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Domain-Specific Knowledge and Why Teaching Generic Skills Does Not Work



Domain-general cognitive knowledge has frequently been used to explain skill when domain-specific knowledge held in long-term memory may provide a better explanation. An emphasis on domain-general knowledge may be misplaced if domain-specific knowledge is the primary factor driving acquired intellectual skills. We trace the long history of attempts to explain human cognition by placing a primary emphasis on domain-general skills with a reduced emphasis on domain-specific knowledge and indicate how otherwise unintelligible data can be easily explained by assumptions concerning the primacy of domain-specific knowledge. That primacy can be explained by aspects of evolutionary educational psychology. Once the importance of domain-specific knowledge is accepted, instructional design theories and processes are transformed.
Domain-Specific Knowledge 1
Domain-Specific Knowledge and why Teaching Generic Skills does not Work
André Tricot
CNRS and University of Toulouse, Toulouse, France
John Sweller
School of Education, University of New-South Wales, Sydney, Australia
Correspondence concerning this article should be addressed to: André Tricot, CLLE Institute,
CNRS and University of Toulouse, 5 Allées Antonio Machado, 31 058 Toulouse cedex,
France <>
Domain-Specific Knowledge 2
Domain-general cognitive knowledge has frequently been used to explain skill when domain-
specific knowledge held in long-term memory may provide a better explanation. An emphasis
on domain-general knowledge may be misplaced if domain-specific knowledge is the primary
factor driving acquired intellectual skills. We trace the long history of attempts to explain
human cognition by placing a primary emphasis on domain-general skills with a reduced
emphasis on domain-specific knowledge and indicate how otherwise unintelligible data can
be easily explained by assumptions concerning the primacy of domain-specific knowledge.
That primacy can be explained by aspects of evolutionary educational psychology. Once the
importance of domain-specific knowledge is accepted, instructional design theories and
processes are transformed.
Domain-Specific Knowledge 3
Domain-Specific Knowledge and why Teaching Generic Skills does not Work
Psychological studies of cognitive performance have been a focus of research for over
130 years. Paradoxically, much of that research emphasised generic or domain-general
cognitive skills despite domain-specific knowledge held in long-term memory being arguably
the most important factor, and possibly the only factor, determining acquired cognitive
performance. In this paper we suggest an alternative to the perspective that teaching generic
skills is important. Instead, we argue that all educationally relevant knowledge acquired
during instruction is, and only is, domain-specific. This view provides the major point of
departure of this paper from the nearly universal consensual view that can best be summarized
by the suggestion that knowledge imparted during instruction includes some mixture of
domain-general and domain-specific information (see for example Greiff, Wüstenberg,
Molnar, Fischer, Funke, & Csapo, 2013).
We will define domain-specific knowledge as memorised information that can lead to
action permitting specified task completion over indefinite periods of time. For example, there
are many different problems that can be solved by using Pythagoras’ theorem. To use the
theorem to solve problems, problem solvers must not only learn the theorem, they also must
learn to recognise the various problems to which the theorem can be applied and the manner
in which it should be applied in each case. We define this set of problems as a “domain” and
Pythagoras’ theorem along with the manner in which it can be used is a constituent of the
domain-specific knowledge required to solve this set of problems. That knowledge, consisting
of large numbers of problem states and the moves associated with those states, is stored in
long-term memory. We will argue that teachable aspects of problem solving skill are entirely
Domain-Specific Knowledge 4
dependent on large amounts of domain-specific information stored in long-term memory,
rather than on other factors such as domain-general skills.
Domain-general skills, by definition, can be used to solve any problem in any area. For
example, learning to solve problems by thinking of similar problems with known solutions is
an example of domain-general knowledge that can be applied to all problems. Such domain-
general knowledge also is stored in long-term memory although as will be argued next, it
belongs to a different knowledge category that for biological evolutionary reasons may be
learnable but unteachable because it already will have been acquired automatically without
instruction, outside of an educational context. We will argue that while people cannot learn an
already learned, domain-general skill, they can learn to apply the skill in a new domain, thus
providing an example of the acquisition of domain-specific rather than domain-general
knowledge (e.g. Youssef, Ayres & Sweller, 2012).
Geary’s Evolutionary Educational Psychology
Geary (2008, 2012) has proposed an evolutionary educational psychology that
transforms our understanding of many aspects of human cognition relevant to instruction. His
proposal suggests knowledge can be divided into biologically primary knowledge that we
have evolved to acquire over many generations and biologically secondary knowledge that
has become culturally important but that we have not specifically evolved to acquire.
Examples of biologically primary knowledge are learning to listen and speak, learning
to recognize faces, engage in social relations, basic number sense or learning to use a problem
solving strategy such as means-ends analysis (Newell & Simon, 1972). Biologically primary
knowledge is acquired easily, unconsciously and without explicit tuition. Barring learning
deficits such as those associated with autism, it will be acquired automatically simply as a
consequence of membership of a normal society. For example, it can be argued that despite its
importance we do not teach people how to use a means-ends problem-solving strategy
Domain-Specific Knowledge 5
because they have evolved to learn how to use the strategy automatically. Biologically
primary knowledge is modular (e.g. the modularity of number sense has been demonstrated
by Mandelbaum, 2013), with different skills likely to have been acquired at different
evolutionary epochs. For example, we are likely to have evolved the ability to learn to
recognise faces independently of learning to listen and speak.
The acquisition of biologically secondary knowledge is heavily dependent on the prior
acquisition of primary knowledge. It is knowledge that we have not specifically evolved to
acquire but which a particular culture has deemed to be important. Reading, writing and
arguably, all other content taught in modern educational establishments provide examples of
biologically secondary knowledge. Schools were invented to teach biologically secondary
knowledge because it is unlikely to be acquired just by engaging in environmental or societal
interactions. Secondary knowledge is acquired consciously, with active mental effort and is
facilitated by explicit instruction.
We suggest that humans may have evolved to acquire very general knowledge that can
be applied to a wide variety of otherwise unrelated areas. Such biologically primary
knowledge is likely to be too important to human cognitive functioning to be left to the
biologically secondary system. If so, domain-general cognitive knowledge will be
unteachable because it will have already been acquired as biologically primary knowledge.
Evidence for this suggestion comes from omission: We are unable to find a domain-general,
cognitive strategy that has been described and tested for effectiveness using randomized,
controlled trials varying one factor at a time with far transfer test tasks to eliminate the effects
of domain-specific knowledge. Until a body of research becomes available demonstrating the
existence of teachable, domain-general knowledge, it may be safer to assume that such
procedures are biologically primary and so already acquired by learners. In contrast, domain-
specific knowledge is biologically secondary and undoubtedly teachable.
Domain-Specific Knowledge 6
While biologically primary knowledge may be unteachable, it does not follow that it is
unimportant to instruction. It can be important in at least two respects. (1) People may learn
the different contexts in which an already acquired generic skill can be applied. Learning the
contexts in which a generic skill can be applied provides another example of acquiring
domain-specific knowledge. In other words, general problem-solving strategies are
"teachable" in a very restrictive sense, i.e. indicating to learners that a primary, general
problem-solving strategy, already acquired by the learner, is usable to solve a specific
academic problem (e.g. Youssef, Ayres & Sweller, 2012). (2) In addition, biologically
primary knowledge may facilitate the acquisition of biologically secondary information that
provides the subject matter of instruction. Pointing out to learners that a biologically primary
skill that they have can be used to assist in a biologically secondary task may be useful.
Similarly, instruction that is organized in a manner that facilitates the use of primary skills in
the acquisition of secondary skills may be beneficial (Paas & Sweller, 2012). In other words,
while primary skills may be unteachable because they have already been acquired, they may
be useful in leveraging the acquisition of secondary skills.
If domain-general knowledge is biologically primary and domain-specific knowledge
provides the major, perhaps only form of teachable knowledge, we should be able to find
evidence for this suggestion. In the remainder of this paper, we will analyse a variety of
research areas, including historically important lines of investigation that placed an emphasis
on either domain-general or domain-specific knowledge. Our aim is to indicate that learned
skill, especially problem solving skill, derives from acquired domain-specific, rather than
domain-general, knowledge.
In the following sections, we present a description of some results from the very
beginnings of scientific psychology to more recent work in both general and educational
psychology. Those results provide evidence that the effect of domain-specific knowledge,
Domain-Specific Knowledge 7
even in areas where it was assumed to be largely irrelevant, has always been available, but
that its importance has tended to be down-played.
The Problem of Knowledge and Intelligence
Binet’s (1894) study is well known by expertise psychologists (e.g. Ericsson &
Charness, 1994; Ericsson & Lehmann, 1996; Ericsson, 1985; Ericsson & Chase, 1982), and
historians of psychology (Nicolas, Gounden & Levine, 2011), probably because it was the
first psychological study to discuss chess expertise. The first part of Binet’s book is about
great mental calculators and is still referenced more than 100 years after its initial publication
(e.g. Dehaene, 1997; Rikers, 2009). Binet studied the case of Mr. Inaudi, a great mental
calculator who could carry out seemingly impossible tasks of mental calculation. Binet asked
him to perform a large number of operations and measured how long Inaudi required to carry
out the calculations. He compared the calculation times to several cashiers who, in the days
before mechanical or electronic calculators, were required to carry out mental calculations as
a major component of their employment. Table 1 replicates a table Binet provided, dealing
with mental multiplication of large numbers. The table includes the results of several
participants multiplying numbers mentally without recourse to pen and paper, but we are
primarily concerned with the results of Inaudi and the “1st Cashier”, referred to as Mr. Lour in
Binet’s (1894, p. 97) quote below. (Mr. Diamandi was another prodigious mental calculator.
