Research on the Efficiency of Visual Search for Car Interface Icons Based on Visual Complexity and Spatial Frequency
DOI: 10.3724/sp.j.1089.2023.19591
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Summary
This study investigates the efficiency of visual search for car interface icons, addressing the need for safe and efficient human-machine interaction in complex driving environments where cognitive resources are limited. The research specifically examines how visual complexity and spatial frequency influence a driver’s ability to identify icons. While previous studies focused on mobile or computer interfaces, this work targets automotive contexts, aiming to provide ergonomic guidelines for icon design that minimize cognitive load and distraction. The researchers employed a dual-task rapid serial visual presentation (RSVP) paradigm to simulate limited cognitive resources. Twenty-four university students participated in the experiment. The task involved identifying two targets: Target 1 (T1), a vigilance task requiring identification of a house shape, and Target 2 (T2), a distraction task requiring identification of car interface icons. The icons were manipulated for visual complexity (low vs. high, based on detail count) and spatial frequency (low vs. high, achieved through Fourier filtering). Participants viewed a sequence of stimuli presented at 119 ms each and responded via keyboard. The primary dependent variables were the accuracy and reaction time for identifying T2, conditional on correct identification of T1. The results demonstrated significant main effects for both visual complexity and spatial frequency, as well as a significant interaction between them. Low-complexity icons yielded higher recognition accuracy and shorter reaction times compared to high-complexity icons. Similarly, high-spatial frequency icons were identified more accurately and quickly than low-spatial frequency icons. The interaction analysis revealed that the impact of spatial frequency was more pronounced for high-complexity icons; specifically, high-complexity icons benefited significantly from high-spatial frequency information, whereas low-complexity icons showed no significant difference in performance between spatial frequency levels. Furthermore, low-spatial frequency information was found to be a key factor in expert experience processing, with high-complexity, low-spatial frequency icons showing the poorest search performance. The study concludes that visual complexity determines the visual processing mechanism, with simple icons favoring holistic processing and complex icons requiring detailed feature integration. High-spatial frequency information is identified as critical for icon recognition, particularly for complex designs. The authors propose three design principles for automotive interfaces: prioritize low visual complexity to reduce cognitive load; ensure sufficient high-spatial frequency details (such as clear boundaries) for complex icons; and utilize low-spatial frequency information to leverage users' expert experience when high complexity is unavoidable. These findings offer a scientific basis for optimizing in-vehicle interface design to enhance driving safety.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-10 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-10 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.
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- Applied Guidance: design guidelines