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The respective roles of the environment and innate talent have been a recurrent question for research into expertise. The authors investigated markers of talent, environment, and critical period for the acquisition of expert performance in chess. Argentinian chess players (N = 104), ranging from weak amateurs to grandmasters, completed a questionnaire measuring variables including individual and group practice, starting age, and handedness. The study reaffirms the importance of practice for reaching high levels of performance, but it also indicates a large variability: The slower player needed 8 times as much practice to reach master level than the faster player. Additional results show a correlation between skill and starting age and indicate that players are more likely to be mixed-handed than individuals in the general population; however, there was no correlation between handedness and skill within the sample of chess players. Together, these results suggest that practice is a necessary but not sufficient condition for the acquisition of expertise, that some additional factors may differentiate chessplayers and nonchessplayers, and that starting age of practice is important.
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Talent and Practice
Gobet. F. & Campitelli, G. (2007). The role of domain-specific practice, handedness
and starting age in chess. Developmental Psychology, 43, 159-172.
The Role of Domain-Specific Practice, Handedness and Starting Age in Chess
Fernand Gobet and Guillermo Campitelli
Centre for the Study of Expertise
Centre for Cognition and Neuroimaging
Brunel University
Address correspondence to
Fernand Gobet
Centre for Cognition and Neuroimaging
Brunel University
Uxbridge, Middlesex, UB8 3PH
United Kingdom
Phone: +44 (1895) 265484
Fax: +44 (1895) 237573
Authors’ note
We thank Neil Charness, Philippe Chassy, Merim Bilalić, and anonymous referees for
comments on this paper.
Running head: Talent and Practice
Talent and Practice
The respective roles of the environment and innate talent have been a recurrent
question for research into expertise. This paper investigates markers of talent,
environment, and critical period for the acquisition of expert performance in chess.
Argentinian chessplayers (N = 104), ranging from weak amateurs to grandmasters,
filled in a questionnaire measuring variables including individual and group practice,
starting age, and handedness. The study reaffirms the importance of practice for
reaching high levels of performance, but also indicates a large variability, the slower
player needing eight times more practice to reach master level than the faster.
Additional results show a correlation between skill and starting age, and indicate that
players are more likely to be mixed-handed than individuals in the general population;
however, there was no correlation between handedness and skill within the chess
sample. Together, these results suggest that practice is a necessary but not sufficient
condition for the acquisition of expertise, that some additional factors may
differentiate between chessplayers and non-chessplayers, and that the starting age of
practice is important.
chess, critical period, domain-specific practice, expertise, handedness, talent
Talent and Practice
The Role of Domain-Specific Practice, Handedness and Starting Age in Chess
Several theories of expertise have been developed to explain the differences in
performance between experts and non-experts in domains such as music,
mathematics, games and sports. One strand of research has tried to find out whether
expertise is due mainly to domain-specific practice within the task environment
(Ericsson, Krampe, & Tesch-Romer, 1993; Howe, Davidson, & Sloboda, 1998;
Starkes, Deakin, Allard, Hodges, & Hayes, 1996) or to some talent underpinned by
genetic factors (Fein & Obler, 1988; Schneiderman & Desmarais, 1988; Winner,
1996). Another strand has aimed to explain cognitive processes underlying expert
performance and its acquisition (Ericsson & Kintsch, 1995; Gobet & Simon, 1996a;
Simon & Chase, 1973).
This article focuses on the talent vs. practice question, the philosophical roots
of which go back to the nature vs. nurture debate. As can be seen in a recent target
article in Behavioral and Brain Sciences (Howe et al., 1998) and in the commentaries
following it, there is currently insufficient evidence to unambiguously support any of
these two extreme positions. Continuing the efforts of others (e.g., Bronfenbrenner &
Ceci, 1998; Csikszentmihalyi, 1998), we wish to present empirical data to show that
this debate is based on a false opposition, and that both talent and practice have an
important role in the acquisition of expert performance.
We first outline the “innate talent vs. practice” debate generally, and the
hypothesis of a critical period for the development of expertise. We then focus on the
relevance of these topics to chess expertise. When presenting the innate-talent
position, we discuss Cranberg and Albert’s (1988) hypothesis, based on Geschwind
and Galaburda’s theory (1985), that non-righthanders should be more represented in
several fields, such as mathematics, music, and chess, than in the general population.
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When presenting the other extreme emphasizing the primary role of learning from the
environment, we summarize Ericsson et al.’s (1993) framework of deliberate practice,
which proposes that the amount of deliberate practice is the key to top-level
performance. We also discuss hypotheses based on the presence of a critical period in
the development of expertise. Following this, we test hypotheses derived from these
three approaches with data based on a questionnaire given to Argentinian
chessplayers of varying skill levels, and we draw the implications of these data for
The “Innate Talent vs. Practice” Debate
As documented in the literature (e.g., Howe et al., 1998), there is a consensus
that individual differences in performance exist in most, if not all, domains of
expertise. The debate arises when researchers try to explain the source of these
individual differences: some authors, continuing the tradition initiated by Galton
(1869/1979), propose that innate talent accounts for most individual differences, while
others argue that these differences are better explained with the extended period of
intense practice that most experts have to go through. Support for innate talent
theories is offered by the study of precocious attainments such as those of Mozart
(music), Ramanujan Srinivasa (mathematics), and more recently, Bobby Fischer
(chess). Several studies in behavioural genetics also suggest a strong inherited
component for intelligence (see Plomin, De Fries, McClearn, & Rutte, 1997, for a
review; but see Grigorenko, 2000, for critiques of this line of research). Candidate
mechanisms for explaining general intelligence include speed of processing, velocity
of the nervous system, and reaction time, among others (Mackintosh, 1998). Since
these abilities (paradoxically, not cognitive) are very basic, it is thought that they are
genetically determined and not modifiable with practice.
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Geschwind and Galaburda (1985) proposed an influential neuropsychological
theory describing the relationship between brain development, immune disorders, and
cognitive abilities. Great exposure or high sensitivity to intrauterine testosterone in
the developing male foetus would lead to a less developed left hemisphere and thus a
more developed right hemisphere than in the general population, a state of affairs that
they called “anomalous dominance.” This would result in a higher probability of
being non-righthanded and being gifted in visuo-spatial abilities, and as a
consequence, in domains such as mathematics, music, and chess. Geschwind and
Galaburda’s (1985) theory has motivated a large number of studies (e.g., Krommydas,
Gourgoulianis, Andreou, & Molyvdas, 2003; Tan & Tan; 2001; Winner, 1996, 2000),
although the results did not always support its predictions. For example, Bryden,
McManus, and Bulman-Fleming (1994) argue that there are serious theoretical and
methodological difficulties with the concept of anomalous dominance, and that the
data on the relationship between handedness and immune disorders show a mixed
pattern, with some conditions (allergies, asthma, and ulcerative colitis) showing
positive associations with left-handedness, as predicted by the theory, but others
(myasthenia gravis and arthritis) showing negative associations. (For further
discussion of Geschwind and Galaburda’s theory, see the section on innate talent and
chess, below.)
At the other extreme of the continuum talent/practice, one finds Ericsson et
al.’s (1993) framework of deliberate practice, which was influenced by Simon and
Chase’s (1973) earlier work on chess expertise. The main assumption is that the
differences observed in performance in a number of domains are due to differences in
the amount of deliberate practice. Deliberate practice consists of activities
deliberately designed to improve performance, which are typically effortful and not
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enjoyable. Moreover, these activities cannot be extended throughout long periods and
must therefore be limited to a few hours a day. High attainments are possible only if
there is strong family support and a favourable environment—essentially being in the
right place at the right time. Ericsson et al. (1993) report results from music expertise
showing that the higher skilled engage more in deliberate practice. The same pattern
was found in karate (Hodge & Deakin, 1998), soccer and hockey (Helsen et al.,
1998), as well as skating and wrestling (Starkes et al., 1996).
Ericsson et al. (1993) do not rule out the participation of inherited factors, but
they limit their role to motivation and general activity levels, explicitly excluding
cognitive abilities. Evidence supporting the role of deliberate practice and
questioning the role of talent includes a series of longitudinal experiments in the digit-
memory span task. The results show that, with sufficient practice, average college
students could achieve higher levels than those attained by individuals previously
thought to have inherited skills (Chase & Ericsson, 1981).
Critical Period
A third explanation for expert performance, besides innate abilities and
practice, is that there exists a critical (or sensitive) period for starting practice in a
given domain. A number of studies have addressed the question of critical period in
domains such as first language acquisition (Lenneberg, 1967), second language
acquisition (Johnson & Newport, 1989; but see also Hakuta, Bialystok, & Wiley,
2003), American sign language (Newman et al., 2001), bird singing (Doupe & Kuhl,
1999), visual system development (Hubel & Wiesel, 1970), and auditory system
development (Knudsen, 1998).
The critical period hypothesis implies that certain phenotypes are more likely
to appear if particular interactions with the environment occur within a given time
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interval. For example, normal vision depends on exposition to light in an early period
of life, and the mastery of language in humans depends on being exposed to a
language early in life. Hensch (2003) analyzed evidence for two possible sources of
this phenomenon: neural plasticity and neuroanatomy. He concluded that both a
reduction of neural plasticity (hence, a reduction in the possibility of creating new
synapses) and a structural consolidation of anatomical circuits are responsible for the
existence of a critical period.
