Latent Space and Social Psychological Models of Diffusion

The problem of social diffusion has animated sociological thinking on topics ranging from the spread of an idea, an innovation or a disease, to the foundations of collective behavior and political polarization. While network diffusion has been a productive metaphor, the reality of diffusion processes is often muddier. Ideas and innovations diffuse differently from diseases, but, with a few exceptions, the diffusion of ideas and innovations has been modeled under the same assumptions as the diffusion of disease. In this dissertation, I develop two new diffusion models for "socially meaningful" contagions that address two of the most significant problems with current diffusion models: (1) that contagions can only spread along observed ties, and (2) that contagions do not change as they spread between people. I augment insights from these statistical and simulation models with an analysis of an empirical case of diffusion - the use of enterprise collaboration software in a large technology company. I focus the empirical study on when people abandon innovations, a crucial, and understudied aspect of the diffusion of innovations. Using timestamped posts, I analyze when people abandon software to a high degree of detail.

To address the first problem, I suggest a latent space diffusion model. Rather than treating ties as stable conduits for information, the latent space diffusion model treats ties as random draws from an underlying social space, and simulates diffusion over the social space. Theoretically, the social space model integrates both actor ties and attributes simultaneously in a single social plane, while incorporating schemas into diffusion processes gives an explicit form to the reciprocal influences that cognition and social environment have on each other. Practically, the latent space diffusion model produces statistically consistent diffusion estimates where using the network alone does not, and the diffusion with schemas model shows that introducing some cognitive processing into diffusion processes changes the rate and ultimate distribution of the spreading information. To address the second problem, I suggest a diffusion model with schemas. Rather than treating information as though it is spread without changes, the schema diffusion model allows people to modify information they receive to fit an underlying mental model of the information before they pass the information to others. Combining the latent space models with a schema notion for actors improves our models for social diffusion both theoretically and practically.

The empirical case study focuses on how the changing value of an innovation, introduced by the innovations' network externalities, influences when people abandon the innovation. In it, I find that people are least likely to abandon an innovation when other people in their neighborhood currently use the software as well. The effect is particularly pronounced for supervisors' current use and number of supervisory team members who currently use the software. This case study not only points to an important process in the diffusion of innovation, but also suggests a new approach -- computerized collaboration systems -- to collecting and analyzing data on organizational processes.

Psychological and Interpersonal Implications of Believing that Everything is One: Identity, Personality, Values, and Worldviews

For thousands of years, people from a variety of philosophical, religious, spiritual, and scientific perspectives have believed in the fundamental unity of all that exists, and this belief appears to be increasingly prevalent in Western cultures. The present research was the first investigation of the psychological and interpersonal implications of believing in oneness. Self-report measures were developed to assess three distinct variants of the belief in oneness – belief in the fundamental oneness of everything, of all living things, and of humanity – and studies examined how believing in oneness is associated with people’s self-views, attitudes, personality, emotions, and behavior. Using both correlational and experimental approaches, the findings supported the hypothesis that believing in oneness is associated with feeling greater connection and concern for people, nonhuman animals, and the environment, and in being particularly concerned for people and things beyond one’s immediate circle of friends and family. The belief is also associated with experiences in which everything is perceived to be one, and with certain spiritual and esoteric beliefs. Although the three variations of belief in oneness were highly correlated and related to other constructs similarly, they showed evidence of explaining unique variance in conceptually relevant variables. Belief in the oneness of humanity, but not belief in the oneness of living things, uniquely explained variance in prosociality, empathic concern, and compassion for others. In contrast, belief in the oneness of living things, but not belief in oneness of humanity, uniquely explained variance in beliefs and concerns regarding the well-being of nonhuman animals and the environment. The belief in oneness is a meaningful existential belief that is endorsed to varying degrees by a nontrivial portion of the population and that has numerous implications for people’s personal well-being and interactions with people, animals, and the natural world.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.

Socialization of Early Prosocial Behavior: Parents' Talk about Emotions is Associated with Sharing and Helping in Toddlers.

What role does socialization play in the origins of prosocial behavior? We examined one potential socialization mechanism, parents' discourse about others' emotions with very young children in whom prosocial behavior is still nascent. Two studies are reported, one of sharing in 18- and 24-month-olds (n = 29), and one of instrumental and empathy-based helping in 18- and 30-month-olds (n = 62). In both studies, parents read age-appropriate picture books to their children and the content and structure of their emotion-related and internal state discourse were coded. Results showed that children who helped and shared more quickly and more often, especially in tasks that required more complex emotion understanding, had parents who more often asked them to label and explain the emotions depicted in the books. Moreover, it was parents' elicitation of children's talk about emotions rather than parents' own production of emotion labels and explanations that explained children's prosocial behavior, even after controlling for age. Thus, it is the quality, not the quantity, of parents' talk about emotions with their toddlers that matters for early prosocial behavior.