Tuesday, June 8, 2021

COVID-19 in 18 countries, 6 languages: We observed an early strong upsurge of anxiety-related terms, which was stronger in countries with stronger increases in cases; positive emotions remained relatively stable

Metzler, Hannah, Bernard Rimé, Max Pellert, Thomas Niederkrotenthaler, Anna Di Natale, and David Garcia. 2021. “Collective Emotions During the COVID-19 Outbreak.” PsyArXiv. June 8. doi:10.31234/osf.io/qejxv

Abstract: The COVID-19 pandemic has exposed the world's population to sudden challenges that elicited strong emotional reactions. Although investigations of responses to tragic one-off events exist, studies on the evolution of collective emotions during a pandemic are missing. We analyzed the digital traces of emotional expressions in tweets during five weeks after the start of outbreaks in 18 countries and six different languages. We observed an early strong upsurge of anxiety-related terms in all countries, which was stronger in countries with stronger increases in cases. Sadness terms rose and anger terms decreased around two weeks later, as social distancing measures were implemented. Positive emotions remained relatively stable. All emotions changed together with an increase in the stringency of measures during certain weeks of the outbreak. Our results show some of the most enduring changes in emotional expression observed in long periods of social media data. Words that frequently occurred in tweets suggest a shift in topics of conversation across all emotions, from political ones in 2019, to pandemic related issues during the outbreak, including everyday life changes, other people, and health. This kind of time-sensitive analyses of large-scale samples of emotional expression have the potential to inform mental health support and risk communication.

The absence of pessimism was more strongly related to positive health outcomes than was the presence of optimism

Scheier, M. F., Swanson, J. D., Barlow, M. A., Greenhouse, J. B., Wrosch, C., & Tindle, H. A. (2021). Optimism versus pessimism as predictors of physical health: A comprehensive reanalysis of dispositional optimism research. American Psychologist, 76(3), 529–548. Jun 2021. https://doi.org/10.1037/amp0000666

Abstract: Prior research has related dispositional optimism to physical health. Traditionally, dispositional optimism is treated as a bipolar construct, anchored at one end by optimism and the other by pessimism. Optimism and pessimism, however, may not be diametrically opposed, but rather may reflect 2 independent, but related dimensions. This article reports a reanalysis of data from previously published studies on dispositional optimism. The reanalysis was designed to evaluate whether the presence of optimism or the absence of pessimism predicted positive physical health more strongly. Relevant literatures were screened for studies relating dispositional optimism to physical health. Authors of relevant studies were asked to join a consortium, the purpose of which was to reanalyze previously published data sets separating optimism and pessimism into distinguishable components. Ultimately, data were received from 61 separate samples (N = 221,133). Meta-analytic analysis of data in which optimism and pessimism were combined into an overall index (the typical procedure) revealed a significant positive association with an aggregated measure of physical health outcomes (r = .026, p < .001), as did meta-analytic analyses with the absence of pessimism (r = .029, p < .001) and the presence of optimism (r = .011, p < .018) separately. The effect size for pessimism was significantly larger than the effect size for optimism (Z = −2.403, p < .02). Thus, the absence of pessimism was more strongly related to positive health outcomes than was the presence of optimism. Implications of the findings for future research and clinical interventions are discussed.


