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Journals / Etkileşim / 2026 / Sayı: 17

Intersections of Neuroscience and Design: A Scoping Review of Neurodesign Research

Nörobilim ve Tasarımın Kesişim Noktaları: Nörotasarım Araştırmasının Kapsam İncelemesi

Simge Kırteke(USKUDAR UNIVERSITY, FACULTY OF COMMUNICATION, DEPARTMENT OF VISUAL COMMUNICATION DESIGN)0000-0003-0621-8177
Ertan Toy(YILDIZ TEKNİK ÜNİVERSİTESİ, SANAT VE TASARIM FAKÜLTESİ)0000-0002-7959-7967
Journal
Etkileşim
Pages
238–260
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, the study aims to propose both a methodological roadmap and a theoretical foundation for future neuroscience-based design research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, the use of neuroscience-based measurement tools in design research is gaining importance because they allow for discussing creative processes not only through description but also through measurable variables.
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Key Findings & Data:During the search process, data were extracted from the Scopus and Web of Science databases using the keyword ‘neurodesign’ for the years 2009-2025.
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Conclusion & Impact:In conclusion, the study aims to propose both a methodological roadmap and a theoretical foundation for future neuroscience-based design research.
🔬Design:Review & Systematic Survey
📍Scope:2009-2025 Verileri
⚙️Variables:Tasarım · Nörobilim · Nörotasarım

Abstract

Today, the use of neuroscience-based measurement tools in design research is gaining importance because they allow for discussing creative processes not only through description but also through measurable variables. Observational and interpretive approaches in design apolications are moving towards an evidence-based framework by examining attention, memory, decision-making, and creativity processes in problem-solving and idea generation more concretely using cognitive neuroscience methods such as fMRI, EEG, and eye-tracking. This study aims to review the literature comprehensively on the use of cognitive neuroscience tools in design, in accordance with the PRISMA-ScR principles. During the search process, data were extracted from the Scopus and Web of Science databases using the keyword ‘neurodesign’ for the years 2009-2025. After eliminating duplicate studies, evaluation was performed at the title-abstract and full-text stages using predefined inclusion/exclusion criteria. The studies reviewed were classified according to the tools/techniques used (fMRI, EEG, eye tracking, etc.), sample type (design student/professional designer), task type (idea generation, problem framing, evaluation, prototyping, etc.), and measured cognitive functions (attention, memory, problem solving, creativity). Based on this classification, general trends in the field, conceptual gaps, and recurring methodological choices are discussed; furthermore, feasible suggestions are offered for transferring neuroscience findings into design education and professional practice. In conclusion, the study aims to propose both a methodological roadmap and a theoretical foundation for future neuroscience-based design research.

Özet

Günümüzde tasarım araştırmalarında nörobilim temelli ölçüm araçlarının kullanımı, yaratıcı süreçleri yalnızca betimlemekle kalmayıp ölçülebilir değişkenler üzerinden tartışabilmeye imkân verdiği için her geçen gün önem kazanmaktadır. Tasarım uygulamalarında gözlem ve yoruma dayalı yaklaşımlar, fMRI, EEG ve göz izleme gibi bilişsel nörobilim yöntemleriyle problem çözme ve fikir üretmedeki dikkat, bellek, karar verme ve yaratıcılık süreçlerini daha somut inceleyerek kanıta dayalı bir çerçeveye yönelmektedir. Nörotasarım çalışmaları çerçevesinde kapsam analizi yöntemiyle hazırlanan bu çalışma, PRISMA-ScR ilkeleri doğrultusunda, tasarım alanında bilişsel nörobilim araçlarının kullanımına ilişkin literatürü kapsamlı biçimde taramayı amaçlamaktadır. Tarama sürecinde Scopus ve Web of Science veri tabanlarından 2009-2025 tarihleri kapsamında ‘neurodesign’ anahtar kelimesi kullanılarak veriler çıkarılmış, önceden belirlenen dahil etme/dışlama ölçütleriyle değerlendirme yapılmıştır. İncelemeye alınan çalışmalar, kullanılan araç/teknik (fMRI, EEG, göz izleme vb.), örneklem türü, görev türü ve ölçülen bilişsel işlevler gibi boyutlarda sınıflandırılmıştır. Bu sınıflandırma üzerinden alanın genel eğilimleri, kavramsal boşlukları ve tekrarlayan yöntemsel tercihleri tartışılmakta; ayrıca nörobilim bulgularının tasarım eğitimine ve profesyonel uygulamaya aktarılabilmesi için uygulanabilir öneriler sunulmaktadır. Sonuç olarak çalışma, gelecekte yürütülecek nörobilim temelli tasarım araştırmaları için hem yöntemsel bir yol haritası hem de kuramsal bir zemin önermeyi hedeflemektedir.

