This Special Issue of the Journal of Experimental Psychopathology is designed to provide an overview of metacognition in psychosis. While the term metacognition has been used extensively in the psychological literature since it was formally defined by Flavell in 1979, the application of the construct to psychotic disorders, specifically schizophrenia, has been much slower and has only recently gained significant interest (Figure 1). Reasons for the relatively slow shift of attention to metacognition are unclear; however, several developments likely support the increased, and now sustained, focus on this topic. First, the definition of metacognition, and how it may conceptually relate to schizophrenia, has recently been clarified. Instead of presenting metacognition as “thinking about thinking,” which could be assumed to be a singular construct that is either intact or impaired in schizophrenia, it is now acknowledged as an overarching term that includes a spectrum of processes spanning discrete to more integrated activities. At the discrete end of the continuum are activities consistent with self-assessment such as immediate awareness of the accuracy of one’s judgments (i.e., introspective accuracy (IA)). Knowledge of cognitive processes, and the biases that may be present within them, is also included along this continuum as well as the ability to regulate or improve these processes. And, as Lysaker and colleagues argue (this issue), metacognition also includes more complex and integrated processes that allow individuals to combine multiple pieces of information into an idea or representation about the self. For example, multiple episodes of forgetting important information may lead to the idea that one has a poor memory. Recent formulations of social processes, such as the Research Domain Criteria (RDoC) framework of the National Institute of Mental Health, have also helped to refine the conceptualization of metacognition and separate it from related constructs like mentalizing or Theory of Mind. Specifically, the RDoC matrix parses the perception and understanding of the self (e.g., self-knowledge) from perception and understanding of others (e.g., understanding mental states). Thus, metacognitive processes like IA or metacognitive regulation can be distinguished from related constructs in that they are primarily self-focused. Metacognition therefore represents a distinct domain, and as demonstrated by the articles included in this Special Issue, the spectrum view provides numerous avenues for metacognitive research to improve our understanding of psychosis and to help promote functional recovery. Second, multiple strategies and measures are now available to assess metacognition. To address the more discrete activities, such as IA, participant reports of ability can be compared to objective task performance (e.g., Koren, Seidman, Goldsmith, & Harvey, 2006; Pinkham, Klein, Hardaway, Kemp, & Harvey, 2018) or to reports of knowledgeable informants (Gould et al., 2015). Sophisticated methods that utilize the signal detection framework to control for bias are also now available (e.g., Davies et al., 2018), and advanced statistical techniques offer improved methods for assessing both accuracy and bias (Pinkham et al., this issue). The integrated aspects

中文翻译：

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精神病中的元认知

本期《实验精神病理学杂志》特刊旨在概述精神病中的元认知。自从Flavell在1979年正式定义元认知这个术语以来，它在心理学文献中已被广泛使用，但该结构在精神病（特别是精神分裂症）中的应用却要慢得多，并且直到最近才引起人们的兴趣（图1）。注意元认知转移相对缓慢的原因尚不清楚；但是，一些事态发展可能会支持这个主题的增加，并且现在已经持续。首先，元认知的定义以及它在概念上与精神分裂症的关系最近得到了澄清。与其将元认知呈现为“思考思考”，”可以被认为是一个完整的结构或在精神分裂症中受损的单一结构，现在被认为是一个总括性的术语，它包括一系列从离散到更综合的活动的过程。在连续过程的离散末端进行的活动是与自我评估相一致的活动，例如立即意识到自己的判断的准确性（即内省性准确性（IA））。认知过程的知识以及其中可能存在的偏见，也包括在这一连续体中，以及调节或改善这些过程的能力。而且，正如Lysaker及其同事所论证的（这个问题），元认知还包括更复杂和集成的过程，使个人可以将多条信息组合成关于自我的观念或表示。例如，多次忘记重要信息可能会导致人们记忆力下降的想法。社会过程的最新表述，例如国家心理健康研究所的研究领域标准（RDoC）框架，也有助于完善元认知的概念，并将其与诸如心理化或心理理论之类的相关结构分开。具体而言，RDoC矩阵从对他人的感知和理解（例如，对精神状态的理解）解析对自我（例如，自我知识）的感知和理解。因此，像IA或元认知调节这样的元认知过程可以与相关结构区分开来，因为它们主要是自我聚焦的。因此，元认知代表了一个独特的领域，正如本期特刊所载文章所证明的那样，频谱视图为元认知研究提供了许多途径，以增进我们对精神病的理解并帮助促进功能恢复。其次，现在有多种策略和措施可用于评估元认知。为了解决诸如IA等更离散的活动，可以将参与者的能力报告与客观任务绩效进行比较（例如，Koren，Seidman，Goldsmith和Harvey，2006； Pinkham，Klein，Hardaway，Kemp和Harvey，2018）或到知识渊博的线人的报告（Gould等，2015）。利用信号检测框架来控制偏差的复杂方法现在也已可用（例如，Davies等，2018），而先进的统计技术为评估准确性和偏差提供了改进的方法（Pinkham等，本期）。整合方面 现在有多种策略和措施可用于评估元认知。为了解决诸如IA等更离散的活动，可以将参与者的能力报告与客观任务绩效进行比较（例如，Koren，Seidman，Goldsmith和Harvey，2006； Pinkham，Klein，Hardaway，Kemp和Harvey，2018）或到知识渊博的线人的报告（Gould等，2015）。利用信号检测框架来控制偏差的复杂方法现在也已可用（例如，Davies等，2018），而先进的统计技术为评估准确性和偏差提供了改进的方法（Pinkham等，本期）。整合方面 现在有多种策略和措施可用于评估元认知。为了解决诸如IA等更离散的活动，可以将参与者的能力报告与客观任务绩效进行比较（例如，Koren，Seidman，Goldsmith和Harvey，2006； Pinkham，Klein，Hardaway，Kemp和Harvey，2018）或到知识渊博的线人的报告（Gould等，2015）。利用信号检测框架来控制偏差的复杂方法现在也已可用（例如，Davies等，2018），而先进的统计技术为评估准确性和偏差提供了改进的方法（Pinkham等，本期）。整合方面 Pinkham，Klein，Hardaway，Kemp和Harvey，2018年）或举报知识渊博的举报人（Gould等人，2015年）。利用信号检测框架来控制偏差的复杂方法现在也已可用（例如，Davies等，2018），而先进的统计技术为评估准确性和偏差提供了改进的方法（Pinkham等，本期）。整合方面 Pinkham，Klein，Hardaway，Kemp和Harvey，2018年）或举报知识渊博的举报人（Gould等人，2015年）。利用信号检测框架来控制偏差的复杂方法现在也已可用（例如，Davies等，2018），而先进的统计技术为评估准确性和偏差提供了改进的方法（Pinkham等，本期）。整合方面