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#320 — Constructing Self and World

Making Sense with Sam Harris

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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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