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value="rel">Relevance</option><option value="a-title">A-Z By Title</option><option value="z-title">Z-A By Title</option><option value="a-author">A-Z By Author</option><option value="z-author">Z-A By Author</option><option value="asc">Date Ascending</option><option value="desc">Date Descending</option></select></div></div><input type="hidden" name="start" form="facetForm" value="0"/></div><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-thesis">Thesis</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/6735d0s8"><div class="c-clientmarkup">A Cognitively Informed and Network Based Investigation of Human Neural Activities, Behaviors, and Performance in Human-Autonomy Teaming Tasks</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ABales%2C%20Gregory">Bales, Gregory</a> </li><li class="c-authorlist__begin"><span class="c-authorlist__heading">Advisor(s):</span> <a href="/search/?q=author%3AKong%2C%20Zhaodan">Kong, Zhaodan</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_etd">UC Davis Electronic Theses and Dissertations</a> (2023)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><p>Human-autonomy teams are expected to provide solutions in a wide range of applications, such as human directed search and rescue, hazard containment and mobilization, and space exploration. These teams consist of autonomous agents that coordinate their actions with the human partner to achieve common goals. Despite the advancements of current autonomous systems, it is the human's ability to engage their knowledge and expertise that makes human-autonomy teams especially effective in tasks dominated by dynamic and uncertain conditions. The human and their autonomous teammate should have shared plans and a similar focus of attention. However, studies have shown that a human's miscomprehension of an autonomous system's state, decisions, or course of action can result in misuse or disuse of the agent, causing a reduction in team performance. The aim of this dissertation is to improve human-autonomy team task proficiency by investigating methods to measure changes in human cognitive state as reflected in neurophysiological measures using methods derived from network science. This work is comprised of two primary studies. In the first study, we examined human behaviors and brain activity acquired via electroencephalography (EEG) to probe the interactions between cognitive processes, behaviors, and performance in a human-multiagent team task. We showed that measurable changes in brain activity indicate a higher burden on the cognitive resources associated with visual-spatial reasoning required to estimate a more complex kinematic state of robotic agents. These conclusions were reinforced by complementary behavioral shifts in gaze and pilot inputs. Next, we showed that EEG inter-channel connectivity network metrics distinguish gaze behaviors associated with the attention process more effectively than traditional single-channel features. In the second study we explored the relationship between neurophysiological features and human trust in an autonomous system while performing a team task. Trust prediction models were constructed using a variety of feature types determined from an EEG timeseries. A comparison of model performance between traditional EEG signal powers with inter-channel connectivity network metrics revealed that measures of dynamic changes in synchronous behavior between distant brain regions can capture cognitive activities that predict a human's trust in an autonomous system. We showed that both single-channel powers and network-metrics defined from brain regions associated with reasoning and attention have the greatest impact on trust prediction. In a third study, we explore the interaction between behaviors and performance for subjects of various skills in a manual grinding task. We show that there were observable and distinguishable sensorimotor behaviors associated with two distinct techniques utilized by the individual subjects, and that task performance is affected by these techniques.</p></div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/6735d0s8"><img src="/cms-assets/9f674c943b64e66095a7c0b92d431c8e76ab03fa8ed6a54c5883fcbf699f72d9" alt="Cover page: A Cognitively Informed and Network Based Investigation of Human Neural Activities, Behaviors, and Performance in Human-Autonomy Teaming Tasks"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/8t7967wf"><div class="c-clientmarkup">Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ADas%2C%20Jayanti">Das, Jayanti</a>; </li><li><a href="/search/?q=author%3ABales%2C%20Gregory%20L">Bales, Gregory L</a>; </li><li><a href="/search/?q=author%3AKong%2C%20Zhaodan">Kong, Zhaodan</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ALinke%2C%20Barbara">Linke, Barbara</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2018)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Due to its high versatility and scalability, manual grinding is an important and widely used technology in production for rework, repair, deburring, and finishing of large or unique parts. To make the process more interactive and reliable, manual grinding needs to incorporate "skill-based design," which models a person-based system and can go significantly beyond the considerations of traditional human factors and ergonomics to encompass both processing parameters (e.g., feed rate, tool path, applied forces, material removal rate (MRR)), and machined surface quality (e.g., surface roughness). This study quantitatively analyzes the characteristics of complex techniques involved in manual operations. A series of experiments have been conducted using subjects of different levels of skill, while analyzing their visual gaze, cutting force, tool path, and workpiece quality. Analysis of variance (ANOVA) and multivariate regression analysis were performed and showed that the unique behavior of the operator affects the process performance measures of specific energy consumption and MRR. In the future, these findings can be used to predict product quality and instruct new practitioners.