The gaps in the table are Binet’s gaps.)
"We see that while Mr. Inaudi usually has a marked superiority, it is less, for the
multiplication of small numbers, to a cashier, Mr. Lour. He is the best and fastest “Bon
Marche” cashier, who takes only 4 seconds in a case where Mr. Inaudi takes 6.4 seconds to
solve the same problem. These are small operations. Mr. Lour could not continue his
superiority for more complex operations, because his memory failed him. The discussion of
these numerical results raises an interesting question of psychology.”
Domain-Specific Knowledge 8
Table 1. Binet’s table (Binet, 1984, p. 98)
Note: The data refer to minutes and seconds to mentally complete the multiplication in the
first row.
It is fascinating to observe that Binet was, on the one hand, an ingenious and creative
psychologist, a pioneer in the history of scientific psychology, and, on the other hand,
seemingly blind, unable to see that, for 7286x5397, the cashier performed much faster than
Inaudi. For Binet, Inaudi was a highly intelligent freak of nature who had to be superior to a
mere cashier. Binet incorrectly interpreted his results accordingly.
It is also interesting to note that in a previous publication about Inaudi, Binet (1892)
reported a well-known anecdote about Mozart and his ability to remember Allegri’s Miserere.
Domain-Specific Knowledge 9
When visiting Rome as a fourteen-year-old, Mozart heard the piece during a Sistine Chapel
Wednesday service. Later that day, he wrote it down entirely from memory, returning to the
Chapel that Friday to make minor corrections. According to Binet, this feat is explained by
Mozart’s musical memory, which Binet attributed to a natural disposition in the same manner
as he interpreted Mr. Inaudi’s ability to mentally calculate. (Binet also thought that painters
like Doré and Vernet have a naturally superior visual memory.)
There was little sign that Binet was able to think in terms of expertise due to domain-
specific knowledge. Such knowledge, can readily explain Mozart’s ability to remember a
musical piece. Mozart understood that Allegri’s piece was tonal music, following the
established rules of tonal music. Those rules are known to experienced musicians who know
the structure of such music and can reproduce it in a manner very similar to Mozart. Mozart
was a genius, but it does not require a genius to remember a music piece belonging to a well-
known category. In other words, the transcription of this piece of music is likely to be a
routine exercise for highly knowledgeable musicians. It was more than 75 years after Binet
and more than 300 years after Mozart for the field to realise, following the work of Ericsson
and his colleagues (Ericsson & Charness, 1994; Ericsson & Lehmann, 1996; Ericsson, 1985;
Ericsson & Chase, 1982), that when performing a cognitive task requiring domain-specific
knowledge, that the presence or absence of this knowledge is the best predictor of
Years after Binet’s 1892 and 1894 publications, he was asked to design a standardized
test to evaluate if a pupil was likely or unlikely to succeed in secondary school. Subsequently,
this test, used to determine the probability of success in school, was given a new name:
“Intelligence”. Binet and those who followed him assumed that they were primarily
measuring a natural, basic trait rather than acquired knowledge
Domain-Specific Knowledge 10
There are more recent findings indicating the importance of acquired knowledge in
intelligence. Some of the strongest evidence for the influence of knowledge on intelligence
comes from an experiment conducted by Cahan and Cohen (1989). They were concerned with
the differential effects on intelligence of increases in age versus increases in schooling. We
know that children’s intelligence increases with age because different tests are required to
measure the intelligence of children and adults but to what extent is this increase a natural
increase simply due to increasing age and to what extent is it due to the increase in knowledge
acquired in school? Obviously, a true experiment on this issue could not be carried out in an
ethical fashion. Cahan and Cohen circumvented this problem by a quasi-experimental design
using the fact that for any given school year, children’s ages can normally vary by up to one
year. Thus, in a given school year, children with the same amount of schooling can vary in
age by up to one year depending on whether their birthday fell just before or just after the cut-
off for school entrance. Correspondingly, children in adjacent school years can be very close
in age but vary in amount of schooling by one year. Cahan and Cohen found that the increase
in intelligence due to one additional year of schooling was twice the increase for one
additional year of age. Similar results were obtained by Cliffordson and Gustafsson (2008)
and Stelzl, Merz, Ehlers and Remer (1995). Other methods, such as assessing the effect of
school reform, provide the same evidence: increasing time spent in school increases
intelligence (Brinch, 2012). It should be noted that other studies that reverse the direction of
causality by suggesting that intelligence has a positive effect on school performances (e.g.
Herrnstein & Murray, 1994) rather than that schooling can increase intelligence, are not based
on controlled experiments but on correlational analyses that cannot determine causality.
While these results can be interpreted in a variety of ways, one conclusion is that
knowledge plays a critical role in intelligence. Based on these results, it may be inappropriate
to assume that intelligence is a basic, biologically determined measure that increases with age.
Domain-Specific Knowledge 11
The accumulation of knowledge in long-term memory during schooling provides an obvious
candidate for the role of the major factor in the development of intelligence.
One hundred years after the publication of Binet’s book on prodigious calculators, “The
Bell Curve” was published (Herrnstein & Murray, 1994), emphasising intelligence and its
links with performances and achievement in many different aspects of life. In response, the
Board of Scientific Affairs (BSA) of the American Psychological Association (APA)
concluded that there was an urgent need for an authoritative report on these issues: - one that
all sides could use as a basis for discussion. Acting by unanimous vote, the BSA established a
Task Force charged with preparing such a report. Neisser was appointed Chair. Here are some
quotations from this report (Neisser et al., 1996):
“… schooling itself changes mental abilities, including those abilities measured on
psychometric tests. This is obvious for tests like the SAT that are explicitly designed to assess
school learning, but it is almost equally true of intelligence tests themselves.” (Neisser et al.,
1996, p. 87).
“There is no doubt that schools promote and permit the development of significant
intellectual skills, which develop to different extents in different children. It is because tests of
intelligence draw on many of those same skills that they predict school achievement as well
as they do”. (Neisser et al., 1996, p. 87).
Some of the conclusions of this work also are very important. Neisser et al. claimed that
we do not know the links between (psychometric) intelligence and genetic endowment. There
is an important quote on the Flynn Effect discovered by Flynn (2007) who found that over a
half century, intelligence scores had been rising substantially:
“Mean scores on intelligence tests are rising steadily. They have gone up a full standard
deviation in the last 50 years or so, and the rate of gain may be increasing. No one is sure why
these gains are happening or what they mean.” (Neisser et al., 1996, p. 97.)
Domain-Specific Knowledge 12
Based on these conclusions, after one hundred years, we apparently still know very little
about intelligence. Of course, as these quotes suggest, many of the paradoxes associated with
intelligence could be resolved had the history of intelligence testing included a heavier
reliance on the acquisition of biologically secondary, domain-specific knowledge held in
long-term memory. Many of the puzzling findings associated with intelligence testing
including the Flynn Effect, become understandable if we assume that at the very least, the
possession of a large store of domain-specific knowledge is an indispensable component of
intelligent behaviour (see e.g. Ackerman, 2000; Brinch, 2012).
While we have attributed increasing intelligence scores to the acquisition of biologically
secondary, domain-specific knowledge, these increases could just as easily be caused by
changes in biologically primary, domain-general knowledge such as general problem solving
skills. Our failure to identify teachable/learnable general problem solving skills argues in
favour of domain-specific skills. The centrality of domain-specific skills in problem solving
expertise is discussed below in the section entitled “Recognising Domain-Specific Knowledge
and Expertise”. In the next two sections we continue to indicate the historical significance of a
failure to recognise the importance of domain-specific knowledge and the equally important
failure to find teachable, domain-general knowledge.
The Problem of Expertise and Disappearing Short-Term Memory Limits
Miller’s (1956) paper can be considered as one of the main events in the birth of
cognitive psychology, but also, the paper that defined the concept of capacity of processing
information or short-term memory capacity. Even as the short-term memory concept was
progressively replaced by working memory (Atkinson & Shiffrin, 1968; Baddeley & Hitch,
1974; Miller, Galanter, & Pribram, 1960), the linked concept of capacity did not disappear
(Cowan, 2005; Conway, Jarrold, Kane, Miyake, & Towse, 2007). The powerful idea of Miller
was that this capacity is universal, applying to everyone in every domain. But, again, a short
Domain-Specific Knowledge 13
quotation from his 1956 article is relevant. In this passage, Miller reported results concerning
absolute judgment of tones. After presenting some results that accorded with his argument, he
“Most people are surprised that the number is as small as six. Of course, there is
evidence that a musically sophisticated person with absolute pitch can identify accurately any
one of 50 or 60 different pitches. Fortunately, I do not have time to discuss these remarkable
exceptions. I say it is fortunate because I do not know how to explain their superior
performance. So I shall stick to the more pedestrian fact that most of us can identify about one
out of only five or six pitches before we begin to get confused.” (Miller, 1956, p. 84.) Of
course, as is the case for intelligence, expertise in the form of domain-specific knowledge can
explain these differing results between experts and novices.
