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. 2020 Dec 7;35(2):486–496. doi: 10.1002/acp.3770

Irrational beliefs differentially predict adherence to guidelines and pseudoscientific practices during the COVID‐19 pandemic

Predrag Teovanović 1,2, Petar Lukić 2, Zorana Zupan 2,3, Aleksandra Lazić 2, Milica Ninković 2, Iris Žeželj 2,3,✉
PMCID: PMC7753549  PMID: 33362344

Summary

In the coronavirus “infodemic,” people are exposed to official recommendations but also to potentially dangerous pseudoscientific advice claimed to protect against COVID‐19. We examined whether irrational beliefs predict adherence to COVID‐19 guidelines as well as susceptibility to such misinformation. Irrational beliefs were indexed by belief in COVID‐19 conspiracy theories, COVID‐19 knowledge overestimation, type I error cognitive biases, and cognitive intuition. Participants (N = 407) reported (1) how often they followed guidelines (e.g., handwashing, physical distancing), (2) how often they engaged in pseudoscientific practices (e.g., consuming garlic, colloidal silver), and (3) their intention to receive a COVID‐19 vaccine. Conspiratorial beliefs predicted all three outcomes in line with our expectations. Cognitive intuition and knowledge overestimation predicted lesser adherence to guidelines, while cognitive biases predicted greater adherence, but also greater use of pseudoscientific practices. Our results suggest an important relation between irrational beliefs and health behaviors, with conspiracy theories being the most detrimental.

Keywords: cognitive biases, conspiracy theories, COVID‐19 health behavior, knowledge overestimation, pseudoscience

With the coronavirus pandemic, societies are forced to introduce new measures to curb the infection rate. This means that ordinary people are asked to adopt enhanced protective health behaviors, such as physical distancing and frequent handwashing. However, along with these official recommendations, people are exposed to pseudoscientific information and unverified content pertaining to COVID‐19, which have proliferated rapidly through social media (Depoux et al., 2020; Kouzy et al., 2020; Mian & Khan, 2020; Zarocostas, 2020). In fact, we are “not just fighting an epidemic; we're fighting an infodemic. Fake news spreads faster and more easily than this virus, and is just as dangerous” (WHO, 2020). Pseudoscientific recommendations such as consuming garlic, drinking ginger tea or rinsing nose with saline to prevent contracting the virus, became so pervasive that the WHO (n.d.) had to officially debunk the claims about their effectiveness. Certain pseudoscientific practices (PSPs) are extremely dangerous—for example, more than 700 Iranians were reported dead of methanol poisoning falsely believing it was a miracle cure for COVID‐19 (Associated Press, 2020). Another “victim” of the infodemic is the COVID‐19 vaccine, which is still in development. Even amid the pandemic, the topic of vaccination has provoked an online backlash (e.g., Mooney, 2020). Given the grave consequences of vaccination refusal, such as failure to reach herd immunity, it is important to understand why some people might be reluctant to get immunized.

Both adherence to official public health recommendations and the use of PSPs might be embedded in a set of irrational beliefs. We refer to irrational beliefs as an umbrella term that covers beliefs which lack a solid evidence base or defy principles of normative rationality (Žeželj & Lazarević, 2019). It encompasses beliefs that differ in content (e.g., paranormal beliefs, conspiracy beliefs or anti‐science attitudes) and form, but what they have in common is that they may inhibit reasoning processes (Rizeq et al., 2020). Irrational beliefs have also been referred to as “epistemically‐suspect” (Pennycook, Cheyne, et al., 2015; Pennycook, Fugelsang, & Koehler, 2015) or “contaminated mindware” (Rizeq et al., 2020; Stanovich et al., 2016).

In this study, we explored whether people who differ in their predisposition to form irrational beliefs also differ in their tendency to follow appropriate preventive measures for COVID‐19. More precisely, whether irrational beliefs such as belief in COVID‐19 conspiracy theories, overestimation of one's own COVID‐19 knowledge, susceptibility to type I error cognitive biases, and cognitive intuition predict adherence to COVID‐19 guidelines, use of PSPs, and intention to receive a COVID‐19 vaccine if it were available. In addition to cognitive intuition and cognitive biases that are more general, knowledge overestimation (i.e., a calibration error relating to the discrepancy between actual, objectively measured and subjective, self‐estimated knowledge), and belief in conspiracy theories (potential source of false knowledge about a particular subject) are measures that are content‐laden and refer to a specific context, event or a class of events. Content laden beliefs, in comparison to cognitive biases and cognitive intuition, may be more or less pertinent for different health behaviors in the current pandemic.

The COVID‐19 pandemic is a public health crisis and, as such, a fertile ground for conspiracy theories (Gonçalves‐Sá, 2020; van Prooijen & Douglas, 2017). This aspect of the infodemic might be especially dangerous since medical conspiracy theories have been consistently associated with a range of risky health behaviors including less sunscreen use, not getting annual check‐ups or vaccinations, less contraceptive use, and HIV medication nonadherence (e.g., Bogart et al., 2010; Jolley & Douglas, 2014; Oliver & Wood, 2014; Setbon & Raude, 2010; Thorburn & Bogart, 2005). However, recent studies examining the relation between belief in COVID‐19 conspiracy theories and self‐reported adherence to recommended behaviors have produced inconsistent results. While some found a relation with adherence to health guidelines (Imhoff & Lamberty, 2020; see also Swami & Barron, 2020), others did not (Čavojová et al., 2020; see also Plohl & Musil, 2020). Furthermore, conspiracy theories might be predictive of some, but not other types of recommended protective behaviors—for example, believing in COVID‐19 conspiracy theories was related to less social‐distancing but unrelated to personal‐hygiene behaviors (Pummerer & Sassenberg, 2020). As for pseudoscientific practices, it was shown that people more prone to conspiratorial thinking were more likely to endorse claims related to the effectiveness of complementary and alternative medical treatments in general (Lamberty & Imhoff, 2018; Lobato et al., 2014; Pennycook, Cheyne, et al., 2015) and that they reported greater use of PSPs to prevent contracting coronavirus (Čavojová et al., 2020; Pummerer & Sassenberg, 2020; see also Imhoff & Lamberty, 2020). A possible mechanism through which conspiracy theories may influence health behaviors is by amplifying distrust toward institutions, which makes people less willing to follow official COVID‐19 recommendations (Pavela Banai et al., 2020). In order to stay healthy, some people may then turn to PSPs, which are not recommended by authorities due to absence of evidence for their effectiveness. Thus, although there is converging evidence suggesting that conspiracy theories are predictive of PSPs, more studies are needed to explore their influence on adherence to COVID‐19 guidelines. We expected that stronger beliefs in COVID‐19 conspiracy theories would predict lesser adherence to COVID‐19 guidelines (H1a), greater use of PSPs (H1b), and a weaker intention to get vaccinated (H1c).

