像专家一样推理：利用多模态大语言模型进行基于绘画的精神分析
Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis
AI总结：
本文提出PICK框架，利用多模态大语言模型对房树人绘画进行层次化分析与知识注入，实现专家级精神分析，实验证明其显著提升心理分析能力。
摘要：
多模态大语言模型（MLLMs）在各种客观多模态感知任务中表现出卓越的性能，但其在主观、情感细腻的领域（如心理分析）中的应用仍 largely 未被探索。在本文中，我们引入了 PICK，一个多步骤框架，旨在通过层次分析和知识注入实现精神分析图像理解，特别关注房树人（HTP）测验，这是一种在临床实践中广泛使用的心理评估工具。首先，我们将包含多个实例的绘画分解为语义上有意义的子绘画，构建一个层次化表示，在三个层面捕捉空间结构和内容：单对象层面、多对象层面和整体层面。接下来，我们在每个层面针对性地分析这些子绘画，从其视觉线索中提取心理或情感洞察。我们还引入了一个 HTP 知识库，并设计了一个特征提取模块，通过强化学习训练，为单对象层面分析生成心理画像。该画像捕捉整体风格特征和动态对象特定特征（如房子、树或人的特征），并将其与心理状态相关联。最后，我们整合这些多方面的信息，以产生一个与专家级推理一致的、信息充分的评估。我们的方法弥合了 MLLMs 与专业专家领域之间的差距，提供了一个结构化和可解释的框架，通过视觉表达理解人类心理状态。实验结果表明，所提出的 PICK 显著增强了 MLLMs 在心理分析中的能力。通过扩展到情感理解任务，它进一步被验证为一个通用框架。
英文摘要：
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance across various objective multimodal perception tasks, yet their application to subjective, emotionally nuanced domains, such as psychological analysis, remains largely unexplored. In this paper, we introduce PICK, a multi-step framework designed for Psychoanalytical Image Comprehension through hierarchical analysis and Knowledge injection with MLLMs, specifically focusing on the House-Tree-Person (HTP) Test, a widely used psychological assessment in clinical practice. First, we decompose drawings containing multiple instances into semantically meaningful sub-drawings, constructing a hierarchical representation that captures spatial structure and content across three levels: single-object level, multi-object level, and whole level. Next, we analyze these sub-drawings at each level with a targeted focus, extracting psychological or emotional insights from their visual cues. We also introduce an HTP knowledge base and design a feature extraction module, trained with reinforcement learning, to generate a psychological profile for single-object level analysis. This profile captures both holistic stylistic features and dynamic object-specific features (such as those of the house, tree, or person), correlating them with psychological states. Finally, we integrate these multi-faceted information to produce a well-informed assessment that aligns with expert-level reasoning. Our approach bridges the gap between MLLMs and specialized expert domains, offering a structured and interpretable framework for understanding human mental states through visual expression. Experimental results demonstrate that the proposed PICK significantly enhances the capability of MLLMs in psychological analysis. It is further validated as a general framework through extensions to emotion understanding tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
