A STUDY ON THE BEHAVIOR UNDER
MULTITASKING CONDITIONS IN A
DYNAMIC TASK SCENARIO IN THE
CONTEXT OF
HUMAN-MACHINE-INTERACTION
vorgelegt von
Dipl.-Psych. Jürgen Kiefer
von der Fakultät V - Verkehrs- und Maschinensysteme
der Technischen Universität Berlin
GRK PROMETEI
zur Erlangung des akademischen Grades
Dr. phil.
genehmigte Dissertation
Promotionsausschuss:
Berichterstatter: Prof. Dr. Ing. Leon Urbas
Berichterstatter: Prof. Dr. Phil. Manfred Thüring
Tag der wissenschaftlichen Aussprache: 05.10.2009
Berlin 2010
D83
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Dipl.-Psych. Jürgen Kiefer: A Study on the Behavior under Multitasking
Conditions in a Dynamic Task Scenario in the Context of Human-Machine-
Interaction , vorgelegt von ,
©
Tag der wissenschaftlichen Aussprache:
05.10.2009
pour JULES
ABSTRACT
The work presented here is focusing on the behavior of participants
in situations of daily life, in which several demands apparently at the
same time need to be dealt with. After introducing into the topic
labeled as "human multitasking", embedded in situation of routine life,
reasons for choosing the topic and approaching it are provided (chapter
1). In chapter 2, an overview of the history in human multitasking and
task switching is given. First approaches starting at the beginning of
last century up to recent approaches and ideas are presented and their
impact for psychological science is displayed. Entering chapter 3, the
empirical work is presented: study 1portrays a driving simulation in a
driving simulator, in which a primary task (driving) plus a concurrent
task (a test of attention which was adapted for the in-car scenario) are
applied. The secondary task featured three different levels. The first
study gives an impression about how people manage such scenarios.
Please note that the main task (driving in the simulator) was considered
a dynamic task. The second study mimics study one and is a replication
with the additional aspect of training and its impact on performance.
With the help of study two, strategies how to handle the scenario
are derived and a heuristic is described which is applied by a bunch
of people. Setting of the study was taken from study one. As task
configuration was expected to strongly moderate the behavior of the
participants during the task scenario, main task (driving) and secondary
task (test of attention) were varied: applying the lane change task (LCT),
a computer-simulation for driving behavior, it was possible to better
analyze the lane derivation during driving (which was taken as a
measure of performance for the main task). As for the secondary task,
the variations of the test of attention used in the previous two studies
were systematically extended. Results of study 3were used to illustrate
the impact of task configuration for the scenario. In the last study,
time pressure as additionally component was applied and its impact
was measured on task performance both for main task (driving) and
secondary task (test of attention). The last chapter (chapter 4) resumes
the results and provides design recommendations. The work closes
with a conclusion and mentions aspects that were not considered due
to time constraints.
v
ich hatte nur diese zeit (rainer werner fassbinder)
ACKNOWLEDGMENTS
thank you:
• Dr. Dirk Schulze-Kissing ("Ulysses")
• Joachim ("Jolle") Wutke
• Prof. Leon Urbas
• Prof. Manfred Thüring
• Prof. Hartmut Wandke
• Prof. Anthony Jameson
• Prof. Werner H. Tack
• Dipl.-Ing. Marcus Heinath
• Dipl.-Psych. Robert Lischke
• Daniel Doering
• Dipl.-Psych. Tobias Katus
• Dipl.-Psych., Dipl.-Ing. Holger Schultheis
• Dipl.-Psych. Cordula Krinner ("Miss LateX")
• Dipl.-Psych. Nicola Fricke
• Dipl.-Psych. Diana Woelki (apt pupil)
• Dipl.-Psych. Necla Soyak
• (cand.) Dipl.-Psych. Bob Kaldasch
• (cand.?) Dipl.-Psych. Michael Schulz
• Antti Oulasvirta (PhD)
• Adam Chuderski (PhD)
• Jing Qian (PhD)
• Joscha Bach (PhD)
• Inessa Seifert (PhD)
• Dipl.-Psych. Nadya Dich
• Michal B. Paradowski (PhD)
• Stefan Mattes (Daimler)
• Dirk Weishaar (pour Messiaen)
• Karin Scherinsky-Pingel
• Birgit Trogisch
• Elke Fadel
• Mario Lasch
•... and a few others
vii
CONTENTS
i introduction 1
1 introductory note 5
1.1Preface 5
1.2Multitasking and human-machine interaction 7
1.3Technology can do multitasking 8
1.4An excerpt of recent studies 9
1.5An ability to multitask? 10
ii theory 11
2 theoretical background 15
2.1A short historical survey 16
2.1.1The 1920‘s 16
2.1.2The 1930‘s 17
2.1.3The 1940‘s 17
2.1.4The 1950‘s 17
2.1.5The 1960‘s 18
2.1.6The 1970‘s 18
2.1.7The 1980‘s 19
2.1.8The 1990‘s 20
2.1.9 2001: The cognitive bottleneck 20
2.1.10 2005: A general multitasking component 21
2.2Multitasking or task interruption? 23
2.2.1Characteristics of task interruption 23
2.2.2The task switching question 25
2.2.3A grammar for task scheduling 26
2.3Single or multiple resources? 26
2.4Summary and criticism 28
2.4.1
Need for continuous tasks in multitasking stud-
ies 29
2.4.2Training and task repetition 29
2.4.3Cognitive heuristics - human multitasking 29
iii studies 31
3 empirical studies 35
3.1Study I: Identification of multitasking heuristics 35
3.1.1Method in study I 36
3.1.2Hypothesis: D2-Drive under multitasking 39
3.1.3Results of study I 40
3.1.4Discussion of study I 41
3.2Study II: Practice - multitasking heuristics 42
3.2.1Method in study II 42
3.2.2Hypotheses for study II 44
3.2.3Results of study II 44
3.2.4Discussion of study II 46
3.3Study III: The role of task configuration 47
3.3.1Method in study III 47
3.3.2Hypotheses for study III 50
3.3.3Results of study III 51
3.3.4Discussion of study III 52
3.4Study IV: Amplification via time pressure 52
ix
xcontents
3.4.1Method in study IV 53
3.4.2Hypotheses for IV 55
3.4.3Results of study IV 56
3.4.4Discussion of study IV 57
iv discussion 59
4 critical discussion 63
4.1Scope and findings 63
4.2Cognitive modeling 64
4.3Design recommendations 65
4.4Criticism and outlook 67
4.4.1
The role of memory in human multitasking
68
4.4.2Domain independence 69
4.4.3
Need for a computational model of human multi-
tasking 69
4.5fMRI studies on multitasking 70
4.6Popular stereotypes about multitasking 71
4.6.1Multitasking and happiness 71
bibliography 73
a appendix 81
a.1Appendix: Structured interview 81
LIST OF FIGURES
Figure 1A sketch on MT 6
Figure 2Human-machine-interaction in daily life 7
Figure 3Advertisement, Berlin (2007)8
Figure 4Robert Rauschenberg: First Landing Jump 10
Figure 5A cartoon on multitasking 15
Figure 6Jersild (1927): task switching paradigm 16
Figure 7Model of Rasmussen (1983)19
Figure 8Cognitive bottleneck (Pashler, 1993)21
Figure 9Multitasking models - Salvucci (2005)22
Figure 10 Interruption scenario 25
Figure 11 A grammar for task scheduling 27
Figure 12 Wickens‘ Model of multiple resources 28
Figure 13 Study I: Scenario 36
Figure 14 Study I: D2test of attention 37
Figure 15 Study I-IV: D2-Drive 38
Figure 16 Study I: performance D2-Drive 40
Figure 17 Study I-IV: the merge heuristic 41
Figure 18 Study II: performance D2-Drive 45
Figure 19 Study II: the impact of training 46
Figure 20 Study III: the lane change task 48
Figure 21 Study III: analyzing LCT 49
Figure 22 Study III: performance D2-Drive 51
Figure 23 Study IV: scenario 53
Figure 24 Study IV: performance D2-Drive 54
Figure 25 Study IV: the impact of time pressure 56
Figure 26 Study IV: performance D2-Drive 57
Figure 27 Study IV: time pressure and D2-Drive 57
Figure 28 Study IV: eye movements 58
Figure 29 Overview of cognitive modeling 64
Figure 30 Pattern processing in D2-Drive 65
Figure 31 Modeling of D2-Drive 66
Figure 32 Pandoras box (Source: www) 67
Figure 33 Modification of LCT (Soyak, 2008)68
Figure 34 General executive for multitasking 70
LIST OF TABLES
Table 1Controlled vs. automatic processing 18
xi
Part I
INTRODUCTION
3
1
INTRODUCTORY NOTE
1.1 preface
efficiency matters! In modern western society (and not only
there), time is money, and the less time required to do a job or task the
more efficient your work is considered to be. Even before the word
multitasking
itself was applied, psychological approaches towards the
phenomenon of how to handle the demand of multiple tasks were
reported. The intellectual debate goes back even to the ancient Greek
times. "To do two things at
once - is to do neither.
"
to do two things at once is to do neither. With these words,
roman philosopher Publilius Syrus
1
describes a phenomenon which
thousand of years later released a core discussion in psychology lasting
almost a hundred years, and a plethora of studies investigate whether
people in fact turn out to be able to perform several tasks concurrently
or not.
Multitasking madness
, some scientists say, leads to a waste of
time. The myth of human multitasking not only remains but even more
gains popularity in the era of mobile computing and human computer
interaction. Is there in fact an illusion of concurrency? This question
does not define the main issue of this work albeit it plays a (minor) role.
Moreover, the author aims to show how people handle the demands
of multiple tasks at the same time, put in a real-life situation which
might occur day by day. This work starts with the word "efficiency",
meaning the degree of target achievement in relation to necessary costs,
be them mental, physical, or financial. It is efficient to save time, but
do we really save time via multitasking?
procrastination
is a type of behavior which is characterized by defer-
ment of actions or tasks to a later time. Psychologists often cite procrastina-
tion as a mechanism for coping with the anxiety associated with starting or
completing any task or decision.
(taken from:
http://en.wikipedia.org/
wiki/Procrastination.
efficiency matters! According to Steel [2007], procrastination is
closely related to perfectionism and workaholism. Ego syntonic perfec-
tionists tend to be less likely to procrastinate than non-perfectionists.
This is remarkable: employers more and more concentrate on efficiency
and most of them assert the more we can do at the same time the
better our job. On the opposite, researchers in the field of psychology
find in their studies that multitasking seems to be counterproductive
or inefficient. Fast switching between tasks leads not only to lower
performance in each task individually but also takes more time, as, for
instance, Rogers and Monsell [1995] report.
A variety of empirical investigations on multitasking do already exist
and one might ask: "Why more studies?" or "What is the benefit of
1
Publilius Syrus was a native of Syria and a Latin writer of maxims in the 1st century BC.
The legacy of his work is a collection of sentences (Sententiae), a series of moral maxims
in iambic and trochaic verse.
5
6 introductory note
Figure 1.: To do two things at once is to do neither?
(Source: unknown)
this investigation?". To reply to these objections, let me analyze com-
mon problems of former studies on task switching, dual-task - and
multitasking scenarios:
1.Most studies in the context of psychology are not applied stud-
ies.
Systematic control as a precondition for proper research
prevents from a direct connection to real life. Only recently (and
with the help of the vast development in computer technology),
more and more researchers dare to investigate applied studies in
the field.
2.Most studies lack task repetition and systematic analysis.
In
order to derive how humans perform several tasks which are
presented concurrently, it is necessary to provide a large number
of task execution. Otherwise, the conclusions drawn from the
experiments are based solely on a small number of observations.
In his studies, Saluvicci [2005] uses cell phone dialing as a secondary
task in a driving situation. Although the studies within his paradigm
provide a deep inside into human multitasking in real life, the sec-
ondary task in his scenarios (dialing) misses issues which are included
in the studies reported within my work. To properly investigate and
analyze a multitasking scenario including two tasks, I claim that a
secondary task should be
1. fully controllable
1.2 multitasking and human-machine interaction 7
2. context-independent
3. interruptable
4. observable
Finding a balance between proper, science-based research on the one
side without losing touch to reality on the other side is the challenge
of this work. Nowadays, multitasking takes place everywhere: most
people walking on the street use their phone and speak to friends.
Rapid switches from one task to another, even without being aware of
it, occur. Most of us do not necessarily become aware of simultaneous
"actions" (e.g., walking and speaking at the same time). To my mind,
many situations closely relate to the use and application of modern
technological systems. This is the starting point of the following work.
1.2 multitasking and human-machine interaction
Figure 2.: Human machine interaction in daily life
An increasing development of technological systems in the beginning
of the 21st century puts us into situations in which we have the lure
of choice when interacting with a mobile system, a cash machine
or a portable data assistant (PDA). This is especially true for new
technology features in cars Strayer and Johnston [2001], and McCarley
et al. [2004] describe this phenomenon as "
burgeoning popularity of in-
vehicle technology
". In the last decades, so-called
in-vehicle information
systems
(IVIS) aim to support the driver by offering a multitude of
possibilities while driving. Green [1999], for instance, postulates a
"15-second rule for driver information systems", meaning 15 seconds to
be the maximum time for a task duration while driving. According to
Green, the time a task requires is strongly correlated to crash risk. Also,
Green promotes to measure task time instead of applying eye tracking
(e.g., eyes-off-the-road-time) due to its instability. His rule became an
international standard and highly focuses on in-car security to prevent
human errors. According to Green [1999], the "15-Second rule"
8 introductory note
•
is consistent with existing national and trade association guide-
lines
• is consistent with accepted vehicle design practice
• is a feature to minimizes harm to drivers
Technological development is inevitable. Using the availability of
(more or less) intelligent systems can support us in daily routines, but
it can also turn out to be a burden. Almost twenty years ago, this has
been referred to as "techno stress".
