Patterns Defined with Examples

A pattern is defined to as a collection of features sufficient to identify an object. A pattern is not a word, although often a word will describe the same collection of features as a pattern. An essential difference is that the side of our brain which does not control language uses patterns to capture aspects of external reality and to process them into a mostly consistent internal world view. Patterns are compared against remembered occurrences. The result is a reduction in the profusion of external reality (abstraction).

Both sides of the brain perform this process. The language-dominant side with words and the non-language side with patterns.

Pattern is a generic term selected so that it can be used at various levels of abstraction and integration, from optical sensation to directing muscles to catch a baseball.

Many Featured Objects

Here’s a case of what I mean. A tree. That’s an object. You and I can point at it. Yep, it’s a tree. That thing over there—that’s another tree, although it doesn’t look like the first one. The first was a sycamore. The second a Canadian hemlock. Yet they’re both trees. There’s a third object, a garage. It’s not a tree. How do we know? Because it doesn’t have a trunk or leaves and it doesn’t grow or need to be trimmed.

We know things by feature collections or patterns. What difference does that make?

The implication is that we categorize, name objects, by considering their features, as we see and experience and understand them. Naming objects is not a choice foisted on us by nature, but a convention. As Plato cautioned, our taxonomy (patterns or categories)  should “carve nature at its joints.”  Yet our convention, which serves us well, implies that we are carving up reality by our choices, not natures.

Of course, the rest of this site’s ideas (particularly Almost Gate and Neural Cascade) need to be brought in to round out the discussion.

Abstraction

Let’s go up a level of abstraction.  By that, I mean proceeding from immediate sensory processing to processing that result. For example, after the visual data is processed in the occipital lobe from area V1 to V7, the result is sent forward towards the temporal lobe, where it will eventually be combined with auditory information.

James Garlick gives us a good encapsulation of abstractions in Intelligence and the Brain (p 14).

First, abstractions actually represent a reduction in information. … Second, abstractions represent information that is consistent or in common across situations.

A reduction in information. Isn’t that interesting? The dissimilarities between raw sensory information and the patterns used in subsequent mental processing is not passed forward (only the pattern is).

Also the pattern (the abstraction) represents information common between the sensory input and the named category.

The Letter E

Which of these would you call an ‘E’?

Many Es of various shapes. Recognition by features, not exact shape

Recognition by features, not exact shape

Maybe all of them. Maybe there’s one or two that you have doubts about. By the way, they are all capital Es according to Microsoft Word symbol chart.

This identification is a mid-level pattern analysis.  The occipital lobe has already processed the pixel-type input from the eyes. That is sent further up, not as a purified visual image, but as a set of features identified in the visual image.  Then our brains do a comparison of those features, for instance E, against remembered E features from school, reading, and signage. This is performed before conscious awareness typically.

Patterns and Pattern Matching

One top-level pattern usage which is easily observable are metaphors. In poems and casual conversation, things are compared to make points. The notions have some features in common and others in conflict. We are expected to see a relationship. We usually do, although we might not agree that the non-metaphorical features can be ignored.

Let’s consider another top-level pattern usage, reacting to actions in the stock market. And let’s make it more interesting by considering two different reactions. Here’s the diagram I want to discuss.

Believing the 2007 stock market mirrored 1929 depends on the height of your Almost Gate is

Stock Market Pattern

Think back to the financial crash in 2007-2008. I first noticed it during 2007. Perhaps you were like me with money in your retirement accounts sliding downward. Did you agree with the financial talking heads that said this was just like 1929 again? Or did you ignore those Cassandras and hold steady?

The pattern was the action of the stock market in 2007. There was a rhythm to its ups and downs. The volume traded fluctuated. This brings us to an important point. You may not have the time, the inclination, or the background to investigate the action of the stock market further than that reported on the daily news (more in Knowledge section). Also, the various pieces of the market, S&P 500, small caps, and NASDAQ all moved a bit differently. Did you ignore that? Or even notice it? It matters for your decision, but it doesn’t matter in the sense that you still had a decision—stay the course or get out of stocks—to make.

The Almost Gate

Back to the diagram. Everyone has some image of the stock market back in 1929 and into the Great Depression. That memory is compared against your understanding of the 2007 stock market action. There are certain features in common and other features different. When you sum up the neural inputs, do they exceed the threshold to fire, to declare a match? If you have a lower threshold for your Almost Gate, perhaps they did. You felt like 2007 was the beginning of the next Great Depression. Others of us, may have a higher threshold, a larger barrier at the Almost Gate, which wasn’t exceeded. We felt the two periods were not similar and we acted on that understanding.

I do not ignore that who say they used deductive logic and not neural processes in high level thinking.  My response is that there were cross-currents in the data, our information is not perfect nor complete, and the theory of stock prices is by no means settled. The logic used must be probabilistic along with some data ignored and other guessed at. The conclusions are probable or improbable with the deciding factor being closeness or distance, that is, the threshold of the Almost Gate.

Patterns permeate our cognitive processing, from sensory perception to meaningful internal understanding of the world to the decision-making in the highest quarters of our prefrontal cortex.

 

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