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

TL;DR

This paper introduces MIND, a hierarchical visual encoder with a Status Judgment module, and a new dataset and metric for psychological analysis in conversations, significantly improving micro-expression detection and visual reasoning.

Contribution

The paper presents MIND, a novel disentanglement model, along with ConvoInsight-DB and PRISM, a comprehensive ecosystem for psychological analysis in the wild.

Findings

MIND achieves an +86.95% gain in micro-expression detection.

Status Judgment module is critical for disentanglement performance.

The ecosystem enables verifiable evaluation of visual grounding and reasoning.

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.