<p>Experiment One: The Effect of Noise on Autonomic Arousal</p><p><br></p><p>In response to the growing demand for research that helps us understand the complex interactions between Autonomic Arousal (AA) on behavior and performance there is an increasing need for robust techniques to efficiently utilize stimuli, such as sound, to vary the level of AA within a study. The goal of this study was to look at the impact of several factors, including sound intensity, order of presentation, and direction of presentation on skin conductance level, a widely utilized technique for approximating levels of AA. To do this we had 34 young adults ages 18- 34 listen to a series of 2-minute blocks of a sound stimuli based off a heating, ventilation, and air conditioning system (HVAC). Blocks included 5 single intensity conditions each block differing in 10 dBA steps ranging from 35-75 dBA. We presented blocks in both rising and falling level of intensity, with half the participants hearing them in a rising order first and half in a falling order first. The evidence found by this study suggests that increasing the sound level plays an important role in increasing AA and habituation is an extremely important factor that must be accounted for as it, in the case of typical young adults, quickly dampens the response to stimuli and subsequent stimuli. These findings suggest that researchers can best efficiently maximize the range of AA they can use while keeping their participants comfortable by starting out with the most intense stimuli and proceeding to the less intense stimuli, working with habitation instead of against it.</p><p> </p><p><br></p><p> Experiment Two: The Effect of Autonomic Arousal on Visual Attention</p><p><br></p><p>The goal of this study was to better understand how various levels of autonomic arousal impact different components of attentional control and if ASD-related traits indexed by Autism Quotient scores (AQ) might relate to alterations in this relationship. This study had 41 young adult participants (23 women, 17 men, 1 prefer not to say), ages ranging from 18 to 38 years old. Participants listened to varying levels of noise to induce changes in AA, which were recorded as changes in skin conductance level (SCL). To evaluate attentional control, participants preformed pro and anti-saccade visual gap–overlap paradigm tasks as measures of attentional control. The findings of this study suggest that increased levels of autonomic arousal are helpful for improving performance on anti-saccade tasks, which are heavily dependent on top-down attentional control. Additionally, increases in AQ scores were related to having less of a benefit from increasing levels of arousal on anti-saccade tasks. Additional interactions were also found and are discussed in this paper.</p>

<p>Volatile organic compounds (VOCs) are endogenous byproducts of metabolic pathways that can be altered by a disease or condition, leading to an associated and unique VOC profile or signature. Current methodologies for VOC detection include canines, gas chromatography-mass spectrometry (GC-MS), and electronic nose (eNose). Some of the challenges for canines and GC-MS are cost-effectiveness, extensive training, expensive instrumentation. On the other hand, a significant downfall of the eNose is low selectivity. This thesis proposes to design a breathalyzer using chemiresistive gas sensors that detects VOCs from human breath, and subsequently create an interface to process and deliver the results via Bluetooth Low Energy (BLE). Breath samples were collected from patients with hypoglycemia, COVID-19, and healthy controls for both. Samples were processed, analyzed using GC-MS and probed through statistical analysis. A panel of 6 VOC biomarkers distinguished between hypoglycemia (HYPO) and Normal samples with a training AUC of 0.98 and a testing AUC of 0.93. For COVID-19, a panel of 3 VOC biomarkers distinguished between COVID-19 positive symptomatic (COVID-19) and healthy Control samples with a training area under the curve (AUC) of receiver operating characteristic (ROC) of 1.0 and cross-validation (CV) AUC of 0.99. The model was validated with COVID-19 Recovery samples. The discovery of these biomarkers enables the development of selective gas sensors to detect the VOCs. </p><p><br></p><p>Polyethylenimine-ether functionalized gold nanoparticle (PEI-EGNP) gas sensors were designed and fabricated in the lab and metal oxide (MOX) semiconductor gas sensors were obtained from Nanoz (Chip 1: SnO₂ and Chip 2: WO₃). These sensors were tested at different relative humidity (RH) levels, and VOC concentrations. Contact angle which measures hydrophobicity, was 84° and the thickness of the PEI-EGNP coating was 11 µ m. The PEI-EGNP sensor response at RH 85% had a signal 10x higher than at RH 0%. Optimization of the MOX sensor was performed by changing the heater voltage and concentration of VOCs. At RH 85% and heater voltage of 2500 mV, the performance of the sensors increased. Chip 2 had higher sensitivity towards VOCs especially for one of the VOC biomarkers identified for COVID-19. PCA distinguished VOC biomarkers of HYPO, COVID-19, and healthy human breath using the Nanoz. A sensor interface was created to integrate the PEI-EGNP sensors with the printed circuit board (PCB) and Bluno Nano to perform machine learning. The sensor interface can currently process and make decisions from the data whether the breath is HYPO (-) or Normal (+). This data is then sent via BLE to the Hypo Alert app to display the decision.</p>

<p dir="ltr">Infographics are increasingly used as visual communication tools for conveying health information to diverse audiences. However, research is lacking on how specific infographic design factors influence learning outcomes. This study aimed to determine the comparative effectiveness of automatically advanced (Group A) versus manually advanced (Group B) infographics for promoting breast cancer awareness and knowledge. A mixed-methods quasi-experimental pretest-posttest design was utilized. The sample comprised 42 participants for analysis. Of these, the majority, 41 persons self-reported as female, with one participant indicating their gender as 'other.' Participant ages ranged from 25 to 55 years (M = 40.5, SD = 7.62). Most participants were well-educated, with graduate degrees or other advanced education beyond a bachelor's degree. Participants were randomly assigned to either the automatically advanced infographic group (Group A) or the manually advanced infographic group (Group B). Results indicated that Group B had significantly higher scores on the knowledge post-test compared to Group A, suggesting improved recall and comprehension of key information. There were no significant differences in cognitive load ratings or viewing duration between the groups. Qualitative feedback from participants suggested that Group B's manually advanced infographic facilitated better self-pacing and absorption of content. While the study's findings provide preliminary evidence supporting the efficacy of manually advanced infographics in learning complex health information, limitations are acknowledged. The research contributes to the design of patient education materials and underscores the necessity for further investigations across varied populations and health topics to understand the impact of infographic design more comprehensively on learning and behavior.</p>

<p dir="ltr">Humans are social beings and throughout our evolution we have survived and thrived thanks to our ability to cooperate [7]. Overcoming our current societal challenges from sustainability and energy conservation [8] to democracy, public health, and community building [9] will all require our continued cooperation. Yet, many of these present us with a dilemma where our short-term personal goals are at odds with the collective long-term benefits. For example, many of us listen NPR radio but never make a donation to help cover its operational costs. The success of cooperation during such dilemmatic situations often depends on communication, reward and punishment structures, social norms and cues [10], [11], [12], [13]. But how to encourage cooperation online where social cues are not readily available?</p><p dir="ltr">Accelerated by the COVID-19 pandemic and the prevalence of digital technologies, cooperation among individuals increasingly happens online where data-based feedback supports our decisions. Problematically, people online are often not only remote and asynchronous, but often also anonymous, which has resulted in de-individuation and antinormative behavior [14]. Social data, information that users share about themselves via digital technologies, may offer opportunities for social feedback design that affords perceptions of social cohesion and may support successful cooperation online.</p><p dir="ltr">This dissertation seeks to answer the normative question of how to design for cooperation in social data feedback charts in dilemmatic situations online. I conducted mixed methods design research by combining theory-driven design with a series of controlled experiments on Amazon Mechanical Turk to understand the perceptual and behavioral effects of visually unifying social data feedback charts. To achieve this, I mapped the design space for home energy feedback (<i>Chapter 2</i>) to guide my iterative and user-centered theorizing about how visual unity in social feedback charts might prime viewers with unified group perceptions (<i>Chapter 3</i>). I then validated my theorizing with controlled perceptual (<i>Chapter 4</i>) and decision experiments (<i>Chapter 5</i>).</p><p dir="ltr">The triangulated results offer evidence for visually unifying cues in feedback charts affecting social data interpretation (<i>Chapter 4</i>) and cooperation online (<i>Chapter 5</i>). Two visual properties: data point <i>proximity</i> and <i>enclosure</i> -, trigger variable levels of perceivable social unity that play a partial role in participants’ decision to cooperate in a non-monetary social dilemma situation online. I discuss the implications for future research and design (<i>Chapter 6</i>).</p>