Summary of Modeling Arousal Potential Of Epistemic Emotions Using Bayesian Information Gain: Inquiry Cycle Driven by Free Energy Fluctuations, By Hideyoshi Yanagisawa et al.
Modeling arousal potential of epistemic emotions using Bayesian information gain: Inquiry cycle driven by free energy fluctuations
by Hideyoshi Yanagisawa, Shimon Honda
First submitted to arxiv on: 14 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Theory (cs.IT); Neurons and Cognition (q-bio.NC); Applications (stat.AP)
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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 The paper proposes a novel formulation of epistemic emotions like curiosity and interest using information gain generated by the free energy minimization principle. It introduces two types of information gain: Kullback-Leibler divergence (KLD) representing recognition-based free energy reduction, and Bayesian surprise (BS) representing expected information gain from prior updates. The authors show that KLD and BS form an upward-convex function similar to Berlyne’s arousal potential functions or the Wundt curve. They suggest that this framework unifies the free energy principle with arousal potential theory, explaining the Wundt curve as an information gain function and proposing an ideal inquiry process driven by epistemic emotions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how our emotions, like curiosity and interest, make us want to learn more. It creates a new way to measure these emotions using math formulas that combine two types of “gain” from learning: how much we recognize what we already know (Kullback-Leibler divergence), and how surprised we are by what’s new (Bayesian surprise). The authors show that these gains form a special shape, like a curve. They think this curve helps explain why we get excited to learn when things are new and challenging. |
