How metacognitive architectures remember their own thoughts: a systematic scoping review
Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents but remains a fragmented field: diverse theories, terminologies, and design choices have led to disjointed developments and limited comparability across systems. Existing overviews remain at a conceptual level that is undiscerning to the underlying algorithms, representations, and their respective effectiveness. We address this gap by performing a systematic scoping review. Reports were included if they described techniques enabling Computational Metacognitive Architectures (CMAs) to model, store, remember, and process their episodic metacognitive experiences, one of Flavell’s (1979) three foundational components of metacognition. Searches were conducted in 16 databases between December 2023 and June 2024. Data were charted using a 20-item framework considering pertinent aspects and analysed via structured narrative synthesis. A total of 101 reports on 35 distinct CMAs were included. Our findings show that metacognitive experiences may boost system performance and explainability, e.g., via self-repair. However, lack of standardisation and limited evaluations may hinder progress: only 17% of CMAs were quantitatively evaluated regarding this review’s focus, and significant terminological inconsistency limits cross-architecture synthesis. Systems also varied widely in memory content, data types, and employed algorithms. Limitations include the non-iterative nature of the search query, heterogeneous data availability, and an under-representation of sub-symbolic CMAs. Future research should focus on standardisation and evaluation, e.g., via community-driven challenges, and on transferring promising principles to emergent systems.