Set

There is a demonic card game that I have been playing obsessively called Set. And I think I'm going to take a break from it for a while; go play solitaire or something.
The basics of how to play are as such:
Pick up three cards that are a Set, as in Set Theory. You have four traits (shape, color, shade and number), and each trait has three variations (ie. squiggle, oval or diamond for shape). All possibilities are in a deck, and each card in a deck is unique, resulting in 81 cards total. 3variations^4traits.
a hollow, three, green, oval
hollow, three, red, oval
and hollow, three, purple, oval
would be a set,
where if you replaced one of the cards with a card that showed only One oval, that would not be a set. Rule of thumb is that you can't have just two of any trait. If I find my camera cord I'll get some pictures.

Pieces of the puzzle:
1. Set is a pattern recognition game. Some people are better at it than others, and most people get better with practice.
2. I went to Alfred Hublert's workshop on Tuesday (mad scientist in actuality), who had a high voltage experiment that had parallels to neurology. Which brought me back to my days of going through the cognitive psychology section at the public library, minimal as it was,
3. and reading Spitzer's The Mind Within the Net at the same time that I read Minsky's The Society of Mind and probably some Piaget too, in order to find the bouncing ideas that they all touched on.
4. Alfred's experiment involved a petri dish with castor oil, ball bearings, about 10,000volts, and three points in the dish. He'd leave one electrode alone, sitting actually In the castor oil. The other, he also put in the oil. The ball bearings (which were swimming/floating in the oil) would connect the two electrodes as a sort of wire. Then he took the second electrode out of the oil and put it to a second point, also in the oil. The ball bearing would jump from their last positions to reform a wire between the two electrodes. But not all of them. Some sort of came towards but didn't quite make it. So if you were to switch from point to point, it would eventually create a split wire between the first electrode and the two points where the second electrode had been placed repeatedly. Which is how neural nets are created and sustained. Neural nets were far from a new concept for me on Tuesday.
5. As well as a description of the actual hardware, Spitzer also presented some ideas about the difficulties for Artificial Intelligence. One such was that where a computer functions in a linear fashion: one signal is sent, which triggers other signals; we have many many many neurons triggering and firing at once.
6. Neurons also act on a threshold trigger while computers act on binary functions. A neuron has the whole range of numbers between 0 and 1 and might fire at, say, .3, or .6, or .75. For a computer to pass on the trigger, it needs to have a full 1 current. These threshold weights are adjusted based on positive and negative feedback (dopamine) as a result of the firing of the neuron. (Where was I going with this?)
7. Neurons that are close to each other (measured both by number of in-between connections and also by physical distance) might activate eachother and are subject to the same weight adjustments mentioned above.
8. Something about schema (like electrical schema) and concept maps and how if you activate a node(gate) in a schema it might or might not activate another but always to the same degree that it was itself activated. But if you activate a node(concept) in a cmap (or, as a book I wish I had on hand once called it a "cognitive schema"), it seems natural that all nodes connected From that node should be activated to a lesser degree, and those should activate the ones coming From them to a still lesser degree and so on. With, of course, consideration of the weights of the links.
9. This neurological behavior relates to pattern recognition in that human minds look at the whole picture at once, with their many neurons (connected to the many visual sensors in our eyes), instead of having to look at one small pixel (piece) at a time like a computer does.
So when you're looking at a cat, you're actually seeing the Whole cat, and not just looking at it and thinking "It has four legs, a tail, has such and such sort of ears, is around such and such height, and has such and such behavior. It must be a cat." If, for some reason, it has only three legs, would it still be a cat? Of course.
What we're doing is taking those traits, activating the neuron (or group of neurons) that represents those traits, which are linked to the neuron (or group thereof) that represents the concept "cat". If the concept "cat" is activated past its firing threshold, we recognize the object as a cat. Which very likely triggers a whole new layer of links to other concepts.

Back to Set. Finding sets works the same way as recognizing a cat. You can go at it linearly and look one by one by one at all the cards to find a set. Which is difficult to do, since our memories are not quite so pristine as we might like them to be (or maybe they are; a discussion for another time), and our speed of thought comes from parallel processing, so to speak.
A more natural approach is simply to run your eyes over the cards and wait for a set to jump out at you. You're mind processes multiple cards at once, and if those cards trigger the right part of the higher level, you'll recognize a set. Sometimes the cards don't fire right and we recognize sets that aren't really sets. Or they don't fire when they should fire, and we miss true sets. What we do when we play Set repeatedly is fine-tune the weights of the connections between our neurons.

That's a big jump, from neurological behavior to recognizing sets, but I'm not sure how to explain it better. It also doesn't really answer the question of why some people are better than others at the game. It might give some pointers, but mainly to more, Huge questions, simply multiplying the main question. :D
I'm satisfied for now. Rather, done mashing ideas together for the night.