Knowledge overestimation is typically calculated as a difference between self‐assessed and objectively assessed knowledge on a certain subject (Ackerman et al., 2002; Harvey, 1997; Kleitman & Stankov, 2001; Kruger & Dunning, 1999; Stankov, 2000). Pennycook et al. (2017) showed that intuitive individuals tended to be more overconfident on the cognitive reflection test, rating themselves as relatively reflective, despite their test scores showing otherwise. Thus, it is possible that some people would overestimate their COVID‐19 related knowledge because their nonreflexivity prevents them from recognizing their ignorance (see also Dunning, 2011; Kruger & Dunning, 1999), which would in turn make them less likely to engage in preventive behaviors during the pandemic. In fact, knowledge overestimation has been widely documented in the health domain (see Dunning et al., 2004). The inability to recognize one's lack of skill or knowledge (Kruger & Dunning, 1999) can, in this context, prevent people from familiarizing themselves enough with the official guidelines. In this study, we expected that higher levels of COVID‐19 related knowledge overestimation would predict lesser adherence to COVID‐19 guidelines (H2a), greater use of PSPs (H2b), and weaker intention to get vaccinated (H2c).

Cognitive biases, as systematic departures from what is normatively defined rational behavior, can be viewed as a relatively broad category of irrational beliefs. Taking into account that the cognitive biases space is considerably heterogeneous (see, for example, Kahneman & Frederick, 2005; Pohl, 2004; Stanovich, 2009; Teovanović et al., 2015), we focused on a subset which concerns a general tendency to make a type I error. Such a choice was motivated by Haselton and Buss' (2000) notion on cost asymmetry between two types of errors (type I and type II) that can occur when judgments are made under uncertainty. More precisely, as the probability of making either type of error cannot be simultaneously minimized, people tend to decrease the likelihood of making the costlier one (Haselton et al., 2009). Since the consequences of type II errors (i.e., false‐negatives) refer to failures to notice actual relations between phenomena, they are usually considered as costlier. Thus, people are typically biased toward type I errors (i.e., false‐positives) which refers to making incorrect conclusions about the existence of relations between unrelated phenomena. In our study, biases that are based on making a type I error were represented by the illusory correlation detection (Smedslund, 1963), base‐rate neglect (Tversky & Kahneman, 1974), gambler's fallacy (Tversky & Kahneman, 1974), and hot‐hand fallacy (Gilovich et al., 1985) bias. Although cognitive biases have shown to be predictive of some paranormal (Bressan, 2002; Pennycook et al., 2012; Šrol, 2020; van Prooijen et al., 2017) and pseudoscientific beliefs (Pennycook, Cheyne, et al., 2015; Redelmeier & Tversky, 1996; Šrol, 2020), they remain underexplored in the domain of both PSPs and adherence to public health guidelines. Starting from the general hypothesis about relation between irrational beliefs and health behaviors, we expected that a higher susceptibility to such cognitive biases would predict lesser adherence to COVID‐19 guidelines (H3a), greater use of PSPs (H3b), and weaker intention to get vaccinated (H3c).

Cognitive intuition is often assessed with the cognitive reflection test (CRT; Frederick, 2005), which consists of three items that lead most people to answer quickly and incorrectly. While cognitive biases are related to specific types of predictable errors on heuristics‐and‐biases tasks, cognitive intuition refers to an inability to “resist reporting the response that first comes to mind” (Frederick, 2005, p. 27). Previous research has shown that misleading intuitions predict paranormal beliefs (Pennycook et al., 2012; Ståhl & van Prooijen, 2018) as well as religious beliefs (Pennycook, Fugelsang, & Koehler, 2015; Shenhav et al., 2012). In the health domain, cognitive intuition was related to beliefs about the effectiveness and self‐reported use of complementary and alternative treatments both before (Browne et al., 2015; McPhetres & Pennycook, 2019; see also Lindeman, 2011) and during the COVID‐19 pandemic (Čavojová et al., 2020; Erceg et al., 2020; Pennycook et al., 2020). However, recent findings on the relation between CRT performance and adherence to official COVID‐19 guidelines are mixed – while some found a negative relation (Stanley et al., 2020), others failed to establish any link (Čavojová et al., 2020; Erceg et al., 2020; Pennycook et al., 2020; cf. Stanley et al., 2020). We expected that higher cognitive intuition would predict lesser adherence to COVID‐19 guidelines (H4a), greater use of PSPs (H4b), and weaker intention to get vaccinated against COVID‐19 (H4c).

In sum, the present study builds upon emerging research on evidence and nonevidence based COVID‐19 related recommendations by examining the predictiveness of different irrational beliefs for COVID‐19 related health behaviors in a single design.

1. METHODS

1.1. Sample and procedure

We recruited a total of 754 participants via a snowball procedure and through social networks (Facebook and Viber groups), between April 10 and April 22, 2020. The final sample (N = 407) included participants who met all of the following criteria: fully completed the questionnaires, accurately responded to all three attention check items, and confirmed that they did not search for information online while completing the questionnaires. The mean age of participants was 34.88 years (SD = 12.81). Females were overrepresented in the sample (76.9%), as were participants with higher education: about 0.5% of participants completed elementary school, 42.5% completed high‐school, 30.2% completed undergraduate studies, and 26.3% completed graduate studies. The questionnaire was administered in Serbian language.

This study is a part of a larger project (https://osf.io/9njp3/?view_only=f778fab66d8b4a52acbc01bce9bfcc14). The full list of measures is available at https://osf.io/qk9nf/?view_only=f778fab66d8b4a52acbc01bce9bfcc14.

1.2. Instruments and variables

Belief in COVID‐19 conspiracy theories scale was developed for the purpose of this study. It consisted of 13 items representing most popular conspiracy theories circulating in digital media and conversations on social networks (e.g., “5G electromagnetic field exposure played a role in the coronavirus pandemic”). Response options ranged from 1 (Completely Disagree) to 5 (Completely Agree). The scale was highly reliable (α = .90). We averaged responses for the 13 items to form a total score.

COVID‐19 knowledge overestimation was calculated as a difference between standardized scores of subjectively estimated and objectively assessed knowledge. Subjective estimation of knowledge related to COVID‐19 was represented by a single item (“How would you rate your knowledge about the new coronavirus?”), on a scale ranging from 1 (Insufficient) to 5 (Excellent). Objective knowledge was assessed using a previously developed test (Lep et al., 2020), consisting of nine true or false statements related to COVID‐19 (e.g., “The coronavirus is transmitted through respiratory droplets”); correct responses were summed to create the overall score. To convert them into a common metric, both subjective and objective scores were transformed to a standard scale; finally, the objective knowledge z‐score was subtracted from the subjective knowledge z‐score.

Type I error cognitive biases were measured with six heuristics‐and‐biases tasks which tap into peoples' tendency to erroneously recognize relations between unrelated phenomena. They were represented with two covariation detection problems as measures of illusory correlation (Smedslund, 1963), two base‐rate problems (Tversky & Kahneman, 1974), and two probability judgment tasks measuring hot‐hand fallacy (Gilovich et al., 1985) and gambler's fallacy (Tversky & Kahneman, 1974). We calculated the total score as the average of biased responses across tasks (α = .52).

Cognitive Intuition was assessed via the CRT (Frederick, 2005), consisting of three items which cue a fast but incorrect response. 1 One example of an item is the following question: “A racket and a ball cost 1100 RSD [Serbian currency] in total. The racket costs 1000 RSD more than the ball. How much does the ball cost?.” Although the correct answer is “50 RSD” approximately 40% of participants answered “100 RSD”. A total score was calculated as a mean of intuitive responses (range 0–1; α = .63).

