Biometric Analysis in Design Cognition Studies: A Systematic Literature Review
Abstract
:1. Introduction and Background
2. Research Method
2.1. Systematic Literature Review
2.2. Selection of Database and Keywords
2.3. Database Search and Priority Selection of Articles
2.4. Screening of Articles
2.5. Content Analysis
3. Results
3.1. Publication Trends in Design Cognition Based on Biometric Analysis
3.2. Content Analysis of Review Articles
4. Discussion
4.1. Biometric Techniques and Measurements
4.1.1. EEG and Associated Measurements
4.1.2. Eye-Tracking and Associated Measurements
4.1.3. fMRI and Associated Measurements
4.1.4. fNIRS and Associated Measurements
4.1.5. Other Biometric Techniques
4.2. Using Biometric Analysis to Explore Design Thinking and Creativity
4.2.1. Design Problem Solving
4.2.2. Design Thinking Strategies—Divergent and Convergent Thinking
4.2.3. Design Creativity and Design Fixation
4.3. Using Biometric Analysis to Explore Design Cognitive Load and Visual Stimulation
4.3.1. Measuring Design Cognitive Load Using Biometric Analysis
4.3.2. Visual Attention in Relation to Design Representation Studies Based on Biometric Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Li, S.; Becattini, N.; Cascini, G. Correlating design performance to EEG activation: Early evidence from experimental data. In Proceedings of the Design Society, Cavtat, Croatia, 26–29 October 2020; pp. 771–780. [Google Scholar]
- Vieira, S.; Benedek, M.; Gero, J.; Li, S.; Cascini, G. Design spaces and EEG frequency band power in constrained and open design. Int. J. Des. Creat. Innov. 2022, 10, 193–221. [Google Scholar] [CrossRef]
- Yao, S.-N.; Lin, C.-T.; King, J.-T.; Liu, Y.-C.; Liang, C. Learning in the visual association of novice and expert designers. Cogn. Syst. Res. 2017, 43, 76–88. [Google Scholar] [CrossRef]
- Cao, J.; Zhao, W.; Guo, X. Utilizing EEG to Explore Design Fixation during Creative Idea Generation. Comput. Intell. Neurosci. 2021, 2021, 6619598. [Google Scholar] [CrossRef] [PubMed]
- Gero, J.S.; Kannengiesser, U. The situated function-behaviour-structure framework. Des. Stud. 2004, 25, 373–391. [Google Scholar] [CrossRef]
- Yu, R.; Gu, N.; Ostwald, M.J. Computational Design: Technology, Cognition and Environments, 1st ed.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2021. [Google Scholar]
- Kumar, J.S.; Bhuvaneswari, P. Analysis of Electroencephalography (EEG) Signals and Its Categorization—A Study. Procedia Eng. 2012, 38, 2525–2536. [Google Scholar] [CrossRef] [Green Version]
- Wang, M.; Yin, X.; Zhu, Y.; Hu, J. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey. Sensors 2022, 22, 5111. [Google Scholar] [CrossRef]
- Liu, L.; Li, Y.; Xiong, Y.; Cao, J.; Yuan, P. An EEG study of the relationship between design problem statements and cognitive behaviors during conceptual design. AIEDAM 2018, 32, 351–362. [Google Scholar] [CrossRef]
- Hu, W.-L.; Reid, T. The Effects of Designers’ Contextual Experience on the Ideation Process and Design Outcomes. J. Mech. Des. 2018, 140, 101101. [Google Scholar] [CrossRef]
- Vieira, S.; Gero, J.S.; Delmoral, J.; Gattol, V.; Fernandes, C.; Parente, M.; Fernandes, A.A. The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving. Des. Sci. 2020, 6, e26. [Google Scholar] [CrossRef]
- Liang, C.; Liu, Y.-C. Effect of musical stimuli on design thinking: Differences between expert and student designers. Cogent Psychol. 2018, 5, 1510298. [Google Scholar] [CrossRef]
- Vieira, S.L.d.S.; Gero, J.S.; Delmoral, J.; Gattol, V.; Fernandes, C.; Fernandes, A.A. Comparing the Design Neurocognition of Mechanical Engineers and Architects: A Study of the Effect of Designer’s Domain. In Proceedings of the Design Society: International Conference on Engineering Design; Cambridge University Press: Cambridge, UK, 2019; pp. 1853–1862. [Google Scholar]
- Lohmeyer, Q.; Meboldt, M.; Matthiesen, S. Analysing visual strategies of novice and experienced designers by eye tracking application. In Proceedings of the DS 76: Proceedings of E&PDE 2013, the 15th International Conference on Engineering and Product Design Education, Dublin, Ireland, 5–6 September 2013; pp. 202–207. [Google Scholar]
- Goucher-Lambert, K.; McComb, C. Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task. In Proceedings of the Design Society: International Conference on Engineering Design; Cambridge University Press: Cambridge, UK, 2019; pp. 1873–1882. [Google Scholar]
