Quantum Computing, Fast and Slow

Behavioral Economics taught us about System 1, System 2, and Cognitive Bias. We combine them to create a mathematical formula for the human soul. 🎭

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The Horse and the Rider

The Orch OR Theory presented in the previous chapter provides a great description for how and why electrons move around in our heads, but it doesn’t do much to describe what those electrons represent. So now we shift our conversation from Neuroscience to Psychology—we switch from a hardware model of the brain to its software.

One of the earliest and most useful models of how humans operate comes from ancient Greece. The ancient Greeks believed that inside each person was actually two animals: a Horse and its Rider. Sometimes the horse and rider cooperate together, but sometimes they compete against each other.

The Horse corresponds to the body. It is strong and powerful and gets the Rider where he needs to go. The Horse is also kinda stupid. My body hates drinking water no matter how many times “I” lead it there. My Horse doesn’t like getting cold, complains when it’s hot, and should rarely be allowed to eat what it wants. If I let my Horse eat after midnight, it turns into a monster so I might as well live inside a Gremlin. 🧌

The Rider corresponds to the soul. The Rider can’t afford to spend his/her attention on the mundane tasks of life like walking and breathing. Those tasks are handled by the Horse. Sometimes the Rider has to make the Horse slow down—like chewing our food or breathing during stressful situations. Sometimes the Rider has to make the Horse speed up—usually during work and workouts. 🏇

To make this concept easier for our kids, our family calls the Rider, “I”, and calls the Horse, “Me”. The faster we could teach their I to control their Me, the faster we could “delegate our parenting” to the child to develop their own autonomy. That frees us (parents) up, so that we can spend more time parenting ourselves. I make enough of my own problems every day without having to find a kid’s other shoe or remind them to put their dishes in the dishwasher. I’m busy doing that to my Me. 👮

The Horse and the Rider concept enables us to see that we actually have more than one brain so to speak. One is more interested in comfort and protection today (the Horse), and the other is more interested in comfort and protection tomorrow (the Rider).

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System 1 and System 2

Over the past 50 years, psychologists Amos Tversky and Daniel Kahneman performed science experiments on people to learn more about these two “systems of thinking”. They essentially invented the science known today as Behavioral Economics. Kahneman eventually won a Nobel Prize for their work and published the research in his book, “Thinking, Fast and Slow”.

It’s best to hear Kahneman’s descriptions for what he calls System 1 and System 2. See if you can guess which one is the Horse and which one is the Rider.

System 1: operates automatically and quickly, with little or no effort and no sense of voluntary control.

System 2: allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.

When we think of ourselves, we identify with System 2, the conscious, reasoning self that has beliefs, makes choices, and decides what to think about and what to do. Although System 2 believes itself to be where the action is, the automatic System 1 is the hero of the book. I describe System 1 as effortlessly originating impressions and feelings that are the main sources of the explicit beliefs and deliberate choices of System 2. The automatic operations of System 1 generate surprisingly complex patterns of ideas, but only the slower System 2 can construct thoughts in an orderly series of steps. I also describe circumstances in which System 2 takes over, overruling the freewheeling impulses and associations of System 1. You will be invited to think of the two systems as agents with their individual abilities, limitations, and functions.

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@aigeeks: two individual neural network agents competing against each other sounds like a GAN, right? 😉

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In rough order of complexity, here are some examples of the automatic activities that are attributed to System 1:

• Detect that one object is more distant than another.

• Orient to the source of a sudden sound.

• Complete the phrase “bread and . . .”

• Make a “disgust face” when shown a horrible picture.

• Detect hostility in a voice.

• Answer to 2 + 2 = ?

• Read words on large billboards.

• Drive a car on an empty road.

• Find a strong move in chess (if you are a chess master).

• Understand simple sentences.

• Recognize that a “meek and tidy soul with a passion for detail” resembles an occupational stereotype.

The highly diverse operations of System 2 have one feature in common: they require attention and are disrupted when attention is drawn away. Here are some examples:

• Brace for the starter gun in a race.

