Doctor of Philosophy, Case Western Reserve University, 2022, Management

The early twenty-first century zeitgeist has been characterized by a cultural and corporate fascination with leveraging mind-body practices such as meditation and yoga as tools for professional performance. At the same time, executive coaches trained in body-mind approaches to coaching make strong but as-yet unsubstantiated claims about the transformative power of body-based behavioral learning. Practitioner literature suggests that developing embodied self-awareness (ESA) enhances well-being, resilience, and relationships while building the emotional and social intelligence (ESI) that sets outstanding leaders apart from ordinary ones. These claims are consistent with theoretical relationships between brain, body, and behavior, but they have yet to be put to the empirical test. This mixed methods research project seeks to challenge, clarify, and validate these claims by examining the antecedents and outcomes of embodied self-awareness through both a theoretical and an empirical lens. Starting with a qualitative study based on critical incident interviews and thematic analysis, the research proceeds to gather survey-based data from over 550 professional coaches about their experience of embodied self-awareness, its potential outcomes, and the activities likely to produce it. Using factor analysis and structural equation modeling, results show that ESA has strong and significant effects on all dependent variables tested and that ESA can be cultivated through multiple avenues, including body-oriented coach training, yoga, meditation, and hands-on bodywork. Ultimately, by triangulating across methods and studies three convergent conclusions emerge: (1) Body-oriented coach training appears to have stronger effects on ESA than more commonly practiced pursuits such as yoga, mindfulness, and bodywork; (2) Developing ESA strengthens one's capacity for resilience, adaptability, and flourishing; and (3) ESA builds interpersonal competencies including empathy, connectedne (open full item for complete abstract)

Committee: Richard Boyatzis (Committee Chair); Anthony Jack (Committee Member); Ellen Van Oosten (Committee Member); Avi Turetsky (Committee Member) Subjects: Behavioral Sciences; Cognitive Psychology; Management; Neurobiology; Organizational Behavior; Social Psychology

Doctor of Philosophy (PhD), Ohio University, 2016, Electrical Engineering & Computer Science (Engineering and Technology)

Machine Intelligence, and all of its associated fields and specialties, is a wide and complex area actively researched in laboratories around the world. This work aims to address some of the critical problems inherent in such research, from the most basic neural network structures, to handling of information, to higher level cognitive processes. All of these components and more are needed to construct a functioning intelligent machine. However, creating and implementing machine intelligence is easier said than done, especially when working from the ground up as many researchers have attempted. Instead, it is proposed that the problem be approached from both bottom-up and top-down level design paradigms, so that the two approaches will benefit from and support one another. To clarify, my research looks at both low level learning, and high level cognitive models and attempts to work toward a middle ground where the two approaches are combined into a single cognitive system. Specifically, this work covers the development of the Motivated Learning Embodied Cognition (MLECOG) model, and the associated components required for it to function. These consist of the Motivated Learning approach, various types of memory, action monitoring, visual and mental saccades, focus of attention, attention switching, planning, etc. Additionally, some elements needed for processing sensory data will be briefly examined because they are relevant to the eventual creation of a full cognitive model with proper sensory/motor I/O. The development of the Motivated Learning cognitive architecture is covered from its initial beginnings as a simple Motivated Learning algorithm to its advancement to a more complex architecture and eventually the proposed MLECOG model. The objective of this research is to show that a cognitive architecture that uses motivated learning principles is feasible, and to provide a path toward its development.

Committee: Janusz Starzyk (Advisor); Mehmet Celenk (Committee Member); Savas Kaya (Committee Member); Jeff Dill (Committee Member); Jeff Vancouver (Committee Member); Annie Shen (Committee Member) Subjects: Cognitive Psychology; Computer Science; Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2019, Electrical Engineering & Computer Science (Engineering and Technology)

Artificial intelligence (AI) is being widely applied to various practical problems, and researchers are working to address numerous issues facing the field. The organizational structure and learning mechanism of the memory is one such issue. A cognitive agent builds a representation of its environment and remembers its experiences to interpret its inputs and implements its goals through its actions. By doing so it demonstrates its intelligence (if any), and it is its learning mechanism, value system and sensory motor coordination that makes all this possible. Memory in a cognitive agent stores its knowledge, knowledge gained over a life-time of experiences in a specific environment. That is, memory includes the “facts”, the relationships between them, and the mechanism used to learn, recognize, and recall based on the agent's interaction with the world/environment. It remembers events that the agent experienced reflecting important actions and observations. It motivates the agent to do anything by providing assessment of the state of the environment and its own state. It allows it to plan and anticipate. And finally, it allows the agent to reflect on itself as an independent being. Hence, memory is critical for intelligence, for it is the memory that determines a cognitive agent's abilities and learning skills. Research has shown that while memory in humans can be classified into different types, based on factors such as their longevity and cognitive mechanisms used to create and retrieve them, they all are achieved using a similar underlying structure. The focus of this dissertation was on using this principle, i.e. different memories created using the same underlying structure, to implement memory for cognitive agents using a biologically plausible model of neuron. This work was an attempt to demonstrate the feasibility of implementing self-organizing memory structures capable of performing the various memory related tasks necessary for a cognitive agent using a c (open full item for complete abstract)

Committee: Wojciech Jadwisienczak (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2009, Electrical Engineering (Engineering and Technology)

In this work, a framework of designing embodied intelligence (EI), along with the essential elements and their design principles, is proposed. This work intends to deploy the following design principles. Firstly, hierarchical self-organizing learning systems in the form of network made of neurons are the essential elements for building machine intelligence. The supervised, unsupervised and reinforcement learning are all necessary aspects of learning and are studied for machine intelligence building. In supervised learning, an efficient learning method for hierarchical multi-layered network structure is proposed and studied. In addition, a quantitative measure is proposed to quantify overfitting of a network in a given learning problem to determine proper network structure or proper learning period. In unsupervised learning, a sparsely-connected hierarchical network is developed to build the neural representations effectively and efficiently for densely-coded sensory inputs, and to enable the memory with large memory capacity and great fault tolerance. Secondly, the memory-based intelligence is not only for passive information processing and pattern storage. One of the critical capabilities of intelligence is continuous and intentional learning. Therefore, a goal creation system (GCS), also as a type of hierarchical self-organizing learning system based on simple and uniform structure, is presented that acts as the trigger for the agent's goal creation, memory management, active interaction and goal-oriented learning. As a self-organizing structure, it is responsible for evaluating actions according to goals, stimulating the learning of useful associations and representations for sensory inputs and motor outputs. It enables the more powerful hierarchical reinforcement learning, finds the ontology among sensory objects, creates the needs, and affects the agent's attention and perception. Biologically inspired structural design concept and the framework of EI propose (open full item for complete abstract)

Committee: Janusz Starzyk (Committee Chair); Jundong Liu (Committee Member); Jeffery Dill (Committee Member); Savas Kaya (Committee Member); Jeffery Vancouver (Other); Sergiu Aizicovici (Other) Subjects: Electrical Engineering