Adherence to COVID‐19 guidelines was measured with 12 items based on the official WHO and the Serbian Ministry of Health COVID‐19 guidelines (α = .69). Five items related to newly introduced (e.g., physical distancing) or enhanced (e.g., thorough handwashing) health behaviors, in the previous 2 weeks, rated on a scale ranging from 1 (Never) to 5 (Very Often). Additional seven items referred to behaviors recommended to be avoided during the outbreak (e.g., visiting other households or attending social gatherings). Participants rated the frequency of these behaviors in the previous 2 weeks by entering a number. These seven items were multiplied by −1 so that higher scores would always correspond to greater adherence to COVID‐19 guidelines. To convert into a common metric, all item scores were standardized. To handle outliers, z values above 3.29 were winsorized. A total score was calculated as an average of all 12 items.

Use of PSPs related to COVID‐19 was assessed via a 12‐item scale created for the purpose of this study (α = .73). Five items were based on the list of common myths indicated on the WHO website (WHO, n.d.), while the remaining seven were based on PSPs against COVID‐19 commonly reported in digital media outlets. Participants rated how often they used PSPs in the previous 2 weeks as a means to protect themselves against COVID‐19 on a scale ranging from 1 (Never) to 5 (Very Often). We averaged the participants' responses for all 12 items to create a total score.

Vaccination intention was assessed by asking participants to rate their willingness to receive a COVID‐19 vaccine if it were available at that time. The scale ranged from 1 (Definitely would not) to 5 (Definitely would).

2. RESULTS

Table 1 shows frequencies for each health related behavior during the pandemic, indicating that participants reported high adherence to the official recommendations. When the frequencies regarding adherence to COVID‐19 guidelines were aggregated, they indicated that 76.7% (n = 312) of the participants reported adhering to at least three out of five newly introduced or enhanced health behaviors often or very often. As for behaviors that were recommended to be avoided by the guidelines (e.g., visiting other households), as much as 27.8% (n = 113) of participants reported always avoiding all of them. In terms of using PSPs, responses were more diverse. While 67.6% (n = 275) of participants reported having used at least one PSP often or very often, 11.3% (n = 46) had rarely or never used any of the listed PSPs. Finally, although 49.1% (n = 200) of participants reported they would definitely or probably receive a COVID‐19 vaccine, a significant percentage (26.8%; n = 109) of participants stated they definitely or probably would not.

TABLE 1.

Frequencies of adherence to COVID‐19 guidelines (prevention and risk avoidance) and pseudoscientific practices

Adherence to COVID‐19 guidelines (prevention)
In the last 2 weeks, how often have you done the following to protect yourself from COVID‐19? Never Rarely Sometimes Often Very often
Washed/disinfected your hands after coughing or sneezing 12.3% (50) 8.1% (33) 14.7% (60) 29.2% (119) 35.6% (145)
Avoided touching your face 4.9% (20) 11.3% (46) 23.3% (95) 38.3% (156) 22.1% (90)
Cleaned and disinfected frequently used surfaces 9.3% (38) 8.4% (34) 26.5% (108) 34.4% (140) 21.4% (87)
Washed your hands with soap and water for at least 20 s 2.2% (9) 2.2% (9) 11.5% (47) 29.0% (118) 55.0% (224)
Kept at least 1 m distance from people outside your household 5.4% (22) 5.7% (23) 5.7% (23) 23.8% (97) 59.5% (242)
Adherence to COVID‐19 Guidelines (risk avoidance)
In the last two weeks, how often have you… Never Once Twice Three times > Three times
Had direct physical contact (hugging, kissing, handshaking) with someone outside of your household 72.0% (293) 7.6% (31) 8.6% (35) 3.2% (13) 8.6% (35)
Visited someone 71.3% (290) 12.3% (50) 9.1% (37) 3.9% (16) 3.4% (14)
Had someone visit your place 64.1% (261) 16.5% (67) 9.1% (37) 6.1% (25) 4.2% (17)
Attended a public gathering (e.g. a party) 93.9% (382) 2.9% (12) 2.0% (8) 0.5% (2) 0.7% (3)
Had contact with people outside of your household 60.0% (244) 14.5% (59) 10.8% (44) 4.9% (20) 9.8% (40)
Left your house for no essential reason 59.7% (243) 9.6% (39) 6.1% (25) 3.4% (14) 21.1% (86)
Visited a park or a picnic area for a reason other than walking your dog 79.1% (322) 7.1% (29) 3.4% (14) 2.2% (9) 8.1% (33)
Pseudoscientific practices
In the last 2 weeks, how often have you engaged in the following behaviors with the aim of preventing to contract coronavirus Never Rarely Sometimes Often Very often
Drank water every 15 min 51.6% (210) 21.1% (86) 14.7% (60) 9.3% (38) 3.2% (13)
Consumed garlic 44.0% (179) 23.8% (97) 18.9% (77) 9.8% (40) 3.4% (14)
Drank alcoholic beverages 53.3% (217) 16.5% (67) 20.1% (82) 6.9% (28) 3.2% (13)
Drank ginger tea, baking soda with lemon, or similar drinks 46.7% (190) 17.7% (72) 17.9% (73) 11.5% (47) 6.1% (25)
Used essential oils 83.0% (338) 7.6% (31) 6.4% (26) 2.0% (8) 1.0% (4)
Used colloidal silver 95.8% (390) 1.0% (4) 1.5% (6) 1.7% (7) 0.0% (0)
Followed special diet 53.3% (217) 19.9% (81) 16.5% (67) 7.4% (30) 2.9% (12)
Inhaled saline solution 90.4% (368) 5.9% (24) 2.0% (8) 1.5% (6) 0.2% (1)
Consulted an astrologer 95.6% (389) 2.5% (10) 1.5% (6) 0.2% (1) 0.2% (1)
Consumed honey or similar products 34.4% (140) 19.2% (78) 20.9% (85) 17.9% (73) 7.6% (31)
Taken large amounts of vitamin C 16.2% (66) 18.7% (76) 27.5% (112) 25.1% (102) 12.5% (51)
Disinfected surfaces with natural products (e.g. vinegar, baking soda) 51.6% (210) 14.0% (57) 15.5% (63) 11.1% (45) 7.9% (32)
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Descriptive statistics and bivariate correlations between all measured variables are presented in Table 2. Overall, participants showed a low to moderate susceptibility to intuitive reasoning on the CRT and type I error cognitive biases. As for content‐laden measures of irrational beliefs, participants moderately endorsed COVID‐19 conspiracy theories and only modestly overestimated their knowledge about COVID‐19. 2 Eight out of 12 zero order correlation coefficients between four types of irrational beliefs and the three health behaviors were statistically significant (ps < .05), all in the expected direction.

TABLE 2.

Descriptive statistics and correlations

Min Max M SD Skewness Kurtosis 1 2 3 4 5 6
1. Cognitive intuition (0–1) .00 1.00 .32 .35 .65 −.92
2. Type 1 error cognitive biases (0–1) .00 .83 .24 .22 .70 −.41 .39 ***
3. COVID‐19 conspiracy beliefs (1–5) 1.00 4.38 2.25 .79 .28 −.87 .28 *** .33 ***
4. COVID‐19 knowledge overestimation −3.27 6.03 .00 1.37 .37 .89 .04 .05 .08
5. Adherence to COVID‐19 guidelines −1.47 .62 .02 .40 −1.11 1.52 −.14 ** .05 −.17 * −.12 *
6. PSP (1–5) 1.00 3.42 1.83 0.52 0.53 −0.20 .20 *** .28 *** .31 ** −.07 .14 **
7. COVID‐19 vaccine (1–5) 1.00 5.00 3.34 1.29 −0.32 −0.96 −.11 * −.09 −.53 * −.07 .33 *** −.07
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Note: SESkewness = .12; SEKurtosis = .24.