In cognitive tasks such as second language acquisition, the early stimulation in
a critical period may enormously facilitate the acquisition of the skill, but it may not
be a necessary condition for attaining a high-level performance. For example,
although there is substantial evidence for a critical period in second language
acquisition (e.g., Johnson & Newport, 1989), there is also evidence of high
performance in late starters (Birdsong, 1992).
The deliberate practice framework recognizes that there are skills, most
notably absolute pitch (Takeuchi & Hulse, 1993), that can be acquired effortlessly
only during a specific and limited phase of development, perhaps because of
biological maturation. However, the most important aspect of the starting age for the
deliberate practice framework is that the earlier one starts practicing, the more hours
of deliberate practice one accumulates (Ericsson et al., 1993, p. 388).
Research on Chess Expertise
Chess has been an important research domain in the study of expertise (for reviews,
see Saariluoma, 1995, and Gobet, De Voogt, & Retschitzki, 2004), and, more
recently, in the study of individual differences (Frydman & Lynn, 1992; Gobet,
Campitelli & Waters, 2002; Howard, 1999, 2001, 2005; Waters, Gobet, & Leyden,
2002; see Holding, 1985, for earlier research). One invaluable feature of chess is the
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presence of a rating scale used internationally (Elo, 1978), which measures ability
from world-class players down to novices. The World Chess Federation (FIDE,
Fédération Internationale des Echecs) publishes rating lists of its members every
three months and awards the titles of grandmaster, international master and FIDE
master. Grandmasters (GMs) are usually rated above 2500 Elo, international masters
(IMs) above 2400, masters between 2200 and 2400 (players above 2300 are often
called FIDE masters), Experts between 2000 and 2200, class A players between 1800
and 2000, class B players between 1600 and 1800, and so on. In spite of the presence
of these titles, it is important to realise that the Elo scale makes it possible to
continuously measure the level of expertise, instead of separating individuals in
arbitrary categories such as experts, intermediates, and novices. The existence of a
continuous variable of chess skill, as opposed to a discrete variable, makes the use of
some powerful statistical analysis, such as regression and correlation analysis, more
Innate Talent
Based upon Geschwind and Galaburda’s (1985) theory, Cranberg and Albert
(1988) hypothesize that the primary neurological components of chess skill are
located in the right hemisphere of the brain, and that chess skill develops more in
males and non-righthanders than in females and righthanders, respectively. They
argue that individuals with enhanced right-hemisphere development might have an
advantage at chess, because the right hemisphere is known to engage spatial reasoning
and pattern recognition, which both directly relate to chess skill (e.g., Simon & Chase,
1973). Cranberg and Albert’s (1988) reasoning runs as follows: chess is a visuo-
spatial task, visuo-spatial tasks are performed by the right hemisphere, non-
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righthanded individuals have the right hemisphere more developed, so non-
righthanders should be more represented in the chess population.
There is extensive literature suggesting that visuo-spatial tasks are mainly
performed by the right hemisphere, although it should be recognized that the left
hemisphere is often engaged in these tasks. The involvement of the right hemisphere
seems particularly strong for tasks engaging coordinate or metric relations,
recognition of patterns as wholes, and spatial reasoning (e.g., Benton, 1985; Bever,
1975; Corballis, 2003; Kogure, 2001).
The link between visuo-spatial abilities and chess is more tenuous (see Gobet,
de Voogt, & Retschitzki, 2004, for a review). On the one hand, Charness (1976),
Robbins et al. (1996), and Saariluoma (1991) showed that when chessplayers were
presented with a visuo-spatial secondary task, their performance in a chess task
decreased, but when the secondary task was verbal, the performance remained
unchanged. On the other hand, the relationship between visuo-spatial abilities and
chess skill has turned out to be more difficult to document than expected, with studies
such as Waters et al. (2002) failing to find such a link with adults, and other studies,
such as Frydman and Lynn (1992), finding a link between chess and performance IQ
with a sample of young chessplayers. Waters et al. (2002) attempted to reconcile
these results by suggesting that visuo-spatial skills may be important in the early
development of chess skill, but other skills become important over time.
There is some empirical support for the role of the right hemisphere in chess
skill. Cranberg and Albert (1988) found that extended lesions of the left hemisphere
hardly affect chess performance; however, they did not present evidence with
extended right-hemisphere lesions, which would offer a more direct test of their
hypothesis. In addition, they recorded the EEG of a chessplayer while he was playing
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blindfold chess. The player presented normal left-hemisphere activity, but
abnormally high right-hemisphere activity. Chabris and Hamilton (1992) performed a
divided-visual-field experiment with male chessplayers. They showed that the right
hemisphere performs better than the left hemisphere at parsing according to the
default rules of chess chunking, but that the left hemisphere performs better than the
right at grouping pieces together in violation of these rules. Onofrj et al. (1995)
performed an experiment with single photon emission computerized technology
(SPECT) while chessplayers were solving a chess problem. They found a non-
dominant dorso-prefrontal activation and also a lower non-dominant activation on the
middle temporal cortex. The four righthanders presented activation on the right
hemisphere, and contrary to the predictions of Geschwind and Galaburda’s (1985)
theory, the left-hander presented activation on the left-hemisphere. Finally, Atherton,
Zhuang, Bart, Hu, and He (2003) found that brain activity was either bilateral or
larger in the left hemisphere. In summary, although there is some evidence in favour
of the use of the right hemisphere in chess, the results of the last two experiments are
problematic for Geschwind and Galaburda’s theory.
Sending an informal questionnaire to 396 US chessplayers, Cranberg and
Albert (1988) collected data on handedness to test another prediction derived from
Geschwind and Galaburda’s (1985) theory—that there should be proportionally more
non-righthanders in the chess population than in the general population. They found
that there were 18% of non-righthanders in the chess population, which is
significantly different from the rate in the general population (10 to 13.5%; Bryden,
1982; Geschwind, 1983; Gilbert & Wysocki, 1992). However, they could not find
differences between a group of high-level players and a group of low-level players.
The higher prevalence of non-righthanded individuals in the chess population as
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compared to the normal population can be seen as a marker of the role of right-
hemisphere processing.
Domain-Specific Practice
In their seminal study of perception in chess, Simon and Chase (1973) pointed
out that a decade of intense commitment with the game is necessary in order to reach
grandmaster level. They estimated that a master has spent roughly from 10,000 to
50,000 hours playing or studying chess, and that a class A player has spent from 1,000
to 5,000 hours. Thus, it takes about 10 years of study and practice to become an
expert. As we have seen, Ericsson et al. (1993) have taken these results to their
extreme by stating that levels of performance are not limited by factors related to
innate individual differences, but that they can be further increased by deliberate
efforts. Note that Simon and Chase (1973) themselves were open to the possibility of
individual differences due to genetic factors.
The proponents of deliberate practice (e.g., Ericsson et al., 1993; Ericsson &
Charness, 1994; Howe et al., 1998) reject the existence of innate cognitive talent,
arguing that there is no evidence for it and that expert performance is directly related
to the amount of deliberate practice. Charness, Krampe and Mayr (1996) tested this
theory in the field of chess by asking players to report the number of hours spent both
studying chess alone and playing or analyzing games with others. The results showed
a strong correlation between chess skill—measured by the Elo rating—and the
number of hours spent studying alone. Charness et al. also found a strong but less
important correlation between chess skill and the number of hours spent studying or
practicing with others. Thus, they proposed that the number of hours of study alone,
rather than the number of hours of studying and practicing with others, best measures
the concept of deliberate practice.
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Biographies of world chess champions and other strong grandmasters (e.g.,
Botvinnik, 2000; Brady, 1973; Forbes, 1992) show that intense dedication to chess is
needed to attain high levels of performance. Krogius (1976) presents data showing
that former world champion Bobby Fischer—the case mostly discussed by the
proponents of the innate talent hypothesis—is almost within the bounds of the 10-year
practice rule. Fischer attained his first grandmaster (GM) result 9 years after he
started playing chess. Even Judith Polgar, GM at 15 years and 4 months 28 days
(15,4,28), started intensive practice at 4 (Forbes, 1992). However, there are more
recent cases that do not seem to respect the 10-year rule. World champion Ruslan
Ponomariov attained the GM title at the age of 14,0,17 and Peter Leko at 14,4,22. In
interviews, both of them reported that they had started playing chess at the age of 7.
Also, Ponomariov attained 2550 Elo points (considered GM level) at the age of 12,8,0
and Leko at the age of 13,9,0. More recently, Teimour Radjabov obtained the GM
title at the age of 14,0,14. More impressively, Sergey Karjakin obtained the GM title
at the age of 12,7,0 and he was recruited at the age of 11 to help Ponomariov in his
World Championship match. Finally, Magnus Carlsen obtained the GM title at the
age of 13,3,27 and reported: “I learned the moves when I was 5 or 6 but hardly played
until I turned 8. I played my first (children’s) tournament in July 99 at the age of 8.5”
(Friedman, 2003). Hence, although there is substantial evidence suggesting that
domain-specific practice is essential for the acquisition of high-level expert
performance, it may be the case that inter-individual variability has been
underestimated in previous research.
Critical Period
A number of studies have investigated the role of a critical period in chess.
