Facial Attention Pattern

In this chapter, I will introduce a new term: FAP.

FAP

Facial Attention Patterns (FAPs) are visual forms that we recognise easily in a face. Our nervous system can easily grasp such facial gestalts through our eyes.
These images can be effectively retained in the memory of our brain (in occipital lobe). The configuration of our neuronal circuits has evolved in such a way that the nervous system can systematically 'capture and retain' certain simple figurations. This systems-dynamic of pattern recognition is an intrinsic property of our brain; this ability arises from the way our neural circuits are constructed.
Through genetic evolution over millions of years, these neural brain circuits have acquired anatomical structures to effectively retain certain forms of perception. This neuronal retention-function is called: Intrinsic Pattern Matching.

We often find these kind of figures attractive. Therefore, FAP also means Facial Attraction Pattern. In other words: A person's face can acquire certain tissue-growth patterns (Facial Attraction Patterns) that are found attractive by another person. These patterns draw attention from the other person (Facial Attention Patterns). 

ANN

Incidentally, software programmers can nowadays program Artificial Neural Networks on computers. These ANNs can be taught to recognize visual patterns by ‘feeding’ them with imagery. The processing-images produced by these algorithms show us how certain shapes are intrinsically fixed by the structural properties of the information network. In other words: the hardware (computer) partly determines the shape boundaries of what the software (ANN) produces as a result (digital image).
If you compare this with our brain function: the nerve cells in our brain (hardware: visual neurons) partially determine the shape boundaries of what our mind (software: thought generator) produces as result (internal visual memory representation of external eye-observations). 

So, the intrinsic fabric of ANNs as well as of brain-cell networks, have a similar functional property: both can hold representation of images in short term memory. And both can be trained to recognize or compare images. This is an intrinsic property of network communication. One day, a similar mechanism will emerge within the Internet: a network of millions of connections and data storage, governed by AI, that will contain representations of worldly phenomena.