Biological organisms display remarkably flexible behaviours. This is an area of active investigation, in particular in the fields of artificial intelligence, computational and cognitive neuroscience. While inductive biases and broader cognitive functions are undoubtedly important, the ability to monitor and evaluate one’s performance or oneself -- metacognition -- strikes as a powerful resource for efficient learning. Often measured as decision confidence in neuroscience and psychology experiments, metacognition appears to reflect a broad range of abstraction levels and downstream behavioural effects. Within this context, the formal investigation of how metacognition interacts with learning processes is a recent endeavour. Of special interest are the neural and computational underpinnings of confidence and reinforcement learning modules. This review discusses a general hierarchy of confidence functions and their neuro-computational relevance for adaptive behaviours. It then introduces novel ways to study the formation and use of meta-representations and nonconscious mental representations related to learning and confidence, and concludes with a discussion on outstanding questions and wider perspectives.

生物有机体表现出非常灵活的行为。这是一个积极研究的领域，特别是在人工智能、计算和认知神经科学领域。虽然归纳偏见和更广泛的认知功能无疑很重要，但监控和评估一个人或自己的表现的能力——元认知——作为有效学习的强大资源而发挥作用。通常以神经科学和心理学实验中的决策信心来衡量，元认知似乎反映了广泛的抽象水平和下游行为影响。在这种情况下，对元认知如何与学习过程相互作用的正式调查是最近的一项努力。特别感兴趣的是信心和强化学习模块的神经和计算基础。本综述讨论了置信函数的一般层次结构及其与自适应行为的神经计算相关性。然后介绍了研究与学习和信心相关的元表征和无意识心理表征的形成和使用的新方法，