Elo (1978) suggested that early introduction to the game and to organized competition
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is a prerequisite to the attainment of mastery. He presented data of 60 contemporary
masters, whose mean starting age was 9.6 (range: from 5 to 16) and whose mean age
of starting organized competition was 14.8 (range: from 10 to 18). Krogius (1976)
presented data of grandmasters and international masters whose mean starting age
was 10.5 years. He found that a group of “early starters” (mean starting age, 6.5)
obtained the first GM result earlier (mean age 22.8) than a group of “late starters”
(mean starting age, 13.6; mean age of first GM result, 25.3). However, the first group
required more time to reach the GM result (16.3 years and 11.7 years, respectively).
In Charness et al.’s (1996) study, the mean starting age was 10 ± 4.8 and the mean age
of becoming serious at chess was 16.7 ± 8.8. The correlation between these variables
and chess rating was -.35 and -.36, respectively. However, when entered into a
multiple regression, these variables did not account for more variance than what was
already accounted for by the cumulative number of hours of serious study alone;
hence, Charness et al. concluded that younger starting age in their sample was not
associated with greater achievement when hours of cumulative practice were taken
into account (Charness et al., 1996, p. 71). Doll and Mayr (1987) found a
nonsignificant correlation between starting age and rating (r = -.27). The starting age
of the national players of their sample was 10.3 years and that of international players
was 7.25 years. The same trend was obtained in the age at which players joined a
chess club (13.8 and 10.5 years, respectively). Ericsson et al. (1993) used some of
these data to support their hypothesis of deliberate practice: basically, the younger the
players start playing chess, the more hours they spend studying it.
Overview of the Study
We submitted a large sample of players both to the Edinburgh Handedness Inventory
(Oldfield, 1971) and a questionnaire similar to that used by Charness et al. (1996).
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The results allowed us to systematically address the issues identified in the
introduction. First, we tested Cranberg and Albert’s (1988) hypothesis that
handedness is a marker for chess ability. Second, we tested Ericsson’s et al. (1993)
hypothesis that individuals’ current performance is directly related to the amount of
deliberate practice. Third, we tested Simon and Chase’s (1973) hypothesis that it
takes at least 10,000 hours of study and practice to reach master level. Our fourth
hypotheses relates to the possibility—verified in our study—that deliberate practice
fails to account for all of the variance, beyond limits in measurement. We tested the
possibility that starting age may be crucial for the later development of expertise, as
suggested by Elo (1978). (We discuss the detail of the practice activities and the
dynamics of the co-evolution of practice and performance in a separate paper.)
The participants were 104 Argentinian chessplayers (101 males and 3
females). They filled in a three-section questionnaire that was left visible on a desk in
the Círculo de Ajedrez Torre Blanca, one of the most important chess clubs in Buenos
Aires (Argentina). Posters asking for volunteers were also put on the notice board of
the club. One of the authors went to several tournaments, both in the Círculo de
Ajedrez Torre Blanca and other chess clubs in Buenos Aires, and distributed the
questionnaires to the players participating in these tournaments. Three grandmasters
(mean age = 31 years, standard deviation (±) 3.5), 10 international masters (29.1 ±
10.7), 13 FIDE masters (27.1 ± 8.9), 39 untitled players with international rating (30.2
± 13.9), and 39 players without international rating (33.2 ± 17.8) filled in the
questionnaire. The mean age of the sample was 30.8 ± 14.6 (range: from 10 to 78
years, median = 28 years). Since not all players had international rating, we used the
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national rating in order to measure chess skill. Note that the two ratings were closely
related: for the 65 players having both international and national rating, the
correlation between the two scales was .89.
The range of the sample was 983 points
(from 1490 to 2473), with a mean of 1990.8 and a standard deviation of 221.5. Since
the Elo rating has a normal distribution with a theoretical standard deviation of 200,
our sample had a range of nearly 5 standard deviations.
The questionnaire was divided into three sections. (Not all players answered
all questions, with the result that the number of data points varies across our
measures.) The first section (see appendix 1 for an English translation) contained
questions about date of birth, age, profession, international rating, national rating,
speed chess rating (rating of the Círculo de Ajedrez Torre Blanca),
chess title, chess
category, age when starting to play chess (henceforth, starting age), age when starting
to play chess seriously (henceforth, serious age),
age at joining a chess club (club
age), years of coaching, number of chess books owned, number of speed games
played, and type of training (blindfold chess, reading games without seeing the board,
use of chess databases, and use of chess programs). The second section contained a
grid in which the participants had to fill out the number of hours per week they spent
studying chess alone in each year (henceforth, individual practice). They also had to
fill out a second row with the number of hours per week they spent studying or
practicing with other players, including tournament games (henceforth, group
practice). We estimated the number of hours studied per year by multiplying the
figures reported by 52, and then we calculated the sum of the total hours spent with
individual and group practice in the whole chess career. In some analyses, we added
the values of these two variables to obtain a single variable called total practice. The
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unit of analysis for individual practice, group practice, and total practice was the
cumulative number of hours.
The third section contained a Spanish translation of a modified version (Ransil
& Schachter, 1994) of the Edinburgh handedness inventory (Oldfield, 1971). The
questionnaire had 10 items enquiring about hand preference for a variety of activities
such as writing, drawing, or using a knife. For each item, the possible responses were
“always left,” “sometimes left,” “no preference,” “sometimes right,” and “always
right,” which were coded as 1, 2, 3, 4, and 5, respectively. Moreover, we asked the
participants whether they considered themselves righthanded, lefthanded, or
ambidextrous. When computing the prevalence of righthandedness, we used self-
reported handedness in order to compare our results to Cranberg and Albert’s (1988).
When computing the correlation with other variables, the total score of the Edinburgh
inventory was used as a measure of the direction of handedness (the minimum of 10
indicating extreme left-handedness, and the maximum of 50 indicating extreme right-
handedness). In line with current literature (Barnett & Corballis, 2002; Niebauer &
Garvey, 2004; Propper & Christman, 2004), we also computed an index of degree of
handedness. We first re-centred the data around zero, extreme left-handedness being
now denoted by –100, and extreme right-handedness being denoted by +100, and we
then took the absolute value of the scores.
The individual and group practice variables warrant some comments.
Charness et al. (1996) as well as Charness, Tuffiash, Krampe, Reingold, and
Vasyukova (2005) argue that individual practice is better than group practice as a
measure of deliberate practice, which means that competition should be excluded as a
deliberate practice activity leading to expert performance (see also Ericsson et al.,
1993, p. 368). However, in Charness et al.’s study (1996, Table 2.4), players
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considered that active participation in chess tournaments is the most important
activity to improve performance. In addition, competitive chess enables interaction
with stronger players, in particular during the post-mortem analysis of the game,
where valuable information can be gained. (See Helsen et al., 1998, and Janelle &
Hillman, 2003, for the role of competition in sport). As a result, we used three
measures of deliberate practice: individual practice, group practice (which includes
tournament games), and total practice. In order not to confuse these measures with
Ericsson et al.’s (1993) definition of deliberate practice, we did not use the label
“deliberate practice” for them.
Table 1 shows the descriptive statistics of all variables as a function of level of
expertise. Table 2 displays the correlation matrix for all variables. Note that, for the
variables submitted to a log-transformation in Table 2, Table 1 shows the value of
these variables before transformation.
The three women were excluded from this analysis since the trend in
handedness is different for women and men (Cranberg & Albert, 1988; Gilbert &
Wysocki, 1992). Six men did not fill out the inventory; therefore, the following
analyses were carried out on 95 participants. We found that 17.9% in our male
sample, which is close to the 18% found by Cranberg and Albert, were self-defined as
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either lefthanders or ambidextrous (from now on, we use Cranberg & Albert’s
terminology and call this group “non-righthanders”). We also asked a male control
sample (N = 98), matched for age and education level, to fill in the Edinburgh
questionnaire and to report their pattern of handedness. In this control sample, 10.2%
self-defined as non-righthanders, which was consistent with what had been found in
the general population in other studies (10 to 13.5% of non-righthanders; Bryden,
1982; Geschwind, 1983; Gilbert & Wysocki, 1992). The mean of the inventory raw
scores, a measure of direction of handedness, was 41.2 (SD = 11.3) for the chess
sample and 43.9 (SD = 9.7) for the control sample. A t-test showed that the difference
was statistically significant (t (191) = 1.78, p < .05, one-tailed). However, a test of
proportion between two independent samples showed that the difference in proportion
between the chess sample and the control sample is only marginally significant (z =
1.54, p = .06, one-tailed). The mean scores for degree of handedness were 76.7 (SD =
21.1) for the chess sample and 83.1 (SD = 17.6) for the control sample. A t-test
showed that the difference was statistically significant (t (191) = 2.28, p < .025, two-
tailed). (A two-tailed test was used as Cranberg and Albert, 1988, do not make any
prediction about degree of handedness.) Within the chess sample, there were no
reliable differences in the percentage of non-righthandedness between titled players (n
= 24; 8.3%) and untitled players (n = 71, 21.1%; χ
(1) = 1.98, p = .16), and the trend
was even opposite to the prediction. Titled and untitled players did not differ with
respect to the degree of handedness (t (96) = .56, ns). Finally, there was no reliable
correlation between the degree or direction of handedness and national rating or speed
rating (see Table 2).
