Browsing by Subject "cognitive science"

Now showing 1 - 3 of 3
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    Human Guidance Behavior Decomposition and Modeling
    (2017-12) Feit, Andrew
    Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics.
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    Investigating Novel Virtual Reality and Telehealth Mindfulness-Based Interventions for Training Interoceptive Awareness
    (2023-10) Haley, Alexander
    Interoception – the ability to sense and integrate internal body signals – plays a critical role in how complex organisms survive and function. It is essential for maintaining stable conditions within the body (e.g., keeping warm), for meeting daily needs within a changing world (e.g., quenching thirst), and for adapting to future needs (e.g., remembering seasonal changes in foraging spots). While research into interoception started more than a hundred years ago, it is not well understood today. Researchers are still mapping out all of the brain and body pathways through which interoception operates. Additionally, research into the most optimal methods for manipulating and measuring interoception is at an early stage. Despite these uncertainties, prior research suggests that a person’s conscious awareness of internal body signals, known as interoceptive awareness, is not a fixed capacity, rather it can be altered through training. This dissertation investigates how mindfulness practices can be combined with emerging technology to train interoceptive awareness. First, we introduce a novel virtual reality (VR) mindfulness-based intervention that is designed for cultivating greater interoceptive awareness. As part of this work, we also introduce a new qualitative methodology to understand users’ experiences of interoceptive awareness in VR. We found that the methodology elicited valuable responses from participants regarding their interoceptive awareness experiences within the novel VR mindfulness-based intervention. Most significantly, our work represents the first attempts to qualitatively investigate a multi-dimensional model of interoceptive awareness in VR. It also establishes a critical foundation for conducting future follow-on comparative studies that can provide more complete design guidelines for how best to train interoceptive awareness in VR. Next, we assessed the efficacy of a novel group telehealth mindfulness intervention, compared to an active control, for enhancing interoceptive awareness. While this second intervention is distinct from the prior VR mindfulness-based intervention, it answers the critical question of whether interoceptive awareness can be trained via a group intervention delivered remotely versus alone in a lab. We found that the remote, group mindfulness intervention can improve interoceptive awareness and that these gains are relatively stable at six and twelve month follow-up time points. Lastly, we confirmed that the telehealth intervention can be delivered by non-mindfulness experts, which points to the promise of scalable, group telehealth mindfulness interventions. Finally, we examine potential predictive factors related to interoceptive awareness outcomes by conducting a hierarchical regression analysis. Knowledge of potential predictive factors is useful for optimizing interventions to enhance interoceptive awareness outcomes for various populations. We found that several factors influence post-intervention interoceptive awareness outcomes. Specifically, the factors of age, baseline mindfulness, and change in mindfulness from baseline to intervention completion significantly influence interoceptive awareness. In terms of baseline mindfulness, current literature has under-investigated this factor even though there is evidence that prior experience with mindfulness is very widespread in the United States. In summary, our work is a first step in the longer journey of weaving together emerging technologies with evidence-based interventions to positively impact public health. By studying two novel interventions individually before pursuing their combination, we hope to establish a solid foundation from which to pursue our larger, long-term vision. This larger vision includes the potential of VR as a powerful computing medium for embodied simulations to leverage telehealth as a critical mode of healthcare delivery to bring evidence-based health interventions outside the confines of traditional healthcare settings. We envision a future where clinicians, computer scientists, artists, and community members co-create immersive, social VR applications that connect geographically distant users to cultivate greater health and wellbeing around the world.
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    Time allocation and meta-cognition: A computational approach towards the organization of motivation
    (2019-01) Mussack, Dominic
    How do people allocate time and effort across tasks? This dissertation takes a computational psychology perspective, and puts forward the theory that motivation solves the meta-cognitive problem of allocating resources to different tasks by computing task priority. Motivation research has previously distinguished between two dissociable components of motivation: directing and energizing. These two components refer to different resources that must be allocated: time and effort. We explore the way humans allocate effort by taking advantage of simple decision making tasks and manipulating either task or background information. We develop a novel method that allows researchers to integrate an array of biometrics that capture how decision processes are modulated. We then extend work from optimal foraging theory to account for human tasks in order to analyze how humans allocate time. We derive various results that match time use behavior across domains. Finally, we apply the structural implications of this theory to make predictions in large scale time use data sets. Humans must often schedule mutually exclusive goals to fulfill mutually exclusive needs, which requires us to allocate time across tasks via a priority computation. Re-framing motivation from a resource allocation perspective, and highlighting the unique problem of time allocation, has implications across human decision making behavior, and we demonstrate its relevance in multiple domains.