Saturday, July 31, 2021

Individual selfishness in high-impact decisions affecting a large group is compatible with prosociality in bilateral low-stakes interactions; human beings can simultaneously be generous with others and selfish with large groups

Generous with individuals and selfish to the masses. Carlos Alós-Ferrer, Jaume García-Segarra & Alexander Ritschel. Nature Human Behaviour, July 29 2021.

Abstract: The seemingly rampant economic selfishness suggested by many recent corporate scandals is at odds with empirical results from behavioural economics, which demonstrate high levels of prosocial behaviour in bilateral interactions and low levels of dishonest behaviour. We design an experimental setting, the ‘Big Robber’ game, where a ‘robber’ can obtain a large personal gain by appropriating the earnings of a large group of ‘victims’. In a large laboratory experiment (N = 640), more than half of all robbers took as much as possible and almost nobody declined to rob. However, the same participants simultaneously displayed standard, predominantly prosocial behaviour in Dictator, Ultimatum and Trust games. Thus, we provide direct empirical evidence showing that individual selfishness in high-impact decisions affecting a large group is compatible with prosociality in bilateral low-stakes interactions. That is, human beings can simultaneously be generous with others and selfish with large groups.

Are partisans able to accurately describe their opponents’ position, or do they instead generate unrepresentative “straw man” arguments?

The straw man effect: Partisan misrepresentation in natural language. Michael Yeomans. Group Processes & Intergroup Relations, July 20, 2021.

Abstract: Political discourse often seems divided not just by different preferences, but by entirely different representations of the debate. Are partisans able to accurately describe their opponents’ position, or do they instead generate unrepresentative “straw man” arguments? In this research we examined an (incentivized) political imitation game by asking partisans on both sides of the U.S. health care debate to describe the most common arguments for and against ObamaCare. We used natural language-processing algorithms to benchmark the biases and blind spots of our participants. Overall, partisans showed a limited ability to simulate their opponents’ perspective, or to distinguish genuine from imitation arguments. In general, imitations were less extreme than their genuine counterparts. Individual difference analyses suggest that political sophistication only improves the representations of one’s own side but not of an opponent’s side, exacerbating the straw man effect. Our findings suggest that false beliefs about partisan opponents may be pervasive.

Keywords: intergroup perception, natural language processing, perspective-taking, political psychology

The evidence presented here confirms that, in open-ended text, partisans did not represent their opponents’ arguments well. However, in contrast to the colloquial understanding of straw man arguments, our results suggest that this kind of partisan disagreement is often not a deliberate tactic. The straw man effect was robust to incentives for accuracy, implying that partisans were often unable—not just unwilling—to take their opponents’ perspective. Furthermore, our results suggest that partisans are not particularly accurate at detecting imitations of genuine positions either. Instead, we found that machine learning algorithms could detect imitations with substantially higher accuracy than human judges.

Theoretical Implications

While other recent research has shown that incentives can reduce or even extinguish partisan gaps on factual questions (Bullock et al., 2015Prior et al., 2015), these results showed a small reduction. However, that earlier work relied on questions that asked participants to guess the correct number on a scale (e.g., percentage of GDP growth under Obama). In these cases, the question itself provides the range of possible answers, and it is easy for partisans to deliberately adjust their answer to suit their goals. However, in open-ended tasks, the range of possible responses is very wide, and it is more difficult for partisans to intuit the correct adjustment from the question. To be sure, incentives do not guarantee that the responses were valid measures of partisans’ actual mental representations of their opponents. For example, incentives may induce them to recite familiar stereotypes rather than their true beliefs about their opponents. But these stereotypes might still serve as signal of meta-knowledge about the contours of a debate. Regardless, incentives are necessary to distinguish the mechanism here from the Talisse and Aikin model (2006) of straw men as deliberate distortions for partisan gain. And our results suggest that at least some straw man arguments persist even when partisans have sincere intentions.

Our results also suggest that the straw man effect is exacerbated by political sophistication. Participants with more political knowledge wrote better descriptions of their own position, but that knowledge was of no help when describing their opponents’. The natural language-processing models also suggested that the language of imitators was more similar to that of genuine moderates than to that of genuine extremists, even though previous research has shown that partisans believe that their opponents hold extreme positions. This perhaps is related to why participants were overconfident in their ability to distinguish imitations from genuine arguments.

These results suggest that perceived polarization might be a natural consequence of asymmetric expertise, whereby partisans gather evidence to buttress their own preferred conclusions. Also known as the rationalizing voter theory (Lodge & Taber, 2013), this is supported by mechanisms at many cognitive levels (e.g., Frenda et al., 2012Kahan, 2015Lord et al., 1979Robinson et al., 1995Toner et al., 2013), and is a compelling explanation for why, in this research, partisans who could so faithfully defend their own position were at a loss when asked to describe their opponents’ point of view. This lack of insight is typical in social judgment (Dunning et al., 2003Nisbett & Wilson, 1977Pronin et al., 2002), but it poses particular difficulties for intergroup research because the very processes that divide partisans may also distort their construal of others’ positions.

Limitations and Future Research

In this research, we did not find an intervention to reduce partisan misrepresentation. Study 1 showed that incentives were, at best, a weak moderator of the straw man effect. However, this was a short-term intervention and could not be effective if partisans simply lacked the knowledge base to accurately take their opponents’ perspective. To make an analogy to memory, our incentives could plausibly affect participants’ biases in recall, but it would have been too late to make any impact on their biases during encoding. This suggests some skepticism is warranted for the potential of other short-term interventions (though see Saguy & Kteily, 2011Stern & Kleiman, 2015). In addition, any potential encoding biases may not be eliminated by self-directed information search, since our results suggested that politically sophisticated partisans were no more accurate than political naives in their imitations (Keltner & Robinson, 1993Lord et al., 1984Thompson & Hastie, 1990). We also found essentially no effect of intergroup contact on accuracy for opponents, which agrees with a recent review suggesting that the effects of contact are more varied and context-dependent that is often acknowledged (Paluck et al., 2018). Our analysis of the extremity of partisans’ imitations may even provide a mechanism for a potential backfire effect of intergroup contact (similar to Bail et al., 2018). Specifically, partisans’ imitations tended to be more moderate than the actual positions of their opponents—perhaps if they learned how extreme their opponents’ positions tended to be, this would have other negative consequences for intergroup harmony and understanding.

Another limitation in this research is our focus on a single topic. This focus facilitated a rich language model, but it is important to consider whether the topic of debate might have moderated our results. We focused on a high-stakes political topic that featured a wide range of competing evidence as well as genuine differences in preferences and values, so that many texts could be collected from enthusiastic partisans on both sides. However, some topics face a clearer divide where the preponderance of evidence stands against one side—for example, antivaccination debates, global warming denialism, or other conspiracy theories (Hornsey et al., 2018Rutjens et al., 2018Stoknes, 2015). In these cases, it is possible that the straw man effect would be asymmetrical, as beliefs based on false premises may also disproportionately reinforce themselves with false beliefs about the opposing arguments. Additionally, many topics of disagreement engage less partisan vigor, where personal preferences are more respected. In cases where partisans are not trying to win a public debate, people may be more genuinely curious about one another, lessening the effect of the rationalizing voter mechanism. Future work could pack these mechanisms across a range of topics and domains.

The current research also demonstrates how machine learning can be applied to develop psychological theory. Initially, it was difficult to interpret the judges’ low accuracy in Study 1—were partisans bad at being judges, or good at being imitators? The results of Study 2 indicated the former was true, as the language model made it clear that human judges had room for improvement. This result confirmed our central hypothesis that the writers, too, were often failing at their task of recreating their opponents’ perspectives. Similar methods may be useful in other interpersonal judgment tasks—mind perception is hard, and inaccuracy is ubiquitous (Epley & Waytz, 2009). But perspective-taking is often studied in cases where the perspective taker has all the needed information available. In the world outside of the lab, however, social interactions are filled with ambiguity, and the blame for inaccuracy is perhaps better apportioned across both the mind perceiver and the mind perceived. To distinguish the two, researchers must estimate how much information is actually available, and our research demonstrates an empirical framework for that process. The accuracy of social perceptions is an oft-debated topic (e.g., Judd & Park, 1993Jussim & Zanna, 2005Zaki & Ochsner, 2011). Our research demonstrates how natural language can be used to study interpersonal accuracy in other domains where data are rich, but misunderstanding is common.

How colonialist attitudes toward native fishes were rooted in elements of racism and sexism; the term “rough fish” is pejorative and degrading to native fish

Goodbye to “Rough Fish”: Paradigm Shift in the Conservation of Native Fishes. Andrew L. Rypel et al. Fisheries, July 21 2021.