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Cite This Article

Kırteke, S., Toy, E. (2026). Intersections of Neuroscience and Design: A Scoping Review of Neurodesign Research. Etkileşim, Sayı: 17, 238–260. https://doi.org/10.32739/etkilesim.2026.9.17.337

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References (43)

  1. Ahram, T., Falcão, C., Barros, R. Q., Soares, M. M., & Karwowski, W. (2016). Neurodesign: Applications of neuroscience in design and human–system interactions. In M. M. Soares & F. Rebelo (Eds.), Ergonomics in design: Methods & techniques. CRC Press. https://doi.org/10.1201/9781315367668
  2. Alexiou, K., Zamenopoulos, T., Johnson, J. H., & Gilbert, S. J. (2009). Exploring the neurological basis of design cognition using brain imaging: Some preliminary results. Design Studies, 30(6), 623–647. https://doi.org/10.1016/j.destud.2009.05.002
  3. Alsharif, A. H., & Isa, S. M. (2024). Electroencephalography studies on marketing stimuli: A literature review and future research agenda. International Journal of Consumer Studies, 49(1). https://doi.org/10.1111/ijcs.70015
  4. Auernhammer, J., Sonalkar, N., & Saggar, M. (2021). NeuroDesign: From neuroscience research to design thinking practice. In C. Meinel & L. Leifer (Eds.), Design thinking research: Understanding innovation (pp. 347-355). Springer. https://doi.org/10.1007/978-3-030-62037-0_16
  5. Auernhammer, J., Liu, W., Ohashi, T., Leifer, L., Byler, E., & Pan, W. (2021). NeuroDesign: Embracing neuroscience instruments to investigate human collaboration in design. In T. Ahram, R. Taiar, K. Langlois, & A. Choplin (Eds.), Human interaction, emerging technologies and future applications III (Advances in Intelligent Systems and Computing, Vol. 1253, pp. 284-289). Springer. https://doi.org/10.1007/978-3-030-55307-4_43
  6. Auernhammer, J., Bruno, J., Booras, A., McIntyre, C., Hasegan, D., & Saggar, M. (2023). NeuroDesign: Greater than the sum of its parts. In C. Meinel & L. Leifer (Eds.), Design thinking research: Understanding innovation (pp. 197–211). Springer. https://doi.org/10.1007/978-3-030-55307-4_43
  7. Balters, S., Weinstein, T., Mayseless, N., Auernhammer, J., Hawthorne, G., Steinert, M., Meinel, C., Leifer, L. J., & Reiss, A. L. (2023). Design science and neuroscience: A systematic review of the emergent field of design neurocognition. Design Studies, 84, 101148. https://doi.org/10.1016/j.destud.2022.101148
  8. Chowdhury, A., & Chakraborty, P. (2021). Memes that evoke emotions: A neurodesign strategy for brand communication and experience. In Design for Tomorrow (Vol. 1, pp. 147–156). Springer. https://doi.org/10.1007/978-981-16-0041-8_13
  9. Çaydere, O. (2015). Faculty and student opinions on graphic design programs (Unpublished doctoral thesis). Gazi University Institute of Educational Sciences.
  10. Chenais, N., & Görgen, A. (2024). Immersive interfaces for clinical applications: Current status and future perspective. Frontiers in Neurorobotics, 18. https://doi.org/10.3389/fnbot.2024.1362444
  11. Cutellic, P., & Lotte, F. (2013). Augmented iterations: Integrating neural activity in evolutionary computation for design. HAL Open Science.
  12. Cutellic, P. (2014). Le cube d’après: Integrated cognition for iterative and generative designs. In Proceedings of the ACADIA Conference. https://doi.org/10.13140/RG.2.1.5186.5444
  13. . An event-based generative design software implementing fast discriminative cognitive responses from visual ERP BCI. In Human–computer interaction in design (Vol. 2, pp. 131-138). https://doi.org/10.52842/conf.ecaade.2018.2.131