*

p < .05.

**

p < .01.

***

p < .001.

To discern the predictive power of a single variable in the set while controlling for effects of the other variables in the model, we ran three multiple regression models with adherence, PSPs, and vaccination intention as outcome variables. Cognitive intuition, susceptibility to type I error cognitive biases, overestimation of COVID‐19 knowledge, and belief in COVID‐19 conspiracy theories served as predictors (see Table 3).

TABLE 3.

Multiple regression analysis for adherence to COVID‐19 guidelines, PSP, and intentions to vaccinate against COVID‐19

Adherence to COVID‐19 guidelines PSP COVID‐19 vaccine
b SE 95% CI p b SE 95% CI p b SE 95% CI p
LL UL LL UL LL UL
Cognitive intuition −.18 .06 −.30 −.06 .003 .10 .08 −.05 .25 .192 .04 .17 −.30 .37 .826
Type 1 error cognitive biases .32 .10 .13 .52 .001 .42 .12 .18 .66 .001 .56 .28 .02 1.10 .042
COVID‐19 conspiracy beliefs −.09 .03 −.14 −.04 .001 .16 .03 .10 .22 <.001 −.92 .07 −1.07 −.78 <.001
COVID‐19 knowledge overestimation −.03 .01 −.06 .00 .028 −.04 .02 −.07 .00 .037 −.03 .04 −.11 .04 .390
R 2 .07 .14 .29
R 2 adjusted .06 .14 .29
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Abbreviations: CI, confidence interval; LL, lower limit; UL, upper limit.

The regression model predicting adherence to COVID‐19 guidelines had relatively low explanatory power (F[4402] = 7.78, R 2 = .07, p < .001). As expected, belief in COVID‐19 conspiracy theories, overestimation of COVID‐19 related knowledge, and cognitive intuition negatively predicted adherence to COVID‐19 guidelines (H1a, H2a, and H4a respectively). Contrary to our hypothesis (H3a), susceptibility to type I error cognitive biases positively predicted adherence to COVID‐19 guidelines.

When it comes to the use of PSPs, belief in COVID‐19 conspiracy theories, COVID‐19 knowledge overestimation and susceptibility to type I error cognitive biases significantly contributed to the model, explaining 14% of the variance (F[4402] = 16.89, R 2 = .14, p < .001). This suggested that those with stronger beliefs in COVID‐19 conspiracy theories, less COVID‐19 knowledge overestimation and greater susceptibility to type 1 cognitive biases were more likely to follow pseudoscientific advice, which is in line with H1b, and H2b, but not with H3b.

Regarding vaccination intentions, the model explained a substantial amount of the variance, that is, 29% (F[4402] = 41.89, R 2 = .29, p < .001). In line with H1c, belief in COVID‐19 conspiracy theories negatively predicted COVID‐19 vaccination intentions, suggesting that those endorsing COVID‐19 conspiracy theories may be less likely to get vaccinated when a vaccine becomes available. Contrary to our expectations (H3c), we observed that susceptibility to type I error cognitive biases positively contributed to the model, suggesting that those that were more prone to cognitive biases may be more likely to get vaccinated against the virus. To make sure that the inclusion of a vaccine‐related conspiracy question (i.e., “One should be careful when a vaccine against coronavirus is developed because no one knows what they will inject in us”) in the conspiracy beliefs measure did not artificially increase the predictivity of the model, we conducted a sensitivity analysis and re ran the regression omitting this question. This model accounted for 26% of the variance (F[4402] = 35.2, p < .001), corroborating the robustness of the relation between beliefs in COVID‐19 conspiracy theories and COVID‐19 vaccination intentions.

Across all regression models, belief in COVID‐19 conspiracies and type I error cognitive biases were the most consistent predictors of health behaviors related to COVID‐19.

To gain more insight about the relations between the outcome variables, and to make sure that the significant regression models were not the result of p value inflation due to multiple comparisons (Gordi & Khamis, 2004), we conducted a canonical correlation analysis, a multivariate type of the general linear model (Thompson, 2005). The results are presented in Table 4, suggesting that two out of three canonical correlations were significant.

TABLE 4.

Canonical correlation analysis

Canonical function R Wilk's Λ F p
1 .60 .58 20.38 <.001
2 .25 .91 6.85 <.001
3 .18 .97
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The first canonical function reflects the relations between beliefs in COVID‐19 conspiracy theories and, marginally, susceptibility to type I error cognitive biases and cognitive intuition, on the one hand, and weaker COVID‐19 vaccination intentions and greater use of PSPs, on the other (Table 5). These results indicate that vaccination hesitancy and use of PSPs have relations with belief in conspiracy theories and to some extent, intuitive and biased thinking. This component explained more than 10% of the variance across the two sets of variables. Moreover, the proportions of explained variance support the examination of irrational beliefs as predictors of health behaviors.

TABLE 5.

Canonical loadings

Canonical function 1 Canonical function 2
Set 1 Cognitive intuition −.33 .23
Type 1 error cognitive biases −.34 .81
COVID‐19 conspiracy beliefs −1.00 −.01
COVID‐19 knowledge overestimation −.06 −.49
Set 2 Adherence to COVID‐19 guidelines .28 .63
PSP −.53 .80
COVID‐19 vaccine .88 .40
R 2 Set 1 by Set 1 .31 .24
Set 1 by Set 2 .11 .02
Set 2 by Set 2 .38 .40
Set 2 by Set 1 .14 .03
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The second canonical function reflects the relations between a greater susceptibility to type I error cognitive biases and lesser COVID‐19 knowledge overestimation, on the one hand, and a higher rate of acceptance of all three available preventive practices, on the other. This suggests that following all types of health practices is related to a greater susceptibility to type I error cognitive biases and lesser COVID‐19 knowledge overestimation. However, since this component explained only 3% of the variance across the variable sets, this finding should be interpreted with caution.

3. DISCUSSION

We found that health behaviors related to COVID‐19—adherence to COVID‐19 guidelines, use of PSPs, and intentions to get vaccinated against COVID‐19—were all predicted by irrational beliefs to some degree.

Belief in COVID‐19 conspiracy theories was the most consistent predictor of each type of health behavior. This is in line with previous findings showing positive relations between conspiratorial thinking and use of PSPs (Čavojová et al., 2020; Oliver & Wood, 2014), nonadherence to medical or public health recommendations (Imhoff & Lamberty, 2020; Oliver & Wood, 2014), and unwillingness to get vaccinated (e.g., Setbon & Raude, 2010). It further strengthens the view of conspiratorial beliefs as a part of contaminated mindware (Rizeq et al., 2020; Stanovich et al., 2016) or unwarranted beliefs detrimental to one's rational thought process, which may influence decisions that lead to unfavorable health outcomes. In our study, the relation between conspiratorial beliefs and unwillingness to get vaccinated against COVID‐19 was particularly strong. Importantly, this result remained even when the overall conspiracy theory measure excluded the vaccination conspiracy theory item, and was based on theories about the origin of the virus and political abuse of the health crisis which should not necessarily affect vaccination intention. For example, one may assume that a person believes that the virus was fabricated in a laboratory, but still considers it dangerous and is willing to protect themselves with a vaccine. Our data, however, suggest that even beliefs relating to the origin of the virus are related to a weaker vaccination intention. This could be due to a more general assumption common to both phenomena: that key information, such as the truth about the harmfulness of vaccines or about the source of pandemic, is hidden from the general public and not included in the official narrative about it (Lukić et al., 2019; Wood et al., 2012).