Abstract: Perspectives of white males have overwhelmingly dominated fisheries science and management in the USA. This dynamic is exemplified by bias against “rough fish”—a pejorative ascribing low-to-zero value for countless native fishes. One product of this bias is that biologists have ironically worked against conservation of diverse fishes for over a century, and these problems persist today. Nearly all U.S. states retain bag limits and other policies that are regressive and encourage overfishing and decline of native species. Multiple lines of evidence point towards the need for a paradigm shift. These include: (1) native species deliver critical ecosystem services; (2) little demonstration that native fish removals deliver intended benefits; (3) many native fishes are long-lived and vulnerable to overfishing and decline; and (4) fisher values and demographics shifting towards native fish conservation. Overall, existing native fish policies are unacceptable and run counter to the public trust doctrine where government agencies manage natural resources for public use. We encourage agencies to revisit their policies regarding native fishes and provide suggestions for developing more holistic, protective, and inclusive conservation policy.


Popular version: Cultural Biases Impact Native Fish, Too | UC Davis

From art to religion to land use, much of what is deemed valuable in the United States was shaped centuries ago by the white male perspective. Fish, it turns out, are no exception.

A study published in Fisheries Magazine, a journal of the American Fisheries Society, explores how colonialist attitudes toward native fishes were rooted in elements of racism and sexism. It describes how those attitudes continue to shape fisheries management today, often to the detriment of native fishes.

The study, led by the University of California, Davis, with Nicholls State University and a national team of fisheries researchers, found that nearly all states have policies that encourage overfishing native species. The study maintains that the term “rough fish” is pejorative and degrading to native fish.

“That has bothered me for a long time,” said lead author Andrew Rypel, co-director of the Center for Watershed Sciences and the Peter B. Moyle and California Trout Chair in Coldwater Fish Ecology at UC Davis. He and others have been disturbed by images of “glory killings” of native fish that periodically pop up on the internet, as well as the lump categorization of less preferred species as “rough” or “trash” fish.

“When you trace the history of the problem, you quickly realize it’s because the field was shaped by white men, excluding other points of view,” Rypel said. “Sometimes you have to look at that history honestly to figure out what to do.”

The study offers several recommendations for how anglers and fisheries managers can shift to a new paradigm that’s more inclusive and beneficial to all fish and people.

The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment

Falandays, James B., and Paul E. Smaldino. 2021. “The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment.” PsyArXiv. July 28. doi:10.31234/

Abstract: Cultural attractor landscapes describe the time-evolution of cultural variants over successive transmission events. Because these landscapes are statistical patterns that emerge from the interactions of dynamic learners in dynamic populations/environments, stable landscapes cannot be taken for granted. However, they are often modeled as such, and little is known about how attractors form, change, and/or stabilize. We present a model of cultural attractor dynamics, which adapts a model of unsupervised category learning in individuals to a multi-agent setting, wherein learners provide the training input to each other. Agents in our populations spontaneously align their cognitive category structures, producing emergent cultural attractor points. We highlight three interesting behaviors exhibited by our model: (1) noise enhances stability of cultural category structures; (2) short “critical” periods of learning early in life enhance stability; and (3) larger populations produce more stable but less complex attractor landscapes, and cliquish network structure can mitigate the latter.


