Staroverov, Vladimir M.

Background. Situations that are characterized by unexpected scenarios, unpredictable developments, and risks to life and health facilitate beliefs in conspiracy theories. These beliefs – together with reliable information, intentional and unintentional misinformation and rumors – determine attitudes toward the situations and ways to overcome them.

Objective. To examine the effect of belief in conspiracy theories on the recognition of the need for quarantine during the COVID-19 pandemic; the effect of personality traits on belief in conspiracy theories and on the recognition of the need for quarantine; the relationship of belief in conspiracy theories with assessment of the dangers of COVID-19 and with feelings of hopelessness.

Design. The study was conducted over a period when the number of coronavirus cases was growing, during the first three weeks of the lockdown in Russia. The sample included 667 undergraduate and graduate students aged 16–31 (M = 20.44, SD = 2.38); 74.2% of the participants were women. Respondents filled out two online questionnaires. The first related to perceptions about the COVID-19 pandemic; the second was a brief HEXACO inventory.

Results. Belief in Conspiracy Theories accounts for 13% of variance in Recognition of the Need for Quarantine; together with Dangers of COVID-19 and Hopelessness, conspiracy beliefs account for more than a quarter of the variance. Personality traits defined in the context of the 6-factor personality model have a small effect on conspiracy beliefs about the coronavirus and on perception of the need for quarantine.

Conclusion. Belief in conspiracy theories is associated not only with irrational views of reality, but also with the adoption of ineffective behaviors.

Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity.

In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress) based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects.