Metacognition, public health compliance, and vaccination willingness.

Metacognition, our ability to reflect on our own beliefs, manifests itself in the confidence we have in these beliefs, and helps us guide our behavior in complex and uncertain environments. Here, we provide empirical tests of the importance of metacognition during the pandemic. Bayesian and frequentist analyses demonstrate that citizens with higher metacognitive sensitivity-where confidence differentiates correct from incorrect COVID-19 beliefs-reported higher willingness to vaccinate against COVID-19, and higher compliance with recommended public health measures. Notably, this benefit of accurate introspection held controlling for the accuracy of COVID-19 beliefs. By demonstrating how vaccination willingness and compliance may relate to insight into the varying accuracy of beliefs, rather than only the accuracy of the beliefs themselves, this research highlights the critical role of metacognitive ability in times of crisis. However, we do not find sufficient evidence to conclude that citizens with higher metacognitive sensitivity were more likely to comply with recommended public health measures when controlling for the absolute level of the confidence citizens had in their COVID-19 beliefs.

Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation.

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence-a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.

Household preferences for reducing greenhouse gas emissions in four European high-income countries: Does health information matter? A mixed-methods study protocol.

BACKGROUND: It is now universally acknowledged that climate change constitutes a major threat to human health. At the same time, some of the measures to reduce greenhouse gas emissions, so-called climate change mitigation measures, have significant health co-benefits (e.g., walking or cycling more; eating less meat). The goal of limiting global warming to 1,5° Celsius set by the Conference of the Parties to the United Nations Framework Convention on Climate Change in Paris in 2015 can only be reached if all stakeholders, including households, take actions to mitigate climate change. Results on whether framing mitigation measures in terms of their health co-benefits increases the likelihood of their implementation are inconsistent. The present study protocol describes the transdisciplinary project HOPE (HOuseholds' Preferences for reducing greenhouse gas emissions in four European high-income countries) that investigates the role of health co-benefits in households' decision making on climate change mitigation measures in urban households in France, Germany, Norway and Sweden. METHODS: HOPE employs a mixed-methods approach combining status-quo carbon footprint assessments, simulations of the reduction of households' carbon footprints, and qualitative in-depth interviews with a subgroup of households. Furthermore, a policy analysis of current household oriented climate policies is conducted. In the simulation of the reduction of households' carbon footprints, half of the households are provided with information on health co-benefits of climate change mitigation measures, the other half is not. Households' willingness to implement the measures is assessed and compared in between-group analyses of variance. DISCUSSION: This is one of the first comprehensive mixed-methods approaches to investigate which mitigation measures households are most willing to implement in order to reach the 1,5° target set by the Paris Agreement, and whether health co-benefits can serve as a motivator for households to implement these measures. The comparison of the empirical data with current climate policies will provide knowledge for tailoring effective climate change mitigation and health policies.

When high working memory capacity is and is not beneficial for predicting nonlinear processes.

Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.

Why metacognition matters in politically contested domains.

Emerging evidence highlights the importance of metacognition - the capacity for insight into the reliability and fallibility of our own knowledge and thought - in politically contested domains. The present synthesis elucidates why metacognition matters in politically charged contexts and its potential impact on how individuals form beliefs, process evidence, and make decisions.

Testing the Social Function of Metacognition for Common-Pool Resource Use.

Metacognition, the ability to monitor and evaluate our own cognitive processes, confers advantages to individuals and their own judgment. A more recent hypothesis, however, states that explicit metacognition may also enhance the collective judgment of groups, and may enhance human collaboration and coordination. Here, we investigate this social function hypothesis of metacognition with arguably one of the oldest collaboration problems humans face, common-pool resource use. Using an agent-based model that simulates repeated group interactions and the forming of collective judgments about resource extraction, we show that (1) in "kind" environments (where confidence and judgment accuracy correlate positively), explicit metacognition may allow groups to reach more accurate judgments compared to groups exchanging object-level information only; while (2) in "wicked" environments (where confidence and judgment accuracy correlate negatively), explicit metacognition may protect groups from the large judgment errors yielded by groups using metacognitive information for individual-level learning only (implicit metacognition). With explicit metacognition, this research highlights a novel mechanism which, among others, provides a testable explanation of the often-observed finding that groups all over the world communicate to enhance common property use.

Polarized climate change beliefs: No evidence for science literacy driving motivated reasoning in a U.S. national study.