- Tsai, Y.-P.; Hung, S.-H.; Huang, T.-R.; Sullivan, W.C.; Tang, S.-A.; Chang, C.-Y. What part of the brain is involved in graphic design thinking in landscape architecture? PLoS ONE 2021, 16, e0258413. [Google Scholar] [CrossRef] [PubMed]
- Manandhar, U.; Hu, M.; Milovanovic, J.; Shealy, T.; Gero, J. Concept Maps Decrease Students’ Neurocognitive Demand When Thinking about Engineering Problems. In Construction Research Congress; American Society of Civil Engineers: Reston, VA, USA, 2022; pp. 244–253. [Google Scholar] [CrossRef]
- Colombo, S.; Mazza, A.; Montagna, F.; Ricci, R.; Dal Monte, O.; Cantamessa, M. Neurophysiological evidence in idea generation: Differences between designers and engineers. Proc. Des. Soc. DESIGN Conf. 2020, 1, 1415–1424. [Google Scholar] [CrossRef]
- Fernberg, P.; Tighe, E.; Saxon, M.; Spencer, C.; Johnson, S.; Stefanucci, J.; Creem-Regehr, S.; Chamberlain, B. Measuring Perception of Urban Design Elements in Virtual Environments Using Eye Tracking: Benefits and Challenges. J. Digit. Landsc. Archit. 2022, 2022, 463–470. [Google Scholar] [CrossRef]
- Gero, J.S.; Milovanovic, J. A framework for studying design thinking through measuring designers’ minds, bodies and brains. Des. Sci. 2020, 6, e19. [Google Scholar] [CrossRef]
- Li, X.; Jiang, Z.; Guan, Y.; Li, G.; Wang, F. Fostering the transfer of empirical engineering knowledge under technological paradigm shift: An experimental study in conceptual design. Adv. Eng. Inform. 2019, 41, 100927. [Google Scholar] [CrossRef]
- Nelius, T.; Doellken, M.; Zimmerer, C.; Matthiesen, S. The impact of confirmation bias on reasoning and visual attention during analysis in engineering design: An eye tracking study. Des. Stud. 2020, 71, 100963. [Google Scholar] [CrossRef]
- Pei, W.; Guo, X.; Lo, T. Pre-Evaluation method of the experiential architecture based on multidimensional physiological perception. J. Asian Archit. Build. Eng. 2022, ahead-of-print. [Google Scholar] [CrossRef]
- Self, J.A. Communication through design sketches: Implications for stakeholder interpretation during concept design. Des. Stud. 2019, 63, 1–36. [Google Scholar] [CrossRef]
- Smith, M.A.B.; Youmans, R.J.; Bellows, B.G.; Peterson, M.S. Shifting the Focus: An Objective Look at Design Fixation; Springer: Berlin/Heidelberg, Germany, 2013; pp. 144–151. [Google Scholar] [CrossRef]
- Weber, B.; Neurauter, M.; Burattin, A.; Pinggera, J.; Davis, C. Measuring and Explaining Cognitive Load during Design Activities: A Fine-Grained Approach. In Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2017; Springer International Publishing: Cham, Switzerland, 2017; pp. 47–53. [Google Scholar]
- Hu, L.; Shepley, M.M. Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools. J. Inter. Des. 2022, 47, 31–50. [Google Scholar] [CrossRef]
- Kim, N.; Chung, S.; Kim, D.I. Exploring EEG-based Design Studies: A Systematic Review. Arch. Des. Res. 2022, 35, 91–113. [Google Scholar] [CrossRef]
- Hay, L.; Duffy, A.H.B.; Gilbert, S.J.; Grealy, M.A. Functional magnetic resonance imaging (fMRI) in design studies: Methodological considerations, challenges, and recommendations. Des. Stud. 2022, 78, 101078. [Google Scholar] [CrossRef]
- Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019, 104, 333–339. [Google Scholar] [CrossRef]
- Alankarage, S.; Chileshe, N.; Samaraweera, A.; Rameezdeen, R.; Edwards, D.J. Organisational BIM maturity models and their applications: A systematic literature review. Archit. Eng. Des. Manag. 2022, 1–19. [Google Scholar] [CrossRef]
- Hettithanthri, U.; Hansen, P.; Munasinghe, H. Exploring the architectural design process assisted in conventional design studio: A systematic literature review. Int. J. Technol. Des. Educ. 2022, 1–26. [Google Scholar] [CrossRef]
- Nguyen, B.N.; London, K.; Zhang, P. Stakeholder relationships in off-site construction: A systematic literature review. Smart Sustain. Built Environ. 2022, 11, 765–791. [Google Scholar] [CrossRef]
- Vieira, S.; Benedek, M.; Gero, J.; Li, S.; Cascini, G. Brain activity in constrained and open design: The effect of gender on frequency bands. Artif. Intell. Eng. Des. Anal. Manuf. 2022, 36, e6. [Google Scholar] [CrossRef]
