The rapid growth of cities and the absence of effective regulatory frameworks, have disrupted the reciprocal interaction between humans and built environments. These challenges have compromised cities’ ability to address the cognitive and physiological needs of their citizens. Neuro-urbanism, as an emerging approach, integrates urban planning, environmental psychology, and neuroscience to analyze neural feedback from the brain in response to urban environments, aiming to restore positive human-environment interaction. Among the various factors, visual complexity -directly linked to human physiology- can be reexamined within this framework from a novel perspective. This study explores the concept of visual complexity in urban design, introduces the optimal spectrum of visual complexity, and identifies the capabilities of neuro-urbanism in examining this environmental factor. Recent studies in neuro-urbanism reveal that the relationship between unity and diversity in urban environments can be assessed through the analysis of neural processing speed for visual data. Empirical evidence indicates a direct correlation between beta waves and the complexity of fractal forms. Data obtained from electroencephalography (EEG) demonstrate that this tool can effectively examine cognitive experiences related to unity and diversity as well as neural processing speed. Additionally, theta frequency oscillations in frontal and prefrontal areas show significant associations with cognitive functions such as working memory, episodic memory, and spatial orientation. Increases in EEG signal amplitude often signify reduced salience of environmental elements, while decreases in amplitude indicate heightened salience. Finally, technical limitations of neuroimaging tools in real-world environments -such as sensitivity to environmental noise and movement- have been identified as major challenges. To address these issues, virtual reality (VR) environments are proposed as innovative tools to control environmental variables and mitigate technical constraints. This approach enables systematic manipulation of visual complexity variables and enhances the accuracy of neural data recording.
Highlights
Introduction of the visual complexity spectrum in relation to the urban form, rather than the abstract stimuli commonly used in laboratory studies.
Presentation of neuro-urbanism and its methodological potential as an emerging interdisciplinary approach with applicability in studying visual complexity.
Examination of various brainwave recording techniques, with a focus on electroencephalography (EEG) as an appropriate tool for studying the neurological effects of visual complexity in the urban form within the framework of cognitive electrophysiology.
Collection and analysis of electroencephalography studies related to the visual complexity spectrum, and the introduction of key factors for evaluation.
Exploration of technical and contextual limitations in the neuro-urbanism approach when studying the visual complexity spectrum in urban environments.
Adli, M. (2011). Urban stress and mental health. Nature, 474(7349), 452–454.
Banaei, M., Yazdanfar, A., Nooreddin, M., & Yoonessi, A. (2015). Enhancing urban trails design quality by using electroencephalography device. Procedia-Social and Behavioral Sciences, 201, 386-396.
Boeing Geoff, 2018, Measuring the Complexity of Urban Form and Design, pre print of Urban design International.
Cassarino M and Setti A ,2016, Complexity As Key to Designing Cognitive-Friendly Environments for Older People. Front. Psychol. 7:1329. doi: 10.3389/fpsyg.2016.01329
Chiao, J. Y., & Ambady, N. (2010). Cultural neuroscience: Parsing universality and diversity across levels of analysis. Handbook of Cultural Psychology, 237–254. (oup.com)
Cohen, M. X. (2014). Analyzing neural time series data: theory and practice. The MIT Press.
Cupchik, G. C., & Berlyne, D. E. (1979). The perception of collative properties in visual stimuli. Scandinavian journal of psychology, 20(1), 93-104.
Donderi, D. C. ,2006, Visual complexity: A review. Psychological Bulletin, 132(1), 73-97. https://doi.org/10.1037/0033-2909.132.1.73
Ellard, C. (2015). Places of the heart: The psychogeography of everyday life. Bellevue Literary Press.
Ellard, C. (2015). Places of the heart: The psychogeography of everyday life. Bellevue literary press.
Elsheshtawy Yasser, 1997, Urban Complexity: Toward The Measurment of the Physical Complexity of treet-scape, Journal of Architectural and Planning Research 14, No., pp. 301 316,Published by: Locke Science Publishing Company, Inc.
Frumkin, H., Bratman, G. N., Breslow, S. J., Cochran, B., Kahn, P. H., Lawler, J. J., Levin, P. S.,.
Hagerhall, C. M., Purcell, T., & Taylor, R. (2004). Fractal dimension of landscape silhouette outlines as a predictor of landscape preference. Journal of environmental psychology, 24(2), 247-255.
Huo Juan, 2015, A measurement method for the mismatch between the image target and salient points as a metric for image complexity, in Science and Information Conference (SAI), pp. 645–649.
Huo Juan, 2016, An Image Complexity Measurement Algorithm with Visual Memory Capacity and an EEG Study, SAI Computing Conference, July 13-15, 2016, London, UK.
Kacha, L., Matsumoto, N., & Mansouri, A. (2015). Electrophysiological evaluation of perceived complexity in streetscapes. Journal of Asian Architecture and Building Engineering, 14(3), 585-592.
Lynch, K. (1960). The image of the city. MIT Press.
Malik, A. S., & Amin, H. U. (2017). Designing EEG experiments for studying the brain: Design code and example datasets. Academic Press.
