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

Published online by Cambridge University Press:  27 February 2023

Mo Hu Open the ORCID record for Mo Hu [Opens in a new window] ,
Christopher McComb  and
Kosa Goucher-Lambert Open the ORCID record for Kosa Goucher-Lambert [Opens in a new window]
Mo Hu*
Affiliation:
Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA
Christopher McComb
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Kosa Goucher-Lambert
Affiliation:
Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA
*
Author for correspondence: Mo Hu, E-mail: mohu@berkeley.edu
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Abstract

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.

Keywords

Concept generationdesign cognitionHMMneurocognition for design
Type
Research Article
Information
AI EDAM , Volume 37 , 2023 , e8
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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