This is a guest post written by Nicholas Barr. Nicholas is a behaviour specialist and memory & cognition tutor based in Palmerston North New Zealand. Cognitive psychology is his research area of interest, and he has used chess to test ideas, such as embodied cognition. He has travelled extensively and played street chess all over the world, for example, in the USA, Indonesia, and Ukraine. He has used chess in his behaviour work to help teenagers with challenging behaviours to improve their impulse control. He only played competitive tournament chess infrequently and won nothing of note––to be fair, a chess intellectual rather than a doer. Currently, he is conducting a chess study to improve how we study chess.
An important question in cognitive science is “What are the processes that underlie expert decision making?” (Ericsson & Staszewski, 1989; Van Harreveld et al., 2007). Every domain, from medical decision-making to engineering, has demonstrated that expertise involves both slow processes, such as selective search, and fast processes, such as the recognition of meaningful patterns (Ericsson & Staszewski, 1989; Van Harreveld et al., 2007). Which is more important between fast and slow processes is also critical in the intellectually demanding sport of chess; therefore, research in chess is an important domain to understand what accounts for expertise (Blanch et al., 2020; Van Harreveld et al., 2007).
The Skill of Chess
De Groot (1946) found that the important difference in skill between grandmasters and candidate masters is that grandmasters have superior recognition memory of the patterns of chess configurations rather than a greater ability to calculate variations (search). Research into the skill difference in expertise has supported De Groot’s findings that the crème de la crème will search somewhat deeper than the mere crème, but there is no difference in how wide they search (Charness, 1981; Holding & Reynolds, 1982; Saariluoma, 1990). Specifically, grandmasters calculate variations slightly deeper, but they calculate the same number of variations as candidate masters (Charness, 1981). Gobet and Simon (1996a) replicated and extended De Groot’s (1946) finding that fast pattern recognition has greater significance than slow search in answering the variability in chess skill (Gobet & Simon 1996a, 2000; Lassiter, 2000). Studies in blitz and simultaneous chess have demonstrated that reducing the thinking time of highly skilled chess players does not have much effect on their performance, as might be expected if search was the more relevant element in chess skill (Calderwood et al., 1988; Gobet, 1998b; Gobet & Simon, 1996b). What precisely underlies these fast, intuitive processes is contested but is mostly considered to be forms of pattern recognition, which are understood as implicit, or nonconscious, processes that give highly skilled chess players the ability to rapidly match from memory a previously encountered chess configuration relationship with a present configuration (Chase & Simon, 1973; Gobet & Chassy, 2009; Montero, 2019; Newell & Simon, 1972). In other words, highly skilled chess players have the ability to look at a chess position and quickly rule out other moves before “intuitively” choosing a good move (Campitelli & Gobet, 2004; De Groot, 1946; Gobet, 2012; Klein et al., 1995; Masters, 1992; Montero, 2019).
Amatzia Avni, an Israeli psychologist and a FIDE Master, examined in his book The Grandmaster’s Mind how grandmasters found good chess moves. He interviewed twelve grandmasters between July 2003 and January 2004. In his introduction, he highlighted the need “to elicit knowledge that is hidden in an expert’s mind” (Avni, 2004, p. 8). However, when he asked Grandmaster Alik Gershon, who was World Under-14 champion in 1994 and World Under-16 champion in 1996, if he had a systematic and logical thinking process about a chess move, Gershon answered: “In the majority of cases I can’t articulate how I reach a certain decision. I rely on intuition” (Avni, 2004, p. 56).
A chunk in chess is a unit of information that a player has stored in long-term memory containing a meaningful grouping of some of the chess pieces on squares that appear on a chessboard, also associated moves and theories (Chase & Simon, 1973). For example, a chunk of a standard white castled position would be the unit Rf1, Kg1, Pf2, Pg2, Ph2. A lot of research supports the idea that highly skilled chess players rely on an intuitive, nonconscious process and has concluded that the difference in chess skill level is importantly explained by how fast and easily a player’s chunks can be accessed from their chess knowledge (Chase & Simon, 1973). This conclusion has been challenged by another line of research that argues that slow processes, such as search, are more important to high-level skill in chess (Chabris & Hearst, 2003; Gobet & Simon, 1996b, 1998; Holding, 1985, 1992; Holding & Pfau, 1985; Montero, 2019). Highlighted in this research is the process of conscious evaluation—such as looking ahead to the outcome of a sequence of chess moves as well as strategizing—as the important factor in the selection of a good chess move rather than pattern recognition (Bilalić et al., 2008b; Charness, 1981; De Groot, 1946/1978; Gobet, 1986, 1998a; Montero, 2019; Saariluoma, 1995).
