Hey everyone!

So I thought I might want to revisit one theorem that I spoke about before and some of you actually appreciated, and links back to last week’s issue about Ray Kurzweil’s Pattern Recognition Theory of Mind (PRTM).

Let’s revisit Bayes’ Theorem.

Bayes' Theorem is a fundamental mathematical concept used to describe the relationship between prior probabilities and new evidence. It’s an incredibly powerful way of thinking that can help you sidestep cognitive traps and make smarter decisions than you would otherwise.

A is our hypothesis, B is our data available; P(A|B) (aka posterior probability) = probability of A occurring given dataB; P(B|A) = probability of data B given Event A occurred; **P(A) (**aka prior probability) = probability of A occurring in isolation; P(B) = probability of B occurring

Let me give you a simple example of how Bayes’ Theorem works. Let's say that you're trying to identify a person in a police lineup. You only have one clue: they are wearing glasses. Based on this one piece of evidence, what are the chances that the person wearing glasses (B) is the criminal (A)?

To answer this question, we need to consider two things: (1) How many people in the lineup wear glasses? (2) How many of those people actually committed the crime? Using Bayes' Theorem, we can calculate the probability that the person wearing glasses is guilty by simply dividing the number of criminals who wear glasses by the total amount of people in the lineup who do so:

• P(A|B) = probability of person being a criminal given he wears glasses
• P(B|A) = probability of person wearing glasses given he committed criminals
• P(A) = probability of person being a criminal
• P(B) = probability of person wearing glasses

Where does Bayes’ Theorem lies in PRTM

The brain is a prediction machine, computing probabilities of what it expects to encounter

Remember that the theory shows that the patterns follow sequential hierarchy, going from lower order/zoomed in patterns towards higher order/big picture patterns.

In order for the brain to recognise the pattern, a firing threshold must be crossed.

Going from lower order to higher order is more of a bottom-up approach.

Bayesian models have been proposed to explain how the brain combines prior knowledge (prior beliefs or expectations) with sensory evidence to infer the most likely interpretation of a given situation, which is considered top-down.

And to end our recap..

The power of Bayes’ theorem lies in its ability to update our beliefs as more information comes in. When we encounter new evidence that contradicts our initial assumptions, we don't discard it - instead we use it to adjust our current understanding so that it fits with reality better.

This allows us to be more reflective and less biased when making decisions, enabling us to learn from our mistakes rather than repeating them over and over again.

Even better, it encourages more patterns to form with our pre-existing knowledge, to see if everything makes sense, by challenging our beliefs and preconceptions about issues and other people.

At its core, learning about Bayesian reasoning teaches us humility; it gives us an appreciation for our limits and encourages us not only think critically but also encourage wide exploration before committing ourselves too quickly.

One final word about this

This is not the end of the debate.

There is still ongoing research.

Have a great week

Mark x

Content

​Top Down and Bottom Up Processing in Human Perception - as mentioned above, the brain uses both top-down and bottom-up approaches when it comes to thinking and perception, and I have a dedicated video talking about both of these.

Quote