The results of the present reanalyses confirm the findings from earlier quantitative and qualitative reviews. The presence of optimism combined with the absence of pessimism (as assessed by the overall/combined scale) is a reliable predictor of physical health. This was true for an analysis that pooled all of the outcomes together and also true for the majority of analyses that examined subgroups of outcomes separately. This replication of prior findings is noteworthy inasmuch as over 80 percent of the studies included in the present reanalyses were not included in the previous meta-analysis (Rasmussen et al., 2009). The novel findings concern the relative strength of optimism and pessimism in contributing to associations with health. Although each was a significant predictor of physical health, the Optimism, Pessimism, and Health 19 effect sizes associated with the absence of pessimism were generally greater in size than those associated with the presence of optimism. The magnitude of these differences was great enough to be significantly different for the analysis aggregating across outcomes, as well as for several of the analyses that investigated subgroups of outcomes separately. Adjustment of the findings for publication bias did little to alter the basic nature of the primary findings. Moderator analyses were conducted on the effect sizes from the overall/combined scale, as well as the two subscales. These analyses failed to identify any significant moderator. It is of interest that there were no significant differences in effect sizes as a function of the type of study employed. Cross-sectional studies are open to a number of methodological criticisms, most notably the issue of reverse causality. Longitudinal studies examine associations across time, but without provisions for equating the health of participants at baseline. As such, longitudinal studies are subject to many of the same criticisms as are cross-sectional studies. Prospective studies provide the gold standard, in that they offer an assessment of the change in the outcome variable overtime (or otherwise start with participants who can be assumed to be equivalent in health at baseline). Given these considerations, it is especially striking that the moderator analyses revealed that study design did not significantly impact the magnitude of the effect sizes that were obtained. The foregoing discussion speaks to the statistical reliability of the effects that emerged. A few words also need to be said about the magnitude of the effects that emerged. The effects sizes reported here appear small. Several considerations should be borne in mind, however, when evaluating the effect sizes obtained. First, as just noted, the effect sizes reported are adjusted for a host of factors, including those related to demographics, study design, and other confounding psychosocial factors. Thus, the effect sizes reported are unique to optimism and pessimism. It is not surprising that the effect sizes are somewhat small, especially so inasmuch as shared variance with related psychosocial factors had been removed. The second point to make is that statistical effects, even small ones, can be quite meaningful when applied to large numbers of people. Take for example, the effect size Optimism, Pessimism, and Health 20 characterizing the association between the pessimism subscale and mortality. The corresponding adjusted odds ratio for this effect in the present reanalysis is 1.074 [95% CI (1.024, 1.126)]. In terms of the number of people who lived and died in the United States in 2016 (the year the most recent study in these reanalyses was published), this odds ratio implies that a 1-point change in the pessimism direction of the pessimism subscale corresponds to an increase in 97,914 deaths from all causes [95% CI (32,540, 162,641)]. Finally, it is worth mentioning that the size of the effects obtained using the present metaanalytic techniques are quite comparable to effects reported in other meta-analyses of psychosocial factors and physical health when the studies are put on this same metric [see, e.g., Richardson et al. (2012) for a meta-analysis of perceived stress and incident coronary heart disease and Kivimäki et al., 2012 for a meta-analysis of job strain and coronary heart disease]. Taken together, these considerations suggest that from a public health standpoint the magnitude of the effects obtained in the present analysis are nontrivial and quite comparable to other findings in the literature. The present set of reanalyses has several potential limitations that should be highlighted. First, search terms for the present analysis relied heavily on the framework used by Rasmussen et al. (2009). The scheme used here is only one of many that could be adopted. Different search terms could yield a different corpus of studies, and the findings obtained using those different studies could be somewhat different. Second, the yield rate for relevant studies was 32%. It is difficult to evaluate this yield rate compared to other meta-analytic studies. This is the case because the data required for the present study could not be extracted from published studies. Rather, the analysis was contingent on authors of those published studies reanalyzing their data and forwarding on the results of those re-analyses. It is likely that this extra requirement lowered the yield rate to some extent. The third limitation concerns the homogeneous nature of the gender and racial composition of the participants. Although these factors differed somewhat from study to study, over 90% of the overall sample were white and women. Additionally, over 90% of the studies were conducted Optimism, Pessimism, and Health 21 in the United States. More studies are clearly needed to determine if the effects reported here are replicable in more diverse populations. Fourth, the conduct of the present research was a group effort. The analyses could not have been done if consortium members had not conducted the needed analyses and forwarded their findings to the primary authors for further meta-analytic processing. On the positive side, the project represents one of the best examples of collaborative science in the truest sense of the term. On the negative side, the more people