Figure 3.: Advertisement, Berlin (2007)
1.3 technology can do multitasking
Technology can do multitasking forever but humans can not! Caig Brod,
author of the ground-breaking bestseller
Techno Stress: The Human Cost
of the Computer Revolution
Brod [1984], directed to the implicit danger
of the vast development of modern technology. Rosen and Weil
1
pro-
vide an excellent explanation why nowadays, modern communication
like email is so distracting. They call this phenomenon "multitasking
madness":
Human beings have brains that allow them to appear as though
they can comfortably perform more than one task at a time. In reality, our
brains have an excellent filtering mechanism that helps switch our attention
rapidly from one thought to the next.
To overcome this problem and stop the multitasking madness, Rosen
and Weil recommend the following:
1.
Precise time estimation: typically, we underestimate time needed
to perform or fulfill a task. This bias creates expectations we
cannot meet. Realistic time estimation is a first step towards
handling the demand of several tasks (almost) concurrently.
2.
External memory: letting go off memory traces reduces cognitive
load and helps us to focus more intensively to the current task.
1published on www.contextmag.com
1.4 an excerpt of recent studies 9
3.
Task perseverance: a full focus on one task at a time without
maintaining thoughts for other tasks decreases time needed to
perform a task and increases task accuracy.
4.
Down time: work will be more efficient after a refresh, be it
playing with children, watching TV, or reading a book.
A study by the Institute for the Future reported that employees of Fortune
1.000 companies send and receive 178 messages a day and are interrupted an
average of at least three times an hour!
1.4 an excerpt of recent studies
In chapter two, an overview on the history of task switching and
multitasking is given. But before that, let me mention a few exemplary
studies to illustrate the importance nowadays.
•
Rogers and Monsell [1995] point out that people are faster in
repeating a task compared to task switching. This is also true
for familiar tasks which can easily be anticipated. Given more
time between the trials did not help to completely eliminate the
switching costs. According to Rogers and Monsell, switch costs
are explained by (a) the need for mental control for the new
setting, and (b) carry-over effects from the previous trial. Proper
preparation did not have any significant improvements.
•
Meuter and Allport [1999] did a study in which subjects were
asked to name digits in their first or second language (depending
on the background color). Not surprisingly, response time for
digits in the first language was faster compared to the second
language (in a repetitive task setting). But also, subjects were
slower when the language changed (task switching).
•
Rubinstein et al. [2001] showed in a serial of four famous studies
using a variety of tasks (e.g., maths, geometry) that task switching
causes tremendous time loss. Additionally they were able to show
that performance was strongly influenced by task complexity.
•
Yeung and Monsell [2003] present a modeling of experimental
interactions between task dominance and task switching, illus-
trating the importance of so-called
prospective memory
(we will
come back to that concept later within this work). It seems that
remembering where to continue a task plays a key role in the
context of task switching.
These four excerpts demonstrate that multitasking and task switch-
ing play a key role, both in scientific research as well as in real life.
In context of the design of new technological products, insights how
people handle several, apparently concurrent tasks, is of special im-
portance: knowing the cognitive mechanisms behind allows to adapt
human-machine-systems to the user‘s need already in early stages of
the design process. This we call prospective design.
Prospective design means to develop and integrate tools and meth-
ods in order to investigate the human-machine interaction already in
the early development stages of technical systems. Design includes
aesthetic as well as functional aspects. User-centered design (UCD) is
10 introductory note
an approach to integrate knowledge about and aspects of the user, e.g.
cognitive limitations. UCD includes multiple stages, such as analyzes,
development, testing, or re-design. Norman [1999] simply describes
UCD as design based on the needs of the user. The earlier a designer
is familiar with these insights (i.e., the users‘ needs) before a product
is finalized, the better (see also Chin et al. [1988]). This work aims to
provide exactly this knowledge about the user. In four empirical stud-
ies, multitasking scenarios are applied to analyze how users interact
with a system (i.e. doing a secondary task) while concentrating on a
dynamic activity (i.e., the primary task). Findings will provide helpful
recommendations for a prospective design of human-machine-systems.
1.5 an ability to multitask?
More than half a century ago, Cherry [1953] mentioned that we have
a
natural ability and predisposition to multitask
. At that time, for sure, the
technological demands remained rather limited. Almost at the same
time, Robert Rauschenberg
1
argued that "
technology is contemporary
nature
". The author would rather go with the last quote than with the
first one. And to show how we adapt to this "nature" is the focus of
this work."There’s been an
exponential explosion
of available
information. Part of
the responsibility of
people developing this
technology -
computer
manufacturers, ’big
Bill’ Gates out in
Seattle - should be
taking into account
the multitasking
limitations of people
using it." David
Meyer (2001,
Interview)
Figure 4.:
Combine painting: cloth, metal, leather, electric fixture, cable, and oil
paint on composition board, with automobile tire and wood plan (R.
Rauschenberg, 1961)
1
In the 1950s, artist Robert Rauschenberg (1925-2008) created the concept known as
"Combines": he put non-traditional materials and objects in innovative combinations,
thus combining both painting and sculpture
Part II
THEORY
13
2
THEORETICAL BACKGROUND
For all but the most routine tasks (and few mental undertakings are truly
routine) it will take more time for the brain to switch among tasks than it
would have to complete one and then turn to the other. (David Meyer)1
In his LA Times article, David Meyer - an expert in the field of
research on human multitasking - communicates a strong and direct
message, which is already summarized in the headline of his article:
we
are all multitasking, but what is the costs?
Meyer points out that "costs" do
not only refer to time but also to mental fatigue and a loss of attention,
i.e. accuracy. His quote opens this chapter which is meant to provide
an overview of the history of (human) multitasking, with other words,
the theoretical background of this work. Before the journey starts, let
us be aware that we are interested in
human
multitasking. However, the
term multitasking has been used in several areas, such as:
computer multitasking
- the apparent simultaneous performance
of two or more tasks by a computer’s central processing unit.
media multitasking could involve using a computer, mp3, or any
other media in conjunction with another.
human multitasking:
the ability of a person to perform more than
one task at the same time
Figure 5.: A cartoon on multitasking
(Source: EDUCATION 2.0, http://atedu20.blogspot.com)
1taken from LA Times, Monday, July 19,2004
15
16 theoretical background
2.1 a short historical survey
Defining the concept of "multitasking" has been challenging scientists
from various disciplines for decades (or even centuries). Long time
before the word itself was used, psychological approaches towards
the phenomenon of how to handle the demand of multiple tasks were
reported. Chapter 2.1gives a short introduction into the history of
task switching and multitasking, from the early beginning (Jersild and
early task switching paradigms) to nowadays studies (Saluvicci [2005],
Taatgen [2005], etc.). This summary is not meant to provide a full
description of all studies in this area, moreover I mention and describe
important steps towards the current state of the research by giving an
overview of the history of human multitasking.
2.1.1The 1920‘s
1927
.Jersild [1927] confronted participants with a list of stimuli to
investigate the ability to alternate between different tasks. He was
interested in how people switch from one task to another. In his
studies, two conditions had to be executed, one in which the same task
was performed on each item (so-called
pure task blocks
) and a second
one in which different tasks were performed (alternating task blocks).
STIM - 3 STIM - 3
task A task A
r(A-A)
STIM - 3
task A
STIM - 3
task A
TASK REPETITION
TASK REPETITION TASK SWITCHING
TASK SWITCHING
r(A-A) r(A-A)
STIM - 3 STIM + 6
task A task B
r(A-B)
STIM - 3
task A
STIM + 6
task B
r(B-A) r(A-B)
PURE TASK BLOCK CONDITION
ALTERNATING TASK BLOCK CONDITION
Figure 6.: Early task switching study by Jersild (1927)
In the
pure task block condition
, task A was "subtracting three from
each number on the list". In the
alternating task block condition
, Task B
was "adding six to the number on the list". As we can see, already in this
early period of psychological studies, Jersild assumed that, though both
tasks being mathematical operations, he assumed them to be different
in terms of cognitive processing, long time before the term "cognitive"
was used. A further, second distinction was between univalent (i.e.,
each stimulus is a potential input only for the appropriate task) and
bivalent (i.e., every stimulus is a potential input for either task) item
lists (see also Fagot, 1994). The tasks mentioned above imply a
bivariate
condition.
Univariate
lists, in contrast, contained words (input for task
2.1 a short historical survey 17
A) and numbers (input for task B).
Switch costs
(in terms of reaction
times) were measured as the difference between a switch (r(A-B) or
r(B-A)) and a no-switch (r(A-A)). To my best knowledge, Jersild was the
first one to introduce the term
switch costs
. Even nowadays, the notion
"switch cost" still holds (Pashler [2000], Rubinstein et al. [2001]), as well
as "mental set" (see Spector and Biederman [1976], Meiran [1996]).
Main findings in the studies by Jersild [1927] are:
1.
For bivalent item lists, performance time is slower in the alternat-
ing condition.
2.
For univalent item lists, performance is slower in the pure condi-
tion
These early, surprising findings remark a first step towards the dis-
tinction between modalities required to properly execute a specific task.
The definition of switching costs still nowadays is used in many studies
on task switching. A few years after Jersild and his task switching
paradigm, the aspect of concurrency became of deeper interest.
2.1.2The 1930‘s
1931
.Telford [1931] asked the question what happens if two tasks
overlap, i.e. the second task appears with a temporal delay to the
first one. In contrast to reported studies in which tasks are presented
sequentially (
task switching
), i.e. one after the other, this scenario is
called the
psychological refractory period
(
PRP
). The
PRP
- paradigm is as
follows: a stimulus
S2
of a task
T2
is presented shortly after the onset
of a stimulus
S1
of a task
T1
. The difference of these two onsets (
S2-
S1) is called "simulus onset asynchrony" (SOA). This extension of task
coordination is a first step into the investigation of task concurrency, i.e.
the fact that for a short moment two tasks appear concurrently. Main
results in the context of
PRP
is that the smaller the (temporal) distance
between
S1
and
S2
(the shorter the
stimulus onset asynchrony
,
SOA
), the
longer the reaction time (
RT
) for
S2
.
RT
is measured for both
T1
and
T2
. Following the argumentation of Jersild [1927],
RT
for
S2
should be
shorter after
T1
than
T2
. Unfortunately, many studies in the context of
PRP
do not include a pure task condition (task repetition) as applied in
Jersild [1927].
2.1.3The 1940‘s
Vince, M. (1949). The connection between the psychological refractory
period and rapid response sequences (Vince [1949]) is investigated by
Margaret Vince. She could confirm PRP - results by Telford [1931].
2.1.4The 1950‘s
1952
. A processing
bottleneck
is proposed by Welford [1952]. Two
decisions about two responses to two different stimuli at the same time,
Welfold claims, is impossible. Imagine two visual stimuli and two
necessary responses (e.g., button presses). Participants had to respond
both stimuli by pressing a button, respectively. Welford found that
the reaction time for the second stimulus is slower than reaction time
for the first stimulus. He called this delay psychological refractory period
18 theoretical background
controlled processes automatic processes
slow fast
flexible use no easy modification
reduce capacity do not reduce capacity
conscious (attention) unconscious (no attention)
Table 1.: Controlled vs. automatic processing
and argues that it is always present, even for quite different stimuli.
Welford [1952], to my best knowledge, was the first one to introduce
the concept of a bottleneck. Many subsequent studies support his
assumptions for a general bottleneck in dual-task processing, implying
serial processing of cognitive steps. Welford did not disclaim that
this bottleneck is sometimes small or can be reduced, e.g. by training.
The impact of training (which is also a denotative feature within the
presented studies) will get more attention later within this chapter, in
context of assumptions about single vs. multiple resources in dual task
performance.
2.1.5The 1960‘s
1963
.Borger [1963] investigated the refractory period and serial choice
reactions. He found
PRP
- effects with visual and auditory stimuli. In
the studies, some participants applied a queuing strategy, i.e. reaction to
task one (
R1
) is buffered and given shortly before reaction to the second
task (R2). Pashler [2000] calls this behavior
grouping strategy
: it can be
avoided by giving appropriate instructions. Meyer and Kieras [1997a]
instructed to produce
R1
as fast as possible. This, one might object,
potentially evokes time pressure but prevents from answer queuing (see
also Meyer and Kieras [1997b]. Learning from instructions became even
more important in recent years (Taatgen et al.,Taatgen et al. [2006]). In
the presented studies in chapter three, to come to the point, the applied
main task will be instructed as priority task. By doing so,
"grouping"
effects of task response are implicitly excluded. Before further
PRP
-
or task switching studies are presented, it is necessary to address our
attention to the way a task is processed - a diminutive but not exiguous
aspect in context of human multitasking.
2.1.6The 1970‘s
1977
.Schneider and Shiffrin [1977] emphasize the necessity of a distinc-
tion between
controlled
and
automatic
processes. Later in this chapter
we will see why this distinction is of deeper interest for human multi-
tasking and dual task performance.
The price for flexibility (controlled processes) is a reduce of speed.
Of special interest related to the studies present in the next chapter and
the issue (human multitasking), automatic processes are not necessarily
consciously accessible. Automatic processing is the result of training
and practice. Automatic processing is a typical feature of skill acqui-
2.1 a short historical survey 19
sition (e.g., Anderson [1982], Lee and Taatgen [2002]). One model to
contribute to this phenomenon was proposed by Rasmussen.
Figure 7.: Skills, rules, and behavior (Rasmussen [1983])
2.1.7The 1980‘s
1983
.Rasmussen [1983] developed a model about skill, rules and
knowledge to explain the essential features of human skilled behaviour.
