Abstract
Contemporary visual warning systems primarily rely on conspicuity—using strong contrasts in color, brightness, and spatial arrangement—to attract attention. However, these systems often fail to ensure accurate interpretation and appropriate user response, particularly among cognitively vulnerable populations and within complex environments. To complement this limitation, this study introduces the concept of distinctiveness as a perceptual factor that enhances discrimination and interpretation in visual warning perception. When auditory access is limited—as in hearing-impaired users or high-noise industrial contexts—visual warnings that depend solely on cons pic uity often lead to interpretive failure due to the lack of perceptual differentiation. The study proposes the Integrated Warning Experience (IWE) as a novel theoretical framework encompassing the full user perceptual sequence—recognition, interpretation, and behavioral response—through direction ally and distance- dependent design strategies. The framework is constructed by analyzing international visual warning standards, user information processing models (e.g., C-HIP), and real-world accident cases involving cognitively vulnerable and hearing-impaired users. Additionally, biological warning strategies— including aposematism, deimatic behavior, mimicry, and camouflage reversal—are examined to derive design principles integrating cons pic uity and distinctiveness. Based on this multi-source analysis, the study proposes a user-centered visual warning model that adapts visual elements by distance range and incorporates visual feedback for cognitive closure. The proposed model represents the first phase in developing the IWE framework, limited to the visual modality. While empirical validation lies beyond the present scope, the theoretical structure provides a foundation for future quantitative modeling, experimental verification, and multimodal expansion. Ultimately, this research contributes to design theory by reframing visual warning as a design problem of perception, expanding the conventional focus on cons pic uity toward a distinctiveness-based, inclusive design approach applicable to Human–Robot Interaction (HRI) and safety-critical environments.
Keywords
Integrated Warning Experience (IWE); Bio-inspired Design; Visual Warning Systems
DOI
https://doi.org/10.21606/iasdr.2025.755
Citation
Pak, H.,and Pak, K.(2025) The Integrated Warning Experience (IWE): A Theoretical Framework for Visual Warning Design Based on Biological Warning Signs, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.755
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 1 - More Than Human-centered Design
The Integrated Warning Experience (IWE): A Theoretical Framework for Visual Warning Design Based on Biological Warning Signs
Contemporary visual warning systems primarily rely on conspicuity—using strong contrasts in color, brightness, and spatial arrangement—to attract attention. However, these systems often fail to ensure accurate interpretation and appropriate user response, particularly among cognitively vulnerable populations and within complex environments. To complement this limitation, this study introduces the concept of distinctiveness as a perceptual factor that enhances discrimination and interpretation in visual warning perception. When auditory access is limited—as in hearing-impaired users or high-noise industrial contexts—visual warnings that depend solely on cons pic uity often lead to interpretive failure due to the lack of perceptual differentiation. The study proposes the Integrated Warning Experience (IWE) as a novel theoretical framework encompassing the full user perceptual sequence—recognition, interpretation, and behavioral response—through direction ally and distance- dependent design strategies. The framework is constructed by analyzing international visual warning standards, user information processing models (e.g., C-HIP), and real-world accident cases involving cognitively vulnerable and hearing-impaired users. Additionally, biological warning strategies— including aposematism, deimatic behavior, mimicry, and camouflage reversal—are examined to derive design principles integrating cons pic uity and distinctiveness. Based on this multi-source analysis, the study proposes a user-centered visual warning model that adapts visual elements by distance range and incorporates visual feedback for cognitive closure. The proposed model represents the first phase in developing the IWE framework, limited to the visual modality. While empirical validation lies beyond the present scope, the theoretical structure provides a foundation for future quantitative modeling, experimental verification, and multimodal expansion. Ultimately, this research contributes to design theory by reframing visual warning as a design problem of perception, expanding the conventional focus on cons pic uity toward a distinctiveness-based, inclusive design approach applicable to Human–Robot Interaction (HRI) and safety-critical environments.