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

TL;DR

This paper investigates the use of hierarchical personality traits, from broad traits to nuanced levels, to improve personality recognition from audiovisual data, demonstrating that finer-grained models significantly outperform broader trait models.

Contribution

It introduces a hierarchical approach to personality recognition, showing that nuance-level features enhance prediction accuracy over traditional trait-level models.

Findings

Nuance-level models outperform facet and trait models.

Mean squared error reduced by up to 74%.

Transformer-based cross-modal attention improves predictions.

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.