# Computational Metacognition

arXiv: https://arxiv.org/abs/2201.12885 · HTML: https://mlsanity.com/paper/2201.12885/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 73 references that are in the map.
- First posted on arXiv 30 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning](https://mlsanity.com/paper/1711.01431/) (2017)
- [Anticipatory Thinking: A Metacognitive Capability](https://mlsanity.com/paper/1906.12249/) (2019)
- [Reflecting After Learning for Understanding](https://mlsanity.com/paper/1910.08243/) (2019)

## Built on by (most cited first, 3 of 3)

- [Metacognition is all you need? Using Introspection in Generative Agents to Improve Goal-directed Behavior](https://mlsanity.com/paper/2401.10910/) (2024)
- [Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses](https://mlsanity.com/paper/2502.15365/) (2025)
- [The Need for a Meta-Architecture for Robot Autonomy](https://mlsanity.com/paper/2207.09712/) (2022)

## 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.