Emily Kubin

Universität Koblenz-Landau · Department of Psychology (Landau)

Significance All Americans are affected by rising political polarization, whether because of a gridlocked Congress or antagonistic holiday dinners. People believe that facts are essential for earning the respect of political adversaries, but our research shows that this belief is wrong. We find that sharing personal experiences about a political is...

Rising political polarization is, in part, attributed to the fragmentation of news media and the spread of misinformation on social media. Previous reviews have yet to assess the full breadth of research on media and polarization. We systematically examine 94 articles (121 studies) that assess the role of (social) media in shaping political polariz...

Americans disagree about many things, including what threats are most pressing. We suggest people morally condemn and dehumanize opponents when they are perceived as rejecting the existence or severity of important perceived threats. We explore perceived “threat rejection”across five studies (N=2,404) both in the real-world COVID-19 pandemic and in...

Meaning in life is tied to the stories people tell about their lives. We explore whether one timeless story—the Hero’s Journey—might make people’s lives feel more meaningful. This enduring story appears across history and cultures, and provides a template for ancient myths (e.g., Beowulf) and blockbuster books and movies (e.g., Harry Potter). Eight...

The media is increasingly blamed for inflaming political animosity, but it may also bridge partisan divides—with the right strategies. Past research highlights the outgroup‐experience effect: Sharing personal experiences (and not facts) helps to reduce partisan animosity. However, sharing facts is a pillar of good journalism and is essential for me...

The media is increasingly blamed for inflaming political animosity, but it may also bridge partisan divides—with the right strategies. Past research highlights the outgroup experience effect: sharing personal experiences (and not facts) helps to reduce partisan animosity. However, sharing facts is a pillar of good journalism, and is essential for m...

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied ma...

For as long as people have expressed ideas, others have tried to censor them. Questions of censorship sit at the center of legal and political debates but are relatively unstudied in psychology. Here we explore when—and why—people endorse censorship. Across 5 studies, using both qualitative and quantitative data, we find that people want to censor...

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and str...

Efforts to bridge political divides often focus on navigating complex and divisive issues. However, nine studies suggest that we should also focus on a more basic moral divide: the erroneous belief that political opponents lack a fundamental sense of right and wrong. This “basic morality bias” is tied to political dehumanization and is revealed by...

Previous research reveals that political scandals often have negative consequences for political candidates and may even result in resignation. However, politicians like Donald Trump frequently survive accusations of political misbehavior and intensive forms of scandalizing news coverage. Scholars have so far neglected to explain this phenomenon. W...

In two questionnaire studies, participants rated whether disagreement about 95 attitude pairs or 190 individual attitudes were characterized by threat, complexity, morality, politics, and harm. The attitudes cover a range of different topics, from abstract concepts to social groups and political figures and are the same attitudes in the Attitudes,...

Data was collected from 552 people from the United States every two weeks for one year for a 26-wave panel study. Participants recruited on Prolific completed measures of political attitudes, political identification, perceived threat, perceived stress, and social distance at every wave. They completed demographic measures at the first wave. They c...

Liberals and conservatives disagree, but are there some domains where we are more or less likely to observe ideological differences? To map the types of attitudes where we may be more or less likely to observe ideological differences, we draw on two approaches, the elective affinities approach, which suggests individual differences explains differe...

Worldview conflict is a regular part of life. We typically encounter information that disagrees with or disconfirms the way we understand and make sense of the world. People usually respond negatively to such experiences; however, do responses depend, in part, on people’s individual religious beliefs or orientations? We tested whether religious ori...

We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits w...

We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits w...

We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds....

We present a Pedestrian Dominance Model (PDM) to identify the dominance characteristics of pedestrians for robot navigation. Through a perception study on a simulated dataset of pedestrians, PDM models the perceived dominance levels of pedestrians with varying motion behaviors corresponding to trajectory, speed, and personal space. At runtime, we u...

We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds....

We present a real-time, data-driven algorithm to enhance the social-invisibility of autonomous robot navigation within crowds. Our approach is based on prior psychological research, which reveals that people notice and--importantly--react negatively to groups of social actors when they have negative group emotions or entitativity, moving in a tight...

We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based...