The Psychology of COVID-19 Booster Hesitancy, Acceptance and Resistance in Australia.

COVID-19 booster vaccinations have been recommended as a primary line of defence against serious illness and hospitalisation. This study identifies and characterises distinct profiles of attitudes towards vaccination, particularly the willingness to get a booster dose. A sample of 582 adults from Australia completed an online survey capturing COVID-related behaviours, beliefs and attitudes and a range of sociodemographic, psychological, political, social and cultural variables. Latent Profile Analysis (LPA) identified three subgroups: Acceptant (61%), Hesitant (30%) and Resistant (9%). Compared to the Acceptant group, the Hesitant and Resistant groups were less worried about catching COVID-19, used fewer official COVID-19 information sources, checked the news less, were lower on the agreeableness personality dimension and reported more conservatism, persecutory thinking, amoral attitudes and need for chaos. The Hesitant group also reported checking the legitimacy of information sources less, scored lower on the openness to new experiences personality dimension and were more likely than the Resistant and Acceptant groups to report regaining freedoms (e.g., travel) and work requirements or external pressures as reasons to get a booster. The Resistant group were higher on reactance, held more conspiratorial beliefs and rated their culture as being less tolerant of deviance than the Hesitant and Acceptant groups. This research can inform tailored approaches to increasing booster uptake and optimal strategies for public health messaging.

I Choose to Opt-Out of Answering: Individual Differences in Giving Up Behaviour on Cognitive Tests.

Under the Meta-reasoning model, the process of giving up when a solution may not be feasible reflects an adaptive metacognitive strategy, where individuals opt-out of responding to mitigate error and resource costs. However, research is still needed to determine whether individuals systematically vary in this behaviour and if so, which variables it meaningfully relates with. The current study (N = 176) is the first to examine factorial stability in giving up tendencies and its relationships with on-task confidence, cognitive ability, decision-making predispositions, and academic performance. To measure giving up tendencies, participants completed three cognitive tests which allowed for opting out, thereby capturing giving up frequency within each test and its consistency across tests. Participants also completed five other cognitive tasks embedded with confidence ratings, and a decision-making styles questionnaire. Confirmatory factor analyses were conducted on all giving up, confidence, and accuracy variables, with a three-factor solution having the best fit (containing a giving up factor, confidence factor, and cognitive ability factor). Supporting the proposed adaptive nature of giving up tendencies, the giving up factor correlated positively with cognitive ability, rational decision making, and academic performance. This research establishes factorial stability in giving up tendencies and provides a foundation for further investigation into its role within Meta-reasoning theory.

It's the deceiver and the receiver: Individual differences in phishing susceptibility and false positives with item profiling.

Phishing email is one of the biggest risks to online information security due to its ability to exploit human trust and naivety. Prior research has examined whether some people are more susceptible to phishing than others and what characteristics may predict this susceptibility. Given that there are no standardised measures or methodologies to detect phishing susceptibility, results have conflicted. To address this issue, the current study created a 40-item phishing detection task to measure both cognitive and behavioural indicators of phishing susceptibility and false positives (misjudged genuine email). The task is based on current real-life email stimuli (i.e., phishing and genuine) relevant to the student and general population. Extending previous literature we also designed a methodology for assessing phishing susceptibility by allowing participants to indicate perception of maliciousness of each email type and the actions they would take (keep it, trash it or seek further information). This enabled us to: (1) examine the relationships that psychological variables share with phishing susceptibility and false positives-both captured as consistent tendencies; (2) determine the relationships between perceptions of maliciousness with behavioural outcomes and psychological variables; and (3) determine the relationships between these tendencies and email characteristics. In our study, 150 undergraduate psychology students participated in exchange for partial course credit (98 Females; Mean age = 19.70, SD = 2.27). Participants also completed a comprehensive battery of psychometric tests assessing intelligence, pre- and on-task confidence, Big 6 personality, and familiarity/competence in computing and phishing. Results revealed that people showed distinct and robust tendencies for phishing susceptibility and false positives. A series of regression analyses looking at the accuracy of both phishing and false positives detection revealed that human-centred variables accounted for a good degree of variance in phishing susceptibility (about 54%), with perceptions of maliciousness, intelligence, knowledge of phishing, and on-task confidence contributing significantly, directly and/or indirectly via perception of maliciousness. A regression model looking at discriminating false positives has also shown that human-centred variables accounted for a reasonable degree of variance (41%), with perceptions of maliciousness, intelligence and on-task confidence contributing significantly, directly and/or indirectly via perception of maliciousness. Furthermore, the characteristics of the most effective phishing and misjudged genuine email items were profiled. Based on our findings, we suggest that future research should investigate these significant variables in more detail. We also recommend that future research should capture consistent response tendencies to determine vulnerability to phishing and false positives (rather than a one off response to a single email), and use the collection of the most current phishing email obtained from relevant sources to the population. It is important to capture perceptions of maliciousness of email because it is a key predictor of the action taken on the email. It directly predicts accuracy detection of phishing and genuine email, as well as mediating the relationships between some other predictors whose role would have been overlooked if the perceptions were not captured. The study provides the framework of human-centred variables which predict phishing and false positive susceptibility as well as the characteristics of email which most deceive people.

It's the deceiver, not the receiver: No individual differences when detecting deception in a foreign and a native language.

Individual differences in lie detection remain poorly understood. Bond and DePaulo's meta-analysis examined judges (receivers) who were ascertaining lies from truths and senders (deceiver) who told these lies and truths. Bond and DePaulo found that the accuracy of detecting deception depended more on the characteristics of senders rather than the judges' ability to detect lies/truths. However, for many studies in this meta-analysis, judges could hear and understand senders. This made language comprehension a potential confound. This paper presents the results of two studies. Extending previous work, in Study 1, we removed language comprehension as a potential confound by having English-speakers (N = 126, mean age = 19.86) judge the veracity of German speakers (n = 12) in a lie detection task. The twelve lie-detection stimuli included emotional and non-emotional content, and were presented in three modalities-audio only, video only, and audio and video together. The intelligence (General, Auditory, Emotional) and personality (Dark Triads and Big 6) of participants was also assessed. In Study 2, a native German-speaking sample (N = 117, mean age = 29.10) were also tested on a similar lie detection task to provide a control condition. Despite significantly extending research design and the selection of constructs employed to capture individual differences, both studies replicated Bond and DePaulo's findings. The results of Study1 indicated that removing language comprehension did not amplify individual differences in judge's ability to ascertain lies from truths. Study 2 replicated these results confirming a lack of individual differences in judge's ability to detect lies. The results of both studies suggest that Sender (deceiver) characteristics exerted a stronger influence on the outcomes of lie detection than the judge's attributes.