Other irrational beliefs were also somewhat predictive of COVID‐19 health behaviors. Those who overestimated their COVID‐19 knowledge reported lesser adherence to COVID‐19 guidelines, but were also somewhat less prone to using PSPs. We have initially assumed that knowledge overestimation may be predictive of greater use of PSPs, however it seems plausible that it operates in a similar fashion for both types of health behaviors. For example, overconfidence in one's knowledge may decrease interest in learning about preventive behaviors, regardless of whether these are evidence‐based or not. We also found that those that rely on cognitive intuition and follow their “gut feelings” may be l more relaxed when it comes to adhering to behaviors recommended by COVID‐19 guidelines. Although there was some indication of a trend for cognitive intuition being related to greater use of PSPs, this result was marginal and we will not interpret it further.

Type I error cognitive biases were directly related (zero order correlation) only to PSPs, however after controlling for other irrational beliefs in the regression analyzes they positively predicted all three health behaviors. This finding may be due to the cost asymmetry between false‐positive and false‐negative errors (Haselton & Buss, 2000) particularly in critical situations such as a global pandemic. More precisely, the cost of a type II error—not following any preventive practice which may result in an avoidable COVID‐19 infection—could be perceived as higher than the cost of a type I error—following all proposed practices, including those with no evidence of effectiveness. Thus, the susceptibility toward type I error cognitive biases, despite the lower reliability of the measure, was consistently positively associated with uncritical pursuit of any health recommendation. This pattern of results of cognitive biases predicting greater engagement in both pseudoscientific and recommended health behaviors by COVID‐19 guidelines and cognitive intuition predicting lesser engagement in health behaviors is in line with the conceptual distinction between type I error cognitive biases and cognitive intuition. Namely, the opposite pattern of results is suggestive of type 1 cognitive biases being related to greater compliance, and intuition to lesser compliance with any kind of health advice.

Of note, adherence to COVID‐19 guidelines was the most weakly predicted health behavior. A relatively modest predictive power of irrational beliefs on adherence to COVID‐19 guidelines might be due to ceiling effects that have likely resulted from lockdown policies and campaigns (e.g., #StayAtHome) that were in effect both world‐wide and locally at the time of the study. This has left people with fewer behavioral choices, particularly with regards to physical distancing. On the other hand, engaging in PSPs was optional and, at the time of data collection, there was no vaccine developed against COVID‐19 (no vaccination policies were in place). Therefore, vaccination was assessed as an intention, as opposed to retrospective self‐reporting. All this may have permitted a greater influence of intrinsic dispositions such as irrational beliefs on these health behaviors.

3.1. Strengths, limitations, and suggestions for future work

Our study adds to the existing literature in several ways. First, we have examined a comprehensive set of irrational beliefs in relation to COVID‐19 health behaviors, which allowed us to compare their predictive power. Second, we examined different types of health behaviors pertaining to evidence‐based and pseudoscientific recommendations related to the COVID‐19 pandemic. In addition, the instruments we developed for their assessment proved to be reliable. Third, the fact that the data were collected during the peak of the pandemic (Serbia Coronavirus, n.d.) testifies to their validity.

Several limitations of the study merit comment. We have included preventive behaviors included in the official COVID‐19 guidelines at the time when the study was conducted, which precluded other preventive behaviors that were not included in these guidelines such as wearing masks or gloves. Importantly, at the time of the study, the guidelines were supported by lockdown policies, which may have reduced the variability and influence of irrational beliefs on these behaviors. In addition, the health behaviors were self‐reported. Future studies could examine observed behaviors (e.g., assessing physical distancing using location‐tracking apps) to increase the confidence attached to the current findings.

There was relatively low reliability of composite scores derived from different cognitive biases tasks (Toplak et al., 2011; West et al., 2008) as well as the summary score derived from the three CRT items (Frederick, 2005; Thomson & Oppenheimer, 2016), however, these are typical findings within the field. As a direct consequence of this, their relations with other measures are attenuated (see, for example, John & Benet‐Martínez, 2000). In that sense, the reported coefficients of correlation can be seen as conservative estimates of strength of the association between type I cognitive biases, cognitive intuition, and health‐related behaviors. Another possible limitation is that the current study did not control for cognitive ability when exploring the effects of cognitive intuition. We did not include measures of cognitive ability in the design since the current study focuses on irrationality, which is conceptually and theoretically distinct from measures of intelligence (e.g., Frederick, 2005; Stanovich, 2009). However, previous studies reported a moderate correlation between cognitive reflection and measures of intelligence (e.g., Shenhav et al., 2012; Toplak et al., 2011). Therefore, it is unclear whether the relations observed between CRT and health‐related behaviors reflect solely the inability to resist intuitive responses or more general limitations in cognitive capacity. Future studies should address this question.

Finally, as we pointed out in the results, scores on the COVID‐19 knowledge test were negatively skewed, indicating that the test was easy for the participants. This was likely due to heavy media coverage of COVID‐19 at the time of the study and that people were continuously learning about the virus. In addition, although we tried to reach as a diverse sample of participants as possible by seeding the links to different social media groups, a probability sample representative of the population would certainly provide a greater variance in the objective knowledge measure; further, results obtained via snowballing recruitment are typically less generalizable than those obtained via probability recruitment (King et al., 2014). Nonetheless, despite these limitations, knowledge overestimation and other assessments included in our study revealed meaningful relations between irrational beliefs, namely conspiratorial thinking, and relevant health behaviors during the COVID‐19 pandemic.

Future research should explore factors contributing to conspiracy beliefs. We believe that distrust in official institutions and related constructs such as political cynicism are the most promising candidates (see e.g., Brotherton et al., 2013; Swami et al., 2010, 2011); there is already evidence that such constructs could be important for COVID‐19 related conspiracy theories (Pavela Banai et al., 2020). Importantly, the rapid proliferation of pandemic misinformation through social media (Depoux et al., 2020; Kouzy et al., 2020; Mian & Khan, 2020; Zarocostas, 2020) may also have the potential to substantially impact health outcomes. Some of the promising interventions to counter science misinformation and conspiracy theories include presenting people with factual corrections (e.g., Porter et al., 2018; Porter et al., 2019), combining factual corrections with personal/social narratives (Lazić & Žeželj, 2020), “inoculating” them by presenting anti‐conspiracy information prior to conspiracy theories (e.g., Jolley & Douglas, 2017), and exposing misleading argumentation techniques (e.g., Roozenbeek & van der Linden, 2019).

4. CONCLUSION

A starting point for social and behavioral sciences to mitigate the effects of global pandemics (Van Bavel et al., 2020) is to understand the psychological underpinnings of health behaviors during the course of the crisis. Our study highlights that people prone to a particular set of irrational beliefs are less likely to follow official COVID‐19 guidelines, more likely to engage in PSPs and have weaker intentions to vaccinate. Once again, and strongly corroborating our hypotheses, conspiracy theories have shown to pose a serious threat to public health—one that demands future attention to prevent unfavorable health outcomes and spread of diseases. The pattern of results for cognitive intuition and COVID‐19 knowledge overestimation was less robust, but mostly in line with initial expectations predicting lesser adherence to COVID‐19 guidelines. Somewhat unexpectedly, susceptibility to type I error cognitive biases predisposed people to engage in any type of preventive behavior, whether evidence‐based or pseudoscientific. Together, the findings show that irrational beliefs are an important factor to consider when tailoring behavioral health policies, especially in a global health crisis.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest concerning the authorship or the publication of this article.

