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Abstract Context effects occur when the preference between two alternatives is affected by the presence of an extra alternative. These effects are some of the most well studied phenomena in multi-alternative, multi-attribute decision making. Recent research in this area has revealed an intriguing pattern of results. On the one hand, these effects are robust and ubiquitous. That is, they have been demonstrated in many domains and different choice settings. On the other hand, they are fragile and they disappear or even reverse under different conditions. This pattern of results has spurred debate and speculation about the cognitive mechanisms that drive these choices. The attraction effect, where the preference for an option increases in the presence of a dominated decoy, has generated the most controversy. In this registered report, we systematically vary factors that are known to be associated with the attraction effect to build a solid foundation of empirical results to aid future theory development. We find a robust attraction effect across the different conditions. The strength of this effect is modulated by the display order (e.g., decoy top, target middle, competitor bottom) and mode (numeric vs. graphical) but not display layout (by-attribute vs. by-alternative). 

Job loss is a common and disruptive life event. It is known to have numerous long-term negative effects on financial, health, and social outcomes. While the negative effects of becoming unemployed on health and well-being are well understood, the influence of job loss on financial decisions has received little attention. Across a large-scale survey ( $N=37,854$), spending data from a bank ( $N=404,470$), and two online experiments (total $N=1,403$), we find that job loss increases financial risk-taking. First, in survey data, job loss is associated with elevated levels of self-reported financial risk-taking and lottery ticket purchases. Next, using administrative data from a large bank, we find consistent causal evidence of the influence of job loss on gambling spending. Although total spending decreases after job loss, gambling spending is less affected than our control categories. Finally, we turn to two incentive-compatible manipulations of job loss operationalized in a lab setting. We find that this experimental manipulation increases the take-up of financial risks. The current finding that job loss increases financial risk-taking could accentuate long-term negative financial effects of job loss. 

Abstract In early March 2020, two crises emerged: the COVID-19 public health crisis and a corresponding economic crisis resulting from business closures and skyrocketing job losses. While the link between socioeconomic status and infectious disease is well-documented, the psychological relationships among economic considerations, such as financial constraint and economic anxiety, and health considerations, such as perceptions of disease spread and preventative actions, is not well understood. Despite past research illustrating the strong link between financial fragility and a wide range of behaviors, surprisingly little research has examined the psychological relationship between the economic crisis and beliefs and behaviors related to the co-occurring health crisis. We show that financial constraint predicts people’s beliefs about both their personal risk of infection and the national spread of the virus as well as their social distancing behavior. In addition, we compare the predictive utility of financial constraint to two other commonly studied factors: political partisanship and local disease severity. We also show that negative affect partially mediates the relationship between financial constraint and COVID-19 beliefs and social distancing behaviors. These results suggest the economic crisis created by COVID-19 spilled over into people’s beliefs about the health crisis and their behaviors. 

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.