Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances

2026-02-05

AI Summary

This study addresses the limitations of traditional personality recognition approaches that rely on coarse-grained Big Five trait labels, which fail to capture contextual behavioral diversity and hinder model generalization. To overcome this, the work proposes leveraging finer-grained constructs within the Big Five model—specifically facets, aspects, and nuanced sub-traits—and presents the first systematic comparison of their predictive efficacy in personality recognition. A novel cross-modal Transformer-based architecture is introduced, integrating audiovisual attention mechanisms with a dyadic interaction-aware module to model interpersonal dynamics in conversations. Experiments on the UDIVA v0.5 dataset demonstrate that modeling at the sub-trait level significantly outperforms coarser granularities, achieving up to a 74% reduction in mean squared error on average. These results underscore the effectiveness and necessity of fine-grained personality representation for advancing computational personality recognition.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Personality is a complex, hierarchical construct typically assessed through item-level questionnaires aggregated into broad trait scores. Personality recognition models aim to infer personality traits from different sources of behavioral data. However, reliance on broad trait scores as ground truth, combined with limited training data, poses challenges for generalization, as similar trait scores can manifest through diverse, context dependent behaviors. In this work, we explore the predictive impact of the more granular hierarchical levels of the Big-Five Personality Model, facets and nuances, to enhance personality recognition from audiovisual interaction data. Using the UDIVA v0.5 dataset, we trained a transformer-based model including cross-modal (audiovisual) and cross-subject (dyad-aware) attention mechanisms. Results show that nuance-level models consistently outperform facet and trait-level models, reducing mean squared error by up to 74% across interaction scenarios.

Problem

• personality recognition
• Big-Five Personality Model
• trait-level prediction
• behavioral data
• model generalization

Innovation

• nuance-level modeling
• cross-modal attention
• dyad-aware transformer
• hierarchical personality recognition
• audiovisual interaction