Design science and neuroscience: A systematic review of the emergent field of Design Neurocognition
Section snippets
Search strategy
We conducted a systematic literature search following PRISMA guidelines (Page et al., 2021). We utilized PubMed, Web of Science and Google Scholar as search databases and additionally checked the reference lists of the selected articles for further relevant publications. Our search query combined (1) a set of Design Science terminologies that are particularly relevant for the emerging field of Design Neurocognition, and (2) a set of terminologies describing commonly used brain imaging and brain
Development of the field
We identified a total of 82 publications that are relevant for this review. In Table 2 (see Supplementary Material), we provide a summary of each publication including author(s), name of journal/conference, title, research topic, description of the experimental task, brain imaging method, number of participants, and study population (e.g., level of expertise). When inspecting the titles of journals and conferences, we observed that contributions targeted either the Design Science or
Discussion
In this paper, we systematically reviewed the existing literature in the emerging field of Design Neurocognition following the standardized PRISMA guidelines. We included literature that assessed brain function during design activities (i.e., while designing) and excluded research focusing on external design assessment (e.g., customer/stakeholder decision on a design outcome). We identified a total of 82 publications that met the inclusion criteria of our systematic review. The review
Conclusion
This systematic literature review provided an overview of the existing research in the emerging field of Design Neurocognition. The advances have been mainly driven by two research communities, Design Science and Neuroscience. We identified nine research topics (i.e., designing versus problem-solving, psychophysiology, stages of the design process, design methods and tools, design training and experience, specific design activities, design interventions, algorithmic predictions, and team
Funding
This work was supported by a Hasso Plattner Design Thinking Research Program Grant and a gift from the Kelvin Foundation.
Declaration of competing interest
The authors declare no conflict of interest.
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