Web of Science Veritabanında Nörobilim ve Mekânsal Tasarım Bibliyometrik Analizi

Öz

Bu makale, nörobilim ve mekânsal tasarım araştırmalarının kesişimi üzerine kapsamlı bir bibliyometrik analiz sunmaktadır. İki aşamalı bir süreç kullanarak, ilk anahtar kelime araması 'EEG' ve 'Neuro' gibi terimlerle birlikte 'Mimarlık,' 'Kentsel Tasarım,' 'Ürün Tasarımı' ve 'İç Mimarlık' terimlerini içeren 296 makale belirlemiştir. Yayın tarihi (2003-2023), dil (İngilizce), belge türü ve kategorilere göre yapılan sonraki filtreleme ile bu sayı 64 makaleye indirilmiştir. Son trendler, mimarlık odaklı çalışmalardan iç mimarlık ve sanal gerçekliğin bir araştırma aracı olarak kullanıldığı çalışmalara doğru bir kayma olduğunu göstermektedir. 2018'den bu yana artan yayın sayısı, 2022'de zirve yaparak, akademik ilginin arttığını göstermektedir. Bu çalışma, insan refahını artırmak için nörobilimin mekânsal tasarıma entegrasyonunun potansiyelini vurgulamakta ve mekânsal tasarımcılar için gelecekteki araştırma yönlerini öne çıkarmaktadır. Bulgular, stres azaltma, biyofilik tasarım ve insan refahının tasarım yoluyla iyileştirilmesine yönelik evrilen bir odağı ortaya koymaktadır. Bu makale, kullanıcı merkezli ve estetik açıdan hoş mekanlar için bilimsel bir temel sağlamayı amaçlamaktadır.

Anahtar Kelimeler

• İç mimarlık
• Mekansal tasarım
• Bibliyometrik analiz
• Nörobilim

Neuroscience and Spatial Design Bibliometric Analysis in Web of Science Database

Öz

This paper presents a comprehensive bibliometric analysis on the convergence of neuroscience and spatial design research. Using a two-step process, an initial keyword search identified 296 papers with terms like 'EEG' and 'Neuro' alongside 'Architecture,' 'Urban Design,' 'Product Design,' and 'Interior Design.' Subsequent filtering by publication date (2003-2023), language (English), document type, and categories refined this to 64 papers. Recent trends show a shift from architecture-focused studies to those emphasizing interior architecture and the use of virtual reality as a research tool. The increase in publications since 2018, peaking in 2022, indicates growing scholarly interest. This study underscores the potential of integrating neuroscience in spatial design to improve human well-being and highlighting future research directions for spatial designers. The findings reveal an evolving focus on stress reduction, biophilic design, and the enhancement of human well-being through design. This paper aims to provide a scientific foundation for user-centered and aesthetically pleasing environments.

