you drive?
Daily
Three or Four Times a Week
Once or Twice a Week
Three or Four Times a Month
Once or Twice a Month
Once or Twice a Year
Never 12. Does your job involve the operation of motor vehicles on public roads?
Yes
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No 13. How many times have you been ticketed for infractions while driving? (E.g., Speeding, unsafe lane change, running a red light, DUI, etc. - Parking tickets do not count.) How many times have you been ticketed for infractions while driving? (E.g., Speeding, unsafe lane change, running a red light, DUI, etc. - Parking tickets do not count.)
0
1 2 3 4 5+
14. Have you ever been in a passenger vehicle collision?
Have you ever been in a passenger vehicle collision? Yes
No 15. If you answered "Yes" to question 14, were you the driver or a passenger?
Driver
Passenger
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Domain-Specific Risk-Taking (Adult) Scale – Risk Perceptions People often see some risk in situations that contain uncertainty about what the outcome or consequences will be and for which there is the possibility of negative consequences. However, riskiness is a very personal and intuitive notion, and we are interested in your gut level assessment of how risky each situation or behaviour is. For each of the following statements, please indicate how risky you perceive each situation. Provide a rating from Not at all Risky to Extremely Risky, using the following scale:
_______________________________________________________________________________________ 1 2 3 4 5 6 7
Not at all Slightly Somewhat Moderately Risky Very Extremely Risky Risky Risky Risky Risky Risky 1. Admitting that your tastes are different from those of a friend. 2. Going camping in the wilderness. 3. Betting a day’s income at the horse races. 4. Investing 10% of your annual income in a moderate growth mutual fund. 5. Drinking heavily at a social function. 6. Taking some questionable deductions on your income tax return. 7. Disagreeing with an authority figure on a major issue. 8. Betting a day’s income at a high-stake poker game. 9. Having an affair with a married man/woman. 10. Passing off somebody else’s work as your own. 11. Going down a ski run that is beyond your ability. 12. Investing 5% of your annual income in a very speculative stock. 13. Going whitewater rafting at high water in the spring. 14. Betting a day’s income on the outcome of a sporting event 15. Engaging in unprotected sex. 16. Revealing a friend’s secret to someone else. 17. Driving a car without wearing a seat belt. 18. Investing 10% of your annual income in a new business venture. 19. Taking a skydiving class. 20. Riding a motorcycle without a helmet. 21. Choosing a career that you truly enjoy over a more secure one. 22. Speaking your mind about an unpopular issue in a meeting at work. 23. Sunbathing without sunscreen. 24. Bungee jumping off a tall bridge. 25. Piloting a small plane. 26. Walking home alone at night in an unsafe area of town. 27. Moving to a city far away from your extended family. 28. Starting a new career in your mid-thirties. 29. Leaving your young children alone at home while running an errand. 30. Not returning a wallet you found that contains $200.
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Driving Behaviour Questionnaire Nobody is perfect. Even the best drivers make mistakes, do foolish things, or bend the rules at some time or another. For each item below you are asked to indicate HOW OFTEN, if at all, this kind of thing has happened to you. Base your judgments on what you remember of your driving over, say, the last month. Please indicate your judgments by circling ONE of the numbers next to each item. Remember we do not expect exact answers, merely your best guess; so please do not spend too much time on any one item.
How often do you do each of the following (for example, in the past month)?
Never Hardly ever
Occasion-ally
Quite often
Frequent-ly
Nearly all the time
a Try to pass another car that is signaling a left turn.
0 1 2 3 4 5
b Select the wrong turn lane when approaching an intersection.
0 1 2 3 4 5
c Fail to ‘Stop’ or ‘Yield’ at a sign, almost hitting a car that has the right of way.
0 1 2 3 4 5
d Misread signs and miss your exit.
0 1 2 3 4 5
e Fail to notice pedestrians crossing when turning onto a side street.
0 1 2 3 4 5
f Drive very close to a car in front of you as a signal that they should go faster or get out of the way.
0 1 2 3 4 5
g Forget where you parked your car in a parking lot.
0 1 2 3 4 5
h When preparing to turn from a side road onto a main road, you pay too much attention to the traffic on the main road so that you nearly hit the car in front of you.