• Focus attention on the clowns in the circus.

• Focus on the voice of a particular person in a crowded and noisy room.

• Look for a woman with white hair.

• Search memory to identify a surprising sound.

• Maintain a faster walking speed than is natural for you.

• Monitor the appropriateness of your behavior in a social situation.

• Count the occurrences of the letter a in a page of text.

• Tell someone your phone number.

• Park in a narrow space (for most people except garage attendants).

• Compare two washing machines for overall value.

• Fill out a tax form.

• Check the validity of a complex logical argument.”

System 1 runs automatically and System 2 is normally in a comfortable low-effort mode, in which only a fraction of its capacity is engaged. System 1 continuously generates suggestions for System 2: impressions, intuitions, intentions, and feelings. If endorsed by System 2, impressions and intuitions turn into beliefs, and impulses turn into voluntary actions. When all goes smoothly, which is most of the time, System 2 adopts the suggestions of System 1 with little or no modification. You generally believe your impressions and act on your desires, and that is fine—usually.

When System 1 runs into difficulty, it calls on System 2 to support more detailed and specific processing that may solve the problem of the moment. System 2 is mobilized when a question arises for which System 1 does not offer an answer, as probably happened to you when you encountered the multiplication problem 17 × 24. You can also feel a surge of conscious attention whenever you are surprised. System 2 is activated when an event is detected that violates the model of the world that System 1 maintains. In that world, lamps do not jump, cats do not bark, and gorillas do not cross basketball courts. The gorilla experiment demonstrates that some attention is needed for the surprising stimulus to be detected. Surprise then activates and orients your attention: you will stare, and you will search your memory for a story that makes sense of the surprising event. System 2 is also credited with the continuous monitoring of your own behavior—the control that keeps you polite when you are angry, and alert when you are driving at night. System 2 is mobilized to increased effort when it detects an error about to be made. Remember a time when you almost blurted out an offensive remark and note how hard you worked to restore control. In summary, most of what you (your System 2) think and do originates in your System 1, but System 2 takes over when things get difficult, and it normally has the last word.

The division of labor between System 1 and System 2 is highly efficient: it minimizes effort and optimizes performance. The arrangement works well most of the time because System 1 is generally very good at what it does: its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate. System 1 has biases, however, systematic errors that it is prone to make in specified circumstances. As we shall see, it sometimes answers easier questions than the one it was asked, and it has little understanding of logic and statistics. One further limitation of System 1 is that it cannot be turned off. If you are shown a word on the screen in a language you know, you will read it—unless your attention is totally focused elsewhere.

It took me five years to finish reading Kahneman's book. If you think that quote was difficult to read, his book is about 500 more pages just like that. The insights and wisdom are so densely packed that I had to read the book with my System 2 the entire time. So I guess it took “I” five years to finish reading Kahneman's book. 🤪

System 2, our consciousness, is like the captain of a large cruise ship. If everything is working well, on schedule, with no surprises, then there’s not much to do. System 2 is free to be creative and innovative and conserve its attention. But if something unexpected happens “below deck”, the captain is the only person who can save the day. The more our System 2 can train our System 1 to handle situations without panic, the less often our System 2 needs to be called into action.

A great example of System 2 training System 1 is sports. Great basketball shooters will tell you that the more you think about how your shot works, the worse you shoot. That’s because shooting a basketball is a System 1 activity. The muscles that produce a basketball shot are informed by a very complex sequence of neurons that don’t have time for you to think. We learned in the previous chapter how each time those neurons are excited, oligodendrocyte cells wrap those neuronal connections in myelin. Myelin is how we physically manufacture “muscle memory”. 💪🧠

My favorite book that connects myelin in Neuroscience to myelin in Psychology is called, “The Talent Code: Greatness Isn't Born. It's Grown. Here's How.”

Author Daniel Coyle unveils why talent isn’t evenly distributed over time. For example, how many famous painters can you name that are not Impressionists or from the Italian Renaissance? Who else can you name that’s not Manet, Monet, Van Gogh, or a Teenage Mutant Ninja Turtle?