AUTHOR CONTRIBUTIONS

Predrag Teovanović, Petar Lukić, and Zorana Zupan designed the study. Iris Žeželj, Aleksandra Lazić, Zorana Zupan, Milica Ninković, Petar Lukić, and Predrag Teovanović drafted the manuscript. Milica Ninković, Predrag Teovanović, and Petar Lukić conducted the analyzes. All authors revised and approved the manuscript.

ACKNOWLEDGEMENT

The study design and data collection were approved by the Institutional Review Board of the Department of Psychology, University of Belgrade (Protocol no. #2020‐018).

Teovanović P, Lukić P, Zupan Z, Lazić A, Ninković M, Žeželj I. Irrational beliefs differentially predict adherence to guidelines and pseudoscientific practices during the COVID‐19 pandemic. Appl Cognit Psychol. 2021;35:486–496. 10.1002/acp.3770

[Corrections made on 15 December 2020, after first online publication: ORCID iDs have been added and the Funding information removed in this version.]

Endnotes

1

We have initially included three additional items to examine if an extended version of instrument would provide a more reliable measure. However, their inclusion decreased internal consistency to α = .58; thus, we decided to proceed with the original 3‐item version.

2

Since scores on all variables had a non‐normal distribution, we re‐ran all the analyzes on normalized scores using Blom's transformation. Results of the analyzes on normalized scores are detailed at https://osf.io/wfr5a/?view_only=455508f470e94cd0bade1fd6d4386061.

DATA AVAILABILITY STATEMENT

The full dataset and analysis code for this paper are available at the (https://osf.io/cpe5t/?view_only=f778fab66d8b4a52acbc01bce9bfcc14).

