Woodliceandmen

Methods & Techniques

• Utilizes Bayesian inference to model cognitive processes and perceptual states.
• Applies variational Bayes for ensemble learning and probability density dynamics.
• Examines hierarchical structures in the brain to understand spatial and temporal processes in cognition.
• Analyzes the reduction of surprise and informational entropy in relation to sensory data and hypotheses.

Key Findings

• The free energy principle provides a deflationary account of cognition that emphasizes simplicity.
• Cognitive states can be modeled as posterior probabilities that evolve through Bayesian belief updating.
• The framework distinguishes between predictive coding and Bayesian models in terms of data processing and hypothesis updating.
• The relationship between the free energy principle and predictive coding reveals insights into top-down and bottom-up processing in the brain.

Prerequisites

• Probability theory and Bayesian inference basics
• Free Energy Principle and generative models

🎯 Consulting & Tutoring

Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.

Related Skills

See BIBLIOGRAPHY.md for the complete publication catalog and related papers.