The problem with the limited capacity of working / short term memory that seems to
disappear as a limit for some people should be linked to the way Miller thought about the
issue. He considered working / short term memory capacity as a general capacity, not
depending on the domain being tested. In fact, it is virtually impossible to measure working /
short term memory capacity in a “pure” fashion uninfluenced by knowledge held in long term
memory for whatever material is being used such as digits, words, letters, tones, pictures, etc.
There are huge differences between individuals’ knowledge held in long-term memory and
that is precisely what the pioneers of expertise psychology discovered in the late 1960’s (see
Domain-Specific Knowledge and Cognitive Development
Historically, the lack of an appropriate emphasis on biologically secondary, domain-
specific knowledge has also bedevilled the field of cognitive development, in particular,
Piaget’s stage theory of cognitive development. His stage theory (Piaget, 1972) documents a
series of cognitive stages through which children develop, beginning with the sensorimotor
Domain-Specific Knowledge 14
stage and progressing through the pre-operational and concrete operational stages culminating
in the formal operational stage. These stages indicate changes in the general ability of
children to engage in logical thought. Each stage was initially assumed to be domain-
independent (Piaget, 1928). The thought processes were assumed to progress in a fixed,
necessary sequence. Progress through the stages could vary in speed but not in sequence.
While the stage theory worked reasonably well, some apparent inconsistencies began to
appear. Piaget demonstrated that preoperational children have difficulty conserving number,
mass and volume. Objects that are spread out frequently are usually assumed by pre-
operational children to have increased in number, liquids poured into a differently shaped
container may be assumed to have altered in volume while solid objects whose shape changes
may be assumed to alter in mass. These errors, according to Piagetian theory, are due to the
predominance of perceptual over logical reasoning in preoperational children. In the next
stage, the concrete operational stage, logical reasoning becomes dominant and the errors are
no longer made.
The difficulty with this explanation is that the point at which the errors disappear,
varies. Children may, for example, conserve number earlier than they conserve mass. If we
assume that learning to conserve number, volume and mass are simply domain-specific
concepts that must be acquired, the fact that a child acquires them at different times is easily
explained. If we assume that the acquisition of these concepts is dependent on the
development of a biologically primary, domain-general ability to handle logic, their
appearance at different times in the same child becomes problematic.
The issue became overwhelming in the case of the ultimate developmental stage, formal
operational thought. Formal operational thought was assumed to develop at about 12-13 years
of age. It allows us to consider issues that may or may not exist except in our minds. We can
propose hypotheses in a scientifically appropriate fashion. Piaget initially tested for formal
Domain-Specific Knowledge 15
operational thought using children from some of the better schools in Geneva. The tasks
included asking children to set up valid experiments testing simple scientific hypotheses such
as establishing the factor or factors that determine the frequency of oscillation of a pendulum.
Formal operational children could accomplish this task successfully by altering one variable
at a time and observing its effect. Concrete operational children were more likely to vary
multiple variables simultaneously indicating their failure to understand the logic of hypothesis
Towards the end of his career, Piaget (1972), realised that there were serious problems
associated with formal operations. Using his scientific tasks as a test, many apparently
capable people seemed never to attain the formal operational stage. The solution, he
suggested, was not to abolish the notion of formal operations but rather, to only test for formal
operations in an area that a person had ability, interest and knowledge. In other words, we
cannot ignore domain-specific knowledge.
We would like to go a step further. Our acquired ability to reason logically is due to
biologically secondary, domain-specific knowledge. A person who is able to reason logically
in science may show no such ability in his or her personal life or in any areas outside of his or
her areas of science. Knowing that we should only test one variable at a time when
conducting a scientific experiment is critical. Outside of hypothesis testing, it may be
irrelevant, with other knowledge being pre-eminent.
The extent to which biologically secondary, domain-specific knowledge held in long-
term memory can explain skill that appears to be due to highly general abilities or teachable
general skills can be surprising. In the next section, we discuss research into expertise and
what that research tells us of the relation between biologically secondary, domain-specific
skill and biologically primary, domain-general skills.
Recognizing Domain-Specific Knowledge and Expertise
Domain-Specific Knowledge 16
Air traffic control and chess are probably the two most common areas where the effect
of domain-specific knowledge has been demonstrated. We will begin by discussing research
on air traffic control.
The Nature of Air Traffic Controller Expertise
Air traffic controller memory has been widely studied during the past 50 years (see
Bainbridge, 1975; Stein, Garland & Muller, 2007 for reviews). The first reported results that
we can find are those of Yntema (Yntema & Mueser, 1960, 1962; Yntema, 1963). Yntema’s
goal was to understand why “card players, air-traffic controllers, and people going about their
ordinary business demonstrate an ability to keep track of a number of things at once” (Yntema
& Mueser, 1960, p. 18). His hypothesis, in conformity with the times, was contrary to a
domain-specific knowledge hypothesis. Following Miller (1956), Yntema tested whether air
traffic controllers had an enhanced general ability to chunk information. Accordingly, he
tested air traffic controllers on laboratory tasks such as letters associated with shapes, colours,
signs, etc. The results indicated that air traffic controllers were no better at chunking
information than the general population.
Ten years later, Bisseret (1970) used the same kinds of tasks but approached them from
a different perspective: understanding performance at work using meaningful materials rather
than laboratory tasks unrelated to an enhanced knowledge base. His experiment included a
description of several aircraft with each description using seven variables. Two factors were
manipulated: The number of aircraft and the experience of the air traffic controller. He found
an increase in memory scores with an increase in experience. The average number of variable
values recalled was 22.8 for advanced air traffic control students and 30 for more expert
professionals, with both of these scores far in excess of Miller’s 7+/-2. Knowledge had a
dramatic effect on working memory.
Domain-Specific Knowledge 17
These effects on performance depending on levels of expertise provided an early
suggestion that working memory capacity depends on domain-specific knowledge. In a
personal communication, Bisseret provided an interpretation of his results 40 years later. He
indicated that he would have been better positioned to interpret his results had Ericsson and
Kintsch’s (1995) concept of long-term working memory (see below) been available to him at
the time of publication.
Why Chess Masters Win
Historically, the above work concerning the consequences of domain-specific
knowledge on cognition using aircraft controllers had minimal impact. The work on chess had
a much greater impact although the full implications of that work are still to be realised, we
believe. That work was initiated by De Groot.
De Groot’s work was first published in 1946 in Dutch and had a limited impact on the
field. It was re-published in 1965 in English. It had a substantial impact on the field of
cognition, especially after Chase and Simon’s (1973) work (see below), but only a limited
impact on issues associated with instructional design.
De Groot was concerned with the factors that allow chess masters to consistently defeat
lower ranked players. Chess is validly seen as a game of problem solving but the problem
solving factors that allow masters to defeat lower ranked players were obscure. One
possibility is that masters engage in a greater search in depth by considering more possible
moves ahead or a greater search in breadth by considering more alternative moves at each
choice point. We might expect that increased search would increase the possibility of finding
a good move but De Groot found no evidence of increased search by chess masters compared
to lower ranked players. Differential problem solving search did not distinguish masters from
other players.
Domain-Specific Knowledge 18
The only distinction De Groot could find between masters and lower ranked players was
in memory for board configurations taken from real games. Players were shown a board
configuration for 5 seconds before the board was removed and the players were asked to
replicate the configuration they had just seen. Masters were good at this task with a 70 – 80%
accuracy rate. Lower ranked players had an accuracy rate of 30 – 40%. Chase and Simon
(1973) replicated these results but in addition demonstrated that if random board
configurations were used, the difference between masters and lower ranked players
disappeared with all having a low success rate.
These results altered our view of human problem solving and, indeed, of human
cognition. Masters were superior to lower ranked players not because they had acquired
complex, sophisticated general problem solving strategies, nor general memory capacity, but
rather, because they had acquired an enormous domain-specific knowledge base consisting of
tens of thousands of problem configurations along with the best move for each configuration
(Simon & Gilmartin, 1973). No evidence, either before or after De Groot’s work has revealed
differential, general problem solving strategies, or indeed, any learned, domain-general
knowledge, that can be used to distinguish chess masters from lower ranked players. The only
difference between players that we have is in terms of domain-specific knowledge held in
long-term memory. Furthermore, no other difference is required to fully explain chess
problem solving skill.
In our view, these results provide some of the strongest evidence for the suggestion that
learned skill, especially problem-solving skill, derives primarily from the accumulation of a
large store of biologically secondary, domain-specific knowledge stored in long-term
memory. As far as we are aware, there is no evidence that learned problem solving skill in
chess derives from domain-general knowledge. Domain-general strategies such as means-
ends analysis (Newell & Simon, 1972) clearly exist and are presumably used by chess
Domain-Specific Knowledge 19
masters, but there is no body of evidence indicating that they are teachable. We suggest that
for evolutionary reasons, we have been selected for our ability to acquire domain-general
knowledge. Such knowledge is too important for us to not acquire it. As a consequence, we
may acquire domain-general knowledge automatically as biologically primary knowledge. If
so, we cannot be taught domain-general knowledge in educational institutions because it
already has been acquired.
