References
- Spence, C. Senses of place: architectural design for the multisensory mind. Cognitive Research: Principles and Implications, 2020, vol. 5, no. 1, pp. 1–26. https://doi.org/10.1186/s41235-020-00243-4
- Leach, N. Architecture in the Age of Artificial Intelligence: An introduction to AI for architects. Bloomsbury Visual Arts, 2021. https://doi.org/10.5040/9781350165557
- Guo, Z., Zhang, Z., Li, Z., Hu, Y., Qian, Y., Cheng, N., Yuan, P. F. Brain-computer interface based generative design framework: an empirical multi-domain application exploration based on human-factors and form-generation interactive mechanisms. Architectural Intelligence, 2024, vol. 3, no. 1, pp. 1–16. https://doi.org/10.1007/s44223-024-00047-2
- Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., Tan, C. Towards a science of human-AI decision making: An overview of design space in empirical human-subject studies. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, pp. 1369–1385. https://doi.org/10.1145/3593013.3594087
- Rokhsaritalemi, S., Sadeghi-Niaraki, A., Choi, S. M. Exploring emotion analysis using artificial intelligence, geospatial information systems, and extended reality for urban services. IEEE Access, 2023, vol. 11, pp. 92478–92495. https://doi.org/10.1109/ACCESS.2023.3307639
- Wilson, M. Six views of embodied cognition. Psychonomic Bulletin & Review, 2002, vol. 9, pp. 625–636. https://doi.org/10.3758/BF03196322
- Sano, S., Yamada, S. AI-assisted design concept exploration through character space construction. Frontiers in Psychology, 2022, vol. 12, p. 819237. https://doi.org/10.3389/fpsyg.2021.819237
- Fontana, M., Giannini, F., Meirana, M. Free form features for aesthetic design. International Journal of Shape Modeling, 2000, vol. 6, no. 2, pp. 273–302. https://doi.org/10.1142/S0218654300000168
- Pernot, J. P., Falcidieno, B., Giannini, F., Léon, J. C. Fully free-form deformation features for aesthetic shape design. Journal of Engineering Design, 2005, vol. 16, no. 2, pp. 115–133. https://doi.org/10.1080/09544820500031617
- Stylidis, K., Rossi, M., Žukas, J. and Söderberg, R. Addressing information asymmetry during design: customer-centric approach to harmonization of car body split-lines. Procedia CIRP, 2021, vol. 104, pp. 110–115. https://doi.org/10.1016/j.procir.2021.11.019
- Nagamachi, M. (ed.). Kansei Engineering, 2 Volume Set. Boca Raton: CRC Press, 2010. https://doi.org/10.1201/b16799
- Alcaide-Marzal, J., Diego-Mas, J. A., Acosta-Zazueta, G. A 3D shape generative method for aesthetic product design. Design Studies, 2020, vol. 66, pp. 144–176. https://doi.org/10.1016/j.destud.2019.11.003
- Brand, N., Odom, W., Barnett, S. Envisioning and understanding orientations to introspective AI: Exploring a design space with Meta. Aware. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, pp. 1–18. https://doi.org/10.1145/3544548.3581336
- Zhang, Z., Fort, J. M., Giménez Mateu, L. Decoding emotional responses to AI-generated architectural imagery. Frontiers in Psychology, 2024, vol. 15, p. 1348083. https://doi.org/10.3389/fpsyg.2024.1348083
- Kleyko, D., Rachkovskij, D., Osipov, E., Rahimi, A. A survey on hyperdimensional computing aka vector symbolic architectures, part II: Applications, cognitive models, and challenges. ACM Computing Surveys, 2023, vol. 55, no. 9, pp. 1–52. https://doi.org/10.1145/3558000
- Guridi, J. A., Cheyre, C., Goula, M., Santo, D., Humphreys, L., Souras, A., Shankar, A. Image generative AI to design public spaces: a reflection of how AI could improve codesign of public parks. Digital Government: Research and Practice, 2025, vol. 6, no. 1, pp. 1–14. https://doi.org/10.1145/3656588
- Draguhn, A., Sauer, J. F. Body and mind: how somatic feedback signals shape brain activity and cognition. Pflügers Archiv- European Journal of Physiology, 2023, vol. 475, no. 1, pp. 1–4. https://doi.org/10.1007/s00424-022-02778-5
- Niu, H., Van Leeuwen, C., Hao, J., Wang, G., Lachmann, T. Multimodal natural human–computer interfaces for computer-aided design: A review paper. Applied Sciences, 2022, vol. 12, no. 13, p. 6510. https://doi.org/10.3390/app12136510
- Naga, P., Marri, S. D., Borreo, R. Facial emotion recognition methods, datasets and technologies: A literature survey. Materials Today: Proceedings, 2023, vol. 80, pp. 2824–2828. https://doi.org/10.1016/j.matpr.2021.07.046
- Khan, M. A., Abbas, S., Raza, A., Khan, F., Whangbo, T. Emotion based signal enhancement through multisensory integration using machine learning. Computers, Materials & Continua, 2022, vol. 71, no. 3, pp. 5911–5931. https://doi.org/10.32604/cmc.2022.023557
- Barsoum, E., Zhang, C., Canton Ferrer, C., Zhang, Z. Training deep networks for facial expression recognition with crowd-sourced label distribution. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI ’16), 2016, pp. 279–283. https://doi.org/10.1145/2993148.2993165
- Caetano, I., Leitão, A. Architecture meets computation: an overview of the evolution of computational design approaches in architecture. Architectural Science Review, 2020, vol. 63, no. 2, pp. 165–174. https://doi.org/10.1080/00038628.2019.1680524
- Li, Y., et al. Effects of illuminance and correlated color temperature of indoor light on emotion perception. Scientific reports, 2021, 11.1: 14351.
- Lu, X., et al. On shape and the computability of emotions. In: Proceedings of the 20th ACM international conference on Multimedia. 2012. pp. 229–238.
- Blazhenkova, O., Kumar, M. M. Angular versus curved shapes: Correspondences and emotional processing. Perception, 2018, vol. 47, no. 1, pp. 67–89.