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Probabilistic Foundations for Metacognition via Hybrid-AI

8 February 2025
Paulo Shakarian
Gerardo I. Simari
Nathaniel D. Bastian
    AI4CE
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Abstract

Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems. This paper reviews a hybrid-AI approach known as "error detecting and correcting rules" (EDCR) that allows for the learning of rules to correct perceptual (e.g., neural) models. Additionally, we introduce a probabilistic framework that adds rigor to prior empirical studies, and we use this framework to prove results on necessary and sufficient conditions for metacognitive improvement, as well as limits to the approach. A set of future

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@article{shakarian2025_2502.05398,
  title={ Probabilistic Foundations for Metacognition via Hybrid-AI },
  author={ Paulo Shakarian and Gerardo I. Simari and Nathaniel D. Bastian },
  journal={arXiv preprint arXiv:2502.05398},
  year={ 2025 }
}
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