High-level chunking allows to operate with up to 80 object in your working memory, using computer-like data structures. Since the size of working memory is correlated with IQ, this actually make you smarter.
When dealing with large amount of information you may choose several strategies to encode it. High level visualization is probably the most accurate, but also the less analytical and more time consuming method. Hyperlinking is very fast and analytical, but it works best with stylized markers. Eventually I failed to describe the method in the middle, which I call here high-level chunking.
Simple chunking
In low level chunking we detect similar markers and chunk them in groups of 3-5 markers per chunk.In this way we effectively increase our working memory from 7 objects to 20 objects (5 chunks of 4 objects). Moreover we can perform marker manipulation between the chunks and between markers within each chunk.
Intermediate chunking
In mid-level chunking we work with data structures. These include linked lists, trees and maps.
Linked lists
Linked lists are ordered chunks of markers connected by some sort of linking “animation” that allows us effortlessly and accurately transverse forward and backwards between them. A good example of a list are task lists, like what to buy in grocery store.
Trees
A great way to remember texts are trees of markers. We have markers per articles, sections, paragraphs and sometimes sentences and terms within sentence. The linking here is hierarchical from parent to kids and similar to zoom-in animation on Prezi. Often we find it useful to connect the children of the same node as linked lists to increase accuracy. It is also a very natural way to follow the text logic.
Maps
Maps are fully associative ways to remember pairs of markers. We can use them to connect object of seemingly unrelated trees. This is a great way to encode out-of-the-box thinking or interdisciplinary connections. References we see in any scientific article are examples of such maps.
High-level chunking
In high-level chunking, the goal is to manage up to 80 items in working memory. To achieve this, we create several data structures based on different types of objects and link them mentally to facilitate navigation between them. This process is akin to hyperlinking but involves more complex markers and highly organized data structures. For instance, organizing 5 tree data structures with 16 items each allows for the memorization and active manipulation of 80 objects in working memory, which is close to the maximum capacity supported by scientific research.
I personally find high-level chunking challenging to master and typically use it only for complex, multidisciplinary projects. When working with these mental chunks, I often supplement my efforts with a spreadsheet or presentation to ensure I don’t overlook any details.
