Perceived personality state estimation in dyadic and small group interaction with deep learning methods

TL;DR

This paper presents a transformer-based deep learning model that estimates perceived personality traits in small group interactions, revealing insights into group dynamics and performance prediction.

Contribution

The study introduces a novel transformer model for predicting perceived personality traits from multimodal interaction data, outperforming human annotators and analyzing group behavior.

Findings

Transformer model performs similarly to human annotators.

Perceived personality traits cluster in collaborative groups.

Group perceived traits better predict group performance than self-reports.

Abstract

Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We find that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-five personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we find that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.