Summary of Surprise! Using Physiological Stress For Allostatic Regulation Under the Active Inference Framework [pre-print], by Imran Khan and Robert Lowe
Surprise! Using Physiological Stress for Allostatic Regulation Under the Active Inference Framework [Pre-Print]
by Imran Khan, Robert Lowe
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes integrating two frameworks – allostasis and active inference – to explain long-term adaptation in living systems. Allostasis suggests that physiological and behavioral adjustments minimize long-term prediction errors, while active inference framework (AIF) explains action through minimizing future errors by learning statistical contingencies of the world. The authors suggest framing prediction errors through biological hormonal dynamics proposed by allostasis. They develop a model grounding prediction errors into secretion of cortisol as an adaptive mediator on homeostatically-controlled physiology. Using a computational agent in simulations, they evaluate allostatic functions of cortisol providing adaptive advantages to long-term physiological regulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two ideas – allostasis and active inference – to help living things adapt over time. Allostasis says that our bodies and behaviors adjust to make sense of the world, while active inference is about learning from experience to predict what might happen. The researchers want to connect these ideas by looking at how our body’s stress response (like cortisol) helps us adapt. They made a computer model to test this idea and found that it can help systems regulate themselves over time. |
Keywords
» Artificial intelligence » Grounding » Inference
