Queueing Network Modeling of Human Performance in Complex Cognitive Multi-task Scenarios.

As the complexity of human-machine systems grows rapidly, there is an increasing need for human factors theories and computational methods that can quantitatively model and simulate human performance and mental workload in complex multi-task scenarios. In response to this need, I have developed and evaluated an integrated cognitive architecture named QN-ACTR, which integrates two previously isolated but complementary cognitive architectures – Queueing Network (QN) and Adaptive Control of Thought-Rational (ACT-R). Combining their advantages and overcoming the limitations of each method, QN-ACTR possesses the benefits of modeling a wider range of tasks including multi-tasks with complex cognitive activities that existing methods have difficulty to model. These benefits have been evaluated and demonstrated by comparing model results with human results in the simulation of multi-task scenarios including skilled transcription typing and reading comprehension (human-computer interaction), medical decision making with concurrent tasks (healthcare), and driving with a secondary speech comprehension task (transportation), all of which contain important and practical human factors issues. QN-ACTR models produced performance and mental workload results similar to the human results. To support industrial applications of QN-ACTR, I have also developed the usability features of QN-ACTR to facilitate the use of this cognitive engineering tool by industrial and human factors engineers. Future research can apply QN-ACTR – which is a generic computational modeling theory and method – to other domains with important human factors issues.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102477/1/shicao_1.pd

The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task.

Models of human behavior provide insight into people’s choices and actions and form the basis of engineering tools for predicting performance and improving interface design. Most human models are either cognitive, focusing on the information processing underlying the decisions made when performing a task, or physical, representing postures and motions used to perform the task. In general, cognitive models contain a highly simplified representation of the physical aspects of a task and are best suited for analysis of tasks with only minor motor components. Physical models require a person experienced with the task and the software to enter detailed information about how and when movements should be made, a process that can be costly, time consuming, and inaccurate. Many tasks have both cognitive and physical components, which may interact in ways that could not be predicted using a cognitive or physical model alone. This research proposes a solution by combining a cognitive model, the Queuing Network – Model Human Processor, and a physical model, the Human Motion Simulation (HUMOSIM) Framework, to produce an integrated cognitive-physical human model that makes it possible to study complex human-machine interactions. The physical task environment is defined using the HUMOSIM Framework, which communicates relevant information such as movement times and difficulty to the QN-MHP. Action choice and movement sequencing are performed in the QN-MHP. The integrated model’s more natural movements, generated by motor commands from the QN-MHP, and more realistic cognitive decisions, made using physical information from the Framework, make it useful for evaluating different designs for tasks, spaces, systems, and jobs. The Virtual Driver is the application of the integrated model to driving with an in-vehicle task. A driving simulator experiment was used to tune and evaluate the integrated model. Increasing the visual and physical difficulty of the in-vehicle task affected the resource-sharing strategies drivers used and resulted in deterioration in driving and in-vehicle task performance, especially for shorter drivers. The Virtual Driver replicates basic driving, in-vehicle task, and resource-sharing behaviors and provides a new way to study driver distraction. The model has applicability to interface design and predictions about staffing requirements and performance.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75847/1/hjaf_1.pd

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

University of Nebraska at Omaha 2017-2018 Course Catalog

The University of Nebraska Omaha (UNO) is a premier metropolitan university that combines the resources of a doctoral research institution with a thriving community in the heart of Omaha. With a global reach and vision, UNO is large enough to provide opportunities students seek, yet personal enough to include the mentorship they need to achieve academic excellence, creativity, and engaged learningat competitive tuition rates. UNO is committed to and engaged with the city surrounding it, allowing students unique hands-on opportunities, internships, service learning,applied research, and other collaborative activities that enhance time in the classroom. This is the ”O” we want you to know – forward thinking, student centered,and devoted to the city we call home. #KnowThe

University of Nebraska at Omaha 2018-2019 Course Catalog

Located in one of America’s best cities to live, work and learn, the University of Nebraska at Omaha (UNO) is Nebraska’s premier metropolitan university. With more than 15,000 students enrolled in 200-plus programs of study, UNO is recognized nationally for its online education, graduate education, military friendliness, and community engagement efforts.Founded in 1908, UNO has served learners of all backgrounds for more than 100 years and is dedicated to another century of excellence both in the classroom and in the community

On looking into words (and beyond): Structures, Relations, Analyses

On Looking into Words is a wide-ranging volume spanning current research into word structure and morphology, with a focus on historical linguistics and linguistic theory. The papers are offered as a tribute to Stephen R. Anderson, the Dorothy R. Diebold Professor of Linguistics at Yale, who is retiring at the end of the 2016-2017 academic year. The contributors are friends, colleagues, and former students of Professor Anderson, all important contributors to linguistics in their own right. As is typical for such volumes, the contributions span a variety of topics relating to the interests of the honorand. In this case, the central contributions that Anderson has made to so many areas of linguistics and cognitive science, drawing on synchronic and diachronic phenomena in diverse linguistic systems, are represented through the papers in the volume. The 26 papers that constitute this volume are unified by their discussion of the interplay between synchrony and diachrony, theory and empirical results, and the role of diachronic evidence in understanding the nature of language. Central concerns of the volume include morphological gaps, learnability, increases and declines in productivity, and the interaction of different components of the grammar. The papers deal with a range of linked synchronic and diachronic topics in phonology, morphology, and syntax (in particular, cliticization), and their implications for linguistic theory