involved, the more potential there is for error. This concern is mitigated by the fact that the researchers involved had already published peer reviewed papers with these same data, and as such had already demonstrated significant capability with these analyses. Finally, the outcomes examined in the present study all involved physical health. It is unclear if similar findings would obtain if mental health outcomes were examined. Perhaps optimism and pessimism would be equally robust as predictors of psychological well-being. Perhaps optimism would be stronger. It is important not to extrapolate the findings obtained with the present set of outcomes to possible findings involving other outcomes. Future research on psychological well-being should report results for the optimism and pessimism subscales separately, in order to evaluate the relative strength of the two dimensions in predicting outcomes in that domain. There is a more nuanced point to be made here than simply to acknowledge that the differential impact of optimism and pessimism on psychological well-being needs to be explored. That is, stress has been identified as one potentially important factor that might mediate the impact of optimism (and pessimism) on physical health (Scheier & Carver, 2018). How? The idea is that stress (and stress-related emotions) might modulate downstream biological systems that underlie health and disease. Optimists cope with and psychologically react to adversity in a different way than do pessimists (Segerstrom et al., 2017). It would be interesting to see within this context if the presence or absence of optimism and the presence or absence of pessimism relate differentially Optimism, Pessimism, and Health 22 to the various emotions that arise in reaction to stressful circumstances. It would further be interesting to see if these potentially different emotions (that characterize the reactions of optimists and pessimists to stress) might themselves be more or less strongly related to physical health outcomes. Answering questions such as these could further in a significant way our understanding of why it might be that the absence of pessimism is more strongly related to physical health outcomes than is the presence of optimism. Limitations aside, the present findings have at least three implications. First, future research should, as a matter of course, provide effect size information for the overall/combined scale and the two subscales separately—a suggestion that has been made previously (Scheier et al., 1994). Such a practice is even more important now that quantitative data exist documenting the differential associations of the two subscales with physical health. With the complete complement of effect sizes reported, future research could continue to evaluate the importance of the separate contributions of optimism versus pessimism without the need to establish consortiums. The present findings also hold important implications for positive psychology (Peterson & Park, 2003; Seligman & Csikszentmihalyi, 2000). Positive psychology emphasizes those characteristics that enable people to experience full, industrious, and resilient lives. As such, it stands in contrast to traditional views that tend to focus on negative attributes, such as depression, anxiety, and other characteristics which undermine successful living. Dispositional optimism is often described as a good example of a variable falling within the positive psychology domain (e.g., Dunn, 2018). As the present data make clear, however, the presence of optimism does not provide the whole story. Optimism is important, but it does not appear to be as important as the absence of pessimism in predicting physical health. In the future, researchers in positive psychology might benefit from taking these findings into account when planning and conducting research. Researchers should examine more closely the predictor variables they are using to see if negative and positive characteristics might be intermingled in the measures employed. If so, an effort should be made to tease apart the positive Optimism, Pessimism, and Health 23 and negative components of the measures to determine what is in fact responsible for doing the predicting. Ultimately, it may turn out that it is the positive aspects of the measures that are important, but it also possible that the negative features are the ones driving the observed associations. Only by explicitly evaluating these possibilities will we know for sure. The final implication concerns interventions. Future efforts to design and adapt interventions to promote better health should keep in mind the differential links between optimism, pessimism, and physical health. In this regard, it is interesting that some cognitive behavior therapies seem to put a greater emphasis on lessening pessimism than they do on promoting optimism. One example of such an intervention concerns cognitive restructuring (Leahy & Rego, 2012), in which participants are trained to challenge the automatic thoughts, beliefs, and expectancies underlying negative feelings. Participants confront their automatic, negative thinking by systematically, and explicitly monitoring their moods and assessing in a more objective fashion the information in the ongoing context that either supports or challenges their negative thoughts. Perhaps existing interventions that focus more on lessening pessimism such as those involving cognitive restructuring will be more successful in promoting better health than will those that place a greater weight on promoting optimism, or even those that place an equal weight on both components. Note that it is not a matter of causing harm, but more a matter of targeting the component that offers the most gain. It is also possible, however, that things are more complicated. Perhaps what works best will depend on the nature of the outcome of interest (e.g., health behaviors versus biological pathways). Intervention efforts with respect to optimism, pessimism, and physical health are still in their infancy. As research in the intervention domain continues to evolve, it would seem prudent to keep the distinction between optimism and pessimism in mind. Doing so may prove profitable both practically and theoretically.