All human groups possess group-specific behavioral repertoires involving cultural variants—things such as tools, linguistic behavior, social norms, religious beliefs, and artistic styles. As cultural variants are observed and copied, they are liable to change over time due to the accumulation of errors in transmission. However, even in the absence of strong selection for specific outcomes, cultural variants may nevertheless converge over successive transmission events toward culture-specific “attractor” points (Sperber, 1996). This effect can be attributed to the fact that individuals within a cultural group tend to have similar cognitive biases, such that they tend to perceive, remember, and reproduce information in consistent ways (Heyes, 2018). Without this "cognitive alignment," cultural transmission would be far less reliable, and the potential for cumulative cultural evolution would be limited. But how does cognitive alignment first emerge in dynamic populations? Current models of cultural evolution take cognitive alignment as given. However, this assumption may not always be justified, since many aspects of culture depend on cognitive biases that are themselves socially learned (Heyes, 2018; Karmiloff-Smith, 1994). As each new generation learns through exposure to the cultural products of the previous generation, they may acquire different cognitive biases than their teachers, in turn resulting in a new set of cultural products in the next generation. An unstable feedback loop of this kind could disrupt the accumulation of cultural knowledge in a population, as later generations may no longer perceive, remember, and reproduce information in ways consistent with their ancestors. Furthermore, populations are not static: new individuals are born or enter the population from elsewhere, while others die or leave for new lands. Within such shifting populations, cognitive alignment needs to be actively and continuously maintained in order for cultural knowledge to be successfully preserved across generations. In this paper, we develop an agent-based model of the emergence and maintenance of cognitive alignment in dynamic populations, where individuals act as both teachers and learners to each other, and refine their cognitive biases over the course of interaction. Our initial explorations with this model suggest that achieving and maintaining cognitive alignment may depend upon a finely tuned balance of factors at the levels of cognition, development, and demographic structure. We highlight three interesting and potentially counter-intuitive behaviors exhibited by our model that are not accounted for in other models of cultural evolution: First, we find that some noise is beneficial to stabilizing cognitive alignment. Second, we find that long learning times may destabilize and limit the complexity of cultural repertoires, while critical or sensitive periods of learning enhance stability. Third, we find that larger populations develop less complex, but more stable patterns of alignment, and that this effect can be moderated by network structure. These results suggest that additional complexity may be needed in models of cultural evolution to adequately understand how human-level culture can get off the ground and develop. We conclude by highlighting several ways that our model may be extended to complement 40 existing models of cultural evolution and gene-culture co-evolution.

U.N. climate panel confronts implausibly hot forecasts of future warming

U.N. climate panel confronts implausibly hot forecasts of future warming. Paul Voosen. Science Magazine, Jul. 27, 2021.

[Excerpts. Full text with original sources at the link above]

The motif, the recurrent and dominant platitudes:

Next month, after a yearlong delay because of the pandemic, the U.N. Intergovernmental Panel on Climate Change (IPCC) will begin to release its first major assessment of human-caused global warming since 2013. The report, the first part of which will appear on 9 August, will drop on a world that has starkly changed in 8 years, warming by more than 0.3°C to nearly 1.3°C above preindustrial levels. Weather has grown more severe, seas are measurably higher, and mountain glaciers and polar ice have shrunk sharply. And after years of limited action, many countries, pushed by a concerned public and corporations, seem willing to curb their carbon emissions.


