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Bartling, B. A.

Publications and source records attributed to Bartling, B. A..

2 recordsLinked to original sources
bioRxiv2026

A Deep Dive into the Cognitive Soundscape of Flow: Finding Your Groove

Flow state, characterized by optimal engagement and performance, represents a key concept in understanding human performance and cognitive resource allocation. Grounded in Csikszentmihalyis and Sherrys flow theory and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), this study investigated physiological and neural correlates of flow state during a simulated driving task under different music conditions and difficulty levels. Using a 2 x 3 factorial design with 20 participants, this study examined self-selected versus non-self-selected music across three difficulty levels, testing the relationship between task switching, cognitive resource allocation, and flow state. Physiological measures included heart rate and EEG (alpha/theta power) using a 4-channel Muse 2 headband, alongside a self-report measure of flow experience. Hierarchical linear modeling revealed significant physiological changes during self-selected music: heart rate decreased ({beta} = -5.15, p < .001), while alpha ({beta} = 5829.77, p < .001) and theta power ({beta} = 7637.24, p < .001) increased. Task difficulty also showed significant effects, with heart rate decreasing during hard ({beta} = -6.70, p < .001) and moderate ({beta} = -3.40, p = .001) conditions. In particular, while physiological measures showed robust changes, the self-reported flow state did not reach significance. Task switching rates showed significant decreases during self-selected music ({beta} = -0.86, p < .001) and hard difficulty ({beta} = -0.61, p < .001), supporting the LC4MP frameworks predictions regarding cognitive resource allocation. These findings demonstrate how task switching and cognitive resource allocation relate to flow state induction. The results highlight the importance of multimodal measurement approaches and demonstrate that personal relevance through music selection and task difficulty significantly influence physiological and neural responses during performance. Future research should employ more comprehensive measurement approaches to better capture the complexity of flow-related neural activity and its relationship to task switching and cognitive resource allocation.

animal behavior and cognition↗
bioRxiv2026

Modeling Synthetic Audience Reactions to Social-Cognitive Narratives: A Generative GSR Model to Predict Real-World Autonomic Alignment during Film Viewing

While media consumption can be a solitary act, it produces a shared, socially coordinated experience where audiences bodies align in response to shared narrative events that are often social-affective in nature. Despite this recognition, traditional descriptive models of Galvanic skin response (GSR) have existed for decades, yet the socially coordinated aspect remains to be fully reflected in physiological models with the field of communication often treating the underlying generators of autonomic activity as a black box. To bridge this gap, we introduce a computational framework that models the underlying neural driver and its convolution to sweat gland physiology to explain how narrative events translate into measurable conductance. By leveraging multimodal AI models to "interpret" the social-cognitive content of a film, we generated a predictor timeline for a synthetic audience comprised of digital agents (i.e. artificial body systems responding to the film events with GSR responses). We then test this computational audience model by comparing its predictions against an empirical dataset collected as audience members (N = 96) processed the same stimulus, finding that AI-identified social triggers, like moments of comedic violence or shared emotional shifts, significantly predict the GSR time-course of audience engagement. In sum, this paper moves beyond simple and often retrospective labels like "arousal" to offer a computational account of how shared social narratives grip the human nervous system. We provide a scalable and expandable framework and a set of tools to predict media impact and understanding the psychophysiological basis of media.

animal behavior and cognition↗