In his eyes, a skill is a combination of open- and closed-loop behavior.
In his model, he claims three levels of behavior:
1.
Skill-based behavior (SBB): Automatic processing, without con-
scious attention or control, relying on signals.
2.
Rule-based behavior (RBB): Behavior is based on familiar rules
and consists of a sequence of subroutines (e.g., mathematical
problem solvin, driving)
3.
Knowledge-based behavior (KBB): Relying upon a "mental model"
(of the system), no rules needed.
With other words, behavior turns from a
cognitive
stage to an
asso-
ciative
stage and finally to an
autonomous
stage. Conscious processing,
thus, becomes unconscious processing, including human error behavior
(
from error-prone to error-free
) and speed (
from slow to fast processing
). In
contrast to the assumptions from Schneider and Shiffrin [1977] whose
theoretical "explanation" remains more descriptive than explanatory,
the approaches on skill acquisition from the decade of the 1980s provide
a comprehensive and plausible framework for the question how pro-
cesses become automatic and thus resource-saving. Later in this chaper,
a model by Chris Wickens will be introduced. This model postulates
multiple cognitive resources. But before that, let us have a deeper look
into further relevant studies on task switching and dual tasking.
20 theoretical background
2.1.8The 1990‘s
1995
.Rogers and Monsell [1995] introduce
task set reconfiguration
to ex-
plain the phenomenon of
alternation costs
even without item repetitions.
Monsell [1967] claims that different processing modules are needed for
different aspects of a task. Alternation costs are defined as difference
between so-called pure and alternating-task blocks. Monsell used an
"alternating runs" procedure. Though the empirical approach is rather
abstract and not intuitively transferable into a real-life context, Monsell
also provides an illustrating example to explain what is meant by a
task
set:
a professor
sits at a computer, attempting to write a paper. The phone
rings, he answers. It is an administrator, demanding a completed module re-
view form. The professor sighs, thinks for a moment, scans the desk for the
form, locates it, picks it up and walks down the hall to the administrators
office, exchanging greetings with a colleague on the way. Each cognitive task
in this quotidian sequence (sentence-composing, phone-answering, conversa-
tion, episodic retrieval, visual search, navigation, social exchange) requires
an appropriate configuration of mental resources, a procedural "schema" or
"task-set".
1992-2000
. Hal Pashler summarizes recent analyzes of dual-task studies
in Pashler [2000] by putting them into two categories, namely
1. studies of task switching or mental sets
2. studies on divided attention or dual task performance
Pashler claims that people show limitations when they have to per-
form two tasks concurrently, and these limitations are strongest in
central stages of decision, memory retrieval, and response selection,
with other words, in cognitive aspects where tasks are "
intellectually
demanding
" (p. 287). It is widely known that training and practice
supports performance, especially in perception and motor response.
Hazeltine et al. [2002], for instance, strongly promote a simultaneous
dual-task performance with parallel response selection afer sufficient
training (see also Ruthruff et al. [2003]). Instead of practice or training,
some authors (e.g., Meiran and Daichman [2005], Sohn and Anderson
[2005]) use the expression
advanced task preparation
to emphasize the
preparatory control. In their study, task switching produced a perfor-
mance decrease (
"task errors"
) which disappeared after
"advanced task
preparation" (i.e., extensive task practising).
Doing two things at the same time (Pashler [1993]) is inevitably con-
nected to restrictions of a so-called
bottleneck
(Pashler [1984], Greenwald
and Shulman [1973], Greenwald [1972]). A few year ago, a discussion
about the central bottleneck took place starting after a paper published
by Meyer and Kieras [1997a] (see also: Meyer and Kieras [1997b]).
2.1.92001: Uncorking the central cognitive bottleneck
2001
. A ground-breaking article published in
Psychological Science
reani-
mated an old discussion about the simultaneous performance of two or
more tasks involving perception, cognition and action. As mentioned
by Pashler [2000], human multitasking is restricted and main reason for
this restriction is a central bottleneck. The
response-selection bottleneck
2.1 a short historical survey 21
PERCEPTION EXECUTION
RESPONSE SELECTION
AND PROGRAMMING
S1 R1
PERCEPTION EXECUTION
RESPONSE SELECTION
AND PROGRAMMING
S2 R2
RT 1
SOA
RT 2
Figure 8.: Bottleneck theory (adapted from Pashler(1993)
(RSB)
hypothesis assumes the steps "perception - response selection
and programming - execution" and claims that response selection to
a stimulus
S2
from a Task
T2
can only be executed after the response
selection to a stimulus
S1
from a Task
T1
has been finished (see Pashler
and his bottleneck theory, Pashler [1993]). According to that, parallel
processing is possible during perception (early stage of information
processing) and execution (late stage of information processing). How-
ever, processing of response selection is serial.
Schumacher et al. [2001] argue in their article that even after "moderate"
training, people reach a state in which they perform two tasks in paral-
lel and the authors call this
virtually perfect time sharing
. But they also
mention that not all participants in their studies were able to reach this
state (individual differences) and the question arises whether extensive
practice would enable virtually perfect time sharing for all participants.
Dual-task interference is explained by conservative executive control
postponing one task while another one is not yet executed. Main claim
in their approach, in sum, is that intensive training and practice allow
human multitasking without dual tasking costs for switching or re-
sponse selection time according to a central bottleneck. Based on these
results, study II of my work investigates allows participants a large
amount of practice in order to overcome limitations and to become
skilled for the applied multitasking scenario.
2.1.10 2005: A general multitasking component
2005
. Salvucci categorizes multitasking studies related to real-world
tasks as illustrated in Fig. 9(taken from Saluvicci [2005]). In contrast
to many psychological approaches within this subject, he highlights
the fact that in "real life", many situations should be understood as
multitasking scenarios.
While Lee and Taatgen [2002] define multitasking as "
the ability to
handle the demands of multiple tasks simultaneously
", Saluvicci [2005] sees
human multitasking as the "
ability to integrate, interleave, and perform
22 theoretical background
Figure 9.:
Examples of multitasking models developed in a cognitive architec-
ture (from: Saluvicci [2005])
multiple tasks and/or component subtasks of a larger complex task
". For a
classification, he divides discrete (duration < 10 s) and continuous
(duration > 10 s) tasks and proposes four categories:
1. Models of discrete successive tasks
2. Models of discrete concurrent tasks
3. Models of elementary continuous tasks
4. Models of compound continuous tasks
Models of discrete successive tasks
are task switching studies like
those already examined in the 1920‘s. Alternating simple choice-
reaction tasks are applied to investigate switching costs. In these
scenarios, the aspect of concurrency is not given. For this reason, i do
not consider them as multitasking studies per se.
Models of discrete concurrent tasks
include a temporal delay. PRP-
studies in the context of dual task performance belong to this section.
Stimulus onset asynchrony defines when the second task begins. As
already mentioned before, Pashler [2000] and others assume a central
bottleneck which does not allow absolute concurrency.
Elementary continuous tasks
build the bridge to multitasking in daily
life: one continuous task (e.g., driving) is performed while at some
points a discrete task (e.g., a simple choice reaction task) is presented.
To the authors belief, integrating these aspects of concurrency is a first
2.2 multitasking or task interruption?23
step into human multitasking in a realistic context.
Even more important and relevant for this work are
compound con-
tinuous tasks
. As the former category refers to tasks with a duration
shorter than 10 seconds, this last section captures many scenarios, be
it in the context of air traffic control, driving, or mobile computing.
Salvucci mentions multiple recent examples (see Fig. 9) such as a model
of manual tracking by Meyer and Kieras [1997a] and Meyer and Kieras
[1997b], a radar-operator model, identification if new aircraft on radar,
or a scenario in which driving and phone dialing is modeled (Salvucci
[2001]. According to Saluvicci [2005],
"...all these efforts contribute to a
broader understanding of multitasking through study of both overall measures
of task performance and particular measures of multitasking performance".
2.2 multitasking or task interruption?
The following section focuses on task interruption, on its characteristic
features and gives some example of task interruption studies. To the
author‘s mind, task switching from an unfinished task (which later is
resumed) to a secondary task is, strictly spoken, a task interruption of
this primary task. For this reason, both cases directly refer to human
multitasking.
2.2.1Characteristics of task interruption
Already in
1927
, Bluma Wulfovna Zeigarnik, a soviet psychologist
and student of Kurt Lewin and Lev Vygotsky, showed that people
remember interrupted tasks better than uninterrupted tasks (Zeigarnik
effect
, see Zeigarnik [1927] and Zeigarnik [1967]). She observed that
waiters seem to have a better memory for unpaid orders (van Bergen
[1968]). Similar to Zeigarnik, Maria Ovsiankina showed that people
tend to resume unfinished, but not finished tasks (Ovsiankina [1928].
Her assumptions closely relate to Zeigarnik and this effect is referred
to as
Ovsiankina effect
. A few years later, H. [1941] was interested in
the impact of feedback (success and failure) on the resumption of
a task (motivational component). However, these effects could not
always be reproduced, but at least it shows that interrupting people
affects their task performance, be it in terms of stress (Cohen, 1980),
decrease in task performance (Gillie and Broadbent [1989]), producing
mistakes (McFarlane and Latorella [2002]) or recalling information.
McFarlane [1998] defines an interruption as "the process of coordinating
abrupt changes in people’s activities" and in reference to this definition,
McFarlane and Latorella [2002] classify interruptions using a taxonomy
which is presented here with slight modifications modified by the
author:
source of interruption
The interruption can be taken by the per-
son who is doing a task (i.e., self,
internal interruption
), by another
person (i.e.,
external interruption
), or by a machine (e.g., computer,
external interruption
). In many classical studies on task switch-
ing, the source of the interruption can easily be controlled, for
instance by stopping task one and allowing to fully focus on task
two. However, in the context of human multitasking, the situation
looks rather different when one ongoing task is not stopped even
though a second task starts.
24 theoretical background
individual differences
Humans are bounded and rely on per-
sonal limitations. Cognitive processing is limited, and so are
processes of perception and motor response. Individual differ-
ences play an important role in the field of human interruption,
but are not of deeper interest within this work.
method of coordination
Immediate interruptions occur without
coordination, in contrast to negotiated interruption. Further meth-
ods are (human- or machine-) mediated interruption and sched-
uled interruption based on an explicit agreement or by convention
for repetitive interruptions.
meaning of interruption
We all know the most common mean-
ing of interruptions in our daily life. Alarms clocks during a
meeting remind us to stop the current activity (task) and turn
to another task/appointment/activity. Simply spoken, we are
reminded that now, starting with the alert, our attention has to be
focused on a specific action. Interruptions can also beckon us to
ultimately stop our current task.
method of expression
Physical expression (verbal, paralinguistic,
kinesic), expression for effect on face-wants (politeness),a signal-
ing type (by purpose, availability, and effort), metal-level expres-
sions to guide the process, adaptive expression of chains of basic
operators, intermixed expression, expression to afford control.
channel of conveyance
Face-to-face, other direct communica-
tion channel, mediated by a person, mediated by a machine,
meditated by other animate object.
changed human activity
Internal or external, conscious or sub-
conscious, asynchronous parallelism, individual activities, joint
activities (between various kinds of human and non-human par-
ticipants), facilitation activities (language use, meta-activities, use
of mediators).
effect of interruption
An interruption can cause multiple chang-
es in human activity. It can influence motor behavior but also
memory, awareness and the focus of attention. Especially in the
context of multitasking situations, an interruption might create a
complete new situation to which the person who is interrupted
must adapt.
2002
. Beginning of this decade, Altmann and Trafton [2002] describe
a sequence of actions within an interruption situation, as illustrated
in Fig. 10. A primary task is performed and interrupted by an
alert
.
The period between this alert and the start of a secondary task is the
interruption lag
. In their studies, Trafton et al. [2003] focus on two
characteristics of an interruption lag:
•
the availability of the primary task during the interruption period
• the duration of the interruption lag
Quite obviously, this time lag is a function of both the time the
secondary task starts and a person‘s reaction time that defines when
to start the secondary task. The secondary task itself is performed and
ends at a certain moment in time. Before the primary task is resumed,
2.2 multitasking or task interruption?25
BEGIN
OF 1st
TASK
ALERT
FOR 2nd
TASK
BEGIN
OF 2nd
TASK
END
OF 2nd
TASK
RESUME
OF 1st
TASK
INTERRUPTION
LAG
RESUMPTION
LAG
Figure 10.: Interruption situation (taken from: Altman and Trafton(2002))
time passes. This interval is called the
resumption lag.
Fig. 10 clearly
illustrates the complete sequence of a scenario in which two tasks are
executed, but they lack the integration of one or even more continuous
tasks which might be partially executed in parallel. While driving, for
instance, people seem to be able to switch their visual attention to the
phone for a split second and dial a number. Nevertheless, they continue
driving (without visual awareness for that moment).
2.2.2The task switching question
One of the core questions in the context of task switching and multi-
tasking is: "when do people switch between tasks?" Kushleyeva et al.
[2005] mention three criterions (referred to as "major skill sets") which
have to be met for "satisfactory" multitasking performance, namely
1. the ability to create and schedule future intentions
2. the facility to remember and prioritize these intentions
3. the ability to switch from carrying out one to another task when
the appropriate moment in time is finally reached
The first and the second condition resemble a concept which gains
more and more interest recently: memory for future intentions is of-
ten named
prospective memory
(remembering to remember, Winograd
[1988]). McDaniel and Einstein [2000] distinguish between
event-based
prospective memory (cue is an event, e.g., pressing a button or an-
swering a question) and
time-based
prospective memory (recalling to
continue a task at a certain time, e.g. going to a meeting at 4pm).