But as climate scientists face this alarming reality, the climate models that help them project the future have grown a little too alarmist. Many of the world’s leading models are now projecting warming rates that most scientists, including the modelmakers themselves, believe are implausibly fast. In advance of the U.N. report, scientists have scrambled to understand what went wrong and how to turn the models, which in other respects are more powerful and trustworthy than their predecessors, into useful guidance for policymakers. “It’s become clear over the last year or so that we can’t avoid this,” says Gavin Schmidt, director of NASA’s Goddard Institute for Space Studies.
Ahead of each major IPCC report, the world’s climate modeling centers run a set of scenarios for the future, calculating how different global emissions paths will alter the climate. These raw results, compiled in the Coupled Model Intercomparison Project (CMIP), then feed directly into the IPCC report. The results live on as other scientists use them to assess the impacts of climate change, insurance companies and financial institutions forecast effects on economies and infrastructure, and economists calculate the true cost of carbon emissions, says Jean-François Lamarque, a lead climate modeler at the National Center for Atmospheric Research (NCAR) and CMIP’s new director. “This is not an ivory tower type of exercise.”
In the past, most models projected a “climate sensitivity”—the warming expected when atmospheric carbon dioxide (CO2) is doubled over preindustrial times—of between 2°C and 4.5°C. Last year, a landmark paper that largely eschewed models and instead used documented factors including ongoing warming trends calculated a likely climate sensitivity of between 2.6°C and 3.9°C. But many of the new models from leading centers showed warming of more than 5°C—uncomfortably outside these bounds.
The models were also out of step with records of past climate. For example, scientists used the new model from NCAR to simulate the coldest point of the most recent ice age, 20,000 years ago. Extensive paleoclimate records suggest Earth cooled nearly 6°C compared with preindustrial times, but the model, fed with low ice age CO2 levels, had temperatures plummeting by nearly twice that much, suggesting it was far too sensitive to the ups and downs of CO2. “That is clearly outside the range of what the geological data indicate,” says Jessica Tierney, a paleoclimatologist at the University of Arizona and a co-author of the work, which appeared in Geophysical Research Letters. “It’s totally out there.”
To find out why, modelers probed the guts of the simulations, focusing on their representation of clouds, long the wild card of climate change. The models can’t simulate clouds directly, so they rely on known physics and observations to estimate cloud properties and behavior. In previous models ice crystals made up more of the low clouds in the midlatitudes of the southern Pacific Ocean and elsewhere than satellite observations seemed to justify. Ice crystals reflect less sunlight than water droplets, so as these clouds heated and the ice melted, they became more reflective and caused cooling. The new models start with more realistic clouds containing more supercooled water, which allows other dynamics driven by warming—the penetration of dry air from above and a subduing of turbulence—to thin the clouds.
But that fix has allowed scientists to spy another bias previously countered by the faulty cooling trend. In both the old and new climate models, the patchy cumulus clouds that form in the tropics thin out in response to warming, allowing in more heat than satellite observations suggest, according to a study by Timothy Myers, a cloud scientist at Lawrence Livermore National Laboratory. “Even though one feature of the climate is now more realistic, another that’s persistently biased has been revealed,” Myers says.
Graphic [Overheated
Climate models used by next month’s Intergovernmental Panel on Climate Change report project more warming over an 1850–1900 baseline than those in a 2013 report. Scientists are using recent observed warming to rein them in.]
By the time modelers exposed that bias, the supercomputing runs were already done and the IPCC report was nearing completion. And many of the hot models otherwise simulate the climate extremely well overall, doing a better job than their predecessors at capturing atmospheric connections between remote ocean basins and the distribution of rainfall. “You want a way you can use those models for what they have without getting stuck with their climate sensitivity,” Schmidt says.
So the IPCC team will probably use reality—the actual warming of the world over the past few decades—to constrain the CMIP projections. Several papers have shown how doing so can reduce the uncertainty of the model projections by half, and lower their most extreme projections. For 2100, in a worst-case scenario, that would reduce a raw 5°C of projected warming over preindustrial levels to 4.2°C. It’s good news for the modelers—but also a clear, and dismaying, sign that global warming has gone on long enough to help chart its own path, says Aurélien Ribes, a climate scientist at France’s National Centre for Meteorological Research. “Observations now provide a clear view for what climate change will be.”
The IPCC report is also likely to present the spatial impacts of different amounts of warming—2°C, 3°C, 4°C—rather than saying how quickly those impacts will be felt. That heat-based technique worked well in an interim IPCC report in 2018, on the impacts of 1.5°C of warming, and would preserve good information from the hot models, even if they suggest these thresholds will come too soon.
The modelers hope to do better next time around. Lamarque says they may test new simulations against recent paleoclimates, not just historical warming, while building them. He also suggests that the development process could benefit from more time, with updates every decade or so rather than the current report interval of every 7 years. And it could be helpful to divide the modeling process in two, with one track focused on scientific experimentation—when a large range of climate sensitivities is helpful—and the other on providing a best estimate to policymakers. “It’s not easy to reconcile these two approaches under a single entity,” Lamarque says.
A cadre of researchers dedicated to the task of translating the models into useful projections could also help, says Angeline Pendergrass, a climate scientist at Cornell University who helped develop one technique for weighting the model results by their accuracy and independence. “It’s an actual job to go between the basic science and the tools I’m messing around with,” she says.
For now, policymakers and other researchers need to avoid putting too much stock in the unconstrained extreme warming the latest models predict, says Claudia Tebaldi, a climate scientist at Pacific Northwest National Laboratory and one of the leaders of CMIP’s climate projections. Getting that message out will be a challenge. “These issues don’t translate very well in practice,” she says. “It’s going to be hard for people looking to make some projection of a water basin in the West to make sense of it.”

Already scientific papers are appearing using CMIP’s unconstrained worst-case scenarios for 2100, adding fire to what are already well-justified fears. But that practice needs to change, Schmidt says. “You end up with numbers for even the near-term that are insanely scary—and wrong.”

COVID-19: The compliants (90%) reported greater worries, & perceived protective measures as effective, whilst the non-compliant group (about 10%) saw them as problematic, were lower on agreeableness & more extraverted, & reactant

To comply or not comply? A latent profile analysis of behaviours and attitudes during the COVID-19 pandemic. Sabina Kleitman, Dayna J. Fullerton, Lisa M. Zhang, Matthew D. Blanchard, Jihyun Lee, Lazar Stankov, Valerie Thompson. PLoS One, July 29, 2021.