Canadians who feel disgust towards sitting on the toilet seat of a public bathroom are in general more socially conservative, tend to vote for conservatives, & favor conservative policies on issues like gay rights & immigration

The political phenotype of the disgust sensitive: Correlates of a new abbreviated measure of disgust sensitivity. Patrick Fourniera, Michael Bang Petersen, Stuart Sorok. Electoral Studies, Volume 72, August 2021, 102347. https://doi.org/10.1016/j.electstud.2021.102347

Abstract: The fields of political psychology and election studies often live separate lives. One reason has been the difficulty of including long psychological question batteries in the high-quality, representative samples that are the hallmark of election studies. In this study, we examine a novel one-item measure of psychological differences in sensitivity to one particular emotion: disgust. We demonstrate that disgust sensitivity serves as a foundational political difference that colors a very large range of social and political attitudes and behaviors: including ideology, political engagement, reactions towards outgroups, support for government intervention, behavior during a pandemic, and vote choice.

Keywords: Disgust sensitivityElection studyIdeologyVote choicePolicy attitudesGroup ratings

Eveningness, a preference for later sleep and rise times, is significatively linked to Major Depressive Disorder (MDD), but the effect is small

Diurnal preference and depressive symptomatology: a meta-analysis. Ray Norbury. Scientific Reports volume 11, Article number: 12003. Jun 7 2021. https://www.nature.com/articles/s41598-021-91205-3

Abstract: Eveningness, a preference for later sleep and rise times, has been associated with a number of negative outcomes in terms of both physical and mental health. A large body of evidence links eveningness to Major Depressive Disorder (MDD). However, to date, evidence quantifying this association is limited. The current meta-analysis included 43 effect sizes from a total 27,996 participants. Using a random-effects model it was demonstrated that eveningness is associated with a small effect size (Fisher’s Z = − 2.4, 95% CI [− 0.27. − 0.21], p < 0.001). Substantial heterogeneity between studies was observed, with meta-regression analyses demonstrating a significant effect of mean age on the association between diurnal preference and depression. There was also evidence of potential publication bias as assessed by visual inspection of funnel plots and Egger’s test. The association between diurnal preference and depression is small in magnitude and heterogenous. A better understanding of the mechanistic underpinnings linking diurnal preference to depression and suitably powered prospective studies that allow causal inference are required.


The current findings demonstrate a small but significant association between diurnal preference and depressive symptomatology. All of the reported studies indicated a positive association between eveningness and depression, ranging between − 0.52 and − 0.03. The summary effect size for the random effects model was − 0.24 which is largely consistent with an earlier meta-analysis30 that reported an effect size of − 0.2 and together these data suggest a small but reliable association between eveningness and depression. Contrary to the findings of Au and Reece, in the current analysis evidence of a potential publication bias (i.e. statistically significant or favourable results being more likely to be published than studies with non-significant or unfavourable results) was observed. The adjusted effect size (Fishers Z = − 0.21), however, remained significant. Subgroup analyses demonstrated no moderating effect of sample characteristics, eveningness or depression measure, or studies published in 2020 vs. any other year. Meta-regression showed a significant effect of age on the association between eveningness and depression symptomatology, but no evidence for a moderating effect of sample size, gender ratio, or year of publication.

A long-standing question in the literature is one of directionality; does eveningness cause depression or is eveningness a consequence of the disorder? The cross-sectional studies quantified here cannot speak directly to this question. However, the current results demonstrated no significant difference between clinical and non-clinical samples, a finding consistent with Au and Reece30. Eveningness may therefore represent a risk-factor for depression rather than a consequence of the depressed state. The vulnerability-stress hypothesis of depression96,97 proposes that depression emerges through an interaction between psychological vulnerability factors (e.g., negative biases/preferential processing of negative material) and an environmental stressor (e.g., bereavement, financial insecurity). Importantly, previous work suggests that eveningness is associated with aspects of negative thinking (i.e. psychological vulnerability factors) in never-depressed individuals. For example, eveningness has been associated with greater recall for negative personality trait words, greater recognition of sad facial expressions63,98 and maladaptive emotion regulation strategies93,99. Similarly, high neuroticism (i.e. individuals who are emotionally reactive and tend to experience more negative emotions and depression) has also been associated with eveningness100. Converging evidence, therefore, suggests that in healthy, never-depressed individuals, eveningness is associated with depressogenic personality types, negative biases in emotional processing and impaired emotion regulation which, if combined with adversity, may lead to depression. These findings also suggest modifiable markers that could be therapeutically targeted to prevent the onset of depression in evening type individuals.

