mlsanityData to 7 Oct 2026

Computational Metacognition

arXiv 2201.12885 ↗First posted 30 Jan 2022cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 73 references that are in the map. First posted on arXiv 30 Jan 2022.

Builds on

3 of 73 references.

References · 3

  1. 2017The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learningcited by 1
  2. 2019Anticipatory Thinking: A Metacognitive Capabilitycited by 1
  3. 2019Reflecting After Learning for Understandingcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.