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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Resting-State Functional Connectivity Correlates Of Attentional Bias In An Emotional Free Viewing Paradigm: An Eye-Tracking Investigation, Andrew Hauler
All NMU Master's Theses
Threat detection, the process of searching complex environments for harmful stimuli, represents a vastly important job that promotes the biological fitness of the organism. Decades of experimental evidence suggests individuals either diagnosed, or at risk for, affective disorders display altered patterns of attentional engagement (hypervigilance or maintenance) with external stimuli; referred to as attentional biases. To date, the extent to which underlying neural mechanisms drive attentional biases, both in affective disorders as well as unselected populations, remain to be resolved. Thus, using eye-tracking and a passive emotional free viewing task, this study set to clarify resting-state network contributions from three …
Fraction Magnitude Understanding Across Learning Formats: An Fmri Study, Chloe A. Henry
Fraction Magnitude Understanding Across Learning Formats: An Fmri Study, Chloe A. Henry
Electronic Thesis and Dissertation Repository
Knowledge of fraction magnitudes are an important, but notoriously difficult mathematical concept to master. Behavioural work has begun to explore and compare the instructional tools used for fraction learning. However, how fraction instructional tools are processed in the brain remains an underexplored question. Therefore, in the present thesis, we used functional brain MRI methodology to examine the neural activity of adult participants while completing a fraction verification task using the number line and area model, two common methods of fraction learning. We found that both models commonly recruited fronto-parietal activity, the neural regions typically implicated in number processing. However, we …
Beyond Machine Learning: An Fmri Domain Adaptation Model For Multi-Study Integration, Lauryn Michelle Burleigh
Beyond Machine Learning: An Fmri Domain Adaptation Model For Multi-Study Integration, Lauryn Michelle Burleigh
LSU Doctoral Dissertations
Traditional machine learning analyses are challenging with functional magnetic
resonance imaging (fMRI) data, not only because of the amount of data that needs to be
collected, adding a particular challenge for human fMRI research, but also due to the change in
hypothesis being addressed with various analytical techniques. Domain adaptation is a type of
transfer learning, a step beyond machine learning which allows for multiple related, but not
identical, data to contribute to a model, can be beneficial to overcome the limitation of data
needed but may address different hypothesis questions than anticipated given the analysis
computation. This dissertation assesses …