0 1 2 3 4 5
i When you back up, you hit something that you did not observe before but was there.
0 1 2 3 4 5
j Pass through an intersection even though you know that the traffic light has turned yellow and may go red.
0 1 2 3 4 5
k When making a turn, you almost hit a cyclist or pedestrian who has come up on your right side.
0 1 2 3 4 5
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l Ignore speed limits late at night or very early in the morning.
0 1 2 3 4 5
m Forget that your lights are on high beam until another driver flashes his headlights at you.
0 1 2 3 4 5
n Fail to check your rear-view mirror before pulling out and changing lanes.
0 1 2 3 4 5
o Have a strong dislike of a particular type of driver, and indicate your dislike by any means that you can.
0 1 2 3 4 5
p Become impatient with a slow driver in the left lane and pass on the right.
0 1 2 3 4 5
q Underestimate the speed of an oncoming vehicle when passing.
0 1 2 3 4 5
r Switch on one thing, for example, the headlights, when you meant to switch on something else, for example, the windshield wipers.
0 1 2 3 4 5
s Brake too quickly on a slippery road, or turn your steering wheel in the wrong direction while skidding.
0 1 2 3 4 5
t You intend to drive to destination A, but you ‘wake up’ to find yourself on the road to destination B, perhaps because B is your more usual destination.
0 1 2 3 4 5
u Drive even though you realize that your blood alcohol may be over the legal limit.
0 1 2 3 4 5
v Get involved in spontaneous, or spur-of-the moment, races with other drivers.
0 1 2 3 4 5
w Realize that you cannot clearly remember the road you were just driving on.
0 1 2 3 4 5
x You get angry at the behaviour of another driver and you chase that driver so that you can give him/her a piece of your mind.
0 1 2 3 4 5
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Appendix B: Call for Participation Document -------------------------------------------------------------------------------- Call for Participation in a Human Factors research --------------------------------------------------------------------------------- Screening We are seeking people who:
· are between 18 to 35 years old; · have a driver's license; · can read texts on a display (approx. 1cm for each character) without wearing glasses (wearing contact
lends is ok. This requirement is due to a limitation of our eye-tracking system). · have no difficulty in hearing or understanding English instructions; · have no problem distinguishing between red, blue, purple, green, yellow, orange, brown and gray; · have no difficulty in controlling a foot pedal with the right foot.
Please do not sign up to participate in this study unless you meet these criteria. All volunteers for this experiment, who meet the above criteria, will be asked to first participate in an online survey on attitudes towards risk. Based on your results on that survey we will decide whether to invite you to participate in the experiment. You will be paid $5 for participating in the screening portion of the study. Payment can either be by PayPal (preferred) or by arranging to pick up the cash from the researchers at the University of Toronto at a mutually agreeable time. Experiment For those volunteers who are invited to participate in the experiment (based on their screening results) the details of the experiment are as follows: Period: from November 15, 2012 to February 15, 2013 Place: 4th floor in the Rosebrugh building at St. George campus of University of Toronto We are studying how different tasks affect mental workload, and we are seeking people who have a variety of cognitive styles and levels of risk tolerance. Participants will participate in the experiment individually. First, they will perform three tasks presented on a computer: colour identification, card sorting, and colour order monitoring. Then they will perform list monitoring tasks (counting targets in a list) under 6 different conditions while performing a one dimensional tracking task using a foot pedal. Each task will take only a few minutes, and the entire experiment should finish within one and a half hours. Participants will get paid $30 for their participation, and there is an addition $20 bonus for good performance in the experiment. If you are interested in participating, please send an email to [email protected]. You will soon receive a response telling the link to the online questionnaire. The online questionnaire will be closed when we receive sufficient number of responses. Questions are also welcome to the same address.