Here are a few questions from the introduction of his book:

• How does a penniless Russian tennis club with one indoor court create more top-twenty women players than the entire United States?

• How does a humble storefront music school in Dallas, Texas, produce Jessica Simpson, Demi Lovato, and a succession of pop music phenoms?

• How does a poor, scantily educated British family in a remote village turn out three world-class writers?

Training System 1 enhances our skills and manufactures our intuition, but System 2 is still very important in sports. Practicing a sport is extremely expensive in attention, calories, and effort. No one else can make that sacrifice for you. Like the famous coach Vince Lombardi said, “The will to win is not nearly so important as the will to prepare to win.”

Here are a few simple examples:

• Your “rider” has to get your “horse” to the gym on time.

• Your “rider” has to push your “horse” to sprint.

• Your “rider” has to make your “horse” drink water.

• Your “rider” has to remind your “horse” what your coach wants you to work on.

• Your “rider” has to get your “horse” to bed on time.

• Your “rider” has to feed your “horse” healthy vegetables and protein.

• Your “rider” has to calm your “horse” when it’s anxious before the game.

• Your “rider” has to remind your “horse” to have fun.

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Cognitive Biases

Tversky and Kahneman spent most of their careers probing the limits of System 1 and System 2. They devised experiments that reveal how these agents work, but more importantly they demonstrated when, where, how, and why they fail. They designed experiments to show how humans aren’t always the “rational actors” that traditional economists use in their financial models.

Tversky and Kahneman call the quirks of the systems, Cognitive Biases. Here’s a list of cognitive biases described in Kahneman’s book, followed by a simple explanation.

• Anchoring — Once we accept a theory, it’s really difficult to notice the flaws.

• Hindsight Bias — We tend to view past events as being predictable.

• Correspondence Bias — We rely too heavily on personality‑based explanations for the behavior seen in others.

• Implicit Bias — We attribute positive and negative qualities to entire groups of individuals, which can be racist and non-factual.

• Confirmation Bias — We tend to search for information in a way that confirms our own views, and discredits any information that doesn’t support them.

• Affinity Bias — We tend to like people who are like us.

• Self‑Serving Bias — We claim more responsibility for successes than for failures.

• Belief Bias — We evaluate the logical strength of an argument based on our current beliefs.

• Status Quo Bias — We prefer to hold on to the current situation, rather than risking any losses through change.

• Overconfidence Effect — We overrate our abilities and skills as decision makers. The less we know about a subject, the more certain we are that we are right.

• Physical Attractiveness Stereotype — We assume people who are physically attractive also are good at other stuff too.

• Availability Heuristic — We think something is common just because it’s easy to remember.

• Planning Fallacy — We believe tasks will take less time than they actually do.

• Framing Effect — The same facts feel different depending on how they’re worded. We react more strongly to “90% survival rate” than “10% death rate”.

• Representativeness Heuristic — We judge by appearances instead of digging in to find out what’s going on.

• Sunk Cost Fallacy — Once we invest in things, we tend to over-invest in them to avoid any loss.

• Base Rate Fallacy — We ignore how common something really is when making judgments.

• Prospect Theory — Losing something hurts worse than gaining that same thing feels good.

• Loss Aversion — We would rather avoid losing $10 than to gain $10.

• Endowment Effect — We overvalue things just because we own them.

• Regression Toward the Mean — Really lucky or unlucky things tend to become more average next time. We get “fooled by randomness” constantly.

• Illusion of Control — We believe we can control stuff that's actually random.

• Priming — Anything we have just seen or heard is likely to affect our next thoughts, even if they are unrelated.

• Substitution (Heuristic) — When a question is too tricky, our brains answer easier questions instead—without us noticing.

• What You See Is All There Is — We assume the limited info we have is the full story, even when it’s not.

As you can see when our System 2 relies on our System 1 too much it leads to errors in judgment. And the opposite is also true—when our System 2 relies on our System 1 too little it also leads to errors in judgment.