- Nguyen, P.; Nguyen, T.A.; Zeng, Y. Segmentation of design protocol using EEG. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 2019, 33, 11–23. [Google Scholar] [CrossRef]
- Dietrich, A.; Kanso, R. A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol. Bull. 2010, 136, 822–848. [Google Scholar] [CrossRef]
- Miller, E.K.; Freedman, D.J.; Wallis, J.D. The prefrontal cortex: Categories, concepts and cognition. Philos. Trans. R. Soc. Lon. Ser. B Biol. Sci. 2002, 357, 1123–1136. [Google Scholar] [CrossRef] [Green Version]
- Gonzalez-Sanchez, J.; Baydogan, M.; Chavez-Echeagaray, M.E.; Atkinson, R.K.; Burleson, W. Chapter 11—Affect Measurement: A Roadmap Through Approaches, Technologies, and Data Analysis. In Emotions and Affect in Human Factors and Human-Computer Interaction; Jeon, M., Ed.; Academic Press: San Diego, CA, USA, 2017; pp. 255–288. [Google Scholar] [CrossRef]
- Sussman, A.; Hollander, J. Cognitive Architecture: Designing for How We Respond to the Built Environment, 2nd ed.; Routledge: New York, NY, USA, 2021; pp. 1–193. [Google Scholar] [CrossRef] [Green Version]
- Zhu, M.; Bao, D.; Yu, Y.; Shen, D.; Yi, M. Differences in thinking flexibility between novices and experts based on eye tracking. PLoS ONE 2022, 17, e0269363. [Google Scholar] [CrossRef]
- Mehta, P.; Malviya, M.; McComb, C.; Manogharan, G.; Berdanier, C.G.P. Mining design heuristics for additive manufacturing via eye-tracking methods and hidden Markov modeling. J. Mech. Des. Trans. ASME 2020, 142, 124502. [Google Scholar] [CrossRef]
- Lavdas, A.A.; Salingaros, N.A.; Sussman, A. Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment. Appl. Sci. 2021, 11, 6197. [Google Scholar] [CrossRef]
- Sussman, A.; Ward, J.M. Eye-Tracking Boston City Hall to Better Understand Human Perception and the Architectural Experience. New Des. Ideas 2019, 3, 53–59. [Google Scholar]
- Hay, L.; Duffy, A.H.B.; Gilbert, S.J.; Lyall, L.; Campbell, G.; Coyle, D.; Grealy, M.A. The neural correlates of ideation in product design engineering practitioners. Des. Sci. 2019, 5, e29. [Google Scholar] [CrossRef] [Green Version]
- Shen, T.; Gao, C. Sustainability in community building: Framing design thinking using a complex adaptive systems perspective. Sustainability 2020, 12, 6679. [Google Scholar] [CrossRef]
- Ferrari, M.; Quaresima, V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage 2012, 63, 921–935. [Google Scholar] [CrossRef] [PubMed]
- Scarapicchia, V.; Brown, C.; Mayo, C.; Gawryluk, J.R. Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies. Front. Hum. Neurosci. 2017, 11, 419. [Google Scholar] [CrossRef] [PubMed]
- Hu, M.; Shealy, T.; Milovanovic, J.; Gero, J. Neurocognitive feedback: A prospective approach to sustain idea generation during design brainstorming. Int. J. Des. Creat. Innov. 2022, 10, 31–50. [Google Scholar] [CrossRef]
- Cross, N. Design Cognition: Results from Protocol and other Empirical Studies of Design Activity. In Design Knowing and Learning: Cognition in Design Education; Charles, E., Michael, M., Wendy, N., Eds.; Elsevier Science: Oxford, UK, 2001; pp. 79–103. [Google Scholar] [CrossRef]
- Xia, T.; Kang, M.; Chen, M.; Ouyang, J.; Hu, F. Design Training and Creativity: Students Develop Stronger Divergent but Not Convergent Thinking. Front. Psychol. 2021, 12, 695002. [Google Scholar] [CrossRef]
- Jauk, E.; Benedek, M.; Neubauer, A.C. Tackling creativity at its roots: Evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. Int. J. Psychophysiol. 2012, 84, 219–225. [Google Scholar] [CrossRef] [Green Version]
- Ericsson, K.A.; Simon, H.A. Protocol Analysis: Verbal Reports as Data; MIT Press: Cambridge, MA, USA, 1993; p. viii. 426p. [Google Scholar]
- Gero, J.S.; McNeill, T. An approach to the analysis of design protocols. Des. Stud. 1998, 19, 21–61. [Google Scholar] [CrossRef]
- Asimow, M. Introduction to Design; Prentice-Hall: Englewood Cliffs, NJ, USA, 1962; p. 135. [Google Scholar]
- Schön, D.; Wiggins, G. Kinds of seeing and their functions in designing. Des. Stud. 1992, 13, 135–156. [Google Scholar] [CrossRef]
- Cross, N. Design Thinking: Understanding How Designers Think and Work; English ed.; Berg Publishers: New York, NY, USA, 2011. [Google Scholar]
- Dorst, K.; Cross, N. Creativity in the design process: Co-evolution of problem-solution. Des. Stud. 2001, 22, 425–437. [Google Scholar] [CrossRef] [Green Version]
