https://orcid.org/0000-0002-3419-0952
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Abstract
The representation and recognition of icons play a crucial role in interface interaction efficiency and user experience within human–computer interaction. However, the intricate relationship between product icon types, feature types, and abstraction in cognitive contexts has yet to be clarified. This study aimed to delve into the cognitive mechanisms concerning practical and hedonic product icons across varying abstraction levels using EEG analysis. Moreover, it investigated how the explicitness and implicitness of these icons and their abstraction levels influence recognition efficiency via eye-tracking studies. In Experiment A, a high abstraction level led to prolonged reaction times (RTs), reduced accuracy rate (ACC), more negative N400, and decreased late positive potential (LPP) amplitudes. These outcomes suggested increased effort, heightened semantic conflict, and negative emotional engagement. Moreover, at middle abstraction levels, RTs were consistently longer for hedonic product icons compared to practical ones. Additionally, both N400 and LPP amplitudes were notably larger for hedonic product icons. In Experiment B, eye-tracking results revealed that compared with implicit features, the change of abstraction degree of explicit features is more likely to increase the number of fixation points and RTs of users. Specifically, functional explicit features wielded the greatest impact on overall icon cognitive efficiency. Synthesizing both experiments revealed significant differences in abstraction requirements for practical vs. hedonic product icons. A practical implication arises: moderate overall abstraction suits practical product icons, while higher abstraction suits hedonic ones. Among features, it’s essential to avoid excessive abstraction in functional and symbolic aspects, while simplifying or removing area line features and chamfered elements can be beneficial. This study contributes to Internet of Things (IoT) icon research, offering potential guidance for graphic designers in crafting user-friendly icons.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data used to support the findings of this study are available from the corresponding author upon request.
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Notes on contributors
Jinchun Wu
Jinchun Wu is currently working as a PhD candidate in Industrial Design at Southeast University, China. His research interests include human–computer interaction, neurodesign, ergonomic research, creative design thinking, light ergonomics, and human factors of nuclear power plants.
Yixuan Liu
Yixuan Liu is currently working as an EngD student in Industrial Design at Southeast University, China. His research interests include human–computer interaction, ergonomic research, aging design, and medical equipment design.
Lulu Gan
Lulu Gan received her Bachelor’s degree in Mechanical Engineering from Southeast University in 2021. She is currently studying for a master’s degree at Southeast University. Her research interests include human–computer interaction in interface.
Mu Tong
Mu Tong is a PhD candidate at Southeast University. He received his BS degree in the Department of Industrial Design from Southeast University in 2019. His research interests include cognitive psychology, human factors, and visual perception.
Chengqi Xue
Chengqi Xue is a Professor in the Mechanical School at Southeast University, China. His research interests lie in human–computer interaction, neurodesign, and ergonomic research.