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Human Artificial Intelligence

Now that we understand System 1 and System 2, it will be much easier to see how humans are very sophisticated generative artificial intelligence. Remember, artificial doesn’t mean “fake” or “digital” or “plastic”, it simply means “designed”. Now, here is Wikipedia to explain the Generative Adversarial Network (GAN).

Wiki: A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014. Two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

@ChatGPT: what’s better about the generative adversarial network architecture?

ChatGPT: The Generative Adversarial Network (GAN) architecture is known for its ability to generate realistic data, such as images or videos, by pitting two neural networks against each other. This approach can create more diverse and complex outputs compared to traditional generative models, making it better suited for tasks that require creativity and innovation. Additionally, GANs can learn from unstructured data, which allows them to generate new and unique content without relying on pre-defined rules or templates.

@ChatGPT: how do GANs work?

ChatGPT: GANs, or Generative Adversarial Networks, work by training two neural networks, a generator and a discriminator, in a competitive process. The generator network takes random noise as input and generates outputs, while the discriminator network takes inputs from both the generator network and real data, and tries to distinguish between the two.

During training, the generator network learns to create outputs that are similar to the real data, while the discriminator network learns to better distinguish between the generated and real data. This process continues until the generator network can produce outputs that are realistic enough to fool the discriminator network.

Once the training is complete, the generator network can be used to create new data that is similar to the real data. This can be applied to various tasks, such as image synthesis, text generation, and music composition, among others.

Our conscious and subconscious minds are pitted against each other like this too. In the second chapter, we learned how quantum mechanics only “renders reality” around each observer like a first-person video game. In the third chapter, we learned how our subconscious minds “generate” our first person experience by making quantum calculations with the universe billions of times per second. Our conscious “discriminator” minds are much slower, operating on clock cycles between 2-50 times per second. It may be difficult to believe, but:

The rest of this chapter will explore our generator and discriminator networks with more than 50 books from psychology. We will see how the first person experience from our “generator” networks includes all kinds of lies and hallucinations for our “discriminator” networks to uncover. From the moment of our birth, our System 1 has been performing little science experiments like knocking over wooden blocks to see what happens next. Our System 1 mastered Newtonian Physics a decade before our System 2 learned the equation, F=MA. Our “lying self” gets a 5-year head start on our “remembering self”.

Here’s how Kahneman describes the division between our two neural networks. He wrote, “Odd as it may seem, I am my remembering self, and the experiencing self, who does my living, is like a stranger to me.”

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@christians: Our Generative Adversarial Network architecture has really important implications for the Bible. For example, the English word “soul” is not in the original Greek Bible. The word that Matthew, Mark, Luke, John, and Paul wrote was the Greek word “ψυχή”, which is pronounced “psyche”.

The word soul may not mean exactly what you think it means. In the North American Standard Bible, “ψυχή” is only translated as “soul” 47% of the time. The NASB also translates that exact same word as:

• heart (2)

• heartily (1)

• life (43)

• mind (2)

• person (4)

• soul (47)

• suspense (1)

• thing (1).

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Uncertainty is my Theory of Everything and every theory is only as good as its best equation, right? So here’s my best equation. I am asserting the following equivalences for all philosophers, scientists, and spiritual explorers:

1. System 1 = subconscious = horse = Quantum GAN Generator = body = flesh

2. System 2 = consciousness = rider = Quantum GAN Discriminator = psyche = ψυχή = soul

These equations are unprovable for all the Bayesian reasons: there is nothing but uncertainty all the way down to the pixels of reality. So maybe a better name for this theory would be the Theory of Every Uncertainty because everything is a subset of uncertainty.

The reason I reference Wikipedia and ChatGPT so often is because they are the most decentralized sources of truth from man and machine, respectively. Their information has some of the lowest uncertainty in the knowable universe. All the other sources of truth in this book comes directly from the world’s leading scientists—the most centralized sources of truth in their fields.

You’ll have to decide for yourself if any of this is true, so watch all the videos and read all these books for yourself. 🙌

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Continue reading…

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