Cryptocurrency trading, gambling and problem gambling.

Some forms of speculative trading share similarities with gambling. Decisions are often based on limited information, short-term motives for gain, and highly volatile and uncertain outcomes. Given these similarities, there is evidence to show that people who are attracted to gambling are also statistically more likely to engage in higher risk speculation such as day-trading of stocks and crypto-currency trading. In this study, involving 543 people (M = 388, F = 155, 85% aged 18-40 years) who reported at least monthly sports-betting, crypto-currency trading or both, we examined whether gambling and problem gambling were reliable predictors of the reported intensity of crypto-currency trading. The results showed that gambling and problem gambling rates were highest among those who reported both activities and that problem gambling scores (PGSI) and engaging in stock trading was significantly related to measures of crypto-currency trading intensity as based on the time spent per day, number of trades and level of expenditure. Future research should examine whether gambling history and involvement influences how people manage their investments in crypto-currencies, including their propensity for making riskier decisions and experiencing more negative outcomes.

Autistic traits as a potential confounding factor in the relationship between schizotypy and conspiracy beliefs.

BACKGROUND: Conspiracy Theories (CT) are complex belief systems that view the world as being manipulated by multiple actors collaborating in the pursuit of malevolent goals. Although culture, education and sociological factors have been implicated in their development, psychological factors are recognized as important. Certain individual differences, including schizotypy and cognitive processing style, have been shown to make some individuals susceptible to CTs. However, the finding that schizotypy often co-occurs with autism spectrum disorder raises a question as to the relative and potentially confounding role of autistic traits in increasing vulnerability to CT beliefs. METHOD: A total of 508 adults were recruited from an international online panel. The study included measures of conspiracy beliefs, schizotypy and autistic traits as well as measures of information searching and cognitive style. RESULTS: The results confirmed that both autistic and schizotypy traits were positively associated with CT beliefs, but that schizotypy traits were the strongest predictor. Exploratory analyses of cognitive style measures indicated potential avenues for further investigation in relation in differences in cognitive processes that might underlie the development of CTs for in people with autistic traits as opposed to schizotypal traits. LIMITATIONS: The study was based on a self-report methodology and did not utilise a clinical sample. CONCLUSION: Both schizotypal and autistic traits are reliable predictors of conspiracy beliefs, but schizotypy appears to be the stronger predictor and that autistic traits are not a strong confounding factor in this relationship. However, autistic traits may pose an additional risk factor for CT beliefs.

Measuring Gambling Harm: The Influence of Response Scaling on Estimates and the Distribution of Harm Across PGSI Categories.

Recent research has shown that harm is not just a feature of problem gambling, but can also be observed in other lower risk categories. Some debates exist, however, as to the distribution of harm across these categories and how harm should be best measured. This study was designed to examine how estimates of self-reported harm are affected by the methodology used. A particular focus was on how harm estimates for low and higher risk gambling (as classified by the PGSI) varied when respondents were able to make more graded attributions of their harm to gambling. An online panel sample of 554 gamblers responded to a brief survey that included the PGSI, measures of gambling harm drawn from Browne et al. (Assessing gambling-related harm in Victoria: a public health perspective, Victorian Responsible Gambling Foundation, Melbourne, 2016) as well as questions about demographics and gambling habits. The recruitment was designed to obtain good representation of each PGSI group, with 23% found to be problem gamblers; 36% moderate risk and 21% low risk gamblers. In support of Browne et al. (2016), the findings showed that higher proportions of harm in low risk gamblers is likely to be identified when one uses binary or 'any harm' scoring, but that this effect mostly disappears when more graded scoring or attribution of harm measures are used. Higher risk PGSI groups consistently reported more harms and more serious harms than lower risk groups. It was concluded that the measurement of gambling harm and its estimated distribution over PGSI categories is quite sensitive to how it is measured.

Positive play and its relationship with gambling harms and benefits.

BACKGROUND: Engagement in responsible or 'positive play' strategies is known to be negatively associated with problem gambling, as indexed by measures such as the Problem Gambling Severity Index (PGSI). Less is known about whether positive play is associated with reduced harm or a greater ability to enjoy the recreational benefits of gambling. AIMS: This study investigated the relationship between positive play and gambling harm after controlling for PGSI scores and whether positive play moderated the relationship between PGSI scores and harm. It also examined whether positive play was related to perceived benefits associated with gambling. METHODS: The study utilised an online panel sample of 554 respondents who completed a survey that included the PGSI, measures of gambling harm drawn from Browne et al. (2016), and the newly developed Positive Play Scale (Wood et al., 2019). The study involved predominantly monthly gamblers with higher levels of gambling risk: 23% problem gamblers; 36% moderate risk; and 21% low risk gamblers. RESULTS: The results indicated that positive play was negatively associated with reduced gambling harm. The behavioural Positive Play subscales relating to pre-commitment and honesty and control explained additional variation in harm after controlling for PGSI scores. Higher levels of positive play also moderated and reduced the relationship between the PGSI and gambling harm. Perceived benefits were, unexpectedly, found to be higher in problem gamblers and negatively related to positive play. CONCLUSION: Behavioural measures of positive play appear to be useful moderating factors in understanding the relationship between problem gambling and harm. Higher-risk gamblers appear to experience both greater costs as well as benefits from gambling, which likely reflects a stronger personal need to engage in the activity.

COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs.

Previous studies have down that erroneous Conspiracy Theory (CT) beliefs develop more strongly in people who have underlying conspiratorial reasoning styles and psychopathological traits and particularly when they are faced with stressful external events (Swami et al., 2013; van Prooijen, 2018). In this study, we test this proposition by examining the individual differences associated with the development of COVID-19-related CT beliefs during the pandemic. A total of 660 adults completed a survey that captured COVID-related CT beliefs and broader conspiracy beliefs, education, perceived stress and attitudes towards government responses. The results showed that COVID-19 related CT beliefs were: strongly related to broader CT beliefs, higher in those with lower levels of education; and, positively (although weakly) correlated with more negative attitudes towards government responses. However, no relationship was found between COVID-19 beliefs and self-reported stress. These findings hold implications for why some people are more likely to be resistant to public health interventions relating to COVID-19. The findings encourage more detailed exploration of the causes and sources of CTs and, in particular, the role of social media use and other information sources in the development and perpetuation of health-related CT beliefs.