Differential diagnosis of mental disorders remains a fundamental challenge in real-world clinical practice, where multiple conditions often exhibit overlapping symptoms. However, most existing public datasets are developed under single-disorder settings and rely on limited data elicitation paradigms, restricting their ability to capture disorder-specific patterns. In this work, we investigate differential mental disorder detection through psychology-inspired multimodal stimuli, designed to elicit diverse emotional, cognitive, and behavioral responses grounded in findings from experimental psychology. Based on this paradigm, we collect a large-scale multimodal mental health dataset (MMH) covering depression, anxiety, and schizophrenia, with all diagnostic labels clinically verified by licensed psychiatrists. To effectively model the heterogeneous signals induced by diverse elicitation tasks, we further propose a paradigm-aware multimodal framework that leverages inter-disorder differences prior knowledge as prompt-guided semantic descriptions to capture task-specific affective and interaction contexts for multimodal representation learning in the new differential mental disorder detection task. Extensive experiments show that our framework consistently outperforms existing baselines, underscoring the value of psychology-inspired stimulus design for differential mental disorder detection.

翻译：精神障碍的鉴别诊断在实际临床实践中仍是一个基本挑战，因为多种疾病常表现出重叠症状。然而，现有的大多数公开数据集是在单一疾病设置下开发的，并依赖于有限的数据诱发范式，这限制了它们捕捉疾病特异性模式的能力。在本研究中，我们通过基于心理学启发的多模态刺激来探索差异化精神障碍检测，这些刺激旨在基于实验心理学的研究发现，诱发多样化的情绪、认知和行为反应。基于这一范式，我们收集了一个大规模的多模态心理健康数据集（MMH），涵盖抑郁症、焦虑症和精神分裂症，所有诊断标签均由持证精神科医生进行临床验证。为了有效建模不同诱发任务所产生的异质性信号，我们进一步提出了一种范式感知的多模态框架，该框架利用疾病间的差异先验知识作为提示引导的语义描述，以捕捉任务特异性的情感和交互上下文，从而进行差异化精神障碍检测新任务中的多模态表示学习。大量实验表明，我们的框架始终优于现有基线方法，这彰显了基于心理学启发的刺激设计在差异化精神障碍检测中的价值。