Abstract Our understanding of mental disorders has traditionally focused on syndromes and symptom clusters rather than on the nature of the symptom and signs themselves. Using a core symptom of depression, anhedonia, as an extended example, this paper illustrates how the development of multiple models of symptoms, at various scales of analysis, may advance the explanation and classification of mental disorders. We begin by outlining the Phenomena Detection Method (PDM), which links different phases of the inquiry process to provide a methodology for conceptualizing the symptoms of psychopathology and for constructing multi-level models of the pathological processes that comprise them. Next, we apply the PDM to anhedonia, building a compositional explanation of this core symptom by way of multiple models across four scales (levels): molecular, neural, cognitive, and phenomenological. Finally, we evaluate our approach in comparison to existing strategies for understanding mental disorders.

中文翻译：

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对精神病理学的症状进行建模：一种多元化的方法

摘要 我们对精神障碍的理解传统上侧重于综合征和症状群，而不是症状和体征本身的性质。本文使用抑郁症的核心症状快感缺乏症作为扩展示例，说明了在不同分析尺度下开发多种症状模型如何推进对精神障碍的解释和分类。我们首先概述了现象检测方法 (PDM)，该方法将探究过程的不同阶段联系起来，以提供一种方法来概念化精神病理学的症状，并构建包含这些症状的病理过程的多层次模型。接下来，我们将 PDM 应用于快感缺失，通过跨四个尺度（水平）的多个模型构建这一核心症状的组合解释：分子、神经、认知和现象学。最后，我们将我们的方法与现有的理解精神障碍的策略进行比较。