Design science and neuroscience: A systematic review of the emergent field of Design Neurocognition

Balters, Stephanie; Weinstein, Theresa; Mayseless, Naama; Auernhammer, Jan; Hawthorne, Grace; Steinert, Martin; Meinel, Christoph; Leifer, Larry; Reiss, Allan L.

doi:10.1016/j.destud.2022.101148

Cited by 15 publications

(10 citation statements)

References 90 publications

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“…In addition to changes in data capture, greater samples sizes would be required for statistically significant results. Neuroscience focused studies in the field of design research have mean sample sizes of 20.97 (SD=14.55) and in the field of neuroscience 27.44 (SD=12) (Balters et al, 2022). These indicate the typical sizes of study required for a full run of the study presented in this paper.…”

Section: Limitations and Further Workmentioning

confidence: 79%

Investigating the Process, Design Outputs and Neurocognitive Differences Between Prototyping Activities With Physical and Digital Lego

McClenaghan,

Goudswaard,

Hicks

2023

Proc. Des. Soc.

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Design neurocognition is an emerging research area that can provide insights into the black box of designers’ cognitive processes. However, work to date has focused on neurocognition on its own, without integrating this with other design measures. This paper presents the results of a pilot study which brings together designer neurocognition with design output and assessment of the design process followed in a constrained prototyping activity comparing use of physical and digital Lego. This was achieved via EEG data capture, a TLX survey and measures of design output variance. Differences between physical and digital prototyping methods were found with respect to Task Related Powers of EEG signals and the design process followed with digital prototyping methods found to take longer, require more effort and cause more frustration. No differences were found with regard to design output. Whilst the sample size used (n=12) was small, future studies will use large sample sizes to increase their statistical power and will consider alternative EEG or fNIRS to capture brain activity due to challenges with the headset used in this study.

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Section: Limitations and Further Workmentioning

confidence: 79%

Investigating the Process, Design Outputs and Neurocognitive Differences Between Prototyping Activities With Physical and Digital Lego

McClenaghan,

Goudswaard,

Hicks

2023

Proc. Des. Soc.

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“…Design neurocognition is the neurocognitive study of designers. Most design neurocognition studies adopt one of three non-invasive brain activity measurement tools EEG, fNIRS, or fMRI (Balters et al, 2023;Borgianni & Maccioni, 2020;Gero & Milovanovic, 2020). The discussion of fNIRS and fMRI is out of the scope of this work, since it uses EEG.…”

Section: Brain Anatomy and Brain Wavesmentioning

confidence: 99%

“…Research into design and creativity has evolved in the last two decades (Cascini et al, 2022). Neurophysiological measurement of designers' cognitive processes represents one of the innovative approaches (Balters et al, 2023;Gero & Milovanovic, 2020). Whilst multiple neurocognitive studies have produced significant results, they are still far from results that can be directly used in real design situations (Borgianni & Maccioni, 2020).…”

Section: Introductionmentioning

confidence: 99%

Chronobiology in Divergent Thinking: How Designers Are Affected by Time of Day

2023

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Chronobiology is the science that studies the role of time in biology. The study of time in human bodies revealed the presence of internal rhythms related to the time of day. Considering divergent thinking as one of the essential cognitive activities of conceptual design, this paper presents the results of investigating the effect of time of day on designers’ brain activity while performing divergent thinking tasks. An experiment was run with a revised Alternative Uses Task, measuring brain activity with an electroencephalogram (EEG) device. Students with different educational backgrounds were recruited for this experiment, including engineering and industrial design students, to determine if the time of day affected them differently. The brain waves and related power results show significant differences with respect to the time of day and educational background. The differences are particularly evident considering the interaction of these factors. Further studies are required to understand the relationship between the differences detected and the designers’ behavioural performance and to identify which time of day is most effective for idea-generation activities for designers.

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“…However, these approaches are limited by their intrinsic subjective nature and extensive qualitative data processing requirements (Chiu and Shu, 2011; Hay et al , 2017). To overcome some of these limitations and combine more quantitative methodologies in design cognition research, an emerging research area in the design research community, often referred to as “design neurocognition”, is seeking to apply techniques from cognitive neuroscience to measure brain activity related to design and advance knowledge of design cognition (Liu et al , 2018; Goucher-Lambert et al , 2019; Hu and Shealy, 2019; Gero and Milovanovic, 2020; Vieira et al , 2020; Zhao et al , 2020; Balters et al , 2023; Hay et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

McComb

Goucher-Lambert

2023

AIEDAM

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The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

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Design science and neuroscience: A systematic review of the emergent field of Design Neurocognition

Cited by 15 publications

References 90 publications

Investigating the Process, Design Outputs and Neurocognitive Differences Between Prototyping Activities With Physical and Digital Lego

Investigating the Process, Design Outputs and Neurocognitive Differences Between Prototyping Activities With Physical and Digital Lego

Chronobiology in Divergent Thinking: How Designers Are Affected by Time of Day

Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

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