Our results show the same pattern as that found by Cranberg and Albert
(1988): chessplayers are more likely to be non-righthanded in comparison to the
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general population, but, within chessplayers, handedness does not correlate with chess
skill. To explain the latter result, Cranberg and Albert hypothesized that the group of
weaker chessplayers contained young non-righthanded players who could become
masters in the future; this may lead to an under-estimate of the proportion of non-
righthanders in the group of stronger chessplayers, and thus to a weaker correlation
than the real one. In our sample, the age gap between the two groups was not as wide
as in Cranberg and Albert’s sample, so this explanation does not seem to apply. We
will present alternative explanations in the discussion.
Amount of Variance Explained by Deliberate Practice
In order to compare our results with Charness et al.’s (1996), we followed their
procedure. We entered the eight variables they used into a multiple-regression
analysis (see Table 3). In Charness et al.’s study, the eight variables together
accounted for 55% of the variance, with individual practice and log number of books
being the significant predictors. When they entered only the significant predictors
into the regression analysis, the amount of Elo rating variance accounted for was
59%. (Charness et al., 2005, using a slightly different set of predictors, found that the
regression analysis accounted for 39% and 28% of the variance in their two samples.)
In our data, the eight variables jointly accounted for 34% of the variance of
national rating. The significant predictors were log group practice and coaching (0,1).
The regression equation including only the significant predictors was:
national rating = 946 + 243 * log (group practice) + 168 * coaching (0,1)
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with an adjusted R
of .364 (F(2,85) = 25.9, p < .001); the 95% confidence intervals
were 162.1 - 324.1 for log group practice, and 79.1 - 257.1 for coaching (0,1). This
means that there was an increase of 243 points in national rating for each log unit of
group practice (e.g., from 100 hours of group practice—2 log units—to 1,000 hours of
group practice—3 log units) and an increase of 168 points in national rating for the
players that had received coaching at some point of their chess career.
The bivariate correlations (see Figure 1) suggest that national rating and speed
chess rating are better predicted by group practice than by individual practice. Both
variables are significantly correlated with national rating, but individual practice is not
correlated with speed chess rating at the .01 level. However, a t test for the difference
between two non-independent correlation coefficients did not show reliable
differences between the correlations involving individual practice and those involving
group practice (national rating: t(86) = 1.42, ns; speed rating: t(60) = 1.55, ns).
Test of Simon and Chase’s (1973) Hypothesis
Simon and Chase (1973) estimated that it was necessary to dedicate between
10,000 and 50,000 hours to chess for achieving master level. We tested this
hypothesis by calculating the cumulative number of hours spent in group and
individual practice until players reached 2200 Elo points (i.e., master level). As we
had access to archives containing the Elo lists with the rating of Argentinian players,
we were able to find out at which age the rated players of our sample achieved 2200
Elo points.
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Based on 34 players, the mean number of hours of total practice accumulated
when players attained master level was 11,053, with a standard deviation of 5,538,
and a range of 20,592 (from 3,016 to 23,608). Thus, the lower bound of Simon and
Chase’s estimate roughly coincides with the mean of our data. However, we should
also highlight the variability of our data. One player attained master level with just
3,016 hours, while another needed 23,608 hours (a 1:8 ratio). Furthermore, some
players in our sample had spent more than 25,000 hours of total practice (i.e., more
hours than the “slowest” master) without attaining the master level.
From these data, we can draw two main conclusions. First, the mean number
of hours of total practice supports Simon and Chase’s claim that a long period of
practice and study is required to reach master level. Second, as shown by the
measures of variability in the number of hours practicing and studying chess, total
practice is not a sufficient condition for becoming a master. The second part of this
conclusion might raise the objections that (a) by combining individual and group
practice we may have artificially inflated the variability of the data, and (b) individual
practice, and not total practice, is the closest marker of deliberate practice, as
indicated by Charness et al. (1996). To meet these objections, we also report the data
of group and individual practice separately. The mean number of hours of group
practice until reaching master level was 6,727, with a standard deviation of 3,298
hours, and a range of 12,584 hours (from 1,612 hours to 14,196 hours). The ratio
between the slowest and the fastest player was thus 1:9. With individual practice, the
mean was 4,325 hours, with a standard deviation of 3,266 hours and a range of 15,392
hours (from 728 hours to 16,120 hours). Thus, the slowest player spent 22 times more
hours than the fastest player! The variability in the number of hours of individual
practice to reach master level is so great that it supports our conclusion, based on
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hours of total practice, that domain-specific practice is not a sufficient condition for
expert performance.
Critical Period
In order to disentangle total practice and onset ages, we performed partial
correlations between the onset variables (starting age, serious age, and club age) and
ratings (national and speed rating), controlling for total practice. In all the analyses
below, ages were log-transformed, because of the non-normality of the data and the
non-linear relationship between age and rating. The partial correlations between
national rating and starting age, serious age, and club age were -.23 (p < .02), -.40 (p
< .001) and -.36 (p < .001), respectively. In all cases, the correlations were calculated
with over 80 players; missing values were discarded pairwise and, since it was
predicted that starting earlier would lead to better performance, the test of significance
was one-tailed. Without controlling for hours of total practice, the bivariate
correlations were -.28 (p < .003), -.37 (p < .001), -.34 (p < .001), respectively
(calculated over 100 players) (see Figure 2). Similar partial correlations were found
with speed chess rating, where the correlations were computed with 60 players:
starting age = -.18 (p < .08), serious age = -.47 (p < .001) and club age = -.41 (p <
.002). Without controlling for hours of total practice, the bivariate correlations
(calculated with over 70 players) were -.23 (p < .03), -.46 (p < .001), and -.40 (p <
.001), respectively.
The partial correlations were similar when current age is
partialled out in addition to total practice, with the difference that the correlation
between starting age and speed chess rating is now only -.09 (p > .20). A test of the
difference between two non-independent correlations with listwise deletion shows
that the correlations were significantly higher for speed than for normal chess with
serious age, t (67) = 4.01, p < .05, and club age, t (67) = 4.09, p < .05.
Talent and Practice
The scatterplots in Figure 2 may give the impression that the results reported
above can be explained by only a few participants that started playing seriously or
joined a chess club late in life. We computed the partial correlations removing the
players that started playing seriously or joined a chess club after the age of 30
(respectively n = 4 and n = 6). The correlations, although smaller, were still
statistically significant (serious age: -.23, p < .03, and club age: -.21, p < .04 for
national rating, and -.38, p < .003 and -.32, p < .009 for speed rating, respectively).
In summary, both for national and speed ratings, the age at which players start
playing chess seriously and enter a club correlates with current rating, even when the
amount of practice has been partialled out. Therefore, our data are consistent with
Elo’s (1978) proposal of the presence of a critical period. This conclusion is further
supported by an analysis of the absolute age at which the strong players start playing
chess seriously. The means and standard deviations (±) for the different levels were
the following: grandmasters: 11.3 years ± 1.1 (n = 3), international masters: 10.3 ± 3.6
(n = 9), FIDE masters: 11.6 ± 3.1 (n = 13), rated players: 14.2 ± 3.9 (n = 39), and non-
rated players: 18.6 ± 11.5 (n = 36). Almost all players with title started playing chess
seriously no later than the age of 12. In our sample, the probabilities to become an
international level player (grandmaster or international master) are about 1 in 4 (.24)
for players starting to play seriously at the age of 12 or before, and only 1 in 55 (.018)
for players starting after the age of 12 (χ
(1) = 12; p < .002), suggesting that one is
very unlikely to achieve international level when serious play begins after the age of
12. On the other hand, a cut-off age of 12 is not apparent in our sample with respect
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to achieving a national level (2000 Elo points), since 54.5% of the players who started
to play seriously after the age of 12 reached the national level. This is not far, but still
statistically different, from 75.6% with the players who started to play seriously at the
age of 12 or before (χ
(1) = 4.7; p < .03).
This paper has investigated different variables in order to uncover which ones
predict chess skill best. The results shed new light on the practice vs. talent debate, in
particular on the roles of handedness, domain-specific practice, and starting age in the
development of skill.
As a possible source of individual differences not related to the expertise
environment, we focused on handedness. Using a well-validated measure (the
Edinburgh Inventory), we found that handedness and chess were related (non-
righthanders tended to be more represented in our chess sample than in the general
population, and chessplayers’ degree of handedness was less strong than for the
control group). However, there was no relation between handedness and skill level
within our sample of moderately to highly skilled chess players. In general, these
results replicate Cranberg and Albert’s (1988), but also add new information by
showing evidence of reduced degree of handedness with chessplayers.
One possible explanation for the relation between chess and handedness, but
the lack of relation between handedness and skill level, is that having a more
developed right hemisphere does not necessarily mean that one is not righthanded
(Geschwind & Behan, 1984). In other words, there may be chessplayers with more
developed right hemisphere who are righthanders. Indeed, there is evidence that only
one third of the people with more developed right hemisphere are not righthanded
Talent and Practice
(Geschwind & Behan, 1984). If this is the case, our failure to identify a correlation
between skill and handedness does not mean that brain asymmetry is irrelevant, but
that other measures of brain asymmetry, including measures of structural differences
using MRI, are needed to test this hypothesis. Another possibility is that non-
righthanders are more likely to consider and choose a visuospatial discipline such as
chess, and then easily improve during the earlier stages (that is why there are more
non-righthanders in the chess population), but thereafter the commitment to the
discipline is the factor that causes the largest improvement (that is why there are no
differences in handedness between skill levels). A third possibility, in line with
research into mathematical talent, is that the link between non-righthandedness and
visuospatial ability is underpinned more by enhanced inter-hemispheric interaction
than by an enhanced right hemisphere (Benbow, 1987; Singh & O’Boyle, 2004). This
explanation receives direct supported from our data on degree of handedness showing
that chessplayers tended to be more mixed-handed than the control group. Finally,
although our sample spanned five standard deviations of skill, it did not cover the full
range from absolute beginners to world champion. Therefore, it is not impossible that
restriction of range may have affected our results and that a correlation might emerge
with the full range of chess skill.
