Gambling, Addiction and Prediction

Why do people love to gamble? True, there is a range of how much various people like to gamble. At one end there are people are (or pretend to be) bored by it (I exclude people who, for whatever reason, won’t even try games of chance or skill). At the other end are people who lose their savings, homes, and families, or even fingers and lives to legal and illegal gambling operations.

It may all have to do with the brain’s built-in prediction mechanisms.

According to Jeff Hawkins’s theory of the sequential and predictive basis of human intelligence, the primary intelligent function of the brain is to remember sequences of data and use that memory to predict future sequences of data. This is true both for sensory data that we think of as naturally sequential, like the notes in a music tune, and for sensory data that we normally think of as non-sequential, like the features in a human face. According to the theory we construct a model of a human face from sequences of visual input (supplemented by other data, like touching one’s own face). If we see an eye, we expect to see a sequence of other features: another eye, a nose, mouth, eyebrows, hair, chin, ears. We expect to hear a voice when a mouth moves, but not when an eye blinks.

An important element in building a model of the world is surprise. Using memory, the brain predicts what it expects to see, hear, and feel next. When something different occurs, the element of surprise turns on higher functions in the brain. We may look at something more closely. Perhaps it is a face, but it is a strange face. Is this a sign of danger? Friend or foe, we need to memorize the new face and whatever we associate with it (perhaps the face belongs to someone who is being introduced by a friend; we need to remember that association, or the physical place where we met the person).

There is a bit of a nervous rush when a prediction is made that turns out to be false. The mind gears up to determine why it made a false prediction, both in order to deal with the unexpected situation and to be able to predict it in the future. Probably this rush is partly neurochemical. We’ll call it a bit of adrenaline, although it is possibly based on another neurotransmitter or hormone. Adrenaline rush is the lay term I will use.

Games can teach, and they can while the time away. Even when they are not played for money or prizes, they can have an addictive quality. In almost every game an element of prediction is involved. The outcome provides relief if it was predicted, and a rush of neural activity if it was not predicted.

Generally, people like adrenaline rushes, especially in small doses. They like a thrill more than they like boredom. Games are played partly because they provide small, safe levels of thrill in situations that would otherwise being predictable and boring.

Gambling involves making a prediction. It involves two highs for the human brain. Most of us enjoy winning. Getting money from winning gives the same motivation as getting a snack, affection, or a pay check.

I believe that it is losing, however, that makes gambling addictive. When you lose in a random game of chance, your brain is fooled. It made a prediction. It was surprised by the outcome. Now it is designed to frantically memorize what happened and construct a way of making a correct prediction.

Gamblers often come to believe, after a string of losses, that they are about to make a correct prediction or a series of correct predictions. This is how your brain works: long experience has taught it that nature is a set of sequences, and past sequences can predict future sequences. The brain is fooled by the artificial construction of random events like dice throws, roulette wheel spins, and card deals. Worse, there is a sometimes-confusing element of predictability to some of this (two dice will roll a seven more often than a two; in poker three of a kind comes up more often than four of a kind) that tends to reinforce the brains belief that it can make sense out of randomness. The brain persists in the false belief that random events become predictable if only you keep placing bets long enough.

There is a rush when losing. People, on a chemical level, don’t feel pain when they lose. The chips or money they gamble is abstract, removed from the necessities or pleasures they could buy if had not lost. But the loss forces the higher intelligence centers of the brain to gear up. It is like taking a tiny bit of a stimulant. It is pleasurable.

This is often why otherwise highly intelligent individuals will lose vast sums of money in casinos. It is precisely like drug addiction. They may, at some conscious level, when away from a casino, coldly say that they understand they cannot beat a random game with odds set so that the house wins. But in the casino they cannot get out of the loop of feeding themselves tiny bits of adrenaline with each wager, win or lose.

Welcome to Machine Understanding

I have been interested in the idea of intelligent, understanding, conscious machines (computers and robots) since I was a child. How could a fan of science fiction books and movies not be? At some point I started wondering how the human mind itself could be intelligent, understanding, and conscious. During my life I have periodically returned to that theme.

A relatively recent wake-up call for me was On Intelligence by Jeff Hawkins. I read the book last year and I am re-reading it now. So most of these early blog entries are inspired by statements from the book. I am more than half-way through the book, well into the nuts-and-bolts of Chapter 6, so I will probably both comment on what I am reading and go back to various passages in the front half of the book.

A few years ago my article Indexing Books: Lessons in Language Computations was published by the Key Words, the Bulletin of the American Society for Indexing. I don't want to discourage you from reading the article, but let me sum it up: if a machine can't read with comprehension, it can't create a high-quality index for a text. Most people think using a computer to generate a good index of a text should be an easy accomplishment.

I chose the term "machine understanding" over AI, artificial intelligence, because I agree with Jeff that the AI field has mainly been a failure. Perhaps I should have called it "human understanding," since we don't really have a good understanding of why humans can understand things, exhibit intelligence, and are conscious.

This blog is mainly for me to keep a record of my thoughts, but if anyone else stumbles across it I hope I can introduce them to the ideas of people who work in this fascinating field.