Generalisation of the Work on Chess to Other Areas
Unsurprisingly, similar results have been obtained in a variety of other areas including
areas of greater interest than chess to the education research community. Findings indicating
that experts have a better memory for problem solving states than novices have been obtained
in areas such as understanding and remembering text (Chiesi, Spilich, & Voss, 1979),
electronic engineering (Egan & Schwartz, 1979), programming (Jeffries, Turner, Polson, &
Atwood, 1981), and algebra (Sweller & Cooper, 1985). Based on these results, competence in
any area requires knowledge of the problem states that can be found in the area along with the
best moves associated with those states. For complex, extensive areas, that knowledge may
consist of tens of thousands of problem states (Simon & Gilmartin, 1973). Those innumerable
problem states and the best moves associated with those states are stored in long-term
memory. It is that knowledge that constitutes expertise. We should at least consider the
possibility that such knowledge is all the teachable skill that is required for expertise and
Expertise Theory
Ericsson and his collaborators provided data and theory for the phenomena associated
with expertise and its reliance on domain-specific knowledge held in long-term memory.
Initially, the emphasis was on the outstanding performance of particular individuals on
memory test tasks such as memorising a list of dozens of randomly presented digits after one
Domain-Specific Knowledge 20
presentation (Chase & Ericsson, 1982). Contrary to popular opinion, studies indicated that the
techniques used by exceptional performers to memorise lists of random digits or random
letters are readily learnable. People who perform at a high level in memory tests are simply
experts in memory test tasks because they have domain-specific knowledge concerning these
tasks. Investigation of the strategies used indicated that they were domain-specific rather than
general (Ericsson & Charness, 1994). Learning to remember long strings of digits does not
transfer to learning to remember long strings of letters.
Subsequently, in work on deliberate practice, Ericsson and his collaborators
demonstrated that expertise in any substantial domain requires years of practice with the
intention of improving performance (Ericsson & Charness, 1994; Ericsson, Krampe, & Tesch-
Romer, 1993). It is likely to take a minimum of 10 years of practice to reach the highest levels
of performance such as attaining grand master status in chess. Interestingly, the three cashiers
who participated to Binet’s (1894) experiment indicated that a period of about 10 years was
required to reach their high levels of mental calculation. Due to the work of Ericsson and his
colleagues, it is reasonable to assume that during those 10 years, experts are acquiring
domain-specific knowledge held in long-term memory.
In effect, the work carried out by Ericsson and his colleagues indicated that the well-
known capacity and duration limits of working memory disappear when working memory
deals with familiar information from long-term memory. Working memory’s capacity and
duration limits apply only to novel, not familiar, information. From a theoretical perspective,
there are two ways of handling this fact. We can assume that working memory deals
differently with organised information stored in long-term memory compared to information
obtained from the environment that is yet to be organised. Alternatively, we can specify a
structure to deal with information from long-term memory that differs from short-term,
working memory. Ericsson and Kintsch (1995) chose to specify a new structure, long-term
Domain-Specific Knowledge 21
working memory to explain how working memory handles information from long-term
memory. Long-term working memory does not have the same capacity and duration limits as
short-term working memory. It may have no measurable limits.
Whether we subscribe to a working memory with differing characteristics depending on
the source of its information or separate structures to deal with environmental information and
information from long-term memory, the outcome is identical. In both cases, knowledge held
in long-term memory dramatically changes performance.
In sum, the psychology of expertise has shown that the major factor determining the
performance of experts is acquired, domain-specific knowledge. The more complex is the
domain, the more important is domain-specific knowledge. As indicated above, data on
expertise in areas such as chess can be fully explained by the assumption that the only factor
that alters as expertise develops is the accumulation of domain-specific knowledge held in
long term memory. As far as we are aware, there is no evidence that chess experts have
acquired some form of domain-general knowledge that permits them to play at such a high
level. There is every reason to suppose that the same cognitive factors apply to educationally
relevant curriculum areas.
According to Ericsson and Charness (1994), it probably took such a long time to
discover the importance of knowledge because we are fascinated by exceptional performance
and genius. This fascination may have led us to seek extraordinary explanations.
Nevertheless, Ericsson and Charness’ emphasis on the role of our fascination with genius may
only be partially correct because when considering non-exceptional people, the contribution
of domain-specific knowledge has also tended to be overshadowed by an assumption that
learners are also acquiring domain-general knowledge. In fact, we suggest that expertise in
complex areas can be fully explained by the acquisition of domain-specific knowledge.
From Expertise Research to Educational Psychology
Domain-Specific Knowledge 22
The influence of expertise research with its emphasis on domain-specific knowledge has
affected educational psychology and the process is ongoing. In this section, we look at the
changing role of biologically secondary, domain-specific knowledge.
Categorisation and the Representation of Physics Problems by Experts and Novices
Some of the earliest work concerning the effect of domain specific knowledge in
education was provided by Chi and her colleagues (Chi, Feltovich & Glaser, 1981). The 1981
study described four experiments devoted to problem solving in physics. Chi and her
colleagues examined the differences between experts and novices in problem representation,
i.e. “the cognitive structure corresponding to a problem, constructed by a solver on the basis
of his domain-related knowledge and its organization” (p. 122). Prior to the Chi et al. (1981)
paper, Simon and Simon (1978) and Larkin, McDermott, Simon and Simon (1980) had found
that novices work backwards from the goal on physics problems using a means-ends strategy
(Newell & Simon, 1972) in which problem solvers locate differences between a current
problem state and the goal state and search for problem solving operators to reduce those
differences while experts work forward from the givens. These results were interpreted as
indicating differences in problem solving strategies between experts and novices.
Chi was convinced that these differences between experts and novices in physics
problem solving could be interpreted in terms of representation (see Chi, 1993, for the genesis
of the Chi et al., 1981, article). She presented novices and experts with a task in which they
were presented with a variety of physics problems that they had to sort into categories. The
experts were advanced PhD students in physics and the novices were physics undergraduates.
The results showed that experts sorted the problems based on structural cues relevant to
problem solution while novices used superficial, physical cues. For example, novices might
group problems together because they included an inclined plane while experts were more
likely to group problems together because, for example, they all relied on conservation of
Domain-Specific Knowledge 23
energy for their solution. “The basic expert-novice result, that experts' knowledge is
represented at a "deep" level (however one characterizes "deep"), while novices' knowledge is
represented at a more concrete level, has been replicated in many domains, ranging from
knowledge possessed by scientists to taxi drivers” (Chi, 1993, p. 12).
The Chi et al. article emphasised the differences between experts and novices in
educationally relevant problems. In the field of problem solving, moving from puzzle
problems treated as a prototype for all problems, to educationally relevant problems was a
major step in recognizing the importance of domain-specific knowledge in education. We can
see the change by considering the Anzaï and Simon (1979) paper concerned with problem
solving using the Tower of Hanoï puzzle. There is no mention in this important paper
concerning the effects of knowledge on problem solving or on knowledge acquisition as a
factor in problem solving performance. The Chi et al. paper was one of the first to apply to
educationally relevant problems the information described above concerning the importance
of domain-specific knowledge on problem solving performance.
Schneider, Korkel and Weinert (1989) replicated the domain specific knowledge effect
in a very different way. They presented memory tasks and text comprehension to two groups
that differed in domain-specific knowledge and in verbal aptitude (vocabulary, sentence
completion, and word classifications) measured by a cognitive ability test. The participants
were soccer experts and novices. The results indicated that low aptitude experts outperformed
high-aptitude novices on all memory and comprehension measures. These results were
analogous to those obtained by Chi (1978) who found that younger, chess playing children
had a better memory for chess board configurations taken from real games than older children
with less knowledge of the game.
Chi’s work contributed to the body of evidence concerning the domain-specificity of
expert knowledge. It was particularly important because the subject matter, physics, was
Domain-Specific Knowledge 24
unambiguously educationally relevant and the novices and experts were all students with
differing levels of expertise rather than established experts. Following Chi’s work, several
studies took domain-specific knowledge into account by controlling it, but only a few focused
on analysing the effects of domain-specific knowledge on learning (see Fayol, 1994 for a
review; and more recently Amadieu, Tricot & Mariné, 2009; Duncan, 2007; Gijlers & de
Jong, 2005). A very limited number of studies have demonstrated the effect of domain-
specific knowledge when it is presented a few minutes before a main learning task (Mayer,
Mathias & Wetzel, 2007; Pollock, Chandler & Sweller, 2002). Thus, if domain-specific
knowledge is central to the intellectual performance of students, techniques designed to assist
students in acquiring domain-specific knowledge seemed to be a logical next step.
Cognitive Load Theory
If domain-specific knowledge held in long-term memory is central to learnable aspects
of intellectual performance, we might expect instructional design research and theories to
place their emphasis on the acquisition of biologically secondary, domain-specific knowledge.
One theory that places a heavy emphasis on the acquisition of domain-specific knowledge is
cognitive load theory (Chanquoy, Tricot & Sweller, 2007; Sweller, 2011, 2012; Sweller,
Ayres & Kalyuga, 2011).
Cognitive load theory was designed and has been continuously developed to account for
cognitive processes that facilitate the acquisition of domain-specific knowledge via new
instructional procedures. The current version of that cognitive architecture places a heavy
emphasis on biological evolution in two respects. First, it uses Geary’s evolutionary
educational psychology (Geary, 2008, 2012) to distinguish between biologically primary and
secondary knowledge. It is the cognitive architecture associated with biologically secondary
knowledge that is used by cognitive load theory. The information processes used by that
architecture are closely analogous to the information processes used by evolution by natural
Domain-Specific Knowledge 25
selection and that analogy provides the second way in which cognitive load theory relies on
evolutionary theory.
