Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

In this thesis paper, the mental health consequences of the COVID-19 pandemic are discussed. Chapter 1 discusses what inspired me to write this thesis and follows with a discussion of social isolation during the COVID-19 pandemic. Chapter 2 takes a step back and discusses biological effects of social isolation in general. Chapter 3 discusses the psychological effects of social isolation. Finally, this thesis concludes with a discussion of what can be done to help those experiencing social isolation during the pandemic.

My research aims to determine the effectiveness of meditation and sleep applications (apps) on the reduction of anxiety and stress in college students, with a focus on sedative piano music. Results showed a significant reduction of stress and anxiety levels in college students when listening to sedative piano music versus non-sedative piano music. Music along with other therapy modalities in meditation and sleep apps show promise in reducing students’ anxiety and stress and promoting their successes.

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior as a reflection of personality – has been the subject of a great deal of misunderstanding, with professionals and nonprofessionals alike questioning whether profiling represents an art or a science and what its function in forensic science should be. To provide a more thorough understanding of criminal profiling’s capabilities and its efficacy as a law enforcement tool, this thesis will examine the application of criminal profiling to investigations, various court rulings concerning profiling’s admissibility, and the role that popular media plays in the perception and function of the practice. It will also discuss how future research and regulatory advancements may strengthen criminal profiling’s scientific merit and legitimacy.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

For my thesis/creative project, I created a prototype for a mental health app. Each section of the prototype has a purpose of instilling mindfulness and healthy habits that can promote and lead to sustainable mental health. Throughout the paper I explain my reasoning for starting this project, the science of mindfulness and how it can bring about positive mental and physical changes, and the design theory behind the prototype.

This paper explores the psychological experiences of domestic workers in three contemporary Latin American films: Roma (Mexico, 2018), Crímenes de familia (Argentina, 2020) and Que Horas Ela Volta? (Brazil, 2015). Specifically, the motherhood of these three protagonists is explored and analyzed using psychological research that pertains to motherhood, trauma, and the relationships between domestic workers and the families that employ them. This paper reveals that contemporary Latin American cinema portrays domestic workers as having negative experiences of motherhood as a direct result of their occupation and proposes for further protections, policy change, and psychological research to take place for domestic workers in Latin America and beyond.