The Design and Evaluation of an Ambient Biofeedback Display

People use non-verbal cues, such as facial expressions, body language and tonal variations in speech, to help communicate emotion; however, these cues are not always available in interactive computer environments. For example, in computer-mediated communication, these cues don’t exist, and in interactive art, it is difficult to convey and represent emotion. Without being able to effectively communicate emotion, we can have difficulty relating to other people, and can lack self-regulation of our own emotional states. In this thesis, we propose to use abstract visual representations of emotion when regular emotion cues either don’t exist or are not appropriate to the medium. Through pilot testing and two user studies, we create abstract visual representations of emotional state and show that the visualizations are naturally interpretable and suitable for at-a-glance understanding. Finally, to demonstrate their utility, we incorporate the visual representations of emotion into a biofeedback task using ambient displays. We show that participants are able to use the visualizations to self-regulate their physiological arousal

Emotional Biosensing: Exploring Critical Alternatives

Emotional biosensing is rising in daily life: Data and categories claim to know how people feel and suggest what they should do about it, while CSCW explores new biosensing possibilities. Prevalent approaches to emotional biosensing are too limited, focusing on the individual, optimization, and normative categorization. Conceptual shifts can help explore alternatives: toward materiality, from representation toward performativity, inter-action to intra-action, shifting biopolitics, and shifting affect/desire. We contribute (1) synthesizing wide-ranging conceptual lenses, providing analysis connecting them to emotional biosensing design, (2) analyzing selected design exemplars to apply these lenses to design research, and (3) offering our own recommendations for designers and design researchers. In particular we suggest humility in knowledge claims with emotional biosensing, prioritizing care and affirmation over self- improvement, and exploring alternative desires. We call for critically questioning and generatively re- imagining the role of data in configuring sensing, feeling, ‘the good life,’ and everyday experience

THE EFFECT OF COLOR ON EMOTIONS IN ANIMATED FILMS

Lighting color in animated films is usually chosen very carefully in order to portray a specific mood or emotion. Artists follow conventional techniques with color choices with the intention to create a greater emotional response in the viewer. This study examined the relationship between color variations in videos and emotional arousal as indicated by physiological response. Subjects wore a galvanic skin response (GSR) sensor and watched two different videos: one portraying love and one portraying sadness. The videos were watched multiple times, each with variations in the lighting color. No significant effects on emotion for either hue or saturation were observed from the GSR sensor data. It was concluded that the hue and saturation of lighting are not likely to cause a significant impact in the strength of emotions being portrayed in animated films to a degree in which it can be measured by electrodermal activity

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Sensitive Pictures:Emotional Interpretation in the Museum

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction.Comment: Accepted for publication in CHI 202

Debunking Sustainability Excuses with Instrumentality and Expectancy Visualizations: A Physiological Perspective

This study advances the IS literature by investigating the effects of visualization on promoting sustainability knowledge and pro-environmental behaviors. Specifically, drawing on the visualization literature, we explain how the key visualization features, expectancy illustration, and interactivity affect individuals’ understanding of the impact of their behaviors on the environment, encouraging pro-environmental behaviors. Additionally, we draw on the pedagogy literature to explicate that the effects of visualization on learning outcomes and pro-environmental practices can be explained through individuals’ psychological responses in their course of interpreting the visualization. Collectively, this study presents our endeavor in understanding the roles of visualization in ecological discourse by integrating the visualization literature and sustainability research. Moreover, by unboxing individuals’ psychological processes in interpreting visualization, we offer a fresh perspective to understanding the promises and challenges of using visualization for knowledge acquisition

MINDtouch: Embodied mobile media ephemeral transference

Copyright @ 2013 ISAST.This article reviews discoveries that emerged from the author's MINDtouch media research project, in which a mobile device was repurposed for visual and non-verbal communication through gestural and visual mobile expressivity. The work revealed new insights from emerging mobile media and participatory performance practices. The author contextualizes her media research on mobile video and networked performance alongside relevant discourse on presence and the embodiment of technology. From the research, an intimate, phenomenological and visual form of mobile expression has emerged. This form has reconfigured the communication device from voice and text/SMS only to a visual and synesthetic mode for deeper expression

