Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer the question of whether a similar interaction leads to savings, a model-free process that is described as faster relearning when experiencing something familiar. This was tested in a two-week reaching task conducted on a robotic arm capable of perturbing movements. The task was designed so that the two sessions differed in their history of errors. By measuring the change in the learning rate, the savings was determined at various points. The results showed that the history of errors successfully modulated savings. Thus, this supports the notion that the two complementary systems interact to develop savings. Additionally, this report was part of a larger study that will explore the organizational structure of the complementary systems as well as the neural basis of this motor learning.

For the past six months, I have been working with Black Lives Matter Phoenix Metro on the Tempe’s People’s Budget coalition. I served as a member on the research team as well as a temporary member of the Black Lives Matter’s nucleus leadership team. I joined weekly meetings for both groups, and conducted research on city budget proposals, initiatives, and resources. I also lead discussions and conversations about progress, next steps, and goals of the coalition with over 50 volunteers within the coalition. The Tempe’s People Budget crafted a survey in late October and sent it out mid-November to members of the Tempe community, asking them what community resources they would use and which resources and investments they believed the city of Tempe should commit to. After five months of survey collection, we presented a preliminary budget proposal to Tempe’s financial office for consideration for the 2022-2023 budget, using data from 318 survey responses. With the creation of the survey among the adaption of the Tempe’s coalition, we wanted to look at what preventive community resource most respondents wanted Tempe to reinvest in. We found that the majority of survey respondents would like investments in renewable energy, housing stability, and alternatives to police. It is the city’s job to protect and serve all members of their communities, and public safety should be prioritized by investing in preventive measures instead of remedial punishments. Public safety concerns would be most effectively resolved by addressing issues such as: little to no income, housing instability, lack of access to food and water and other basic necessities to survive. Currently, Tempe is investing in the police, who most often only serve punishments.