用多模态大模型和多智能体协作，实现绘画心理测试的自动评估
From Visual Perception to Deep Empathy: An Automated Assessment Framework for House-Tree-Person Drawings Using Multimodal LLMs and Multi-Agent Collaboration
- 通过多智能体分工，分离视觉识别与心理推理
- 模型解释与专家判断的语义相似度达0.75，结构类数据更高至0.85
- 可生成具生态效度和内在一致性的心理报告，适合数字心理健康服务
House-Tree-Person（HTP）绘画测试由约翰·巴克于1948年提出，至今仍是临床心理学中广泛使用的投射性技术。然而，该方法长期面临评分标准不一、依赖评估者主观经验、缺乏统一量化编码体系等挑战。定量实验显示，多模态大语言模型（MLLM）的解释与人类专家解释之间的平均语义相似度约为0.75（标准差约0.05），在结构导向的专家数据集中这一数值提升至0.85，表明已达到专家级理解水平。定性分析表明，多智能体系统通过整合社会心理视角与去污名化叙事，有效纠正了视觉幻觉，并生成具有高生态效度与内部一致性的心理报告。研究证实，多模态大模型具备作为标准化投射评估工具的潜力。所提出的多智能体框架通过角色划分，实现特征识别与心理推断的解耦，为数字心理健康服务提供了新范式。
原文摘要 · Abstract (English)
Background: The House-Tree-Person (HTP) drawing test, introduced by John Buck in 1948, remains a widely used projective technique in clinical psychology. However, it has long faced challenges such as heterogeneous scoring standards, reliance on examiners subjective experience, and a lack of a unified quantitative coding system. Results: Quantitative experiments showed that the mean semantic similarity between Multimodal Large Language Model (MLLM) interpretations and human expert interpretations was approximately 0.75 (standard deviation about 0.05). In structurally oriented expert data sets, this similarity rose to 0.85, indicating expert-level baseline comprehension. Qualitative analyses demonstrated that the multi-agent system, by integrating social-psychological perspectives and destigmatizing narratives, effectively corrected visual hallucinations and produced psychological reports with high ecological validity and internal coherence. Conclusions: The findings confirm the potential of multimodal large models as standardized tools for projective assessment. The proposed multi-agent framework, by dividing roles, decouples feature recognition from psychological inference and offers a new paradigm for digital mental-health services. Keywords: House-Tree-Person test; multimodal large language model; multi-agent collaboration; cosine similarity; computational psychology; artificial intelligence
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