Thus, skilled chess players’ use of fast and slow processes is widely accepted by researchers (Montero, 2019). What is contested is which of these types of processes are more important to a highly skilled player’s performance in chess (Chabris & Hearst, 2003; Gobet & Simon, 1996b; Holding & Pfau, 1985; Montero, 2019).
Knowing the changes that occur as expertise develops is an essential part of understanding the relevance of fast and slow processes to chess skill. The development of problem-solving strategies is important to understand as these are assumed to be crucial in the acquisition of expertise (Anderson, 1993; Bilalić et al., 2008a). Fast, nonconscious processes are more important to an expert’s performance than they are for an amateur’s according to a line of research that emphasizes the role of automatization in high-level skill (Beilock & Carr, 2001). Declarative and implicit knowledge are assumed to be the two main ways information is stored (Anderson, 1983, 1987). Declarative knowledge (also known as propositional knowledge) collects facts and rules that one is conscious about; thus, one is capable of articulating it. Implicit knowledge (also known as practical knowledge, procedural knowledge, or non-declarative knowledge) involves one’s ability to do something, to produce actions for the task using (declarative) representations, and is the knowledge applied when not consciously performing a task, and therefore one is incapable of articulating it (Anderson, 1982, 1987; Krivec et al., 2021; Masters, 1992). The standard view in expertise research is that these two types of knowledge form a developmental progression when acquiring a high-level skill (Anderson, 1982; Patel, et al., 1999, p. 5; Masters, 1992, p. 622). Firstly, declarative knowledge is attained through conscious attention of information about the skill and then this information is stored in long-term memory (LTM). Secondly, the skill is practiced repeatedly until it becomes proceduralied or automatic, thus, not consciously attending to the skill. Finally, the skill should leave fragmentary memory traces of a consciously retrievable memory.
In addition to expert chess players’ superior knowledge of chess patterns and the storage of chess configurations in LTM, they have a neurally more efficient brain functioning of the frontal cortices when presented with chess-related tasks (Blanch et al., 2020; Grabner et al., 2006; Saariluoma et al., 2004,). There are also important distinctions between novices and experts in chess problem-solving (Grabner et al., 2006; Saariluoma et al., 2004). Expert chess players increase the effectiveness and speed of slow processes in comparison to novices because of their engagement with thorough training and practice exercises, and the fact that they also play games more often (Blanch et al., 2015; Campitelli & Gobet, 2011; Grabner et al., 2007; Howard, 2012, 2013; Simon & Chase, 1973; Van Harreveld et al., 2007). In the same way as other sports, demanding preparation – such as physical fitness, solving tactics, opening preparation, and playing training games – in chess is fundamental for a highly skilled sports performance (Blanch, 2018; Eccles et al., 2009).
You can contribute to our understanding of the processes that contribute to chess expertise by taking part in an online cognitive study that is looking at the process of finding a chess move. Participants must be over 16 years of age and be able to read and type in English. You need to have played 15 fully rated games and either have a FIDE or USCF rating of 1900 and above or a USCF rating of 1400 and under.
Recruitment will be completed when there are either 120 participants—60 over FIDE 1900 and 60 under National Chess Federation of 1400—or by 1-31-2023. Recruitment is happening worldwide.
This study aims to build on cognitive skill studies that examine the thought processes for finding a chess move. Direct benefits of participating in this study include an increased awareness and knowledge of the processes involved in research by actively participating in it and a satisfaction in knowing that you are contributing to cognitive skill studies and chess. Foreseeable risks, adverse effects, and discomforts that one may encounter by taking part in this study are minimal.
The time to complete this study will vary between approximately 20-40 minutes.
Participants have a right to access the information collected about them as part of this study. It is important to us that we maintain privacy throughout this study. The names and contact information will be held electronically and stored on the principal investigator’s computer only for data recording purposes. Each participant in the study will be allocated a number. Staff involved in analysis will have access to participants’ numbers only. All data from test sessions will be recorded against the participant’s ID number, and their name will never be used in any report, correspondence, or publication.
Please email NICHOLAS BARR- [email protected] – your age, gender, nationality, or chess federation you are active in, and your FIDE or USCF rating. A link to the study with your access code will then be emailed for you to access the study. Once the study is completed, a follow-up blog will be written about the findings.
This Chess study is part of my (NICHOLAS BARR) Master of Science degree at Massey University, Palmerston North, New Zealand. My supervisor is Dr Stephen Hill (https://www.massey.ac.nz/massey/expertise/profile.cfm?stref=908630).
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