If we rely only on our senses we would notice we are seeing a small part of an object. Our eyes capture a small patch of colours but we see a bee on a flower. How does the brain construct this image from the minuscule sensory data?

One theory is our brains infer the most likely state of the world (coming up with a "best guess") given our current sensory data and our prior knowledge about the world.

How does our brain perceive the world based given the noisy and incomplete sensory data?

1. We have the World creating events.
2. A small % of that reality creates sensory data.
3. As Agents in the world, we perceive the World. Meaning ur brains have to come out with the best hypothesis for the features of the world given the sensory data.

Bayesian theorem is the optimal mathematical solution for this mathematical problem. A very computationally explosive problem. We can get an inspiration from AI systems that are addressing it.

Neural Networks (which are heavily used in AI) appear to be the solution to the complexity of the Bayesian Perception Problem. They break the problem in levels.

You feed a collection of images of faces to a neuronal network and it predicts:

• low-level features: edges and corners
• mid-level features: noses or eyes
• high-level features: facial details

Since this hierarchical predictions are computationally efficient, we can think our mind also solves the complexity of a Bayesian prediction by breaking up the problem in hierarchical levels.

It is a hypothesis that our brain works through hierarchical predictive processing:

• the brain is constantly generating predictions about the world and then comparing those predictions to incoming sensory data
• the brain is organised into a hierarchy of processing levels, each of which generates predictions about the level below it. Ex: the highest level of the hierarchy might generate predictions about the overall scene that we are looking at, while lower levels of the hierarchy might generate predictions about specific objects or features in the scene.

What I believe I am experiencing right now is actually what I am experiencing (called a Posterior Belief). So our reality is weighted average of prior beliefs and sensory data.

These prior beliefs are acting like the hierarchical levels separating a neural network. These included, starting from the levels closer to the sensory data:

• sensations
• gesture, voices, happiness, disgust
• drawings, motions
• memory, math, faces
• reasoning, concepts

Not only we are hallucinating our world. We halucinate ourselves and we halucinate how our self integrates in the constructed world. This is based on:

• sensory integration
• our sense of agency
• experience of what it means to be "me"
• perspective

Ofcourse, in daily lif,e we are not aware of our hallucinations. Meditation or psychedelics are ways to break through our model of ourselves.

Meditation allows us:

• to over-emphasise our senses (as opposed to our priors) through attentional meditations (where we count our breaths for example)
• to become aware of our priors (higher level models in the Bayesian brain like our ethical foundations) and the ways we build reality through deconstruction type meditations like mindfulness
• to reconstruct ourselves through constructive meditations like love&kindness.

If we place the 3 types of meditation of on a 3D chart we can plot the evolution of meditator from "monkey mind" to enlightenment. Just like the Jhanas, states of deep mental concentration and absorption that are cultivated in Buddhist meditation.

In short meditation:

• lowers the weight of the priors compared to our senses
• attenuates our high-level priors: beliefs, attitudes etc
• allows us to tweak our change our priors or create new ones