Exploring Methodological Approaches of Experimental Studies in the Field of Neuroarchitecture: A Systematic Review

Abstract

Objectives: This systematic review aims to strengthen the relationship between architecture and neuroscience by classifying data measurement techniques in the field of neuroarchitecture with a focus on the most practical and common methodological approaches. It classifies data recording techniques in different architectural categories (e.g., interior, urban, built environment). Backgrounds: With regard to urban life developments and technological breakthroughs, studies of human interactions with environments have been expanding in recent years. Additionally, recent advances in neuroscience have allowed architects to find out more about human experiences in built environments, but there are few valid frameworks about what methodologies and instruments are more common to conduct experimental tasks in this interdisciplinary field. Methods: Twenty-eight experimental studies were selected based on the preferred reporting items for systematic reviews and meta-analyses literature search extension (PRISMA) systematic review protocol and a comprehensive analysis. The task-space of selected articles was categorized into three subfields, namely, “interior design,” “urban design,” and “building design” based on environments and their stimuli. As for this context-based categorization, recording techniques and methodology were distinguished for each subfield division. Results: More than 50% of the studies were incorporated in the first two categories, and the EEG recording was the most frequently employed neuroimaging technique thanks to the technical efficacy of its setup and the high temporal resolution of its electrophysiological signals. Conclusion: In this study, a summary of techniques and methodological approaches applied in the field is provided in a nut shell, and a general framework of instruments is presented to help scholars to carry out more practical research in the future leading to designing built environments more efficiently.