The ongoing COVID-19 pandemic has significantly limited social contacts, thus contributing to deepening isolation. Therefore, SARS-CoV-2 exerted on humanity not only a physical impact but also a psychological one, often increasing the feeling of stress. The long-term effects of such a state could include the management of depression, so our study aimed to analyze groups of medical students in different periods of the pandemic (at the beginning of the pandemic, after half a year of the pandemic, after one year of the pandemic) in order to assess the impact of this situation on coping with stress. The impact of the pandemic on the development of stress factors such as alcohol consumption and smoking was also studied. The level of physical activity in the context of coping with an uncertain situation was also assessed. The impact of the above-mentioned factors on the behavior of students, including the Mini-COPE questionnaire, AUDIT test, the Fagerström test and the IPAQ questionnaire was analyzed. It has been shown that as the pandemic and the lockdown progressed, patients consumed more often or larger amounts of alcohol, smoked more cigarettes, and levels of physical activity decreased. All these factors may have had some impact on the deterioration of coping with stress among the respondents, which would indicate that the COVID-19 pandemic significantly contributed to an increase in the sense of stress among the students.
Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time‐consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision‐makers to learn what information to attend to from one another, rather than from an expert. In the first experiment, eye movements of N = 100 subjects were recorded while they repeatedly performed a standard behavioral finance investment task. Consistent with previous studies, we found that a significant proportion of subjects were affected by decision bias. In the second experiment, a different group of N = 100 subjects faced the same task but, after each choice, they received individual, machine learning‐generated feedback on whether their pre‐decision eye movements resembled those made by Experiment 1 subjects prior to good decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their successful peers, which in turn reduced their decision bias. Furthermore, subjects with low Cognitive Reflection Test scores gained more from the proposed form of process feedback than from standard behavioral feedback based on decision outcomes.
Recent studies reported that the attraction effect, whereby inferior decoys cause choice reversals, fails to replicate if the choice options are presented in a pictorial rather than abstract numerical form. We argue that the pictorial setting makes the similarity between decoy and target salient, while the abstract one emphasizes the inferiority relationship between them, crucial for the effect to occur. Thus, we used a novel experimental design in which both similarity and inferiority are equally easy to judge, their relative strength simple to manipulate, and choices incentivized rather than hypothetical. Using eye-tracking, we found that both the transfer of attention towards an undesirable target and choice reversal likelihood increase when the decoy is more strongly inferior but less similar to the target. This suggests that a key mechanism in the attraction effect is that, by virtue of its inferiority, a decoy projects a spotlight of attention towards the target, making it more attractive.
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