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What are the key ingredients in a generative model?What’s the first decision a modeler must make?
What kinds of models are used in each?Which real-life phenomena are better suited to each approach?
What’s the process for using Active Inference to interpret behavioural data?What’s one challenge in applying this method to group-level data?
What two forces does it balance?How does it relate to curiosity and goal-seeking behaviour?
What quantities map to specific brain processes?How does precision relate to dopamine, according to this framework?
Name two fields or models that Active Inference integrates.What’s one critique or limitation the authors acknowledge?
Part 2
🧪 Active Inference Scavenger Hunt (Part II: Applied Modeling)
Mission: You've completed your training as a Bayesian Brain Apprentice. Now you must apply your skills in the field. Follow these clues to simulate sentient behavior and interpret data through the lens of Active Inference.
🛠️ Clue 6: The Model Builder’s Cookbook
Objective: Find the chapter that introduces a recipe for designing Active Inference models.
🧭 Clue 7: Discrete Time vs. Continuous Time
Objective: Compare how Active Inference works in discrete time and continuous time.
🦉 Clue 8: Learning from Behaviour
Objective: Find the section on model-based data analysis (e.g., Chapter 9).
💡 Clue 9: Planning and Policy
Objective: Identify where the book discusses expected free energy and its role in planning.
🤖 Clue 10: From Normative to Process Theory
Objective: Track how the book bridges from formal models to neurobiological realism.
🔮 Bonus Clue: The Unified Field Theory (of Mind?)
Objective: In Chapter 10, the book links Active Inference to other major theories.
🏁 Completion Reward
If you’ve followed all the clues, you’ve effectively enacted your generative model and updated your beliefs to better match the structure of the book. Congratulations—your surprise is minimised. 🧠💫
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