REFERENCES

  1. Ackerman, P. L. , Beier, M. E. , & Bowen, K. R. (2002). What we really know about our abilities and our knowledge. Personality and Individual Differences, 33(4), 587–605. 10.1016/s0191-8869(01)00174-x [DOI] [Google Scholar]
  2. Associated Press . (2020, April 27). Over 700 Iranians dead from methanol poisoning over false belief the chemical cures COVID‐19. TIME. https://time.com/5828047/methanol-poisoning-iran/
  3. Bogart, L. M. , Wagner, G. , Galvan, F. H. , & Banks, D. (2010). Conspiracy beliefs about HIV are related to antiretroviral treatment nonadherence among African American men with HIV. Journal of Acquired Immune Deficiency Syndromes, 53(5), 648–655. 10.1097/qai.0b013e3181c57dbc [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bressan, P. (2002). The connection between random sequences, everyday coincidences, and belief in the paranormal. Applied Cognitive Psychology, 16(1), 17–34. 10.1002/acp.754 [DOI] [Google Scholar]
  5. Brotherton, R. , French, C. C. , & Pickering, A. D. (2013). Measuring belief in conspiracy theories: The generic conspiracist beliefs scale. Frontiers in psychology, 4, 279. 10.3389/fpsyg.2013.00279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Browne, M. , Thomson, P. , Rockloff, M. , & Pennycook, G. (2015). Going against the herd: Understanding the psychosocial factors underlying the ‘vaccination confidence gap’. PLoS One, 10(9), e0132562. 10.1371/journal.pone.0132562 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Čavojová, V. , Šrol, J. , & Mikušková, E. B. (2020). Scientific reasoning as a predictor of health‐related beliefs and behaviors in the time of COVID‐19 . 10.31234/osf.io/tfy5q [DOI] [PubMed]
  8. Depoux, A. , Martin, S. , Karafillakis, E. , Preet, R. , Wilder‐Smith, A. , & Larson, H. (2020). The pandemic of social media panic travels faster than the COVID‐19 outbreak. Journal of Travel Medicine, 27(3), taaa031. 10.1093/jtm/taaa031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dunning, D. (2011). The Dunning‐Kruger effect: On being ignorant of one's own ignorance. In Olson J. M. & Zanna M. P. (Eds.), Advances in experimental social psychology. Advances in experimental social psychology (Vol. 44, pp. 247–296). Cambridge, MA: Academic Press. 10.1016/B978-0-12-385522-0.00005-6 [DOI] [Google Scholar]
  10. Dunning, D. , Heath, C. , & Suls, J. M. (2004). Flawed self‐assessment: Implications for health, education, and the workplace. Psychological Science in the Public Interest, 5(3), 69–106. 10.1111/j.1529-1006.2004.00018.x [DOI] [PubMed] [Google Scholar]
  11. Erceg, N. , Ružojčić, M. , & Galić, Z. (2020). Misbehaving in the corona crisis: The role of anxiety and unfounded beliefs . 10.31234/osf.io/cgjw8 [DOI] [PMC free article] [PubMed]
  12. Frederick, S. (2005). Cognitive reflection and decision making. The Journal of Economic Perspectives, 19(4), 25–42. 10.1257/089533005775196732 [DOI] [Google Scholar]
  13. Gilovich, T. , Vallone, R. , & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295–314. 10.1016/0010-0285(85)90010-6 [DOI] [Google Scholar]
  14. Gonçalves‐Sá, J. (2020). In the fight against the new coronavirus outbreak, we must also struggle with human bias. Nature Medicine, 26(3), 305–305. 10.1038/s41591-020-0802-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gordi, T. , & Khamis, H. (2004). Simple solution to a common statistical problem: Interpreting multiple tests. Clinical Therapeutics, 26(5), 780–786. 10.1016/S0149-2918(04)90078-1 [DOI] [PubMed] [Google Scholar]
  16. Harvey, N. (1997). Confidence in judgment. Trends in Cognitive Sciences, 1(2), 78–82. 10.1016/s1364-6613(97)01014-0 [DOI] [PubMed] [Google Scholar]
  17. Haselton, M. G. , Bryant, G. A. , Wilke, A. , Frederick, D. A. , Galperin, A. , Frankenhuis, W. E. , & Moore, T. (2009). Adaptive rationality: An evolutionary perspective on cognitive bias. Social Cognition, 27(5), 733–763. 10.1521/soco.2009.27.5.733 [DOI] [Google Scholar]
  18. Haselton, M. G. , & Buss, D. M. (2000). Error management theory: A new perspective on biases in cross‐sex mind reading. Journal of Personality and Social Psychology, 78(1), 81–92. 10.1037/0022-3514.78.1.81 [DOI] [PubMed] [Google Scholar]
  19. Imhoff, R. , & Lamberty, P. (2020). A bioweapon or a hoax? The link between distinct conspiracy beliefs about the coronavirus disease (COVID‐19) outbreak and pandemic behavior. 10.31234/osf.io/ye3ma [DOI] [PMC free article] [PubMed]
  20. John, O. P. , & Benet‐Martínez, V. (2000). Measurement: Reliability, construct validation, and scale construction. In Reis H. T. & Judd C. M. (Eds.), Handbook of research methods in social and personality psychology (pp. 339–369). Cambridge, England: Cambridge University Press. [Google Scholar]
  21. Jolley, D. , & Douglas, K. M. (2014). The effects of anti‐vaccine conspiracy theories on vaccination intentions. PLoS ONE, 9(2), e89177. 10.1371/journal.pone.0089177 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jolley, D. , & Douglas, K. M. (2017). Prevention is better than cure: Addressing anti‐vaccine conspiracy theories. Journal of Applied Social Psychology, 47(8), 459–469. 10.1111/jasp.12453 [DOI] [Google Scholar]
  23. Kahneman, D. , & Frederick, S. (2005). A model of heuristic judgment. In Holyoak K. J. & Morrison R. G. (Eds.), The Cambridge handbook of thinking and reasoning (pp. 267–293). Cambridge, England: Cambridge University Press. [Google Scholar]
  24. King, D. B. , O'Rourke, N. , & DeLongis, A. (2014). Social media recruitment and online data collection: A beginner's guide and best practices for accessing low‐prevalence and hard‐to‐reach populations. Canadian Psychology, 55(4), 240–249. 10.1037/a0038087 [DOI] [Google Scholar]
  25. Kleitman, S. , & Stankov, L. (2001). Ecological and person‐oriented aspects of metacognitive processes in test‐taking. Applied Cognitive Psychology, 15(3), 321–341. 10.1002/acp.705 [DOI] [Google Scholar]
  26. Kouzy, R. , Abi Jaoude, J. , Kraitem, A. , El Alam, M. B. , Karam, B. , Adib, E. , … Baddour, K. (2020). Coronavirus goes viral: Quantifying the COVID‐19 misinformation epidemic on twitter. Cureus, 12(3), e7255. 10.7759/cureus.7255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kruger, J. , & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self‐assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. 10.1037/0022-3514.77.6.1121 [DOI] [PubMed] [Google Scholar]
  28. Lamberty, P. , & Imhoff, R. (2018). Powerful pharma and its marginalized alternatives? Social Psychology, 49(5), 255–270. 10.1027/1864-9335/a000347 [DOI] [Google Scholar]
  29. Lazić, A. , & Žeželj, I. (2020). Telling stories to counter anti‐vaccination conspiracy theories: A systematic review of narrative interventions and implications for public communication. Manuscript submitted for publication.
  30. Lep, Ž. , Babnik, K. , & Kacin Beyazoglu, K. (2020). Emotional responses and self‐protective behavior within days of the COVID‐19 outbreak: The promoting role of information credibility. Frontiers in Psychology, 11, 1846. 10.3389/fpsyg.2020.01846 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lindeman, M. (2011). Biases in intuitive reasoning and belief in complementary and alternative medicine. Psychology & Health, 26(3), 371–382. 10.1080/08870440903440707 [DOI] [PubMed] [Google Scholar]
  32. Lobato, E. , Mendoza, J. , Sims, V. , & Chin, M. (2014). Examining the relationship between conspiracy theories, paranormal beliefs, and pseudoscience acceptance among a university population. Applied Cognitive Psychology, 28(5), 617–625. 10.1002/acp.3042 [DOI] [Google Scholar]
  33. Lukić, P. , Žeželj, I. , & Stanković, B. (2019). How (ir)rational is it to believe in contradictory conspiracy theories? Europe's Journal of Psychology, 15(1), 94–107. 10.5964/ejop.v15i1.1690 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. McPhetres, J. , & Pennycook, G. (2019). Science beliefs, political ideology, and cognitive sophistication. 10.31219/osf.io/ad9v7 [DOI] [PubMed]
  35. Mian, A. , & Khan, S. (2020). Coronavirus: The spread of misinformation. BMC Medicine, 18(1), 89. 10.1186/s12916-020-01556-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mooney, T. (2020, April 14). Anti‐vaxxers spread fear about future coronavirus vaccine. CBS News. https://www.cbsnews.com/news/anti-vaxxer-fear-coronavirus-vaccine/
  37. Oliver, J. E. , & Wood, T. (2014). Medical conspiracy theories and health behaviors in the United States. JAMA Internal Medicine, 174(5), 817. 10.1001/jamainternmed.2014.190 [DOI] [PubMed] [Google Scholar]
  38. Pavela Banai, I. , Banai, B. , & Mikloušić, I. (2020). Beliefs in COVID‐19 conspiracy theories predict lower levels of compliance with the preventive measures both directly and indirectly by lowering trust in government medical officials. 10.13140/RG.2.2.29313.89443 [DOI] [PMC free article] [PubMed]
  39. Pennycook, G. , Cheyne, J. A. , Barr, N. , Koehler, D. J. , & Fugelsang, J. A. (2015). On the reception and detection of pseudo‐profound bullshit. Judgment and Decision Making, 10(6), 549–563. [Google Scholar]
