The process of understanding difficult texts

• The reason why difficult sentence is difficult is that the word is not used in the dictionary sense (i.e., it is a non-parallel symbol).
  • Felt Sense and Symbol Diagrams

1: Symbols are not parallel, so even if you know the meaning of the symbols themselves used in the book, you don’t know what is represented by their combination. - There are two patterns of “I don’t know.”

2: Then I said, “Oh! Is this what you mean?” And then there is a realization. This is a new felt-sense imagination, and there is no confirmation that this is what the author wanted to express - Understanding is a hypothesis

3: By repeating this process, the relationships (RELEVANCE) between symbols become visible, and as they form a consistent network, the confidence that “this is what the author meant” increases. - Grounded in consistency

The process from 2 to 3 is strongly related to Gaining Stability in [Interference Effects of Ideas

Scrapbox facilitates the identification of the felt sense an author has for a non-parallel symbol by presenting a summary of “where the non-parallel symbol is used” for that symbol.

• So don’t [Auto Bracketing
  • Mixture of parallel symbols in a set of pages with links is detrimental
  • It’s not about whether it’s a string match, but whether it’s consistent with the author’s felt sense.
  • If it’s a string match but you don’t feel comfortable linking to it, the string side should change. - There are three patterns in the results of direct matching

The “integrity network” being created in 3 is related to the “author’s emulator” spoken of in The weight of “read.”.

Thinking about this process from a digital perspective

• If the felt sense is a word2vec-like n-dimensional vector.
• There are 2^N ways to mix two felt senses.
• I don’t know which one the author intended.
• Select from among the possible mixtures those that seem to be related (vectors are not orthogonal) when randomly sampled and matched.

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This page is auto-translated from /nishio/難解な文章を理解するプロセス using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I’m very happy to spread my thought to non-Japanese readers.