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Description
Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement

Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement of a virtual ball in a target-hitting task. Preliminary results indicate that a method in which the position of the virtual object directly relates to the amplitude of brain signals is most conducive to success. In addition, this research explores learning in the context of neural signals during training with a BCI task. Specifically, it investigates whether subjects can adapt to parameters of the interface without guidance. This experiment prompts subjects to modulate brain signals spectrally, spatially, and temporally, as well differentially to discriminate between two different targets. However, subjects are not given knowledge regarding these desired changes, nor are they given instruction on how to move the virtual ball. Preliminary analysis of signal trends suggests that some successful participants are able to adapt brain wave activity in certain pre-specified locations and frequency bands over time in order to achieve control. Future studies will further explore these phenomena, and future BCI projects will be advised by these methods, which will give insight into the creation of more intuitive and reliable BCI technology.
ContributorsLancaster, Jenessa Mae (Co-author) / Appavu, Brian (Co-author) / Wahnoun, Remy (Co-author, Committee member) / Helms Tillery, Stephen (Thesis director) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
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Description
It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect is also beneficial in category learning. In a set of

It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect is also beneficial in category learning. In a set of experiments, Carvalho & Goldstone (2013), demonstrated that a blocked presentation showed an advantage during learning, but that ultimately, the distributed presentation yielded better performance during a post-learning transfer test. However, we have identified a major methodological issue in this study that we believe contaminates the results in a way that leads to an inflation and misrepresentation of learning levels. The present study aimed to correct this issue and re-examine whether a blocked or distributed presentation enhances the learning and subsequent generalization of categories. We also introduced two shaping variables, category size and distortion level at transfer, in addition to the mode of presentation (blocked versus distributed). Results showed no significant differences of mode of presentation at either the learning or transfer phases, thus supporting our concern about the previous study. Additional findings showed benefits in learning categories with a greater category size, as well as higher classification accuracy of novel stimuli at lower-distortion levels.
ContributorsJacoby, Victoria Leigh (Author) / Homa, Donald (Thesis director) / Brewer, Gene (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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Description
Estradiol (E2) and Levonorgestrel (Levo) are two hormones commonly used in hormone therapy (HT) to decrease symptoms associated with menopause. Both of these hormones have been shown to have beneficial effects on cognition when given alone in a rodent model of menopause. However, it is unknown whether these hormones, when

Estradiol (E2) and Levonorgestrel (Levo) are two hormones commonly used in hormone therapy (HT) to decrease symptoms associated with menopause. Both of these hormones have been shown to have beneficial effects on cognition when given alone in a rodent model of menopause. However, it is unknown whether these hormones, when taken in combination, are beneficial or harmful to cognition. This is a critically important question given that these hormones are most often given in combination versus separately. This thesis is composed of two studies examining the cognitive effects of E2 and Levo using a rat model of surgical menopause. Study 1 assessed how the dose of E2 treatment in rats impacted cognitive performance, and found that low dose E2 enhanced working memory performance. Next, based on the results from Study 1, Study 2 used low dose E2 in combination with different doses of Levo to examine the cognitive effects of several E2 to Levo ratio combinations. The results from Study 2 demonstrated that the combination of low dose E2 with a high dose of Levo at a 1:2 ratio impaired cognition, and that the ratio currently used in HT, 3:1, may also negatively impact cognition. Indeed, there was a dose response effect indicating that working and reference memory performance was incrementally impaired as Levo dose increased. The findings in this thesis suggest that the E2 plus Levo combination is likely not neutral for cognitive function, and prompts further evaluation in menopausal women, as well as drug discovery research to optimize HT using highly controlled preclinical models.
ContributorsBerns-Leone, Claire Elizabeth (Co-author) / Prakapenka, Alesia (Co-author) / Pena, Veronica (Co-author) / Northup-Smith, Steven (Co-author) / Melikian, Ryan (Co-author) / Ladwig, Ducileia (Co-author) / Patel, Shruti (Co-author) / Croft, Corissa (Co-author) / Bimonte-Nelson, Heather (Thesis director) / Glenberg, Arthur (Committee member) / Conrad, Cheryl (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12