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Description
In everyday life, unfocused attention can be detrimental to life either through driving vehicles, cutting vegetables, and learning new languages. In order to increase sustained attention, Binaural Beats were introduced. Binaural Beats have two different frequency tones which oscillate to produce different effects. Each of these beats operates at different

In everyday life, unfocused attention can be detrimental to life either through driving vehicles, cutting vegetables, and learning new languages. In order to increase sustained attention, Binaural Beats were introduced. Binaural Beats have two different frequency tones which oscillate to produce different effects. Each of these beats operates at different frequencies thus having different sound production: theta, delta, alpha, beta, and gamma. In the experiment beta Binaural Beats with frequencies of 200 and 216 Hertz, Hz (unit of measurement for frequency), were used with a control of 200 Hz flat noise. The experiment utilized the psychomotor vigilance task to measure changes in response times and utilized Affectiva to measure changes in facial musculature. The response times were then analyzed to explain why the Binaural Beats condition had significantly shorter response times compared to the control group throughout the task. In terms of emotional change, some trends were noticed for the Binaural Beats and the control condition; however, none of the trends were significant. Moving forward with the experiment, an increased number of participants can be recorded to strengthen the validity and test different frequencies of Binaural Beats for accuracy.
ContributorsObulasetty, Mohitha (Author) / Brewer, Gene (Thesis director) / Wingert, Kimberly (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
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DescriptionStudy invetsigated impacts of TDCS of the trigeminal region of the face, which resulted in improved reaction times and numerically, though not significantly, larger pupil diameters, indicating increased arousal.
ContributorsPope, Ben Alexander (Author) / Corbin, William (Thesis director) / Brewer, Gene (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12