abstract: With the growth of autonomous vehicles’ prevalence, it is important to understand the relationship between autonomous vehicles and the other drivers around them. More specifically, how does one’s knowledge about autonomous vehicles (AV) affect positive and negative affect towards driving in their presence? Furthermore, how does trust of autonomous vehicles correlate with those emotions? These questions were addressed by conducting a survey to measure participant’s positive affect, negative affect, and trust when driving in the presence of autonomous vehicles. Participants’ were issued a pretest measuring existing knowledge of autonomous vehicles, followed by measures of affect and trust. After completing this pre-test portion of the study, participants were given information about how autonomous vehicles work, and were then presented with a posttest identical to the pretest. The educational intervention had no effect on positive or negative affect, though there was a positive relationship between positive affect and trust and a negative relationship between negative affect and trust. These findings will be used to inform future research endeavors researching trust and autonomous vehicles using a test bed developed at Arizona State University. This test bed allows for researchers to examine the behavior of multiple participants at the same time and include autonomous vehicles in studies. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019

Indiana University-Purdue University Indianapolis (IUPUI) / Background: People with schizophrenia experience significant deficits in empathic skills, which are important for effective interpersonal relationships. Researchers have speculated about the roles of personal distress, emotion regulation, and metacognition in empathic interaction, but the impact of these constructs on empathy has yet to be empirically investigated. This study examines the relationships among these constructs in a sample of people with schizophrenia receiving community-based treatment (N = 58). It was hypothesized that better emotion regulation and metacognition, as well as reduced personal distress, would predict empathy. Further, emotion regulation was expected to mediate the relationship between personal distress and empathy, and metacognition was expected to moderate the relationship between personal distress and empathy. Method: Participants with schizophrenia or schizoaffective disorder completed self-report questionnaires of emotion regulation and personal distress, a performance-based measure of empathy, and an observer-rated interview to assess metacognition. Results: Metacognition, but not emotion regulation or personal distress, significantly predicted cognitive empathy performance, with a trend-level association for affective empathy performance. Mediation analyses revealed that emotion regulation mediates the relationship between personal distress and affective empathy performance, and moderation analyses revealed that metacognition moderates the same relationship. Moderation results suggest the relationship between personal distress and affective empathy performance is significant for those with low metacognition, but that the relationship is the opposite of hypotheses – increased personal distress is associated with better performance. Conclusions: This study is the first of its kind to examine performance-based empathy with personal distress, emotion regulation, and metacognition. Results suggest interventions targeted to improve metacognition may be useful in enhancing empathic skills. Future work is needed to improve existing measures of empathy and personal distress, and to parse apart the intricacies of the relationships among personal distress, emotion regulation, and empathy.

Indiana University-Purdue University Indianapolis (IUPUI) / Depression is an emerging risk factor for obesity; however, it is unclear whether certain depressive symptoms drive this relationship. Recent evidence suggests that atypical major depressive disorder (MDD) – whose key features include the reversed somatic-vegetative symptoms of hyperphagia (increased appetite) and hypersomnia (increased sleep) – is a stronger predictor of future obesity than other MDD subtypes. The present study sought to examine food attentional bias (increased attention to food cues), emotional eating (eating in response to negative emotions), and external eating (eating in response to external food cues) as candidate mechanisms of the depression-to-obesity relationship. This cross-sectional laboratory study hypothesized that total depressive symptom severity, hyperphagia severity, and hypersomnia severity would all be positively associated with measures of food attentional bias, emotional eating, and external eating. Data were collected from a sample of 95 undergraduate students. Depressive symptom severity was measured using the Hopkins Symptom Checklist (SCL-20); two measures of food attentional bias were obtained from eye tracking with high calorie food images: direction bias and duration bias; and emotional eating and external eating were assessed using the Dutch Eating Behavior Questionnaire. Simultaneous regression models (adjusted for age, sex, race/ethnicity, body mass index, and subjective hunger) revealed total depressive symptom severity and hypersomnia severity were not associated with measures of food attentional bias, while hyperphagia severity was negatively associated with direction bias but not associated with duration bias for high and low calorie food images. Findings related to emotional and external eating are consistent with previous literature: total depressive symptom severity and hyperphagia severity were positively associated with both emotional eating and external eating, and the pattern of results suggests that hyperphagia may be driving relationships between depressive symptoms and these eating behaviors. Hypersomnia severity was not associated with emotional eating and external eating, suggesting this symptom does not play an important role in the relationships between depressive symptoms and these eating behaviors. Future studies should examine prospective associations of hyperphagia severity with food attentional bias, emotional eating, and external eating in larger, more representative samples.

Indiana University-Purdue University Indianapolis (IUPUI) / Textual emotion classification is a task in affective AI that branches from sentiment analysis and focuses on identifying emotions expressed in a given text excerpt. It has a wide variety of applications that improve human-computer interactions, particularly to empower computers to understand subjective human language better. Significant research has been done on this task, but very little of that research leverages one of the most emotion-bearing symbols we have used in modern communication: Emojis. In this thesis, we propose several transformer-based models for emotion classification that processes emojis as input tokens and leverages pretrained models and uses them , a model that processes Emojis as textual inputs and leverages DeepMoji to generate affective feature vectors used as reference when aggregating different modalities of text encoding. To evaluate ReferEmo, we experimented on the SemEval 2018 and GoEmotions datasets, two benchmark datasets for emotion classification, and achieved competitive performance compared to state-of-the-art models tested on these datasets. Notably, our model performs better on the underrepresented classes of each dataset.

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