Domain-Specific Practice
While the role of practice has been emphasized for a long time (e.g., by De
Groot, 1946/1978), Ericsson et al. (1993) have taken the extreme position that
domain-specific practice is a sufficient, not merely necessary, condition for expertise.
Our data are not consistent with this position. Although the overall correlation
between individual and group practice and chess skill shows a reliable pattern, this
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variable on its own explained less than 50% of the variance. Thus, our data indicate
that domain-specific practice is necessary, but not sufficient, to acquire master level.
According to Simon and Chase (1973), one has to spend between 10,000 to
50,000 hours of practice and study to become a chess master, which would take at
least 10 years. In our sample, the mean of total practice (11,053 hours) coincided
with the lower bound of Simon and Chase’s range. However, there was also a
remarkable amount of variability, which was apparent in the scatter-plots of Figure 1
and in the numerical estimates of variability we have provided. Some players with
relatively few hours of total practice (even as low as 3,016 hours) achieved master
level, while others needed much more time (up to 23,608 hours). This 1:8 ratio is so
large as it is very unlikely that it can be explained by errors in measurement alone. In
addition, some players with a huge amount of practice (more than 25,000 hours) did
not reach the master level. Thus, while our data support Simon and Chase’s claim
that a long period of practice and study is required to become a master, the substantial
variability in the number of practice hours is not consistent with the view that practice
alone is sufficient for becoming a master. This result must count against Ericsson et
al.’s (1993) theory of deliberate practice, and in particular the “monotonic benefits
assumption” that “the amount of time an individual is engaged in deliberate practice
activities is monotonically related with that individual’s acquired performance (p.
Interestingly, our estimates are much below the upper bound of Simon and
Chase’s range (50,000 hours). This result is consistent with other measures (e.g., the
number of years needed to reach grandmaster level, which we have discussed in the
introduction; see also Howard, 1999, 2001), which show that there has been recently a
speeding-up in the time to reach high levels of expertise. Whether this speeding-up
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can be best explained by a rise in the general level of intelligence (Howard, 1999,
2001) or by changes in training methods (e.g., apparition of computerized databases)
and in the structure of the chess environment (e.g., increased opportunity to play in
tournaments) is still debated (e.g., Gobet et al., 2004; Howard, 2005).
Starting Age
The final goal of our study was to explore the possibility that there was a
critical period in the acquisition of chess expertise. In order to disentangle measures
of onset age (starting age, age of becoming serious, and age of joining a chess club)
and practice, we carried out a partial correlation between these measures and current
rating (national and speed rating, respectively), controlling for the number of hours of
individual practice.
The results indicated that the correlation between current rating and the age at
which players started playing chess seriously or joined a club was significant even
when controlling for the number of hours of practice. This correlation was even
stronger with speed chess, although not significantly different. Moreover, almost all
players who obtained a title started studying seriously or joined a chess club when
they were 12 years old or before. Interestingly, the only two exceptions—a FIDE
master and an international master who were taught the rules at 14 years of age and
joined a club at 15 years of age—were non-righthanders (self-defined ambidextrous
and left-handed, respectively). Thus, being actively exposed to a chess environment
at an early age (i.e., not just playing chess with friends or relatives, but reading chess
books, solving problems, and receiving feedback from advanced players) is important
for developing skills. These results support Elo’s (1978) proposal of a critical period
in skill development.
Talent and Practice
To explain how differences in starting age may lead to individual differences,
independently of the amount of practice, we suggest two explanations. First, it is
known that young children pay attention to different features than teenagers or adults
(e.g., Siegler, 1986), including that they are more tuned to concrete patterns than
adults, who direct their attention to more abstract patterns (Piaget & Inhelder, 1955).
Thus, starting at different ages may lead to differences in what is learned and how
knowledge is organized. This is consistent with the well-established role of chunking
and pattern recognition in chess and other domains (De Groot, 1946/1978; Gobet,
1998; Gobet et al., 2001; Gobet & Simon, 1996b; Simon & Chase, 1973). Chunking
offers a well-specified mechanism for explaining the acquisition of implicit
knowledge (see Gobet et al., 2001, for details), and differences in the efficiency of
chunking mechanisms or in what is being learnt could explain individual differences
in skill. Calderwood, Klein and Crandall (1988) as well as Gobet and Simon (1996b)
have proposed that knowledge-based pattern recognition is essential to play high-
quality games in speed chess, because there is little time to carry out look-ahead
search. Indeed, this hypothesis, combined with Elo’s hypothesis of a critical period,
leads to two predictions that can be tested in our data: (a) there should be a high
correlation between normal ratings and speed chess ratings, because these two forms
of chess require essentially the same type of procedural, recognition-based
knowledge; and (b) starting age should correlate higher with speed chess than with
normal chess, because reduced thinking time in speed chess enhances the role of
pattern recognition skills, which should be easier to learn when young. Consistent
with Burns (2004), who found that speed chess accounts for 81% of the variance of
slow chess, the first hypothesis was supported by our data, which showed a high
correlation between national and speed rating (r (72) = .83, p < .001). The second
Talent and Practice
hypothesis was also supported by our results, as serious age and club age correlated
higher with speed chess than with normal chess.
The second explanation, which is not inconsistent with the first one, is that
starting to interact with a specific environment at earlier ages may facilitate the
acquisition of knowledge later used in pattern recognition because the brain shows
more plasticity to environmental stimulation at young ages. There is substantial, but
not always uncontroversial, evidence for the idea that young peoples’ brains show
more plasticity in learning than those of adults (Elman et al., 1996; Hensch, 2003;
Johnson & Newport, 1989). For example, Hensch (2003) proposed that both a
reduction of neural plasticity outside a critical period and a structural consolidation of
neuroanatomical circuits during the critical period explain why several skills are much
more easily acquired during the critical period.
Conclusions and Future Directions
In summary, starting to play seriously not later than the age of twelve, carrying
out individual practice such as reading books, playing with others, and receiving
feedback from a coach seem to be all important factors to attain a high level of
expertise in chess. There was some evidence that individual differences in abilities
not related to the chess environment differentiate between players and non-
chessplayers (direction and degree of handedness). Together, these results suggest
that the talent/practice debate is based on a false opposition, and hint at the need to
promote developmental theories of expertise which provide mechanisms reconciling
these two radical positions.
In comparison to Charness et al. (1996) and Charness et al. (2005), we used a
measure of handedness and designed a few additional questions about the amount of
practice (e.g., use of computer databases and computer programs). A further
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improvement was that, in addition to the dependent variable of Elo rating for standard
games, we also used the rating for speed chess. This allowed us to address questions
related to the skills required by this special modality of chess, in particular pattern
recognition (Gobet & Simon, 1996b).
We acknowledge three limits of our study. First, although often used within the
framework of deliberate practice, retrospective questionnaires do not possess ideal
reliability. This being said, empirical research has shown that they correlated
reasonably well with independent measures (Ericsson et al., 1993, p. 380). In
addition, this methodology leads to replicable results. For example, one key result in
our study—the correlation between cumulative hours of individual practice and skill
level (r = .42)—is reasonably similar to that estimated by Charness et al. (1996; r =
.60), and Charness et al. (2005; r = .54 for the extended sample from their 1996 study,
and r = .48 for an independent sample), in spite of the fact that the data were collected
in three continents.
Second, being a correlational study, this investigation cannot establish causation.
Even if a correlation of 1 is obtained between the number of hours of practice and
chess rating, this does not necessarily mean that practice is the cause of high-level
performance; it may well be the case that good players practice more than weak
players because they are rewarded by their victories. However, correlational studies
have the advantage that they provide key information in order to carry out further
studies which require more time and resources (e.g., longitudinal studies). Third,
although our sample was a fair representation of the population of chessplayers in
Argentina with respect to gender and ethnicity, this population underrepresents
women and some ethnic groups compared to the general population. This may affect
the generalizability of our findings
Talent and Practice
Traditional research on expertise has mainly relied upon standard experiments,
aptitude tests, and questionnaires. The next step requires novel approaches. A first
possibility is to submit a few experts to a large variety of measures, addressing both
issues related to talent and to practice, and to analyze these data individually in order
to provide computational theories of these individuals (Gobet & Ritter, 2000). We are
currently collecting such data in our laboratory, subjecting chessplayers from novice
to grandmasters to a number of measures, including brain activation, eye movements,
visuospatial memory, reaction times, and domain-specific memory tasks. A second
approach—actually an extension of the first one—is to carry out longitudinal studies
following the development of novices over many years until they hopefully become
experts, again collecting a variety of data aimed both at identifying individual
differences and the role of practice, and at developing computational models of these
data. The use of computational modelling is inescapable, due to the complexity of the
processes under study, the complexity of the environments being assimilated by the
experts, and the amount and dynamic character of the data being collected.