As applied to human cognition, the relevant information processes require: A store of
information in the form of a long-term memory holding very large amounts of domain-
specific information; Machinery to obtain that information from other people; The ability to
create novel information through a random generate and test process during problem solving;
A structure, working memory, to limit the amount of novel information that is acquired during
random generate and test to ensure that useful information held in long-term memory is not
destroyed, and lastly; Either a structure such as long-term working memory or processes to
allow information held in long-term memory to be brought into working memory to govern
knowledge-based activity. Together, these cognitive structures and processes constitute a
cognitive architecture that can be used to generate instructional procedures. (See Sweller &
Sweller, 2006, for details of the analogy between this cognitive architecture and evolution by
natural selection.) These instructional procedures are concerned entirely with facilitating the
acquisition of biologically secondary, domain-specific knowledge. Recent summaries of the
various cognitive load effects and their instructional implications can be found in Sweller
(2011, 2012). Detailed, comprehensive summaries may be found in Sweller, Ayres and
Kalyuga (2011) and will not be repeated here. While all of the effects are intended to facilitate
the acquisition of domain-specific knowledge, two of the effects, the worked example effect
and the expertise reversal effect, provide particularly good examples of the importance of
biologically secondary, domain-specific knowledge to instructional design issues. These two
effects will be discussed within a context of acquiring domain-specific knowledge.
The worked example effect. This effect is demonstrated when learners, provided
problems to solve, learn less than learners provided the same problems using a worked
example format. In a worked example, each problem is associated with a detailed solution.
Domain-Specific Knowledge 26
Despite solving fewer problems, on subsequent problem solving tests, the worked example
condition characteristically performs at a higher level. Why is this result obtained?
According to cognitive load theory, studying a worked example reduces extraneous (or
unnecessary) working memory load compared to having to search for a problem solution and
that reduction allows working memory resources to be devoted to learning to recognise
problem states associated with their appropriate moves. In other words, studying a worked
example is congruent with the biologically secondary, domain-specific knowledge hypothesis
that suggests that good problem solvers have learned to recognise a large number of problem
states and the best moves associated with each state. Work examples place their emphasis on
precisely those problem states and their moves, leading to the worked example effect.
Expertise reversal effect. The worked example effect occurs using novices in a
domain. As levels of expertise increase, the effect first disappears and then reverses with
problem solving proving superior to studying worked examples (Kalyuga, Chandler,
Tuovinen, & Sweller, 2001). While novices require worked examples to help them acquire the
domain-specific knowledge that is central to problem-solving skill, why are worked examples
deleterious to the acquisition of skill once levels of expertise increase?
More expert problem solvers have already acquired the knowledge necessary to solve a
given class of problems. They do not need to be shown how to solve such problems because
they do not need to engage in an extensive problem-solving search process to find a suitable
solution. Reading a worked example is a redundant activity (see Sweller, et al., 2011, for a
discussion of the redundancy effect) that increases extraneous cognitive load. Instead, learners
may need practice at solving the problems so that they can automatically recognise the
relevant problem states and their associated moves. For these reasons, worked examples are
needed by novices while problem solving is more important for more expert problem solvers
in a domain leading to the expertise reversal effect. Again, the effect was generated by
Domain-Specific Knowledge 27
cognitive load theory and relies on the central importance of biologically secondary, domain-
specific knowledge to skilled problem solving. (It should be noted that the expertise reversal
effect modifies a range of cognitive load effects, not just the worked example effect.)
Both the worked example effect and other associated effects such as the expertise
reversal effect are predicated on the assumption that the purpose of instruction is to allow
learners to acquire vast amounts of biologically secondary information stored in long-term
memory. It is assumed that that information transforms our cognitive processes and indeed,
transforms us. This assumption can be contrasted with alternative views of human cognition
that place a greater emphasis on the acquisition of domain-general knowledge (see Kirschner,
Sweller & Clark, 2006). We suggest it can be argued that domain-general information is
unteachable because it consists of biologically primary knowledge that is acquired easily and
automatically without instruction. We have evolved to acquire such knowledge.
We have argued that expertise based on biologically primary, domain-specific
knowledge held in long-term memory is by far the best explanation of performance in any
cognitive area. Furthermore, in contrast to domain-general cognitive knowledge, there is no
dispute that domain-specific knowledge and expertise can be readily taught and learned.
Indeed, providing novice learners with knowledge is the main role of schools. We might
guess that most school teachers in most schools continue to emphasise the domain-specific
knowledge that always has been central, making little attempt to teach domain-general
knowledge. Based on our argument, they should continue to do so. At school, children acquire
knowledge that overcomes the need to engage in inefficient problem solving search and other
cognitive processes. That knowledge, allows people to function in a wide variety of tasks
outside of school. Given the overwhelming importance of domain-specific knowledge, indeed
its sole importance if the argument presented in this paper is valid, it is puzzling that our field
Domain-Specific Knowledge 28
has tended to place considerable emphases elsewhere for most of its existence as an area of
research. There are several possible reasons.
At any given time, we are unaware of the huge amount of domain specific knowledge
held in long-term memory. The only knowledge that we have direct access to and are
conscious of must be held in working memory. Knowledge held in working memory tends to
be an insignificant fraction of our total knowledge base. With access to so little of our
knowledge base at any given time, it is easy to assume that domain-specific knowledge is
relatively unimportant to performance. It may be difficult to comprehend the unimaginable
amounts of organised information that can be held in long-term memory precisely because
such a large amount of information is unimaginable. If we are unaware of the large amounts
of information held in long-term memory, we are likely to search for alternative explanations
of knowledge-based performance. Those alternatives tend to consist of domain-general
strategies. We have suggested that such strategies are likely to be unteachable because they
are too important for humans not to acquire. As a consequence, we have evolved to acquire
very general strategies easily and quickly as biologically primary knowledge.
Not only is the amount of domain-specific knowledge held in long-term memory hidden
from us, the nature of that knowledge tends to be hidden from us as well. We may know that
we have learned Pythagoras’ theorem because it is explicitly learned. We may not know that
we must also learn to recognise the various problem states to which the theorem applies and
that knowledge may be considerably more extensive and difficult to learn than simply
learning the theorem itself because the problem states to which the theorem applies are
effectively infinite. Based on the current, predominant literature, it is still easy to assume, for
example, that learning mathematics involves no more than learning the rules of mathematics
or learning to play chess is no more than just learning the rules of chess. Mathematicians and
chess players are fully aware that they need to learn the appropriate rules in order to function
Domain-Specific Knowledge 29
in their area. They may be quite unaware of what else needs to be learned in order to function
at a high level. It may not be surprising that in the absence of information concerning the
extensive knowledge of problem states and their moves, hypotheses associated with
frequently unnamed and undescribed general cognitive strategies arose instead. It took us a
very long time to discover exactly what is learned when dealing with a substantial domain.
Once we have learned a substantial domain, we tend to forget how difficult and how
long it took us to learn it. As many secondary teacher trainers can testify, it can be difficult to
convince trainees that they should not enter their first classroom and attempt to tell students
everything they have learned about a particular topic in 45 minutes. Once we have learned
something, we tend to assume it is simple and obvious (because it is simple and obvious for
us) and forget how complex and difficult it was to learn.
For these reasons, the extent, complexity, difficulty and sheer time needed to acquire
domain-specific knowledge can be hidden from us. Suggestions that domain-specific
knowledge held in long-term memory may be all that is needed to explain very high and very
sophisticated levels of performance may appear to be counter-intuitive. Instead, complex but
frequently unspecified cognitive strategies may appear to be the main drivers of our cognitive
processes. While sophisticated, general strategies are likely to exist, we should expect them to
be biologically primary.
The search for powerful, general strategies that transform and enhance our performance
can provide an irresistible siren-call but such strategies, because of their importance and
power, are likely to be biologically primary and so automatically acquired without assistance
from instructors. Humans are likely to have evolved to acquire important cognitive strategies
and do so easily and automatically. In contrast, biologically secondary information is rarely
obtained easily or automatically. We should at least consider the possibility that all learning
of the biologically secondary information that is central to modern education is based on the
Domain-Specific Knowledge 30
acquisition of domain-specific rather than domain-general knowledge. If so, an appropriate
role for cognitive processes and instructional design researchers is to devise techniques to
assist students to acquire this domain-specific knowledge rather than already learned generic
skills. As indicated in the previous section, such a strategy can lead to novel instructional
Domain-Specific Knowledge 31
Ackerman, P. L. (2000). Domain-specific knowledge as the "Dark matter" of adult
intelligence: Gf/Gc, personality and interest correlates. Journals of Gerontology Series
B-Psychological Sciences and Social Sciences, 55, P69-P84.
Amadieu, F., Tricot, A., & Mariné, C. (2009). Effects of prior knowledge diversity on
learning with a non-linear electronic document: disorientation and coherence of the
reading sequence. Computers in Human Behavior, 25, 381-388. doi:
Anzai, Y., & Simon, H. A. (1979). Theory of learning by doing. Psychological Review, 86,
124-140. doi: 10.1037//0033-295x.86.2.124
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its
control processes. In K.W. Spence & J. T. Spence (Eds.), The psychology of learning
and motivation: Advances in research and theory (Vol. 2, pp. 89-195). New York:
Academic Press.
Baddeley, A.D., & Hitch, G. (1974). Working memory. In G.H. Bower (Ed.), The psychology
of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New
York: Academic Press.
Bainbridge, L. (1975). Working memory in air-traffic control. Unpublished paper, University
of Reading. Retrieved December 11, 2011, from
Binet, A. (1892). Le calculateur Jacques Inaudi [The calculator Jacques Inaudi]. Revue des
deux Mondes, 111, 905-924.