Following Smith and Bayen [2004] and Smith [2003], maintaining an
intention always requires attention resources, whereas McDaniel and
Einstein [2000] note that cue identification can be automatic or effort-
ful, depending on a variety of parameters. The connection between
prospective memory and the area of interruption becomes quite visi-
26 theoretical background
ble: Altmann and Trafton [2002] highlight that in order to resume an
interrupted task, two essential conditions have to be met:
1. prospective goal encoding ("what was I about to do?")
2. retrospective rehearsal ("what was I doing?")
In their eyes, prospective goal encoding constitutes "a key mechanism
behind prospective memory". Retrospective rehearsal is connected to
what Kushleyeva (Kushleyeva et al. [2005]) calls "facility to remember"
and can be suppressed by tasks which prevent from rehearsal, such as
the n-Back task (McElree [2001], Owen et al. [2005], Juvina and Taatgen
[2007]). The third and last criterion mentioned above focuses on the
moment in time when precisely a switch has to be executed. This
decision, however, determines not only a switch of attention but also
the activation of another task set, i.e. the task set which is necessary to
perform the resumed task. In Chapter 3, I claim and provide evidence
that this decision is both conscious (and reported) but also unconscious
(and thus only available in eye tracking data). In their approach, Dario
Salvucci and his team use a computational grammar (an algorithm)
called "information requirements grammar" to describe when people
switch from one task to another.
2.2.3A grammar for task scheduling
Andrew Howes and his collegues propose a "theory of competence
for tasks", so-called information requirements grammar, IRG (Howes
et al. [2005]). IRG implies the assumptions that (a) information and
control requirements constrain the execution of a task and (b) available
resources constraint the performance of a task (i.e., of their compo-
nent processes and the necessary information). Two different kinds
of constraints define task scheduling, namely information and control
constraints on the one side and resource constraints on the other side.
This is illustrated in the following example:
IRG, however, does not allow a delay in performance when the
information necessary to execute a task or subtask is available. In
addition, this grammar proposed perfect task switching.
2.3 single or multiple resources?
Single-resource theories
assume one central, unique resource (General-
Purpose-Limited-Capacity Central Processor). Nobel prize winner
Daniel Kahneman claims that we have only one global resource, and if
we reach this available capacity, e.g. by demands of multiple concur-
rent tasks, we feel cognitive load (Kahneman [1992]). Single-resource
theories postulate a direct connection between number and difficulty
of concurrent tasks on the one side and resulting cognitive load on the
other side. Performance decreases with increasing number of tasks
and directly influences our limited, cognitive resource. The more dif-
ficult a task, the more reduced the available cognitive resource and
consequently the performance. Especially in dual task studies, this
theoretical assumptions by Kahneman [1992] became popular. Two
tasks can be performed concurrently until the limit is exceeded. If this
happens, cognitive resources are no longer available and both tasks
cannot be performed in parallel. Mainly, a decrease in reaction time
2.3 single or multiple resources?27
Figure 11.: Howes (2005): a grammar for task scheduling
results from that. Performance errors are expected to increase in this
case.
In contrast to theories on one central cognitive resource, so-called
theories of multiple resources postulate different and specific moduls
for information processing (see Fig. 12). Similar to assumptions by
Kahneman [1992], a limitation of the cognitive system is not denied.
But the main difference to single-resource theories is that the central
capacity is a product of different, independent individual capacities.
Wickens (Wickens [2002], Wickens [1984], Wickens and Liu [1988], and
Wickens [2004]) proposes a model of multiple resources, as shown in
Fig. 12 with the following categorial, dichotomic dimension:
1. processing stages (perception, cognition, responding)
2. perception modalities (visual vs. auditory)
3. cisual processing (focal vs. ambient)
4. processing codes (spatial vs. verbal)
Following Wickens and his model of multiple resources, it is generally
possible to perform multiple tasks under the same conditions without
distraction or loss in performance. Division of attention, for instance,
turned out to be more robust under "cross-modal time-sharing" com-
pared to "intra-modal time-sharing". The model by Wickens (Wickens
[2004], Wickens [2002]) allows different, divided resources for indi-
vidual stages of information processing: resources for perception and
resources for response do not interfere,thus both processes theoretically
run in parallel without performance loss. For the presented studies in
the next chapter, these implications play a key role.
28 theoretical background
Figure 12.:
Model of multiple resources, taken from: Wickens and Liu [1988]
and Wickens [2004]
2.4 summary and criticism
The presented theoretical approaches towards human multitasking
serve to give an short overview and can be summarized in the following
main messages:
1.
First empirical approaches to investigate human multitasking go
back to the task switching studies (Jersild [1927]) in which simple
choice reaction tasks were applied.
2.
These studies were enriched by the aspect of overlapping tasks
(Telford [1931]) and the concept of psychological refractory period
was introduced.
3.
In the following decades until the late 1980s, many variations of
the early findings systematically analyzed human task switching.
4.
Pashler [2000] emphasizes the role of a central bottleneck and
aims to show the inevitability to fully parallelize two tasks.
5.
With their Psychological Science article on how to reach virtually
perfect time-sharing, Schumacher et al. [2001]
6.
In recent studies (Saluvicci [2005], Taatgen [2005]), real-life scenar-
ios in the context of human multitasking gain increasing attention
and the importance of continuous tasks in such investigations is
highlighted.
7. Core questions in explaining human multitasking still remain:
a) Is it sufficient to apply discrete tasks in multitasking studies?
b) How much training do people need to optimize task schedul-
ing?
c) When and how do people decide to switch from one task to
another?
d) Which strategies do people apply in human multitasking situa-
tions?
2.4 summary and criticism 29
2.4.1Need for continuous tasks in multitasking studies
In Pashler [2000], the main focus is on discrete tasks. This is a rather
common handling in the context of studies on dual task performance.
Altmann and Trafton [2002] use discrete tasks in their task switching
scenario. As Saluvicci [2005] remarks, to draw conclusions about
human multitasking behavior in real life, it is necessary to investigate
continuous tasks in a dynamic environment. Driving as the most
prominent example for a continuous task underlines this demand. But
also various other situations in daily life like walking in the street
and concurrently using a mobile phone support a call for applied
studies including continuous tasks. For this reason, my studies rely on
scenarios of "compound continuous tasks", as proposed by Saluvicci
[2005].
2.4.2The importance of training and task repetition
Schumacher et al. [2001] highlight the importance of task training in the
context of multitasking studies. They claim that participants become
skilled in that specific task set. Lee and Taatgen [2002] considers skill
acquisition as a method to "learn" how to do multitasking. Within the
cognitive architecture ACT-R (Anderson [2007]), Taatgen refers to a
mechanism called "production compilation" as main explanation how
to perform successful multitasking. The following nice example (taken
from: Taatgen [2005]) helps to illustrate how this works:
Put water in kettle, put water on stove until it boils, put tea leaves in teapot,
pour boiling water in teapot, and wait 3to 5min. These five instructions
for making tea can be stored almost literally in declarative memory. The sim-
plicity of the representation explains why this is the starting point for a new
skill: Declarative items of knowledge can be added as single items to mem-
ory. The disadvantage of declarative representations is that they cannot act
by themselves; instead they need, according to Anderson’s theory, produc-
tion rules to be retrieved from memory and interpreted. This explains why
initially processing is slow, because the declarative representations must be
retrieved before they can be carried out, and it is prone to errors because the
right declarative fact might not be retrieved at the right time.
In study II within chapter III, the role of training and practise is
illustrated. We will see to what extend this will contribute to handle
the demands of concurrent multiple tasks in a dynamically changing
environment.
2.4.3Cognitive heuristics under human multitasking
Even though the approaches by Taatgen [2005], Saluvicci [2005] and
others provide (computational) models which accurately predict hu-
man performance under multitasking in their concrete task scenarios, I
doubt that human task scheduling behavior follows a grammar like IRG
(see Howes et al. [2005]) or a formal description. Instead, I claim that in
a human multitasking scenario, people adapt to the environment and
develop strategies to optimally "survive" in such situations. Addition-
ally, people might not necessarily be aware of their applied strategies,
i.e. they arise either consciously (strategic) or unconsciously. They do
not need to be precise, either. Therefore, I use the word "heuristic"
which comes from the same Greek root as Eureka! And means "to find".
30 theoretical background
In my understanding, a heuristic is a "rule of thumb", with other words
a rule which is simple, efficient and can easily be learned through
experience and training. Kahneman and Tversky [1973] propose the
availability heuristic as a a heuristic for judging frequency and proba-
bility where people base their prediction of the frequency of an event
or the proportion within a population based on how easily an example
can be brought to mind. With other words, the ease of imagining an
example has more weight for the judgment than the actual statistical
probability. Because an example is easily brought to mind or mentally
"available", the single example is considered as representative of the
whole rather than as just a single example in a range of data. Stuart
Sutherland illustrates this heuristic using a plausible example (Stewart
et al. [1994]). Asked whether there are more words with "r" as the
first letter than with "r" in the third position and also whether there
are more words beginning with "k" than with "k" as the third letter,
most people tend to reply that in both cases there are more words
with "r" on 1st position than on the 3rd position. The same counts for
the "k" - example. Nevertheless, people make a mistake, because in
both cases, there are more words with "r" (same for "k") on the 3rd
position. Words are arranged according their initial letter. Retrieving
from memory thus is facilitated for words starting with a letter, e.g.
with "r" (road, run) whereas it is more difficult to retrieve words with
this letter on the 3rd position (like street, care). The statistical frequency
is completely ignored and people judge based on the availability of
words in their mind. This heuristic is used to explain findings in the
area of probability judgment and people in general are not aware that
they make use of a cognitive heuristic. The unconscious application of
a heuristic implies that cognitive resources should suffer less compared
to a strict propositional processing. Heuristics furthermore are not, as
some scientists state, a bias or a failure: According to Gigerenzer and
Selten [2002], heuristics can be
fast, frugal and accurate all at the same time
by exploiting the structure of information in natural environments
(page 9).
With other words, the development is an adaptation mechanism to the
environment. This characteristic is of main importance for the four
empirical studies, now presented in the upcoming chapter.
Part III
STUDIES
33
3
EMPIRICAL STUDIES
Purpose of the following four studies and scope of the entire work
is to convince and give ample evidence that, in a real life scenario,
people do not follow principles of pure optimization when doing
multitasking. Multiple approaches (e.g., Brumby et al. [2007]; Saluvicci
[2005]) propose a task switching behavior according to free resources
(e.g., visual attention or manual action) and available information
required for performing a specific task. In study I-IV, the author tries
to illustrate how people do multitasking by adapting to the structure of
the environment. In contrast to many (if not even most) psychological
studies in the last century, a dynamic main task in man machine
interaction will be used. Study I introduces the multitasking-scenario
which was applied in all of the four studies. Main focus in the first
study lies on investigating how people manage a multiple task situation
in a real-life context. Study II goes one step further by concentrating on
the impact of practice and thus analyzes multitasking strategies to a
deeper extend. Study III shows the importance of task configuration:
with a systematic variation of the involved secondary task, it becomes
obvious how people manage the demand of several tasks according to
their design. The last and final study (IV) refers to an aspect of daily
life, i.e. time pressure: in dynamic man-machine-interaction, available
time is often rather precious and especially high time pressure plays a
key role in how we handle multitasking in everyday life.
Study I and II were conducted in a driving simulator. In study III and
IV, a driving simulation on a PC was used. Please note that for the first
two studies, ecological validity turns out to be higher compared to the
last two studies, but these studies (I and II) lack a complete systematic
control of external influences. For this reason, study III and IV were run
on a PC with a simulated driving environment and perfectly controlled
conditions.
Findings of the four studies provide helpful insight and consequently
support the prospective design of human machine interaction already
in early stages of system development. Understanding how people
behave in a multitasking scenario, what kind of strategies they use and
how to support their performance allows a direct intervention during
the design process and helps to reduce human errors in daily life.
3.1 study i:identification of multitasking heuristics
Most studies in the context of human multitasking mainly include
standard, PC-based psychological tasks. As illustrated in 2, psycholo-
gists have been analyzing multiple-task coordination for quite a long
time, starting already in the 1920s. Task shifting studies (Altmann and
Trafton [2002]) or PRP-studies (Pashler [2000], Pashler [1993]), however,
lack a direct connection to human behavior in daily life. The catego-
rization of multitasking studies by Saluvicci [2005] nicely reflects that
only recently, researchers started to pay more and more interest in
dynamic task environments. Study I, for this reason, is interested in
the adaptation and the allocation of attention in a scenario in which
35
36 empirical studies
people have to perform a continuous task (driving) and concurrently
somehow handle the demands of a secondary task which is connected
to the main task. It is well-known that a driver is multitasking while
driving: everywhere, everyday. Be this eating, reading email, or talking
on the phone. Inattention, distraction, and mental fatigue still are the
most dangerous contributing factors leading to an accident. The design
of an in-car-system which requires less attention and is easy to use,
thus, can prevent from vehicle crashes. To do this, it is necessary to un-
derstand how people behave in a naturalistic multitasking environment.
3.1.1Method in study I
Participants in study I
Twenty four male and female undergraduate students (age between
20 and 30) of Technical University Berlin participated the first study.
All participants had a driving license and were experienced in driving.
Wearers of glasses were excluded from the study. Gender effects were
of no interest for the study.
