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Paranoia and belief updating during the COVID-19 crisis.

ABSTRACT: The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour.

SUBMITTER: Suthaharan P

PROVIDER: S-EPMC8458246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Project description:Student engagement in online learning enhance students performance and the outcomes of the learning process in online learning environment. The existed literature revealed various factors influencing student engagement in online leaning, however these studies were before the COVID-19 crisis. The purpose of the current paper is to explore the factors that influence student engagement in online learning during the COVID-19 crisis in middle school settings in developing countries where is a lack of studies about the factors influencing student's engagement in emergency remote learning during the crisis. A qualitative approach was used for data collection and analysis. Semi-structured interviews with 34 participants (14 students, 13 teachers, and 7 parents) were conducted for 20-30 min. Furthermore, online class observations were used for data collection; 13 online classes were observed. Each class was 40 min. A thematic analysis was used to categorize the findings into themes and subthemes. The findings of the study revealed that various factors influence student engagement in online learning during the crisis including infrastructure factors, cultural factors, digital inequality, and the threat to digital privacy. Cultural factors were the important factor that influences females because of parents' culture and their bias against females using online learning compared to male students. Teachers' presence and quality of content were the major factors that influence student engagement, where parental concerns, norms, and traditions emerged as the major factors in the crisis, influencing engagement. Most of the participants reported that teaching and learning online during the crisis has broadened the digital inequality and threatened their digital privacy which influenced negatively student engagement. The limitations of this research included the limited number of participants covering a large geographic area, and the research design using diverse and often limited educational software and delivery methods. Future studies could utilize a mixed-method approach and include more participants.Supplementary informationThe online version contains supplementary material available at 10.1007/s10639-021-10566-4.

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Paranoia and belief updating during the COVID-19 crisis.

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