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(English translation from Spanish)
Answer all the questions, please. Leave a blank space only if you do not possess the
ratings requested. If you do not know your rating/ratings, you can ask the secretary
for it/them. Alternatively, you can write down your name to allow us to look up your
ratings. Moreover, fill out the form of hours of study and practice in chess following
the instructions. Thank you for your participation.
1) How old are you?____________________________________________________
2) What is your profession? ______________________________________________
3) What is your national Elo rating?________________________________________
4) What is your speed chess rating?________________________________________
5) What is your category?________________________________________________
6) What is your international Elo rating? ___________________________________
7) Do you have any title (GM, IM, FM)? Which one?__________________________
8) At what age did you learn how to play chess?______________________________
9) At what age did you start playing chess seriously?__________________________
10) How many hours per week (on average) have you studied alone during the current
year? __
11) How many hours per week (on average) have you studied or practiced chess with
other chess players (including tournament games) during the current year?________
12) Have you ever joined a chess club?_____________________________________
If yes, at what age for the first time?_______________________________________
13) Have you ever received formal chess instruction from a chess coach?__________
Individual coaching: from (age)______to (age)_______________________________
Group coaching: from (age)________to (age)________________________________
14) How many books do you have? (excluding chess journals)___________________
15) Do you play blindfold chess?_________________________________________
16) Do you reproduce chess games from journals without using the
17) Do you use any computer database to study chess?_________________________
18) Do you play games against chess software?_______________________________
19) Do you play speed chess games?_______________________________________
How many per week?________________________________________________
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Table 1. Descriptive statistics of all the variables measured in this study, as a
function of skill.
TOTAL Non-rated Rated FIDE IM GM
N mean
sd mean
sd mean
sd mean
sd mean
sd mean
National rating 104 1991 221 1780 125 2030 103 2165 136 2300 82 2445 39
Speed rating 72 1958 208 1787 124 2003 134 2194 122 2315 50 2403 0
Dir. of handedness
98 41.3 11.2 41.7 11.1 40.6 12.2 44.6 4 41.0 11.6 31.7 19
Deg. of handedness
98 76.4 21.1 76.0 25.2 78.2 18.4 73.1 19.9 76.1 16.7 75.0 25.0
Total practice 89 13,325
8,303 7,900
Individual practice
90 5,375
3,744 5,236
4,567 4,767
Group practice
89 7,921
4,557 3,586
7,101 5,044
d d
Age 104 30.8 14.6 33.18 17.8 30.2 13.9 27.1 8.9 29.1 10.7 31 3.5
Starting age 104 8.8 4.3 10.3 5.1 8.7 3.8 7.5 2.9 6.5 3.1 5.7 1.1
Serious age 100 15 8 18.6 11.5 14.2 3.9 11.6 3.1 10.3 3.6 11.3 1.1
Club age 102 15 8.2 18.9 11.1 14.2 4.8 10.8 3.6 9.9 3 11.7 2.1
Number of books 99 66.3 98.2 24.4 23.1 81.4 113.3
78.4 88.7 116.7 85
Coaching (0,1) 103 0.81 0.4 0.67 0.5 0.85 0.4 0.92 0.3 1 0 1 0
Chess bases (0,1) 104 0.67 0.5 0.51 0.5 0.72 0.4 0.85 0.4 0.8 0.4 1 0
Chess program (0,1) 104 0.66 0.5 0.59 0.5 0.67 0.5 0.85 0.4 0.6 0.5 1 0
Blindfold reading (0,1)
104 0.56 0.5 0.46 0.5 0.54 0.5 0.77 0.4 0.7 0.5 0.67 0.6
Blindfold chess (0,1) 104 0.23 0.4 0.15 0.4 0.26 0.4 0.23 0.4 0.5 0.5 0 0
Speed games (0,1)
104 0.84 0.4 0.67 0.5 0.9 0.3 1 0 1 0 1 0
Speed games
102 17.3 33.7 8.7 10 18.4 21 19 26.1 13.3 15.1 121.7 156.8
For the direction of handedness, the scale ranges from 10 (extreme left-handedness) to 50
(extreme right-handedness); for the degree of handedness, the scale ranges from 0 (mixed handedness)
to 100 (strong handedness).
Group and individual practice were measured as the cumulative number
of hours studying or practicing with others (group practice) or practicing alone (individual practice).
Speed games (0,1) measures whether or not the participants play speed games, and speed games is the
average number of speed games played per week.
No GM answered these questions.
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Table 2. Correlations and descriptive statistics for chess ratings, handedness, practice variables, activities, and age variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
1. National rating .83**
-.05 .04 .57**
.08 -.28**
.11 .24* .16 .27**
2. Speed rating .10 -.01 .38**
.25* .43**
-.04 -.23 -.46**
.13 -.05 .05 .04 .25* .30*
3. Direction of handedness .26**
.13 .11 .16 .18 -.18 .00 .00 .17 -.17 -.04 -.23*
-.12 -.08 -.04 -.17
4. Degree of handedness .17 .10 .12 .26** .13 .13 .17 .25* -.14 -.12 -.13 -.15 -.01 -.13 -.11
5. Log total practice
.43** -.17 -.08 -.07 .59**
.10 .26* .18 .31**
.10 .08 .03
6. Log individual practice
.17 -.14 -.16 -.15 .41**
.19 .15 .15 .30**
.15 .07 .05
7. Log group practice
-.19 -.14 -.11 .60**
.05 .26* .14 .27* .04 .07 -.02
8. Log Age
-.13 .03 -.06 -.17 -.34**
9. Log starting age
-.11 -.33**
.18 -.12 -.05 -.33**
10. Log serious age
-.12 -.42**
-.11 -.09 -.15 -.35**
11. Log club age
-.10 -.48**
-.03 -.08 -.11 -.38**
12. Log number of books
.10 .28**
.06 .17 .09 .16 .11
13. Coaching (0,1)
.07 .15 .14 .31**
14. Use of chess databases (0,1)
.09 .47**
15. Use of chess programs (0,1)
.05 .07 .02
16. Blindfold reading (0,1)
.23* .21*
17. Blindfold chess (0,1)
.12 .14
18. Playing speed chess (0,1)
19. Log number of speed games
Mean 1991
41.3 76.4 3.9 3.1 3.7 1.4 0.9 1.1 1.1 1.5 0.8 0.7 0.7 0.6 0.2 0.8 0.8
sd 221 208 11.2 21.1 0.4 1.3 0.4 0.2 0.2 0.2 0.2 0.5 0.4 0.5 0.5 0.5 0.4 0.4 0.7
Note. Correlations with * are statistically significant at p < .05, and those with ** are significant at p < .01 (two-tailed).
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Table 3. Multiple regression predicting national rating (using the same variables as
Charness et al., 1996).
Variable B SE Beta t p 95% CI
1233.3 269.7 0 4.57 < .001
695.5 - 1771.2
Coaching (0,1)
137.6 58.5 0.264 2.35 < .03 21.0 - 254.2
Log group practice
136.8 66.4 0.272 2.06 < .05 4.4 - 269.2
Log age
327.3 167.3 0.312 1.96 > .05 -6.5 - 661.1
Log serious age
-318.6 204.8 -0.288 -1.55 > .1 -727.2 - 89.9
Log starting age
136.1 129.4 0.123 1.05 > .2 -122 - 394.1
Log individual practice
17.8 18.2 0.110 0.97 > .3 -18.5 - 54.1
Log club age
-147.5 215.6 -0.141 -0.68 > .4 -577.4 - 282.4
Log number of books
3.3 42.8 0.009 0.07 > .9 -82.1 - 88.7
Note. R = .642, R
= .412, Adjusted R
= .345, F(8,70) = 6.14, p < .001. Missing
values were handled by excluding cases list-wise.
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Figure captions
Figure 1. Scatter plots of national rating and speed rating as a function of log
individual practice and group practice. The unit of analysis of individual and group
practice is the cumulative number of hours. With group practice, there are 89 data
points for national rating and 63 for speed chess rating; with individual practice, there
are 81 data points for national rating and 55 for speed chess rating. (The plots for
individual practice have excluded nine players who reported zero hours of practice.
With these players included, the equations are 1754.508 + 73.490x (r
= 0.175; N =
90) for national rating, and 1817.242 + 43.808x (r
= 0.063; N = 64) for speed
Figure 2: Scatter-plots of national rating as a function of log starting age, log serious
age, and log club age.
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Figure 1
National rating
2 3 4 5
Log individual practice
y = 1358.086 + 183.292x r
= 0.240
2 3 4 5
Log group practice
y = 1048.802 + 252.020x r
= 0.288
Speed chess rating
2 3 4 5
Log individual practice
y = 1635.741 + 95.306x r
= 0.070
2 3 4 5
Log group practice
y = 1258.013 + 191.327x r
= 0.184
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Figure 2
0 0.25 0.5 0.75 1 1.25 1.5 1.75
Log (Starting age
National Rating
0 0.25 0.5 0.75 1 1.25 1.5 1.75
Log(serious age)
National Rating
0 0.25 0.5 0.75 1 1.25 1.5 1.75
Log (Club age)
National Rating
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The scores were somewhat lower in the national rating, due to differences in the
results taken into account. For instance, the four best players had 2520, 2491, 2490
and 2488 in the international rating and 2438, 2473, 2400 and 2463 in the national
rating, respectively.
Standard games are played with an average of three minutes per move; in speed
chess, each player has only five minutes for the entire game. The speed chess rating is
computed independently from the national rating. In some cases, the calculation for
the former rating is based on more than one thousand games.
