(Georgia Institute of Technology, 2020-08)
Han, Andrew Taekyu
The purpose of this study is to see how manipulating boundaries impact one’s spatial memory in unfamiliar spaces. To test this, after we measured our participants’ Sense of Direction (SOD) and memory capacities, they were equally divided up into three separate training conditions: an abstract environment, a translucent environment, and a control environment. Afterwards, they were evaluated using wayfinding and pointing tasks. Our results indicated that the abstract training significantly impacted those with varying SOD’s. Those with low SOD’s in the control condition outperformed their abstract counterparts in wayfinding, and those with high SOD’s in the opaque abstract condition outperformed their control counterparts in the pointing tasks. This could be due to their reliance on different navigation strategies. In this case piloting versus path integration, respectively. Regardless, this study emphasizes the need to further investigate other methods of boundary manipulation that will potentially affect people’s spatial abilities.
(Georgia Institute of Technology, 2020-05)
Nakirikanti, Anudeep Sai
The impact of Alzheimer’s Disease (AD) on today’s society and healthcare is unprecedented. As a larger portion of today’s population enters an age for which AD becomes a health concern, there is growing support among health practitioners to prevent the disease’s progression and development. Early identification of the disease may serve as a critical step towards combating the disease, allowing earlier interventions in the disease process to foster healthy aging. The focus of such interventions includes alleviating risk factors of AD, two of which include cortisol and degeneration in the basal forebrain. Importantly, increased levels of cortisol and reduced volume in the basal forebrain are attributed to higher risks of AD. In the present study, we make use of machine learning and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to characterize individuals with AD by using data from cortisol levels and basal forebrain degeneration. This allowed us to test whether cortisol and basal forebrain degeneration were predictively valuable for AD diagnosis. Our data partially supported our prediction—the machine learning classifier yielded significantly above chance classification accuracy for basal forebrain degeneration, but the classification accuracy for cortisol was not significantly above chance. Consequently, our results indicate that basal forebrain degeneration might serve as a diagnostically useful biomarker for AD, while cortisol’s role in AD characterization necessitates further investigation.
(Georgia Institute of Technology, 2020-05)
Freeman, Sarah Austin
Stress, a common feature of everyday life, has been demonstrated in numerous studies to profoundly impact memory function, particularly functions dependent on the hippocampus. The impacts of acute stress on statistical learning are still unknown, as statistical learning has only recently been demonstrated to rely on hippocampal mechanisms. In order to examine the impact of acute stress on statistical learning, as well as investigate individual differences in statistical learning performance, I induced acute stress via shock on healthy young adults during either the encoding or retrieval phase of a previously established statistical learning tasks that is based on implicit learning of temporal community structures. Preliminary results suggest that stress applied during either encoding or retrieval can disrupt statistical learning, though further data collection is needed to generate a more robust model of these effects. A thorough definition of the interactions between stress and statistical learning of temporal relationships has implications for understanding maladaptive effects of stress mechanisms and potential interventions for improving learning and memory – and thus quality of life - for people who suffer from chronic stress disorders, such as generalized anxiety disorder and post-traumatic stress disorder.