In contexts where people lack prior knowledge and risk awareness—such as the COVID-19 pandemic—even truthful visualizations of data can seem surprising. This can lead people to mistrust the veracity of the data and to discount it, leading to poor risk decisions. In this work, we illustrate how narrative visualizations can achieve a balance between the benefits of three common risk communication mediums (static visualizations, interactive simulations, and affect-laden anecdotes). We demonstrate empirically that viewing a narrative visualization mitigates the reduced concern induced by a static visualization when communicating COVID-19 transmission risk (Study 1). Through mediation analysis, we show that narrative visualizations are more effective than static visualizations at increasing concern about large risks because they increase one’s perceived understanding and trust in data (Study 2). We argue that narrative visualizations deserve attention as a distinct class of visualizations that have the potential to be powerful tools for scientific communication (especially in contexts where data are surprising, and empiricism is important).

Understanding the systematic ways that human decision making departs from normative principles has been important in the development of cognitive theory across multiple decision domains. We focus here on whether such seemingly “irrational” decisions occur in ethical decisions that impose difficult tradeoffs between the welfare and interests of different individuals or groups. Across three sets of experiments and in multiple decision scenarios, we provide clear evidence that contextual choice reversals arise in multiples types of ethical choice settings, in just the way that they do in other domains ranging from economic gambles to perceptual judgments (Trueblood et al., 2013; Wedell, 1991). Specifically, we find within-participant evidence for attraction effects in which choices between two options systematically vary as a function of features of a third dominated and unchosen option—a prima facie violation of rational choice axioms that demand consistency. Unlike economic gambles and most domains in which such effects have been studied, many of our ethical scenarios involve features that are not presented numerically, and features for which there is no clear majority-endorsed ranking. We provide empirical evidence and a novel modeling analysis based on individual differences of feature rankings within attributes to show that such individual variations partly explains observed variation in the attraction effects. We conclude by discussing how recent computational analyses of attraction effects may provide a basis for understanding how the observed patterns of choices reflect boundedly rational decision processes.

With increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy.