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Understanding expertise: A multidisciplinary approach


What makes an expert? What strategies do they use? If you are an expert in one domain, are you more likely to become an expert in a second? This book provides a comprehensive overview of the field of expertise. With a discussion of research from psychology, neuroscience, sociology, philosophy, education, law and artificial intelligence, this is the definitive guide to the subject. It considers expertise on a number of levels, ranging from the neural to the psychological and the social. It critically evaluates current theories and approaches, and addresses issues of key importance for society.
List of Illustrations xiii
Preface xv
Acknowledgements xvii
Publisher’s Acknowledgements xviii
List of Abbreviations andAcronyms xx
1 Introduction 1
1.1 Preview of Chapter 1
1.2 The Dual Meaning of the Term “Expertise” 1
1.3 Denitions of Expertise 2
1.4 Why Study Expertise? 6
1.5 Preview of Book 7
1.6 Chapter Summary 10
1.7 Further Reading 10
2 Perception and Categorisation 11
2.1 Preview of Chapter 11
2.2 De Groot’s Seminal Research 11
2.3 Medical Expertise 14
2.4 Holistic Perception and Anticipatory Schemata 16
2.5 Perception in Sport 17
2.6 Perception in Music 19
2.6.1 Basic Skill Differences in Perception 20
2.6.2 Absolute Pitch 21
2.6.3 Laypeople’s Implicit Musical Expertise 23
2.6.4 Sight-Reading 24
2.7 Perceptual Learning, Perceptual Expertise and Categorisation 27
2.8 Chapter Summary 28
2.9 Further Reading 28
3 Memory 29
3.1 Preview of Chapter 29
3.2 Chase and Simon’s Research 29
3.2.1 The Key Empirical Results 29
3.2.2 Chunking Theory 30
3.3 Generalisability of Experts’ Superiority in Recall Tasks 33
3.4 Is Knowledge Structured as Chunks? 33
3.5 How Many Chunks Are Stored in LTM? 36
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viii Contents
3.6 Does Randomisation Eliminate Experts’ Superiority? 37
3.7 Is STM Capacity Limited and Are LTM Encoding Times Slow? 39
3.8 The Intermediate Effect in Medicine 44
3.9 Memory in Sports 46
3.10 Memory in Music 47
3.11 Theoretical Accounts 48
3.11.1 Chase and Simon’s Chunking Theory 48
3.11.2 Skilled Memory Theory 49
3.11.3 Long-Term Working Memory 49
3.11.4 Revisions of Chunking Theory 51
3.11.5 Constraint Attunement Theory 58
3.12 Chapter Summary 60
3.13 Further Reading 60
4 Problem Solving 61
4.1 Preview of Chapter 61
4.2 De Groot’s Research 62
4.3 Phases of Problem Solving 63
4.4 Expertise Effects in Progressive Deepening 64
4.5 Macrostr ucture of Search 66
4.6 Directionality of Search 67
4.7 Planning 67
4.8 Evaluation 68
4.9 The Role of Pattern Recognition in Problem Solving 69
4.10 The Role of Perception in Problem Solving 70
4.11 The Role of Schemata and Conceptual Knowledge in
Problem Solving 70
4.12 The Role of Representations 73
4.12.1 Physics 73
4.12.2 Economics 74
4.13 Automatisation and Rigidity of Thought 75
4.13.1 Automatisation 75
4.13.2 Rigidity of Thought 76
4.14 Theories of Problem Solving 78
4.14.1 The Selz-de Groot Framework 78
4.14.2 Newell and Simon’s Problem-Space Theory 78
4.14.3 Chunking Theory and Template Theory 79
4.14.4 Holding’s Theory 80
4.14.5 Computer Models of Human Search 80
4.15 Chapter Summary 84
4.16 Further Reading 84
5 Decision Making 85
5.1 Preview of Chapter 85
5.2 Rationality and Bounded Rationality 85
5.3 The Heuristics and Biases Approach 86
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Contents ix
5.4 Biases in Exper ts 87
5.5 Fast and Frugal Heuristics 88
5.6 Naturalistic Decision Making 89
5.7 The SOS Ef fect 91
5.8 Shanteau’s Framework 92
5.9 Decision Making in Sports 93
5.9.1 Using Task-Specic Probabilities 93
5.9.2 Option Selection 94
5.10 Chapter Summary 95
5.11 Further Reading 96
6 Intuition, Insight and Creativity 97
6.1 Preview of Chapter 97
6.2 Exper t Intuition 97
6.2.1 Simon’s Theory 98
6.2.2 Dreyfus and Dreyfus’s Theory 99
6.2.3 Template Theory of Intuition 100
6.2.4 Too Much of a Good Thing? 100
6.3 Insight 101
6.4 Creativity 103
6.4.1 Are Estimations of Creativity Reliable? 103
6.4.2 Tests of Creativity 105
6.4.3 Factors Supporting the Development of Creativity 106
6.4.4 Theories of Creativity 108
6.5 Chapter Summary 113
6.6 Fur ther Reading 114
7 Talent, Individual Differences and Gender Differences 115
7.1 Preview of Chapter 115
7.2 Talent Approaches Based on Intelligence 115
7.2.1 A Brief Overview of Early Intelligence Research 115
7.2.2 Two Classic Studies on Intelligence and Talent 117
7.2.3 Gardner’s Approach 118
7.2.4 IQ as Predictor of Expert Performance 119
7.2.5 Components of Intelligence 120
7.2.6 Intelligence: Discussion 120
7.3 Talent Approaches Not Based on Intelligence 121
7.3.1 Talent in Chess 121
7.3.2 Talent in Music 123
7.3.3 Talent in Sports 124
7.4 Personality 127
7.4.1 Creativity 128
7.4.2 Other Domains of Expertise 129
7.5 Psychopathology 129
7.6 Gender Dif ferences 131
7.6.1 General Explanations 132
7.6.2 Explanations Based on Intelligence 134
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x Contents
7.7 Chapter Summary 137
7.8 Further Reading 137
8 Learning and Education 138
8.1 Preview of Chapter 138
8.2 Approaches Based on Talent 138
8.3 Approaches Based on Practice 139
8.3.1 Identifying Strategies 139
8.3.2 Chunking Theory 139
8.3.3 Template Theory 140
8.3.4 ACT-R and Intelligent Tutoring Systems 141
8.3.5 Deliberate Practice 142
8.3.6 Discussion: Talent vs. Practice Revisited 149
8.4 The Question of Transfer 150
8.4.1 Differential Predictions of the Talent and Practice
Approaches 150
8.4.2 Early Specialisation vs. Diversication in Sports 151
8.5 Expert Teachers and Learners 153
8.5.1 Expert Teachers 153
8.5.2 Expert Learners 155
8.6 Chapter Summary 156
8.7 Fur ther Reading 157
9 Development and Ageing 158
9.1 Preview of Chapter 158
9.2 Exper tise and Development 158
9.2.1 Domain-General Mechanisms 158
9.2.2 Domain-Specic Mechanisms 159
9.2.3 The Role of Strategies 162
9.2.4 Gifted Children 162
9.2.5 Savants 163
9.3 Exper tise and Ageing 164
9.3.1 Effects of Age 165
9.3.2 A Paradox 165
9.3.3 Expertise as a Moderating Variable 166
9.3.4 Theories 166
9.3.5 The Careers of Great Creators as a Function of Age 167
9.4 Chapter Summary 169
9.5 Fur ther Reading 169
10 Neuro-Expertise 170
10.1 Preview of Chapter 170
10.2 Skill Acquisition in Novices 170
10.3 Typical Data in Neuro-Expertise 172
10.3.1 Mental Calculation 172
10.3.2 Memory Champions 172
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Contents xi
10.3.3 Chess 173
10.3.4 Music 173
10.3.5 Taxi Drivers 174
10.3.6 Sports 174
10.4 Proposed Mechanisms 176
10.4.1 The Fixed Localisation vs. Perceptual Expertise
Debate 176
10.4.2 Mechanisms Linked to Intelligence 178
10.4.3 Functional Reorganisation of the Brain: The Role
of Retrieval Structures and Templates 180
10.4.4 Geschwind and Galaburda’s (1987) Theory 180
10.5 Gender Differences 181
10.6 Smart Drugs 182
10.7 Chapter Summary 183
10.8 Further Reading 184
11 Experts and Society 185
11.1 Preview of Chapter 185
11.2 The Difculty of Making Correct Predictions 185
11.3 A Miscarriage of Justice 186
11.4 When Experts Fail 187
11.4.1 Difculties with Scientic Research 188
11.4.2 Predictions in Political Science 190
11.5 The Role of Media 193
11.6 Fraud and Cheating in Science 194
11.7 The Internet 196
11.8 Group Phenomena 197
11.9 Why Do We Believe Experts? 200
11.10 Situated Action 201
11.11 Chapter Summary 201
11.12 Further Reading 202
12 Sociology 203
12.1 Preview of Chapter 203
12.2 The Sociology of Professions 203
12.2.1 Early Work 203
12.2.2 Abbott’s Seminal Work 205
12.2.3 Experts as Heuristics 206
12.3 Communication and Expertise 207
12.4 Experts in Court 208
12.5 Classication of Experts 210
12.5.1 Mieg’s Classication 210
12.5.2 Collins and Evans’s (2007) Periodic Table of Expertises 211
12.5.3 Classications of Expertise: Evaluation 214
12.6 Chapter Summary 215
12.7 Further Reading 216
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xii Contents
13 Philosophy 217
13.1 Preview of Chapter 217
13.2 Ancient Greek Philosophy 217
13.3 Knowing-How and Tacit Knowledge: Ryle and Polanyi 219
13.4 Disagreement between Experts 221
13.5 Identication of Experts 222
13.6 Dreyfus’s Critique of Expert Systems 224
13.7 Rationality and Expertise 225
13.8 Philosophy and Expertise: Applications 226
13.9 Chapter Summary 228
13.10 Further Reading 229
14 Articial Intelligence andExpert Systems 230
14.1 Preview of Chapter 230
14.2 Knowledge Representation 230
14.3 Expert Systems 231
14.4 Knowledge Elicitation Techniques 233
14.5 Decline of Expert Systems Research 234
14.6 Contributions of Expert Systems Research 235
14.7 Chapter Summary 236
14.8 Further Reading 236
15 Putting It All Together 237
15.1 Preview of Chapter 237
15.2 Good and Bad News 237
15.3 Transversal Themes 238
15.3.1 Denition and Identication 239
15.3.2 Rationality 239
15.3.3 Knowledge 241
15.3.4 Search 241
15.3.5 Generativity 242
15.3.6 Diachronicity 243
15.3.7 Nature vs. Nurture 243
15.3.8 Environment and Society 243
15.4 Methods and Theories 244
15.5 Four Tensions (Almost) Resolved 245
15.6 Practical Implications 246
15.7 How to Become an Expert 248
15.7.1 Performance-based Expertise 248
15.7.2 Reputation-based Expertise 249
15.8 Conclusion: Toward an Integration of Research
on Expertise? 249
15.9 Chapter Summary 251
15.10 Further Reading 251
References 252
Index 293
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Chapter 1
1.1 Preview of Chapter
We live in a complex environment, where new technological developments
regularly challenge our wits. With the development of the Internet, the
amount of information that is available has increased exponentially over
the last decade. It is therefore essential that we improve our understanding
of the way people learn to cope with these challenges. In the last century or
so, a tremendous amount of information has been acquired regarding learning
in psychology, neuroscience, education, sociology and other elds, with a
substantial portion derived from research into expertise. The aim of this book
is to review the most important results stemming from this line of research
and to evaluate their implications for society. In particular, we will be interested
in the educational methods that have beneted from expertise research and in
the implications that this research has on how society can develop ways to help
citizens cope with these new challenges.
A good way to start is to illustrate, with a few examples, what we mean by
experts. A list of top-level experts would include Wolfgang Amadeus Mozart in
music, Marie Curie in science, Magnus Carlsen in chess, Bill Gates in business
and Jessica Ennis-Hill in sports. A list of more ordinary experts would include
a physician, an engineer, a lawyer but also a baker, a orist and a nurse.
From the outset, we face a few central questions on the nature of expertise.
The most obvious is: what is expertise? We will spend some time discussing
some of the many denitions that have been proposed and evaluating the
extent to which they are successful. This will lead to a working denition
that we will use in most of this book. Another important question relates
to the reasons why it is important to study expertise. We will see that there
are both basic scientic reasons and more applied ones. However, before we
address these questions, we need to clear up an important issue about the dual
meaning of the word “expertise”.
1.2 The Dual Meaning of the Term “Expertise”
Whatever the detail of the denitions, which we will consider in the next
section, one must recognise from the outset that the term “expertise” has
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2 Understanding Expertise
two basic meanings, which are not necessarily consistent with each other. For
example, the Oxford Talking Dictionary (1998) denes expertise as “Expert
opinion or knowledge; know-how, skill, or expertness in something”. The rst
part of the denition emphasises knowledge or even opinion – knowing-that.
The second part emphasises skill – knowing-how, as indeed mentioned in the
denition. This is a fundamental divide reected in several of the elds we will
consider in this book. On the one hand, sociology, law and – to some extent –
philosophy are more interested in the rst part of the denition (knowing-
that). On the other hand, psychology, neuroscience and education essentially
use the second part of the denition (knowing-how). Interestingly, some
languages such as French accept only the rst meaning of the term “expertise”
in everyday language.
These two meanings raise the irksome question as to whether they are
related, and indeed whether it makes sense to devote a book to expertise
as a single concept. This book will argue that this is not only a meaningful
endeavour but also an important one. Bringing together traditions of research
that have focused on either meaning of the word will help integrate two bodies
of knowledge that have essentially evolved independently. It also raises new
and important questions that will spur new research and bring about new
1.3 Denitions of Expertise
Having cleared up the question of the two basic meanings of “expertise”, we
can consider some of the denitions of expertise that have been proposed in
the literature. Note that not all denitions neatly t with the two meanings we
have just discussed.
Intuitively, the term “expertise” brings to mind individuals such as physicians,
engineers, chess masters and lawyers. Most people would also consider that
good examples of experts are offered by the pundits (such as academics,
journalists or business consultants) who proffer their views about their area of
expertise (and even sometimes well beyond) on TV/radio and in newspapers.
But what about occupations such as bricklaying and cigar making, or abilities
such as language and walking, which most people carry out uently? Obviously,
some activities are more likely to be labelled as “expertise” than others. Is this
reasonable or is it just a reection of the prejudices of our society?
In research papers, expertise is often dened using experience and the
amount of time an individual has spent in a domain. Unfortunately, while the
amount of dedicated practice predicts expertise fairly well (see Chapter 8),
experience in itself is often a poor predictor of true expertise (Ericsson et al.,
1993; Meehl, 1954; Richman et al., 1996). Everybody knows amateur tennis
players or pianists who fall short of expert performance despite having practised
their favourite activity for years. In fact, there is direct empirical evidence from
research on clinical expertise (Meehl, 1954) and chess (Gobet et al., 2004)
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Introduction 3
indicating that the correlation between expertise level and the number of years
spent in a eld is weak.
Another reasonable approach is to use diplomas: PhDs, honorary titles
and certicates from ofcial professional associations. There are at least four
weaknesses with this approach. First, diplomas are often based not only on an
objective measure of performance but also on sociocultural criteria. Second,
diplomas often do not test the skills that will be used later, but rather test
declarative knowledge. This is the case, for example, in medical schools
and most elds in universities (psychology is a case in point). Thus, future
medical doctors are tested on their knowledge of anatomy, biochemistry and
pathology, and not on their ability to diagnose and treat patients. Third, unless
detailed grades are supplied, diplomas do not provide much information
about the skill level obtained. Fourth, some individuals can be experts without
formal qualications. A striking example is provided by Epstein (1996), who
showed that some AIDS activists had acquired considerable knowledge about
microbiology and statistics, which, added to their knowledge of AIDS culture,
allowed them to make substantial contributions to research. As Gallo, who
co-discovered the human immunodeciency virus (HIV) and who was originally
lukewarm to AIDS activists’ work, put it: “It’s frightening sometimes how
much they know and how smart some of them are” (Epstein, 1996, p. 338).
Some elds offer more reliable measures of expertise, measures that are
also ecological, in the sense that they are part of the culture of the domain.
Researchers of business expertise can use the wealth accumulated by different
individuals; students of expertise in science can use the number of citations
that scientists have accrued during their career; and researchers of writing
expertise can use the number of books an author has sold. While having the
advantage of being quantitative, these measures have shortcomings as well.
In particular, they can be sensitive to factors unrelated to expertise, such as
market uctuations in business, popularity of a specic school of thought in
science and fashion in literature.
In an ideal world – at least for scientic research – experts would be rank-
ordered as a function of their level of expertise, or even better, they would
have their expertise quantied. When absolute measures are involved (e.g.
time to run 100 metres or the amount of weight that an athlete can lift),
there is no debate, barring accusations of cheating. Rank ordering is used
in sports such as football, where the International Federation of Association
Football (FIFA) publishes a monthly ranking of national teams, using a rather
byzantine formula. Tennis uses the ranking of the Association of Tennis
Professionals (ATP): the sum of the best 18 results from the immediate past
52 weeks. From the point of view of expertise research, the ATP rating has two
weaknesses. First, it measures skill only over the last year, and second, it only
takes points won in entire tournaments into account and ignores the strength
of the opponents as well as the outcomes of specic matches.
The best available system so far is the Elo rating (Elo, 1978), developed for
measuring chess skill but now also used in other domains such as Scrabble and
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4 Understanding Expertise
table tennis. The Elo rating takes into account both the outcome of a game
(win, loss or draw) and the skill level of the opponent. It can be used after each
game or match, producing a nely graded and up-to-date measure of skill. It
also has the advantage that it is based on a sound mathematical model. Having
such a quantitative measure is a real bonus, and this in fact partly explains why
a considerable amount of research has been carried out on chess expertise.
While researchers in most other domains of expertise have to satisfy themselves
with coarse comparisons between novices, intermediates and experts, chess
researchers can differentiate between a grandmaster with 2,620 Elo points and
another with 2,680 Elo points, and even compute the expected outcome of a
game between those two players.
Some researchers emphasise that expertise is something that can only be
acquired with effort and intentionally, with a clear goal in mind (Bereiter &
Scardamalia, 1993). This seems an unnecessary requirement. How expertise is
acquired is of course important, but it does not seem wise to include this in a
denition. Similarly, whether somebody is talented or not in a specic domain
should not be part of the denition of expertise, not least because there is
considerable disagreement about this question. We shall take up these issues
in Chapters 7 and 8.
In a similar vein, it has been proposed that the hallmark of experts is that they
display uid behaviour, requiring few conscious decisions (Dreyfus & Dreyfus,
1988; Fitts, 1964). We shall see that this description captures expertise in some
but not all situations. Moreover, it should also be pointed out that almost
the opposite denition of expertise has sometimes been proposed. Bereiter
and Scardamalia (1993, p.11) argue that “the expert addresses problems
whereas the experienced nonexpert carries out practiced routines”. A similar
view is shared by Ericsson et al. (1993), who argue that just performing
routine actions hinders the development of expertise, and that experts must
deliberatively practice selected components of their skill. We will discuss this
idea in considerable detail in Chapter 8 when dealing with deliberate practice.
The importance of knowledge has often been emphasised, in particular
when human expertise is compared to the expertise (or the lack thereof) of
computers. For example, it has been proposed that expertise is made possible
by the acquisition of a large number of domain-specic patterns. While this
is true in many domains (see Chapters 2 and 3), it seems prudent to not
include putative mechanisms in the denition of expertise, in part because the
nature of these mechanisms is still the topic of vigorous debate. In any case,
investigating expertise will require reecting on, and questioning, long-held
views about the status of knowledge in cognition. An important question
will be the link between knowledge and real-time cognitive processing. In
intelligence research, these two forms of cognition are called crystallised and
uid intelligence, respectively (Cattell, 1971).
Based on the seminal work of de Groot (1965), who asked chess players
of various skill levels to nd the best move in a given chess position, Ericsson
has repeatedly emphasised (e.g. Ericsson, 1996a; Ericsson & Smith, 1991a)
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Introduction 5
that expert performance should be replicable in the laboratory, when tasks
representative of the domain are used. For example, when studied in the
laboratory and compared to non-experts, chess experts should nd better
moves, physicists should provide better solutions to physics problems and
medical doctors should provide better diagnoses. As we shall see in this book,
this is in fact what has been found in the three examples just given, and indeed
in most (although by no means all) domains of expertise. Thus, Ericsson’s
requirement seems a valid one, at least with domains where it is feasible to set
up laboratory tasks that are ecologically valid. But this is not always possible.
A counter-example is expertise in developing novel and ground-breaking
scientic theories in physics; by denition, such events are rare, and thus
unlikely to be captured in the laboratory.
Finally, we would be remiss to not mention some denitions where the
social aspects of expertise play a central role. These denitions emphasise
that “expertise” is a label that society or other groups give to individuals,
sometimes irrespectively of the real competences of these individuals. Support
for this view comes from the fact that selection criteria differ from one domain
to the next, and indeed even differ within a domain (Sternberg, 1997). Labels
can be ofcial, such as university and professional titles, or informal, such as
the label of the “local technology wizard”, but this is immaterial when it comes
to societal recognition. Stein (1997) argues that the term “expertise” can only
be used within a specic context. According to him, it is incorrect to say that
expertise resides solely in the expert: while individual knowledge and skills
are obviously important, these gain their meaning only within the context
provided by the social system of which the expert is a part. We will take up
these issues in Chapters 11 and 12 when dealing with the social aspects of
expertise and the sociology of professions.
In most of this book, we will dene an expert as somebody who obtains
results that are vastly superior to those obtained by the majority of the
population. This denition has the advantage that it can be applied recursively
and that we can dene a super-expert: somebody whose performance is vastly
superior to the majority of experts (Gobet, 2011).1 This denition also has the
advantage of providing a means to deal with domains where most individuals
have a high level of natural ability (e.g. language, walking). It is still possible
to identify an expert in language (e.g. somebody who possesses a large
vocabulary) and an expert in walking (e.g. somebody who has won an Olympic
medal in the 20 km race walking event). Indeed, even with an ability as basic
as breathing, it could be argued that practitioners of hatha yoga are experts,
in that they have mastered breathing techniques unknown to most people.
Finally, this denition can be applied to the two meanings of “expertise” we
have highlighted earlier. The application is trivial with the know-how meaning:
we can simply observe whether an expert does better than a non-expert. Does
Lionel Messi dribble more successfully than a third-division player, or does an
1A super-expert might correspond to what is sometimes called a “genius”.
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6 Understanding Expertise
experienced surgeon operate better than a newcomer? The application is more
delicate, but still possible, with the know-that meaning. The difculty is not in
testing the amount of knowledge – simple questionnaires can do this – but in
the fact that knowledge itself can be of variable quality. For example, we would
doubt the scientic quality of the knowledge used by an astrologer, but not
by a civil engineer. This issue will be dealt with at great length in Chapter 12.
1.4 Why Study Expertise?
The study of expertise is important for society in several ways. First, it sheds
important light on learning and the acquisition of knowledge, which can be
used to develop better methods of instruction and training. Given the pace at
which technology advances in our society, this is a signicant contribution. For
example, research on physics and mathematics expertise, together with other
studies, has led to the development of articial tutoring systems in mathematics
that perform better than human teachers (see Chapter 8).
Second, research on expertise can lead to better ways of coaching experts. The
clearest illustration of this comes perhaps from sport and music. In athletics,
world records are improved every year due to better training techniques,
and the difference between current and previous achievements is sometimes
stunning. The winners of Olympic medals in the marathon one century ago
recorded times similar to today’s amateur runners. In swimming, the seven
world records that earned Mark Spitz as many gold medals at the Munich
Olympic Games in 1972 would not have been sufcient for qualication for
the semi-nals in the 2008 Beijing Olympic Games.
Third, research on human expertise can inform the development of articial
expert systems performing at high or even human-like levels, as we shall see in
Chapter 14. Expert systems are much cheaper, do not tire and do not move to
other jobs – considerable advantages from the point of view of industry. Thus,
expert systems can make valuable contributions to the economy.
With respect to cognitive psychology, research on expertise has shed
important light on human cognition, and several general cognitive mechanisms
have rst been identied in expertise research. These include the role of
pattern recognition in decision making and problem solving, progressive
deepening and selective search. (We will discuss these mechanisms in detail
in Chapter 4.) Thus, just as neuropsychology illuminates human cognition
by studying a “special” population characterised by brain damage, expertise
research provides critical information on cognition by focusing on individuals
who go beyond the limits that mar most of us. In both cases, looking at an
atypical population offers a unique window on typical cognition.
Positive psychology, which is now a very inuential approach in psychology,
was created from the observation that most psychology devoted all its energy
to negative aspects of human psychology, such as pathology, while ignoring
its more positive aspects (Linley et al., 2006; Seligman & Csikszentmihalyi,
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Introduction 7
2000). By contrast, positive psychology focuses on hope, optimism and other
human virtues. It might be worth emphasising that research on expertise,
which focuses on humans’ creativity and their potential to achieve extraordinary
performances, had unequivocally anticipated at least some of the claims of
positive psychology.
1.5 Preview of Book
The following chapters deal with the psychology of expertise. Chapter 2 focuses
on perception and categorisation. It shows that perception lies at the heart
of expertise: experts literally “see” things differently compared to novices,
enabling them to categorise situations and problems better. Chapter 3 argues
that this superior perception is due to the vast amount of knowledge that has
been stored in long-term memory (LTM) during the years of practice necessary
to reach expertise. Numerous theories have been developed to explain expert
memory, and this chapter reviews the main candidates.
In Chapters 4 and 5, we shall see how these differences in perception and
knowledge affect problem solving and decision making. They also affect experts’
intuition, insight and creativity, topics of Chapter 6. In all cases, non-cognitive
factors are involved as well. These include personality and intelligence, which
are covered in Chapter 7. This chapter examines different approaches, mostly
from differential psychology, that defend the role of talent, and it also addresses
the issue of gender differences. In domains such as mathematics, science and
chess, men vastly outperform women; is the origin of these differences social
or biological? Finally, the chapter examines the hypothesis that creativity might
benet from psychopathologies such as manic depression and schizophrenia.
When discussing these issues, these chapters provide an overview of the key
empirical results, the methods used to obtain these results, and the main theories
developed to explain them.
Chapter 8 covers the links between expertise, learning and education. It
is concerned with four broad issues. First, it addresses the implications of
theories based on talent for education. Second, it discusses the role of practice
in acquiring expertise, and what theories focusing on practice tell us about
the training of experts. If the theories presented in Chapters 2, 3 and 4 are
correct, then it should be possible to isolate the components of knowledge
that experts must acquire and design instruction and training methods that
optimise their transmission to budding experts. Suitable practice schedules can
then be designed and optimal feedback can be provided. In the extreme case,
aspects of coaching could be automated with intelligent tutoring systems. Great
attention will be devoted to the deliberate practice framework, which has been
very inuential in recent years. Proponents of deliberate practice argue that
there is no empirical evidence for the role of talent in the development of
expertise, and this claim will be discussed. The third issue addressed in this
chapter is that of transfer. Do skills acquired in one domain transfer to others?
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8 Understanding Expertise
How do some experts appear to move to a different domain of expertise
seamlessly, for example from being a biochemist to university vice-chancellor,
while others fail to make such transitions? Finally, the chapter addresses the
question of expert learners and expert teachers. Are some individuals just
better than the majority at acquiring new information? Are some individuals
particularly efcient at transmitting information to others? If so, what does
this tell us about education in general?
Chapter 9 covers expertise across the life span. How does expertise develop
with children? What are the respective roles of knowledge (including strategies)
and biological maturation? What light do savants throw on expertise in
general? Is the talent of gifted children limited to a single domain? At the
other side of the life span, we will consider how ageing affects expertise, and
whether expertise acts as a moderating variable in the ageing process. We will
also consider how the careers of creative people evolve across time.
Chapter 10 addresses the links between expertise, biology and neuroscience.
It discusses the inuential theory proposed by Geschwind and Galaburda
(1987), which ties together data from psychopathology (e.g. dyslexia and
autism), developmental neuroscience and expertise in a large variety of
domains including mathematics, visual arts and music. Recently, important
discoveries have been made with the advent of novel brain imaging techniques
(e.g. functional magnetic resonance imaging) as well as new developments
with older techniques (e.g. electro-encephalography), and this chapter reviews
the most important of them. These cover a large variety of expertise domains,
most notably sports and music. The key notion of brain plasticity, which
impinges on the interpretation of some of these data, is also examined. Finally,
a better understanding of the biological mechanisms underpinning expertise
raises the possibility of creating new drugs that will speed up the development
of experts and enhance their performance. How far are we from this Brave
New World?
Chapters 11 and 12 deal with expertise and its place in society. In some
domains, the distinction between experts and non-experts is obvious. If one
doubts that Maryam Mirzakhani, who in 2014 was the rst woman, Muslim
and Iranian to win the prestigious Fields Medal, is an expert in mathematics
and more specically the symmetry of curved surfaces, one can always try
to identify errors in her proofs. However, as we have just seen, there are
other domains – perhaps most domains in “real life” – where the denition
of expertise is controversial. More generally, there is the issue that expertise
criteria vary from one domain to the next, and that criteria are sometimes
used inconsistently within the same domain of expertise. This particularly
applies to the professions, which are the main kind of institutionalised expertise
in industrialised countries (most notably lawyers and the medical profession).
How then are experts selected and labelled by society? Are ofcial titles (such
as those awarded by universities) always necessary? To what extent do specic
contexts create new types of expertise and new experts? Is expertise just the
product of an arbitrary selection from a particular group? What are the specic
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Introduction 9
practices that enable social and cultural authority? Do experts in Scientology
and astrology have the same status as experts in neuroscience and astronomy?
What is the role of scientic knowledge in validating experts? Are today’s experts
tomorrow’s non-experts? These considerations are answered by results from
sociology research.
Another key topic of these chapters concerns the power of experts, at least in
industrialised societies. Directly or indirectly, experts played a role in the recent
global nancial crisis either by condoning nancial practices that were – with the
benet of hindsight – too risky or failing to predict the consequences of these
practices on the dynamics of markets. Similarly, experts have a considerable
impact on political decisions (consider, for example, global warming or the
2009 swine u pandemic), even though the science itself is a matter of dispute
amongst experts. This raises complex questions about experts’ legitimacy and
These chapters also address the extent to which it is possible to communicate
expert knowledge – an issue that is crucial in legal settings, for example with
expert testimony. Authors such as Luhmann (1995) have argued that experts
essentially cannot communicate knowledge outside their constituency. This is
because social communication systems each make sense of their environment
using their own code. Others, such as Mieg (2001), have been more sanguine
about experts’ ability to do so. Finally, the chapters address the question as
to how the mass media and more recently the Internet affect the way expert
knowledge is communicated.
The nal theme addressed in these chapters is the issue of the legal status
of the expert. There are vast differences in the way experts are dened and
selected in different legal systems. These chapters compare and contrast practices
in the common law jurisdictions of Anglo–American courts with the civil law
jurisdictions within continental Europe. Key questions include an analysis of
current systems of appointment of expert witnesses and, more generally, of the
designation of someone as an “expert”. Another issue is that the legal coding
of information will be different to that used, for example, in engineering. As
a consequence, expert opinion will have a different meaning and signicance
within the legal system to those within the domain from which the expertise
originated, often creating serious misunderstandings and distortions.
The discussion of the philosophy of expertise in Chapter 13 will allow us to
revisit some of the central questions of this book: the question of rationality,
the nature of knowledge acquired by experts (knowing-that and/or knowing-
how), and the nature of scientic knowledge. Anticipating the following chapter,
it will also address the philosophical implications of articial systems emulating
human experts.
A motivation for some of the research discussed in Chapters 2 and 3 was
that a sound understanding of the cognitive processes underlying expert
behaviour should make it possible to develop articial systems that are able
to perform as well as, or even better than, human experts. The eld of expert
systems is a recognised and active discipline of computer science, and there
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10 Understanding Expertise
are a number of expert systems developed to the point that they are crucial
to some industries (for example, banking and geology). Chapter 14 discusses
strengths and weaknesses of such systems as well as other related issues. What are
the differences between expert systems and human experts? How is knowledge
elicited from experts? Can experts really communicate their perceptual and
procedural knowledge? What do expert systems teach us about human expertise
and human psychology more generally?
Finally, the conclusion weaves together several of the strands that were
discussed in previous chapters. It proposes a synthesis, highlighting the issues
that should be addressed in future research.
1.6 Chapter Summary
This chapter started with a discussion of the two key meanings of expertise:
knowing-that and knowing-how. It then considered a number of denitions
of expertise, each emphasising a different aspect (e.g. type of measurement
or place in society). It was noted that many of these denitions suffer from
weaknesses. A fair amount of space was devoted to the question as to why we
should study expertise. The main reasons were: the development of better
methods for coaching and instruction in general, the prospect of building
articial-intelligence programs that can emulate human experts and to improve
our understanding of human cognition.
1.7 Further Reading
Several edited books provide worthwhile overviews of the various ways expertise
has been studied. Chi et al. (1988), Ericsson and Smith (1991b), Ericsson
(1996b) and Staszewski (2013a) focus on cognitive psychology, although
other viewpoints are occasionally discussed. Feltovich et al. (1997) discuss both
human and machine expertise, with a special interest in the role of context.
Ericsson et al.’s handbook (2006) provides a comprehensive overview of the
psychology of expertise, with a strong emphasis on deliberate practice. Another
handbook (Simonton, 2014) focuses on extreme forms of expertise – genius.
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In this index f represents gure and t
represents table.
AARON (computer program),
Abbott, A., 205–206, 214
Abnormalities, 91
Absolute pitch (AP), 21–23
Academic appointments, gender
differences and, 131–132
Action, chunking and, 32, 140
ACT-R (Adaptive Control of Thought)
tutor, 141–142
Adaptive expertise, 242
Adequacy criterion, 85
Advanced beginner stage, of intuition
expertise, 99
Affordances, 201
Against Method (Freyerabend), 220
intelligence and, 116
memory and, 159–160
strategies, memory and, 162
Ageing, expertise and
careers of great creators as function of,
deliberate practice and, 166–167
effects of, cognition and, 165
moderating variable, expertise and,
paradox, 165
theories of, 166–167
AI. See Articial intelligence
Alchemy and Articial Intelligence
(Dreyfus), 224
American functionalism, 204
Anticipation tasks, 176
Anticipatory schemata, 16–17
Anti-intellectualism, 219
Applied philosophy, 226
Arational thought, 225, 226
Architectonic ear, 123
Aristotle, 218
Articulatory loop, 52
Articial intelligence, creativity and,
Articial intelligence (AI)
Dreyfus’s critique of, 224
expert system research, contributions,
expert systems and, 231–232
knowledge and, 241
knowledge elicitation techniques,
knowledge representation, 230–231
psychology, knowledge representations
and, 232
research, decline of, in expert systems,
search and, 241–242
Asperger’s, 134
Assumption of monotonicity, 146, 148
Assumption of rationality, 239–240
Atonal music, 22
Attributions, gender and, 133
Automatisation, problem-solving and,
Autonomous phase, behaviour and, 75–76
Backward search, 67
Base rate neglect, 87
Beer-mat knowledge, 212
Behaviours, in/out group, 199
Being-in-the-world, 224
conrmation, 200
decision making and, 87–88
egocentric epistemic, 222
gender and, 133
publication, 189
Binet, Alfred, 115–116
Binet-Simon test, 116
Biological age, 116
Biological markers, for talent/
intelligence, 122
Biological mechanisms, of neuro-
expertise, 178–180
Biomedical knowledge, 45
Bipolar disorder, 130
Birth order effect, intelligence and, 106
Bodily-kinaesthetic intelligence, 118
Bounce (Syed), 143
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294 Index
Bounded rationality, decision making
and, xvi, 85–86, 114, 240–241
Brain imaging
chess and, 173
episodic memory and, 172
functional reorganisation of, retrieval
structures, templates and, 180
fusiform face area and, 177
intelligence, gender, head size and,
intelligence, gender differences and,
knowledge acquisition and, 174
memory experts and, 172–173
skill acquisition in novices, 170–172
smart drugs and, 182–183
sports, neuro-expertise and, 174–176
visuospatial function and, 180–181
Brain imaging techniques, 171
Brain plasticity, 175, 180, 182
Brainstorming, 199
British Journal of Psychology, 57
Brute force, 235
Bureaucracies, 204
Calibration measures, 191
CaMeRa computation model
(Tabachnek-Schijf), 75
Career age, 167
Careers, ageing and, 167–169
Carlsen, Magnus, 148
Cartesian dualism, 219
Categorisation, 27–28
Charmides (Plato), 222
Chase, W. G., 29–33, 79–80
Chassy, P., 100
in science, fraud and, 194–195
in sports, 195–196
Check congurations, 173
age, memory and, 160
blindfolded playing, 141
gender and, 133–134
intelligence and, 120
memory, 29–39
neuro-expertise and, 173
perception, 11–14
problem solving, 62–73
and reliable measure of expertise, 144
talent and, 121–123
Chi, M. H., 160
CHREST simulation program, 16, 36,
37–38, 244, 250
evaluation of, 56–58
template theory, chunking and,
CHUMP, computer simulation program,
Chunking theory (Chase & Simon),
30–33, 48–60
age and, 165
ageing model and, 166
digit span test and, 161
EPAM-IV and, 52
expert’s superiority, recall tasks and, 33
knowledge structured as, 33–36
long-term memory storage and, 36–37
practice, talent identication and,
problem-solving and, 79–80
random positions and, 39
revisions of, 51
template theory, CHREST and, 52–56
Chunks, memory and, 30
Circumferential scan pattern, 15
Classication, of expert roles, 210–211
evaluation, 214–215
periodic table of expertises, 211–214
Clinical knowledge, 45
Clinical vs. Statistical Prediction
(Meehl), 90
Closure, professions and, 204
location, 37
LTM and, 41
musical stimuli, 25
Cognition, 4
deceptive moves and, 176
decision making and, 88–89
domain general mechanisms and,
situated action and, 201
smart drugs and, 182–183
Cognitive biases, 88
Cognitive liberty, 183
Cognitive phase, behaviour and, 75–76
Cohen, Harold, 111–112
Collaboration, research and, 197–200
Collins, M. H., 211–214
Collins and Evan’s periodic table of
expertises, 211–214
Common law, 208
Communication, stories and, 200
Compensatory mechanisms, age and,
Competence stage, of intuition expertise,
Computer simulations, 58
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Index 295
Concept formation, 27–28
Concept learning, 27–28
Concept of Mind, The (Ryle), 219
Conceptual knowledge, problem-solving
and, 70–71
Condition, chunking and, 32, 140
Conrmation bias, 200
Conict resolution rules, 82
Connectionist models, computer
simulation program, 81–82
Connoisseurship, 97
Conscientiousness, 129
Constraint attunement theory,
Contributory expertise, 211t, 213
Control, professions and, 204
Control condition, 47
Copy task, 30
encoding and, 232
templates and, 54
Core mirror neuron system, 176
Corticomotor system, 174
Cost-benet analysis, 86
Court, experts in, 208–209
Creating Minds (Gardner), 118
Creating thinking, tests of, 105
articial intelligence and, 111–112
intelligence and, 119
mental health and, 130
personality and, 128–129
stages of, 108
tacit knowledge/knowing,
Creativity, expert, 103–113
education/training, development of
and, 106–107
estimations of, reliability, 103–104
family environment/socioeconomic
conditions, development of and,
sociocultural contexts, development of
and, 107–108
tests of, 105–106
Creativity, theories of
as product of unconscious
mechanisms, 108–109
as search through problem space,
selection mechanisms and, 110–113
Creativity test, 76–77
Credentials, 211t, 214, 222–223
Csikszentmihalyi’s phenomenon of
ow, 146
Darwinian mechanisms of variation and
selection, 110
Data, fraud/cheating, science and,
Data mining, 235
Deception identication, 176
Decision experts, 211
Decision making
biases approach to, 87–88
fast/frugal heuristics, 88–89
group phenomena, experts and,
heuristics approach to, 86–87
intuition and, 97–98
naturalistic, 89–91
rationality/bounded rationality,
85–86, 239–241
satisfaction of search effect and, 91
Shanteau’s framework for, 92
in sports, 93–95. See also Sports,
decision making in
Declarative memory, 182–183
Dening expertise, 2–6, 239
de Groot, Adriaan, 11–14, 62–63, 78
Deliberate play, 152
Deliberate practice, 142–149
ageing and, 166–167
assumption of monotonicity,
146, 148
data supporting, 143–144
evidence against, 144–149
herding and, 199–200
individual vs. team, 146–147
lack of enjoyment, 146
logical/methodological issues,
other interests, training in, 147
talent vs. practice, 149
theory of, 142–143
violation of 10-year, 10,000-hour
rules, 146
DENDRAL expert system, AI and,
231–233, 242
Detection tasks, 19
Deutsch’s scale illusion, 21
Development, expertise and
diachronicity and, 243
domain-general mechanisms, 158–159
domain-specic mechanisms,
of expert systems, 236
gifted children, 162–163
savants and, 163–164
Diachronicity, expertise and, 243
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296 Index
Differential predictions of talent/
practice, 150–151
Digital Equipment Corporation (DEC),
Digit span test, 40, 53, 57, 161
Direction identication, 176
Disagreement, between experts,
Discipline integration, expertise and, 250
Discrimination, expertise and,
Discrimination measures, 191
Discrimination network, 31, 32f, 53f
Dispositions, 211t, 212, 219
Divergent production tests, 105
Divergent semantic units, 105
Diversication, specialisation in sports
and, 151–152
Domain general mechanisms,
development and, 158–159
Domain selection, 249
Domain-specic mechanisms,
development and, 159–161
Doping, sports cheating and, 195–196
Downward discrimination, 214
Dreyfus, H. L., 99–100, 224–225
Dreyfus, S. E., 99–100
Dreyfus & Dreyfus theory of expert
intuition, 99–100
Dreyfus & Dreyfus theory of expertise,
225, 226, 244
smart, neuro-function and, 182–183
sports, cheating and, 195–196
DSM-III (Diagnostic and Statistical
Manual of Mental Disorders), 130
Dualism, 219
Dual theories, of intuitive expertise,
Durkheim, Emile, 204
Economic null hypothesis, prediction
failure and, 187
Economic representations, problem-
solving and, 74–75
Economics, rationality and, 240
Economy, professions and, 204
Economy and Society (Weber), 203–204
creativity development and, 106–107.
See also Learning
environment, society and, 243–244
generativity and, 242
knowledge and, 241
nature vs. nurture, 243
rationality and, 241
smart drugs and, 182–183
Egocentric epistemic bias, 222
Ego strength, 130
Ehrlich Paul, 187–188
Einstellung effect, 77, 88
Electroencephalography (EEG), 171,
Elo, A., 122, 167
Elo rating, 3–4, 72, 121
Emotional responses, 154
Empathizing-systemizing theory, 134
Empirical data, chunking theory and,
Energy, personality and, 128
expertise, society and, 243–244
gifted children and, 162–163
EPAM-IV, memory theory and, 52,
Episteme (Aristotle), 218
Epistemic peer disagreement, 221–222,
Epistemic peers, 221–222
Epstein, D., 3
Equal-eight view, 222
Ericsson, K. A., 49–51, 143
Errors, in scientic research/publication,
Ethical issues, expertise and, 247
of expert classication, 214–215
problem-solving and, 68–69
Evans, R., 211–214, 225–226
Event-related potentials (ERP), 171
Existential intelligence, 118
Expansive mode, 228
Expected utility theory, 86
Experience, 211t, 214
Expert analysts, 210
Expert creativity, 103–113. See also
Creativity, expert
Expert insight, 101–102
Expert intuition. See Intuition, expert
communication and, 207–208
dening/identication and,
2–6, 239
diachronicity and, 243
dual meaning of, 1–6
ethical issues, 247
failure of, 91
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Index 297
ve-stage model of (Dreyfus &
Dreyfus), 225
four tensions, 245–246
generativity and, 242
interactional, 208
knowledge and, 241
medical, 14–16
methods/theories, 244–245
nature v. nurture, 243
perceptual superiority and, 17–18
performance-based, 248–249
performance/reputation-based, 239
periodic table of, 211–214
philosophy, applications of and,
positive/negative aspects, 237–238
practical implications, 246–248
rationality and, 225–226, 239–241
reputation-based, 249
search and, 241–242
specialisation effects in, 72–73
transversal themes of, 238–239
why study?, 6–7
Expertise reversal effect, 154
Expertise stage, of expert intuition, 99
Expert knowledge, 250
Expert learners, 155–156
Expert Mirror Neuron System, 176
Expert-novice comparisons, 154
Expertocracy, society and, 193
Expert patients, 197
Expert performance, IQ as predictor of,
Expert researchers, 210
Expert roles, 210–211
becoming an, 248–249
biases in, 87–88
in court, 208–209
disagreements between, 221–222
failure of, 187–188
identication of, 222–224, 248
society and. See Society, experts and
super, 250–251
why do we believe, 200–201
Experts, classication of
Collins and Evans’s periodic table of
expertises, 211–214
Mieg’s, role classication, 210–211
Expert systems, 209
AI and, 231–233
Dreyfus critique of, 224–225
knowledge elicitation techniques,
research, contribution of, 235–236
research, decline of, 234–235
Expert teachers, 153–155
Exposure time, 46
Expressive ear, 123
External expertise, 211t, 213
External problem space, 78–79
Extra-weight view, 222
Extroversion, 128, 129
Eye-hand span, 26
Eye movement patterns, 14–16, 24, 56,
70, 78, 82
Eye-voice span, 26
Facial recognition, 176–177
Factor analysis, 116
Fads, prediction, experts and, 188
Failure, of experts, 187–188
political science, predictions in,
scientic research and, 188–190
Fake experts, 222–223
Family conditions, creativity
development and, 106
Fast and frugal heuristics, 88–89
Fast Company, 143
Fear of success, 133
File-drawer problem, 190
Final phase, of problem-solving, 64
First-person subjective experience, 224
First phase problem-solving, 63–64
Five-stage model of expertise (Dreyfus &
Dreyfus), 225
Fixation of abnormality, 15
Fixed localisation vs. perceptual expertise,
Flexibility, teaching and, 154
Flow (Csikszentmihalyi’s theory of),
128, 146
Formal experts, 211
Formalisms, 231
Forward search, 67
Four tensions, talent vs. practice,
Frame, 232
Frames of Mind (Gardner), 118
Framing effect, 87
Framing phase, of problem-solving, 64
fake experts, 222–223
in science, 194–195
Functional Magnetic Resonance Imaging
(fMRI), 171, 177
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298 Index
Functional reorganisation, 174, 180
Fusiform face area, 177
Future of Management, The (Hamel &
Breen), 188
Galton, Francis, 115
Gardner, Howard, 118
Gardner’s approach, to intelligence and
talent, 118
Gaze-contingent window paradigm,
13–14, 15
Gender differences
Asperger’s, 134
brain functions and, 178
empathizing-systemizing theory, 134
females, verbal IQ/speed tasks and,
gaps, in sciences/arts and, 131–136
intelligence and, 134–136, 181–182
in mathematics, 132
in sports performance, 124
statistical explanations, 132–134
in talent, 117, 119
testosterone exposure in utero, 180
Gene-constellation hypothesis,
intelligence and, 120–121
General Intelligence, 134
Generalisability of skills, 150–152
General Problem Solver (Newell &
Simon), 224
General theories, 103
Generation of solutions, creativity and,
Generativity, expertise and, 242
Generic description, 50–51
Genetic markers, sports and, 124–126
Genetics of talent, 149
Genius in All of Us, The (Shenk), 143
Geschwind and Galaburda’s theory of
talent, 122, 180–181
Gestalt psychology, 16
Gifted children, development and,
Gilmartin, K., 36
Glass ceiling, 133
Global-focal search model, 15
Goal-directed activities, 142, 151
Gobet, F., 33–36, 100
Greek philosophy, 217–218
Group phenomena, experts, decision
making and, 197–200
Groupthink, 199
Guilford’s theory of intelligence, 105
Halo effect, media and, 193
of chess players, 122
talent, visuospatial domains and,
Head size, intelligence and, 179
Herding effect, 199–200
Hereditary Genius (Galton), 115
Heredity, intelligence and, 120–121
personality, talent and, 127–128
sports and, 124–125
Heterarchy, 232
Heuristic approach, to decision making,
articial intelligence and,
decision making and, 88–89
expert insight and, 102
experts as, 206–207
Hierarchical clustering, 234
Hierarchical organisations, 204
Hierarchies, 232
Hindsight bias, 87
Holding’s theory, problem-solving and,
Holistic perception, 16–17
Holistic understanding, 226–227
Homing heuristic, 66
Horizontal décalages (Piaget), 159
Human rationality, tests of, 89
Human search, computer models of,
connectionist models, 81–82
NSS, MATER and, 80–81
Identical elements, theory of (Thorndike
and Woodworth), 151
Identifying expertise, 239
Idiographic approach, 103
IF-THEN rules, 232
Illumination stage, of creativity, 108
Illusion of control, 200
Immediate reinvestigation, 64–66
Implicit knowledge, 234
Implicit learning, 234
Implicit memory, 234
Incubation stage, of creativity, 108, 109
Individual differences, in talent, 117
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Index 299
Inert knowledge, 142
Inexibility (rigidity of thought),
Information, knowledge and, 197
Information processing, intelligence and,
Information theory, 59
In-group behaviors, 199
Insight, expert, 101–102
Intellectualism, 219
Intelligence, talent and, 115–121
biological mechanisms linked to,
components of, 120
Gardner’s approach (5 intelligences),
gender differences and, 134–136,
gene-constellation hypothesis,
IQ as predictor of expert performance,
performance and, 129
Intelligent tutoring, 141–142
Interactional expertise, 208, 211t,
Interfering task, 25, 39–40
Inter-individual variability, deliberate
practice and, 145
Intermediate effect, in medicine, 44–46
Internal expertise, 211t, 213
Internal meta-expertises, 211t, 213
Internal problem space, 78–79
Internet, society, expertise and,
Interpersonal intelligence, 118
Inter-piece latencies, 31
Inter-rater correlation, 117
Intersection positions, 55
Intuition, expert, 97–101
Dreyfus & Dreyfus, 99–100
dual theories, 100–101
in nursing, 226
Simon’s theory and, 98–99
template theory of, 100
Inverted U-curve, 44
Invulnerability, 199
IQ (intelligence quotient), 90, 116
birth order effect and, 106
chess and, 120
gender differences and, 132–133
as predictor of expert performance,
savants and, 164
Jurisdiction, professions and, 205
Justice, miscarriage of, expertise and,
KEKADA production system, 112
Kinematic cues, 175
Kintsch, W., 49–51
Knowing how, 227
Knowing how, philosophy and, 219–220
Knowing How and Knowing That (Ryle),
Knowing that, 227
acquisition of, 141, 174
assessment of, 227–228
cognitive development and, 159
communication and, 207–208
compensatory mechanisms, age and, 165
conceptual, problem-solving and, 70–71
elicitation techniques, expert systems,
AI and, 233–234
expertise and, 241
expertise classication and, 212–213
information and, 197
metacognitive skills and, 159
organisation, schemas and, 42–43
procedural, 234
professions and, 205
search and, 241–242
stereotypical thinking and, 107
testability of, 220
ubiquitous tacit, 215
Knowledge, chunks and, 33–36
Knowledge representation
AI and, 230–231
psychology and, 232
Labour division, 204
Language learning, 56
Large complexes, 70–71
Latencies, 35
Laypeople, musical expertise and, 23
Learners, expert, 155–156
expert learners, 155–156
expert teachers, 153–155
machine, 235
practice, based on, 139–149. See also
Practice, talent and
talent, based on, 138–139
transfer and, 150–152
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300 Index
Learning by example, 140
Linear performance, 44
Linguistic experience, 214
Linguistic uency, 212
Linguistic intelligence, 118
Local discrimination, 211t, 213
Location coding, 37, 48
Logic, formalisms and, 231
Logical-mathematical intelligence, 118
Logic of Scientic Discovery (Popper),
Long-term memory (LTM)
chunks and, 31, 32, 33
chunk storage and, 36–37
schemas and organisation of
knowledge, 42–43
slow encoding times, 39–43