Thank you, Sachi Mizobuchi (Project leader) Chief Scientist, Vocalage Inc. Visiting scientist, Mechanical and Industrial Engineering department, University of Toronto
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Appendix C: Client Information Sheet and Informed Consent Form for the Study: Investigating the Effects of Cognitive Ability and Interface Modality Preferences on Dual-Task Performance Date: [Month] [Day], 2012 Dear Sir/Madam: Thank you for your interest in this research project. This letter has been created to provide you with the information needed so that you may decide whether or not you would like to participate in this study. Participation is voluntary and you are free to withdraw or stop at any time. Other than in the case where withdrawal occurs prior to the commencement of the main experiment, withdrawal of consent will not affect your compensation for participation. If at any point you feel as though any of the following details are unclear, or if you have any other questions, comments, or concerns, please feel free to contact me using the contact information at the end of this letter. If you decide that you would like to participate, please date and sign the third page of this letter then return one copy to me and keep the other for your reference. If you do not wish to participate there is no need to return the form. Please note, you may request a copy of our final study if desired. The long-term goal of our research is to minimize driver's distraction caused by in-vehicle information systems. This study aims to understand how different aspects of a task in a multitasking environment affect primary driving-related task performance. The design of the experiment is as follows. All participants will participate in the experiment individually, one at a time. First, each person will be asked to perform a series of tasks presented on a computer. These include simple target identification, colour identification, card sorting, and a colour-order monitoring task. Next, a list monitoring task (counting targets in a list) will be performed at the same time as a one dimensional tracking task (keeping a target within a specified area using a foot pedal). This will be repeated 8 times at different difficulty levels so as to provide data on what works best. At the end of some tasks, you will be asked for your opinions concerning how easy the software was to use and how much workload you experienced while performing the task. You may decline to answer any of these questions. Frequent breaks will be scheduled so that no one becomes fatigued. However, if at any time during the experiment you feel physical discomfort or eye strain please let the experimenter know so that you can take a break. During the experiment, your eye gaze will be tracked using a camera attached to eye-tracking software. This will provide information on which screen you are looking at for those tasks where you are using two screens. This gaze-tracking camera is to be used solely to let us know where people are looking during the experiment. The data we collect will be anonymized and kept in a secure office. No personal or identifying information will be included in written reports or presentations, and your confidentiality and privacy will be respected at all times. Any data and information received will be kept confidential. Any study reports and presentations will have all personal identifiers removed. Data and participant information will be kept in my possession or stored in a locked office
73
accessible only by me and the other investigators. Electronic information will be password protected. All data will be securely stored until March 31 2018. All data will be destroyed after March 31 2018.
The experiment should take between 1.5 and 2 hours and will be held in the Rosebrugh building at 164 College Street on the University of Toronto St. George Campus (near University Avenue and College Street). As compensation for participating in this study, you will be given $30 for your participation, and up to $20 bonus for points earned based on task performance. Everyone is free to withdraw or stop the experiment at any time without affecting baseline compensation. However, those who withdraw prior to the start of the main experiment will not be eligible for the $20 bonus. As mentioned previously, if you have any questions, you may contact me at [email protected] or 416-978-8951. Alternatively, you may call the Office of Research Ethics at [email protected] or 416-946-3273. Thank you for your consideration, _____________________________ Mark Chignell
To be completed by participants:
I have read this consent form and I understand the research and what is expected of me.
I understand that: - My eye gaze data will be recorded - I am free to withdraw before or anytime during the study without the need to give any
explanation - I am free to elect to skip parts of the study without the need to give any explanation
I agree to participate in this study. If I do not wish to participate in the research, I can just keep the form without signing it. ______________________ (Signature) ______________________ (Name, please print) _________________ (Date) ______________________ (Investigator) ______________________ (Name, please print) ______________________ (Date)
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Appendix D: Correlations Between DOSPERT and DBQ Total and Subscale Scores
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Appendix E: Relationship between Ethical and Health & Safety DOSPERT Subdomains and DBQ Violation scores A review of results found that (1) Ethical, and Health & Safety subdomain scores were highly
significantly correlated (r = .57, p < .001), and (2) Violation scores had low correlations with
other DOSPERT subdomains (.04 < |r| < .15), although all but one (Investment scores, r = .09, p
= .57) were inversely correlated. This suggests that there is a common factor underlying the
Ethical and Health & Safety domains that is not shared with other subdomains.