Anahtar Kelimeler

• Interior architecture
• Spatial design
• Bibliometric analysis
• Neuroscience

Kaynakça

1. Ahlquist, S. (2020). Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind. International Journal of Architectural Computing, 18(2), 174–193. https://doi.org/10.1177/1478077120919850
2. Al-Barrak, L., Kanjo, E., & Younis, E. M. G. (2017). NeuroPlace: Categorizing urban places according to mental states. PLOS ONE, 12(9), e0183890. https://doi.org/10.1371/journal.pone.0183890
3. Albdour, A., Agiel, A., & Ghoudi, K. (2022). Assessing the Emotional Affordance of Brand Image and Foreign Image Based on a Physiological Method Using Examples from Dubai: Exploratory Study. Buildings, 12(10), 1650. https://doi.org/10.3390/buildings12101650
4. Aliverdilou, H., Hajilou, M., Sabokbar, H. a. F., & Faraji, A. (2021). An intelligent method for industrial location selection: application to Markazi Province, Iran. Journal of Regional and City Planning, 32(3), 267–289. https://doi.org/10.5614/jpwk.2021.32.3.5
5. Allahyar, M., & Kazemi, F. (2021). Effect of landscape design elements on promoting neuropsychological health of children. Urban Forestry & Urban Greening, 65, 127333. https://doi.org/10.1016/j.ufug.2021.127333 Ambrosini, E., Arbula, S., Rossato, C., Pacella, V., & Vallesi, A. (2019). Neuro-cognitive architecture of executive functions: A latent variable analysis. Cortex, 119, 441–456. https://doi.org/10.1016/j.cortex.2019.07.013
6. Asim, F., Chani, P. S., Shree, V., & Rai, S. (2023). Restoring the mind: A neuropsychological investigation of university campus built environment aspects for student well-being. Building and Environment, 244, 110810. https://doi.org/10.1016/j.buildenv.2023.110810
7. Awada, M., Becerik-Gerber, B., Liu, R., Seyedrezaei, M., Lu, Z., Xenakis, M., Lucas, G. M., Roll, S. C., & Narayanan, S. (2023). Ten questions concerning the impact of environmental stress on office workers. Building and Environment, 229, 109964. https://doi.org/10.1016/j.buildenv.2022.109964
8. Azzazy, S., Ghaffarianhoseini, A., Ghaffarianhoseini, A., Naismith, N., & Doborjeh, Z. G. (2020). A critical review on the impact of built environment on users’ measured brain activity. Architectural Science Review, 64(4), 319–335. https://doi.org/10.1080/00038628.2020.1749980

