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What pages are they on?Which road appeals more to philosophical reasoning? Which to computational modeling?
Summarize what it means for living organisms to actively sample their world.How is this connected to self-evidencing?
How do they differ?Why is free energy the key objective for perception and action?
What are the prior and likelihood values?What are the surprise and Bayesian surprise scores for the jumping observation?
What’s the key distinction?How does this difference relate to the concept of subjectivity in perception?
Part I
🔍 Active Inference Scavenger Hunt (Part I: Theoretical Foundation)
Mission: You are an Agent equipped with a Generative Model. Your goal is to minimize epistemic uncertainty and maximise conceptual reward. Find the following clues and fill in your observations.
🧠 Clue 1: The High and Low Roads
Objective: Find and explain the difference between the High Road and Low Road to Active Inference.
🔁 Clue 2: Action-Perception Loops
Objective: Identify the figure that illustrates the action-perception loop.
🎯 Clue 3: Prediction Error vs. Free Energy
Objective: Locate the explanation of prediction error minimization vs variational free energy minimisation.
🧩 Clue 4: Bayesian Frog
Objective: Find the "frog vs. apple" example used to explain Bayesian inference.
🔬 Clue 5: Generative Model vs. Generative Process
Objective: Locate the section with a diagram showing the difference between a generative model and a generative process.
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