This paper introduces MetaRAG, a metacognitive retrieval-augmented generation framework that integrates large language models (LLMs) with human introspective reasoning for multi-hop question answering (QA). The framework combines retrieval-augmented generation with metacognition, enabling the model to monitor, evaluate, and plan its response strategies. By doing so, the model can identify inadequacies in its initial cognitive responses and fix them. The three-step metacognitive regulation pipeline allows the model to assess the quality of its current response, evaluate the reasons for potential inaccuracies, and plan targeted improvements. Empirical evaluations show that MetaRAG significantly outperforms existing methods on two multi-hop QA datasets. The contributions of this paper include: (1) introducing a metacognitive retrieval-augmented generation framework for multi-hop QA; (2) identifying three primary challenges in multi-hop QA causing wrong answers: insufficient knowledge, conflicting knowledge, and erroneous reasoning; and (3) devising a three-step metacognitive regulation pipeline tailored for retrieval-augmented LLMs. The framework leverages metacognitive knowledge and regulation to enhance the accuracy of answer generation. The results demonstrate that MetaRAG achieves higher reasoning capabilities and outperforms existing baselines significantly.This paper introduces MetaRAG, a metacognitive retrieval-augmented generation framework that integrates large language models (LLMs) with human introspective reasoning for multi-hop question answering (QA). The framework combines retrieval-augmented generation with metacognition, enabling the model to monitor, evaluate, and plan its response strategies. By doing so, the model can identify inadequacies in its initial cognitive responses and fix them. The three-step metacognitive regulation pipeline allows the model to assess the quality of its current response, evaluate the reasons for potential inaccuracies, and plan targeted improvements. Empirical evaluations show that MetaRAG significantly outperforms existing methods on two multi-hop QA datasets. The contributions of this paper include: (1) introducing a metacognitive retrieval-augmented generation framework for multi-hop QA; (2) identifying three primary challenges in multi-hop QA causing wrong answers: insufficient knowledge, conflicting knowledge, and erroneous reasoning; and (3) devising a three-step metacognitive regulation pipeline tailored for retrieval-augmented LLMs. The framework leverages metacognitive knowledge and regulation to enhance the accuracy of answer generation. The results demonstrate that MetaRAG achieves higher reasoning capabilities and outperforms existing baselines significantly.