Chunking

Chunking

Chunking in NLP is Changing a perception by moving a "chunk", or a group of bits of information, in the direction of a Deductive or Inductive conclusion through the use of language.

Chunking up or down allows the speaker to use certain language patterns, to utilise the natural internal process through language, to reach for higher meanings or search for more specific bits/portions of missing information.

When we "Chunk Up" the language gets more abstract and there are more chances for agreement, and when we "Chunk Down" we tend to be looking for the specific details that may have been missing in the chunk up.

As an example if you ask the question "for what purpose cars?" you may get the answer "transport", which is a higher chunk and more toward abstract.

If you asked "what specifically about a car"? you will start to get smaller pieces of information about a car.

Lateral thinking will be the process of chunking up and then looking for other examples: For example "for what intentions cars?", "transportation", "what are other examples of transportation?" "Buses!"

Here is what others have said about chunking:

Joseph O'Connor and John Seymour: (or stepping) Changing your perception by going up and down a logical level . Stepping up is going up to a level that includes what you are studying. For example, looking at the intention behind a question chunks ups from that question. Stepping down is going to a level below for a more specific example of what you are studying. This can be done on the basis of member and class or part and whole. For example, the first step in formulating an outcome is to phrase it in the positive.

NLP Comprehensive: Chunk size is the level of specificity. People who are detail-oriented are "small chunkers." People who think in general terms are "large chunkers"--they see the big picture

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