Of the moderators tested here only age was significantly associated with effect size. This contrasts with the findings of Au and Reece (2017) who did not observe a similar relationship. The mean age range in the current study was 19–70, which is broader than included by Au and Reece (19–55, MDD sample only) which may account for the discrepancy. Although it should be noted that for the majority of studies included here (~ 50%) the mean age was less than 30 years of age. Of note, Kim et al. recently reported no difference in prevalence rates for depression in late chronotypes vs. neither types in a population of Korean adults stratified by age (19–40, 41–59 and 60–80 years). However, although the total sample size was large (N = 6382) the number of participants in the older 60–80 years group classified as evening-type was small (N = 22) which may limit interoperability101. Counter to this, eveningness has been associated with increased odds for reporting depression in a large sample of older adults (age range 40–70 years) taken from the UK Biobank102. Similarly, here increasing age was associated with increased depressive symptomatology but the factors underpinning this effect remain to be elucidated. Older individuals that remain more evening-type may gradually lose friendship networks and group allegiances as peers gravitate to a social schedule in synchrony with their changing circadian typology, potentially leaving evening-prone individuals more isolated and potentially more prone to depression. This notion, however, is purely speculative and requires further investigation with suitably powered, prospective studies to determine the potential impact of age on the association between eveningness and depression.

There are several limitations associated with this work which should be considered when interpreting the results. A general limitation of meta-analyses is that by creating a summary of outcomes, important between-study differences are ignored. To formally address this here study inclusion was restricted to adults, for clinical samples mood disorders other than MDD were excluded and only studies that used validated instruments to measure depressive symptomatology and diurnal preference were included. In addition, moderator analysis and meta-regression were employed to explore study heterogeneity. More specifically, the current analysis was unable to account for important factors that may impact the results. Sleep duration and/or sleep quality, for example, were not taking into consideration (zero-order correlations or unadjusted odds-ratios/mean differences were reported). Similarly, social jet-lag, the difference between internal rhythm and external demands (e.g. work or university), which may be more pronounced in evening-types and is associated with increased likelihood of reporting depressive symptoms103,104 was not included in this meta-analysis. The current report, therefore, cannot directly assess the potential impact of social jetlag on the association between eveningness and depressive symptoms. Further, the terms chronotype and diurnal preference are frequently used interchangeably in the literature but reflect different aspects of the same phenomenon. Here, the focus was diurnal preference and the questionnaires included limited to the MEQ, rMEQ and CSM which determine morningness/eveningness preferences based on self-reported preferences for times of activity and rest. These measures, therefore, reflect a personality trait. By contrast, instruments such as the Munich Chronotype Questionnaire (MCTQ)105 measure behaviour (mid-point of sleep on free days) which can be viewed as an indicator of state106. The focus of the current report was unipolar depression, but increasing evidence links eveningness with other affective disorders such as bipolar disorder107 and Major Depressive Disorder with Seasonal Pattern108 and anxiety109. Future meta-analyses that review and synthesise the recent literature related to these disorders is warranted. Finally, it should also be noted that all phases of this review and analyses were conducted solely by the author.

In summary, the current meta-analysis demonstrated that eveningness is associated with depressive symptoms. These data are largely consistent with a previous meta-analysis30 and the extant literature. The underlying causes that lead to depression are likely multifactorial and progress in understanding the links between diurnal preference and depression is predicated on a better understanding of the mechanistic underpinnings and suitably powered prospective studies that allow causal inference.