Binet, A. (1894). Psychologie des grands calculateurs et joueurs d'échecs [Psychology of
great calculators and chess players]. Paris: Hachette.
Domain-Specific Knowledge 32
Bisseret, A. (1970). Mémoire opérationelle et structure du travail [Working memory and work
structure]. Bulletin de Psychologie, 24, 280-294. English summary published in 1971:
Analysis of mental processes involved in air traffic control. Ergonomics, 14, 565-570.
Brinch, C. N. (2012). Schooling in adolescence raises IQ scores. Proceedings of The National
Academy of Sciences of The United States of America, 109, 425–430. doi:
Cahan, S., & Cohen, N. (1989). Age versus schooling effects on intelligence development.
Child Development, 60, 1239-1249. doi: 10.1111/j.1467-8624.1989.tb03554.x
Chanquoy, L., Tricot, A., & Sweller, J. (2007). La charge cognitive. Paris: Armand Colin.
Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In G. H. Bower (Ed.),
The psychology of learning and motivation (Vol. 16, pp. 1-58). New York: Academic
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55-81.
doi: 10.1016/0010-0285(73)90004-2
Chi, M. T. H. (1978). Knowledge structures and memory development. In R. Siegler (Ed.),
Children's thinking: What develops? (pp. 73-96). Hillsdale, NJ: Erlbaum.
Chi, M. T. H. (1993). Experts vs novices knowledge - a citation-classic commentary on
categorization and representation of physics problems by experts and novices by Chi,
M.T.H., Feltovich, P., Glaser, R. Current Contents/Social & Behavioral Sciences, 42, 8-
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of
physics problems by experts and novices. Cognitive Science, 5, 121-152. doi:
Domain-Specific Knowledge 33
Chiesi, H. L., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain-related information
in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal
Behavior, 18, 257-273. doi: 10.1016/s0022-5371(79)90146-4
Cliffordson, C., & Gustafsson, J. E. (2008). Effects of age and schooling on intellectual
performance: Estimates obtained from analysis of continuous variation in age and
length of schooling. Intelligence, 36, 143-152. doi: 10.1016/j.intell.2007.03.006
Conway, A. R. A., Jarrold, C., Kane, M. J., Miyake, A., & Towse, J. (Eds.), (2007). Variation
in working memory. Oxford University Press.
Cowan, N. (2005). Working memory capacity. Hove: Psychology Press.
De Groot, A. (1965). Thought and choice in chess. The Hague, Netherlands: Mouton.
(Original work published 1946).
Dehaene, S., (1997). The number sense. New York: Oxford University Press.
Duncan, R. G. (2007). The role of domain-specific knowledge in generative reasoning about
complicated multileveled phenomena. Cognition & Instruction, 25, 271-336.
Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory &
Cognition, 7, 149-158. doi: 10.3758/bf03197595
Ericsson, K. A. (1985). Memory skill. Canadian Journal of Psychology, 39, 188-231. doi:
Ericsson, K. A., & Charness, N. (1994). Expert performance - its structure and acquisition.
American Psychologist, 49, 725-747. doi: 10.1037/0003-066x.49.8.725
Ericsson, K. A., & Chase, W. G. (1982). Exceptional memory. American Scientist, 70, 607-
Ericsson, K. A., & Kintsch, W. (1995). Long-term working-memory. Psychological Review,
102, 211-245. doi: 10.1037//0033-295x.102.2.211
Domain-Specific Knowledge 34
Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of
maximal adaptation to task constraints. Annual Review of Psychology, 47, 273-305. doi:
Ericsson, K. A., Krampe, R. T., & Teschromer, C. (1993). The role of deliberate practice in
the acquisition of expert performance. Psychological Review, 100, 363-406. doi:
Fayol, M. (1994). From declarative and procedural knowledge to the management of
declarative and procedural knowledge. European Journal of Psychology of Education,
9, 179-190.
Flynn, J. R. (2007). What is intelligence? Beyond the Flynn effect. Cambridge, UK:
Cambridge University Press
Geary, D. C. (2012). Evolutionary educational psychology. In K. Harris, S. Graham & T.
Urdan (Eds.), APA Educational Psychology Handbook (Vol. 1, pp. 597-621).
Washington, D.C.: American Psychological Association.
Geary, D. C. (2008). An evolutionarily informed education science. Educational
Psychologist, 43, 179-195. doi: 10.1080/00461520802392133
Gijlers, H., & de Jong, T. (2005). The relation between prior knowledge and students'
collaborative discovery learning processes. Journal of Research in Science Teaching,
42, 264-282. doi: 10.1002/tea.20056
Greiff, S, Wüstenberg, S, Molnar, G, Fischer, A, Funke, J, & Csapo, B. (2013). Complex
Problem Solving in educational settings – something beyond g: Concept, assessment,
measurement invariance, and construct validity. Journal of Educational Psychology,
105, 364-379. doi: 10.1037/a0031856
Herrnstein, R.J., & Murray, C. (1994). The Bell Curve: Intelligence and class structure in
American life. New York: Free Press.
Domain-Specific Knowledge 35
Jeffries, R., Turner, A., Polson, P., & Atwood, M. (1981). Processes involved in designing
software. In J. R. Anderson (Ed.), Cognitive skills and their acquisition (pp. 255-283).
Hillsdale, NJ: Erlbaum.
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is
superior to studying worked examples. Journal of Educational Psychology, 93, 579-
588. doi: 10.1037/0022-0663.93.3.579
Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does
not work: An analysis of the failure of constructivist, discovery, problem-based,
experiential and inquiry-based teaching. Educational Psychologist, 41, 75-86.
Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Models of competence in
solving physics problems. Cognitive Science, 4, 317-345. doi:
Mandelbaum, E. (2013). Numerical architecture. Topics in Cognitive Science, 5, 367-386.
DOI: 10.1111/tops.12014
Mayer, R. E., Mathias, A., & Wetzell, K. (2002). Fostering understanding of multimedia
messages through pre-training: Evidence for a two-stage theory of mental model
construction. Journal of Experimental Psychology-Applied, 8, 147-154. doi:
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our
capacity for processing information. Psychological Review, 63, 81–97.
Miller, G. A., Galanter, E. & Pribram, KH. (1960). Plans and the structure of behavior. New
York: Holt, Rinehart & Winston.
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., et al. (1996).
Intelligence: Knowns and unknowns. American Psychologist, 51, 77-101. doi:
Domain-Specific Knowledge 36
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice
Nicolas, S., Gounden, Y., & Levine, Z. (2011). The memory of two great mental calculators :
Charcot and Binet’s neglected 1893 experiments. American Journal of Psychology, 124,
Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the
human motor system and collaboration to support the learning of complex cognitive
tasks. Educational Psychology Review, 24, 27-45. doi: 10.1007/s10648-011-9179-2
Piaget, J. (1972). Intellectual evolution from adolescence to adulthood. Human Development,
15, 1-12.
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning
& Instruction, 12, 61-86. doi: 10.1016/s0959-4752(01)00016-0
Rikers, R.M.J.P. (2009). Why is not everyone Albert Einstein? Implications of expertise
research for educational practice. Cognitive Load Theory Conference, Open University
of the Netherlands, Heerlen, March 2-4.
Schneider, W., Korkel, J., & Weinert, F. E. (1989). Domain-specific knowledge and memory
performance: A comparison of high- and low-aptitude children. Journal of Educational
Psychology, 81, 306-312. doi: 10.1037/0022-0663.81.3.306
Simon, D.P., & Simon, H.A. (1978). Individual differences in solving physics problems. In R.
Siegler (Ed.), Children’s thinking: What develops? (pp. 325–348). Hillsdale, NJ:
Simon, H. A., & Gilmarti, K. (1973). Simulation of memory for chess positions. Cognitive
Psychology, 5, 29-46. doi: 10.1016/0010-0285(73)90024-8
Domain-Specific Knowledge 37
Stein, E.S., Garland, D.J., & Muller, J.K. (2010). Air-traffic controller memory. In J.A. Wise,
V.D. Hopkin, & D.J. Garland (Eds.). Handbook of aviation human factors (2nd
Edition). (pp. 21-1 – 21-39). Boca Raton: CRC Press.
Stelzl, I., Merz, F., Ehlers, T., & Remer, H. (1995). The effect of schooling on the
development of fluid and crystallized intelligence: A quasi-experimental study.
Intelligence, 21, 279-296. doi: 10.1016/0160-2896(95)90018-7
Sweller, J. (2011). Cognitive load theory. In J. Mestre & B. Ross (Eds.), The psychology of
learning and motivation: Cognition in education (Vol. 55, pp. 37-76). Oxford:
Academic Press.
Sweller, J. (2012). Human Cognitive Architecture: Why some instructional procedures work
and others do not. In K. Harris, S. Graham & T. Urdan (Eds.), APA Educational
Psychology Handbook (Vol. 1, pp. 295-325). Washington, D.C.: American
Psychological Association.
Sweller, J., & Cooper, G. (1985). The use of worked examples as a substitute for problem
solving in learning algebra. Cognition & Instruction, 2, 59-89.
Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary
Psychology, 4, 434-458.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.
Youssef, A., Ayres, P., & Sweller, J. (2012). Using General Problem-solving Strategies to
Generate Ideas in Order to Solve Geography Problems. Applied Cognitive Psychology,
26, 872–877. DOI: 10.1002/acp.2888
Yntema, D. B. (1963). Keeping track of several things at once. Human Factors, 5, 7-17.