- Maher, M.L.; Poon, J. Modelling design exploration as co-evolution. Microcomput. Civ. Eng. 1996, 11, 195–210. [Google Scholar] [CrossRef]
- Yu, R.; Gu, N.; Ostwald, M.; Gero, J. Empirical support for problem-solution co-evolution in a parametric design environment. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 2015, 29, 33–44. [Google Scholar] [CrossRef] [Green Version]
- Liu, L.; Nguyen, T.A.; Zeng, Y.; Hamza, A.B. Identification of relationships between electroencephalography (EEG) bands and design activities. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers: New York, NY, USA, 2016; p. V007T006A019. [Google Scholar]
- Härkki, T. Mobile gaze tracking and an extended linkography for collaborative sketching and designing. Int. J. Technol. Des. Educ. 2022, 1–35. [Google Scholar] [CrossRef]
- Goldschmidt, G. Linkographic Evidence for Concurrent Divergent and Convergent Thinking in Creative Design. Creat. Res. J. 2016, 28, 115–122. [Google Scholar] [CrossRef]
- Kneller, G.F. Introduction to the Philosophy of Education; Wiley: Hoboken, NJ, USA, 1971. [Google Scholar]
- Hu, Y.; Ouyang, J.; Wang, H.; Zhang, J.; Liu, A.; Min, X.; Du, X. Design Meets Neuroscience: An Electroencephalogram Study of Design Thinking in Concept Generation Phase. Front. Psychol. 2022, 13, 832194. [Google Scholar] [CrossRef]
- Shealy, T.; Gero, J.; Hu, M.; Milovanovic, J. Concept generation techniques change patterns of brain activation during engineering design. Des. Sci. 2020, 6, e31. [Google Scholar] [CrossRef]
- Milovanovic, J.; Hu, M.; Shealy, T.; Gero, J. Characterization of concept generation for engineering design through temporal brain network analysis. Des. Stud. 2021, 76, 101044. [Google Scholar] [CrossRef]
- Said-Metwaly, S.; Noortgate, W.V.d.; Kyndt, E. Approaches to Measuring Creativity: A Systematic Literature Review. Creat. Theor. Res. Appl. 2017, 4, 238–275. [Google Scholar] [CrossRef] [Green Version]
- Runco, M.A.; Jaeger, G.J. The Standard Definition of Creativity. Creat. Res. J. 2012, 24, 92–96. [Google Scholar] [CrossRef]
- Howard, T.J.; Culley, S.J.; Dekoninck, E. Describing the creative design process by the integration of engineering design and cognitive psychology literature. Des. Stud. 2008, 29, 160–180. [Google Scholar] [CrossRef]
- Gero, J.; Yu, R.; Wells, J. The effect of design education on creative design cognition of high school students. Int. J. Des. Creat. Innov. 2019, 7, 196–212. [Google Scholar] [CrossRef]
- Rosenman, M.A.; Gero, J.S. Creativity in design using a design prototype approach. In Modeling Creativity and Knowledge-Based Creative Design; Gero, J.S., Maher, L.M., Eds.; Lawrence Erlbaum Associates: Hillsdale, NJ, USA, 1993; pp. 111–138. [Google Scholar]
- Suwa, M.; Gero, J.; Purcell, T. Unexpected Discoveries And S-Invention Of Design Requirements: A Key To Creative Designs. In Computational Models of Creative Design IV; Gero, J.S., Maher, L.M., Eds.; Key Centre of Design Computing and Cognition, University of Sydney: Camperdown, Australia, 1999; pp. 297–320. [Google Scholar]
- Rominger, C.; Papousek, I.; Perchtold, C.M.; Weber, B.; Weiss, E.M.; Fink, A. The creative brain in the figural domain: Distinct patterns of EEG alpha power during idea generation and idea elaboration. Neuropsychologia 2018, 118, 13–19. [Google Scholar] [CrossRef]
- Yin, Y.; Wang, P.; Childs, P.R.N. Understanding creativity process through electroencephalography measurement on creativity-related cognitive factors. Front. Neurosci. 2022, 16, 951272. [Google Scholar] [CrossRef] [PubMed]
- Jia, W.; Zeng, Y. EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment. Sci. Rep. 2021, 11, 2119. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.-C.; Chang, C.-C.; Yang, Y.-H.S.; Liang, C. Spontaneous analogising caused by text stimuli in design thinking: Differences between higher- and lower-creativity groups. Cogn. Neurodyn. 2018, 12, 55–71. [Google Scholar] [CrossRef]
- Sun, L.; Xiang, W.; Chai, C.; Wang, C.; Liu, Z. Impact of text on idea generation: An electroencephalography study. Int. J. Technol. Des. Educ. 2013, 23, 1047–1062. [Google Scholar] [CrossRef]
