Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild

• URL: http://arxiv.org/abs/2512.04728v1
• Date: Thu, 04 Dec 2025 12:13:18 GMT
• Title: Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild
• Authors: Yigui Feng, Qinglin Wang, Haotian Mo, Yang Liu, Ke Liu, Gencheng Liu, Xinhai Chen, Siqi Shen, Songzhu Mei, Jie Liu,
• Abstract summary: Multilevel Insight Network for Disentanglement(MIND) is a novel hierarchical visual encoder.<n>ConvoInsight-DB is a new large-scale dataset with expert annotations for micro-expressions and deep psychological inference.<n>On our PRISM benchmark, MIND significantly outperforms all baselines, achieving a +86.95% gain in micro-expression detection over prior SOTA.
• Score: 19.78493693965451
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative psychological analysis of in-the-wild conversations faces two fundamental challenges: (1) existing Vision-Language Models (VLMs) fail to resolve Articulatory-Affective Ambiguity, where visual patterns of speech mimic emotional expressions; and (2) progress is stifled by a lack of verifiable evaluation metrics capable of assessing visual grounding and reasoning depth. We propose a complete ecosystem to address these twin challenges. First, we introduce Multilevel Insight Network for Disentanglement(MIND), a novel hierarchical visual encoder that introduces a Status Judgment module to algorithmically suppress ambiguous lip features based on their temporal feature variance, achieving explicit visual disentanglement. Second, we construct ConvoInsight-DB, a new large-scale dataset with expert annotations for micro-expressions and deep psychological inference. Third, Third, we designed the Mental Reasoning Insight Rating Metric (PRISM), an automated dimensional framework that uses expert-guided LLM to measure the multidimensional performance of large mental vision models. On our PRISM benchmark, MIND significantly outperforms all baselines, achieving a +86.95% gain in micro-expression detection over prior SOTA. Ablation studies confirm that our Status Judgment disentanglement module is the most critical component for this performance leap. Our code has been opened.

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