Emotional Intelligence and Individual Visual Preferences: A Predictive Machine Learning Approach

Abstract

Differences in individuals' psychological and cognitive characteristics have been always found to play a significant role in influencing our behaviour and preferences. While a number of studies have identified the impact of these characteristics on individuals' visual design preferences, understanding how emotional intelligence (EI) would influence this process is yet to be explored. This study investigated the link between individuals' EI dimensions (e.g., emotionality, self-control, sociability, and well-being) and their eye movement behaviour in an attempt to build a prediction model for visual design preferences. A total of 136 participants took part in this study. The feature selection and prediction of EI and eye movement data were performed using the genetic search method in conjunction with the bagging method. The results showed that participants high in self-control and emotionality exhibited different eye movement behaviours when performing five visual selection tasks. The prediction results (93.87% accuracy) revealed that specific eye parameters can predict the link between certain EI dimensions and 2 preferences for visual design. This study adds new insights into Human-Computer Interaction, EI and rational choice theories. The findings also encourage researchers and designers to consider EI in the development of intelligent and adaptive systems.

AI for Psychometrics: Validating Machine Learning Models in Measuring Emotional Intelligence with Eye-Tracking Techniques

“…These findings are indeed consistent with the attentional bias discovered in the literature. That is, individuals with higher (vs. lower) EI scores tended to prefer (vs. avoid) positive visual stimuli such as happy faces ( Al-Samarraie et al 2022 ; Davis 2018 ; Lea et al 2018 ). These consistent findings suggest that eye-tracking techniques are indeed ideal for emotional intelligence research.…”