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[To appear in T. Redman (in press), Education and chess]
Training in chess: A scientific approach
Fernand Gobet
University of Nottingham
Peter J. Jansen
Carnegie Mellon University
Address correspondence to:
Dr. Fernand Gobet
ESRC Centre for Research in Development, Instruction and Training
School of Psychology
University of Nottingham
Nottingham NG7 2RD
United Kingdom
+ 44 (115) 951 5402 (phone)
+ 44 (115) 951 5324 (fax)
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Training in chess: A scientific approach
Fernand Gobet & Peter J. Jansen
Research in psychology has made important progress towards unraveling the secrets
of chess players’ minds. It is the goal of this chapter to show how recent findinds in
cognitive psychology can be applied to improve techniques of chess training, teaching,
and learning. We start by giving an overview of the state of research on chess players’
memory, perception and problem solving. In Section 2, we review a recent
psychological theory that synthesizes previous work. Building on this theory, we then
review various training techniques and discuss their pros and cons. We conclude with
some general considerations on chess teaching and learning.
In this chapter, we have in mind a player who has already mastered the basics of
chess (with a level of, say, 1800 Elo) and would like to reach a higher level of expertise
(say, 2400 Elo). Hence, we will not say much about teaching chess to beginners or
about training at grandmaster level, although it is to some extent possible to generalize
from our considerations. We also expect our player to be a strongly-motivated
competitive player. While many players are legitimately interested in learning about
chess history or in enjoying combinations and well-played games, we are talking here
about a player who wants to progress in order to perform at a competitive level. (Our
recommendations would likely be different if excellence in chess were measured, not
by norms and Elo rating, but by an “academic” examination!)
It is almost a tradition in the literature of chess training that authors claim to be the
first to devote a book to this topic. However, as the bibliography at the end of the
chapter shows, they normally stand on the shoulders of a remarkable series of masters
and grandmasters. Although we do acknowledge our intellectual debt in this regard, and
very few of the techniques we discuss are original, we believe this is the first time that
chess training techniques have been systematically organized and criticized from the
point of view of a scientifically motivated theory.
1. Chess psychology: An overview
Since the end of the nineteenth century (Binet, 1894), chess has been a popular topic
of research in cognitive psychology, the field of scientific psychology that studies
perception, memory, learning, and thinking. Classical landmarks include Adriaan De
Groot’s doctoral dissertation (De Groot, 1946), translated into English as De Groot
(1978), Newell and Simon’s (1972) research on problem solving, and Simon and
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Chase’s (1973) work on chess memory and perception.
For a detailed description of
recent work on chess psychology, we refer to the technical literature (e.g., De Groot &
Gobet, 1996; Gobet, 1993; Gobet, de Voogt, & Retschitzki, in press; Holding, 1985;
Saariluoma, 1995).
It is not feasible to discuss the whole history of chess psychology in detail in the
context of this chapter, but the key findings on chess expertise can be summarized in
the following statements:
1. Chess players have a highly efficient mode of (high-level) perception. They
can access the key elements of a position rapidly.
2. Chess players show a remarkable memory for chess positions and games. This
ability typically does not extend beyond chess.
3. Chess knowledge is encoded at several levels, in particular at a low, perceptual
level, where patterns of pieces are stored, and at a high, conceptual level,
where information about plans, evaluation, etc., is stored. These various types
of encoding, with rich indexing and a high level of cross-referencing, account
for chess players’ excellent professional memory.
4. Chess players search highly selectively. It is rare that they analyze more than
one hundred positions in the search tree before choosing a move.
5. There is no difference between the search algorithm of class A players (Elo
1800-2000) and that of Grandmasters.
6. Masters lose relatively little of their skill when they play simultaneous games
or speed chess.
2. The template theory
The components of chess expertise have recently been synthesized in the “template
theory” (Gobet & Simon, 1996), which is a refinement of theories developed by De
Groot, Chase, Newell and Simon. Some aspects of this theory have been implemented
in a computer program that simulates chess players’ behavior in various memory and
perception experiments. Computer programs are an important tool for developing
theories in cognitive psychology. They allow a much more rigorous specification than
Chess psychology has attracted intellectual giants indeed: Allen Newell and Herbert
Simon are recognized as the fathers of modern cognitive psychology and of artificial
intelligence. Simon was awarded the Nobel prize in economics in 1979.
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verbal theorizing: the programs’ operations can be compared to human behavior, and
discrepancies indicate that the theory is either incomplete or incorrect.
The template theory states that the human cognitive system comprises three main
modules: a visuo-spatial imagery system, a short-term memory (STM), where
information is briefly stored, and a long-term memory (LTM), which consists both of
structures indexing the information and the information itself. (Note that players can be
said to use, in addition, an external memory: the board itself with the pieces.) Long-
term memory consists of declarative knowledge (the ‘what’), encoded as schemata, and
of procedural knowledge (the ‘how’), encoded as productions. Perceptual and
conceptual information that can be used as units are called chunks, and constitute the
building blocks out of which knowledge is constructed. Schemata can be visualized as
nodes connected by links, where the nodes refer to concepts, and links to relations
between these concepts. For example, in “Pd4 defends Pe5”, the two concepts Pd4 and
Pe5 are connected by the relation “defense.”
Figure 1 illustrates a more complex example—a subpart of the knowledge related to a
typical isolated Queen’s Pawn:
Figure 1: Example of a schema
has-part has-part
has-part has-part
White Pawns
Pa3 Pd4 Ph2
... ...
Black Pawns
Pa7 Ph6
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Productions are knowledge units made up from a set of conditions and actions. For
example, “IF there is an open file X, AND you have a Rook, THEN place the Rook on
X,” or “IF you have a passed Pawn X, THEN push X.” In the last example, the
condition tests the presence of a passed Pawn, and the action recommends pushing it.
Productions allow information to be processed rapidly and unconsciously, and may
be the mechanism underlying what players call intuition. For example, in the position
shown in Figure 2, most masters will almost immediately consider 1. Bxh7+ as a
plausible move. The critical pattern is perhaps the presence of a Bishop on d3, of
Black’s king-side castle, of a Pawn on e5, and the absence of a defensive Bishop on e7.
Strong players will even “intuitively” anticipate that the Black king will be mated if it is
goes back to g8 after 1.Bxh7+ Kxh7 2.Ng5+, effortlessly proposing the sequence
3.Qh5 Re8 4.Qxf7+ Kh8 5.Qh5+ Kg8 6.Qh7+ Kf8 7.Qh8+ Ke7 8.Qxg7+. Thus,
productions make it possible for strong players to search deeper.
Figure 2: Example of a position eliciting an automatic, intuitive solution.
Access to information is rapid once it has been learned, but learning is slow. It has
been estimated that it takes about ten seconds to learn a new chunk in LTM (Simon &
Chase, 1973). Through recognition processes, patterns of pieces on the external board
or in the visuo-spatial store activate LTM chunks. When the position is relatively well-
known to the player, special chunks—called templates—are activated. Templates are
simply large chunks that possess slots to encode information rapidly. Both chunks and
templates point to information related to the configuration of pieces currently focused
on. This information may include possible moves, the relative strength or weakness of
squares, the opening the position may have come from, links to other templates, and so
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Search for a good move is greatly facilitated when a template has been accessed,
because useful information that cuts the amount of search down is accessible directly.
This information may be available in an explicit form (i.e., it may be accessed
consciously and communicated to other people) or in an implicit form (i.e., the player is
unaware of what information is used, and how it is used). Search implies an interplay
between information on the external board, the visuo-spatial store, information in STM,
and information in LTM. Due to the limits in capacity and access time of STM, and to
the risks of decay and interference in the visuo-spatial store, search is a difficult
process, and prone to errors. The advantage of accessing chunks is that groups of pieces
may be stored and manipulated as units. Templates offer the additional possibility of
updating portions of the board rapidly, since they incorporate slots into which values of
variables may be encoded easily. Accessing chunks and templates thus make search
In summary, becoming a skilled player requires the acquisition of a variety of well-
indexed and cross-referenced kinds of knowledge—chunks, templates, and procedures.
We may infer a few general educational principles from the theory we have sketched:
1. Learning occurs best from the simple to the complex. This principle may also
be described as gradually building up from the known to the unknown. It
follows from the theory, because the building blocks of knowledge must be
acquired first, before they can, for example, be used as variables in templates.
2. Learning occurs best when the elements to be learnt are clearly identified. This
helps provide a context for indexing as well as guidance for generalization.
3. Learning occurs best by following an “improving spiral,” where the learner
comes back to the same position, or material, and adds increasingly more
complex new information to its knowledge-base. This process increases the
chance of creating cross-referencing links.
Principles 1 and 2 have also been proposed by other leading experts in education
(Anderson, 1990; Anderson, Corbett, Koedinger & Pelletier, 1995; Gagné, Briggs &
Wagner, 1989; Travers, 1978). However, this view has not been unchallenged. There is
currently a heated debate whether, instead of learning the material in a progressive way,
learners should be immersed right away in complex problems, possibly with some help
from the teacher (the so-called “situated-learning approach” and “problem-based
approach,” respectively). We will stick to the “traditional approach,” which has
received the strongest empirical support.
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We now employ the conceptual framework offered by the template theory to review
various chess training techniques. In general, it is hard and even useless to train the
STM and imagery components as such, and it is more useful to develop the knowledge
base, because the former relate to hardware variables of the human body, probably
unchangeable even with current progress in neurophysiology, while the acquisition of
the latter is known to be relatively easy. As mentioned earlier, we believe that depth of
search or intuition are side effects of a well constructed knowledge base.
3. Acquiring chess knowledge
What is the best way to acquire what De Groot (1978) calls the “system of playing
methods” that a master has at his disposal and that is presumably necessary for reaching
mastership in chess? We may organize chess knowledge along three dimensions (see
Table 1) which will be used to present the material of this section.
A. Type of encoding
1. Explicit
2. Implicit
B. Diachronic dimension
1. The opening
2. The middle game
3. The endgame
C. Chess contents
1. Tactics
2. Strategy
Table 1: The three dimensions along which knowledge acquisition is discussed in this
3.1. Type of knowledge
We have proposed that (chess) knowledge is encoded with one of two types of data
structures: declarative, which states relations between concepts, and procedural, which
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encodes action(s) to carry out given a set of conditions. Typically, a subset of both
types of knowledge is consciously accessible (“explicit” knowledge), although most is
not (“implicit” knowledge). One could say that explicit knowledge is implicit
knowledge to which special retrieval information—typically verbal information—has
been attached. We now treat these two aspects of chess knowledge in turn.
3.1.1. Explicit knowledge
It is clear that a huge amount of knowledge is explicit. The best examples are
perhaps offered by the theory of openings,
by the theory of endgames, and by various
types of methods to apply in particular types of positions. For example, given a Queen’s
Gambit Defense, exchange variation, a Master can clearly state that a worthwhile plan
for White is to carry out a minority attack, with Rooks placed on b1 and c1, and that a
position like the one shown in diagram 3 is advantageous for White. Rote knowledge of
games or of sections of games is another type of explicit knowledge. What does the
template theory have to say about learning such material? Mainly two (sad) things:
learning is time consuming and forgetting will inevitably occur.
Figure 3: A typical position from the Queen’s Gambit Accepted, Exchange variation.
Masters know that in this type of position, Rooks are best placed on b1 and c1 (position
after White’s 29th move in the game Flohr - Euwe, Amsterdam, 1932).
The term “theory” is used in a peculiar way in the chess literature. It refers to a
compilation and analysis of variations and positions, and not, as is the case in most
sciences, as a set of principles and laws summarizing a collection of observations.
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Several practical recommendations may be inferred from the theory. First, one
should focus on a limited number of types of positions and openings, and learn the
various methods in these positions thoroughly. This focus is necessary due to the
limited study time available and to the wealth of information to learn. As discussed
below, this focus also facilitates access to the information through pattern recognition.
Second, repetition will be necessary, both when learning opening lines and strategic
ideas. It is a good idea to go over the same material several times, if possible using
varying points of view. For example, when studying a game, one could first focus on
the strategic aspects, then on the tactical aspects, and so on. Or when studying an
opening line, one could first try to memorize and understand it using an opening
textbook, then study games using this opening, then carry out one’s own analysis.
approach will offer a richly-indexed encoding of information, which helps prevent
forgetting and allows easier access to memory traces.
Third, one should avoid spending too much time on historical and anecdotal details.
Although such information may be useful in indexing knowledge, it has several
disadvantages. It can become an overwhelming mass of information, dangerously
attractive but not useful for competition. In addition, this information is usually not
acquired and stored in an efficient way for competition. Finally, because of its intrinsic
attraction, it may divert a player’s attention from other, more relevant aspects. For
example, knowing the opening and result of Lasker’s 5th round game in the New York
1924 tournament, what White’s winning percentage is with the Sveshnikov Sicilian,
how to mate with two knights against a pawn, or remembering 1234 endgame studies
can be fun, but take time and do not contribute to a player’s competitive strength.
Fourth, one word about studying classics. This clearly has advantages, as is often
stressed in the literature. For example, ideas are easier to understand in these games
than in contemporary games, because the quality of defense was lower and games were
more centered around a single theme than is the case nowadays. Classical games were
also less obscured by many subtleties in the opening. However, there is the danger that,
in addition to overloading LTM with anecdotal material, studying classics leads to the
creation of schemata that will not be useful in practice, because the type of positions
Carrying out one’s own analysis should not be done at the beginning, because it
inefficient–not all key concepts are available–and will most likely only duplicate
established analysis.
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met in these games is unlikely to be seen again. This disadvantage is not fatal, however,
and may be avoided by studying a judicious mixture of classical and modern games.
3.1.2. Implicit knowledge
While it is easy to describe the type of explicit knowledge that a strong player should
possess, it is harder to describe what type of implicit knowledge should be acquired.
According to the template theory, there are three different types of knowledge, which
are all accessed through recognition processes: chunks and templates, schemata, and
productions. Testing for the presence of implicit information is not easy, because
players are themselves unaware of the details of its encoding.
As with explicit knowledge, the template theory suggests that it is better to learn a
few opening variations in detail —though sufficiently broadly to cover all possible
openings by the opponent—than to cover too many opening variations. Studying fewer
openings enhances the likelihood that chunks and templates learned during training will
occur in tournament games. As discussed below, recognizing plausible moves early on
cuts down the search space. In addition, efficient productions will be encoded with
specific conditions (productions with non-specific conditions are slow to apply because
time is needed to instantiate them), and these conditions are likely to be related to
features of the opening.
The question now arises: How to optimize learning so that key aspects of the
position are recognized rapidly, and so that methods are applied efficiently? We believe
that the best way to achieve this is by using computer technology to display positions
and games, or, even better, to teach concepts: obviously, this technology cuts down the
time spent in searching games and variants, and in moving pieces on the board, but it
also permits one to analyze variations and sub-variations easily, which would only be
visualized in the mind’s eye in traditional teaching. Disadvantages of visualization in
the mind’s eye are that it is slower and more prone to errors than direct perception from
external displays, and therefore it offers a less efficient encoding in LTM. We will
come back to the question of computer instruction at the end of this chapter.
3.2. Diachronic dimension
3.2.1. The opening
Chess players’ natural pragmatism has led most of them to appreciate the importance of
studying openings. It is unclear, however, if they do it in an efficient way. For the
opening phase, we give the following recommendations, which are quite consistent with
our discussion of explicit and implicit knowledge.
First, one should be selective, and focus on a small repertoire of openings, which
may be expanded later. Most books devoted to training agree on this point (for
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example, see Bönsch, 1987; Kotov, 1971). This selectivity is of course made necessary
to some degree by the wealth of information offered by chess theory. But within a
restricted repertoire, the required repetition and the predictive value of generalizations
is likely to lead to rules more usefully applicable to later games than the less specific
and less predictive knowledge derived from multi-opening systems.
Second, one should find a balance between rote learning and understanding. On the
one hand, there is no doubt that a huge amount of rote learning has to take place—chess
is a domain too chaotic for everything to be derived from general principles, and also
complex enough that theoretical variations cannot be calculated in real time without the
risk of making serious errors, errors that have been eradicated during the slow
evolutionary process of opening practice. On the other hand, new positions will be met,
where rote knowledge cannot be applied. Therefore some general principles and rules
of thumb had better be learnt, as well as the key ideas and themes in a given opening.
Third, openings should be studied from different points of view. For example, one
should strive to link the knowledge of openings to typical middle-game positions and
endgame positions, or, even better, to entire games. Doing so will speed up the creation
of templates, which in turn will facilitate search and position evaluation. In addition, as
seen before, this cross-referencing strengthens the memory traces of the material to
Fourth, one should keep information about openings in a central filing system.
Earlier, this role was played by the opening notebooks or card-indices that were
maintained by several GMs and IMs. Nowadays, this data maintenance task is vastly
facilitated by computer databases. Such repositories are important for several reasons.
They allow reviewing the variations over time (memory is fallible!). They also facilitate
the update of one’s repertoire with new games and innovations. And of course, it is also
easier to carry a laptop or a notebook than a library of dozens of volumes!
Finally, we should mention a useful technique, which we may call the
“decomposition method.” It consists in studying and playing basic endgames that may
Some players specialize in gambit openings, and sometimes do quite well even against
nominally stronger players—an illustration of the power of specialization! There is
however the danger that this approach leads to a rather limited game, which may
hamper progression. We suggest that one cycle of the spiral should take the player into
less tactical positions.
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occur from an opening position, by removing all pieces but kings and pawns from the
position, and then gradually adding pieces of various sorts or varying aspects of the
pawn structure (see Figure 4). This allows one to develop proficiency in endgames that
may occur from one’s opening repertoire. Note that keeping the pawns makes more
sense than keeping other aspects in the position, as the pawn structure is less likely to
change, and the position, or a similar one is hence more likely to actually occur in a
game with the opening line under study.
Figure 4: Example of the decomposition method, taken from the Benoni Defense.
Would you rather play White or Black in this endgame?
3.2.2. The middlegame
Since most of what we have to say about the middlegame will be dealt with in the
section on “chess contents,” a few comments will suffice here. In the middlegame,
perhaps more than in the study of openings, we can see the tension between learning
specific facts and learning general principles. While our advice of specialization applies
in this case as well, it would be quite unrealistic to expect all middlegame positions to
fit known schemata. In particular, as there are a much larger number of positions, rote
learning will be much less useful here than for opening positions.
Rather than by opening, the chess literature generally groups sets of positions
around salient features of the position or the strategy employed. Books are available
(e.g., Pachman, 1972) that treat certain middlegame themes in turn (such as the
minority attack we mentioned before, or certain types of king side attacks, or various
types of isolated queen-pawn positions, etc). Similarly, current database software
allows relatively easy retrieval of games with such features. During training, the student
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may thus use these tools as a way to learn about the strategic and tactical features of the
position likely to be met. Again, to make an effective selection of plans, themes, and
positions for study, it is best to use typical positions stemming from one’s chosen
opening repertoire as starting points. Each theme can be looked at from different angles,
studied as it occurs in actual games, possible resulting endgame situations, etc. Rote
learning of a theme as exemplified in an actual game may help with indexing for further
information retrieval.
Some players keep a catalog of certain middlegame positions that were important to
them. They may serve as a comparison for positions encountered/studied later with
certain common characteristics (chunks, templates). Sometimes, it is useful to study
positions around the appropriate action rather than the conditions. Certain middlegame
books bring positions together in which, for example, certain typical sacrificial attacks
are applicable (e.g., the aforementioned bishop sacrifice on h7 or f7, or a rook sacrifice
on g7, etc.).
3.2.3. The endgame
Several authors (Capablanca, 1963; Chéron, 1942) have stressed the necessity of
studying endgames from an early point, because this allows the student to go from
simple to more complicated concepts. There is no doubt that endgames are important,
and that a good knowledge of them may be capitalized on to yield extra points in
competitive games. As with openings, the lore of endgames is huge, and it is important
not to waste time on irrelevant details. We propose three lines of study.
First, it is perhaps here that the concept of an “improving spiral” can be best applied.
The student should start by acquiring basic knowledge in all domains of endgames
(Pawn, Rook, etc.). Special emphasis should be given to Pawn and Rook endgames, as
they are more likely to occur. It is important to pay attention to typical positions, and
avoid all arcane knowledge, however exciting it may be. In further cycles, attention
should be directed to slightly more complex endgames, with a constant check that
knowledge acquired in previous cycles is still there. Only in later cycles of the spiral
can you start spending time on more exotic endgames. We recommend books like
Awerbach (1981a; 1981b) or Pachman (1977) which contain the necessary theoretical
knowledge with enough practical slant. Coaches come in particularly handy with the
study of endgames, as they can present material in the optimal order of difficulty.
Second, one should study well-commented endgames played by strong players (e.g.,
Euwe, 1962; Mednis, 1979; Schereschewski, 1994). This gives a practical gloss that
theoretical books are lacking.
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Third, one should study typical endgames resulting from the openings belonging to
one’s repertoire. In this respect, the “method of decomposition,” mentioned above, is an
excellent way to familiarize oneself with potential endgames.
3.3. Chess content
Traditionally, chess literature categorizes knowledge into two broad classes: tactical
and strategic. From the point of view of our theory, both types of knowledge require the
acquisition of chunks (the key features of a position indicating a combination or a
strategic theme), templates/schemata (with them, positions are easier to search, and they
give access to knowledge useful for this class of positions), and productions (they speed
up thinking).
3.3.1. Tactics
Again, most chess trainers and teachers agree that practice and repetition are
essential (e.g., Bönsch, 1987; Kotov, 1971). Traditional “quiz” books are quite useful,
although we expect computer technology to greatly improve this part of chess teaching
(see below). It is probably best to start with positions ordered by themes, then to move
to positions randomly ordered (as is fairly usual among the quiz books). Again, typical
combinations and themes should be studied when studying an opening. Finally, playing
a computer opponent is a good way to improve one’s tactical skills, particularly for
players who are prone to errors with shallow combinations.
There are quite a few misconceptions about how to improve tactical skills, and three
of them recur in the literature often enough to warrant our comments at this point: (1)
visit each variant only once when thinking ahead; (2) practice with blindfold chess; (3)
practice with artistic chess problems. (We hope the reader will forgive us for
anticipating themes we deal with in more detail later).
(1) Kotov (1971; 1983) proposed that players should strive at calculating a branch of
the search tree only once. We believe that this advice is likely to lead to disaster in
competition games. As a matter of fact, empirical evidence shows that even top-level
players often re-investigate the same variation (De Groot, 1978). We have more
confidence in the technique of progressively diminishing the thinking time in practice
games, also proposed by Kotov.
(2) We believe that playing blindfold chess is at best useless, and at worst harmful to
one’s development. The ability of playing blindfold comes more as a side effect of
having acquired a well organized and easily accessible knowledge base (Ericsson &
Staszewski, 1989; Saariluoma, 1995). Practicing blindfold as such may be harmful
when it interferes with other types of training.
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(3) Finally, training with artistic chess problems, proposed among others by Chéron
(1942), is probably useless for chess competition. Gruber and Strube (1989) have
shown that there is little transfer between these two variants of chess. According to the
theory espoused here, practicing with chess problems will develop chunks of
knowledge that are unlikely to be of use in competition games, because the conditions
of their application are not met in these games. We have a somewhat better opinion of
training with endgame studies (e.g., Dvoretzky, 1991), as long as the positions are not
too far removed from practical play. Studying endgame studies with few pieces is
probably excellent practice and may help develop imaginative and original ideas, but
should come only when the basics on practical endgames have been covered (cf.
discussion on endgames).
3.3.2. Strategy
Perusal of various textbooks on strategy has convinced us that the number of
elements typically taught is rather limited, typically along three axes. There is first an
abstract axis, including the notions of time, space and material; there is then a concrete
axis, which relates to the activity of pieces and includes notions such as open lines,
strong posts, co-ordination of pieces; the last axis deals with the static and dynamic
characteristics of pawn structure. Studying a few textbooks on chess strategy is
certainly useful, but the benefits from additional material decrease rapidly. We
recommend two or three classics (e.g., Euwe, 1972; Kmoch, 1980; Nimzowitsch,
1977), with the addition of a more recent textbook. Again, we believe that most of the
strategic education can be gained by building one’s own opening repertoire. In fact,
quite a few opening books have taken this approach and attempt to illuminate the
positional aspects arising from the opening under consideration. For example: Nunn’s
book on the Benoni (1982), Watson’s book on the Tchigorin Defence (Watson, 1981),
or, at a more popular level, Levy and O’Connel’s book on the Sicilian Defence (1983).
In addition to studying textbooks, it is of course essential to learn and understand
themes that are likely to occur in one’s games. Here again, study of positions related to
one’s opening repertoire and repetition are key to progress.
4. Practical methods for acquiring and consolidating chess knowledge
We may divide practical methods into two broad categories: analytic methods and
practice games. Under the first heading, we include activities such as analysis of
openings, analysis of games (including one’s own games), the decomposition method,
and the technique of guessing the next move of a published game. The second heading
addresses activities such as speed chess, games against computer, correspondence
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games, and training games. There is some overlap between the two categories: for
example, the decomposition method can be coupled with speed chess or games against
computer. As we have illustrated several of these methods in the preceding section,
there is no need to repeat them here. However, a warning may be in order: teaching is a
poor method of improving one’s skill, because it directs attention to the wrong kind of
material, from the point of view of a player aiming at improving his strength. In
particular, teaching beginners and weak players can be particularly detrimental.
5. Methods not based on knowledge acquisition
We have hitherto described improvement techniques based on optimizing knowledge
acquisition. Techniques focusing on other aspects of chess cognition have been
proposed as well. We now review methods aimed at improving visual representation,
short-term memory, and the ability to search ahead.
5.1. Practicing visual representation
Some authors have recommended practicing visual representation as a way to
improve one’s chess skills. However, while chess is obviously a visual and spatial
game, there is no firm empirical data demonstrating the necessity of having strong
general visual or spatial abilities in order to be a master. Indeed, available data suggest
that general visuo-spatial abilities do not correlate with skill, and that these abilities are
not more developed in chessplayers than in non-chessplayers (Waters, Gobet, &
Leyden, in press). Given this lack of evidence, we do not encourage players to train this
ability as such.
5.2. Practicing short-term memory
Again, there is no evidence showing that strong chess players have a superior STM
capacity with material different from chess. Their superiority with chess material seems
to be a side effect of the accumulation of large chunks. Therefore, there seems to be no
need to train short-term memory in isolation.
5.3. Practicing the ability to look far ahead
This is one of the most contentious points of chess psychology and of chess training.
According to our theory, the ability to look ahead is made possible by the co-ordination
of LTM knowledge, STM, and visual imagery. The question for chess training is: which
of these components to train, and how? In general, the theory indicates that good
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understanding of a position through pattern recognition should cut down the need for
looking ahead.
2, 4, 6
1, 4, 7,10 2, 5, 8, 11
3, 12
Figure 5: Illustration of various types of search. Humans typically use search depicted
as III. Numbers indicate the sequence with which positions (indicated by letters) are
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Empirical research has shown that players tend to visit the same nodes several times
(De Groot, 1978). For example, consider Figure 5, which depicts a search tree. The
most economical way to search would be to visit every node only once (panel I). When
considering position B, a player would then store the positions obtained after every
move, and then analyze them in order. This would in general require a very large
memory capacity, far beyond that of humans. Another approach is to come back to
position B after having considered a terminal position, as shown in panel II. In this
case, only one position (position B) has to be stored in memory, as opposed to the three
positions of the first approach. What De Groot found in his classical study is that all
players—including world-class grandmasters—tend to use a third approach. After
having visited a terminal node, they come back to the root position (the position in plain
view on the external board), and then play again the moves in their mind to reach
another terminal position, possibly revisiting some nodes. This is shown in panel III.
While this approach has the disadvantage of visiting more nodes than the previous
approaches, it has the clear advantage of not overtaxing STM, and of reducing the risk
of blundering or playing illegal moves due to errors in remembering the location of
certain pieces (this is not a real danger in our example with a depth of only three half-
moves, but is a real concern with larger depths). In addition, it allows the player to
propagate the information gathered at a given node to other nodes. De Groot (1978; see
also De Groot & Gobet, 1996) calls this search behavior “progressive deepening.”
It is worth pointing out that, even for improving depth of search, it is more efficient
to improve one’s knowledge base than one’s ability to look-ahead. Assume that there
are, on average, 35 legal moves in a position, each generating a new position or a new
node in the search tree (cf. De Groot, 1978). Without knowledge, it is hard to decide
among the possible choices. Assume also that a large amount of knowledge, encoded as
chunks and templates, reduces this choice to 4 plausible moves, on average. Assume
finally that a “fast searcher” looks at 20 positions per minute (i.e., 3 seconds per
position), an “average searcher” looks at 6 positions per minute (i.e., 10 seconds per
position), and a “slow searcher” looks at 2 positions per minute (i.e., 30 seconds per
Table 2 gives the average depth of search for various levels of search ability
and knowledge, as defined above (a thinking time of 10 minutes is assumed).
De Groot’s (1946/1978) data, as well as more recent data, indicate that players,
including Masters and Grandmasters search about four or five moves in a minute, on
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Search speed Knowledge
Branching = 35 moves
Branching = 4 moves
Search rate
Nodes searched
in 10 minutes
Mean depth (in half-moves)
Slow 2 20 0.8 2.2
6 60 1.2 3.0
Fast 20 200 1.5 3.8
Table 2: Mean depth of search for an ideal player with or without knowledge, and with
low, average, or high search abilities. Estimates are based on a thinking time of 10
Assuming a search with constant depth, we find that weak players (slow search
speed and low knowledge) increase their depth of search more by increasing their
knowledge than their search ability. Players with low search speed but with high
knowledge (that is, high selectivity) actually search deeper than players with high
search speed but with low knowledge. Although the assumption of constant depth of
search is not quite plausible, this demonstration makes our point clear: selective search
due to knowledge is more useful than sheer search speed. In addition, searching without
knowledge has a high likelihood of missing a good opportunity (something well known
by chess program designers), because even fast searchers can cover only a very small
portion of the search space.
Thus, training depth of search per se is unlikely to yield good results. Rather, depth
of search should be seen as a consequence of acquiring a large knowledge base, which,
through chunking of moves and creation of templates, leads to a more selective and
efficient search.
Then, what to do with Kotov’s (1971, 1983) famous advice of visiting each branch
of the search tree only once? Certainly, Kotov has good intentions: put some order and
organization into chess players’ thoughts, which are often muddled indeed. Note that
Kotov’s techniques are not so much aimed at improving depth of search than at
avoiding wasting time in analyzing a position.
average. All these experiments used verbal protocols, which may lead to an
underestimation of the number of moves searched.
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It is unclear what Kotov exactly means when he says (Kotov, 1971, p. 28— in bold
in the text): “In analyzing complicated variations one must examine each branch of the
tree once and once only.” Is it valid only for terminal branches, or also for the
intermediate branches between the initial position and the terminal branches? Take for
example the case of a choice intervening after, say, 4 forced moves. Should the players
never replay in their mind these first “introductory” moves? In addition, Kotov’s advice
of visiting each branch of the search tree only once is probably bad. As mentioned
earlier, empirical evidence shows that even top-level players do visit the same branch
several times, what De Groot calls “progressive deepening.”
The fact remains that some players complain of searching in a disorganized way and
of a lack of decision. But another technique proposed by Kotov (reducing the thinking
time in training sessions), seems more appropriate to us: while inducing players to use
their time gradually in a more structured and effective way, it will still leave time for
those aspects of iterative deepening that are necessary for good decision making. When
pushed to the extreme, this technique also provides a useful exercise for practicing play
in time trouble.
6. Media of instructional materials
According to the chunking/template theory, the order of presentation of the material, as
well as the way it is segmented, are crucial for effective training. Typically, it is unwise
for a student to carry these tasks by himself: it is hard to do it efficiently, and it is time
consuming. Therefore, it is important to have a good trainer, good computer software,
or a good book, in order to have the instructional materials chopped into optimal
chunks. Obviously, a trainer is more flexible than a book or than the computer programs
currently available.
6.1. Role of coaches
A coach’s contribution may be divided into two main aspects: a technical contribution,
and a personal contribution. The technical contribution includes preparation of study
materials and study programs, identification of the trainee’s weaknesses and preparation
of a program to fix them, feedback on games and results, and advice on how to play
against the trainee’s opponents, including preparation of specific variations. The
personal contribution includes management of the trainee’s motivation, and
optimization of study time by reducing the time spent in administrative chores (e.g.,
looking for games belonging to the repertoire, subscription to tournaments, or decision
about which competition to attend). Although the necessity of having a coach is
sometimes debated (e.g., Charness, Krampe, & Mayr, 1996), we believe that it is a key
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factor of success—most grandmasters had a coach at some point of their career. Finally,
research in education has shown that students take more advantage of a private tutor
than from a tutor shared in a classroom (Bloom, 1984; Cohen, Kulik, & Kulik). We
believe that this is the case with chess as well.
6.2. Textbooks
Books have been the main vehicle for transmitting chess knowledge. Whether they
are also the optimal vehicle is a debatable question. Unfortunately, few of them follow
recommendations inspired from sound psychological and pedagogical principles. For
example, most books present schemata and methods specific to a small range of
positions, which may not match the positions students will meet in their own practice,
but offer the implicit promise that these schemata and methods will generalize to other
positions. Learning these schemata and methods can be useful, of course—certainly if
you play similar positions—but there is a serious danger for the students of believing
that generalization will come easily. There is actually a paradox in most strategy
textbooks: they advertise learning general principles, but actually, by using a “teaching-
by-example” approach, communicate very specific methods.
In addition, most educational books have the default of placing diagrams either at
the beginning of the game segment that will be discussed, or at some point where there
is a tactical combination. Even books specifically devoted to strategy do not use
diagrams to emphasize the constellations of pieces that are important to memorize.
Obviously, from a pedagogical point of view, diagrams (and perhaps other visual aids)
should be used to emphasize, reinforce, or help index the concepts being taught.
6.3. Computers
Today’s computers offer an invaluable aid for creating and using game databases, for
practicing with an opponent (computer or, on the internet, human), and for support with
analysis of games and positions (e.g., Jansen & Schaeffer, 1990). In particular, we
would recommend playing with a strong computer program to improve one’s tactical
skills, to practice typical positions (opening, middle game, endgame), and to test new
ideas in openings.
We anticipate that the next generation of computer instruction will go beyond
“merely” offering facilities for playing and managing databases, and will also act as a
real coach. For example, it could select the study material (tactical positions, strategic
themes, endgames, etc.) as a function of the strengths and weaknesses of the student.
Similar technology has been recently developed in domains such as the teaching of
geometry or programming (Anderson et al., 1995).
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7. Summary and conclusion
In this chapter, we have applied a scientific theory of chess expertise to the question of
chess training.
The theory emphasizes that skill is derived from the creation of chunks
(perceptual knowledge units) and templates (conceptual knowledge units), and that
search ability directly depends upon them. In general, the practical pieces of advice
inspired by the theory stress the necessity of building knowledge that will likely be of
use in future games. This is achieved by centering knowledge acquisition around one’s
opening repertoire, including knowledge about middlegames, endgames, tactics and
strategy. A few techniques are discussed, such as the decomposition method. Not
surprisingly, applying such a training approach is time consuming—it takes years to
become a master. We finally hinted at some possible developments in educational
computer technology, which may lead to new powerful chess tutoring programs.
What will be required at the next step of chess training, to advance from Master to
Grandmaster? In short: more of the same. The differences are mainly of degree: a
player’s opening preparation becomes more and more detailed, with increasing
emphasis on specific cases; their opening repertoire widens, and their ability to cope
with unknown middlegame and endgame positions increases. The added difficulty—a
serious one—is to maintain motivation during the long years of study and practice. The
advance to higher levels is not always smooth, and may be punctuated by periods
without noticeable improvement, or even relative decline. Only the player with the will
power to overcome these motivational hurdles will manage to make it to full mastery.
There are obviously a number of topics we have not addressed in this chapter, such
as training for endurance, the psychology of errors, playing the opponent (e.g., Krogius,
1976; Kotov, 1971; Munzert, 1990). While these are important questions, the fact is that
they are even less well understood than the topics we have discussed.
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Anderson, J. R. (1990). Cognitive psychology and its implications (3rd ed.). New York:
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive
tutors: Lessons learned. The Journal of the Learning Sciences, 4, 167-207.
Awerbach, J. (1981a). Lehrbuch der Schachendspiele (Band 1). Berlin: Sportverlag.
Awerbach, J. (1981b). Lehrbuch der Schachendspiele (Band 2). Berlin: Sportverlag.
Binet, A. (1894). Psychologie des grands calculateurs et joueurs d’échecs. Paris:
Hachette. [Reedited by Slatkine Ressources, Paris, 1981.].
Bloom, B. S. (1984). The 2-sigma problem: The search for methods of group
instruction as effective as one-to-one tutoring. Educational Researcher, 13, 4–
Bönsch, E. (1987). Schachlehre für Lehrende und Lernende (2 ed.). Berlin: Sportverlag.
Capablanca, J. R. (1963). A primer of chess. New York: Harcourt, Brace, & Cie.
Charness, N., Krampe, R., & Mayr, U. (1996). The role of practice and coaching in
entrepreneurial skill domains: An international comparison of life-span chess
skill acquisition. In K. A. Ericsson (Ed.), The road to excellence (pp. 51-80).
Mahwah, NJ: Lawrence Erlbaum.
Chéron, A. (1942). Nouveau manuel d’échecs du débutant. Paris: Payot.
Cohen, P. A., Kulik, J. A., & Kulik, C. L. C. (1982). Education outcomes of tutoring: A
meta-analysis of findings. American Educational Research Journal, 19, 237–
De Groot, A. D. (1946). Het denken van den schaker. Amsterdam: Noord Hollandsche.
De Groot, A. D. (1978). Thought and choice in chess (Revised translation of De Groot,
1946; 2nd ed.). The Hague: Mouton Publishers.
De Groot, A. D., & Gobet, F. (1996). Perception and memory in chess. Heuristics of the
professional eye. Assen: Van Gorcum.
Dvoretzky, M., (1991). Secrets of chess training. London: Batsford.
Ericsson, K. A., & Staszewski, J. J. (1989). Skilled nemory and expertise : Mechanisms
of exceptional performance. In D. Klahr & K. Kotovski (Eds.), Complex
information processing: The impact of Herbert A. Simon. Hillsdale, NJ:
Lawrence Erlbaum Ass.
Euwe, M. (1962). Die Endspiellehre und ihre praktische Anwendung. Hamburg:
Euwe, M. (1972). Les échecs: jugement et plan. Paris: Payot.
Gagné, R., Briggs, L. J., & Wagner, W. W. (1989). Principles of instructional design.
New York, NY: Holt, Rinehart, & Winston.
Gobet, F. (1993). Les mémoires d’un joueur d’échecs. Fribourg (Suisse): Editions
Gobet, F., de Voogt, A., & Retschitzki, J. (in press). Moves in mind. Hove:
Routledge/Psychology Press.
25/1/05 24 out of 24
Gobet, F., & Simon, H. (1996). Templates in chess memory: A mechanism for recalling
several boards. Cognitive Psychology, 31, 1-40.
Gruber, H., & Strube, G. (1989). Zweierlei Experten : Problemisten, Partiespieler und
Novizen bei Lösen von Schachproblemen. Sprache & Kognition, 8, 72-85.
Holding, D. H. (1985). The psychology of chess skill. Hillsdale, NJ: Erlbaum.
Jansen, P.J., & Schaeffer, J. (1990). On seconding a grandmaster, ICCA Journal, 13,
Keres, P., & Kotov, A. (1964). The art of the middle game. New York, NY: Dover.
Kmoch, H. (1980). La force des pions aux échecs. [Pawn power in chess]. Paris: Payot.
Kotov, A. (1971). Think like a grandmaster. London: Batsford.
Kotov, A. (1983). Chess tactics. Macon, GA: American Chess Promotion.
Krogius, N. (1976). Psychology in chess. London: R.H.M Press.
Levy, D., & O’Connel, K. J. (1983). Comment jouer la défense sicilienne. Paris:
Mednis, E. (1979). Practical endgame lessons. London: Batsford.
Munzert, R. (1990). Schachpsychologie. Hollfeld: Beyer.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ:
Nimzowitsch, A. (1977). My system. A chess treatise. London: Bell & Sons.
Nunn, J. (1982). The Benoni for the tournament player. London: Batsford.
Pachman, L. (1972). Modern chess strategy. New York, NY: Dover.
Pachman, L. (1977). Les finales. Paris: Payot-Diffec.
Saariluoma, P. (1995). Chess players’ thinking: A cognitive psychological approach.
London: Routlege.
Shereshevsky, M. (1994). Endgame strategy. New York, NY: Macmillan.
Simon, H. A., & Chase, W. G. (1973). Skill in chess. American Scientist, 61, 393-403.
Travers, M. W. (1978). An introduction to educational research. New York, NY:
Waters, A., Gobet, F., & Leyden, G. (in press). Visuo-spatial abilities in chess players.
British Journal of Psychology.
Watson, J. L. (1981). Queen’s Gambit: Chigorin Defense. London: Batsford.
We thank Guillermo Campitelli and Peter Lane for useful comments on this chapter.
... Il va se demander quelles sont les facultés cognitives indispensables à la pratique et comment les améliorer le plus efficacement possible. On peut retrouver des exemples variés de domaines qui ont fait évoluer leurs méthodes d'apprentissage grâce aux recherches en psychologie de l'expertise : les échecs (Gobet & Jansen, 2006), le sport (Smith, McEwan, Tod, & Martindale, 2019), l'apprentissage des langues (Soo Von Esch & Schneider Kavanagh, 2018), la détection de mines (Davison, Staszewski, & Boxley (2001)… ...
... La méthode d'apprentissage en trois étapes de Gobet et Jansen (2006) (Gobet, 2005 Les retours seraient également essentiels à l'apprentissage (Gobet, 2005). Grâce à eux il est alors possible de percevoir clairement les bonnes et mauvaises performances qui ont été réalisées durant la pratique. ...
