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

• URL: http://arxiv.org/abs/2602.05650v1
• Date: Thu, 05 Feb 2026 13:35:04 GMT
• Title: Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances
• Authors: Amir Ansari, Jana Subirana, Bruna Silva, Sergio Escalera, David Gallardo-Pujol, Cristina Palmero,
• Abstract summary: Personality recognition models aim to infer personality traits from different sources of behavioral data.<n>We trained a transformer-based model including cross-modal (audiovisual) and cross-subject (dyad-aware) attention mechanisms.<n>Results show that nuance-level models consistently outperform facet and trait-level models, reducing mean squared error by up to 74% across interaction scenarios.
• Score: 37.83859643892549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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