Decoding the Pattern Between Architecture and Emotion

Abstract

<p><strong>In this research, I investigated the interrelation between emotion and visual experience of buildings, specifically from an architect's perspective. To address this question, I extensively reviewed literature from various fields including architecture, psychology, and computer science. Based on this comprehensive investigation, I developed a framework that allowed for a controlled and scientific study of how buildings influence emotions. For testing this framework, I selected two architecturally significant "sacred" buildings: the Sheikh Lotfollah Mosque in Isfahan, Iran, and the Futuna Chapel in Wellington, New Zealand.</strong></p><p>As part of the framework, I created digital twins of the selected buildings using photogrammetry technology, enabling realistic virtual reality (VR) simulations. The high fidelity of these VR simulations greatly contributed to the framework's development and enhanced the ecological validity of the investigation. To gauge low-level emotional responses, I collected physiological data through a biosensor, specifically Electrodermal Activities (EDA), from 12 human subjects. The collected data underwent a series of processing techniques to ensure interpretability and suitability for further analysis.</p><p>Moreover, I developed nine computational algorithms based on the Processing Fluency Theory to quantify users' visual experience during virtual visits to the buildings. These algorithms measured various aspects such as Light Contrast, Visual Complexity, Visual Fractality, Visual Symmetry, and other qualities, providing numerical indicators of the perception of these architectural features. In the initial phase, I conducted standard statistical comparisons between the visual experiences of the two buildings through data analysis. The findings of this phase demonstrated the algorithms' ability to describe architectural qualities using quantitative measures.</p><p>In the subsequent phase, I employed a Machine Learning approach for data analysis. By training Machine Learning models, I aimed to uncover complex patterns between the visual features extracted from participants' architectural experiences and the emotional indicators obtained from the biosensors. The results revealed promising potential for the developed methodology. Consequently, this research suggests that the framework can be further utilized for systematic and scientifically grounded investigations into how the visual experience of architecture influences users' emotions. Overall, this study contributes to the understanding of the intricate relationship between emotion and the visual aspects of buildings, providing valuable insights for architects and researchers interested in designing spaces that elicit specific emotional responses.</p>