Dans cette thèse, nous nous sommes intéressés au raisonnement et à la capacité décisionnelle des experts. A l’exception d’une étude qui est composée de deux populations expertes différentes (Joueurs d’échecs et joueurs de Go), nous nous sommes concentrés sur la population d’expert du jeu d’échecs. Notre objectif initial était de montrer l’influence de certains processus émotionnels dans les décisions expertes. Dans ce travail, nous nous sommes intéressés aux liens pouvant être établis entre la théorie des marqueurs somatiques et les théories en psychologie de l’expertise. Notre idée est que les marqueurs somatiques offrent un cadre intéressant afin d’étudier et de comprendre les performances expertes.Nous avons tout d’abord étudié les capacités de prise de décision générales des experts, en dehors de leur champ d’expertise, à l’aide d’un test spécialement créé pour étudier les marqueurs somatiques (Iowa Gambling Task ; IGT) et d’autres épreuves se focalisant sur les aspects de décision ambiguë (Balloon Analog Risk Task ; BART) et en connaissance des risques (Game of Dice Task ; GDT). L’objectif était de voir si les joueurs d’échecs sont meilleurs que les novices dans ces tâches et de mieux comprendre le type de contexte décisionnel pouvant amener les joueurs experts à dépasser les capacités de la population générale. Nous observons que la prise de décision des experts est meilleure principalement dans le cadre de l’IGT. Ainsi, contrairement à ce qui apparait parfois dans la littérature, les performances des joueurs d’échecs ne semblent pas se limiter exclusivement à leur domaine d’expertise.Nous avons ensuite étudié les décisions des experts au sein de leur domaine de compétence. Nous avons ainsi réalisé deux études utilisant des positions d’échecs. Il s’agit d’une tâche d’amorçage et d’une adaptation de l’effet d’Einstellung (ou effet d’attitude). L’objectif de ces études était d’observer l’influence du traitement automatique des positions sur la performance des joueurs d’échecs. Nos résultats semblent indiquer que les experts procèdent à un traitement automatique des positions pouvant amener à l’activation de schémas et procédures de résolution spécifiques à la situation. Cet activation automatique peut entraîner une amélioration des performances pouvant aller jusqu’à la mise en place d’une décision intuitive pour les joueurs experts. Mais celle-ci peut également venir perturber la décision des joueurs en focalisant leur attention sur des aspects moins pertinents de la situation.Pour ce qui concerne les compétences générales des experts, en dehors de leur champ d’expertise, les résultats obtenus semblent indiquer une utilisation efficace de la voie émotionnelle de la décision responsable de l’activation des marqueurs somatiques. Dans les études menées dans le domaine d’expertise, la théorie des marqueurs somatiques permettrait également, selon nous, d’expliquer les différents modes de décision des experts. Nous proposerons donc dans cette thèse un modèle des décisions expertes incluant la modalité somatique.En résumé, nos résultats semblent indiquer que la théorie des marqueurs somatiques est un cadre interprétatif intéressant pour les décisions expertes. Ces marqueurs sont reliés à de précédentes situations ayant provoqué une réaction émotionnelle et pourraient venir assister les décisions experts dans et hors de leur domaine d’expertise. Néanmoins, de plus amples recherches, incluant des mesures physiologiques, doivent être menées afin de confirmer l’intérêt des marqueurs somatiques dans la décision experte.
... On average, grandmasters could beat a class-B player with a balanced style with less number of moves as compared to a player who is strong at either opening or endgame. This shows the importance of the opening training in the game, which is also supported by other researchers in the chess literature [13,19]. Similarly, the results indicate that the endgame is as important as the opening phase to the game's outcome [13]. ...
... This shows the importance of the opening training in the game, which is also supported by other researchers in the chess literature [13,19]. Similarly, the results indicate that the endgame is as important as the opening phase to the game's outcome [13]. ...
The topic of virtual humans is increasingly vital in entertainment. They offer an influential medium for amusement and learning. In this article, the researcher investigates virtual chess players of different personalities to explore the psychology of competition between two groups of virtual chess players: grandmasters and class-A players. More specifically, the researcher evaluates the different errors made by the two groups of virtual players while competing against each other. The two virtual grandmasters are represented by Anderssen and Leko, who vary in their attack and defense styles. While Anderssen is an aggressive grandmaster, who starts attacking his opponent at an early stage of the game, Leko is known for being a solid defensive player. The class-A players in this study vary in their Knight-Bishop employment preferences. The study reveals many interesting findings of the errors made by different virtual chess players. These findings have their grounds in social sciences and can be beneficial to psychology and computing researchers.
... On average, grandmasters could beat a class-B player with a balanced style with less number of moves as compared to a player who is strong at either opening or endgame. This shows the importance of the opening training in the game, which is also supported by other researchers in the chess literature [13,19]. Similarly, the results indicate that the endgame is as important as the opening phase to the game's outcome [13]. ...
... This shows the importance of the opening training in the game, which is also supported by other researchers in the chess literature [13,19]. Similarly, the results indicate that the endgame is as important as the opening phase to the game's outcome [13]. ...
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Virtual humans emerged as a topic of research in HCI and they have been used for various purposes. This paper explores the behavior of chess players in a virtual chess environment to gain more understanding about chess personalities. In particular, the focus of this research is investigating attack and defense strategies used by virtual chess grandmasters against different virtual class-B personalities who vary in their strength in the different stages of a game. These attack and defense strategies have attracted much attention in the chess community and are considered among the main aspects to chess players. They occur in different phases of the game: opening, middle game and endgame. The researcher examines virtual chess players to understand the psychology of competition between two grandmasters (attacker, defender) and three class-B chess players with different personalities: (a) strong at openings; (b) weak at openings, but strong at endgames and (c) balanced player. The virtual humans in this research represent personalities of real players. The empirical players' results showed that the personalities could influence the error and the number of moves of the game for both grandmasters and class-B players. Such findings can be used in designing virtual chess players.
... On top of the fact that " learning … games from a piece of paper is notoriously difficult " (Gobet, de Voogt, & Retschitzki, 2004, p. 164), many rulebooks are not constructed in a way to facilitate learning or teaching of game systems; " Gobet and Jansen (2006) argue that [rule] books often violate sound psychological and pedagogical principles " (p. 21). ...
... Since modern board games can be so difficult, players " require considerable external support to build new knowledge structures in a relatively efficient manner " (Kalyuga & Plass, 2009, p. 723). Thus, emphasis must be placed on effective face-to-face teaching. Gobet and Jansen (2006) explain how board game teachers can assist with both " a technical contribution: a selection and preparation of study material, identification and remediation of trainee's weaknesses, feedback on performance, and advice " (Gobet, de Voogt, & Retschitzki, p. 168) and also a " personal contribution: management of the trainee's motivation, ...
The board game hobby has rapidly grown and evolved in recent years, but most of the non-digital games lack tips and tutorials and remain difficult to learn and teach effectively. In this project, we integrated a popular hobbyist approach to teaching modern strategy games with classical experiential learning elements (i.e., demonstration, observation, reflection, discussion and repeated experiences). We tested our model by teaching two modern board games to Japanese high school and university students. Questionnaires, gameplay data, self-ratings and discussions showed improved understanding and enjoyment, more strategic play and more interest in modern board games over the course of the instructional sequence. The model's repetition (the participants played each game three times) was rated the most useful in terms of learning the games. Overall, the integrated model was largely successful in teaching strategy board games to new players, and we offer several recommendations for teachers, designers and researchers of board games.
