Dalege, Borsboom, van Harreveld, and van der Maas (this issue) describe a novel framework for the conceptualization of attitudes that draws on principles from statistical mechanics. A core idea in their framework is that systems are often characterized by randomness (i.e., entropy) and that there is both heuristic and predictive value in applying the idea of entropy to the study of attitudes and related phenomena. We applaud their initiative: The attitudinal entropy framework provides an intriguing new perspective on theoretical questions and empirical findings in social psychology. It opens up new avenues for research in many areas and is a timely contribution given the growing popularity of predictive processing theories emphasizing entropy as an important factor in human cognition (for a recent overview, see Metzinger & Wiese, 2017). Nevertheless, we believe that there is still room for improvement. In the first part of this commentary, we present an epistemological analysis that clarifies the way in which the Attitudinal Entropy framework contributes to scientific knowledge. The second section of the commentary focuses on a number of limitations of the framework as it was formulated by Dalege et al. (this issue), most prominently their shallow treatment of inferential processes. Finally, we present empirical data concerning two of the predictions put forward by Dalege and colleagues.

中文翻译：

---

态度熵框架的科学地位和局限性分析及其一些经验预测的初步检验

Dalege、Borsboom、van Harreveld 和 van der Maas（本期）描述了一种新的态度概念化框架，它借鉴了统计力学的原理。他们框架中的一个核心思想是系统通常具有随机性（即熵）的特征，并且将熵的思想应用于态度和相关现象的研究中具有启发性和预测价值。我们为他们的倡议鼓掌：态度熵框架为社会心理学的理论问题和实证发现提供了一个有趣的新视角。它为许多领域的研究开辟了新途径，并且鉴于强调熵作为人类认知的重要因素的预测处理理论的日益普及，这是一项及时的贡献（有关最近的概述，请参阅 Metzinger & Wiese，2017 年）。尽管如此，我们认为仍有改善的空间。在本评论的第一部分，我们提出了一个认识论分析，阐明了态度熵框架对科学知识的贡献。评论的第二部分侧重于 Dalege 等人制定的框架的一些局限性。（这个问题），最突出的是他们对推理过程的浅薄处理。最后，我们提供了有关 Dalege 及其同事提出的两个预测的经验数据。评论的第二部分侧重于 Dalege 等人制定的框架的一些局限性。（这个问题），最突出的是他们对推理过程的浅薄处理。最后，我们提供了有关 Dalege 及其同事提出的两个预测的经验数据。评论的第二部分侧重于 Dalege 等人制定的框架的一些局限性。（这个问题），最突出的是他们对推理过程的浅薄处理。最后，我们提供了有关 Dalege 及其同事提出的两个预测的经验数据。