  40. Pennycook, G. , Cheyne, J. A. , Seli, P. , Koehler, D. J. , & Fugelsang, J. A. (2012). Analytic cognitive style predicts religious and paranormal belief. Cognition, 123(3), 335–346. 10.1016/j.cognition.2012.03.003 [DOI] [PubMed] [Google Scholar]
  41. Pennycook, G. , Fugelsang, J. A. , & Koehler, D. J. (2015). Everyday consequences of analytic thinking. Current Directions in Psychological Science, 24(6), 425–432. 10.1177/0963721415604610 [DOI] [Google Scholar]
  42. Pennycook, G. , McPhetres, J. , Bago, B. , & Rand, D. G. (2020). Predictors of attitudes and misperceptions about COVID‐19 in Canada, the U.K., and the U.S.A. 10.31234/osf.io/zhjkp [DOI]
  43. Pennycook, G. , Ross, R. M. , Koehler, D. J. , & Fugelsang, J. A. (2017). Dunning–Kruger effects in reasoning: Theoretical implications of the failure to recognize incompetence. Psychonomic Bulletin & Review, 24(6), 1774–1784. 10.3758/s13423-017-1242-7 [DOI] [PubMed] [Google Scholar]
  44. Plohl, N. , & Musil, B. (2020). Modeling compliance with COVID‐19 prevention guidelines: The critical role of trust in science. 10.31234/osf.io/6a2cx [DOI] [PubMed]
  45. Pohl, R. F. (Ed.). (2004). Cognitive illusions: A handbook on fallacies and biases in thinking, judgement and memory. Hove, East Sussex, England: Psychology Press. [Google Scholar]
  46. Porter, E. , Wood, T. , & Kirby, D. (2018). Sex trafficking, Russian infiltration, birth certificates, and pedophilia: A survey experiment correcting fake news. Journal of Experimental Political Science, 5(2), 159–164. 10.1017/XPS.2017.32 [DOI] [Google Scholar]
  47. Porter, E. , Wood, T. J. , & Bahador, B. (2019). Can presidential misinformation on climate change be corrected? Evidence from internet and phone experiments. Research & Politics, 6(3), 1–10. 10.1177/2053168019864784 [DOI] [Google Scholar]
  48. Pummerer, L. , & Sassenberg, K. (2020). Conspiracy theories in times of crisis and their societal effects: Case “corona”. 10.31234/osf.io/y5grn [DOI]
  49. Redelmeier, D. A. , & Tversky, A. (1996). On the belief that arthritis pain is related to the weather. Proceedings of the National Academy of Sciences, 93(7), 2895–2896. 10.1073/pnas.93.7.2895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Rizeq, J. , Flora, D. B. , & Toplak, M. E. (2020). An examination of the underlying dimensional structure of three domains of contaminated mindware: Paranormal beliefs, conspiracy beliefs, and anti‐science attitudes. Thinking & Reasoning, 1–25. 10.1080/13546783.2020.1759688 [DOI] [Google Scholar]
  51. Roozenbeek, J. , & van der Linden, S. (2019). Fake news game confers psychological resistance against online misinformation. Palgrave Communications, 5(1), 65. 10.1057/s41599-019-0279-9 [DOI] [Google Scholar]
  52. Serbia Coronavirus: 10,496 Cases and 228 Deaths . (n.d.). Worldometer. Retrieved May 17, 2020, from https://www.worldometers.info/coronavirus/country/serbia/
  53. Setbon, M. , & Raude, J. (2010). Factors in vaccination intention against the pandemic influenza a/H1N1. European Journal of Public Health, 20(5), 490–494. 10.1093/eurpub/ckq054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Shenhav, A. , Rand, D. G. , & Greene, J. D. (2012). Divine intuition: Cognitive style influences belief in god. Journal of Experimental Psychology: General, 141(3), 423–428. 10.1037/a0025391 [DOI] [PubMed] [Google Scholar]
  55. Smedslund, J. (1963). The concept of correlation in adults. Scandinavian Journal of Psychology, 4(1), 165–173. 10.1111/j.1467-9450.1963.tb01324.x [DOI] [Google Scholar]
  56. Šrol, J. (2020). Individual differences in epistemically suspect beliefs: The role of susceptibility to cognitive biases. 10.31234/osf.io/4jcf7 [DOI]
  57. Ståhl, T. , & van Prooijen, J. W. (2018). Epistemic rationality: Skepticism toward unfounded beliefs requires sufficient cognitive ability and motivation to be rational. Personality and Individual Differences, 122, 155–163. 10.1016/j.paid.2017.10.026 [DOI] [Google Scholar]
  58. Stankov, L. (2000). Complexity, metacognition. and fluid intelligence. Intelligence, 28(2), 121–143. 10.1016/s0160-2896(99)00033-1 [DOI] [Google Scholar]
  59. Stanley, M. , Barr, N. , Peters, K. , & Seli, P. (2020). Analytic‐thinking predicts hoax beliefs and helping behaviors in response to the COVID‐19 pandemic. 10.31234/osf.io/m3vth [DOI]
  60. Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri‐process theory. In Frankish K. & Evans J. S. B. T. (Eds.), In two minds: Dual processes and beyond (pp. 55–88). Oxford, England: Oxford University Press. [Google Scholar]
  61. Stanovich, K. E. , West, R. F. , & Toplak, M. E. (2016). The rationality quotient: Toward a test of rational thinking. Cambridge, MA: MIT Press. [Google Scholar]
  62. Swami, V. , & Barron, D. (2020). Analytic thinking, rejection of coronavirus (COVID‐19) conspiracy theories, and compliance with mandated social‐distancing: Direct and indirect relationships in a nationally representative sample of adults in the United Kingdom. 10.31219/osf.io/nmx9w [DOI]
  63. Swami, V. , Chamorro‐Premuzic, T. , & Furnham, A. (2010). Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs. Applied Cognitive Psychology, 24(6), 749–761. 10.1002/acp.1583 [DOI] [Google Scholar]
  64. Swami, V. , Coles, R. , Stieger, S. , Pietschnig, J. , Furnham, A. , Rehim, S. , & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Evidence of a monological belief system and associations between individual psychological differences and real‐world and fictitious conspiracy theories. British Journal of Psychology, 102(3), 443–463. 10.1111/j.2044-8295.2010.02004.x [DOI] [PubMed] [Google Scholar]
  65. Teovanović, P. , Knežević, G. , & Stankov, L. (2015). Individual differences in cognitive biases: Evidence against one‐factor theory of rationality. Intelligence, 50, 75–86. 10.1016/j.intell.2015.02.008 [DOI] [Google Scholar]
  66. Thompson, B. (2005). Canonical correlation analysis. In Everitt B. & Howell D. C. (Eds.), Encyclopedia of statistics in behavioral science (pp. 192–196). Hoboken, NJ: John Wiley & Sons. [Google Scholar]
  67. Thomson, K. S. , & Oppenheimer, D. M. (2016). Investigating an alternate form of the cognitive reflection test. Judgment and Decision Making, 11(1), 99–113. [Google Scholar]
  68. Thorburn, S. , & Bogart, L. M. (2005). Conspiracy beliefs about birth control: Barriers to pregnancy prevention among African Americans of reproductive age. Health Education & Behavior, 32, 474–487. 10.1177/1090198105276220 [DOI] [PubMed] [Google Scholar]
  69. Toplak, M. E. , West, R. F. , & Stanovich, K. E. (2011). The cognitive reflection test as a predictor of performance on heuristics‐and‐biases tasks. Memory and Cognition, 39(7), 1275. 10.3758/s13421-011-0104-1 [DOI] [PubMed] [Google Scholar]
  70. Tversky, A. , & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. 10.1126/science.185.4157.1124 [DOI] [PubMed] [Google Scholar]
  71. Van Bavel, J. J. , Baicker, K. , Boggio, P. S. , Capraro, V. , Cichocka, A. , Cikara, M. , … Willer, R. (2020). Using social and behavioural science to support COVID‐19 pandemic response. Nature Human Behaviour. 4, 1460–12471. 10.1038/s41562-020-0884-z. [DOI] [PubMed] [Google Scholar]
  72. van Prooijen, J. W. , & Douglas, K. M. (2017). Conspiracy theories as part of history: The role of societal crisis situations. Memory Studies, 10(3), 323–333. 10.1177/1750698017701615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. van Prooijen, J.‐W. , Douglas, K. M. , & De Inocencio, C. (2017). Connecting the dots: Illusory pattern perception predicts belief in conspiracies and the supernatural. European Journal of Social Psychology, 48(3), 320–335. 10.1002/ejsp.2331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. West, R. F. , Toplak, M. E. , & Stanovich, K. E. (2008). Heuristics and biases as measures of critical thinking: Associations with cognitive ability and thinking dispositions. Journal of Educational Psychology, 100(4), 930–941. 10.1037/a0012842 [DOI] [Google Scholar]
  75. Wood, M. J. , Douglas, K. M. , & Sutton, R. M. (2012). Dead and alive: Beliefs in contradictory conspiracy theories. Social Psychological and Personality Science, 3(6), 767–773. 10.1177/1948550611434786 [DOI] [Google Scholar]
  76. World Health Organisation . (2020, February 15). Munich Security Conference. WHO. https://www.who.int/dg/speeches/detail/munich-security-conference
  77. World Health Organisation . (n.d.). Coronavirus disease (COVID‐19) advice for the public: Myth busters . https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/myth-busters
  78. Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395(10225), 676. 10.1016/s0140-6736(20)30461-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Žeželj, I. , & Lazarević, L. B. (2019). Irrational beliefs. Europe's Journal of Psychology, 15(1), 1–7. 10.5964/ejop.v15i1.1903 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The full dataset and analysis code for this paper are available at the (https://osf.io/cpe5t/?view_only=f778fab66d8b4a52acbc01bce9bfcc14).

Articles from Applied Cognitive Psychology are provided here courtesy of Wiley

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