Understanding the relationship between Neurodiversity, Encoding, Chunking, and Cognitive Overload

by Renate Otterbach


In the previous blog, we discussed differences in coding and its implications for teaching, especially for neurodiverse students, who tend to encode very differently from neurotypical students. Since neurodiverse students assimilate, store, and retrieve information differently from neurotypical students, they may have problems gaining the same benefits from regular classroom instruction.

In a regular classroom setting, when learning new content, they first must decode the instruction and then recode it in a way that makes the content accessible to them. This extra demand for cognitive resources often leads to cognitive overload, diminishing their ability to learn in traditional classroom settings. It is, therefore, not surprising that neurodiverse students are often labeled as learning disabled and or having ADHD. However, when provided with information in a way that is aligned with their encoding system, they often turn out to be outstanding learners.

Encoding, Chunking, and Information Retrieval

How we encode information determines what information is chunked together and how it is stored in and retrieved from our memories. Over time we become experts in our preferred way of encoding, reducing the energy needed to chunk, store, and retrieve. Chunking is the bundling of information and involves selecting and connecting pieces of information for storage to be recalled later. All chunking consists of the inclusion and exclusion of specific material. Our preferred encoding methods determine how we select what is and is not stored in our memory. Two people may observe the same accident, but their recall may differ based on what aspects were or were not salient to their encoding system. In chess, how we chunk information determines what we see and do not see on the board. Hence, effective chunking reduces chess blindness.

The more efficient we are in chunking or bundling information, the more efficient we are in recalling it. This was illustrated by De Groot’s experiment with chess players, who found that expert and higher-rated players could recognize meaningful chess positions more accurately than novices. Still, there was no difference in recall when presented with random positions. Hence, the mental organization of information enabled recall instead of the presence or absence of memory capacity. De Groot’s experiments were the foundation of the theory of chunking, which led to the development of the field of expertise studies and laid the foundation of AI technology.

What is effective chunking?

Simple-defined chunking is a combination of understanding the principles of chess, our natural cognitive strengths, and our encoding preferences. The key to improving the quality of our information chunks is to find effective connectors that link new information to prior information. Regarding cognitive theory, the size of a chunk does not make any difference in information storage. A piece of information, such as a phone number, takes up the same “space” as a massive block of chunked information. No matter how large, the chunk is counted as one unit; hence, the more efficient a person is in chunking, the more information they can store effectively, regardless of their memory capacity.

Generally, logical propositional statements with validated if… then statements are the best connectors between chunks. In chess, these are often referred to as calculation skills. However, while these connectors allow for superior encoding, they require a high level of mental energy and should be used judiciously, especially during a game.

Another effective way of chunking is pattern recognition. Hence learning typical patterns in chess can be very helpful and reduces the cognitive load. The problem with depending too heavily on pattern recognition is that you may misinterpret the position if the patterns do not apply. Many new opening ideas often take advantage of this natural weakness by finding alternative patterns to accomplish the same goal.

For example, whereas the traditional way of controlling the center was direct control through pawn placement, a more modern approach is indirect control through fianchettoed bishops. Many tactical strategies also depend on breaking patterns. For example, whereas in most cases, people expect “piece value” to be the key determining factor of a trade, in sacrifices, often the determining value is “piece function,” and as Botvinnik pointed out, in the case of Tal, “the initiative or piece activity.”

Implications for Teaching

To help students maximize their learning strategies, i.e., to get the most benefit from each hour of instruction, it is essential for teachers to understand how students encode and chunk information and what factors contribute to cognitive overload for the different types of students. Using this framework, they can then identify the best teaching strategy for each type of student.

On the surface, this seems daunting; however, chess is an excellent tool to meet this challenge. Because chess requires a combination of cognitive skills to excel at the game, different types of errors may indicate various encoding weaknesses. Using targeted strategies, teachers can help students to identify their strengths and weaknesses and to overcome them through well-designed deliberate practice. As far as I know, no other domain besides chess has this educational potential.

How this advantage can be explored further will be discussed in subsequent blogs.


Renate Otterbach
This post is the third part of her Develop learning to learn strategies through chess series.

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