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/8t7967wf"><img src="/cms-assets/a184d3be14f1e89f49647c2f5e9dffafcc84ca1b06dc7ff50270619ae5704fce" alt="Cover page: Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/0kr1t3n9"><div class="c-clientmarkup">Recognizing Gaze-Motor Behavioral Patterns in Manual Grinding Tasks</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ABales%2C%20Gregory">Bales, Gregory</a>; </li><li><a href="/search/?q=author%3ADas%2C%20Jayanti">Das, Jayanti</a>; </li><li><a href="/search/?q=author%3ALinke%2C%20Barbara">Linke, Barbara</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AKong%2C%20Zhaodan">Kong, Zhaodan</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2016)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">This paper reports our progress in developing techniques for “parsing” raw gaze and force data from manual grinding tasks into a principled model. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of her skillset. Based on the joint, gaze, and force data collected from a series of experiments, and by extending existing scanpath methods, we develop a visualization method called Gaze-Motor Space-Time Cube (GMSTC), which can help us gain insight into the joint gaze-motor routine existing in complex manual tasks. For instance, there exists a strong correlation between the spectra of a subject's fixation and force distributions. Such insight might be hard to extract through an examination of either the gaze or the force data separately. Furthermore, by comparing data obtained from operators with different levels of skill, we are able to quantitatively describe characteristics of human manual skill. For instance, we find that an experienced subject exhibits longer fixation durations and smaller fixation variations than an intermediate one. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and have implications in the way in which knowledge and manual expertise is transferred from one generation of practitioners to the next.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/0kr1t3n9"><img src="/cms-assets/69db802d890af603e9f1bb1cfc993a513431b6eaaa166c16a41d8897df8adfbf" alt="Cover page: Recognizing Gaze-Motor Behavioral Patterns in Manual Grinding Tasks"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/1cd833gz"><div class="c-clientmarkup">Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ABales%2C%20Gregory%20L">Bales, Gregory L</a>; </li><li><a href="/search/?q=author%3ADas%2C%20Jayanti">Das, Jayanti</a>; </li><li><a href="/search/?q=author%3ATsugawa%2C%20Jason">Tsugawa, Jason</a>; </li><li><a href="/search/?q=author%3ALinke%2C%20Barbara">Linke, Barbara</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AKong%2C%20Zhaodan">Kong, Zhaodan</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2017)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">This paper presents new techniques to analyze and understand the sensorimotor characteristics of manual operations such as grinding, and links their influence on process performance. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of his or her skillset. Based on the joint gaze, force, and velocity data collected from a series of manual grinding experiments, we have compared operators with different levels of experience and quantitatively described characteristics of human manual skill and their effects on manufacturing process parameters such as cutting energy, surface finish, and material removal rate (MRR). For instance, we find that an experienced subject performs the task in a precise manner by moving the tool in complex paths, with lower applied forces and velocities, and short fixations compared to a novice. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and impact future efforts in workforce training as well as the digitalization of manual expertise, thereby facilitating the transformation of raw data into product-specific knowledge.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/1cd833gz"><img src="/cms-assets/8fd80215c39281cc0362f63920d6d86187515e3ccdc0111c324b9e0192681600" alt="Cover page: Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance"/></a></div></section></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy Statement</a></li><li><a href="/policies">Site Policies</a></li><li><a href="/terms">Terms of Use</a></li><li><a href="/login"><strong>Admin Login</strong></a></li><li><a href="https://help.escholarship.org"><strong>Help</strong></a></li></ul></nav><div class="c-footer__logo"><a href="/"><img class="c-lazyimage" data-src="/images/logo_footer-eschol.svg" alt="eScholarship, University of California"/></a></div><div class="c-footer__copyright">Powered by the<br/><a href="http://www.cdlib.org">California Digital Library</a><br/>Copyright © 2017<br/>The Regents of the University of California</div></footer></div></div></div></div> <script src="/js/vendors~app-bundle-2aefc956e545366a5d4e.js"></script> <script src="/js/app-bundle-3c8ebc2ec05dcc3202fd.js"></script> </body> </html>