Figure 13.: Car interior in study I
Involved tasks in study I
Primary task in study I was a driving task (with lane derivation being
considered a dependent variable): participants were asked to drive with
constant speed (130 km/h) in a driving simulation in a car. The task
itself was quite trivial (keeping the lane): participants were instructed
to keep the lane (a simulation which does not require a deeper spec-
ification here). Performing this task (after being instructed to do so)
turned out to be feasible for all subjects. Also, the focus on driving
as primary task was understood by all subjects and they acted accord-
ingly, considering driving with main importance. As secondary task,
the MODYS research group, especially Marcus Heinath and Jeronimo
Dzaack, build a derivation from the "D2test of attention" (Brickenkamp
3.1 study i:identification of multitasking heuristics 37
[1992]), an in-car version adapted to be displayed on a 10inch screen
in the car interior. In what follows, this test is hence referred to as
"D2-Drive". In comparison to other "in-vehicle information systems"
(IVIS), D2-Drive is a model of a secondary task on a screen-oriented
driver information system and captures the following characteristics:
•Attention: D2-Drive requires full visual attention.
•Access: D2-Drive can easily be learned.
•Interruptability: D2-Drive can be interrupted and resumed.
•Resources: D2-Drive is a measuring tool for residual resources.
•Cognition
: D2-Drive requires perception (read), cognition (de-
cide) and action (motor response).
In this sense, D2-Drive is cultural independent, scalable in terms
of complexity and serves as an optimal tool to measure individual
attention. In this case, attention is needed for the secondary task for
perception ("reading") and action (manual response). As a dependent
variable, Dr-Drive performance was considered in study I and all
upcoming studies.
Figure 14.: Original D2test of attention (Brickenkamp [1992])
Similar to the original paper and pencil version of the D2-test devel-
oped by Brickenkamp (Brickenkamp [1992], in D2-Drive people have to
judge whether a pattern contains the letter "d" and concurrently two
strokes. However, in contrast to the original version, D2-Drive requires
to press a button (Yes or No) instead of crossing the pattern out. This
feature underscores the similarity to many situations in which we inter-
act with systems in daily life. Based on the fact that D2-Drive requires
full visual attention and asks for a decision (a cognitive evaluation),
performing D2-Drive in context of a multitasking scenario means a
visual interruption of a concurrent task.
Three versions of D2-Drive were used in study I (see Fig. 15):
d2-drive-v1.1:
Presentation of a complete row of (five) patterns Fo-
cus only on the pattern in the middle (third pattern) Execution
only of pattern in the middle (1pattern)
d2-drive-v1.2:
Presentation of a complete row of (five) patterns Fo-
cus on complete row Execution of complete row (5patterns)
d2-drive-v1.3:
Presentation of a matrix of patterns (rows and col-
umns) Focus on the row whose number was presented Execution
of this complete row
As can be seen, D2-Drive-v1.2is close to the original version of the D2-
test. D2-Drive-v1.3contains an additional memory element, i.e. while
38 empirical studies
performing a complete row on the n-th screen, the row which needs to
be performed on the next (i.e., n+1- th) screen has to be read and kept
in mind. The design of all three versions assumes increasing complexity
and the demand of different resources. Whereas version D2-Drive-v1.1
requires visual fixation, a cognitive decision process and a response,
the other two versions further include a reading element. D2-Drive-v1.3
additionally relies on vertical as well as horizontal aspects.
Figure 15.: D2-Drive (Urbas et al., 2005)
Design of study I
Complexity of D2-Drive was treated as between-subjects factor (three
groups with 8participants per version) and condition (single- vs. multi-
tasking) as within-subject factor. Measure of performance for main task
(Driving) was lane derivation, for secondary task (D2-Drive) number of
correct patterns per minutes (i.e., trial). Please note that the error rate in
all three versions of D2-Drive approximated zero: hence, the decrease
in performance is reflected in the number of performed patters (i.e.,
patterns per min).
Procedure in study I
In study I, participants were first introduced to the complete procedure
and instructed to perform the primary task, i.e. driving in the simulator
environment with constant speed (130 km/h) and main priority. First,
participants trained the primary task and afterwards, baseline measures
(single task condition) for driving were recorded. Subsequently the
secondary task (D2-Drive) was explained, trained and performed under
single task condition (pretest). Following, the multitasking condition
started: while driving, at four different positions a sound indicated a
D2-Drive test (duration of 60sec). As soon as participants heard this
sound, they were requested to perform the secondary task (D2-Drive)
without neglecting the primary task (driving). Within a lap, D2-Drive
was presented four times. The study concluded with a post test for
D2-Drive and a structured interview in which participants were asked
questions about their experience while performing the study. For data
analysis, a multivariate analysis of variance was conducted.
1. Welcome, introduction and instruction
2. Training "Driving"
3. Baseline "Driving"
4. Training "D2-Drive"
3.1 study i:identification of multitasking heuristics 39
5. Single-Task "D2-Drive" (Pretest)
6. Dual-task session (4x "D2-Drive")
7. Single-Task "D2-Drive" (Posttest)
8. Structured interview
Hypotheses for study I
In collaboration with the MODYS research group as well as from a
explorative perspective, the following hypotheses, mentioned below,
were tested.
Hypothesis: Driving under multitasking
The primary task itself (driving) is a continuous tracking task and
does not require deep cognitive processing. Driving is instructed to
be considered as priority task. Therefore, no performance decrease is
expected under multitasking compared to single tasking (baseline for
driving).
3.1.2Hypothesis: D2-Drive under multitasking
•
For the three versions of the secondary task, complexity is as-
sumed to increase from version 1to 3consecutively and hence
performance to decrease. This assumption is based on a deeper
cognitive processing (visual perception, cognitive processing in
terms of decision making, and action via motor response), espe-
cially for D2-Drive-v1.2and D2-Drive-v1.3. It can be expected
that performance (patterns per minute) in D2-Drive-v1.2is com-
parable to results from the paper and pencil version developed
by Brickenkamp [1992]. Increasing complexity (D2-Drive-v1.1
< D2-Drive-v1.2< D2-Drive-v1.2) should be reflected in perfor-
mance data (correct patterns per minute) and result in significant
differences.
•
D2-Drive requires visual attention, cognitive processing as well
as motor action (response). Therefore performance in D2-Drive is
expected to decrease under multitasking performance. Driving
(main task) was instructed to be performed with priority and
consequently a decrease in the primary task cannot be expected.
In addition, the three different versions of D2-Drive require a dif-
ferent amount of visual attention. For D-Drive-v1.2, performance
is expected to be best: in this version, visual-motor coordination
(as described by Wickens [2002]) is possible. D2-Drive-v1.1and
D2-Drive-v1.2should not differ in performance significantly in
the pretest but in the dual task condition and even more in the
post-test due to stronger learning effects and development of a
"multitasking skill" (Taatgen [2005]). D2-Drive-v1.3is supposed to
be the most complex and thus most demanding version, leading
to worst performance. This is a result of the additional cognitive
(memory) element in it.
40 empirical studies
3.1.3Results of study I
Results: Driving under multitasking
Not surprisingly, driving was not affected by an additional secondary
task. Performance under multitasking was similar compared to single
task performance. Recordings of baseline driving after training confirm
that under multitasking, participants do not improve performance, i.e.
after single tasking, they reached their maximum and learning effects
due to longer practice can be excluded. The results further support
the assumption that participants follow the instructions and consider
driving as main task with main priority.
Results: D2-Drive under multitasking
Figure 16.: Performance in D2-Drive in study I
Pre- and post-tests (single task condition) were applied to check
whether learning effects (see Taatgen [2005]) occur. For D2-Drive-
v1.1, performance did not change significantly whereas in both D2-
Drive-v1.2and D2-Drive-v1.3, participants improved significantly after
multitasking (for both,
p < 0.05
). Overall, there was a strong significant
difference between single and multitasking for all three versions (for
D2-Drive-v1.2:
p < 0.01
, for D2-Drive-v1.2:
p < 0.05
, for D2-Drive-v1.3:
p < 0.06
). Surprisingly, in D2-Drive-v1.2performance was best in all
conditions and performance data are comparable to the paper and
pencil version.
After the complete scenario, participants were asked several questions
concerning the manner in which they performed both tasks individually
as well as concurrently. Based on these interviews, the most prominent
"strategies" how people performed the secondary task were:
1.
(1) For D2-Drive-v1.1, no specific strategy was applied. Partic-
ipants fixated the pattern in the middle and from time to time
turned their visual attention to the street. The behavior remained
constant during the complete procedure of the study.
3.1 study i:identification of multitasking heuristics 41
2.
(2) For D2-Drive-v1.2, participants started to perform the same
way they did in D2-Drive-v1.1, but over time, several patterns
were read sequentially and responses were given sequentially as
well. 14 out of 24 confirmed to use this "strategy", some did so
already in the beginning of the dual task condition while others
"developed" this strategy during the multitasking scenario.
3. (3) For D2-Drive-v1.3, the interviews state that participants used
the same strategy. One additional "adaptation" was an external-
ization of the memory element: 10 out of 24 participants reported
that they did not keep this number in their mind but used the
corresponding finger of their left hand (which was on the wheel).
Mostly, for the secondary task, participants apply a processing or
a strategy which here in this context is referred to as "
merge heuris-
tic
": at the beginning, participants perform the test pattern by pattern.
After a while, participants seem to have "understood" that merging
patterns together and replying them in a sort of block (e.g., reading 2
or 3patterns, scanning the street, responding these 2or 3patterns by
manually pressing keys) is a resource-friendly and clever adaptation to
the environment, i.e. the multitasking situation.
Figure 17.: The "merge heuristic"
3.1.4Discussion of study I
In sum, study I shows that performance under multitasking does not
decrease in both tasks. One reason for this phenomenon is the impact of
training (practice effects) as mentioned by the participants in the struc-
tured interviews. A second, maybe even more important reason, is the
development and application of multitasking heuristics. These heuris-
tics can potentially be derived not only from verbal reports (structured
interviews). In study II, eye tracking data will be applied additionally.
The observation that performance improves over time (effect of practice)
implies a need for a second study in which (a) participants are given
more time to practice both tasks concurrently and (b) eye tracking data
are used to support assumptions about the "merge heuristic". Addition-
ally to these aspects, a third extension is use of another 3versions of
D2-Drive with nine instead of five patterns. These version are referred
42 empirical studies
to as D2-Drive-v2.1(5), D2-Drive-v2.2(5), D2-Drive-v2.3(5) and conse-
quently D2-Drive-v2.1(9), D2-Drive-v2.2(9), D2-Drive-v2.3(9), where the
number in brackets indicates the corresponding pattern lengths.
3.2 study ii:practice and multitasking heuristics
In study I, participants used cognitive heuristics which developed with-
out instruction and not necessarily consciously. The "
merge heuristic
"
was detected and described in detail in the previous paragraph. Main
critical issue of study I is based on the observation that participants
increased in performance over time, especially in D2-Drive-v1.2. Thus,
the question is whether the detected heuristic is moderated by practice
and how practice supports multitasking heuristics. In many studies
on skill acquisition, practice has a tremendous impact on performance.
For this reason the same scenario was applied with some slight modi-
fications. Does more intensive driving amplify the use of the "merge
heuristic"? Will performance increase in a second driving lap with the
help of the "merge heuristic"? Or will participants feel mental fatigue
and produce worse performance under multitasking? And also, in
analogy to the original paper and pencil test of D2, it is of further
interest if longer patterns per version (9instead of 5) even amplify the
gap between the three versions. Especially D2-Drive-v1.2is expected
to benefit from such a modified configuration. Therefore, what is the
impact of pattern length in the secondary task? Is there any connection
between pattern length and the "merge heuristic"? These questions will
be answered in this paragraph.
3.2.1Method in study II
In study II, the same method was applied as in study I.
Participants in study II
Thirty-six undergraduate students of Technical University Berlin took
part in the second study. Gender was equally distributed. Age was
of no further interest. As in study I, all participants of study II had
a driving license and were experienced in driving. Because of the
measuring of eye tracking data, all participants had to confirm to wear
neither glasses nor contact lenses. Participants were male and female,
gender effects were not of any interest in this context.
Involved tasks in study II
Primary task for study II was driving, using the same environment
(driving simulator with identical lane) as in study I. Lane derivation
and performance in D2-Drive are dependent variables in study II. In
contrast to the first study, this time participants were asked to drive
two laps. In fact, study II is a slight variation and a design similar to
the one applied in study I, with the following extensions:
• Providing more training by extensive driving (2laps)
•
Investigating the impact of pattern length in the secondary task
(5vs. 9patterns)
3.2 study ii:practice -multitasking heuristics 43
As in the previous study, secondary task was D2-Drive. This is a
necessary condition in order to compare participants behavior and to
see whether any possible effect is in fact based on practise.
d2-drive-v2.1Same as in study I (for 5as well as 9patterns)
d2-drive-v2.2Same as in study I (for 5as well as 9patterns)
d2-drive-v2.3Same as in study I (for 5as well as 9patterns)
In study II, subjects were instructed to pay main attention to the pri-
mary task (driving in the simulation environment) and at the same time
- without neglecting the driving - to perform D2-Drive as appropriate
as possible. The three versions described in the previous section were
presented with either five patterns (as in the first scenario) or with 9
patterns. This is denoted by an according number in brackets, e.g., D2-
Drive-v2.2(9), indicating that the second version was presented using
nine patterns. Please note that not coincidentally, the numbers refer to
assumptions about working memory capacity: Cowan (2001) claims
that people are able to maintain 4(plus/mins 1) chunks, whereas for-
mer studies (Miller, 1957) promote a "magical number seven", implying
that working memory capacity features a size of seven. Nevertheless,
this work is not meant to focus on people‘s capacity limit when doing
multitasking. Choosing a nine-patterns version just plays to the vari-
ance in working memory performance: participants with a size bigger
than five "have the chance" to make use of their full capacity and do
not find themselves restricted by a five-patterns version.
Design in study II
Exactly like before, "complexity in D2-Drive" (3versions) was treated
as between-subject factor and "pattern length" (five vs. nine) as within-
treatment.