  14. . Towards encoding shape features with visual event-related potential–based brain–computer interfaces for generative design. International Journal of Architectural Computing, 17(1). https://doi.org/10.1177/1478077119832465
  15. Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 306(5695), 447–452. https://doi.org/10.1126/science.1102566
  16. Goucher-Lambert, K., Moss, J., & Cagan, J. (2018). Inspired internal search: Using neuroimaging to understand design ideation and concept generation with inspirational stimuli. In Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. https://doi.org/10.1115/DETC2018-85690
  17. Göktaş, O., Ergin, E., Çetin, G., Özkoç, H. H., Fırat, A., & Gazel, G. G. (2024). Investigation of user–product interaction by determining the focal points of visual interest in different types of kitchen furniture: An eye-tracking study. Displays, 83, 102745. https://doi.org/10.1016/j.displa.2024.102745
  18. Hay, L., Duffy, A. H. B., Gilbert, S. J., Lyall, L., Campbell, G., Coyle, D., & Grealy, M. A. (2019). The neural correlates of ideation in product design engineering practitioners. Design Science, 5, e29. https://doi.org/10.1017/dsj.2019.27
  19. Hermosilla, C., & Magal-Royo, T. (2022). User experience requirements for hyperloop passengers’ cabin. DYNA, 97(4). https://doi.org/10.6036/10480
  20. Hevner, A. R., Davis, C., Collins, R. W., & Gill, T. G. (2014). A neurodesign model for IS research. Informing Science: The International Journal of an Emerging Transdiscipline, 17, 103–132.
  21. Kirkland, L. (2012). Using neuroscience to inform your UX strategy and design. Retrieved May 14, 2025 from https://www.uxmatters.com/mt/archives/2012/07/using-neuroscience-to-inform-your-ux-strategy-and-design.php
  22. Kwon, J., Linihan, S., Iedema, A., Schmidt, A., Luo, C., & Marrufo, K. (2023). How interior design responds to neurodiversity: Implementing wearable technologies in neurodesign processes. Frontiers in Built Environment, 9. https://doi.org/10.3389/fbuil.2023.1211519
  23. Labrada, S. M., Fonseca, E. M., & Marquez-Barja, J. M. (2024). Requirement system for the interface design of applications for children with neurodevelopmental disorders. In Proceedings of the IEEE Global Engineering Education Conference (EDUCON). https://doi.org/10.1109/EDUCON60312.2024.10578799
  24. Liu, Q., Huang, R., Zhu, R., & Chen, Y. (2024). Exploring neurodesign in cultivating empathy and creativity in primary education in China. https://doi.org/10.1145/3675094.3677596
  25. Liu, W., Jin, Y., Li, B., Lyu, Z., Pan, W., Wang, N., & Zhao, X. (2020). NeuroDesign: Making decisions and solving problems through understanding of the human brain. In A. Marcus & E. Rosenzweig (Eds.), Human–computer interaction (LNCS Vol. 12200). Springer. https://doi.org/10.1007/978-3-030-49713-2_14
  26. Liu, W., Lee, K. P., Gray, C. M., Toombs, A. L., Chen, K. H., & Leifer, L. (2021). Transdisciplinary teaching and learning in UX design: A program review and AR case studies. Applied Sciences, 11, 10648. https://doi.org/10.3390/app112210648
  27. Lu, G., & Hou, G. (2020). Effects of semantic congruence on sign identification: An ERP study. Human Factors: The Journal of the Human Factors and Ergonomics Society, 62(5). https://doi.org/10.1177/0018720819854880
  28. Ohashi, T., Auernhammer, J., Liu, W., Pan, W., & Leifer, L. (2022). NeuroDesignScience: Systematic literature review of current research on design using neuroscience techniques. In J. S. Gero (Ed.), Design computing and cognition ’20 (pp. 575–592). Springer. https://doi.org/10.1007/978-3-030-90625-2_34