9. Bacevice, P., & Ducao, A. (2021). Use of biometric data and EEG to assess architectural quality of two office spaces: a pilot experiment. Intelligent Buildings International, 14(4), 433–454. https://doi.org/10.1080/17508975.2021.1921683
10. Baumann, O., & Brooks-Cederqvist, B. (2023). Multimodal assessment of effects of urban environments on psychological wellbeing. Heliyon, 9(6), e16433. https://doi.org/10.1016/j.heliyon.2023.e16433
11. Chang, S., & Jun, H. (2019). Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives. Journal of Asian Architecture and Building Engineering, 18(5), 381–391. https://doi.org/10.1080/13467581.2019.1660663
12. Cheng, P., Chiueh, T., & Chen, J. (2021). A high temporal/spatial resolution neuro-architecture study of rodent brain by wideband echo planar imaging. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-98132-3
13. Djebbara, Z., Fich, L. B., Petrini, L., & Gramann, K. (2019). Sensorimotor brain dynamics reflect architectural affordances. Proceedings of the National Academy of Sciences of the United States of America, 116(29), 14769–14778. https://doi.org/10.1073/pnas.1900648116
14. Djebbara, Z., Jensen, O. B., Parada, F. J., & Gramann, K. (2022). Neuroscience and architecture: Modulating behavior through sensorimotor responses to the built environment. Neuroscience & Biobehavioral Reviews, 138, 104715. https://doi.org/10.1016/j.neubiorev.2022.104715
15. Domjan, S., Arkar, C., & Medved, S. (2023). Study on occupants’ window view quality vote and their physiological response. Journal of Building Engineering, 68, 106119. https://doi.org/10.1016/j.jobe.2023.106119
16. Erkan, İ. (2018). Examining wayfinding behaviours in architectural spaces using brain imaging with electroencephalography (EEG). Architectural Science Review, 61(6), 410–428. https://doi.org/10.1080/00038628.2018.1523129
17. Erkan, İ. (2020a). Cognitive response and how it is affected by changes in temperature. Building Research and Information, 49(4), 399–416. https://doi.org/10.1080/09613218.2020.1800439
18. Erkan, İ. (2020b). A neuro-cognitive investigation of the impact of glass floors on people. Architectural Science Review, 64(4), 336–345. https://doi.org/10.1080/00038628.2020.1858574
19. Erkan, İ. (2023). A neuro-cognitive perspective on urban behavior of people with different moods. Open House International, 48(4), 822–839. https://doi.org/10.1108/ohi-10-2022-0252
20. Essawy, S., Kamel, B., & Elsawy, M. S. (2014). Timeless Buildings and The Human Brain: The effect of spiritual spaces on human brain waves. ArchNet-IJAR, 8(1), 133. https://doi.org/10.26687/archnet-ijar.v8i1.329
21. Gallese, V., & Gattara, A. (2015). Embodied Simulation, Aesthetics, and Architecture: an Experimental aesthetic approach. In The MIT Press eBooks (pp. 161–180). https://doi.org/10.7551/mitpress/10318.003.0010
22. Gharib, Z., Tavakkoli‐Moghaddam, R., Bozorgi-Amiri, A., & Yazdani, M. (2022). Post-Disaster Temporary Shelters Distribution after a Large-Scale Disaster: An Integrated Model. Buildings, 12(4), 414. https://doi.org/10.3390/buildings12040414
23. Guizzo, A. O., Sia, A., & Escoffier, N. (2023). Revised Contemplative Landscape Model (CLM): A reliable and valid evaluation tool for mental health-promoting urban green spaces. Urban Forestry & Urban Greening, 86, 128016. https://doi.org/10.1016/j.ufug.2023.128016
24. Halligan, P. W., Fink, G. R., Marshall, J. C., & Vallar, G. (2003). Spatial cognition: evidence from visual neglect. Trends in Cognitive Sciences, 7(3), 125–133. https://doi.org/10.1016/s1364-6613(03)00032-9
25. He, Z., Zuazua-Ros, A., & Martín-Gómez, C. (2023). Thermoelectric system applications in buildings: A review of key factors and control methods. Journal of Building Engineering, 78, 107658. https://doi.org/10.1016/j.jobe.2023.107658
26. Herman, K., Ciechanowski, L., & Przegalińska, A. (2021). Emotional Well-Being in Urban Wilderness: Assessing States of Calmness and Alertness in Informal Green Spaces (IGSs) with Muse—Portable EEG Headband. Sustainability, 13(4), 2212. https://doi.org/10.3390/su13042212
27. Higuera-Trujillo, J. L., Llinares, C., Aviñó, A. M. I., & Rojas, J. C. (2019). Multisensory stress reduction: a neuro-architecture study of paediatric waiting rooms. Building Research and Information, 48(3), 269–285. https://doi.org/10.1080/09613218.2019.1612228
28. Hollander, J. B., & Foster, V. (2016). Brain responses to architecture and planning: a preliminary neuro-assessment of the pedestrian experience in Boston, Massachusetts. Architectural Science Review, 59(6), 474–481. https://doi.org/10.1080/00038628.2016.1221499
