Research
This is a collection of my research papers (yet to be published)
written fully by me. Click the icon to open the full pdf!
Neuroscience Research
The role of the parahippocampal place area in processing static versus navigational scenes in a naturalistic movie
By Rachel Yoon
Supervised by Professor Joonkoo Park
University of Massachusetts, Amherst
8 November 2024
Figures
Fig. 3. Final correlation matrices for static and navigational scenes, averaged across all 15 subjects. Axes are labeled with all 8 ROIs, each box containing the correlation coefficient between two ROIs. Overall, correlations for static scenes are markedly higher than those for navigational scenes.
Abstract
The parahippocampal place area (PPA) has been studied intensively for its role in scene perception, yet its broader contribution to the visual network remains unclear. Previous studies have highlighted the PPA’s unique anatomical location in-between the ventral and dorsal streams—two highly interactive visual pathways. This study investigates whether the PPA, in alignment with its anatomical position, coordinates information flow between the two streams, acting as an “integration hub” for scene perception. Here, using naturalistic movie-watching data from the StudyForrest dataset, I analyze the PPA’s functional connectivity with both scene- and object-selective regions under two distinct conditions: static scenes with limited movement versus navigational scenes with high dynamicity. The results of the experiment support the hypothesis that the PPA serves as a crucial connector of information flow between the ventral and dorsal pathways, with the right hemispheric PPA playing a larger integrative role compared to the left hemispheric PPA. Furthermore, the viewing of static scenes evoked stronger functional connectivity across the entire network, suggesting a more globally integrated brain response during static visual processing. In contrast, navigational scenes unexpectedly displayed weaker overall functional connectivity but a higher modular network organization, indicating that brain regions prioritize specific connections to maximize speed and efficiency during dynamic visual processing. These results demonstrate that the PPA functions as an integration hub within the visual network, modulating functional connectivity based on scene dynamics to facilitate adaptive processing between the ventral and dorsal pathways.
Fig. 4. (a) Connectivity graphs for static and navigational scenes mapping out the connections between ROIs. Each node represents a specific ROI (labeled accordingly) and lines represent the connectivity between the two regions. Shorter path lengths (edges) represent stronger correlations, and longer path lengths represent weaker correlations.
(b) A bar graph depicting the betweenness centrality values for the right PPA (PPA_R) and the left PPA (PPA_L) in both static and navigational scenes.
Fig. 5. Differences in correlations between static and navigational scenes, as determined by a series of pairwise ttests. A positive difference indicates greater correlation during static scenes, and vice versa. If p < 0.05, the difference is considered statistically significant. Notably, PPA_R has significantly higher correlations with LOC_L, OPA_R, OPA_L, RSC_L, and PPA_L during static scenes than navigational scenes.
Fig. 6. (a) Comparison of modularity scores (q) between static and navigational scenes. Modularity was significantly
higher for navigational scenes compared to static scenes (p = 0.0013), indicating a more modular network during
navigational scenes.
(b) Comparison of characteristic path lengths (CPL) between static and navigational scenes.
CPL was significantly higher for navigational scenes compared to static scenes (p = 0.0009), suggesting longer
communication routes during navigational scenes.
Examining the Intersection of Pollution
and Racism in California
Environmental Justice Research
By Rachel Yoon
AP English Language & Composition
3 March 2024
Abstract
Examining the Intersection of Pollution and Racism in California explores the various political and socioeconomic factors behind environmental injustice, while proposing direct solutions to ameliorate its consequences. Low income and minority communities often face greater
environmental burdens in the form of soil-lead contamination, industrial air pollution, and oil and gas hazards. Notably, CalEnviroScreen 4.0 scores reflect high percentiles of environmental hazards clustered in Disadvantaged Communities (DACs). Dangers among minority and low-income populations support the hypothesis that environmental injustice results from intentional and racist city policy decisions.
Compared with ethnic population estimates from the U.S. Census Bureau, the CalEnviroScreen 4.0 scores collected from the California Office of Environmental Health Hazard Assessment (OEHHA) clearly portrayed disparities among low-income and minority populations. A thorough analysis of past city policies and legislations of Santa Ana and Los Angeles established several patterns in city council decision-making, which explain the observed disparities and provide points to address when detailing a comprehensive solution.
A collection of risk assessments concluded that low-income and minority communities fall victim to significantly higher soil-lead concentration levels, more frequent industrial projects, and the correlated risk of oil and gas hazards. An analysis of health complications in Buttonwillow, California, saw a greater risk of chronic diseases and congenital anomalies associated with environmentally-burdened communities. Observed activity from both the Santa
Ana and Los Angeles city councils from the past decade saw implicit biases affecting authoritative decision-making, particularly for the approval and location of city industrial projects. Based on these trends, in order to properly address environmental injustices in California, youth communities should catalyze a group effort to spread sustainable practices and education across the state. To directly tackle racist policies, city governments must adopt a
three-step methodology to policy-making that puts special attention on reducing environmental
hazards in vulnerable regions.