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Browsing by Author "Thomas, Micheal, committee member"

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    An exploration of varying attentional focus strategies on the exercise experience
    (Colorado State University. Libraries, 2023) Oselinsky, Katrina, author; Graham, Daniel, advisor; Cleary, Anne, committee member; Thomas, Micheal, committee member; Hickey, Matthew, committee member
    Background: Research indicates attentional focus (AF) has a significant impact on the overall exercise experience, however, little is known regarding how AF manipulations via the use of distracting technology exerts a beneficial influence on the exercise experience. Additionally, the effect of varying AF strategies on the exercise experience may vary based on individual characteristics and/or familiarity with the exercise task. Purpose: The goal of Study 1 was to determine if distinct exerciser profiles could be created from a sample of group fitness participants. The goal of Study 2 was to determine if AF mediates the relationship between immersive virtual reality (VR) technology and ratings of perceived exertion (RPE)/enjoyment during an exercise session. Methods: In Study 1, a sample of group fitness participants (n=31) completed one traditional cycling class in which only audio cues were presented (AUD) and one video-enhanced immersive cycling class (IMM) in which a combination of music and video images was presented. After each cycling session, participants complete a brief survey that asked them to rate their perceived exertion, AF, and enjoyment of the exercise sessions. In Study 2, additional study volunteers (n=84) were randomly assigned to complete either an audio-only cycling class or an immersive VR-enhanced cycling class in which a combination of music and video images was presented. After cessation of the exercise session, participants completed a brief survey regarding their experiences in which they reported their recalled, in-task AF, RPE, and level of exercise enjoyment. Results: Study 1 leveraged Latent profile analysis (LPA) which indicated three, distinct classes could be drawn from the sample of 31 group fitness participants. These classes were classified as Low Heart Rate (HR) Dissociator, High HR Dissociator, and Associator. Results of Study 2 indicated AF did not act as a mediator relating immersive technology with RPE and exercise enjoyment (n=84). Additionally in Study 2, experimental condition did not have a significant influence on AF, RPE, or enjoyment directly, however, post-hoc, exploratory analyses revealed that average heart rate and time spent working in a moderate to vigorous heart rate zone (i.e., time spent at 70% or greater of age calculated heart rate maximum) were significantly greater in the immersive video enhanced condition than the audio only. Conclusions: Study 1 expands on the extant literature by elucidating the different attentional focus techniques used by different groups of exercisers and the varying response patterns of these sub-groups on commonly assessed exercise experience variables. Study 1 demonstrates the need for a deeper exploration of how individual characteristics differentially impact the exercise experience and how emerging analytical techniques can be employed to create more targeted interventions. Study 2 suggests that although AF was not a mediator relating immersive technology to RPE and exercise enjoyment, this technology does seem to exert a beneficial influence on the exercise experience as evidenced by the increased work rate found in this study. The results of Study 2 suggest future research should seek to identify other causal mechanisms that explain how immersive technology exerts its beneficial influence on the exercise experience.
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    ItemOpen Access
    Application of the neural data transformer to non-autonomous dynamical systems
    (Colorado State University. Libraries, 2023) Mifsud, Domenick M., author; Ortega, Francisco R., advisor; Anderson, Charles, advisor; Thomas, Micheal, committee member; Barreto, Armando, committee member
    The Neural Data Transformer (NDT) is a novel non-recurrent neural network designed to model neural population activity, offering faster inference times and the potential to advance real-time applications in neuroscience. In this study, we expand the applicability of the NDT to non-autonomous dynamical systems by investigating its performance on modeling data from the Chaotic Recurrent Neural Network (RNN) with delta pulse inputs. Through adjustments to the NDT architecture, we demonstrate its capability to accurately capture non-autonomous neural population dynamics, making it suitable for a broader range of Brain-Computer Inter-face (BCI) control applications. Additionally, we introduce a modification to the model that enables the extraction of interpretable inferred inputs, further enhancing the utility of the NDT as a powerful and versatile tool for real-time BCI applications.