Multiscale Quantitative Analytics of Human Visual Searching Tasks

Chen, Xiaoyu

Multiscale Quantitative Analytics of Human Visual Searching Tasks

dc.contributor.author	Chen, Xiaoyu	en
dc.contributor.committeechair	Jin, Ran	en
dc.contributor.committeemember	Bowman, Douglas A.	en
dc.contributor.committeemember	Gabbard, Joseph L.	en
dc.contributor.committeemember	Lau, Nathan	en
dc.contributor.department	Industrial and Systems Engineering	en
dc.date.accessioned	2021-07-17T08:00:19Z	en
dc.date.available	2021-07-17T08:00:19Z	en
dc.date.issued	2021-07-16	en
dc.description.abstract	Benefit from the recent advancements of artificial intelligence (AI) methods, industrial automation has replaced human labors in many tasks. However, humans are still placed in the central role when visual searching tasks are highly involved for manufacturing decision-making. For example, highly customized products fabricated by additive manufacturing processes have posed significant challenges to AI methods in terms of their performance and generalizability. As a result, in practice, human visual searching tasks are still widely involved in manufacturing contexts (e.g., human resource management, quality inspection, etc.) based on various visualization techniques. Quantitatively modeling the visual searching behaviors and performance will not only contribute to the understanding of decision-making process in a visualization system, but also advance AI methods by incubating them with human expertise. In general, visual searching can be quantitatively understood from multiple scales, namely, 1) the population scale to treat individuals equally and model the general relationship between individual's physiological signals with visual searching decisions; 2) the individual scale to model the relationship between individual differences and visual searching decisions; and 3) the attention scale to model the relationship between individuals' attention in visual searching and visual searching decisions. The advancements of wearable sensing techniques enable such multiscale quantitative analytics of human visual searching performance. For example, by equipping human users with electroencephalogram (EEG) device, eye tracker, and logging system, the multiscale quantitative relationships among human physiological signals, behaviors and performance can be readily established. This dissertation attempts to quantify visual searching process from multiple scales by proposing (1) a data-fusion method to model the quantitative relationship between physiological signals and human's perceived task complexities (population scale, Chapter 2); (2) a recommender system to quantify and decompose the individual differences into explicit and implicit differences via personalized recommender system-based sensor analytics (individual scale, Chapter 3); and (3) a visual language processing modeling framework to identify and correlate visual cues (i.e., identified from fixations) with humans' quality inspection decisions in human visual searching tasks (attention scale, Chapter 4). Finally, Chapter 5 summarizes the contributions and proposes future research directions. The proposed methodologies can be readily extended to other applications and research studies to support multi-scale quantitative analytics. Besides, the quantitative understanding of human visual searching behaviors performance can also generate insights to further incubate AI methods with human expertise. Merits of the proposed methodologies are demonstrated in a visualization evaluation user study, and a cognitive hacking user study. Detailed notes to guide the implementation and deployment are provided for practitioners and researchers in each chapter.	en
dc.description.abstractgeneral	Existing industrial automation is limited by the performance and generalizability of artificial intelligence (AI) methods. Therefore, various human visual searching tasks are still widely involved in manufacturing contexts based on many visualization techniques, e.g., to searching for specific information, and to make decisions based on sequentially gathered information. Quantitatively modeling the visual searching performance will not only contribute to the understanding of human behaviors in a visualization system, but also advance the AI methods by incubating them with human expertise. In this dissertation, visual searching performance is characterized from multiple scales, namely, 1) the population scale to understand the visual searching performance in regardless of individual differences; 2) the individual scale to model the performance by quantifying individual differences; and 3) the attention scale to quantify the human visual searching-based decision-making process. Thanks to the advancements in wearable sensing techniques, this dissertation attempts to quantify visual searching process from multiple scales by proposing (1) a data-fusion method to model the quantitative relationship between physiological signals and human's perceived task complexities (population scale, Chapter 2); (2) a recommender system to suggest the best visualization design to the right person at the right time via sensor analytics (individual scale, Chapter 3); and (3) a visual language processing modeling framework to model humans' quality inspection decisions (attention scale, Chapter 4). Finally, Chapter 5 summarizes the contributions and proposes future research directions. Merits of the proposed methodologies are demonstrated in a visualization evaluation user study, and a cognitive hacking user study. The proposed methodologies can be readily extended to other applications and research studies to support multi-scale quantitative analytics.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:31941	en
dc.identifier.uri	http://hdl.handle.net/10919/104200	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Computational attention	en
dc.subject	Machine learning	en
dc.subject	recommender system	en
dc.subject	visual attention	en
dc.subject	wearable sensing	en
dc.title	Multiscale Quantitative Analytics of Human Visual Searching Tasks	en
dc.type	Dissertation	en
thesis.degree.discipline	Industrial and Systems Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en