template theory, CHREST and,
See also Memory entries
Long-term working memory (LTWM),
Loose hierarchy, 232
Lose-shift hypothesis, 66
LTM. See Long-term memory
LTWM. See Long-term working memory
Lum, G., 227–228
Machine learning, 235
Macrostructure, of search, 66–67
Macysma program, expert system, 234
Mad genius theory, 130
Management science, prediction failure
and, 188
MATER, computer simulation program,
Mathematics, gender differences in, 132,
Matthew effect, 247
Maximisation, 85
McDowell’s theory of rationality,
Media, expertise and, 193
Medical expertise, 14–16, 44–46
Medical research, expert failure and,
Mednick’s theory of creativity, 105
Meehl, P. E., 90
age and, 159–160, 165
Chase & Simon’s Chunking Theory,
chunking theory, 30–33
creativity and, 109
digit span test and, 161
domain-specic, decline in, 166
episodic, brain function and, 172
experts, brain function and,
functional reorganisation of brain and,
intermediate effect, in medicine,
in music, 47
randomisation and, 37–39
savants and, 163–164
smart drugs and, 182–183
in sport, 46–47
STM capacity, LTM encoding times
and, 39–43
task expertise and, 180
Memory, theories of, 48–60
constraint attunement theory, 58–60
long-term working memory, 49–51
revisions of chunking theory, 51
skilled memory theory, 49
Mental age, 116
Mental calculations, brain function and,
Mental energy, 178
Mental health issues, talent and,
Mental imagery, 32, 55–56
Meritocracy, 193
Merton, R. K., 247
Metacognitive skills, 159
Meta-criteria, 211t, 214
Meta-DENDRAL, expert system, AI
and, 232
Meta-expertises, 211t, 213
Method of loci, 40, 49
Methodology, deliberate practice and,
Mieg, H. A., 206–207, 209, 210–211,
Mind over Machine (Dreyfus), 225
Mind-set effect, 77
Mind’s eye, problem-solving and, 32,
Mirror-neuron system, 176
Mnemonics, 39–40
Monotonicity, assumption of, 146
Moral authority, 204
Motivation, personality, creativity and,
128, 163
Motor behaviour, 75–76
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Motor-evoked-potential amplitudes,
Motor skills, neuro-expertise, sports and,
Multidimensional scaling, 234
intelligence and, 120
memory in, 47
neuro-expertise and, 173–174
talent in, 123
Music, perception in, 19–27
absolute pitch, 21–23
eye-hand span, 26
eye movements, 24
laypeople, implicit musical
expertise, 23
proofreader’s area, 26–27
short presentations, 24–25
sight-reading, 24
Musical ear, 123
Musical intelligence, 118
MYCIN, expert system, AI and,
231–232, 234
Naturalistic decision making, 89–91
Naturalistic intelligence, 118
Nature vs. nurture, expertise and, 243,
Neural network simulations, 167,
chess and, 173
environment, society and, 243–244
xed localisation vs. perceptual
expertise, 176–178
functional reorganisation of brain and,
gender differences and, 181–182
Geschwind and Galaburda’s theory,
intelligence, biological mechanisms
and, 178–180
memory experts and, 172–173
mental calculations, data, 172
music and, 173–174
nature vs. nurture, 243
skill acquisition, in novices and,
sports and, 174–176
taxi drivers, knowledge and, 174
Neuroticism, 128, 129
Newel and Simon’s problem-space
theory, 78–79, 112
Nicomachean Ethics (Aristotle), 218
Node, 53, 62f, 231
Nomothetic theories, 103
Non-immediate reinvestigation,
Nootropic drugs, education, brain
function and, 182–183
Novice stage, of intuition expertise, 99
NSS, computer simulation program,
Object discrimination, 176–177
Object recognition, 177
Ontology, 235–236
Optimism, 192
Option selection, sports, decision making
and, 94–95
Ordinary savants, 163
Orientation zone, 70
Outliers (Gladwell), 143
Output quantity, creativity and, 112
Pain sensitivity, sports performance and,
Parafoveal information, 15
Partitioning technique, 35
Pattern recognition
expert intuition and, 98
insight and, 102
problem-solving and, 69–70
search and, 82
theory of search, 83f
Peak, of career, ageing and, 167–168
Pearson, Karl, 116
Peers, disagreement between experts,
PERCEIVER, computer simulation
program, 81
de Groot research on, 11–14
in music, 19–27. See also Music,
perception in
problem-solving role, 70
in sports, 17–19
Perceptual behaviour, 75–76
Perceptual cue, 201
Perceptual expertise, 27–28, 177–178
Perceptual expertise vs. xed localisation,
Perceptual learning, 27–28
Perceptual resources, 91
Perfectionism, 129
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of experts, failure and, 188
rationality and, 240
recall, age and, 165
rehearsal and, 162
Performance-based expertise, 239,
Periodic table of expertise, 211–214
Peripheral information, 15
Personality, talent and, 127–129
creativity and, 128–129
need for achievement, 129
Personal Knowledge (Polanyi), 219–220
Pessimism, 192
Phenomenology, 224
Philosopher kings, 218
ancient Greek, 217–218
disagreements, between experts and,
Dreyfus’s critique, of expert systems
and, 224–225
environment, society and, 243–244
expertise, applications of, 226–228
identication, of experts, 222–224
knowing-how, tacit knowledge and,
rationality, expertise and, 225–226
of science, expertise and, 220–221
Phronesis (prudence), 218
Physical Review Letters, 190
Physics, representations, problem-solving
and, 73–74
Piaget’s theory of development,
158–159, 162
Planning, problem-solving and, 67–68
Plato, 217–218
Platykurtic, 132
Poincaré, Henri, 108
Polanyi, Michael, 219–220
Political anarchy, creativity and, 108
Political science, predictions in,
Politics as a Vocation (Weber), 204
Popular understanding, 212
Population, famine and, prediction
failure, 187–188
Population Bomb, The (Ehrlich), 187–
Positron emission tomography (PET)
scans, 171
professions and, 205–206
talent vs., 245–246
Practice, talent and, 123
ACT-R, intelligent tutoring and,
chunking theory and, 139–140
deliberate practice, 142–149. See also
Deliberate practice
differential predictions, transfer and,
identifying strategies, 139
music, neuro-expertise and,
template theory, 140–141
Predictions, expertise and, 185–186
failure of experts, 187–188
media and, 193
in political science, 190–192
Preparation stage, of creativity, 108
Prescriptive mode, 227–228
Presentation time, age and, 165,
Primary source knowledge, 211t, 212
conceptual knowledge and,
de Groot’s research and, 62–63
directionality of search, 67
evaluation and, 68–69
expert systems and, 233
macrostructure of search, 66–67
mind’s eye and, 32
pattern recognition, role in, 69–70
perception role in, 70
phases of, 63–64
planning and, 67–68
progressive deepening, expertise
effects and, 64–66
representations role in, 73–75. See also
Representations, problem–solving
Problem-solving theories
chunking/template, 79–80
computer models of human search,
80–84. See also Human search,
human search
Holding’s theory, 80
Newell and Simon’s problem-space
theory, 78–79
Selz-deGroot Framework, 78
Problem space theory (Newell &
Simon), 78–79, 109–110
Prodigious savants, 163
Productions (rules of the type), 32, 140,
167, 232
Productive thinking, framework, 78
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Product theories, 58–60
Professions, sociology of
Abbot’s studies of, 205–206
characteristics of, 203–205
communication and, 207–208
early works on, 203–205
heuristics, experts as, 206–207
role classication and, 211
trust and, 209
Prociency stage, of intuition expertise,
Progressive deepening, 63
expertise effects in, 64–66
problem-solving and, 64
Proofreader’s error, 26–27
Protocol analysis, 233
of intelligence, 115, 116
knowledge and, 241
knowledge representations and,
nature vs. nurture, 243
Psychometric tests, 105
Psychopathological mechanisms, 134
Psychopathy, talent and, 129–131
Psychoticism, 128
Publication bias, scientic research and,
Raab, M., 94–95
Rage to master, 163
Randomisation, expert superiority and,
Rationalisation, 199
decision making and, 85–86, 89–91
expertise and, 225–226, 239–241
Rational organisations, 204
Reaction times, age and, 165
Reading music, 24
Reasoning, age-related decline in, 166
Recall task, 29–30, 33
Referred expertise, 211t, 214
Rehearsal strategy, 162
Reingold, E. M., 12–14
Relative experts, 210
Relative pitch, 22
Reliability, of creativity estimations,
Remote Associates Test (RAT) of
creativity, 76–77
Replication, expert failure and, 188,
knowledge, AI and, 230–231
language, 235
situated action and, 201
Representations, problem-solving and,
automatisation/rigidity of thought,
economics, 74–75
physics, 73–74
Republic, The (Plato), 217–218
Reputation-based expertise, 239, 249
Research, scientic
contribution of expert system,
on expertise, 248
further, expertise and, 250
groups and, 198
Research scientic
expert failure and, 188–190
Retraction, scientic literature and,
Retrieval cues, 173
Retrieval structures, 39–40, 42f, 48, 49
EPAM-IV and, 52
functional reorganisation of brain and,
LTWM and, 49–51
Right-reasons view, 222
Rigidity of thought, problem-solving
and, 75, 76–78
Role models, creativity and, 106–107
Rorschach test, 90, 105
Routine expertise, 242
Ryle, Gilbert, 219–220
Satisfaction of search effect, 91
Satiscing, 85
Satiscing mechanism, 80
Savants, development and, 163–164
Savoir comment faire, 227
Savoir faire, 227
knowledge organisation and,
knowledge representations, psychology
and, 232
problem-solving role, 70–71
Schizophrenia, 130
Scholastic Aptitude Test for
Mathematics, 132
Science, fraud/cheating, experts and,
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Science as a Vocation (Weber), 204
Science philosophy, expertise and,
Scientic literature, publication bias and,
Script formation, 45
Scripts, 232
brute, 235
computer models of human, 80–84.
