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Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task

Part of: Human Behaviour in Design and Design Cognition

Published online by Cambridge University Press:  26 July 2019

Kosa Goucher-Lambert  and
Christopher McComb
Kosa Goucher-Lambert*
Affiliation:
University of California, Berkeley;
Christopher McComb
Affiliation:
The Pennsylvania State University
*
Contact: Goucher-Lambert, Kosa, University of California, Berkeley, Mechanical Engineering, United States of America, kosa@berkeley.edu

Abstract

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Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to transition between a few key states. Additionally, the likelihood of occupancy within these states is different for high and low performing designers. This work opens up the door for future research to investigate the patterns of neural activation within the discovered states.

Keywords

Design cognitionConceptual designHuman behaviour in designNumerical methods
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1873 - 1882
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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