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Integrated Neural number representations explain mental associations

First, a link to one of my previous article:

This is about Integrated Neural Representations (INR)

a mental association I define as an immediate following up an idea another idea, which was triggered by the first directly or indirectly.
This includes situations in which the second thought is triggered by something that perceives the person, because everything is perceived in a neural representation (NR) niederschlaegt and thus is thought to himself.

The concept of INR to mental associations explain completely natural. Let A be any NO. A is for reasons of natural selection (see link ) an at least partial reconstruction of INR (RNR). Now let B also any NO. Just as A is of course a RNR.
Then there exists an intersection S of INR, which occur equally in A and B. Depending on how large S, the less modification of A is necessary to obtain B and vice versa. Add to that the strength of the INR is in S of A and B. This is very high with regard to the INR of A that are not in S, very little effort is needed to get to B because the INR in A, outside S is relatively weak then, so do not play a big role. Similarly, the same applies also for example

Interestingly, it shows that you could A which associated with higher probability to B than vice versa. It is even quite unlikely given that in both directions, the same association probability is, because that would each ratio of the strength of the INR in S are exactly equal to the strength of the INR in A and B respectively. Instead of only two
NO as A and B involve the entire model can be extended to any number of NR.

increases the probability for overlaps of course, with decreasing absolute number of existing INR. The semantically optimized integration of NR plays as well with one or more vividly, in subject areas in which one knows about, a rather come as associations elsewhere.