Yntema, D. B., & Mueser, G. E. (1960). Remembering the present states of a number of
variables. Journal of Experimental Psychology, 60, 18-22. doi: 10.1037/h0040055
Domain-Specific Knowledge 38
Yntema, D. B., & Mueser, G. E. (1962). Keeping track of variables that have few or many
states. Journal of Experimental Psychology, 63, 391-395. doi: 10.1037/h0045706
... Ces connaissances sont générales car elles sont mobilisables (transférables) dans des contextes variés sans avoir été enseignées. C'est d'ailleurs parce qu'elles sont d'une importance si cruciale pour l'espèce, qu'elles s'acquièrent naturellement ; il ne serait d'ailleurs pas possible de les enseigner(Tricot & Sweller, 2014 ;). Un apprentissage conscient est néanmoins nécessaire pour utiliser les connaissances primaires dans des domaines spécifiques conduisant aux connaissances secondaires. ...
... Ainsi les connaissances secondaires peuvent et doivent être apprises et enseignées, elles sont spécifiques au domaine et, de fait, se généralisent (ou se transfèrent) difficilement, tandis que les connaissances primaires sont génériques, elles s'acquièrent et se généralisent naturellement(Tricot & Sweller, 2014). La distinction des connaissances primaires et secondaires est devenue un élément central de la théorie de la charge cognitive car elle fournit des voies à exploiter pour comprendre les processus d'apprentissage et les faciliter(Sweller, 2011 ;, la première voie étant de ne plus considérer comme allant de soi le transfert de compétences enseignées considérées comme générales.La programmation est souvent considérée comme un moyen de développer des compétences générales, telles que la résolution de problèmes, mais ce sont pourtant les habiletés spécifiques qu'elle entraîne(Galand ;, en cohérence avec la littérature précédemment exposée. ...
L’objectif de cette thèse est d’évaluer les effets de la pratique précoce de la programmation visuelle avec Scratch sur les performances en mathématiques, l’anxiété en mathématiques (Ashcraft & Kirk, 2001; Ashcraft & Krause, 2007; Ashcraft & Moore, 2009; Hembree, 1990), le sentiment de compétence en mathématiques (Harter, 1985) et la motivation autodéterminée en mathématiques (Guay et al., 2010 ; Ryan & Deci, 2000) dans le cadre du projet Expire (Expérimenter la Pensée Informatique pour la Réussite des Elèves, projet e-fran 2017). Ce projet fait écho à la demande internationale d’introduire la pensée informatique dans les programmes scolaires (Bocconi et al., 2016 ; Tang et al., 2019 ; Wing & Stanzione, 2016) afin de doter les élèves des habiletés numériques indispensables à l’insertion dans une société de plus en plus numérique et favoriser leurs apprentissages, particulièrement en mathématiques, en raison de la proximité des processus cognitifs impliqués dans ces deux domaines (Scherer, 2016 ; Shute et al., 2017). La programmation, considérée comme un moyen d'enseigner, évaluer et exposer les étudiants à la pensée informatique, est massivement introduite au sein des programmes de mathématiques au primaire, comme c’est le cas en France (Bocconi et al., 2016).Nous testons les hypothèses que les performances ainsi que les variables conatives citées précédemment seront positivement influencées par l’utilisation de la programmation en classe. Pour cela nous avons mis en oeuvre un ECR auprès de 2472 élèves du bassin grenoblois en CM1 et CM2, recrutés en 2017-2018. Le groupe expérimental, « programmation » réalise des activités de programmation sur trois séquences d’apprentissage mathématiques tandis que le groupe « contrôle » met en oeuvre une pédagogie classique d’apprentissage pour ces mêmes séquences. Des mesures conatives en amont et en aval de l’expérimentation ainsi que des scores de réussite en mathématiques ciblés sur les notions travaillées ont été prises en pré et post-test pour chaque séquence. Des analyses multi-niveaux nous ont permis de rendre compte d’une détérioration des apprentissages au cours des différentes séquences sous l’effet de la pratique expérimentale. Nous n’avons montré aucun effet de cette pratique sur nos variables conatives.
... Experts can use an extensive library of schemas stored in long-term memory to solve issues more quickly than novices (Ayres & Paas, 2012;Kalyuga, 2009;Kalyuga et al., 2003;Kirschner, 2002). Nevertheless, experts perform worse than novices when given the same scaffolded or directed material as novices, such as worked examples, because of the expertise reversal effect (Blayney et al., 2015;Kalyuga et al., 2003;Tricot & Sweller, 2014). The expertise reversal effect happens when an expert receives too much redundant information (such as too much direction when solving problems), and it overwhelms their working memory (Kalyuga et al., 2003;Plass et al., 2010;Schnotz, 2010;Tricot & Sweller, 2014). is essential to provide the learner/student with the information in written form to prevent transient effects from happening. ...
... Nevertheless, experts perform worse than novices when given the same scaffolded or directed material as novices, such as worked examples, because of the expertise reversal effect (Blayney et al., 2015;Kalyuga et al., 2003;Tricot & Sweller, 2014). The expertise reversal effect happens when an expert receives too much redundant information (such as too much direction when solving problems), and it overwhelms their working memory (Kalyuga et al., 2003;Plass et al., 2010;Schnotz, 2010;Tricot & Sweller, 2014). is essential to provide the learner/student with the information in written form to prevent transient effects from happening. ...
... Experts are able to use a large library of schemas stored in long-term memory to solve issues more quickly than novices (Ayres & Paas, 2012;Kalyuga, 2009;Kalyuga et al., 2003;Kirschner, 2002). Nevertheless, experts perform worse than novices when given the same scaffolded or directed material as novices, such as worked examples, because of the expertise reversal effect (Blayney et al., 2015;Kalyuga et al., 2003;Tricot & Sweller, 2014). The expertise reversal effect happens when an expert receives too much redundant information (such as too much direction when solving problems) and it overwhelms their working memory (Kalyuga et al., 2003;Plass et al., 2010;Schnotz, 2010;Tricot & Sweller, 2014). ...
... Nevertheless, experts perform worse than novices when given the same scaffolded or directed material as novices, such as worked examples, because of the expertise reversal effect (Blayney et al., 2015;Kalyuga et al., 2003;Tricot & Sweller, 2014). The expertise reversal effect happens when an expert receives too much redundant information (such as too much direction when solving problems) and it overwhelms their working memory (Kalyuga et al., 2003;Plass et al., 2010;Schnotz, 2010;Tricot & Sweller, 2014). ...
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In today's globalized environment, learning a language is essential. It is crucial to offer the best strategies for learning a foreign language as a result. More efficient teaching methods could be developed by comprehending how participants' physiological sex and cognitive load theory that connect to one another. This study summarizes some of the findings from studies on the delivery of instruction via digital media in the context of the cognitive load of multimedia learning. Several pedagogical implications are also discussed.
... Self-regulated learning strategies may encompass planning, monitoring progress, evoking prior knowledge, help-seeking, integrating sources of information, creating visual or verbal representations, predicting future performance, and so forth (Zimmerman and Pons, 2016;Fong et al., 2021). In general, these skills are domain-general, impose low mental load (if any), and might be flexibly applied to a variety of tasks (e.g., math and biology) already known (Tricot and Sweller, 2014). However, applying these strategies may be very demanding for novice students, especially when the tasks are complex, the strategy requires other specific knowledge (e.g., learning software to create maps or organize domain concepts), there is little time for study, or it is not clear which strategy is more effective for the task (e.g., narrated or written self-explanations for learning The independence of Latin America) (Van Gog et al., 2011;Baars et al., 2017;Dong et al., 2020). ...
... This lack of consistency may be explained by the characteristics of both self-regulated learning and learning tasks. Mental activities such as planning, monitoring, or relating sources of information seem to be generic skills that do not impose a high mental load when the content or information being processed is already known (Geary, 2008;Tricot and Sweller, 2014). Nevertheless, learning new information or solving problems, even mentally undemanding, requires basic strategies, and some social regulation that includes verbal explanations, reviews to understand, and visual and verbal elaborations to reduce working memory load (Nückles et al., 2020;Roelle et al., 2022). ...
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Measurement of self-regulated learning through self-report questionnaires can help improve educational efforts. The Deep Learning Strategies Questionnaire has been recently validated, which consists of 30 items and four scales: basic self-regulated learning strategies, visual elaboration and summary strategies, deep information processing strategies, and self- regulated social learning strategies. We examined the characteristics of the questionnaire with 694 Ecuadorian students. The exploratory factor analysis resulted in four factors, like the original model. However, the factors identified as basic and social learning strategies included items of visual elaboration and summary and deep processing strategies. Further group comparisons showed that participants with high school finished used fewer visual and verbal elaboration strategies than those with higher education levels and that males use more deep information processing strategies than females. We discuss the difficulty of separating self-regulated learning strategies and conclude with suggestions for future research and recommendations for educational practice.
... PST 6 rejected what he/she described as "traditional didactic manner". In this regard PST 6 manifests the view that principles of learning, potentially emphasising facilitation and co-construction (alignment with "teaching pedagogy evidence"), are preferable to a more didactic approach favoured by the author and East Asian educators (e.g., Huang & Leung, 2004;Lai & Murray, 2012;Leung et al., 2015;Li, 2004), meta-data analysts (e.g., Hattie, 2009;Hattie & Donoghue, 2016), and cognitive load theorists (e.g., Chen et al., 2016;Kirschner, Sweller, & Clark, 2006;Sweller, 2016;Tricot & Sweller, 2014). It needs to be noted, however, that while the views of PST 6 provide a reasonably coherent justification for existing generic mathematics teacher education programs and curriculum courses/subjects, they are counter to those expressed by the majority of the pre-service teachers enrolled in the mathematics course informing this study. ...