Procedure in study II
As in the previous study, participants were welcomed and introduced
to the scenario. As in study I, it was important for them not to have par-
ticipate in a similar driving study (e.g., study I) before (inexperienced).
procedure for extended driving and d2-drive
1. Welcome, introduction and instruction
2. Training "Driving"
3. Baseline "Driving"
4. Training "D2-Drive"
5. Single-Task "D2-Drive" (Pretest)
6. Dual-task session (4x "D2-Drive", lap 1)
7. Dual-task session (4x "D2-Drive", lap 2)
8. Single-Task "D2-Drive" (Posttest)
9. Structured interview
44 empirical studies
3.2.2Hypotheses for study II
Hypothesis: Intensive driving under multitasking
In the previous study, driving performance was not influenced by
multitasking. Therefore, in study II no significant differences were
expected under multitasking compared to driving in the single task
condition. The instruction (i.e., considering driving as man task with
main priority) was expected to fully work, meaning that participants
should perform D2-Drive without neglecting the driving task. Mental
fatigue should not occur.
D2-Drive under multitasking (pattern lengths and practice)
Schneider and Shiffrin [1977] claim that tasks become "automized" after
practice, thus no voluntary control is needed and no interference with
mental operations appears, i.e. simultaneous performance without
interference is possible. However, in their studies, discrete dual task
situations were investigated. In this study, a dual task scenario with
continuous tasks is used. With reference to results from Schumacher
et al. [2001], Schneider and Shiffrin [1977] and Taatgen [2005], practice
not only improves performance but also turns controlled processing of
(part of) a task into automatic processing (from error-prone to error-free
behavior with an increase in speed). Intensive training should thus pro-
mote using the "merge heuristic" (consciously as well as unconsciously)
and thus lead to a better performance for D2-Drive-v2.2, but not for
D2-Drive-v2.1(due to its configuration not supporting the use of the
described heuristic). This counts for the dual task condition (merging
of action parts adaptively to the multitasking situation) as well as for
the post-test (participants will have learned how to optimally use the
"
merge heuristic
"). However, D2-Drive-v2.3is not expected to show sig-
nificantly better performance after training. The memory element in it
prevents from automatically enter responses by demanding resources
in working memory.
The "
merge heuristic
" (described in the previous section) is a smart, cog-
nitive tool to adapt to the multitasking environment. Similar to studies
on perfect-time sharing by Schumacher et al. [2001], two tasks coalesce
and almost become one (as much as possible). Visual attention, ob-
viously, cannot be shared, and both tasks require visual perception:
the driving task requires, even if it is only a minimum, control scans
of the street, and by nature, D2-Drive is based on D2(Brickenkamp
[1992]) which is a test of attention. However, participants in the first
study somehow managed to separate those elements of both tasks that
allow parallel processing (control scans on the street and concurrently
response in terms of manual action). Even more surprisingly, this all
happens
without
instruction: people
understand
the structure of their
(dynamically changing) task environment and automatically adapt to
it.
3.2.3Results of study II
Results: Intensive driving under multitasking
Not surprisingly, driving remained constant and was not influenced
by a secondary task. Even more, under dual task condition, driving
3.2 study ii:practice -multitasking heuristics 45
Figure 18.: Performance in D2-Drive in study II
performance did not significantly change from lap 1to lap 2. This con-
stancy of performance goes in line with results from the previous study
and again shows that participants follow the instructions and consider
driving as main task with main priority. It also shows that mental
fatigue, in case participants felt it, did have no effect on performance.
Results: D2-Drive under multitasking: pattern lengths and learning
The factor "pattern length" (five vs. nine patterns) did not have a
significant influence on performance in the secondary task. In the
nine-patterns version, participants were only slightly better. However,
this result is not worth further mentioning. For this reason, all data is
aggregated, subsumed and hence I refer to D2-Drive overall. As already
confirmed by study I, D2-Drive-v2.2outperforms D2-Drive-v2.1as well
as D2-Drive-v2.3. As can be seen, participants perform rather poor in
D2-Drive-v2.3(the obviously most difficult version). In this version,
performance under dual task performance does not improve (in contrast
to the other versions), only small, but not significant learning effects
(pretest vs. post-test) were found. Comparing D2-Drive-v2.1and D2-
Drive-v2.2, the power of the "merge heuristic" becomes transparent: this
version of the secondary task allows to optimally perform both tasks
(or part of them) concurrently. Combining single parts of each of them
(visual perception, cognition, motor action) is possible via the ability
to separate individual parts of the secondary task and reconfigure
them. Also, please note that the level of performance can be carried
over to the posttest in D2-Drive-v2.2, i.e. learning effects are stronger
in this version than in D2-Drive-v2.1. Remarkably, in D2-Drive-v2.3,
performance in the post-test is even better
Participants in study II passed eight trials, i.e. while driving they were
presented eight times D2-Drive (two laps). 19 displays the performance
over time and clearly shows the improvement from trial 1until trial
8.Interestingly, this effect is strongest for D2-Drive-v2.2. A closer look at
the trials gives insight about the zigzag - sequence in the performance
data: even though it was not applied as a factor, a post observation
gives insight about this phenomenon. Odd trials (i.e., trial one, trial
46 empirical studies
three, trial five (or trial one in the second lap), trial, seven (or trial
two in the second lap)) correspond to a lane curve whereas even trial
include a straight lane. There was no assumption about street behavior
and thus the significance and meaning of this "factor" is rather weak.
Driving behavior on a curved vs. straight lane remains stable.
Figure 19.: Improvement over time in study II
Results: Cognitive heuristics under multitasking
In 3.1, one main critical issue was the lack of eye-tracking data. Study
II accounts for this measure, a comparison of visual time spend on
the primary vs. secondary task was done. On average, participants
spend 47 percent of visual attention (time of gaze in percent for the
defined area of interest, AOI, "lane" and for AOI "D2-DRIVE" during
multitasking) on D2-DRIVE and 51 percent on the lane. The residual
amount (2percent) can be considered as noise. Interestingly, D2-Drive-
v1.2requires only 36 percent of eye gazes, whereas D2-Drive-v1.1as
well as D2-Drive-v1.3both require 48 percent. In combination with the
high performance in D2-Drive-v1.2, this supports the assumption that
participants perform the secondary task while their visual attention is
on the primary task. This adaptive task coordination can be explained
with the help of the "merge heuristics" mentioned in paragraph 3.1.
The explanations of participants of how they perform the individual
tasks and both tasks together (structured interview) again support that
most people use the "merge heuristic" (reported by 22 out of 36), and it
can be assumed that even people who were not able to verbalize their
applied strategy/heuristic, might have used them (as can be derived
from eye tracking data).
3.2.4Discussion of study II
In study II, the goal was to investigate the impact of intensive practise.
Main findings of the second study are:
3.3 study iii:the role of task configuration 47
version of d2 gazes on d2-drive gazes on lane
Total 47 percent 51 percent
D2-Drive-v1.1 48 percent 51 percent
D2-Drive-v1.2 35 percent 62 percent
D2-Drive-v1.3 48 percent 51 percent
Table 2.: Study II: Amount of attention (eye gazes, AOI)
1. Practice supports the development of cognitive heuristics.
2. Task complexity seems to have an decisive influence.
3.
Different task configurations require different amounts of visual
attention.
4.
The driving task (tracking) remains unaffected by the secondary
task.
It seems that the more practice participants have, the stronger the
impact of the task configuration. Eye tracking analyzing supports the
assumption that cognitive heuristics (in particular, the "
merge heuristic
")
are applied. For a deeper analysis of human multitasking, study III
uses a standardized driving simulation as primary task.
3.3 study iii:the role of task configuration
Study I and II provide ample evidence that:
1.
People adapt to the environment with the help of cognitive heuris-
tics.
2. This behavior is more pronounced under intensive training.
The applied secondary task in the previous studies varied in terms of
complexity (three difficult levels) but also in terms of its task configura-
tion. In other words, the task itself determines how people perform it.
To further investigate the impact of task configuration, four versions of
the "D2-Drive"-test were build which systematically offer the potential
for cognitive heuristics to be applied. Even though the first two stud-
ies contain a rather high degree of ecological validity, the systematic
control in a driving simulator is strictly limited. To further investigate
human multitasking in a real life context, in the subsequent studies the
"lane change task" (Mattes [2003]), hence LCT, was applied.
3.3.1Method in study III
Study III has not been conducted in a driving simulator. Instead, a
standardized tool for measuring lane derivation (
LCT
) was used. An
additional feature of
LCT
is a (cognitive demanding) task involved
in it (i.e., to change the lane on the appropriate moment in time).
LCT
is a standard tool in a dynamically changing task environment
and has often been used in the context of human machine interaction
in the area of driving (see Mattes [2003]). In september 2005,
LCT
48 empirical studies
was submitted as measuring tool to the international organization for
standards (ISO) and for the time being,
LCT
is in a testing routine
with the title "ISO/DIS 26022: Simulated Lane Change Test To Asses
Driver Distraction" (International Organization for Standardization,
2007). Based on the fact that
LCT
is "freeware", easy to install and
manage, and also features a tool to analyze driving data (called
LCTA
),
it is a perfect main task for investigating human multitasking like in
study I and II.
Participants
Forty students of Technical University Berlin (same characteristics as
in study I and in study II) fulfilling the same premises (no glasses, age
20-30, experienced in driving) participated study III.
Involved tasks
Figure 20.: Lane Change Task (taken from Mattes, 2003)
Figure 20 illustrates
LCT
: participants drive on a simulated highway
and are asked to change the lane according to predefined road signs.
These signs contain three columns, two with crosses (i.e., the letter "x")
and one with an arrow. This arrow indicates the position on which
participants have to switch. To do
LCT
properly, four steps are included:
1. (1) Perception (road sign)
2. (2) Action (start maneuver)
3. (3) Action (perform lane change)
4. (4) Action (keep lane)
LCT
measures the lane derivation: in Fig. 21, the green (even) line
is the optimal lane calculated from the system itself based on a large
amount of driving samples. This optimal lane can be modified by
changing individual parameters (for a deeper insight, please have a
look at the manual of
LCT
). The red, wiggly line shows the driving
3.3 study iii:the role of task configuration 49
behavior of a participant. The density between the two lines constitutes
a value for the lane derivation. Lane derivation is the dependent
variable for the main task (driving).
LANE DERIVATION UNDER SINGLE TASKING
LANE DERIVATION UNDER DUAL TASKING
Figure 21.: How to calculate lane derivation in the LCT
LCT
is a standardized tool often applied in the context of driving
studies. For a further description of the
LCT
, please see Mattes [2003].
In study III and IV, primary task in the scenario is the LCT, secondary
task is the D2-Drive.
Secondary task in study III was a systematic variation of D2-Drive-v1.2
(i.e., D2-Drive-v2.2) with the following features:
d2-drive-v3.1Constant row, no visual support
d2-drive-v3.2Changing row, no visual support
d2-drive-v3.3Constant row and visual support
d2-drive-v3.4Changing row and visual support
Visual support means that the current pattern was highlighted. This
helps to remember the current pattern and there is no need to keep that
position in mind. Also, a constant row allows (technically) to apply
the "merge heuristic", i.e. the answers for several patterns can be kept
in mind and replied combined. Please note that for all four versions,
different assumptions define the hypotheses for study III (as further
described in section 3.3.2).
Design
In contrast to the previous two studies, complexity of D2-Drive was
treated as within-subjects factor. To avoid learning effects, the order
of the D2-Drive versions was balanced. The condition "single- vs.
multitasking" was also treated as within-subject factor. Measure of
performance for main task (Driving) was lane derivation, for secondary
task (D2-Drive) number of correct patterns per minutes (i.e., trial). As
before, the error rate in all three versions of D2-Drive approximated
50 empirical studies
zero: hence, the decrease in performance is reflected in the number
of performed patters (i.e., patterns per min). Pre- as well as post-tests
were applied to investigate possible learning effects.
Procedure
Each participant had to perform the following (sequence of) steps:
1. Welcome and general introduction
2. Introduction of LCT
3. Training "Driving" (LCT)
4. Single task "Driving" (LCT, Baseline)
5. Introduction and Training "D2-Drive-v3.1"
6. Single task "D2-Drive-v3.1" (Pretest)
7. Introduction and Training "D2-Drive-v3.2"
8. Single task "D2-Drive-v3.2" (Pretest)
9. Introduction and Training "D2-Drive-v3.3"
10. Single task "D2-Drive-v3.3" (Pretest)
11. Introduction and Training "D2-Drive-v3.4"
12. Single task "D2-Drive-v3.4" (Pretest)
13. Introduction to eye tracking measuring
14. Checking of eye tracking system
15. Dual task (session) (including "D2-Drive-v3.1" - "D2-Drive-v3.4"
16. Single task "D2-Drive-v3.1" (Posttest)
17. Single task "D2-Drive-v3.2" (Posttest)
18. Single task "D2-Drive-v3.3" (Posttest)
19. Single task "D2-Drive-v3.4" (Posttest)
20. Structured interview
3.3.2Hypotheses for study III
The following hypotheses in this subsection were derived from results
of the previous studies in 3. As in study I and II, hypotheses refer both
to driving behavior and to performance in the secondary task, under
single as well as dual tasking.
Hypothesis: Independence of primary task (stability)
As in the previous two studies, in study III driving is expected to
remain stable under multitasking as it is instructed as primary task.
No performance decrease is expected. Same counts for lane change
behavior: as an effect of proper instruction and training, participants
should not produce errors (i.e., assuming the constantly change to the
correct lane).
3.3 study iii:the role of task configuration 51
Hypothesis: Influence of task configuration
The investigated main task in study III contains higher cognitive de-
mands than the main tasks in the previous two studies. For this reason,
performance in D2-Drive under multitasking is expected to strongly
decrease under multitasking in comparison to single task performance.