See also Human search, computer
models of
directionality of, 67
expertise and, 241–242
macrostructure of, 66–67
through problem space, creativity and,
SEARCH, computer simulation
program, 82–84
Search for Excellence (Peters &
Waterman), 188
Search tree, 62–63
Season of birth, talent/intelligence and,
Selection mechanisms, creativity and,
Self-regulation, 155, 156
Selz, Otto, 78
Selz-de Groot framework, problem-
solving and, 78
Semantic networks, 232
Semantic reasoning, 37
Semantic web, 235
Sexism, 133
Shanteau’s framework, for decision
making, 92
Short-term memory (STM), 25, 39–43.
See also Memory entries
Sight-reading, musical perception and,
24, 26
Simon, H. A., 29–33, 78–80, 85–86,
109–110, 240
Simon’s theory, of expert intuition,
Simulated eye, 53f
Situated action, experts and, 201
Situational elements, of intuition
expertise, 99
Skill acquisition, in novices, 170–172,
Skilled memory theory, 49
Skills, generativity and, 242
Sloboda, J. A., 24–27
encoding and, 232
templates and, 54
Slotted schemata, 57
Smart drugs, intelligence, neuro-function
and, 182–183
Social closure, 203
Society, experts and, 182–183
believing experts, why?, 200–201
expertise and, 243–244
expertocracy, 193
failure of experts, 187–188
fraud/cheating, in science, 194–195
group phenomena, 197–200
Internet and, 196–197
justice, miscarriage of and, 186–187
media and, 193
political science, predictions and,
predictions, difculty of making
correct, 185–186
scientic research and, 188–190
situated action and, 201
sports, cheating in, 195–196
Sociocultural contexts, of creativity
development, 107–108
Socioeconomic conditions, creativity
development and, 106
diachronicity and, 243
knowledge and, 241
Socrates, 222
SOS effect, decision making and, 91
Spaghetti model, 244f
Span of apprehension, 26
Spatial ability, 123
Spatial intelligence, 118
Spearman, Charles, 116
Spearman’s theory of intelligence, 116,
communication and, 207–208
diversication in sports vs.,
expertise and, 248
Specialisation effects, in expertise,
Specialisation paradigm, 72–73, 193
Specialist expertise, 211t, 212
Specialist knowledge, 212
Specialist tacit knowledge, 211t,
Speed tasks, gender and, 135–136
Spiral of improvement, 140
Spiritual intelligence, 118
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cheating in, 195–196
expertise and, 246–248
genetics, talent, performance and,
memory in, 46–47
neuro-expertise and, 174–176
perception in, 17–19
specialisation vs. diversication in,
Sports, decision making in, 93–95
option selection, 94–95
task-specic probabilities, use, 93–94
Standard deviations, 132
Status, believing experts and, 200–201
Statutory law, 209
STEM disciplines, 119
Stereotype threat, 133
Stereotypical thinking, knowledge and,
STM. See Short-term memory
Stories, communication and, 200
Strategies, development and, 162
Strict hierarchy, 232
Super-expert, 5, 250–251
Symbolic processing, 81
System of playing method, 71
Systems communication, 207
Tacit Dimension (Polanyi), 220
Tacit knowledge, 215, 219–220
based on intelligence, 115–121. See
also Intelligence, talent and
in chess, 121–123
gender differences and, 131–136. See
also Gender differences
genetics of, 149
Geschwind and Galaburda’s theory of,
gifted children and, 162–163
in music, 123
personality and, 127–129. See also
Personality, talent and
practice vs., 149, 245–246
psychopathy and, 129–131
in sports, 124–127
Talent Code, The (Coyle), 143
Talent identication, 138–139
Talent is Overrated (Colvin), 143
Tangled hierarchy, 232
Task-specic probabilities, sports,
decision making and, 93–94
Teachers, expert, 153–155
Team expertise, 250
Techne (craft), 218
Technical connoisseurship, 211t, 213
Technocracy, 193
Template theory
CHREST, chunking and, 52–56
evaluation of, 56–58
of expert intuition, 100
functional reorganisation of brain and,
principles of education, 140
problem-solving and, 79–80
Testimony, experts and, 208
Testosterone exposure, in utero, talent
and, 122, 180
Theoretical discipline, 226
Theoretical inferences, AI and, 112
Theories, of memory, 48–60
Thorndike and Woodworth’s theory of
identical elements, 150–151
Thurstone, Louis, 116
Time cost, of cognitive processes, 52
Torrance’s tests of creative thinking, 105
Track record, 211t, 214
creativity development and, 106–107
deliberate practice and, 147
functional reorganisation of brain and,
perceptual patterns and, 18, 23
skill acquisition and, 170–172
sports, neuro-expertise and, 174–176
Trait approach, 205
Transcranial magnetic stimulation
(TMS), 172, 174, 175–176
Transfer, 150–152
differential predictions, of practice/
talent, 150–152
diversication, specialisation in sports
and, 150–152
expertise and, 250
Transverse themes, in expertise,
Truly random positions, 39
Trust, expertise and, 209
Tutoring, 141–142
Two-stage detection model, 15
Ubiquitous discrimination, 211t, 213
Ubiquitous expertises, 211t, 212
Ubiquitous tacit knowledge, 211t,
212, 215
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Unconscious mechanisms, creativity and,
Unconscious processing, 234
Understanding, learner’s, 228
United States v. Johnson, 208
Value, creativity and, 103
Valuation phase, of problem-solving, 64
Variability, measuring, 145
VAX computer systems, 232–233
Verbal communication, gender
differences, 181–182
Verbal IQ, 120, 135–136
Verication stage, of creativity, 108
Visual processing, 177
Visual recall tasks, 25
Visual search, 19
Visuospatial delayed-match-to-sample
task, 170–171
Visuospatial domains, handedness and
talent in, 122, 180
Voxel-based morphometry, 175
Water-level task, 76
Watson, John, 142
Weber, Max, 203–204
Weighted probabilities, 64
Weschler-Bellvue test, 90
What Computers Cant Do: The Limits
of Articial Intelligence (Dreyfus),
224, 241
Win-stay hypothesis, 66
Wisdom of Crowds, The (Surowiecki),
Witnesses, expert, 208–209
XCON, expert system, AI and, 231–233
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... However, determining expertise solely based on criterion-based measures (e.g., years of teaching experience) can be problematic. Studies have shown that there can be a high degree of expertise variability despite comparable experience duration (e.g., van den Bogert, 2016) and that expertise development is not always linear but an inverted U-shaped (e.g., Gobet, 2015). Therefore, the usage of an objective, performance-based measure, as recommended by Palmer et al. (2005), provides an economical method. ...
... Appendix A, Table 5). According to Barth (2017), Gobet (2015) and Wolff (2015), we are differentiating between description, explanation, and guidance. ...
... Initially, a distinction was made between evaluation and explanation, but following Gobet (2015), these categories were merged for the analyses due to the low number of evaluative statements. So, the code "explanation" was assigned for statements that contained a global rating of a situation or behavior in the video or when judging comments were made about the quality of the observed teaching interaction, when teaching situations were explained in a theoretically sound manner, or when an observed situation was not simply named but cause-effect relationships or means-purpose relationships were also explained (e.g., "I don't think it's okay to put your head down on the table during class." 4 ). ...
We investigate whether differences in professional vision (PV, both in noticing and reasoning) can be found between prospective teachers using a knowledge test as an economic, performance-based expertise indicator. Furthermore, we examine whether novices can be supported in their PV through a specific compared to a general task instruction, activating knowledge schemata promoting top-down processes. An online-based study with N = 85 prospective teachers using video vignettes reveals that PVs' accuracy and velocity depends on knowledge. The specific task instruction does not contribute to more effective PV. Results emphasize the relevance of knowledge transfer during university education for prospective teachers.
... Templates are also easily modified. The fact that templates are easily and rapidly modifiable allow for rapid recall, accounting for the superior memory skills of experts [54]. ...
... While a grandmaster could recall the positions of almost all the pieces, a strong amateur struggled to recreate half of the chess board. Furthermore, experts perceived the chess boards not as individual pieces but in large complexes that included information such as threat, potential moves and move sequences [54]. These large complexes were later called chunks by Chase and Simon, who repeated and modified De Groot's chess experiment [46], [47]. ...
... Experts have better recall than novices for random perceptual stimuli in their domain of expertise. This skill difference in random material is explained by chunking because since experts have more chunks than novices, they are more likely to recognize these chunks even in random positions /configurations [52], [54]. ...
One expected outcome of physics instruction is that students develop quantitative reasoning skills, including evaluation of problem solutions. To investigate students’ use of evaluation strategies, we developed and administered tasks prompting students to check the validity of a given expression. We collected written (N>673) and interview (N=31) data at the introductory, sophomore, and junior levels. Tasks were administered in three different physics contexts: the velocity of a block at the bottom of an incline with friction, the electric field due to three point charges of equal magnitude, and the final velocities of two masses in an elastic collision. Responses were analyzed using modified grounded theory and phenomenology. In these three contexts, we explored different facets of students’ use and understanding of evaluation strategies. First, we document and analyze the various evaluation strategies students use when prompted, comparing to canonical strategies. Second, we describe how the identified strategies relate to prior work, with particular emphasis on how a strategy we describe as grouping relates to the phenomenon of chunking as described in cognitive science. Finally, we examine how the prevalence of these strategies varies across different levels of the physics curriculum. From our quantitative data, we found that while all the surveyed student populations drew from the same set of evaluation strategies, the percentage of students who used sophisticated evaluation strategies was higher in the sophomore and junior/senior student populations than in the first-year population. From our case studies of two pair interviews (one pair of first years, and one pair of juniors), we found that that while evaluating an expression, both juniors and first-years performed similar actions. However, while the first-year students focused on computation and checked for arithmetic consistency with the laws of physics, juniors checked for computational correctness and probed whether the equation accurately described the physical world and obeyed the laws of physics. Our case studies suggest that a key difference between expert and novice evaluation is that experts extract physical meaning from their result and make sense of them by comparing them to other representations of laws of physics, and real-life experience. We conclude with remarks including implications for classroom instruction as well as suggestions for future work.
