# Metacognitive Myopia in Large Language Models

arXiv: https://arxiv.org/abs/2408.05568 · HTML: https://mlsanity.com/paper/2408.05568/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 10 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Susceptibility to Influence of Large Language Models](https://mlsanity.com/paper/2303.06074/) (2023)
- [Towards Healthy AI: Large Language Models Need Therapists Too](https://mlsanity.com/paper/2304.00416/) (2023)

## Built on by (most cited first, 6 of 6)

- [Imagining and building wise machines: The centrality of AI metacognition](https://mlsanity.com/paper/2411.02478/) (2024)
- [MIRROR: A Hierarchical Benchmark for Metacognitive Calibration in Large Language Models](https://mlsanity.com/paper/2604.19809/) (2026)
- [Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report](https://mlsanity.com/paper/2604.17707/) (2026)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)
- [Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets](https://mlsanity.com/paper/2511.09454/) (2025)
- [REPOT: Recoverable Program-of-Thought via Checkpoint Repair](https://mlsanity.com/paper/2605.30052/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework accounting for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that biased samples in the information environment cause five symptoms of metacognitive myopia in LLMs: integration of invalid embeddings, susceptibility to redundant information, neglect of base rates in conditional computation, decision rules based on frequency, and inappropriate higher-order statistical inference for nested data structures. Moreover, it posits that the two main components of metacognition, monitoring and control, could account for these five symptoms. Accordingly, we further outline how monitoring and control could be approximated technically, for instance, through hidden parallel reasoning histories that allow interactive LLMs to evaluate risks of myopic inference before generating overt responses. Our theoretical framework provides a novel perspective on flawed human-machine interactions and agentic AI and raises significant ethical concerns regarding the implementation of LLMs in organizational structures and high-stakes decisions.