During Ashley's free time, she enjoys participating in scientific research projects at Columbia University. In 2018, Ashley joined the The Social Cognitive and Affective Neuroscience (SCAN) Lab as a Research Assistant under Brian Silston and Kevin Ochsner at Columbia, where her main project studied motivational effects on decision making in an interactive digital environment. The manuscript is currently in preparation.
In 2021, she participated in Columbia University's Data Science Institute Health Analytics COVID-19 Data Challenge and won 2nd place for developing a model to predict COVID-19 Social Distancing Adherence (SoDA) on a state and county level based on health, economic and demographic data. With her team, Myles Ingram, a research analyst at Columbia University's Medical Center, and Chin Hur, a physician and research scientist at Columbia University's Medical Center, they turned their findings into a publication which was published by Nature.
Ingram, M., Zahabian, A. & Hur, C. Prediction of COVID-19 Social Distancing Adherence (SoDA) on the United States county-level.
Humanit Soc Sci Commun8, 87 (2021).
Social distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p < 0.00001) and owner-occupied housing unit rate (β = −0.322, p < 0.00001) were the most positively correlated and negatively correlated features to SoDA, respectively. Counties with higher per capita income, older persons, and more suburban areas were positively associated with adherence while counties with higher African American population, high obesity rate, earlier first COVID-19 case/death, and more Republican-leaning residents were negatively correlated with adherence. The base model predicted county SoDA with 90.8% accuracy. The model using only COVID-19-related features predicted with 64% accuracy and the model using the top 25 most substantial features predicted with 89% accuracy. Our results indicate that economic features, health features, and a few other features, such as political affiliation, race, and the time since the first case/death, impact SoDA on a countywide level. These features, combined, can predict adherence with a high level of confidence. Our prediction model could be utilized to inform health policy planning and potential interventions in areas with lower adherence.
Presented by Itsik Pe'er, Columbia University COVID-19 Virtual Symposium
Silston, B., Zahabian, A., Bolger, N., Ochsner, K. (Pending). Motivated Search, Selection and Decision-Making in an Interactive Digital Context
Interactive digital contexts have become ubiquitous and indispensable, capturing hours of attention daily and influencing everyday decision-making, content consumption, purchasing behaviors, and the formation and updating of attitudes in a variety of domains. These carefully designed environments interact with human motivation to influence behaviors and beliefs, including in impactful domains such as science and public policy. Digital information contexts such as Google have become the de facto standard for information search and content delivery, however search results arrive devoid of editorial filtering and with limited procedural transparency. The potential societal costs of “wild information” are immense, especially for areas such as scientific technology that can provide significant benefits with low and manageable risk (i.e. vaccines, or genetically modified foods (GM). Using GM foods as a focal topic, we designed and implemented a custom search engine and content delivery system to identify the stages at which human motivation operates and its effects on search and selection behavior, attitude updating and decision-making in the context of widely-used interactive digital technology. Results demonstrate Google format searching often reflects prior beliefs and background media sentiment about GM foods, while menu-style searching reflects only prior attitudes. Prior attitudes influence search and content selection and in turn, content selection influences both attitudes about GM foods and decision-making in a food selection task. These results demonstrate when and how motivation covertly operates during digital information search and selection, and how search-based interactive technology interacts with motivational and cognitive systems to influence belief and real-world decision-making.