Methods in Emotion Research is a special section of Emotion Review that assists emotion researchers in staying abreast of critical advances in research methods, tools, and techniques. We invite you to submit articles to this section or to contact the editors with suggestions for topics. Articles take a variety of formats. First, articles sometimes introduce and recommend a new method, tool, or technique for emotion research. Recent articles included a computational approach for studying affect (D’Mello et al., 2018), a more complete method of evaluating validity (Miners et al., 2018), and machine learning methods for classification and feature reduction (Miller et al., 2020). This type of article should inspire emotion researchers to try the new method. These articles should describe emotion topics or areas to which the methods are relevant, demonstrate how the methods can be used in emotion research, and explain what these methods add beyond older or simpler methods. These articles should not overburden readers with technical details or formulas. Instead, these articles should introduce readers to the methods, persuade them of their value, and refer readers to accessible resources so they can learn more. Second, articles can provide detailed tutorials regarding an existing method, tool, or technique, providing a crash course for emotion researchers. Like articles on new methods, these tutorial articles should explain when the methods would be useful to emotion researchers. However, articles on well-established methods can focus more on the concrete details of how to implement the methods. Regardless of whether the methods are old or new, articles should base all explanations and examples on emotion research and should refer the reader to additional sources to learn more. Third, articles for Methods in Emotion Research sometimes critique an existing method, tool, or technique for emotion research. For example, Matsumoto and Hwang (2017) examined methodological issues in cross-cultural studies of judgments of facial expressions. As a second example, Maul (2012a), though it predates the creation of this section, would have fit well within this section. This article critiqued the Mayer– Salovey–Caruso Emotional Intelligence Test and was followed by comments (MacCann et al., 2012; Mayer et al., 2012) and a reply (Maul, 2012b). Critique articles should provide essential background about the methods, describe and integrate previous or new concerns about them, and recommend a way forward that addresses the concerns. Fourth, articles in this section sometimes review and compare alternative options. Such articles include reviews of databases of dynamic facial stimuli (Krumhuber et al., 2017), approaches to operationalizing neutral affective states (Gasper, 2018), experimental methods of inducing basic emotions (Siedlecka & Denson, 2019), and methods of coding extrafacial behavioral expressions (Witkower & Tracy, 2019). This type of article should provide a comprehensive overview of all alternatives, including the most commonly used options and the highest quality ones. These articles should provide key information about each option to help researchers select among the alternatives and should make recommendations regarding which options researchers should use in various circumstances. Finally, this type of article should provide information about how to obtain and use each of the options. All Methods in Emotion Research articles should represent the highest quality research methods and should simultaneously be written to be understandable to a broad audience of emotion researchers. Articles that are coauthored by emotion researchers and methodological specialists are welcomed, though not required. To pitch an idea for an article that you would like to write or to request an article on a topic you want to know more about, email the Methods in Emotion Research section editor (kim. barchard@unlv.edu).

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征文：情绪研究方法

情绪研究方法是情绪评论的一个特殊部分，它帮助情绪研究人员跟上研究方法、工具和技术的重要进展。我们邀请您向此部分提交文章或与编辑联系以提出主题建议。文章采用多种格式。首先，文章有时会介绍和推荐情绪研究的新方法、工具或技术。最近的文章包括一种用于研究情感的计算方法（D'Mello 等人，2018 年）、一种更完整的评估有效性的方法（Miners 等人，2018 年），以及用于分类和特征减少的机器学习方法（Miller 等人，2018 年）。 , 2020)。这类文章应该会激发情感研究人员尝试新方法。这些文章应描述与方法相关的情感主题或领域，演示如何在情感研究中使用这些方法，并解释这些方法除了旧的或更简单的方法之外还增加了什么。这些文章不应让读者负担过重的技术细节或公式。相反，这些文章应该向读者介绍这些方法，说服他们相信它们的价值，并向读者推荐可访问的资源，以便他们了解更多信息。其次，文章可以提供有关现有方法、工具或技术的详细教程，为情感研究人员提供速成课程。就像关于新方法的文章一样，这些教程文章应该解释这些方法何时对情绪研究人员有用。然而，关于成熟方法的文章可以更多地关注如何实现这些方法的具体细节。不管方法是旧的还是新的，文章应基于情感研究的所有解释和示例，并应向读者推荐其他资源以了解更多信息。第三，情绪研究方法的文章有时会批评情绪研究的现有方法、工具或技术。例如，Matsumoto 和 Hwang (2017) 研究了面部表情判断的跨文化研究中的方法论问题。作为第二个例子，Maul (2012a)，虽然它早于本节的创建，但很适合本节。本文批评了 Mayer-Salovey-Caruso 情绪智力测试，随后发表了评论（MacCann 等人，2012；Mayer 等人，2012）和回复（Maul，2012b）。评论文章应提供有关方法的基本背景，描述和整合对它们的先前或新关注，并提出解决这些问题的方法。第四，本节中的文章有时会回顾和比较备选方案。此类文章包括对动态面部刺激数据库的评论（Krumhuber 等人，2017 年）、操作中性情感状态的方法（Gasper，2018 年）、诱导基本情绪的实验方法（Siedlecka 和 Denson，2019 年）以及面部外编码方法行为表达（Witkower & Tracy，2019）。此类文章应提供所有替代方案的全面概述，包括最常用的选项和最高质量的选项。这些文章应提供有关每个选项的关键信息，以帮助研究人员在备选方案中进行选择，并应就研究人员在各种情况下应使用哪些选项提出建议。最后，此类文章应提供有关如何获取和使用每个选项的信息。情绪研究中的所有方法文章都应代表最高质量的研究方法，并且应同时编写为广泛的情绪研究人员可以理解。欢迎情绪研究人员和方法论专家共同撰写的文章，但不是必需的。要为您想写的文章提出想法或请求有关您想了解更多主题的文章，请发送电子邮件至情感研究方法部分编辑（kim.barchard@unlv.edu）。情绪研究中的所有方法文章都应代表最高质量的研究方法，并且应同时编写为广泛的情绪研究人员可以理解。欢迎情绪研究人员和方法论专家共同撰写的文章，但不是必需的。要为您想写的文章提出想法或请求有关您想了解更多主题的文章，请发送电子邮件至情感研究方法部分编辑（kim.barchard@unlv.edu）。情绪研究中的所有方法文章都应代表最高质量的研究方法，并且应同时编写为广泛的情绪研究人员可以理解。欢迎情绪研究人员和方法论专家共同撰写的文章，但不是必需的。要为您想写的文章提出想法或请求有关您想了解更多主题的文章，请发送电子邮件至情感研究方法部分编辑（kim.barchard@unlv.edu）。