Most non-human species are cognitively constrained to show only simple forms of reputation-based cooperation

Manrique, Hector, Henriette Zeidler, Gilbert Roberts, Pat Barclay, Flora Samu, Andrea Fariña, Redouan Bshary, et al. 2021. “The Psychological Foundations of Reputation-based Cooperation.” PsyArXiv. June 2. doi:10.1098/rstb.2020.0287

Abstract: Humans care about having a positive reputation, which may prompt them to help in scenarios where the return benefits are not obvious. Various game-theoretical models support the hypothesis that concern for reputation may stabilize cooperation beyond kin, pairs or small groups. However, such models are not explicit about the underlying psychological mechanisms that support reputation-based cooperation. These models therefore cannot account for the apparent rarity of reputation-based cooperation in other species. Here we identify the cognitive mechanisms that may support reputation-based cooperation in the absence of language. We argue that a large working memory enhances the ability to delay gratification, to understand others' mental states (which allows for perspective-taking and attribution of intentions), and to create and follow norms, which are key building blocks for increasingly complex reputation-based cooperation. We review the existing evidence for the appearance of these processes during human ontogeny as well as their presence in non-human apes and other vertebrates. Based on this review, we predict that most non-human species are cognitively constrained to show only simple forms of reputation-based cooperation.

Less educated citizens in democracies are considerably less trustful of science than their counterparts in non-democracies, not due to stronger religiosity or lower science literacy, but for a shift in the mode of legitimation

Jiang, Junyan and Wan, Kin-Man, Democracy and Mass Skepticism of Science (June 3, 2021). SSRN: http://dx.doi.org/10.2139/ssrn.3845857

Abstract: Ever since the Age of Enlightenment, democracy and science have been seen as two aspects of modernity that mutually reinforce each other. This article highlights a tension between the two by arguing that certain aspects of contemporary democracy may aggravate the anti-intellectual tendency of the mass public and potentially hinder scientific progress. Analyzing a new global survey of public opinion on science with empirical strategies that exploit cross-country and cross-cohort variations in experience with democracy, we show that less educated citizens in democracies are considerably less trustful of science than their counterparts in non-democracies. Further analyses suggest that, instead of being the result of stronger religiosity or lower science literacy, the increase in skepticism in democracies is mainly driven by a shift in the mode of legitimation, which reduces states' ability and willingness to act as key public advocates for science. These findings help shed light on the institutional sources of "science-bashing" behaviors in many long-standing democracies.

Keywords: science, democracy, institution, anti-intellectualism, constitution, legitimacy


[democracies are significantly less likely to make references to science in their constitutions, and award a smaller share of high state honors to scientists.]

Poorly educated individuals with highest trust in science: Korea, China, Kazakhstan, Spain, Tanzania, Gambia, Tajikistan, Myanmar, UAE, and Uzbekistan. For college degree+, the highest trust countries are the Philippines, India, Belgium, Denmark, Norway, Ireland, Finland, Spain, Tajikistan, and Czech Republic.

Over the course of the pandemic, we observed that the genetic predisposition to life satisfaction had an increasing influence on perceived quality of life

Warmerdam, Robert, Henry H. Wiersma, Pauline Lanting, Marjolein X. Dijkema, Judith M. Vonk, Marike H. Boezen, Patrick Deelen, et al. 2021. “Increased Genetic Contribution to Wellbeing During the COVID-19 Pandemic.” PsyArXiv. June 7. doi:10.31234/osf.io/uksxt

Abstract: Physical and mental health are determined by an interplay between nature, i.e. genetics, and nurture, which encompasses experiences and exposures that can be short or long-lasting. Depressive episodes, for example, are partly the result of an interaction between stressful life-events and a genetic predisposition to depression The COVID-19 pandemic represents a unique situation in which whole communities were suddenly and simultaneously exposed to both the virus and the societal changes required to combat the virus. We studied 27,537 population-based biobank participants for whom we have genetic data and extensive longitudinal data collected via 19 questionnaires over 10 months, starting in March 2020. This allowed us to explore the interaction between genetics and the impact of the COVID-19 pandemic on individuals’ wellbeing over time. We observe that genetics affected many aspects of well-being, but also that its impact on several phenotypes changed over time. Over the course of the pandemic, we observed that the genetic predisposition to life satisfaction had an increasing influence on perceived quality of life. These results suggest that people’s genetic constitution manifested more prominently over time, potentially due to social isolation driven by strict COVID-19 containment measures. Overall, our findings demonstrate that the contribution of genetic variation to complex phenotypes is dynamic rather than static.