Individual items from the Ethical (E) and Health & Safety (H/S) subdomains focus on heavy
drinking (H/S), unprotected sex (H/S), driving a car without a seatbelt (H/S), riding a
motorcycle without a helmet (H/S), sunbathing without sunscreen (H/S), walking alone at night
through unsafe areas (H/S), leaving children unattended while running errands (E), cheating on
taxes (E), having an affair (E), Plagiarizing (E), revealing secrets to others (E), and failing to
return a wallet found with $200 in it (E). From this list it is quite clear that at least two Health &
Safety items are clearly related to deliberate risk taking with motor vehicles, and so the
relationship with DBQ violations makes sense. With respect to Ethical risks, however, the
relationship is less clear cut until one inspects individual DBQ Violation subscale items. For
example, one Violation item is “Having a strong dislike of a particular type of driver, and
indicate your dislike by any means that you can,” and another is, “Pass through an intersection
even though you know that the traffic light has turned yellow and may go red.” (Parker, Reason
et al., 2005) These items appear to hint at behaviours performed by particular types of
individuals; the same types of individuals that one might expect to perceive lower risk in the
DOSPERT Ethical subdomain. It appears as though potential mechanisms underlying these
relationships are personality traits such as conscientiousness and agreeableness, although the
extent to which this is the case remains an area for future work.
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Appendix F: Speculation as to the Nature of the Relationship Between Risk Measures and Secondary Task Accuracy In assessing the relationship between the DOSPERT Recreational subdomain and secondary
task performance it was decided to review subdomain items (Appendix A). These include
camping in the wilderness, skiing beyond one’s ability, whitewater rafting at high water in
spring, skydiving, bungee jumping, and piloting a small plane. These questions seem to be
related to risk taking and extreme sports. Therefore, it may be the case that individuals who
perceive less risk associated with these types of activities are generally more active or at least
accustomed to intense stimulation, which leads to boredom, a loss of concentration, and lower
scores in the laboratory. Alternatively, it may be the case that individuals who perceive greater
risk in this domain are more adverse to losing a reward and so focus more on optimizing
performance between tasks, although here the question arises why only the Recreational
subdomain makes a significant contribution to the model.
A third option that addresses this concern involves the understanding that the driving context is
one about which some individuals are quite apprehensive. For these individuals, high perceived
speeds while driving, the salience of close calls on the road, and the frequent mention of fatal
roadway accidents in the news may affect how they approach driving-related tasks. Therefore, it
may be that those who are apprehensive about driving tend to treat it as similar to those
situations in the Recreational domain and in doing so perceive greater risk, which leads to a
prioritization of the experiment to a greater extent than do those who perceive less risk. In other
words, those who perceive greater risk while driving (as measured by the recreational risk scale)
pay more attention to the experiment while those who perceive little risk are off thinking about
other things (E.g., what to eat for dinner).
Meanwhile, the positive relationship between self-reported number of lapses while driving and
secondary task accuracy suggests that those who act in an absent-minded fashion more often and
who are prone to failures of attention and memory perform better on non-driving tasks. One
explanation for this is that those who report an increased number of lapses, which Reimer et al.
(2005) define as embarrassing actions, may be those same individuals who are apprehensive
about driving and so perceive greater risk. In other words, the more lapses you have, the more
apprehensive you are (although causality is not assumed), which leads to increased focus and
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better performance on a relatively simple dual-task. However, central to this argument is the
notion that DBQ lapse scores and DOSPERT Recreational scores are related, which does not
seem to be the case from survey scores (r = .03). Therefore an alternative explanation is that
those who are prone to attention failures might be more likely to divert attention away from an
ongoing driving task to perform a suddenly salient secondary task. Here it would be
hypothesized that individuals who commit more lapses are more likely to be distracted by the
sudden onset of a secondary task (cued by a notable audio tone in this experiment) than would
those who commit fewer lapses, leading to superior secondary task performance with as of yet
unknown implications for the primary task.
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Copyright Acknowledgements
Figure 4 on page 24 originally appeared in (Mizobuchi S. , Chignell, Suzuki, Koga, & Nawa,
2012) and has been included in this work with the primary author’s permission.