Low foreign language proficiency reduces optimism about the personal future

"Not me!": Optimistic bias and college students' perceived susceptibility, severity, benefits, and self-efficacy in following COVID-19 preventive guidelines

Objective The COVID-19 pandemic has created unprecedented challenges for university administrators and health professionals to keep doors open and students safe. Optimistic bias and the Health Belief Model serve as foundations for understanding students' perceived susceptibility and severity for contracting the virus and their perceived benefits and self-efficacy for following prevention recommendations. Participants: A total of 1,773 undergraduate and graduate students from a large mid-western university participated. Methods: Students were emailed an online survey examining their perceptions and knowledge of COVID-19 and their adherence to recommended preventive measures. Results: Almost one-half of students do not think they will get COVID-19 but said their friends are at risk. The majority are following most Centers for Disease Control and Prevention recommendations and understand the perceived benefits of doing so. Conclusions: Results could aid university officials in developing effective COVID-19 messages that could lead to reductions in cases numbers, deaths, and spread of the virus.

There is no belief update bias for neutral events: failure to replicate Burton et al. (2022)

In a recent paper, Burton et al. claim that individuals update beliefs to a greater extent when learning an event is less likely compared to more likely than expected. Here, we investigate Burton's et al.'s, findings. First, we show how Burton et al.'s data do not in fact support a belief update bias for neutral events. Next, in an attempt to replicate their findings, we collect a new data set employing the original belief update task design, but with neutral events. A belief update bias for neutral events is not observed. Finally, we highlight the statistical errors and confounds in Burton et al.'s design and analysis. This includes mis-specifying a reinforcement learning approach to model the data and failing to follow standard computational model fitting sanity checks such as parameter recovery, model comparison and out of sample prediction. Together, the results find little evidence for biased updating for neutral events.

Incorporating social knowledge structures into computational models

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points-with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

Risk perception and optimism during the early stages of the COVID-19 pandemic

Slowing the spread of COVID-19 requires people to actively change their lives by following protective practices, such as physical distancing and disinfecting their hands. Perceptions about the personal risk of COVID-19 may affect compliance with these practices. In this study, we assessed risk perception and optimism about COVID-19 in a multinational (UK, USA and Germany), longitudinal design during the early stages of the pandemic (16 March 2020; 1 April 2020; 20 May 2020). Our main findings are that (i) people showed a comparative optimism bias about getting infected and infecting others, but not for getting severe symptoms, (ii) this optimism bias did not change over time, (iii) optimism bias seemed to relate to perceived level of control over the action, (iv) risk perception was linked to publicly available information about the disorder, (v) people reported adhering closely to protective measures but these measures did not seem to be related to risk perception, and (vi) risk perception was related to questions about stress and anxiety. In additional cross-sectional samples, we replicated our most important findings. Our open and partly preregistered results provide detailed descriptions of risk perceptions and optimistic beliefs during the early stages of the COVID-19 pandemic in three Western countries.

Using a Foreign Language Changes Medical Judgments of Preventative Care

Every day, multilinguals around the world make important healthcare decisions while using a foreign language. The present study examined how the use of a native vs. non-native language shapes evaluations and decisions about preventative care. Bilinguals were randomly assigned to evaluate a series of medical scenarios in either their native or non-native language. Each scenario described potential adverse effects of a medical condition and a preventative treatment, as well as the population risk of disease- or treatment-related complications. Participants judged the perceived negativity and likelihood of experiencing adverse effects and indicated how willing they would be to accept the preventative treatment. We found that bilinguals using a foreign language perceived disease symptoms and treatment side effects to be less negative than those using their native tongue. Foreign language users were also more likely to account for the objective risks associated with medical conditions and treatments when making decisions about preventative care. We conclude that the use of a native vs. foreign language changes how people evaluate the consequences of accepting and declining preventative treatment, with potential implications for millions of providers and patients who routinely make medical choices in their non-native tongue.