BLOG POST#4:2. How well does Bayesian inference describe the brain’s use of prior knowledge with new sensory data?

Bayesian Inference is a way to combine what we already know (prior knowledge) with new information to make prediction and decision.

Here is a simple breakdown on how it relates with the brain:

1.Prior knowledge: The brain keep tracks of what it has already learned about the world. For example: If you often see dog with tail your brain forms the brain form the expectation that a new animal with four legs for is probably a dog.

2.New evidence: The brain form new information through vision ,sound or other senses. For example: You see a new animal in the garden

3.Updating Belief: Bayesian inference describes how the brain combine prior knowledge with new evidence to form an updated belief. For example : seeing t that an animal bark confirms your expectation is a dog.

4.Probabilistic thinking : The brain does not toke one guess ,it using likelihoods to different possibilities and often acts on the most probable one.

5.Real World Application: This approach help explain motor control and decision.

SUMMARY:

Bayesian inference is a mathematical way to describe how the brain learn from experience and adapt to new situation by combining old knowledge with new evidence allowing to make smart guesses in an uncertain word.