Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling

Mehta, Priyesh; Malviya, Manoj; McComb, Christopher; Manogharan, Guhaprasanna; Berdanier, Catherine G.P.

doi:10.1115/1.4048410

Cited by 15 publications

(7 citation statements)

References 15 publications

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“…More specifically, this question will be addressed: are there non-observable states (e.g., latent or subconscious) of eye movements (e.g., saccadic jumps) or strategies (e.g., holistic or analytic) that a HMM can successfully predict? Recent research demonstrated that HMM-based approaches may unravel new insights about cognitive functions (e.g., learning, decision making) [ 58 , 59 ]. A HMM allows for the identification of not yet considered commonalities as well as differences between novices and experts during the comprehension of process models, thereby enabling a better support in model comprehension [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

et al. 2021

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Process models are crucial artifacts in many domains, and hence, their proper comprehension is of importance. Process models mediate a plethora of aspects that are needed to be comprehended correctly. Novices especially face difficulties in the comprehension of process models, since the correct comprehension of such models requires process modeling expertise and visual observation capabilities to interpret these models correctly. Research from other domains demonstrated that the visual observation capabilities of experts can be conveyed to novices. In order to evaluate the latter in the context of process model comprehension, this paper presents the results from ongoing research, in which gaze data from experts are used as Eye Movement Modeling Examples (EMMEs) to convey visual observation capabilities to novices. Compared to prior results, the application of EMMEs improves process model comprehension significantly for novices. Novices achieved in some cases similar performances in process model comprehension to experts. The study’s insights highlight the positive effect of EMMEs on fostering the comprehension of process models.

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Section: Discussionmentioning

confidence: 99%

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

et al. 2021

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“…Thus, the participant’s entire design process was recorded by video that could later be analyzed by the research team. More details on findings from the study are available in our published work (Mehta, Berdanier et al, 2019; Mehta et al, 2020).…”

Section: Methods and Methodology For Calculation Of Intercoder Reliab...mentioning

confidence: 99%

Visual and Statistical Methods to Calculate Intercoder Reliability for Time-Resolved Observational Research

Malviya

Buswell

Berdanier

2021

International Journal of Qualitative Methods

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While calculating intercoder reliability (ICR) is straightforward for text-based data, such as for interview transcript excerpts, determining ICR for naturalistic observational video data is much more complex. To date, there have been few methods proposed in literature that are robust enough to handle complexities such as the occurrence of simultaneous event complexity and partial agreement by the raters. This is especially important with the emergence of high-resolution video data, which collects nearly continuous or continuous observational data in naturalistic settings. In this paper, we present three approaches to calculating ICR. First, we present the technical approach to clean and compare two coders’ results such that traditional metrics of ICR (e.g., Cohen’s κ, Krippendorff’s α, Scott’s Π) can be calculated, methods previously unarticulated in literature. However, these calculations are intensive, requiring significant data manipulation. As an alternative, this paper also proposes two novel methods to calculate ICR by algorithmically comparing visual representations of each coders’ results. To demonstrate efficacy of the approaches, we employ all three methods on data from two separate ongoing research contexts using observational data. We find that the visual methods perform as well as the traditional measures of ICR and offer significant reduction in the work required to calculate ICR, with an added advantage of allowing the researcher to set thresholds for acceptable agreement in lag time. These methods may transform the consideration of ICR in other studies across disciplines that employ observational data.

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“…Another critical motivation for applying HMM to neuroimaging data on design ideation comes from prior work that has demonstrated HMM as a valuable tool for capturing patterns and sequence in design behavior data. HMM was adopted by the authors in prior work to represent and stimulate sequential patterns of design behaviors when designing for additive manufacturing (Mehta et al, 2020) and solving configuration problems, including the design of truss structures or internet-connected home cooling systems (McComb et al, 2016(McComb et al, , 2017a(McComb et al, , 2017bBrownell et al, 2021). Design is a dynamic process in a sequence of stages or activities (Howard et al, 2008;Gericke and Blessing, 2011;Cramer-Petersen et al, 2019).…”

Section: Application Of Hmm In Design Researchmentioning

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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Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling

Cited by 15 publications

References 15 publications

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

Visual and Statistical Methods to Calculate Intercoder Reliability for Time-Resolved Observational Research

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

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