In D2-Drive-v3.1and D2-Drive-v3.3, the pattern row does not change
which offers the possibility to apply the "merge heuristic": responses
can be anticipated and entered in a unit of several patterns. During this
action (motor response), visual resources are "free" and participants can
scan the lane and (visually) resume the primary task, i.e. driving.
3.3.3Results of study III
Results are divided into driving behavior and performance in D2-Drive.
Results: Performance in driving
Under multitasking condition, driving was not affected by the sec-
ondary task. Performance in
LCT
while D2-Drive was presented did
not differ significantly from
LCT
- performance under single task con-
dition.
Results: Performance in D2-Drive
Figure 22.: Performance in D2-Drive in study III
Figure 22 shows that performance with D2-Drive is better if the
pattern row remains the same (anticipation and merging is possible)
compared to a version in which the row changes after each response.
With other words, D2-Drive-v3.1and D2-Drive-v3.3outperform D2-
Drive-v3.2and D2-Drive-v3.4. This effect is statistically significant for
single tasking as well as multitasking.
52 empirical studies
Results: Eye tracking
D2.Drive-vrs.
Gazes at
LCT
Gazes at D2-
Drive
Gazes at En-
vironment
D2-Drive-v3.1 60 35 5
D2-Drive-v3.2 44 51 5
D2-Drive-v3.3 71 26 3
D2-Drive-v3.4 50 46 4
The presented eye tracking data in study III support the assumption
of the applied heuristics when doing multitasking. However, a deeper
analysis and interpretation of the precise meaning and implication of
the eye tracking data is not provided due to a missing embedding
within a theoretical context. For upcoming studies, i highly recommend
to synchronize the formulated hypotheses with the corresponding
assumptions on expected eye tracking results.
Results: Structured Interview
The structured interviews from the previous two studies were adapted
and applied in study III.
3.3.4Discussion of study III
In study III,
•
performance in primary task (
LCT
) is not affected by multitasking
•
performance in secondary task (D2-Drive) is highly influenced by
the presence of a higher demanding primary task (LCT)
•
configuration of secondary task (D2-Drive) effects performance,
even though this influence reduces under multitasking
•
configuration of secondary task (D2-Drive) strongly influences
multitasking heuristic
Especially in those versions of D2-Drive in which the "merge heuris-
tic" could not be applied (changing row), participants reported a (sub-
jective) feeling of time pressure which is an inherent component of the
structure of the task itself. Performance under multitasking thus seems
to depend (a) on the task environment as well as (b) on situational
components such as time pressure. These two issues are considered in
study IV, investigating the impact of time pressure and system design.
For this reason, the next study focused on the impact of time pres-
sure under human multitasking, applying the identical experimental
scenario.
3.4 study iv:amplification via time pressure
As study III illustrates, the structure of the involved tasks seems to have
a tremendous influence on participants performance under multitask-
ing. The way D2-Drive is designed supports or hinders performance but
nevertheless, driving performance remains untouched by the applied
secondary tasks.
3.4 study iv:amplification via time pressure 53
Figure 23.: Scenario in study IV
3.4.1Method in study IV
Participants
Forty subjects participated in study IV. Due to technical problems,
four participants were excluded from the data analysis. Therefore,
sample size for this study reduced to thirty six, with an equal gender
proportion. Age distribution (within a range from 20 to 40 years) was
not analyzed as not being of deeper interest for the research questions.
Involved tasks
As in study III, primary (main) task is
LCT
. Secondary task again is
D2-Drive. With reference to the structure of D2-Drive in the previous
studies, two versions of D2-Drive were designed by cand. Dipl.-Psych.
Robert Lischke, with the following requirements:
1. Clear distinction in terms of ease of use (subjective impression)
2. Clear distinction in terms of required time (reaction time)
3.
Clear distinction in terms of
necessary visual attention
(eye tracking
data)
Based on these requirements, the design was as follows:
d2-drive-v4.1Constant row of patterns and constant reply buttons
d2-drive-v4.2
Changing row of patterns and changing reply buttons
D2-Drive-v4.1allows participants to use the "merge heuristic". Sev-
eral patterns can be scanned and replied together. Also, after a while,
entering the solution without visual attention is expected due to the
fact that both reply buttons (yes, no) remain at the same position.
D2-Drive-v4.2does not allow the use of the "merge heuristic" due to its
structure: after each reply (motor action), the patterns change, i.e., a
54 empirical studies
new row with patterns appears. For instance, if a person performs the
first pattern (at the beginning of the row), after pressing a button (yes
or no) this row changes and the person now has to perform the second
pattern (which has not been shown before). The same counts for the
reply buttons: after each button press, the positions of the buttons (yes,
no) change, either one of them or both. Four possible configurations
for the reply buttons are possible, as is shown in Fig. 24.
To check the three claimed preconditions (ease of use, required time,
necessary visual attention), in a pretest of these two versions, eight
participants of the graduate program
prometei
tested both versions
under single task condition.
YES
NO
YES
NO
YES
NO
YES
NO
D2-DRIVE-v4.1 D2-DRIVE-v4.2
YES
NO
Figure 24.: D2-Drive in study IV
Design
Two independent variables (time pressure, complexity level in D2-
Drive) were investigated, both as within-subject factors. Please note
that complexity of D2-Drive is within the secondary task and should
not lead to any confusion plus not be considered the same as doing
multitasking. Complexity in D2-Drive it the label for the difficulty
levels in D2-Drive. As in the previous studies, dependent measures
were performance in main task (
LCT
) and performance in secondary
task (D2-Drive). For each participant, four conditions were applied.
Procedure
1. Welcome
2. Technical preparation (for physiological data measurement)
3. Relaxation period for participants
4. Baseline (Physiology)
5. Calibration of eye tracking equipment
6. Training main task (LCT)
3.4 study iv:amplification via time pressure 55
7. Training secondary task (D2-Drive-v4.1)
8. Single task (D2-Drive-v4.1, Pretest)
9. Training secondary task (D2-Drive-v4.2)
10. Single task (D2-Drive-v4.2, Pretest)
11.
Dual task session (driving and D2-Drive-v4.1): low time pressure
condition
12.
Dual task session (driving and D2-Drive-v4.2): low time pressure
condition
13.
Dual task session (driving and D2-Drive-v4.1): high time pressure
condition
14.
Dual task session (driving and D2-Drive-v4.2): high time pressure
condition
15. Single task (D2-Drive-v4.1, Posttest)
16. Single task (D2-Drive-v4.2, Posttest)
17. Demographical questionnaire (age, driving experience, etc.)
the application of time pressure: Time pressure was applied
via instruction as follows: participants were asked to consider driving
as main task (priority A), and at the same time to perform (1) as many
lance changes as possible and (2) as many patterns in D2-Drive as
possible.
low time pressure Prioritize (safe) driving and perform D2-Drive
without neglecting LCT.
high time pressure
Consider driving as main task, but at the same
time aim to perform as many lane changes (
LCT
) and as many
patterns (D2-Drive) as possible.
3.4.2Hypotheses for IV
Hypothesis I: The impact of time pressure
Hypothesis I claims that time pressure has a negative impact, both on
driving as well as on D2-Drive. Reported statements from study III
recommend to consider time pressure as one of the main situational
influences.
Hypothesis II: The impact of task complexity
As suggested from the quantitative and qualitative data of the previous
studies, D2-Drive-v4.1is expected to provide a better performance
compared to D2-Drive-v4.2. This is based on (1) the possibility to apply
the "merge heuristic".
56 empirical studies
Figure 25.: The influence of time pressure on driving
3.4.3Results of study IV
Results: LCT and time pressure
Performance in
LCT
was not influenced by the secondary task: no
significant difference in lane derivation was found between single
tasking and multitasking. This goes in line with previous results
from study I-IV. However, time pressure highly effects the driving
behavior (
p < 0.01
), as can be seen in figure 27: under time pressure,
lane derivation is pronounced twice as much as without time pressure.
This indicates that the instruction given to the participants was taken
seriously. It is also remarkable that after the driving (single task),
the performance in
LCT
did even imporve (though not statistically
significant). Lane derivation was lower under multitasking without
time pressure compared to the performance under baseline driving.
This can be explained in terms of learning effects.
Results: D2-Drive
An overall comparison shows that D2-Drive-v4.1is permanently better
(in terms of number of correct patterns performed) compared to D2-
Drive-v4.2. Please also note that learning effects (comparing pre-test vs.
post-tests) occur for D2-Drive-v4.1(
p < 0.05
) but not for D2-Drive-v4.2.
Also, for D2-Drive-v4.1, number of performed patterns is significantly
lower under multitasking (
p < 0.05
), but for D2-Drive-v4.2there is 8sta-
tistically) not difference between single- vs. multitasking. Interestingly,
under multitasking, D2-Drive-v4.1seems to receive some losses. For
this reason, a more detailed analysis of the interplay between time
pressure (factor 1) and task difficulty (factor 2) is necessary.
Overall, time pressure has a significant influence on performance of
D2-Drive (
p < 0.05
). Additionally, time pressure shows a stronger im-
pact on D2-Drive-v4.2(
p < 0.01
). This is surprisingly, also considering
the fact that multitasking does not negatively influence D2-Drive-v4.2
3.4 study iv:amplification via time pressure 57
Figure 26.: Performance in D2-Drive in study IV
Figure 27.: Time pressure and performance in D2-Drive
to the same amount as for D2-Drive-v4.1. Taken together all results,
we can conclude that under multitasking and concurrent time pres-
sure, D2-Drive-v4.2performs quite worse and participants seem to fail
completely. A look at figure 25 confirms this assumption.
Results: Eye tracking
Eye tracking data in study IV did not deliver any additional information.
However, as illustrated in figure 28, gaze analysis helped identifying
the (development and) application of the "merge heuristic", which was
also mentioned in verbal reports by the participants.
3.4.4Discussion of study IV
The two main findings in study IV are:
1.
Time pressure highly influences both primary as well as secondary
task.
58 empirical studies
Figure 28.: Tracking eye movements in D2-Drive)
2.
Time pressure accelerates the development of cognitive heuristics.
Participants in study IV reported a strong feeling of time pressure
and felt highly motivated to "optimize" their performance in terms
of (1) number of lane changes (
LCT
), and (2) number of performed
patterns (D2-Drive).
in chapter iv the results of the four empirical studies will be
summarized and a critical analysis is given.
Part IV
DISCUSSION
61
4
CRITICAL DISCUSSION
4.1 scope and findings
Observations of human behavior in real life gave birth to the idea of
this work. Modern technology as the core domain in which multiple
daily tasks occur at the same time was chosen as research area for the
empirical studies presented in this work. An overall review of studies
in the field (see chapter II) of human multitasking showed that
•
many (if not most) psychological studies remain artificial. Espe-
cially the reported task switching or PRP approaches (an excellent
overview provides Pashler [2000]) do not offer the possibility to
draw conclusions about human behavior in daily life.
•
applied studies in the field of human machine interaction mainly
lack task repetition and systematic control (as in the studies
reported by Saluvicci [2005]). Also, theory embedment is missing
quite often.
•
contemporary approaches (e.g., Brumby et al. [2007]) mainly
focus on optimization and do not fully integrate qualitative as-
sumptions about human (conscious and unconscious) information
processing.
•
the aspect of dynamically changing task environments has hardly
been considered.
These critical remarks serve to show the starting point of this work.
After an introduction (chapter I) and a general overview of the history
of human multitasking (chapter II), four applied studies in the field of
human machine interaction were presented (chapter III), illustrating
1.
... how people in general do human multitasking using heuristics
instead of trying optimization (study I).
2.
... to what extend training and extensive task repetition impacts
human multitasking and applied heuristics (study II).
3. ... the importance of task configuration (study III).
4.
... what happens with human behavior given people have a
subjective feeling of time pressure (study IV).
This work, for sure, is not the final cut in studying human multitask-
ing. A specific domain (driving plus a secondary task) was chosen as
research domain. Based on the empirical studies of this work, I would
like to summarize the findings. I supported evidence for the following
results:
cognitive heuristics
highly support human multitasking in dy-
namically changing environments (study I). This was shown for
two different main tasks (a simple driving task in a simulator and
using the lane change task).
63
64 critical discussion
extensive training
does not only support the use of cognitive
heuristics but also improves overall performance (study II).
the configuration of a task
turns out to be determining how
people manage multiple, concurrent tasks (study III).
time pressure
tremendously influences multitasking performance
(study IV). Both primary as well as secondary task highly suffer
under instructed time pressure.
As reported in the theoretical part (chapter II), recent approaches
have been started to simulate human multitasking behavior in a cog-
nitive architecture. To fully develop a multitasking mechanism for
cognitive modeling was not the scope of this work. However, partially
an implementation has been done, as will be reported in the next
section.
4.2 cognitive modeling
To additionally confirm theoretical assumptions about the "merge
heuristic", a computational model within the cognitive architecture
ACT-R (Anderson [2007]) was built, based on version D2-Drive-v1.2.