- Goucher-Lambert, K.; Moss, J.; Cagan, J. A neuroimaging investigation of design ideation with and without inspirational stimuli—Understanding the meaning of near and far stimuli. Des. Stud. 2019, 60, 1–38. [Google Scholar] [CrossRef]
- Tang, Z.; Xia, D.; Li, X.; Wang, X.; Ying, J.; Yang, H. Evaluation of the effect of music on idea generation using electrocardiography and electroencephalography signals. Int. J. Technol. Des. Educ. 2022, 1–19. [Google Scholar] [CrossRef]
- Crilly, N. Fixation and creativity in concept development: The attitudes and practices of expert designers. Des. Stud. 2015, 38, 54–91. [Google Scholar] [CrossRef] [Green Version]
- Jansson, D.G.; Smith, S.M. Design fixation. Des. Stud. 1991, 12, 3–11. [Google Scholar] [CrossRef]
- Hu, M.; Shealy, T.; Milovanovic, J. Cognitive differences among first-year and senior engineering students when generating design solutions with and without additional dimensions of sustainability. Des. Sci. 2021, 7, e1. [Google Scholar] [CrossRef]
- Buettner, R. Cognitive workload of humans using artificial intelligence systems: Towards objective measurement applying eye-tracking technology. In KI 2013: Advances in Artificial Intelligence. KI 2013: 36th German Conference on Artificial Intelligence; Timm, I.J., Thimm, M., Eds.; Springer: Koblenz, Germany, 2013; pp. 37–48. [Google Scholar]
- Granholm, E.; Asarnow, R.; Sarkin, A.; Dykes, K. Pupillary responses index cognitive resource limitations. Psychophysiology 1996, 33, 457–461. [Google Scholar] [CrossRef] [PubMed]
- Hess, E.H.; Polt, J.M. Pupil size in relation to mental activity during simple problem-solving. Science 1964, 143, 1190–1192. [Google Scholar] [CrossRef]
- Holmqvist, K.; Nyström, N.; Andersson, R.; Dewhurst, R.; Jarodzka, H.; Van de Weijer, J. Eye Tracking: A Comprehensive Guide to Methods and Measures; Oxford University Press: Oxford, UK, 2011. [Google Scholar]
- Yu, R.; Gero, J. Exploring Designers’ Cognitive Load When Viewing Different Digital Representations of Spaces: A Pilot Study. In Research into Design for Communities, Volume 1: Proceedings of ICoRD 2017; Springer: Singapore, 2017; pp. 457–467. [Google Scholar]
- Nguyen, T.A.; Zeng, Y. A physiological study of relationship between designer’s mental effort and mental stress during conceptual design. Comput. Aided Des. 2014, 54, 3–18. [Google Scholar] [CrossRef]
- Nguyen, T.A.; Zeng, Y. Effects of stress and effort on self-rated reports in experimental study of design activities. J. Intell. Manuf. 2017, 28, 1609–1622. [Google Scholar] [CrossRef]
- Nguyen, P.; Nguyen, T.A.; Zeng, Y. Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process: An EEG study. Res. Eng. Des. 2018, 29, 393–409. [Google Scholar] [CrossRef]
- Self, J.; Lee, S.-g.; Bang, H. Understanding the Complexities of Design Representation. In Proceedings of the 2013 Ancient Futures: Design and/or Happiness; Asian Digital Art & Design Association & Korean Society of Design Science: Seongnam-si, Republic of Korea, 2013. [Google Scholar]
- Luo, S.; Zhang, Y.; Zhang, J.; Xu, J. A User Biology Preference Prediction Model Based on the Perceptual Evaluations of Designers for Biologically Inspired Design. Symmetry 2020, 12, 1860. [Google Scholar] [CrossRef]
- Self, J.A. Comprehending the designer’s sketch & implications for communication. In Proceedings of the DESIGN 2018 15th International Design Conference, Dubrovnik, Croatia, 21–24 May 2018; Volume 5, pp. 2133–2144. [Google Scholar] [CrossRef]
- Liang, C.; Chang, C.-C.; Liu, Y.-C. Comparison of the cerebral activities exhibited by expert and novice visual communication designers during idea incubation. Int. J. Des. Creat. Innov. 2019, 7, 213–236. [Google Scholar] [CrossRef]
- Guan, Z.; Lee, S.; Cuddihy, E.; Raney, J. The validity of the stimulated retrospective think-aloud method as measured by eye tracking. In Proceedings of the CHI 2006 Conference on Human Factors in Computing Systems, Montréal, QC, Canada, 22–27 April 2006; pp. 1253–1262. [Google Scholar]
- Liang, C.; Lin, C.-T.; Yao, S.-N.; Chang, W.-S.; Liu, Y.-C.; Chen, S.-A. Visual attention and association: An electroencephalography study in expert designers. Des. Stud. 2017, 48, 76–95. [Google Scholar] [CrossRef]
- National Research Council. Beyond Productivity: Information Technology, Innovation, and Creativity; Mitchell, W.J., Ed.; National Research Council: Washington, DC, USA, 2003.
