Neuroaesthetic Indicators from the Perspective of Two-Stage Process Theory: N2 and LPP can Predict Aesthetic Judgment of Human–Computer Interfaces

Abstract

This study presented 101 participants with either interfaces or scrambled interfaces to purify the effects of aesthetic judgment. Both N2 and LPP showed a significant interaction between aesthetic level (aesthetic, medium, and unaesthetic) and image type (interface and scramble). More specifically, N2 was more enhanced for aesthetic interfaces than for medium and unaesthetic interfaces, while N2 for the three types of scrambles showed no difference. Similarly, LPP for aesthetic and medium interfaces was more positive than for unaesthetic interfaces, but LPP for the three kinds of scrambled images showed no significant differences. The results of four classification models consistently showed that N2 and LPP could more accurately classify four different interface types than N1 and P2. These patterns indicated that both N2 and LPP are ERP correlates for aesthetic appreciation, indicating that aesthetic and unaesthetic interfaces might evoke differences in both the early attentional and later evaluation stages.

Keywords:

• N2
• LPP
• neuro-aesthetic
• aesthetic judgment task

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Acknowledgment

Thank Chenxi Wang for collecting the aesthetic rating of interface images. Thank Yilan Yin for technic support.

Authors’ contributions

Yanci Liu: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, and Visualization. Fei Teng: Formal analysis and Writing – Original Draft. Shiyu Zhang: Writing – Review & Editing. Feng Du: Conceptualization, Methodology, Writing – Review & Editing, Supervision, Project administration, and Funding acquisition.

Ethics approval

The study was approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent to participate

Prior to the commencement of this study, informed consent was diligently obtained from each and every participant involved. Throughout the research process, the fundamental right to privacy of all participant s was vigilantly respected and upheld.

Disclosure statement

The authors have no competing interests to declare.

Data availability statement

Data and code are available upon request to corresponding author.

Notes

1 https://www.canalys.com/newsroom/PC-market-forecast-global-region-2024

Additional information

Funding

The study was supported by a grant from the Scientific Foundation of the Institute of Psychology, Chinese Academy of Sciences (E0KM073003) and Space Medical Experiment Project of China Manned Space Program (HYZHXM03002). The funding organizations had no role in the development of the study design or the collection, analysis, and interpretation of the data.

Notes on contributors

Yanci Liu

Yanci Liu is a Ph.D. candidate in the Department of Psychological and Cognitive Sciences at Tsinghua University, with research interest in emotion and time perception.

Fei Teng

Fei Teng is currently pursuing the Master’s degree in the Department of Psychological and Cognitive Sciences at Tsinghua University, with research interest in deep learning and recommendation systems.

Shiyu Zhang

Shiyu Zhang is currently pursuing the Master’s degree in Applied Psychology at University of Chinese Academy of Sciences, Beijing, China. Her research interest includes temporal structure and behavioral rhythm in visual attention.

Feng Du

Feng Du is a professor at the Institute of Psychology, Chinese Academy of Sciences. He is the Chair of the Engineering Psychology Committee under the Chinese Psychological Society and a council member of the Chinese Ergonomics Society. His research interest includes cognitive ergonomics, situation awareness, and human–AI trust.