... Providing guidance for scientists to do this type of work is essential, as training is crucial to ensure success, especially when the new skill involves domain-specific knowledge (Tricot & Sweller, 2014). Without adequate training and support, scientists risk being frustrated or feeling as if their efforts are inadequate or even wasted (Falloon & Trewern, 2013;Laursen et al., 2007;Simis et al., 2016). ...
Conference Paper
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The way science is taught in school does not reflect how scientific research proceeds in universities and research institutions. Connecting scientists with K-12 students via creating instructional materials related to current research may help bridge this gap. The purpose of this qualitative case study is to investigate if a scientist-teacher partnership can support scientists in creating a quality lesson plan. Using a Theory of Planned Behavior framework, this study looks at three cases of graduate student scientists and attempts to characterize how they work with K-12 science teachers to develop a lesson plan that can be implemented in the classroom. It also seeks to understand the impacts of this collaboration on the scientists' attitudes and sense of self-efficacy regarding curricular material creation. The study found that scientists and teachers primarily worked together asynchronously, with teachers providing resources and feedback through email. Scientists sought out support primarily at the outset, with teachers helping to brainstorm how the scientists' ideas could be adapted to classrooms and made to fit science standards. They got feedback on their final product at the end of the process. There is evidence that these partnerships contributed to the scientists' self-efficacy and impacted their attitudes toward the behavior, but further research is needed to support this assertion. Implications for best practices for future partnerships are also discussed.
... According to Tricot and Sweller [58] we should distinguish domain-general information, a form of biologically primary knowledge naturally acquired without instruction (such as fluid abilities), from biologically secondary, domain-specific knowledge which can be taught, learned and memorized to a point where it provides enough guidelines to achieve in various learning areas (such as knowledge about numbers and literacy) [59]. They maintain that the goal of any education enterprise should be to foster domain-specific knowledge rather than improving generic skills. ...
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The present study documented in two distinct population-based samples the contribution of preschool fluid and crystallized cognitive abilities to school achievement in primary school and examined the mediating role of crystallized abilities in this sequence of predictive associations. In both samples, participants were assessed on the same cognitive abilities at 63 months (sample 1, n = 1072), and at 41 and 73 months (sample 2, n = 1583), and then with respect to their school achievement from grade 1 (7 years) to grade 6 (12 years). Preschool crystallized abilities were found to play a key role in predicting school achievement. They contributed substantially to school achievement in the early school years, but more modestly in the later years, due to the strong auto-regression of school achievement. They also mediated the association between fluid abilities and later school achievement in the early grades of school, with the former having modest direct contribution to the latter in the later grades. These findings are discussed regarding their implication for preventive interventions.
... Thus, we tentatively suggest that longitudinal data will yield effects of a similar nature to those found herein. Second, although our fluid reasoning data were based on a test tapping into key aspects of the higher-order factor of general mental ability ("g"; Carroll, 1993;Cattell, 1987), it will be important to expand on this measure to assess related cognitive factors predictive of academic outcomes such as broad general knowledge (i.e., "crystallized intelligence"; Cattell, 1987) and domain-specific prior knowledge (Tricot & Sweller, 2014), as well as personality traits (Ginns et al., 2014). We also point out that the survey's time constraints meant our fluid reasoning measure was somewhat brief and restricted to 30 s per item. ...
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This study drew on Job Demands-Resources theory and data from 500 Australian university students to investigate the role of COVID-related lockdown, perceived adaptability, and fluid reasoning in students' self-efficacy—and the role of these factors in students' engagement and disengagement. Lockdown was associated with higher disengagement; perceived adaptability was associated with higher self-efficacy; and both perceived adaptability and fluid reasoning were significantly and positively associated with engagement. Self-efficacy significantly mediated the relationship between perceived adaptability and engagement and disengagement, while moderation tests revealed that fluid reasoning yielded a significant positive role for the self-efficacy of students in lockdown. These findings shed light on factors during COVID-19 that are implicated in students’ academic development and provide direction for psycho-educational interventions.
The world of education is caught up in trends that affect all corners of contemporary society. Those who are working in the sciences have to help students learn in this new environment of fast-paced expectations and information overload. The intention of this chapter is to present a set of principles—or axioms—about learning that have direct bearing on instructional utilization of technology in the classroom. This will, in turn, provide a guide to an effective integration of technology that promotes learning that lasts within the sciences. The principles introduced herein balance knowledge of cognition as well as application in K12 science classrooms. The principles range from moderation to when to appropriately utilize technology for enhancing science learning to precision and extension in which emphasis is placed upon in what manner to focus technology integration to enhance science learning and also a principle of timely application that clarifies not just ‘when' in terms of content but also in terms of what skills and knowledge are in place among the students.
This study defined Task Knowledge and Learning-Process Knowledge based on current concepts regarding instructors' knowledge. It examined whether instructors' acquisition of each knowledge type changes their desirability judgments on educational methods involving students' activities or teachers' guidance. In a pretest-posttest design experiment, university students were presented with a complex mathematical problem and ten questionnaire items; half of these pertained to classroom situations involving students' activities and half teachers' guidance. Participants in the Task Knowledge condition (N=147) were provided with the solution steps and correct answer for the problem. Those in the Learning-Process Knowledge condition (N=136) were provided with examples of fifth-grade students' incorrect answers and the appearance rates of each answer pattern. Both groups assessed the problem difficulty level for fifth-grade public elementary school students with average academic ability and rated the educational desirability for the ten questionnaire items. Results indicated that participants in both conditions evaluated the problem difficulty level to be higher in the posttest than in the pretest. However, in the two conditions, their desirability judgments changed differently. The Task Knowledge condition participants scored the teachers' guidance items higher in the posttest than the pretest, whereas the Learning-Process Knowledge condition participants scored the students' activities items higher.
Cognitive Load Theory John Sweller, Paul Ayres, Slava Kalyuga Effective instructional design depends on the close study of human cognitive architecture—the processes and structures that allow people to acquire and use knowledge. Without this background, we might recognize that a teaching strategy is successful, but have no understanding as to why it works, or how it might be improved. Cognitive Load Theory offers a novel, evolutionary-based perspective on the cognitive architecture that informs instructional design. By conceptualizing biological evolution as an information processing system and relating it to human cognitive processes, cognitive load theory bypasses many core assumptions of traditional learning theories. Its focus on the aspects of human cognitive architecture that are relevant to learning and instruction (particularly regarding the functions of long-term and working memory) puts the emphasis on domain-specific rather than general learning, resulting in a clearer understanding of educational design and a basis for more effective instructional methods. Coverage includes: • The analogy between evolution by natural selection and human cognition. • Categories of cognitive load and their interactions in learning. • Strategies for measuring cognitive load. • Cognitive load effects and how they lead to educational innovation. • Instructional design principles resulting from cognitive load theory. Academics, researchers, instructional designers, cognitive and educational psychologists, and students of cognition and education, especially those concerned with education technology, will look to Cognitive Load Theory as a vital addition to their libraries.
We describe a set of two computer‐implemented models that solve physics problems in ways characteristic of more and less competent human solvers. The main features accounting for different competences are differences in strategy for selecting physics principles, and differences in the degree of automation in the process of applying a single principle. The models provide a good account of the order in which principles are applied by human solvers working problems in kinematics and dynamics. They also are sufficiently flexible to allow easy extension to several related domains of physics problems.
Working memory - the ability to keep important information in mind while comprehending, thinking, and acting - varies considerably from person to person and changes dramatically during each person's life. Understanding such individual and developmental differences is crucial because working memory is a major contributor to general intellectual functioning. This volume offers an understanding variation in working memory by presenting comparisons of the leading theories. It incorporates views from the different research groups that operate on each side of the Atlantic, and covers working-memory research on a wide variety of populations, including healthy adults, children with and without learning difficulties, older adults, and adults and children with neurological disorders. Each research group explicitly addresses the same set of theoretical questions, from the perspective of both their own theoretical and experimental work, and from the perspective of relevant alternative approaches. Through these questions, each research group considers their overarching theory of working memory, specifies the critical sources of working memory variation according to their theory, reflects on the compatibility of their approach with other approaches, and assesses their contribution to general working-memory theory. This shared focus across chapters unifies the volume and highlights the similarities and differences among the various theories. Each chapter includes both a summary of research positions and a detailed discussion of each position.
The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.
Professor James Flynn is one of the most creative and influential psychologists in the field of intelligence. The ‘Flynn Effect’ refers to the massive increase in IQ test scores over the course of the twentieth century and the term was coined to recognize Professor Flynn’s central role in measuring and analyzing these gains. For over twenty years, psychologists have struggled to understand the implications of IQ gains. Do they mean that each generation is more intelligent than the last? Do they suggest how each of us can enhance our own intelligence? Professor Flynn is finally ready to give his own views. He asks what intelligence really is and gives a surprising and illuminating answer. This book bridges the gulf that separates our minds from those of our ancestors a century ago. It is a fascinating and unique book that makes an important contribution to our understanding of human intelligence.