Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912881455398912 |
|---|---|
| author | Ansari, Amir Subirana, Jana Silva, Bruna Escalera, Sergio Gallardo-Pujol, David Palmero, Cristina |
| author_facet | Ansari, Amir Subirana, Jana Silva, Bruna Escalera, Sergio Gallardo-Pujol, David Palmero, Cristina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05650 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances Ansari, Amir Subirana, Jana Silva, Bruna Escalera, Sergio Gallardo-Pujol, David Palmero, Cristina Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.05650 |