  29. Paoletti, A., & Imbesi, L. (2021). A neurodesign case study: Measuring the emotional index for redesign. Design Principles and Practices: An International Journal, 15(1), 33–44. https://doi.org/10.18848/1833-1874/CGP/v15i01/33-44
  30. Ralph, P., & Wand, Y. (2009). A proposal for a formal definition of the design concept. In K. Lyytinen, P. Loucopoulos, J. Mylopoulos, & B. Robinson (Eds.), Design requirements engineering: A ten-year perspective (Lecture Notes in Business Information Processing, Vol. 14, pp. 103–136). Springer. https://doi.org/10.1007/978-3-540-92966-6_6
  31. Salingaros, N. A. (2025a). Environments that boost creativity: AI-generated living geometry. Multimodal Technologies and Interaction, 9(38). https://doi.org/10.3390/mti9050038
  32. (2025b). Façade psychology is hardwired: AI selects windows supporting health. Buildings, 15(1645). https://doi.org/10.3390/buildings15101645
  33. Shealy, T., & Gero, J. S. (2019). The neurocognition of three engineering concept generation techniques. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 1833–1842. https://doi.org/10.1017/dsi.2019.189
  34. Simon, H. A. (1977). The new science of management decision. MIT Press.
  35. Sola, H. M., Qureshi, F. H., & Khawaja, S. (2025). Human-centered design meets AI-driven algorithms: Comparative analysis of political campaign branding in the Harris–Trump presidential campaigns. Informatics, 12(30). https://doi.org/10.3390/informatics12010030
  36. Thienen, J. V., Borchart, K. P., Jaschek, C., Krebs, E., Hildebrand, J., Ratz, H., & Meinel, C. (2021). Leveraging video games to improve IT solutions for remote work. In Proceedings of the IEEE Conference on Games. IEEE. https://doi.org/10.1109/CoG52621.2021.9618986
  37. Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Moher, D. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
  38. Vartanian, O., Navarrete, G., Chatterjee, A., Fich, L. B., Gonzalez-Mora, J. L., Leder, H., Modroño, C., Nadal, M., Rostrup, N., & Skov, M. (2015). Architectural design and the brain: Effects of ceiling height and perceived enclosure on beauty judgments and approach–avoidance decisions. Journal of Environmental Psychology, 41, 10–18. https://doi.org/10.1016/j.jenvp.2014.11.006
  39. Vecchiato, G., Astolfi, L., Fallani, F. D. V., Toppi, J., Aloise, F., Bez, F., Wei, D., Kong, W., Dai, J., Cincotti, F., Mattia, D., Babiloni, F., & Babiloni, C. (2011). On the use of EEG or MEG brain imaging tools in neuromarketing research. Computational Intelligence and Neuroscience, 2011, 643489. https://doi.org/10.1155/2011/643489
  40. Vieira, S., Gero, J. S., Delmoral, J., Gattol, V., Fernandes, C., Parente, M., & Fernandes, A. A. (2020). The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving. Design Science, 6, e26. https://doi.org/10.1017/dsj.2020.26
  41. Zallio, M., Berry, D., & Leifer, L. J. (2020). Meaningful age-friendly design: Case studies on enabling assistive technology. In T. Ahram & C. Falcao (Eds.), Advances in usability and user experience (Advances in Intelligent Systems and Computing, Vol. 972, pp. 779–790). Springer. https://doi.org/10.1007/978-3-030-19135-1_76
  42. Wang, R. W. Y., Ke, T. M., Chuang, S. W., & Liu, I. N. (2020). Sex differences in high-level appreciation of automobile design–evoked gamma broadband synchronisation. Scientific Reports, 10, 66515. https://doi.org/10.1038/s41598-020-66515-7
  43. Wang, X., Huang, Y., Ma, Q., & Li, N. (2012). Event-related potential P2 correlates of implicit aesthetic experience. NeuroReport, 23(14), 862–866. https://doi.org/10.1097/WNR.0b013e3283587161