Line-based and plane-based icon research for automotive user interfaces

Icons play a critical role in human-machine interaction scenarios such as autonomous driving. Although icons can effectively overcome language barriers, existing research on icon design for such contexts remains limited. In particular, icon classification methods lack consistency, and prior studies often focus on preference or attention while overlooking underlying cognitive mechanisms. Grounded in cognitive load theory, this study classifies icons based on visual features and investigates their effects on recognition and memory using an electroencephalography-based rapid interaction experiment. Data were collected from 43 participants to compare recognizability and memory load across different icon categories. Results show that visual features do not significantly influence recognition accuracy but have a significant effect on memory load. Specifically, line-based icons perform better under low memory load conditions, whereas plane-based icons demonstrate advantages in high memory load tasks. These findings provide empirical guidance for icon design in automotive user interface.