The Science

Our platform is an attempt to create a training tool based upon scientific research on the nature of chess knowledge, how such knowledge is most efficiently created, and how it is retained. Research has demonstrated that the most efficient means to increase chess playing strength is by focusing on increasing chess knowledge (Gobet & Charness, 2006).

Studies have shown that the time spent on increasing chess knowledge and the size of ones chess library predicted chess playing strength better than playing tournament games, the age at which the subject became serious about chess, and presence of coaching (Charness, Krampe, & Mayr, 1996) (Charness, Tuffiash, Krampe, Reingold, & Vasyukova, 2005).

Further, studies suggest that it is more efficient to build chess knowledge than it is to try to increase the number of moves you can look ahead (Gobet, 2016) because the ability to foresee moves as well as “chess intuition” (the feeling that you know a move is good without being able to explain why) are side effects from a well-developed knowledge-base (Gobet & Jansen, 2005).

Our chess training platform is designed to enable chess players to efficiently increase chess knowledge. We believe increasing chess knowledge is best achieved by solving positions that are just outside of your comfort zone and in areas of the game where you are weakest.

What is Chess Knowledge?

Chase and Simon (1973a) proposed an influential theory of chess knowledge, referred to as “chunking” theory, later refined by Gobet and Simon (1996a), among others, as “template” theory. These researchers proposed that the fundamental unit of chess knowledge can be described as a “chunk,” a small arrangement or pattern of pieces stored in long-term memory that is perceptually treated as a whole (Gobet, 2019).

For beginners, chunks consist of individual pieces on a given square. As players progress, the chunks increase in number and size, include larger configurations of pieces, and become associated with particular actions (given this pattern of pieces, knights should be exchanged), referred to as procedures, and other forms of higher-order knowledge, such as tactical motifs (if a queen is on the same diagonal as a knight, consider using a bishop to pin the knight to the queen), strategic principles (two bishops are usually better than two knights) and endgame principles (passed pawns should be pushed) (Gobet, 2019).

With further progression, chunks develop into templates, which are themselves chunks and consist of stable information with slots for information that is variable (Gobet, 2019). For example, a template may consist of a particular pawn structure with a slot consisting of whether a bishop or a knight occupies a certain square relevant to the structure. Just like chunks, templates provide useful information for decision-making, such as plausible moves and standard plans.

Gobet and Simon explain that chunks and templates can be recalled instantly because they are stored in long-term memory (Gobet & Simon, 1996a). This feature explains how chess players are able to come up with plans and find strong moves very quickly without consciously considering all possible moves available (Gobet, 2019).

It also explains chess intuition. Specifically, Gobet and Jansen state that intuition is composed of chunks and templates stored in long-term memory that are associated with emotions (the gut feeling of whether a move is good), but not specific conscious thought (Gobet & Jansen, 2005).Some have estimated that chess masters have learned between 10,000 and 100,000 chunks (by comparison, approximately 50,000 words comprise an average college student’s vocabulary), and that grandmasters may have as many as 300,000 stored in long-term memory (Gobet, 2016).

Chunks and templates do not exist as isolated knowledge units. As chess players become more skillful, chunks, templates, and procedures become better organized or indexed and cross-referenced with one another in the course of a learning process that occurs at both the conscious and subconscious level. This knowledge-base enables chess masters to use strategies adaptively and flexibly. With simple problems or high time pressure, masters may rely more on intuition. With complex problems and enough time to think, they will use a combination of intuition and deliberation (Gobet, 2016).

A chess master’s knowledge-base, therefore, can be characterized as a large set of chunks, templates, and procedures that have been richly indexed and cross-referenced, providing the master with an apparent seamless ability to play better than less skilled players.

How is playing strength increased?

According to the research, increasing playing strength requires chess players to build a highly indexed and cross-referenced base of chess knowledge (chunks, templates, and procedures). To build such a knowledge-base, the information to be learned should be clear and appropriate to the skill level of the learner, is best learned through a specific type of practice, referred to as deliberate practice, and is optimally transferred into long-term memory through a specific type of review, referred to as spaced-repetition learning. There is no short-cut to chess mastery. It has been estimated that it may take 10 years or between 3,000 and 23,000 hours of deliberate practice to become a chess master (Gobet, 2019).

First, regarding how information should be learned, Gobet and Jansen outlined three principles for guiding information processing (Gobet & Jansen, 2005).

The first principle is that learning is optimized when it proceeds “from the simple to the complex.” The basic building blocks of knowledge must first be acquired before more complex knowledge, such as detailed templates, can be created.

The second principle is that learning is optimized when the “elements to be learnt are clearly identified.” This assists with indexing and cross referencing the concepts learned as well as enabling the player to generalize concepts to novel positions.

The third principle is that learning is optimized when following an “improving spiral.” This means that instruction should begin with the basic material, and thereafter regularly return to this material, gradually adding more complex information (Gobet, 2016).

Our training platform facilitates learning in this manner by providing positions that get progressively more difficult as you improve, allowing you to analyze positions on your own and with the help of a chess engine, and by identifying the chess skill represented by the position. Additionally, you can opt to receive positions you’ve gotten wrong again in the future to ensure improved pattern recognition.

Second, chess knowledge is increased (and, as a result, playing strength) through what researchers describe as deliberate practice (Gobet, 2019). Deliberate practice means engaging in goal-directed activities at an appropriate level of difficulty over long periods of time. These activities are highly structured and are designed to improve performance by eliminating weaknesses through optimizing opportunities for error correction. The activities should be monitored so that regular and detailed feedback can be provided, a role typically filled by a coach (Gobet, 2016).

Our training platform will enable you to engage in deliberate practice by empowering you to track your training time and know whether you’re meeting your training goals. It will also provide you with specific feedback on your strengths and weaknesses. With this information you can adjust your training to focus on weak areas.

Finally, research indicates that information to be learned is best transferred into long-term memory (retained or internalized) through a method referred to as spaced repetition learning. This method is based on the theory that information retention is maximized when it is reviewed at certain intervals (days, weeks, and months) following instruction (Smolen, Zhang, & Byrne, 2016). In other words, people begin forgetting information learned very shortly after instruction unless the information is consciously reviewed time and again (Shrestha, 2017). First identified by Hermann Ebbinghaus in 1885, the amount forgotten is exponential in nature, meaning that it falls along a “forgetting curve” (Chun & Heo, 2018).

This means that memory retention is 100% at the time of learning, but may drop to 40% within the first few days. If, for example, the information is reviewed again on the first, third, and sixth day from instruction, research has demonstrated that 80% to 90% of the information may be retained.

As summarized above, a critical aspect of chess players’ playing strength is the ability to recall from memory patterns of pieces and higher order knowledge (chunks, templates, and productions). Our solution facilitates the transfer of chess knowledge into long-term memory by allowing you to receive positions you’ve answered correctly again.

Specifically, our trainer will prompt you to solve positions you’ve gotten wrong again at intervals designed to maximize retention (days, weeks, and months in accordance with an optimal spaced repetition algorithm). By solving positions that are just outside your comfort zone, repeating positions you answered incorrectly, and focusing on areas of the game where you are weakest, chess knowledge, and, therefore, playing strength, will be efficiently and maximally increased.

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Charness, N., Tuffiash, M., Krampe, R., Reingold, E. M., & Vasyukova, E.
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