- Gero, J.S.; Tang, H.-H. Concurrent and Retrospective Protocols and Computer-Aided Architectural Design. In Proceedings of the 4th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA1999), Shanghai, China, 5–7 May 1999; pp. 403–410. [Google Scholar]
- Bilda, Z.; Demirkan, H. An insight on designers’ sketching activities in traditional versus digital media. Des. Stud. 2003, 24, 27–50. [Google Scholar] [CrossRef] [Green Version]
- Tang, H.H.; Lee, Y.Y.; Gero, J.S. Comparing collaborative co-located and distributed design processes in digital and traditional sketching environments: A protocol study using the function-behaviour-structure coding scheme. Des. Stud. 2011, 32, 1–29. [Google Scholar] [CrossRef]
- Meyer, M.W.; Norman, D. Changing Design Education for the 21st Century. She Ji J. Des. Econ. Innov. 2020, 6, 13–49. [Google Scholar] [CrossRef]
Biometric Technique | Authors | Year | Region | Title | Source Title | Focus Area | Measurement Parameters | |
---|---|---|---|---|---|---|---|---|
EEG | Yin, Y., Wang, P., Childs, P.R.N. | 2022 | UK | Understanding creativity process through electroencephalography measurement on creativity-related cognitive factors | Frontiers in neuroscience | Design creativity | Channels Fp1/Fp2/F7/F8/F3/F4 report signals on the frontal lobe, C3/C4/P3/P4 report signals on the parietal lobe, T3/T4/T5/T6 report signals on the temporal lobe and O1/O2 report signals on the occipital lobe. | |
Hu, Y., Ouyang, J., Wang, H., Zhang, J., Liu, A., Min, X., Du, X. | 2022 | China | Design Meets Neuroscience: An Electroencephalogram Study of Design Thinking in Concept Generation Phase | Frontiers in Psychology | Cognitive effort, diverged thinking | Alpha (8–13 Hz), beta (13–30 Hz), theta (3.5 Hz-8), delta (0.5–3.5 Hz) and gamma (31–45 Hz) for task-related power. | ||
Vieira, S., Benedek, M., Gero, J., Li, S., Cascini, G. | 2022 | Italy | Brain activity in constrained and open design: The effect of gender on frequency bands | Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM | Gender differences on neurocognition of professional designers | High cut-off of 28 Hz to maintain only theta, alpha and beta frequency range. | ||
Kim, N., Chung, S., Kim, D.I. | 2022 | Korea | Exploring EEG-based Design Studies: A Systematic Review | Archives of Design Research | Review paper on EEG-based design studies | N/A | ||
Vieira, S., Benedek, M., Gero, J., Li, S., Cascini, G. | 2022 | Italy | Design spaces and EEG frequency band power in constrained and open design | International Journal of Design Creativity and Innovation | Comparison of the design process of mechanical engineers and industrial designers | Frequency bands theta, alpha 1 (7–10 Hz), alpha 2 (10–13 Hz), beta 1 (13–16 Hz), beta 2 (16–20 Hz), beta 3 (20–28 Hz). Band-pass filter of 3.5–28 Hz for theta to beta frequency range. Electrodes O1/2, P7/8, T7/8, FC5/6, F7/8, F3/4, AF3/4. | ||
Cao, J., Zhao, W., Guo, X. | 2021 | China | Utilizing EEG to Explore Design Fixation during Creative Idea Generation | Computational Intelligence and Neuroscience | Design fixation, idea generation | Data were filtered between 0.1 and 40 Hz, with principal component analysis to remove ocular artifacts. | ||
Wang, P., Wang, S., Peng, D., Chen, L., Wu, C., Wei, Z., Childs, P., Guo, Y., Li, L. | 2020 | UK | Neurocognition-inspired design with machine learning | Design Science | 64 electrodes 10–20 system, notch filter 50 Hz. | |||
Zhao, M., Jia, W., Yang, D., Nguyen, P., Nguyen, T.A., Zeng, Y. | 2020 | Canada | A tEEG framework for studying designer’s cognitive and affective states | Design Science | Loosely controlled experiment, complex design process | N/A | ||
Vieira S.; Gero J.S.; Delmoral J.; Gattol V.; Fernandes C.; Parente M.; Fernandes A.A. | 2020 | Italy | The neurophysiological activations of mechanical engineers and industrial designers while designing and problem solving | Design Science | Problem solving, basic design, open design | Filter excluding frequencies above 60 Hz. | ||
Nguyen, P., Nguyen, T.A., Zeng, Y. | 2019 | Canada | Segmentation of design protocol using EEG | Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM | Segmentation of design protocol, methodology paper | N/A | ||
Liang C., Chang C.-C., Liu Y.-C. | 2019 | China | Comparison of the cerebral activities exhibited by expert and novice visual communication designers during idea incubation | International Journal of Design Creativity and Innovation | Experts vs. novice, conceptual imagination | Channels Fp1 and Fp2 only. Filters cutting off 1–50 Hz. | ||
Hu, W.-L., Reid, T. | 2018 | US | The Effects of Designers Contextual Experience on the Ideation Process and Design Outcomes | Journal of Mechanical Design, Transactions of the ASME | Context-specific experience, mental state, creativity, evaluation of solutions | |||
Liu, L., Li, Y., Xiong, Y., Cao, J., Yuan, P. | 2018 | China | An EEG study of the relationship between design problem statements and cognitive behaviors during conceptual design | Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM | Design problems, divergent thinking, convergent thinking, and mental workload | Band-pass filter 0.1 HZ–40 Hz. | ||
Nguyen, P., Nguyen, T.A., Zeng, Y. | 2018 | Canada | Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process: An EEG study | Research in Engineering Design | Concept design, effort, fatigue, and concentration | Alpha (focus 8–15), Beta (16–31), Delta (< 4), Theta (4–7), (Theta + alpha)/beta, Alpha/beta, (Theta +alpha)/(alpha + beta), Theta/beta. | ||
