Al-Samarraie, H orcid.org/0000-0002-9861-8989, Sarsam, SM, Lonsdale, M et al. (1 more author) (2022) Emotional Intelligence and Individual Visual Preferences: A Predictive Machine Learning Approach. International Journal of Human–Computer Interaction. ISSN 1044-7318
Abstract
Differences in individuals’ psychological and cognitive characteristics have been always found to play a significant role in influencing our behavior 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 (eg, emotionality, self-control, sociability, and well-being) and their eye movement behavior 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 behaviors 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 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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 Taylor & Francis Group, LLC. This is an author produced version of an article published in International Journal of Human–Computer Interaction. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jun 2022 09:03 |
Last Modified: | 30 May 2023 00:13 |
Status: | Published online |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/10447318.2022.2075630 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187581 |