Mavros, P., Austwick, M.Z. & Smith, A.H., 2016, Geo-EEG: Towards the Use of EEG in the Studyof Urban Behaviour, Appl. Spatial Analysis , 9: 191. https://doi.org/10.1007/s12061-015-9181-z.
Mavros, P., Coyne, R., Roe, J., & Aspinall, P. A. ,2012, Engaging the brain: implications of mobile EEG for spatial representation. In Proceedings of the 30th International Conference on Education and Research in Computer Aided Architectural Design in Europe, September 12-14 2012, Prague, Czech Republic: Digital Physicality (Vol. 2, pp. 657-665)
Müller, V., Lutzenberger, W., Preißl, H., Pulvermüller, F., & Birbaumer, N. (2003). Complexity of visual stimuli and non-linear EEG dynamics in humans. Cognitive Brain Research, 16(1), 104-110.
Ode, Å., Hagerhall, C. M., & Sang, N. (2010). Analysing visual landscape complexity: theory and application. Landscape research, 35(1), 111-131.
Portella Adriana ,2014, Visual Pollution Advertising, Signage and Environmental Quality, Oxford Brookes University, The Joint Centre for Urban Design, UK .
Post, R. (2016). The beauty of Unity-in-Variety: Studies on the multisensory aesthetic appreciation of product designs.
Rapoport Amos , Hawkes Ron ,1970, The Perception Of Urban Complexity, Journal of the American Institute of Planners36:2, 106-111.
Rapoport Amos , Kantor Robert E. ,1967, Complexity and Ambiguity in Environmental Design, Journal of the American Institute of Planners, 33:4, 210-221.
Rapoport Amos , Kantor Robert E. ,1967, Complexity and Ambiguity in Environmental Design, Journal of the American Institute of Planners, 33:4, 210-221.
Salingaros Nikos A, 2017, How Neuroscience Can Generate a Healthier Architecture,Conscious Cities Journal No.3, Conscious Cities Anthology 2018: Human-Centred Design, Science, and Technology.
Salingaros, N. (2017). Why we need to “grasp” our surroundings: Object affordance and prehension in architecture. Journal of Architecture and Urbanism, 41(3), 163-169.
Salingaros, N. A. (2000). Complexity and urban coherence. Journal of Urban Design, 5(3), 291–297.
Salingaros, N. A., & Masden, K. (2008). Neuroscience, the natural environment, and building design. Biophilic design: The theory, science and practice of bringing buildings to life, 41(3).
Sanei, S., & Chambers, J. A. (2013). EEG signal processing. John Wiley & Sons.
Tawil, N., & Kühn, S. (2024). The built environment and the brain: Review of emerging methods to investigate the impact of viewing architectural design. In Environmental Neuroscience (pp. 169–226). Springer. (springer.com)
Ulrich, R. S., Simons, R. F., Losito, B. D., Fiorito, E., Miles, M. A., & Zelson, M. (1991). Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, 11(3), 201–221.
Van Humbeeck, N., Meghanathan, R. N., Wagemans, J., Van Leeuwen, C., & Nikolaev, A. R. (2018). Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration. Psychophysiology, 55(12), e13267.
Vidal, F. (2017). Exploring the brain in the city: Neuroscience and urbanism. Frontiers in Human Neuroscience, 11, 482–486
Shakibamanesh, A. and َAhmadpour, N. (2025). The Application of Neuro-Urbanism in Urban Design: Evaluating the Effects of Visual Complexity on Pedestrians' Neurocognitive Responses. Iranian Urban Design Studies, 1(2), 243-268. doi: 10.22099/iuds.2025.52288.1027
MLA
Shakibamanesh, A. , and َAhmadpour, N. . "The Application of Neuro-Urbanism in Urban Design: Evaluating the Effects of Visual Complexity on Pedestrians' Neurocognitive Responses", Iranian Urban Design Studies, 1, 2, 2025, 243-268. doi: 10.22099/iuds.2025.52288.1027
HARVARD
Shakibamanesh, A., َAhmadpour, N. (2025). 'The Application of Neuro-Urbanism in Urban Design: Evaluating the Effects of Visual Complexity on Pedestrians' Neurocognitive Responses', Iranian Urban Design Studies, 1(2), pp. 243-268. doi: 10.22099/iuds.2025.52288.1027
CHICAGO
A. Shakibamanesh and N. َAhmadpour, "The Application of Neuro-Urbanism in Urban Design: Evaluating the Effects of Visual Complexity on Pedestrians' Neurocognitive Responses," Iranian Urban Design Studies, 1 2 (2025): 243-268, doi: 10.22099/iuds.2025.52288.1027
VANCOUVER
Shakibamanesh, A., َAhmadpour, N. The Application of Neuro-Urbanism in Urban Design: Evaluating the Effects of Visual Complexity on Pedestrians' Neurocognitive Responses. Iranian Urban Design Studies, 2025; 1(2): 243-268. doi: 10.22099/iuds.2025.52288.1027
)