Liu, Y.-C., Chang, C.-C., Yang, Y.-H.S., Liang, C. | 2018 | China | Spontaneous analogising caused by text stimuli in design thinking: differences between higher- and lower-creativity groups | Cognitive Neurodynamics | Word stimuli, design thinking, high and low creativity groups of designers | Low-pass filter 50 Hz and high-pass filter 1 Hz. Whole-brain analyses. | ||
Liang, C., Liu, Y.-C. | 2018 | China | Effect of musical stimuli on design thinking: Differences between expert and student designers | Cogent Psychology | Musical stimulation, changes in design thinking | Low-pass filter 50 Hz and high-pass filter 1 Hz. Whole brain analyses. | ||
Liu, Q. | 2018 | China | Graphic creative design based on subconsciousness theory | NeuroQuantology | Unconsciousness of the brain, creation within design, creative thinking | Region of interest is frontal electrodes and T7. Frequency range 10–15 Hz. | ||
Liang, C., Lin, C.-T., Yao, S.-N., Chang, W.-S., Liu, Y.-C., Chen, S.-A. | 2017 | China | Visual attention and association: An electroencephalography study in expert designers | Design Studies | Visual attention, visual stimuli on design thinking | High-pass filter 1 Hz and transition band 0.2 Hz. Low-pass filter 50 Hz and transition band of 7 Hz. Whole-bran analyses. | ||
Yao S.-N., Lin C.-T., King J.-T., Liu Y.-C., Liang C. | 2017 | China | Learning in the visual association of novice and expert designers | Cognitive Systems Research | Expert vs. novice designers, visual association task | Frontal, prefrontal and cingulate cortices regions of interest. Los-pass filter 50 Hz and high-pass filter of 1 Hz. Mainly alpha and gamma band investigation. | ||
Sun, L., Xiang, W., Chai, C., Wang, C., Liu, Z. | 2013 | China | Impact of text on idea generation: An electroencephalography study | International Journal of Technology and Design Education | Idea generation, sketching, expert vs. novice | Theta band (theta1 = 4–6 Hz, theta2 6–8 Hz) and alpha band (alpha1 8–10 Hz, alpha2 10–13 Hz). | ||
Eye-tracking | Zhu, M., Bao, D., Yu, Y., Shen, D., Yi, M. | 2022 | China | Differences in thinking flexibility between novices and experts based on eye tracking | PLoS ONE | Flexibility of design thinking, experts vs. novices | Fixation duration and saccade amplitudes. | |
Fernberg, P., Tighe, E., Saxon, M., Spencer, C., Johnson, S., Stefanucci, J., Creem-Regehr, S., Chamberlain, B. | 2022 | US | Measuring perception of urban design elements in virtual environments using eye tracking: Benefits and challenges | Journal of Digital Landscape Architecture | VR-based design, geospatial and temporal patterns of spatial observations/inferences | Gaze tracking, total dwell time and total fixation count for areas of interest (buildings within VR environment). | ||
Härkki, T. | 2022 | Finland | Mobile gaze tracking and an extended linkography for collaborative sketching and designing | International Journal of Technology and Design Education | Design collaboration, convergent and divergent collaboration | Visit frequency, visit duration. | ||
Mehta, P., Malviya, M., McComb, C., Manogharan, G., Berdanier, C.G.P. | 2020 | US | Mining design heuristics for additive manufacturing via eye-tracking methods and hidden Markov modeling | Journal of Mechanical Design, Transactions of the ASME | Design behaviour | Fixation time. | ||
Nelius, T., Doellken, M., Zimmerer, C., Matthiesen, S. | 2020 | Germany | The impact of confirmation bias on reasoning and visual attention during analysis in engineering design: An eye tracking study | Design Studies | Problem-solving, disconfirming and misinterpreted information, visual attention | Eye-tracking glasses measuring fixation duration. | ||
Li, X., Jiang, Z., Guan, Y., Li, G., Wang, F. | 2019 | China | Fostering the transfer of empirical engineering knowledge under technological paradigm shift: An experimental study in conceptual design | Advanced Engineering Informatics | Empirical engineering knowledge, concept design | Fixation durations and counts. | ||
Self, J.A. | 2019 | Korea | Communication through design sketches: Implications for stakeholder interpretation during concept design | Design Studies | Design interpretations, conceptual design representations | Pupil dilation. | ||
fMRI | Hay, L., Duffy, A.H.B., Gilbert, S.J., Grealy, M.A. | 2022 | UK | Functional magnetic resonance imaging (fMRI) in design studies: Methodological considerations, challenges, and recommendations | Design Studies | Review paper on fMRI-based design studies | N/A | |
Tsai, Y.-P., Hung, S.-H., Huang, T.-R., Sullivan, W.C., Tang, S.-A., Chang, C.-Y. | 2021 | China | What part of the brain is involved in graphic design thinking in landscape architecture? | PLoS ONE | 20-channel head coil used to take images. 45 EPI slices were sampled in bottom–up, interleaved order. | |||
Shen, T., Gao, C. | 2020 | China | Sustainability in community building: Framing design thinking using a complex adaptive systems perspective | Sustainability (Switzerland) | Design thinking, complex adaptive systems | oxy-Hb in the participants’ cerebral cortices. | ||
Hay, L., Duffy, A.H.B., Gilbert, S.J., Lyall, L., Campbell, G., Coyle, D., Grealy, M.A. | 2019 | UK | The neural correlates of ideation in product design engineering practitioners | Design Science | Creative ideation | Blood oxygen level dependent (BOLD) signal, various brain regions. | ||