ACT-R is a production system, information processing is simulated via
a collection of rules. ACT-R contains "condition-action-pairs", i.e. if (a
condition is met), then (do the following action) - pairs, and is a hybrid
cognitive architecture: it features a symbolic level (production system,
sequential processes) as well as a sub-symbolic level (parallel processes,
utility-based selection). Main components of ACT-R are modules (e.g.,
the perceptual-motor (PM) module is the interface with the - simulation
of - the real world), buffers, and a pattern matcher. Memory in ACT-R
is either declarative (facts about the world) or procedural (knowledge
about how we do things). Many successful models have been imple-
mented in ACT-R, mainly in the areas of learning and memory, problem
solving and decision making, language and communication, perception
and attention, cognitive development and individual differences. Con-
ceptual reasons for choosing ACT-R in my work are its assumptions
on human memory, the psychological plausibility and the interaction
with environment. Before I introduce results of the ACT-R model of
D2-Drive, let me give a short explanation of cognitive modeling, as
illustrated in Fig. 29:
Figure 29.: Cognitive modeling (following Taatgen [1999])
According to Werner H. Tack (
personal communication
), cognitive mod-
eling is meant to be a "simulation of human problem solving". Tack
4.3 design recommendations 65
[1995] refers to it as "defining symbol structures for specific cognitive
tasks". Cognitive modeling is not a metaphor for the mind. Its goal is
the prediction of human behavior. Mental task processes are specified
and cognitive tasks are performed by computing (Lewis [1999]).
A cognitive architecture
1
contains theoretical assumption (theory)
about cognitive processes. In combination with task knowledge, a task
model is build and derived. This task model produces performance
data (see Fig. 29). Traditional measures are time to perform the task,
accuracy in the task, or neurological (fMRI) data. From another side,
a (psychological) experiment produces data as well. This data is ana-
lyzed and compared to the data produced by the model. The matching
between model data and empirical data defines the goodness of the
model.
Both on a micro-level (performing of a single pattern) as well as on
a macro-level, the model describes the processing how participants
performed D2-Drive.
YES NO
YES
NO
pattern processing
at the beginning
pattern processing
after training
implicit
learning
READ upper
READ lower
READ middle
READ upper
READ lower
READ middle
d-pattern p-pattern
READ upper
READ lower
READ middle
READ upper
READ lower
READ middle
REPLY (p)REPLY (d)
REPLY (p)REPLY (d)
Figure 30.: Processing of a single pattern in D2-Drive
Fig. 30 shows the basic assumptions for the corresponding ACT-R
model (see also Kiefer et al. [2006], for more details). We generally
distinguish "d-patterns" (patterns containing the letter "d") from "p-
patterns" (patterns containing the letter "p"). In both cases, participants
start on one position (i.e., the middle, which is at the same time the
letter-component in the pattern). Next, they scan the upper part and
then the lower part. After a while, implicit learning takes place, partic-
ipants "understand" that with "p-patterns", the last two steps are not
necessary. This leads to shorter reaction times for p-patterns (see left
part of Fig. 31). The implemented Act-R model fits the data pretty well,
especially performance of "p-patterns".
4.3 design recommendations
Visiting national and international conferences to present preliminary
results of my work, it happened quite often that after my presenta-
1an algorithm that simulates a non-linear theory of cognition, based on Taatgen [1999]
66 critical discussion
Figure 31.: Modeling of D2-Drive
tion/talk, people coming from different disciplines (e.g., designers,
engineers) came to me asking me about advice. "What would you
recommend me if i tell you that i am planning to develop an in-vehile
information system?" Or: "Which mistakes can i avoid when I design
an information system which should be used in daily life?"
Derived from the results described in the previous chapter and from
the insight i won during the last years doing theoretical and practical
work in that area, here is my advice. Some of the recommendations
might be helpful ideas, with the hope to prevent from avoidable errors
in multitasking situations:
do not force people to multitask!
Giving advice how to per-
form two tasks concurrently not only prevents from developing
individual strategies or heuristics, but also leads to a direction in
processing which might be suboptimal for participants‘ individ-
ual style. Learning, from a psychological perspective, happens
consciously or unconsciously. Remember that some participants
in the previous four studies could not even report that they used
the "merge heuristic", i.e., they learned implicitly i instead of
rule-based. Instructions do not allow such a possibility.
reduce complexity!
The more complex a system, i.e. the higher
the functionality, the more difficult it is to comprehend. We
saw in the first two studies that D2-Drive-v1.3(and D2-Drive-
v2.3consequently) performed worst. Please keep in mind that a
more demanding system requires a longer learning period until
individual steps can be taken without mental load or cognitive
demand. What is the benefit of a extremely functional mobile
phone if it takes you months (or years) to understand the structure
and the navigation? Cognitive demanding tasks produce human
errors, so always ask yourself: is the benefit worth the effort?
support familiarity!
As study III and IV show, a dynamically
changing secondary task requires visual (re-)orientation as well as
cognitive (resumption) costs. Part of a task needs to be resumed,
but the mental set has changed (i.e., the positions of the answer-
buttons). Familiarity does not require many cognitive resources.
Therefore, a system which is easy to understand, learn and be-
come familiar with, can be accessed immediately and promotes
human multitasking.
apply prospective design!
Do not mainly focus on results from
studies in literature. Even if prominent theories advice you to
4.4 criticism and outlook 67
do this or that (this work is not an exception, by the way), try to
investigate the human-machine interaction already in the early
development phases of your planned technical systems.
These recommendations mainly result from an overall impression of
the studies in the previous chapter, supported by quantitative (perfor-
mance data) as well as qualitative (interview data) measures.
4.4 criticism and outlook
Aim of the presented work was to approach human behavior in multi-
tasking scenarios from a human machine interaction perspective. In-
cluding four copious empirical studies, it can only give an extract of
relevant issues in context of human multitasking. When i started in
2005, i shortly realized that investigating human multitasking and the
necessary, connected domains feels like opening Pandoras box1.
Figure 32.: Pandoras box, found on: www.icarusgirl.blogspot.com
For this work can never be complete by going into all details, three
issues are critically discussed:
1.
The role of (prospective) memory in human multitasking (theo-
retical aspect)
2. Domain (in-)dependence (practical aspect)
3.
Need for a (computational) model of human multitasking (mod-
eling aspect)
The following sections concentrate on these three issues.
1
In Greek mythology, Pandora was the first woman. Each god helped create her by giving
her unique gifts. Zeus ordered Hephaestus to mold her out of Earth as part of the
punishment of mankind for Prometheus theft of the secret of fire, and all the gods joined
in offering this beautiful evil seductive gifts. According to the myth, pandora opened a
jar in modern accounts referred to as Pandoras box, releasing all the evils of mankind
(greed, vanity, slander, envy, pining) leaving only hope inside once she had closed it
again.
68 critical discussion
4.4.1The role of memory in human multitasking
Each task interrupted becomes a prospective task?
In study I-IV, design and complexity of the applied secondary task (i.e.
D2-Drive, developed by Kiefer et al. [2006]) have been systematically
analyzed and varied. However, in study I and II, the same main task
(lane keeping on a rather monotonous street) was applied. To increasing
complexity and cognitively enrich the main task, study III and IV focus
on LCT as main task. In a recent study by Soyak, a modification of
LCT based on the hypotheses in this study throws some new light to
the scenario. Soyak points out the importance of prospective memory
("remembering to remember", Winograd [1988]). In the area of task
interruption, Dodhia and Dismukes claim that "a task interrupted
becomes a prospective task".
Figure 33.: Modification of LCT in a prospective task study
McDaniel and Einstein [2000] distinguish between two kinds of
prospective memory (PM), namely:
event-based pm
Recalling an action or an intention triggered by a
stimulus ("event"), e.g. receiving a reminder-email ("cue") reminds
to submit a paper ("intention")
time-based pm
Recalling an action or an intention triggered by a
time, e.g. watching the news in television at 8pm.
Based on McDaniel and Einstein [2000], Soyak investigated the impact
of disruptions on prospective memory performance. Main task in
her study was a modified version of LCT (see Fig.33: participants
were asked to keep in mind a verbally given city name (Hamburg,
Berlin, München, Stuttgart, Köln, Leipzig) and change the lane at the
moment the road sign with this name appears. Soyak showed a negative
influence of disruptions on successful prospective task performance.
Delayed disruptions require longer reaction times on the target cue.
In her study, Soyak used a more demanding, cognitively enriched main
task and concurrently the two versions of D2-Drive mentioned in study
IV of the previous chapter. Eye tracking data were recorded but not
yet analyzed (purpose of her work was not on cognitive heuristics
under multitasking). Nevertheless, it would be of interest to look
4.4 criticism and outlook 69
for participants processing in the two tasks individually and under
multitasking. We can assume the following:
1.
Modified LCT require to many cognitive resources and heuristics
for D2-Drive cannot be applied.
2. Participants use the "merge heuristic" in a reduced frequency.
3.
Due to the new task configuration, participants develop new
heuristic(s) adaptively.
In context of human multitasking, the author highlights the im-
portance to take memory aspects under deeper consideration. The
importance of the prospective task seems to be a core aspect in each
multitasking scenario.
4.4.2Domain independence
All of the four presented studies in this work include a driving task
(as main task) in the multitasking scenario. The question, hence is
whether this multitasking behavior, the application of heuristics and
information processing, can be transferred to other situations in real
life in which people use technical systems while doing a continuous
"task" (e.g., walking on the street). Studies by Antti Oulasvirta from the
Helsinki Institute of Technology (HIIT) (Oulasvirta and Blom [2008],
Oulasvirta et al. [2007], Oulasvirta and Saariluoma [2006], Oulasvirta
et al. [2005], Oulasvirta and Saariluoma [2005]) show that in fact, in var-
ious situations, an ongoing task needs to be interrupted and resumed.
Oulasvirta provides both empirical data (eye tracking) as well as a
qualitative analysis to explain how multitasking in these scenarios is
explained. Especially the area of mobile computing is a promising field
for further research. Modern technology for us is a constant challenge
to develop fast and frugal (conscious or unconscious) "heuristics" to
adapt to a dynamically changing environment. This direction in the
area of human machine interaction studies in this direction will become
of increasing importance in future research.
4.4.3Need for a computational model of human multitasking
In this chapter, a computational model of the applied secondary task
was introduced and explained. However, a general model for human
multitasking is still missing (though it was not meant to be part of
this work). Saluvicci [2005] proposes an approach in which he aims
to incorporate human multitasking in cognitive modeling, namely
within the ACT-R architecture (Anderson et al. [2004], Anderson [2005].
Salvucci proposes an general executive which is
• an architectural mechanism
• dependent on time
• sensitive to goal representations
Fig34 represents core mechanisms within this idea. Main features of
the general executive proposed by Saluvicci [2005] are:
70 critical discussion
Figure 34.:
Overview of multitasking general executive proposed by Saluvicci
[2005]
•
A cognitive processor manages the concurrency of multiple goals
at the same time.
•
As in earlier frameworks of ACT-R, only one goal can be executed
at the same time.
• Goal switching is moderated by heuristics.
• Urgency defines when to switch to which goal.
Salvucci mentions "natural breaking points" necessary for interleav-
ing tasks. He proposes two core heuristics to decide when to switch
between goals, namely
the iterating heuristic
Salvucci gives the example of a task with
a duration of 100 sec, assuming 50msec per production rule, so
in sum 2.000 rules to fire. When the model returns to a previous
fired rule, task switching should be proposed at this point. The
next iteration is initiated by a new goal. Especially for models
with a long duration in terms of execution time, this heuristic
becomes plausible.
the blocking heuristic
Salvucci mentions "
significant time
" and
the problem how to decide about that. He illustrates that for per-
ceptual motor actions (PM) in particular, ACT-R has to wait until
an action is done. In such a case, the blocking heuristic proposes
to create a new goal which gives permission to a secondary task
to intercede.
4.5 fmri studies on multitasking
The reported work did not consider functional magnetic resonance
imaging (fMRI) scans. However, some studies (e.g., Leber et al. [2008])
have revealed that superior multitasking performance is correlated
4.6 popular stereotypes about multitasking 71
with higher basal ganglia, anterior cingulate cortex, prefrontal cortex,
and parietal cortex activity. Philippe Peigneux, Professor of Clinical
Neuropsychology in Brussels, even talks of a multitasking mind (pub-
lished in April 2006 on
seedmagazine.com
), meaning that even when
we sleep, our mind works constantly, processing several tasks concur-
rently. In future, neuro-scientific studies will dive deeper into the area
of multitasking.
4.6 popular stereotypes about multitasking
4.6.1Multitasking and happiness
Multitasking has been criticized as a hindrance to completing tasks or
feeling happiness. Timothy Ferriss argues that one should rarely do
multitasking and should instead devote full attention to completing
a very small set of defined goals (taken from the interview "
I receive
500 to 1000 emails per day", published in The Economist on 2008-04-04).
Barry Schwartz has noted that, given the media-rich landscape of the
Internet era, it is tempting to get into a habit of dwelling in a constant
sea of information with too many choices, which has been noted to
have a negative effect on human happiness.
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DECLARATION
I hereby declare that:
•
I autonomously carried out the PhD-thesis entitled "
Multitasking
in HMI". All third party assistance has been enlisted.
•
My submission as a whole is not substantially the same as any
that I have previously made or am currently making, whether in
published or unpublished form, for a degree, diploma, or similar
qualification at any university or similar institution.
The thesis has not been submitted elsewhere for an exam, as thesis
or for evaluation in a similar context.
Berlin 2010
D83, Tag der wissenschaftlichen Aussprache: 05.10.2009
Dipl.-Psych. Jürgen Kiefer
A
APPENDIX
a.1 appendix:structured interview
The following questions (qualitative interview) were put to the partici-
pants after the experiment:
• Which of the two tasks did you perform with prioritization?
• Which of the two tasks did you experience as more difficult?
• Please explain how you proceeded the both tasks?
•
On a scale from 1to 5(where 1is the lowest and five is the
highest value), how much mental fatigue during the complete
experiment?
•
Can you in detail describe how you performed the driving task
as a single task?
•
Can you in detail describe how you performed the pattern task
as a single task?
•
Can you in detail describe how you performed the combination
of both tasks?
In addition to these questions, for all of the four reported studies, a
verbal qualitative interview on the task processing of all participants
was applied.
81