... For many researchers the answer seems to be straightforward: expertise substantially relies on perception (cf. Gobet, 2015). It is perception that allows experts to rapidly categorise a problem and therefore 'see' more or different cues than a novice. ...
... What seems to be the case here is that instead of seeing static constellations of pieces, experts notice dynamic possibilities more often. Intelligence is also said to correlate with expertise in several domains (cf. Gobet, 2015). ...
Full-text available
Few phenomena have incited as much passion as the unravelling of what ‘intercultural competence’ means. This book presents a novel, bio-cultural approach towards intercultural competence, arguing that a relevant perceptual architecture must be set up via acting competently in various contexts and situations over time. The enactive framework proposes various levels of integration of cultural differences fundamental for communicating and acting effectively in multicultural environments. Intercultural competence emerges here from the co-activation of specific sets of expertise, such as creativity, morality and gender, for which the integration of cultural otherness provides the pivotal axis. A specific perceptual architecture results from such novel functional connections, via the integration of cultural otherness into highly interlinked perception, cognition, affect and action systems.
... Sala & Gobet, 2017). There is a considerable time investment required to cultivate expertise in any domain (Ericsson, 2006;Gobet & Chassy, 2009;Gobet, 2016), the classic example being the many years of effort required to become a chess grandmaster (Campitelli & Gobet, 2008). Revisiting our language analogy, we can think about how learning a language (particularly a second language) requires considerable time investment in order to attain fluency (Jackson & Kaplan, 1999). ...
Full-text available
Interdisciplinarity is widely promulgated as beneficial to science and society. However, there are three quite serious problems which can limit the success of any interdisciplinary research collaboration. The first problem is expertise (it takes years of effort to cultivate a deep knowledge of even one discipline). The second problem is comprehensibility (experts in different disciplines do not reliably understand each other). The third problem is service (in a given interdisciplinary endeavour, it often occurs that one discipline benefits and the other discipline does not benefit). This essay is an elaboration of these three problems. Parallels are drawn between translation between languages and translation between disciplines (published in "Avant: Trends in Interdisciplinary Studies").
... Beyond the complexity of GP programs in a formal sense, one must also consider complexity as experienced by humans when trying to understand GP programs. A substantial amount of literature in psychology and cognitive science indicates that at least three factors affect complexity: (a) cognitive factors (e.g., short-term memory capacity, learning rate, speed of processing information) (Gobet, Chassy, & Bilalić, 2011;Hunt, 2011;Simon, 1989); (b) the level of expertise and domain knowledge (Gobet, 2016;Shadbolt & O'Hara, 1997), which in our case also includes knowledge of the GP operators used and the type of programs generated; and (c) extrinsic factors (e.g., length of programs, presence of recursion, and type of representations used) (Kotovsky & Simon, 1990;Weinberg, 1998). Ai, Muggleton, Hocquette, Gromowski, and Schmid (2021) explore such ideas with respect to simple two-person games. ...
Full-text available
Genetic programming (GP), a widely used Evolutionary Computing technique, suffers from bloat -- the problem of excessive growth in individuals' sizes. As a result, its ability to efficiently explore complex search spaces reduces. The resulting solutions are less robust and generalisable. Moreover, it is difficult to understand and explain models which contain bloat. This phenomenon is well researched, primarily from the angle of controlling bloat: instead, our focus in this paper is to review the literature from an explainability point of view, by looking at how simplification can make GP models more explainable by reducing their sizes. Simplification is a code editing technique whose primary purpose is to make GP models more explainable. However, it can offer bloat control as an additional benefit when implemented and applied with caution. Researchers have proposed several simplification techniques and adopted various strategies to implement them. We organise the literature along multiple axes to identify the relative strengths and weaknesses of simplification techniques and to identify emerging trends and areas for future exploration. We highlight design and integration challenges and propose several avenues for research. One of them is to consider simplification as a standalone operator, rather than an extension of the standard crossover or mutation operators. Its role is then more clearly complementary to other GP operators, and it can be integrated as an optional feature into an existing GP setup. Another proposed avenue is to explore the lack of utilisation of complexity measures in simplification. So far, size is the most discussed measure, with only two pieces of prior work pointing out the benefits of using time as a measure when controlling bloat.
... Research in cognitive science and psychology focuses on the performance of players, as well as the cognitive and behavioral differences between novices and experts (Gobet, 2016;Reitman et al., 2020), because the discussion of the cognition and behavior of novices and experienced players can help learn the differences in behavior patterns between the two, and understand the selection factors of game behavior and strategy implementation by novices and experienced players, so as to help eSport players plan more appropriate training strategies. Therefore, it is suggested by this study that follow-up research can follow this research direction. ...
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With the growing popularity of eSport games, eSport-related issues have gradually gained attention and discussion in academic research. However, the positive benefits (values) brought by playing eSport have not received too much attention in current research. Therefore, after reviewing related research in the past, this study proposed that eSport has the 5 Cs educational Value of cultivating the cooperative attitude, communication skills, critical thinking, self-confidence and continuous improvement attitude based on the three-domain model (TDM) of cognitive, affective and psychomotor, and developed an eSport educational values scale. In this study, a conceptual sampling method was adopted and players with eSport experience were invited to fill out the questionnaire. A total of 316 participants filled out the questionnaire, 51 invalid samples were deleted, the number of effective participants was 265, and the effective recovery rate was 83.9%. Then SPSS 23.0 and AMOS 20.0 were used to analyze the reliability and validity of the scale, and the verification results show the scale developed by this study has good reliability and validity. In addition, in this study, it was also found that the participants had a positive view (M 3.9) on the 5 Cs educational value of the MOBA type eSport, which shows that eSport is not only a casual game, moderate playing this game can also bring educational significance to players.
... What it means to be an expert has been debated for decades. Traditional definitions of expertise rely on knowledge (the know-that's) and skills (the know-how's) with Gobet offering a more general definition: "an expert obtains results vastly superior to those obtained by the majority of the population" [17]. De Groot established how experts perceive the most important aspects of a situation faster than novices [8]. ...
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Described is a synthetic elderly companion named Lois, short for Loved One’s Information System, able to tend to an elder’s needs, monitor overall well-being and progress, detect decline and signs of further medical problems, and in general, ‘be there’ for an elder. Lois is a cognitive system comprised of computer displays, cameras, speakers, microphones, and various sensors placed throughout the home facilitating both non-invasive monitoring and highly interactive collaboration. Lois maintains a set of models enabling the recognition of events and changes of condition as well as the compiling of a historical record for later use by medical personnel and family members. Since Lois lives with the elder over an extended period of time Lois becomes tailored specifically to the elder learning their schedule, likes, dislikes, and tendencies. By working with the elder, the elder’s family members, and medical personnel, Lois is at the center of a synthetic elderly caregiver ensemble capable of performance superior to any human caregiver.
... It seems you might also consider how the different talent ID method may 388 broaden the net at different sampling periods such that it identifies people with the 389 appropriate genetic profile and potential for cycling but who for whatever reason may 390 have been interested in a different sport and/or just did not realize that cycling fit their 391 body type, etc. 392 393 394 Theorists who do not subscribe to a single factor hypothesis to explain 395 expertisethe practice vs. talent dichotomyendorse a multi-component explanation 396 to expertise (Ackerman, 2014; Gobet, 2015). We suggest that our measure of the 397 period to excellence offers a more holistic approach to identifying the time applied to 398 acquiring expertise, as this not only includes practice but also recovery periods, which 399 allows for physiological adaptations. ...
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The aim of this study was to compare two methodologies employed by the British Cycling talent identification programme. Specifically, we investigated cyclists selected to represent GB cycling team at the London 2012 Olympics using (a) a traditional talent identification methodology (British Cycling Olympic Development Programme), where selection is based upon race results and (b) a detection talent identification methodology (UK Sport Talent Team Programme), which is a multi-Olympic event initiative that identifies athletic potential from a range of generic, physical and skill-based tests. To facilitate this comparison, we calculated the speed with which expertise was acquired. A Mann-Whitney U test (U =16.0, p = 0.031) indicated that the speed of acquiring expertise was quicker in detection talent identification (Mdn. = 5.4) than traditional talent identification (Mdn. = 7.2). Practice started later with detection talent identification than with traditional talent identification (14.12 years vs. 11.23 years, respectively), which affected the period to excellence. Thus, detection talent identification resulted in an absence of early specialization, which suggests a critical period for attaining cycling expertise. We hypothesize a genetic basis of talent and propose that a detection talent identification programme provides a better starting point of deliberate practice, traditionally a weakness in calculating the period to excellence.
Technology is central to human life but hard to define and study. This review synthesizes advances in fields from anthropology to evolutionary biology and neuroscience to propose an interdisciplinary cognitive science of technology. The foundation of this effort is an evolutionarily motivated definition of technology that highlights three key features: material production, social collaboration, and cultural reproduction. This broad scope respects the complexity of the subject but poses a challenge for theoretical unification. Addressing this challenge requires a comparative approach to reduce the diversity of real-world technological cognition to a smaller number of recurring processes and relationships. To this end, a synthetic perceptual-motor hypothesis (PMH) for the evolutionary–developmental–cultural construction of technological cognition is advanced as an initial target for investigation.
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Considerable research has been carried out on chess in the last seventy years. While classic research has centred on perception, memory, and decision making, contemporary research has focused on deliberate practice, individual differences, and education. Contrasting with classical research, which has mainly used experiments and computer modelling, more recent research has tended to use questionnaires, interviews, and analysis of computer databases as source of information. This article reviews these recent research trends, focusing on what has been learnt from chess research with respect to deliberate practice, intelligence, and transfer of skill. It also discusses ageing and risk taking between civilizations as examples of computer database analyses. Results clearly indicate that deliberate practice is a necessary, but not sufficient condition for achieving high levels of expertise. Other factors are important, some of which are innate. One of them is intelligence. Data show that chess players on average are more intelligent than individuals who do not play chess, and that chess skill positively correlates with intelligence. These results are unlikely to be explained by the hypothesis that chess leads to an increase of intelligence, as the results of experiments using chess instruction to bring about far-transfer effects are inconsistent. In addition, experiment designs used in chess instruction research are typically insufficient to allow strong conclusions about causality. Research using chess databases have led to interesting results, but its generalisability is likely to be limited. The article ends with recommendations for future research.
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