Abstract: How and why do people comply with protective behaviours during COVID-19? The emerging literature employs a variable-centered approach, typically using a narrow selection of constructs within a study. This study is the first to adopt a person-centred approach to identify complex patterns of compliance, and holistically examine underlying psychological differences, integrating multiple psychology paradigms and epidemiology. 1575 participants from Australia, US, UK, and Canada indicated their behaviours, attitudes, personality, cognitive/decision-making ability, resilience, adaptability, coping, political and cultural factors, and information consumption during the pandemic’s first wave. Using Latent Profile Analysis, two broad groups were identified. The compliant group (90%) reported greater worries, and perceived protective measures as effective, whilst the non-compliant group (about 10%) perceived them as problematic. The non-compliant group were lower on agreeableness and cultural tightness-looseness, but more extraverted, and reactant. They utilised more maladaptive coping strategies, checked/trusted the news less, and used official sources less. Females showed greater compliance than males. By promoting greater appreciation of the complexity of behaviour during COVID-19, this research provides a critical platform to inform future studies, public health policy, and targeted behaviour change interventions during pandemics. The results also challenge age-related stereotypes and assumptions.


The current study is the first to provide a holistic view of the factors influencing behavioural compliance with protective measures during COVID-19. In doing so, we integrated an extensive battery of constructs based on theories from multiple paradigms, including epidemiology, health, differential, and cultural psychology, revealing a complex picture of behaviour and the need for targeted interventions. The novel person-centred approach offers insight into different clusters/groups within the population based on behaviours, attitudes, and key demographics. The sample clustered into two broad groups: those compliant and those not. Whilst the majority fell into the compliant group (90%); 10% of individuals reported non-compliant behaviours and attitudes, which is enough to be cause for concern given the risk of exponential spread.

The compliant and non-compliant groups differed on a number of variables, including beliefs about protective measures, social attitudes, and personality. There was remarkable consistency across the four countries surveyed; and surprisingly, the non-compliant group was not populated simply by young people. The non-compliant group was the second youngest amongst four identified sub-groups, however, and the youngest individuals formed a distinct cluster within the three compliant groups. In the two-class solution, compliant and non-compliant groups did not differ in age. This finding runs contrary to the oft-promoted media stereotype of the young, COVID-indifferent partygoers neglecting restrictions. The picture we discovered was much more complex and key take-away findings are discussed below.

Compliance rates

Overall, we found a promisingly high (90%) rate of compliance, and each of the four countries displayed relatively similar compliance rates. However, we note that this sample was drawn early in the pandemic. These rates of compliance may change as the pandemic prolongs and people become more complacent. Further, country-level differences are likely to become more pronounced given the major differences between countries’ trajectories and regulations since the first wave.

Differences in demographics and attitudes towards protective measures

The compliant group endorsed protective measures as beneficial and effective in leading to better health-related outcomes. By contrast, the non-compliant group appeared concerned with the social and economic cost of such measures. These findings align with health behaviour frameworks which propose that behaviour change is, in part, motivated by perceptions about the efficacy, benefits, and costs of behaviours [1314]. Compliant and non-compliant groups differed in their level of worry about COVID-19, consistent with pre-COVID and emerging COVID-19 research showing worry or fear is an important driver of positive behaviour change [236812]. These findings support the Health Belief Model and Protection Motivation Theory which suggest that perceptions of severity of the threat, vulnerability to infection, efficacy of protective behaviours, self-efficacy, and perceived benefits and barriers of protective actions are the key beliefs driving health behaviour change and compliance [1314]. Although based on correlational data, these results stress the importance of targeting these perceptions to increase compliance.

Notably, on average, the groups did not differ on age, education, or physical health, nor were there differences in pro- or anti-social behaviours. Consistent with previous research, females showed higher rates of compliance than males [15].

The four-class solution allowed for a more nuanced view of the large compliant group, which split into three distinct groups, offering insight into possible motivations behind compliance. The largest group (Class 2) were the youngest, and largely university students. The second largest group (Class 3) were middle-aged and more highly educated; and a minority fit into a third compliant class (Class 4), who were older and had poorer physical health. To our knowledge, although intuitive, no previous study has demonstrated the existence of these classifications within the compliant population.

