Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition

2025-07-31

AI Summary

Existing automatic Real Personality Recognition (RPR) methods rely predominantly on external observer perspectives, inferring personality impressions solely from audiovisual behavioral cues—thus failing to capture individuals’ latent trait structures and suffering from substantial cognitive bias and low recognition accuracy. Method: We propose a personalized intrinsic cognitive simulation framework: (1) a novel two-dimensional graph structure that maps short-term audiovisual behaviors into a dual-semantic feature graph—where both nodes and edges encode discriminative behavioral and relational semantics; (2) an end-to-end 2D Graph Neural Network (2D-GNN) jointly optimizing cognitive mechanism modeling and personality classification; and (3) dynamic personalized network weight adaptation to accommodate inter-individual variability. Contribution/Results: Our approach significantly bridges the cognitive gap between observable behavior and latent personality traits. Extensive experiments demonstrate substantial improvements in RPR accuracy across mainstream benchmarks, establishing a new paradigm for interpretable, individualized personality computation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from easy-accessible external short audio-visual behaviours expressed by the target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a novel graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules.

Problem

• Recognize real personality traits from external behaviors
• Simulate personalized internal cognition from audio-visual behaviors
• Develop a 2D-GNN for inferring personality from cognition

Innovation

• Simulate personalised internal cognition from audio-visual behaviours
• Encode cognition as 2D graph with node-edge features
• Use 2D-GNN for end-to-end personality recognition