Sonic Movie Memories: Sound, Childhood, and American Cinema

Though the trend rarely receives attention, since the 1970s many American filmmakers have been taking sound and music tropes from children’s films, television shows, and other forms of media and incorporating those sounds into films intended for adult audiences. Initially, these references might seem like regressive attempts at targeting some nostalgic desire to relive childhood. However, this dissertation asserts that these children’s sounds are instead designed to reconnect audience members with the multi-faceted fantasies and coping mechanisms that once, through children’s media, helped these audience members manage life’s anxieties. Because sound is the sense that Western audiences most associate with emotion and memory, it offers audiences immediate connection with these barely conscious longings. The first chapter turns to children’s media itself and analyzes Disney’s 1950s forays into television. The chapter argues that by selectively repurposing the gentlest sonic devices from the studio’s films, television shows like Disneyland created the studio’s signature sentimental “Disney sound.” As a result, a generation of baby boomers like Steven Spielberg comes of age and longs to recreate that comforting sound world. The second chapter thus focuses on Spielberg, who incorporates Disney music in films like Close Encounters of the Third Kind (1977). Rather than recreate Disney’s sound world, Spielberg uses this music as a springboard into a new realm I refer to as “sublime refuge” - an acoustic haven that combines overpowering sublimity and soothing comfort into one fantastical experience. The second half of the dissertation pivots into more experimental children’s cartoons like Gerald McBoing-Boing (1951) - cartoons that embrace audio-visual dissonance in ways that soothe even as they create tension through a phenomenon I call “comfortable discord.” In the final chapter, director Wes Anderson reveals that these sonic tensions have just as much appeal to adults. In films like The Royal Tenenbaums (2001), Anderson demonstrates that comfortable discord can simultaneously provide a balm for anxiety and create an open-ended space that makes empathetic connections between characters possible. The dissertation closes with a call to rethink nostalgia, not as a romanticization of the past, but rather as a reconnection with forgotten affective channels.

A Haptic System for Depicting Mathematical Graphics for Students with Visual Impairments

When teaching students with visual impairments educators generally rely on tactile tools to depict visual mathematical topics. Tactile media, such as embossed paper and simple manipulable materials, are typically used to convey graphical information. Although these tools are easy to use and relatively inexpensive, they are solely tactile and are not modifiable. Dynamic and interactive technologies such as pin matrices and haptic pens are also commercially available, but tend to be more expensive and less intuitive. This study aims to bridge the gap between easy-to-use tactile tools and dynamic, interactive technologies in order to facilitate the haptic learning of mathematical concepts. We developed an haptic assistive device using a Tanvas electrostatic touchscreen that provides the user with multimodal (haptic, auditory, and visual) output. Three methodological steps comprise this research: 1) a systematic literature review of the state of the art in the design and testing of tactile and haptic assistive devices, 2) a user-centered system design, and 3) testing of the system’s effectiveness via a usability study. The electrostatic touchscreen exhibits promise as an assistive device for displaying visual mathematical elements via the haptic modality.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.