Personality differentiates compliant and non-compliant groups

Unsurprisingly, the non-compliant group were more extraverted. This group indicated their plans to visit family and friends in the forthcoming week, characteristically extraverted behaviours. However, such behaviours are especially worrisome at the time of pandemic. Although it is difficult to change a psychological trait, to increase compliance, the self-centredness of certain manifestations of trait extraversion may need to be targeted, as they present a health risk factor to others.

Consistent with some emerging COVID-19 findings, the compliant group scored higher on intellect/openness and agreeableness [1819]. Contrary to what other COVID-19 research suggests [1953] we did not find any differences in conscientiousness or neuroticism between groups. Similarly, perceptions of being resilient and adaptable did not promote compliance during the first wave. This, however, may be the result of sampling during the first few months of the pandemic and the results may change with prolonged exposure to restrictions as the pandemic continues.

Finally, the compliant group were more likely to cope adaptively by self-distraction, planning, and using active strategies; whilst non-compliant people were more likely to cope through denial, substance use, and behavioural disengagement. This research was correlational; thus, no causal mechanism is implied. Instead, we suggest that future studies, should examine whether the promotion and acceptance of more adaptive strategies will lead to better management of isolation and boredom, and help to increase and maintain compliance. If this is the case, intervention strategies should include promotion and education of adaptive strategies, which might be disseminated through mainstream and social media in engaging ways.

Information consumption

The compliant group reported greater use of official government and health information sources than the non-compliant group, suggesting compliant people are better-informed about COVID-19. Also supporting this notion, non-compliant individuals tend to check the legitimacy of sources less than compliant individuals. The groups did not differ in their use of casual information sources (e.g., social media, conversations), highlighting the potential for utilising casual sources for the dissemination of official information. The compliant group checked the news more frequently and expressed greater trust in all information sources than the non-compliant group. Research from the Avian flu pandemic showed that trust in both formal and informal information sources was associated with greater worry, and trust in formal information was linked to greater perceived effectiveness of hygiene behaviours [4]. It is possible that more frequent news-checking has similar impacts on worry and perceptions of protective behaviours, thus promoting compliance. Future studies should determine whether the dissemination of official and reliable information in accessible form (e.g., memes, short messages and videos) via a variety of news outlets, including casual (e.g., social media), may increase rates of compliance in the non-compliant group. However, this would require people to accurately evaluate the legitimacy of information to distinguish between official information and that which is not credible. Thus, targeted interventions focusing on education about how to check the credibility of information, would be of critical importance to foster greater recognition of fake and misleading news.

Attitudes towards government and other cultural factors

Compliant individuals perceived their government as being more truthful than those non-compliant, though there were no differences in reports of satisfaction with their government’s response. Contradictorily, groups differed in their responses when asked whether they thought their government’s reaction to the COVID-19 outbreak was appropriate, too extreme, or insufficient; such that the compliant group was more likely to perceive their government’s reaction to be insufficient compared to the non-compliant group. This aligns with Fetzer et al.’s [34] finding that perceiving the government’s response to be insufficient is associated with greater worry about COVID-19, which in turn may motivate compliance. Conversely, the non-compliant group scored higher on reactance, indicating they are more likely to perceive rules as a threat to their freedom and thus resist them. Consistently, the non-compliant group reported looser cultural norms and higher amorality. A ‘loose’ culture is characterised by valuing freedom, hence is less accustomed to strict social norms such as those imposed during the pandemic. However, we note that the countries sampled are relatively culturally similar. High scores on amorality indicate disregard for moral values within society and are associated with self-interested behavioural choices that ignore COVID-19 guidelines. Whilst no causality is implied, it is possible that emphasising the message of common goals and moral responsibilities at the time of a global health crisis may foster higher compliance rates. Future studies should examine the most efficient messaging to target self-interests, reactance, and perceptions of looser cultural norms.

Implications and future directions

Heterogeneity in the population poses a challenge to implementing widespread behaviour change policies. These strategies should be targeted for different profiles of individuals and focus on increasing the perceived benefits and efficacy of protective measures, reducing barriers, and fostering a functional level of worry. Several directions for future studies have already been proposed. The section below covers further implications of our findings.

Non-compliant individuals appear to distrust and be sceptical of both formal and informal information sources. Further research is needed to identify sources considered trustworthy by this group in order to optimise communication of health advice.

Maintaining compliance as restrictions remain in place for a prolonged period is critically important. Perceptions of being resilient and adaptable did not promote compliance during the first wave in our overall sample. However, it would be fruitful to examine resilience and adaptability beyond the first wave, under the threat of future waves and lockdowns. Further, perceptions of one’s resilience may change as the pandemic prolongs. Some might succumb to the challenges of the pandemic experience, whilst others may discover their strength and experience resilient growth, with both changes having profound effects on mental health.

