
#320 — Constructing Self and World
Making Sense with Sam Harris
Unraveling Bayesian Perception
This chapter explores the principles of Bayesian statistics and Bayes' theorem, emphasizing how probability estimates are updated with new evidence. It discusses the relationship between sensory perception and prior probabilities, illustrating how the brain's predictive coding framework influences our interpretation of visual information. Additionally, the chapter draws parallels between human cognitive processes and deep learning architectures, setting the ground for discussions on consciousness and perception.
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