What did the players consider as “seriously”? Apparently, they assumed that this
term referred to the time they joined a chess club. The question about starting to play
seriously yielded similar results to the question about the age of joining a chess club
(serious age: M = 15.0, SD = 8.0; club age: M = 15.0, SD = 8.2; r =.87, p < .001).
The results are fairly similar when listwise deletion and 2-tailed tests are used. The
respective partial correlations for national rating (80 players) are: starting age, -.14;
ns; serious age, -.39, p < .001; and club age, -.31, p <.005. The respective correlations
for speed rating (56 players) are: starting age, -.28, p < .05; serious age, -.55, p < .001,
and club age, -.49, p < .001.
... The "10-year rule" has been widely used in the sports domain [7,8]. The validity of the "10,000-h rule" has been investigated [9][10][11], and the main critique is the lack of measures for variation in the amount of training leading to expertise. Gobet and Campitelli [10] studied the variation in the number of training hours required to reach the level of expertise and found that chess players spent between 3000 and 23,000 h of training to reach expertise, with the average number of hours being 11,000. ...
... The validity of the "10,000-h rule" has been investigated [9][10][11], and the main critique is the lack of measures for variation in the amount of training leading to expertise. Gobet and Campitelli [10] studied the variation in the number of training hours required to reach the level of expertise and found that chess players spent between 3000 and 23,000 h of training to reach expertise, with the average number of hours being 11,000. Gobet and Campitelli [10] concluded that the number of hours with domain-specific training could not be the only requirement for achieving expertise. ...
... Gobet and Campitelli [10] studied the variation in the number of training hours required to reach the level of expertise and found that chess players spent between 3000 and 23,000 h of training to reach expertise, with the average number of hours being 11,000. Gobet and Campitelli [10] concluded that the number of hours with domain-specific training could not be the only requirement for achieving expertise. In the context of Olympic gymnastics, research found that the athletes had accumulated 18,835 training hours by the age of 16 [12]. ...
Full-text available
A comprehensive understanding of skill acquisition is important for different performance domains, and has practical implications for both sport sciences and public health. The study compared important constraints for expertise development in a physically demanding sport (cross-country skiing) versus a technically demanding sport (freeskiing). Eighteen world-class athletes reported the importance of different constraints for their developmental history subdivided into two age spans: (1) 7–15 years and (2) 16 years until present. The total amount of training did not differ between the groups, but from the age of 16, the cross-country skiers spend approximately 98% of their training specific to their main sport, compared to 75% for freeskiers. No differences were found between the distribution of organized versus non-organized training in main sport, but freeskiers reported a higher amount of unorganized training in other sports after the age of 16. No differences were found in perceived importance of facilities, enjoyment of performing their sport, or the need for early specialization of training. After the age of 16, the cross-country skiers reported a higher need for coach involvement compared to freeskiers. The two sports mainly share common paths to expertise but differ in the need for specific training and coach involvement.
... [40] further asserts that reading can be done at any stage and phase of a human being's life; however, he denotes that children learn faster than adults. [22] indicated that the relevant age to start playing chess is as a result of a scientific correlation between the age of commencing and the skill obtained from playing chess, which emanates from neuronal level, reduction in plasticity and the consolidation of anatomical circuits which tend to occur around the age of twelve. [22] further state that when learners are exposed to chess at a tender age, it is pivotal for many reasons: that is, boosting of the attainment of the acquisition of knowledge in chess also for the ability to calculate moves ahead on a chessboard. ...
... [22] indicated that the relevant age to start playing chess is as a result of a scientific correlation between the age of commencing and the skill obtained from playing chess, which emanates from neuronal level, reduction in plasticity and the consolidation of anatomical circuits which tend to occur around the age of twelve. [22] further state that when learners are exposed to chess at a tender age, it is pivotal for many reasons: that is, boosting of the attainment of the acquisition of knowledge in chess also for the ability to calculate moves ahead on a chessboard. When young people occupy themselves with chess and attend chess club sessions, their minds become active resulting in them developing good habits [22]. ...
... [22] further state that when learners are exposed to chess at a tender age, it is pivotal for many reasons: that is, boosting of the attainment of the acquisition of knowledge in chess also for the ability to calculate moves ahead on a chessboard. When young people occupy themselves with chess and attend chess club sessions, their minds become active resulting in them developing good habits [22]. ...
Full-text available
The study aimed to establish chess influence on learner's performance in mathematics. It adopted a quantitative approach and followed a descriptive research design. 70 Grade 9 learners from seven secondary schools participated in the study. Cluster sampling technique was employed with 70 learners being surveyed using a questionnaire. A close-ended questionnaire was used to gather data. The study found that the largest group of learners in school that did not offer chess could not understand or explain En Passant. It also emerged that in schools that offered chess, all learners agreed that they could explain En Passant. Slightly less than 50% of the participants strongly disagreed that they can algebraically notate the game whereas 17.1% disagreed. In non-chess schools, only 4% (N=2) indicated that they can algebraically notate whereas all 20 learners (100%) in chess schools confirmed that they can notate the game. The study concludes that the learners in those schools that offered chess had ideas/could explain the chess terms and vice versa. The study recommends that since the influence of chess training may have positive impact, chess training can be introduced to schools to enhance the mathematics performance of learners.
... A viable experimental framework to study human information processing is found in the game of chess (Ericsson & Smith, 1991;Simon, 1979). On the one hand, chess is highly complex: The game harbors an astronomical number of possible games and board positions (Allis, 1994;Shannon, 1950), and becoming a chess expert requires not only high cognitive ability but also thousands of hours of practice Gobet & Campitelli, 2007;Grabner, 2014;Hambrick et al., 2014;Howard, 2012;Vaci et al., 2019). At the same time, chess is remarkably simple: the board consists of only 64 squares and six types of pieces, with rules that can be learned by virtually anyone. ...
Full-text available
Chunking theory and previous eye-tracking studies suggest that expert chess players rely on peripheral vision when judging a chess position and determining the best move to play. However, so far, the role of peripheral vision in chess has mostly been inferred rather than based on controlled experimentation. In this study, we used a gaze-contingent paradigm in a reconstruction task initially used by De Groot (1946). It was hypothesized that the smaller the gaze-contingency window while memorizing a chess position, the smaller the differences in reconstruction accuracy between novice and expert players. In the experiment, participants viewed 30 chess positions for 20 seconds, after which they reconstructed this position. This was done for four different window sizes as well as for full visibility of the board. Cohen's d effect sizes between experts and novices of the proportion of correctly placed pieces were found to support the foregoing hypothesis. A complementary find-the-best-move task and additional eye-movement analyses showed that experts had a longer median fixation duration and more concentrated scan patterns than intermediate and novice players. Our findings implicate a key contribution of peripheral vision and are consistent with the prevailing chunking theory.
... It comes only from having more, better, or more efficiently organized knowledge in a domain. Gobet and Campitelli [48] assert that mastery of one's chess skills requires maximum focus and determination as well as investment in time. They further indicate that the best time to commence chess practice or play in local chess clubs is 12 years old. ...
Full-text available
The aim of this study was to explore chess as an alternative strategy to improve the teaching and learning of mathematics. Purposive sampling was used to identify 25 experimental group learners and 26 control group learners. Experimental learners were drawn from two schools that offered chess, while control group learners were drawn from five non-chess-training schools. As such, 51 learners provided the data. The researcher administered a group test in an attempt to triangulate the findings of the study. Using chess as an alternative strategy to improve the teaching and learning of mathematics, it emerged from the main findings that the control group learners made far more contextual errors, compared to the experimental group, on the various problems that were presented. It also emerged that non-experimental learners were poorer in applying required basic steps to arrive at answers to the activities given to them compared to the experimental learners. The study further found that while both groups committed blended contextual and procedural errors, control group learners were dominant as compared to the experimental learners. This implies that the experimental group applied more metacognition in various problems and therefore outperformed the control group in the group test. Based on the above findings, the study concludes that chess training improves both teachers’ and learners’ application of metacognition in supporting learners’ performance in mathematics. The study recommends that since the use of chess serves as an alternative strategy to positively improve the teaching and learning of mathematics, chess training can be introduced to schools to enhance the mathematics performance of learners.
... Similarly, Charness et al. (1996) noted that the current chess rating of older chess players was particularly influenced by their recent level of practice and was less well predicted by accumulated practice across their career when the predictability was compared to young chess players. We believe that the amount of accumulated practice necessary to reach the level of chess master estimated by Gobet and Campitelli (2007) is influenced by these issues. Gobet and Campitelli reported that the number of accumulated hours of solitary practice ranged from 1,612 to 14,196 to reach the level of chess master -a level corresponding to about the top 1% of the tournament players in a given country. ...
Full-text available
This issue on advanced learning focuses on the educational and developmental needs of advanced learners as they develop towards excellence. We speculated that those needs could be observed in at least three ways. The first is that the advanced learner requires educational interventions that are more closely aligned to the “deliberate practice” approach delineated by Ericsson et al. (1993). Ericsson et al. (1993) identified that the number of hours of deliberate practice differentiated among the performance levels of musicians. Deliberate practice can be described as individualised instruction whereby a teacher or coach identifies the goals and activities that need to be adopted by an individual during practice to improve their performance. A second assumption is that advanced learners do not attain high levels of performance in the absence of environmental factors but the factors that support the talent developmental trajectory of advanced learners will not be the same as those that support them at earlier stages. The expertise reversal effect, for example, suggests that the instructional activities designed for novices may have a detrimental effect on more advanced learners Kalyuga (2007). The third premise is the need for more tailored and well-designed learning resources to support talent development. Such learning resources include highly-specialised learning materials and curricula, expert teachers and coaches, mentors, and so on, which are purposefully designed to meet the individual’s specific needs at a specific point in the talent development process. Again, this echoes the deliberate practice approach described earlier.