Lastly, this study was conducted in the early stages of the pandemic. Mobile tracking data from several European countries suggests that people stayed home substantially less during the second wave from late 2020 to early 2021 than they did during the first wave [54]. Thus, further research is needed to examine whether the same profiles and predictors of behaviours emerge in these later stages when rates of compliance and behaviour change have fluctuated.

Strengths and limitations

As a strength, this study addresses the intention-behaviour gap issue prominent in health behaviour research. Our measure of compliance captured behaviours within a critical period rather than asking participants to recall past behaviours. Equally important, we captured a comprehensive range of constructs, providing a holistic view of factors drawn from different psychology paradigms, which extends upon current research by integrating person- and variable-centred approaches to profile and examine characteristics of individuals. These findings may be used to inform strategies for improving and maintaining behaviour change.

We collected data from a large and diverse sample of people in four countries, giving good power and generalisability within that sample. However, a small proportion (18.4%) were collected using snowball recruitment, and using a university student pool (26.3%), contributing to selection bias. We also acknowledge that the interpretation is limited by the fact we sampled ‘WEIRD’ (Western, educated, industrialised, rich, democratic) countries. Though emerging research from other countries has reported findings consistent with ours. For example, distrust in government authorities was associated with non-compliance in Swiss [55], Nigerian [56], Italian, and French samples [57]. Additionally, males and those with less moral values showed lower compliance in Swiss adults [55], as did those with no worry about COVID-19 in Italian and French samples [57]. Further, perceived benefits and efficacy of protective measures have emerged as strong predictors of compliance across samples from a range of countries including Switzerland [58], Ethiopia [59], and China [60]. Nevertheless, we managed to capture a relatively heterogeneous sample, varying in age, gender, levels of education, pre-existing health conditions, and economic situation.

We also used brief versions of several scales and their psychometric properties may limit reliability. However, with few exceptions, most measures had reasonable to excellent reliability estimates. Newly developed measures showed promising preliminary psychometric properties but require further validation.

Moreover, while we captured different levels of education this research did not capture any other socio-economic status (SES) metrics. The emerging results indicate that in the USA, higher SES was related to earlier incidences of COVID-19 cases, but as regulations of social distancing were imposed, the growth of incidents was slower in higher SES countries with lower case fatality rates [61]. Future research needs to determine economic and social conditions that may have disadvantaged different populations as the pandemic progressed within the four countries examined in this research and across the globe. For instance, density of living situations, and reduced capacity to access healthcare, reliable information, and to work from home are important barriers to overall compliance behaviours.

Finally, during the time of data collection, with some caveats, the four countries sampled had employed similar approaches to controlling the spread of COVID-19. This likely contributed to consistency between the four countries surveyed in the profiles identified and their characteristics. Although the consistency in personal characteristics and behavioural patterns across four countries is encouraging, this finding needs to be replicated and extended as the rules and conditions change in these four countries.

Misplaced trust: When trust in science fosters belief in pseudoscience and the benefits of critical evaluation

Misplaced trust: When trust in science fosters belief in pseudoscience and the benefits of critical evaluation. Thomas C. O'Brien, Ryan Palmer, Dolores Albarracin. Journal of Experimental Social Psychology, Volume 96, September 2021, 104184.

Abstract: At a time when pseudoscience threatens the survival of communities, understanding this vulnerability, and how to reduce it, is paramount. Four preregistered experiments (N = 532, N = 472, N = 605, N = 382) with online U.S. samples introduced false claims concerning a (fictional) virus created as a bioweapon, mirroring conspiracy theories about COVID-19, and carcinogenic effects of GMOs (Genetically Modified Organisms). We identify two critical determinants of vulnerability to pseudoscience. First, participants who trust science are more likely to believe and disseminate false claims that contain scientific references than false claims that do not. Second, reminding participants of the value of critical evaluation reduces belief in false claims, whereas reminders of the value of trusting science do not. We conclude that trust in science, although desirable in many ways, makes people vulnerable to pseudoscience. These findings have implications for science broadly and the application of psychological science to curbing misinformation during the COVID-19 pandemic.

Keywords: MisinformationTrust in scienceCritical thinkingMethodological literacy