29. Hsu, W. (2015). A novel image registration algorithm for indoor and built environment applications. Computer-Aided Civil and Infrastructure Engineering, 30(10), 802–814. https://doi.org/10.1111/mice.12156 Hu, M., & Roberts, J. D. (2020). Built Environment Evaluation in Virtual Reality Environments—A Cognitive Neuroscience approach. Urban Science, 4(4), 48. https://doi.org/10.3390/urbansci4040048
30. Hu, S., Lu, M., He, M., Wang, G., Liang, P., Li, T., & Liu, G. (2021). Research on the light comfort characterization method based on visual evoked potential energy. Building and Environment, 197, 107831. https://doi.org/10.1016/j.buildenv.2021.107831
31. Hu, S., Ma, H., Lu, M., & Wang, F. (2023). The use of electroencephalogram to characterize subjective evaluation with illuminance as the independent variable. Indoor and Built Environment, 32(7), 1450–1463. https://doi.org/10.1177/1420326x231166560 IFI (2011). IFI Interiors Declaration (IFI ID). https://ifiworld.org/programs-events/interiorsdeclaration-adoptions/
32. Ji, S., Kang, S. Y., & Jun, H. J. (2020). Deep-Learning-Based Stress-Ratio Prediction Model Using Virtual Reality with Electroencephalography Data. Sustainability, 12(17), 6716. https://doi.org/10.3390/su12176716
33. Jung, C., Jung, C., Samanoudy, G. E., & Qassimi, N. A. (2022). Evaluating the color preferences for elderly depression in the United Arab Emirates. Buildings, 12(2), 234. https://doi.org/10.3390/buildings12020234
34. Jung, D., Kim, D. I., & Kim, N. (2023). Bringing nature into hospital architecture: Machine learning-based EEG analysis of the biophilia effect in virtual reality. Journal of Environmental Psychology, 89, 102033. https://doi.org/10.1016/j.jenvp.2023.102033
35. Kaklauskas, A., Zavadskas, E. K., Bardauskienė, D., Čerkauskas, J., Ubartė, I., Seniut, M., Dzemyda, G., Kaklauskaite, M., Vinogradova, I., & Velykorusova, A. (2019). An Affect-Based built environment video analytics. Automation in Construction, 106, 102888. https://doi.org/10.1016/j.autcon.2019.102888
36. Kalantari, S., Rounds, J. D., Kan, J., Tripathi, V., & Cruz-Garza, J. G. (2021). Comparing physiological responses during cognitive tests in virtual environments vs. in identical real-world environments. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-89297-y
37. Kalantari, S., Tripathi, V., Kan, J., Rounds, J. D., Mostafavi, A., Snell, R. S., & Cruz-Garza, J. G. (2022). Evaluating the impacts of color, graphics, and architectural features on wayfinding in healthcare settings using EEG data and virtual response testing. Journal of Environmental Psychology, 79, 101744. https://doi.org/10.1016/j.jenvp.2021.101744
38. Karakas, T., & Yıldız, D. (2020). Exploring the influence of the built environment on human experience through a neuroscience approach: A systematic review. Frontiers of Architectural Research, 9(1), 236–247. https://doi.org/10.1016/j.foar.2019.10.005
39. Kim, M., Cheon, S., & Kang, Y. (2019). Use of electroencephalography (EEG) for the analysis of emotional perception and fear to nightscapes. Sustainability, 11(1), 233. https://doi.org/10.3390/su11010233
40. Kong, Z., Hou, K., Wang, Z., Chen, F., Li, Y., Liu, X., & Liu, C. (2022). Subjective and Physiological Responses towards Interior Natural Lightscape: Influences of Aperture Design, Window Size and Sky Condition. Buildings, 12(10), 1612. https://doi.org/10.3390/buildings12101612
41. Krauze, W., & Motak, M. (2022). Neurosciences in architecture. Applied research and its potential in architectural design. Teka Komisji Urbanistyki i Architektury Oddział PAN w Krakowie, 50. https://doi.org/10.1016/j.promfg.2015.07.453
42. Li, J., Jin, Y., Lu, S., Wu, W., & Wang, P. (2020). Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy and Buildings, 224, 110259. https://doi.org/10.1016/j.enbuild.2020.110259
43. Li, J., Wu, W., Jin, Y., Zhao, R., & Bian, W. (2021). Research on environmental comfort and cognitive performance based on EEG+VR+LEC evaluation method in underground space. Building and Environment, 198, 107886. https://doi.org/10.1016/j.buildenv.2021.107886
44. Masden, K. G., & Salingaros, N. A. (2014). Intellectual [Dis]Honesty in Architecture. Journal of Architecture and Urbanism, 38(3), 187–191. https://doi.org/10.3846/20297955.2014.941522
