Analyzing the “Opposite” Approach in Additions to Historic Buildings Using Visual Attention Tools: Dresden Military History Museum Case

From past to present, modern additions have continued to transform historic environments. While some argue that contemporary extensions disrupt the integrity of historic buildings, others suggest that the contrast between past and present creates a meaningful architectural dialog. This debate raises a key question: in contrasting compositions, which architectural elements draw more visual attention, the historic or the modern? To address this, a visual attention-based analytical approach is adopted. In this study, eye-tracking-based visual attention analysis is used to examine how viewers perceive the relationship between historical and contemporary architectural elements. Instead of conventional laboratory-based eye-tracking, artificial intelligence-supported visual attention software developed from eye-tracking datasets is employed. Four tools-3M-VAS, EyeQuant, Attention Insight, and Expoze-were used to generate heat maps, gaze sequence maps, hotspots, focus maps, attention distribution diagrams, and saliency predictions. These visualizations enabled both a qualitative and quantitative comparison of viewer focus. The case study is the Military History Museum in Dresden, Germany, known for its widely debated contemporary addition representing an oppositional design approach. The results illustrate which architectural components are visually prioritized, offering insight into how contrasting architectural languages are cognitively perceived in historic settings.

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Publication Details

Journal
Journal of Eye Movement Research
Published
2026-01-12
DOI
https://doi.org/10.3390/jemr19010007
Primary Topic
Visual Attention and Saliency Detection
Type
article
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article

Analyzing the “Opposite” Approach in Additions to Historic Buildings Using Visual Attention Tools: Dresden Military History Museum Case

Fikret Bademci, Hicran Hanım Halac, Nuray Özkaraca Özalp, Mehmet Fatih Özalp
Journal of Eye Movement Research
Visual Attention and Saliency Detection
article

Analyzing the “Opposite” Approach in Additions to Historic Buildings Using Visual Attention Tools: Dresden Military History Museum Case

Fikret Bademci, Hicran Hanım Halac, Nuray Özkaraca Özalp, Mehmet Fatih Özalp
article en

Abstract

From past to present, modern additions have continued to transform historic environments. While some argue that contemporary extensions disrupt the integrity of historic buildings, others suggest that the contrast between past and present creates a meaningful architectural dialog. This debate raises a key question: in contrasting compositions, which architectural elements draw more visual attention, the historic or the modern? To address this, a visual attention-based analytical approach is adopted. In this study, eye-tracking-based visual attention analysis is used to examine how viewers perceive the relationship between historical and contemporary architectural elements. Instead of conventional laboratory-based eye-tracking, artificial intelligence-supported visual attention software developed from eye-tracking datasets is employed. Four tools-3M-VAS, EyeQuant, Attention Insight, and Expoze-were used to generate heat maps, gaze sequence maps, hotspots, focus maps, attention distribution diagrams, and saliency predictions. These visualizations enabled both a qualitative and quantitative comparison of viewer focus. The case study is the Military History Museum in Dresden, Germany, known for its widely debated contemporary addition representing an oppositional design approach. The results illustrate which architectural components are visually prioritized, offering insight into how contrasting architectural languages are cognitively perceived in historic settings.

Journal of Eye Movement ResearchVol. 19(1)
University of Liverpool (GB), Siirt Üniversitesi (TR), Eskisehir Technical University (TR), Düzce Üniversitesi (TR)
Sustainable cities and communities
Openalex Percentile: Top 91%
Visual Attention and Saliency Detection
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