... Chess has been the focus of several studies, which have had considerable theoretical impact on the deliberate practice framework. In the following, I will focus on the study I carried out with my then PhD student Guillermo Campitelli, as it was unique in collecting information both about practice and talent (CAMPITELLI; CAMPITELLI, 2007). ...
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Considerable research has been carried out on chess in the last seventy years. While classic research has centred on perception, memory, and decision making, contemporary research has focused on deliberate practice, individual differences, and education. Contrasting with classical research, which has mainly used experiments and computer modelling, more recent research has tended to use questionnaires, interviews, and analysis of computer databases as source of information. This article reviews these recent research trends, focusing on what has been learnt from chess research with respect to deliberate practice, intelligence, and transfer of skill. It also discusses ageing and risk taking between civilizations as examples of computer database analyses. Results clearly indicate that deliberate practice is a necessary, but not sufficient condition for achieving high levels of expertise. Other factors are important, some of which are innate. One of them is intelligence. Data show that chess players on average are more intelligent than individuals who do not play chess, and that chess skill positively correlates with intelligence. These results are unlikely to be explained by the hypothesis that chess leads to an increase of intelligence, as the results of experiments using chess instruction to bring about far-transfer effects are inconsistent. In addition, experiment designs used in chess instruction research are typically insufficient to allow strong conclusions about causality. Research using chess databases have led to interesting results, but its generalisability is likely to be limited. The article ends with recommendations for future research.
RESUMO O desenvolvimento das funções cognitivas é um processo natural na vida do indivíduo e depende de fatores biológicos e ambientais. Desafios envolvendo estratégias mais avançadas de resolução de problemas podem estimular o processo de desenvolvimento e de reabilitação, porém pouco se sabe sobre o real papel do jogo de xadrez na estimulação cognitiva em crianças. Objetivo: revisar trabalhos que utilizaram o jogo de xadrez em treinamentos de crianças para investigar os benefícios cognitivos e o impacto na vida real desta prática. Métodos: os artigos científicos originais foram obtidos nas bases da Pubmed, da Science Direct e em pesquisas no International Journal of Special Education, publicados nos últimos 10 anos. Resultados: foram selecionados sete artigos de pesquisas originais, que sugerem benefícios da prática do xadrez na escola, cujos resultados positivos nas avaliações cognitivas, impactando nas funções de raciocínio matemático, atenção, planejamento, resolução de problemas, melhorou o desempenho das crianças na escola. Conclusões: o uso do jogo de xadrez pode ser uma ferramenta que estimula o desenvolvimento cognitivo de crianças, mas ainda faltam dados de avaliações neuropsicológicas mais amplas, um número maior de indivíduos avaliados e por prazos mais longos. Estudos futuros devem ser conduzidos para verificar até que ponto as habilidades aprendidas no xadrez são transferidas para a vida diária. Palavras chave: Xadrez. Desenvolvimento. Reabilitação. Cognição. Benefícios. ABSTRACT The development of cognitive functions is a natural process in the life of the individual and depends on biological and environmental factors. Challenges involving more advanced strategies troubleshooting can stimulate the process of development and rehabilitation, but little is known about the real role of chess game in cognitive stimulation in children. Objective: To review studies that used the game of chess in children training to investigate the cognitive benefits and the impact on the real life of this practice. Methods: The original scientific articles were obtained on the basis of Pubmed, the Science Direct and research in the International Journal of Special Education, published in the last 10 years. Results: were selected seven articles of original research that suggest benefits of chess practice at school, whose positive results in cognitive assessments, impacting on mathematical reasoning functions, attention, planning, problem solving, improved children's performance in school. Conclusions: Chess Game use can be a tool that stimulates the cognitive development of children, but there are still data broader neuropsychological evaluations, a greater number of
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Cerebral localization is determined by the separation of incompatible mechanisms.-Lashley7 Clinical and experimental evidence suggests that the left hemisphere of the brain is specialized for speech activity and the right hemisphere is specialized for many nonlinguistic functions. Jackson1 related the hemispheric linguistic differences t o differences in cognitive activity, suggesting that the left hemisphere is specialized for analytical organization, while the right hemisphere is adapted for " direct associations " among stimuli and responses. Modern researchers have substantially generalized this differentiation to encompass a wide range of behaviors in normal subjects. Experimental2 and clinical3 investigators of hemispheric asymmetry generally agree on the fundamental nature of the processing differences between the two sides of the brain: the left hemisphere is specialized for propositional, analytic, and serial processing of incoming information, while the right hemisphere is more adapted for the perception of appositional, holistic, and synthetic relations. This asymmetry raises the question of whether there are essential differences in the way in which the two hemispheres organize behavior and process information. Several theories attribute hemispheric differences to a structural differentiation of some kind. Asymmetries might be due to differences intrinsic to each hemisphere: e. g., in the neurospatial organization of functions4 or the existence of modality-specific differences in capacity,S or to some fundamental differences in the way the elementary neu-rological interactions occur. The structural difference might exist because of forces extrinsic to the brain, e. g., a muscular predisposition for handedness, asymmetries in sensory organs, or socially trained asymmetries in such observable traits as handed-ness and eyedness. Each of these views supposes that there is some physical or social structure that specifically and directly causes functional asymmetry to occur; that is, these proposals are all extremely strong in that they make concrete claims about the nature of the phenomenon. Yet the apparent precision of each claim is of little use t o us, since we d o not know the relevant facts that would critically prove or disprove any of them. I shall argue that unless we have evidence conclusively proving any of the more specific claims, we should view cerebral dominance as the result of certain general properties of the mind and of the relationship between the structures of the mind and the anatomy of the brain. The basic view underlying this proposal is that the mind is composed of a number of partially independent faculties, each of which has certain 25 1
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Evidence for the view that chess skill is dependent on recognitional/perceptual processes has been based primarily on findings from recall tasks and from analyses of verbal protocols. The present study employed a time-pressure manipulation as a possible source of converging evidence, based on assumed differences in the temporal requirements of recognitional versus calculational modes of processing. The rated quality of moves for very strong (master) and weaker (class B) players was compared in tournament games played under regulation and blitz time rules. The findings supported the predicted interaction between skill level and game type wherein the decrement in move quality for blitz games compared with regulation games was greater for the class B players than for the masters. These results were interpreted as supporting the view that the more highly skilled players are able to rely more extensively on rapid recognitional processes than less-skilled players.
The coming of language occurs at about the same age in every healthy child throughout the world, strongly supporting the concept that genetically determined processes of maturation, rather than environmental influences, underlie capacity for speech and verbal understanding. Dr. Lenneberg points out the implications of this concept for the therapeutic and educational approach to children with hearing or speech deficits.
1. Kittens were visually deprived by suturing the lids of the right eye for various periods of time at different ages. Recordings were subsequently made from the striate cortex, and responses from the two eyes compared. As previously reported, monocular eye closure during the first few months of life causes a sharp decline in the number of cells that can be influenced by the previously closed eye.2. Susceptibility to the effects of eye closure begins suddenly near the start of the fourth week, remains high until some time between the sixth and eighth weeks, and then declines, disappearing finally around the end of the third month. Monocular closure for over a year in an adult cat produces no detectable effects.3. During the period of high susceptibility in the fourth and fifth weeks eye closure for as little as 3-4 days leads to a sharp decline in the number of cells that can be driven from both eyes, as well as an over-all decline in the relative influence of the previously closed eye. A 6-day closure is enough to give a reduction in the number of cells that can be driven by the closed eye to a fraction of the normal. The physiological picture is similar to that following a 3-month monocular deprivation from birth, in which the proportion of cells the eye can influence drops from 85 to about 7%.4. Cells of the lateral geniculate receiving input from a deprived eye are noticeably smaller and paler to Nissl stain following 3 or 6 days' deprivation during the fourth week.5. Following 3 months of monocular deprivation, opening the eye for up to 5 yr produces only a very limited recovery in the cortical physiology, and no obvious recovery of the geniculate atrophy, even though behaviourally there is some return of vision in the deprived eye. Closing the normal eye, though necessary for behavioural recovery, has no detectable effect on the cortical physiology. The amount of possible recovery in the striate cortex is probably no greater if the period of eye closure is limited to weeks, but after a 5-week closure there is a definite enhancement of the recovery, even though it is far from complete
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.
This study used participants from the marital arts (karate) to examine the influence of context in the acquisition of novel motor sequences and the applicability of Ericsson, Krampe, and Tesch-Römer's (1993) theory of deliberate practice in this athletic domain. The presence of context did not benefit recall performance for the experts. The performance of the novice group was hindered by the presence of context. Evaluation of the role of deliberate practice in expert performance was assessed through retrospective questionnaires. The findings related to the relationship between relevance and effort, and relevance and enjoyment diverged from Ericsson et al.'s (1993) definition of deliberate practice, suggesting that adaptations should be made if it is to be considered a general theory of expertise.