Differential Mental Disorder Detection with Psychology-Inspired Multimodal Stimuli

2026-04-03

AI Summary

This study addresses the diagnostic challenge posed by symptom overlap among psychiatric disorders such as depression, anxiety, and schizophrenia. To this end, it proposes a psychology-inspired multimodal stimulation paradigm designed to elicit differential responses across emotional, cognitive, and behavioral dimensions. The authors introduce the first large-scale multimodal mental health dataset (MMH) incorporating clinically validated diagnostic labels. Innovatively guided by psychological theory, the stimulation protocol informs a paradigm-aware multimodal framework that leverages inter-disorder difference priors as semantic cues to enhance task-specific modeling of affective and interactive context. Experimental results demonstrate that the proposed approach significantly outperforms existing baselines in differential diagnosis, thereby validating the efficacy of psychology-driven stimulus design in improving the accuracy of psychiatric disorder identification.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• differential diagnosis
• mental disorders
• overlapping symptoms
• multimodal stimuli
• psychology-inspired

Innovation

• differential diagnosis
• psychology-inspired stimuli
• multimodal mental health dataset
• paradigm-aware framework
• prompt-guided representation learning