Beyond shape – An exploration in alternative forms for data visualization

This thesis explores the topic of alternative forms in data visualization and the ways visualization affects the communication of data it is based on. It does this through the creation of a machine learning based data visualization system prototype. It examines norms and ideals of data visualization as a set of systems aimed for simplification, situating visualization as a tool with the potential power to affect how we perceive the complexity of the world by either highlighting or obscuring information. It aims to critically highlight these norms by taking an exploratory aim to visualizing information by increasing potential interpretations of a particular set of data instead of reducing them. Norms prevalent in the field of data visualization are explored, and through this, the concept of alternative is defined. Then the dataset to visualize is defined through an exploration of current discussions around issues of increasing amounts of data, the complexity of the systems producing that data and the interpretations they enforce through the data they produce. Through this, the concept of machine detected human emotions in a text is chosen as a particular example of computational reduction to be explored through the prototype. In order to counteract this identified reduction in complexity, a system which produces a mapping between visual attributes and detected emotional attributes is proposed. The design of this system utilizes recognized critical design concepts by creating a type of post-optimal object: A visualization that causes more interpretations in its reader than reading the data itself. The process of visualization follows prevalent norms within the field but applies identified forms of alternativeness in order to create ambiguity in the visual artifacts created by the prototype. Machine learning methods are applied through a collaborative process in order to create an artificially intelligent system that automatically analyses the emotional values of a given text, and maps those to a particular set of figures. Some of the visual artifacts are then tested on a set of users, in order to assess how the visualization might affect the communication of the data it is based on and how it succeeds in increasing interpretational complexity. While not aimed toward conclusive evidence, the result of the test seems to indicate success in increasing interpretational complexity, but a lack of success in communicating the numeric data the visualizations are based on – in this sense leading to the end-result no longer being a functional data visualization, but rather a form of data-driven illustration.Denna avhandling handlar om alternativa former inom datavisualisering och sätten visualisering påverkar kommunicering av data den byggs utav, genom skapandet av en maskininlärningsbaserad datavisualiseringsprototyp. Genom det, undersöks de ideala normerna inom datavisualisering som fält som en samling konventioner med simplifiering som ändamål. Datavisualisering placeras som ett verktyg med förmågan att ändra hur vi uppfattar världens komplexitet genom att antingen framhäva eller undangömma. Genom att ställa ett explorativt mål – att visualisera data genom att utvidga tolkningar istället för att reducera dem då produceringen av den data som visualiseras är komplext är avsikten att kritiskt examinera dessa normer. Först undersöks fältets normer och genom detta definieras vad kunde anses som alternativ datavisualisering. Sedan identifieras ett komplext problem som kunde visualiseras genom en utforskning av aktuella synpunkter runt den växande mängden data I världen omkring oss och komplexiteten av de system som producerar detta data. Genom detta väljs maskinbaserad detektion av människokänslor som ett problem där maskinbaserad reduktion kan forskas genom visualisering. För att motverka reduktionistisk behandling av komplicerade domän, föreslås ett system som producerar översättningar mellan emotionella egenskaper och visuella egenskaper. Konstruktionen av detta system använder sig av kritiska designmetoder genom att bygga ett postoptimalt objekt: En datavisualisering som inte försöker kommunicera data den består utav så klart som möjligt, men istället försöker orsaka en ökande mängd tolkningar i sin läsare. Processen följer de normer som är rådande I fältet, men med ändamålet att orsaka tvetydighet för läsaren. Maskininlärning används för att implementera en kollaborativt framställd översättningsmodell mellan de emotionella och de visuella egenskaperna. Slutligen testas systemet genom en mätning av effekterna på läsare och på så sätt utvärderas visualiseringens förmåga att öka mängden tolkningar. Undersökningen har inte som mål att ge ett slutligt resultat för funktionaliteten av systemet, men skall fungera som guide för nästa iterationer. Undersökningen verkar visa att de producerade visualiseringarna lyckas i att öka mängden tolkningar för en bild till en nivå som påminner om tolkningarna för text, men lyckas inte att kommunicera känslorna från den lästa texten. Detta gör slutresultatet mer av en data-inspirerad illustration, än en datavisualisering som termen konventionellt används

Trolling Twitter

Political polarization is a defining feature of the contemporary American political landscape. While there is little doubt that elite polarization levels have risen dramatically in recent decades, there is some debate over the existence of a corresponding rise in mass polarization. Recent scholarship on mass polarization has cited evidence related to citizens’ positions on public policy issues, party sorting, and geographic polarization; however, questions remain as to the nature and extent of mass polarization in online spaces. Specifically, more needs to be known regarding how expressions of elite polarization influence the formation of polarized communities within social media. This dissertation examines the question: Does elite polarization contribute to mass polarization in social media? This question is approached in three stages. First, this dissertation tests whether or not a causal link between elite and mass polarization strengthens with temporal proximity to highly politicized and potentially polarizing events over the span of the 2016 Republican presidential primary. Second, this dissertation examines the instant effects of elite polarization by examining a minute-by-minute live stream of reactions on Twitter during the first 2016 presidential debate. Third, this dissertation tests a contemporary theory which claims a presidential candidate’s patterns of speech sows the seeds of mass polarization in the form of resentment, fear, or incivility. This dissertation also employs the use of network analysis tools to measure the extent to which polarized communities form on social media in response to elite cues. The nature of such causal relationships provides insight into the influence polarizing messages by elites may have on mass polarization while taking into consideration the unique characteristics of the social media communications environment. In doing so, this dissertation offers a blueprint for future researchers who seek to better understand how networked technologies shape human interactions