... Since, chess proficiency depends on a combination of slower heuristics and fast pattern recognition, mere exposure is not sufficient. It seems that, just as in reading, there must be a high level of familiarity with the game and a thorough understanding of the dynamics, before subsequent repetition and practice will ultimately lead to fast recognition of game situations (Gobet & Jansen, 2006). However, many years of subsequent practice are necessary to obtain mastery (Charness et al., 2005;Gobet & Campitelli, 2007). ...
... A recent review by Gobet and Jansen (2006), in which various training techniques are compared, sheds further light on the dynamics of successful practice in chess. Gobet and Jansen infer several educational principles from their findings which must result in the emergence of chunks and templates, viz, the elements to be learned must be clearly identified, complexity must increase gradually, the focus must be limited constantly to a small number of positions at a time, vast repetition is vital, resources must be employed efficiently, specific attention must be given to typical situations, and motivation must be maintained for long periods. ...
In this chapter we will engage in a theoretical quest for ways to ameliorate reading fluency in dyslexics. In the first section we will provide an overview of research on dyslexia and dyslexia treatment and we will discuss the limitations of traditional interventions to ameliorate the poor reading fluency of dyslexic children. In the second section of the chapter we will have a closer look on reading fluency, often referred to as the "neglected" aspect of reading. We will discuss the essential role of extensive reading experience in the development of reading fluency and focus on repeated reading, the most familiar and most researched approach to fluency training. A state of the art overview of insights from cognitive neuroscience, concerning fluent and disrupted reading, will be given in the third section of the chapter. In this light we will discuss cognitive, neurobiological and connectionist models on reading development and additionally focus on other areas of skill learning, such as chess. In the fourth section we will amalgamate the various insights, draw several conclusions regarding fluency-oriented instructional practices, and proposed some new directions for dyslexia treatment. Additionally, we will demonstrate the unique possibilities provided by edugames, or computer-game training, for the implementation of the proposed educational principles. As an example we will present an edugame, called LexyLink, which we developed in our own laboratory and which we are currently testing in our institute.
... Research into expertise and template theory have identified a number of processes that have been found to lead to the acquisition of expertise, which may have relevance, or at least should be recognised as factors, in the teaching and coaching of beginners and intermediates (see also Gobet, 2005;Gobet & Jansen, 2006;Gobet & Wood, 1999). These are discussed below. ...
Full-text available
What are the cognitive mechanisms involved in learning in environments beyond the formal context of the classroom? A possible answer to this question lies in the body of literature relating to the nature of expertise. This is arguably a good starting point, as most of this research has studied individuals who have acquired their expertise outside of a classroom environment. This paper addresses the role of practice, feedback, strategies and memory in acquiring expert performance. It considers the extent to which we can apply an understanding of expert behaviour to support beginners and intermediates in formal and non-formal contexts. While there are differences between the processes and behaviours required to attain expertise compared to less intensive forms of learning, there are also important commonalities. Finally, a discussion on the principles based on radical constructivism and situated learning in the light of expertise research shows that, while these approaches correctly emphasise the role of learning by doing and of the social environment, they may be limited by ignoring the role of practice and of teachers’ guidance.
Chess has emerged as a promising domain for psychologists, marketers, computer scientists, and experts from many other domains. In this paper, the author explores attack behavior in chess via the employment of virtual chess players. In other words, he investigates two virtual grandmasters: Chigorin and Waitzkin. Although the two grandmasters have the same level of strength, they have two different chess personalities that determine their attitudes during chess games. While Waitzkin is an attacker grandmaster, Chigorin tends to be a feared attacker, and he leans more toward the defensive side. The investigation is conducted by collecting data from the two grandmasters while competing against three other class-A virtual players. The results were consistent with the previous research that shows the importance of chess strategy in the outcomes of the game. Generally, a chess grandmaster with an attacking strategy performed better than a grandmaster with a defensive strategy. Likewise, the less skilled players (i.e., class-A virtual players) performed differently although their ratings are almost the same. These findings can be valuable to chess software designers and for training chess players of different skills and personalities.Keywordschess personalityattackdefensevirtual humangrandmaster
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Do you need to be a genius to be good at chess? What does it take to become a Grandmaster? Can computer programmes beat human intuition in gameplay? The Psychology of Chess is an insightful overview of the roles of intelligence, expertise, and human intuition in playing this complex and ancient game. The book explores the idea of ‘practice makes perfect’, alongside accounts of why men perform better than women in international rankings, and why chess has become synonymous with extreme intelligence as well as madness. When artificial intelligence researchers are increasingly studying chess to develop machine learning, The Psychology of Chess shows us how much it has already taught us about the human mind.
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Chess is a competitive sport in the classical meaning of the word. One of the most important factors for chess and sport competence is the accumulated time of training. In order to obtain a high level of competence, chess players and athletes alike must spend up 10 years of specific training. In chess and classical sport energy needed for brain activity is first derived from glycogen stores in brain, muscles and liver and later from adipose tissue. Both, chess and classical sport rely on shared energy from glycogen and fat. When the brain needs additional energy, muscles and liver share energy with the brain. When muscles Need additional energy, brain complies with the request of muscles. Energy expenditure, O2 uptake and CO2 production during chess games are similar to those obtained during a marathon. Mental and physical fatigue begin with similar metabolic states: deprivation of glycogen. During competitive chess, athletes must be in good physical condition. Mental profiles of chess players and other athletes correlate with processes such as attention, conflict control, memory, motivation and recognition. In chess there exists no gender-specific excellence; glycogen availability, however, is less developed in female chess players. In chess and in classical sports, the brain, spinal cord, nerves and muscles cooperate in complete harmony. The brain commands everything: in chess the figures, in sport the cellular receptors (baro-, lactate-, gluco-, metabo-, chemo-, thermo-, respiratory-) “send” signals via eyes or metabolic changes to the brain. The brain then decides, what to do: in chess, the player moves a figure; in sports, muscles react according to demand. Physical exercise or chess must be defined by a motor activity completely controlled by the central nervous system (CNS) in combination with a specific competence. In chess as well as in physical exercise, physical stress prepares brain to cognitive stimulation. With respect to biochemical, physiological, neuronal and psychological aspects, chess is equals classical physical exercise and must be recognized as sport.
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Chunking models offer a parsimonious explanation of how people acquire knowledge and have been validated in domains such as expert behaviour and the acquisition of language. In this paper, we review two computational theories based on chunking mechanisms (the chunking theory and the template theory) and show what insight they offer for instruction and training. The suggested implications include the importance of perception in learning, the cost of acquiring knowledge, the significance of segmenting and ordering instruction material, the role of the variability of the instructional material in acquiring schemata, and the importance of taking individual differences into account.
A meta-analysis of findings from 65 independent evaluations of school tutoring programs showed that these programs have positive effects on the academic performance and attitudes of those who receive tutoring. Tutored students outperformed control students on examinations, and they also developed positive attitudes toward the subject matter covered in the tutorial programs. The meta-analysis also showed that tutoring programs have positive effects on children who serve as tutors. Like the children they helped, the tutors gained a better understanding of and developed more positive attitudes toward the subject matter covered in the tutorial program. Participation in tutoring programs had little or no effect, however, on the self-esteem of tutors and tutees.