45. Mavros, P., Austwick, M. Z., & Smith, A. H. (2016). Geo-EEG: Towards the use of EEG in the study of urban behaviour. Applied Spatial Analysis and Policy, 9(2), 191–212. https://doi.org/10.1007/s12061-015-9181-z
46. Mazzone, A., & Khosla, R. (2021). Socially constructed or physiologically informed? Placing humans at the core of understanding cooling needs. Energy Research & Social Science, 77, 102088. https://doi.org/10.1016/j.erss.2021.102088
47. Merhav, M., & Fisher-Gewirtzman, D. (2023). How pathways’ configuration impacts wayfinding in young and older adults. Journal of Environmental Psychology, 90, 102065. https://doi.org/10.1016/j.jenvp.2023.102065
48. Mostafavi, A. (2021). Architecture, biometrics, and virtual environments triangulation: a research review. Architectural Science Review, 65(6), 504–521. https://doi.org/10.1080/00038628.2021.2008300
49. Mostafavi, A., Cruz-Garza, J. G., & Kalantari, S. (2023). Enhancing lighting design through the investigation of illuminance and correlated color Temperature’s effects on brain activity: An EEG-VR approach. Journal of Building Engineering, 75, 106776. https://doi.org/10.1016/j.jobe.2023.106776
50. Nanda, U., Pati, D., Ghamari, H., & Bajema, R. (2013). Lessons from neuroscience: form follows function, emotions follow form. Intelligent Buildings International, 5(sup1), 61–78. https://doi.org/10.1080/17508975.2013.807767
51. Nasab, S.H., Saradj, F.M., Khanmohammadi, M. A., & Ghamari, H. (2022). Evaluation of the Residential Facades in Tehran from the Neuro-Aesthetics Approach. MANZAR, the Scientific Journal of landscape, 14(60), 18-29. https://doi.org/10.22034/MANZAR.2022.317574.2169
52. Nie, W., Jia, J., Mimi, W., Sun, J., & Li, G. (2022). Research on the Impact of Panoramic Green View Index of Virtual Reality Environments on Individuals’ Pleasure Level Based on EEG Experiment. 景观设计学, 10(2), 36. https://doi.org/10.15302/j-laf-1-020059
53. Pektaş, Ş. T. (2021). A scientometric analysis and review of spatial cognition studies within the framework of neuroscience and architecture. Architectural Science Review, 64(4), 374–382. https://doi.org/10.1080/00038628.2021.1910480
54. Rad, P. N., Shahroudi, A. A., Shabani, H., Ajami, S., & Lashgari, R. (2019). Encoding pleasant and unpleasant expression of the architectural window shapes: an ERP study. Frontiers in Behavioral Neuroscience, 13. https://doi.org/10.3389/fnbeh.2019.00186
55. Rhee, J., Schermer, B., & Hyun, S. (2023). Effects of indoor vegetation density on human well-being for a healthy built environment. Developments in the Built Environment, 14, 100172. https://doi.org/10.1016/j.dibe.2023.100172
56. Salingaros, N. A., & Masden, K. G. (2010). TEACHING DESIGN AT THE LIMITS OF ARCHITECTURE. International Journal of Architectural Research: Archnet-IJAR, 4, 19–31. https://doi.org/10.26687/archnet-ijar.v4i2/3.93
57. Shemesh, A., Leisman, G., Bar, M., & Grobman, Y. J. (2021). A neurocognitive study of the emotional impact of geometrical criteria of architectural space. Architectural Science Review, 64(4), 394–407. https://doi.org/10.1080/00038628.2021.1940827
58. Shemesh, A., Leisman, G., Bar, M., & Grobman, Y. J. (2022). The emotional influence of different geometries in virtual spaces: A neurocognitive examination. Journal of Environmental Psychology, 81, 101802. https://doi.org/10.1016/j.jenvp.2022.101802
59. Vijayan, V. T., & Embi, M. R. (2019). Probing phenomenological experiences through electroencephalography brainwave signals in Neuroarchitecture study. International Journal of Built Environment and Sustainability, 6(3), 11–20. https://doi.org/10.11113/ijbes.v6.n3.360
60. Wang, H., Hou, K., Kong, Z., Xi, G., Hu, S., Lu, M., Piao, X., & Qian, Y. (2022). “In-Between Area” design method: An optimization design method for indoor public spaces for elderly facilities evaluated by STAI, HRV and EEG. Buildings, 12(8), 1274. https://doi.org/10.3390/buildings12081274
61. Yeom, S., Kim, H., & Hong, T. (2021). Psychological and physiological effects of a green wall on occupants: A cross-over study in virtual reality. Building and Environment, 204, 108134. https://doi.org/10.1016/j.buildenv.2021.108134
62. Yu, R., Schubert, G., & Gu, N. (2023). Biometric Analysis in Design Cognition Studies: A Systematic Literature review. Buildings, 13(3), 630. https://doi.org/10.3390/buildings13030630
63. Zur, N., Tsoory, S.S., Sterkin, A., & Gewirtzman, D.F. (2023). Perceived density and positive affect ratings of studio apartment: an EEG study. Architectural Science Review, 1-11. https://doi.org/10.1080/00038628.2023.2224284