Goucher-Lambert, K., Moss, J., Cagan, J. | 2019 | US | A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli | Design Studies | Inspirational stimuli, design ideation | Cluster size, location, in various brain regions. | ||
Lazar L. | 2018 | India | The cognitive neuroscience of design creativity | Journal of Experimental Neuroscience | Design creativity, “ill-structured” tasks | Various brain regions including PFC, parietal cortex (PC), the anterior, cingulate cortex (ACC). | ||
fNIRS | Hu, M., Shealy, T., Milovanovic, J., Gero, J. | 2022 | US | Neurocognitive feedback: A prospective approach to sustain idea generation during design brainstorming | International Journal of Design Creativity and Innovation | Design ideation, brainstorming | Oxygenated blood (Oxy-Hb) in the prefrontal cortex (PFC). | |
Milovanovic, J., Hu, M., Shealy, T., Gero, J. | 2021 | US | Characterization of concept generation for engineering design through temporal brain network analysis | Design Studies | Divergent and convergent thinking, design concept generation technique | Central nodes. | ||
Hu, M., Shealy, T., Milovanovic, J. | 2021 | US | Cognitive differences among first-year and senior engineering students when generating design solutions with and without additional dimensions of sustainability | Design Science | Concept generation, sustainable design | Oxy-Hb using fNIRS in the PFC. | ||
Shealy, T., Gero, J., Hu, M., Milovanovic, J. | 2020 | US | Concept generation techniques change patterns of brain activation during engineering design | Design Science | Concept generation techniques, divergent/convergent thinking, ill-defined problem solving | Sampling frequency of 4.44 Hz. change in participants’ oxygenated haemoglobin. Channels over prefrontal cortex. | ||
Combined method | Reviewed EEG, MRI, NIRS, PET | Hu, L., Shepley, M.M. | 2022 | US | Design meets neuroscience: A preliminary review of design research using neuroscience tools | Journal of Interior Design | Review paper on design and neuroscience | N/A |
ECG, EEG | Tang, Z., Xia, D., Li, X., Wang, X., Ying, J., Yang, H. | 2022 | China | Evaluation of the effect of music on idea generation using electrocardiography and electroencephalography signals | International Journal of Technology and Design Education | Creative idea generation, music impact | Heart rate, theta (4–8 Hz), alpha1 (8–10 Hz), and alpha2 (10–12 Hz) frequency bands. | |
EEG, ECG, respiration rate, and GSR | Jia, W., Zeng, Y. | 2021 | Canada | EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment | Scientific Reports | Creative idea generation and evaluation | EEG filtered to 1–30 Hz bends for power analysis and microstate analysis. Whole-brain analysis. | |
EEG, HRV with ECG, eye-tracking, facial action coding system, fMRI | Gero, J.S., Milovanovic, J. | 2020 | US | A framework for studying design thinking through measuring designers’ minds, bodies and brains | Design Science | Design thinking, design analysis and evaluation | N/A | |
EEG, skin conductance | Nguyen, T.A., Zeng, Y. | 2017 | Canada | Effects of stress and effort on self-rated reports in experimental study of design activities | Journal of Intelligent Manufacturing | Mental stress and mental effort during design activities | beta2 power. | |
ECG, EEG, EMG, pulse oximetry, blood pressure, body temperature, galvanic skin response measures and others. | Balters S.; Steinert M. | 2015 | Norway | Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices | Journal of Intelligent Manufacturing | Review on using physiology sensors to capture human emotion reactivity in a products or systems engineering context | N/A | |
EEG, HRV | Nguyen, T.A., Zeng, Y. | 2014 | Canada | A physiological study of relationship between designer’s mental effort and mental stress during conceptual design | CAD: Computer-Aided Design | Designers interaction with design tools and mental effort | EEG data high-pass filtered 0.3 Hz and low-pass filtered at 40 Hz. Notch filtered at 60 Hz. HRV data converted using HRVAS software at each 0.5 s using wavelet transform. |
Biometric Techniques | Advantages | Disadvantages |
---|---|---|
EEG | Relatively affordable; non-invasive; allows head movement. | Low spatial resolution. |
Eye-tracking | Relatively affordable; non-invasive; diverse applicability such as gaze tracking, fixation, pupil size etc., mobile eye-tracking device allows physical movement. | Data limited to eye tracking only. |
fMRI | Effective at locating the brain regions involved in cognition tasks; high spatial resolution. | Relatively cost prohibitive. |
fNIRS | Portability; affordability and greater tolerance for motion. | Relatively poor spatial resolution; limited temporal resolution |
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Yu, R.; Schubert, G.; Gu, N. Biometric Analysis in Design Cognition Studies: A Systematic Literature Review. Buildings 2023, 13, 630. https://doi.org/10.3390/buildings13030630
Yu R, Schubert G, Gu N. Biometric Analysis in Design Cognition Studies: A Systematic Literature Review. Buildings. 2023; 13(3):630. https://doi.org/10.3390/buildings13030630
Chicago/Turabian StyleYu, Rongrong, Gabrielle Schubert, and Ning Gu. 2023. "Biometric Analysis in Design Cognition Studies: A Systematic Literature Review" Buildings 13, no. 3: 630. https://doi.org/10.3390/buildings13030630