A substantial literature shows that public polarization over climate change in the U.S. is most pronounced among the science literate. A dominant explanation for this phenomenon is that science literacy amplifies motivated reasoning, the tendency to interpret evidence such that it confirms prior beliefs. The present study tests the biasing account of science literacy in a study among the U.S. population that investigated both interpretation of climate change evidence and repeated belief-updating. Results replicated the typical correlational pattern of political polarization as a function of science literacy. However, results delivered little support for the core causal claim of the biasing account-that science literacy drives motivated reasoning. Hence, these results speak against a mechanism whereby science literacy driving motivated reasoning could explain polarized climate change beliefs among the science literate. This study adds to our growing understanding of the role of science literacy for public beliefs about contested science. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Contested science: Individuals with higher metacognitive insight into interpretation of evidence are less likely to polarize.

Societal polarization over contested science has increased in recent years. To explain this development, political, sociological, and psychological research has identified societal macro-phenomena as well as cognitive micro-level factors that explain how citizens reason about the science. Here we take a radically different perspective, and highlight the effects of metacognition: How citizens reason about their own reasoning. Leveraging methods from Signal Detection Theory, we investigated the importance of metacognitive insight for polarization for the heavily contested topic of climate change, and the less heavily contested topic of nanotechnology. We found that, for climate change (but not for nanotechnology), higher insight into the accuracy of own interpretations of the available scientific evidence related to a lower likelihood of polarization over the science. This finding held irrespective of the direction of the scientific evidence (endorsing or rejecting anthropogenicity of climate change). Furthermore, the polarizing effect of scientific evidence could be traced back to higher metacognitive insight fostering belief-updating in the direction of the evidence at the expense of own, prior beliefs. By demonstrating how metacognition links to polarization, the present research adds to our understanding of the drivers of societal polarization over science.

Importance of domain-specific metacognition for explaining beliefs about politicized science: The case of climate change.

One of the oldest debates in psychological research into politicized science such as nanotechnology, vaccination, or climate change centers around the role of knowledge. Does increased knowledge of the science affect beliefs about it? While research has traditionally focused on the role of object-level knowledge, here we highlight the importance of meta-knowledge: How much people believe they know about the science. Specifically, we demonstrate the importance of meta-knowledge (measured as confidence in knowledge) for explaining beliefs about science with one of the most contested examples: climate change. For a national Germany sample (N = 509), frequentist and Bayesian analyses demonstrated that climate change meta-knowledge was predictive of climate change beliefs, above and beyond object-level climate change knowledge. These results held for both the belief that climate change is risky, and the belief that climate change is anthropogenic, and when controlling for political attitude, and demographic variables. Furthermore, for a second national German sample (N = 588), confidence in climate change knowledge was a stronger predictor of climate change beliefs compared to confidence in other-domain (biological and physical) science knowledge, suggesting that outside of the respective domain, metacognitive confidence did not explain beliefs. These results highlight the relevance of domain-specific metacognition for explaining beliefs about the contested science of climate change. By demonstrating the relevance of metacognitive, rather than solely object-level thought, these results add to our understanding of the cognitive mechanisms involved in the formation of beliefs about politicized science.

Extrapolation accuracy underestimates rule learning: Evidence from the function-learning paradigm.

Understanding the development of non-linear processes such as economic or population growth is an important prerequisite for informed decisions in those areas. In the function-learning paradigm, people's understanding of the function rule that underlies the to-be predicted process is typically measured by means of extrapolation accuracy. Here we argue, however, that even though accurate extrapolation necessitates rule-learning, the reverse does not necessarily hold: Inaccurate extrapolation does not exclude rule-learning. Experiment 1 shows that more than one third of participants who would be classified as "exemplar-based learners" based on their extrapolation accuracy were able to identify the correct function shape and slope in a rule-selection paradigm, demonstrating accurate understanding of the function rule. Experiment 2 shows that higher proportions of rule learning than ruleapplication in the function-learning paradigm is not due to (i) higher a priori probabilities to guess the correct rule in the rule-selection paradigm; nor is it due to (ii) a lack of simultaneous access to all function values in the function-learning paradigm. We conclude that rule application is not tantamount to rule-learning, and that assessing rule xlearning via extrapolation accuracy underestimates the proportion of rule learners in function-learning experiments.

Evaluating the Application of the Mental Model Mapping Tool (M-Tool).

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool's application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool's convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool's construct validity by relating mental model complexity to participants' education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.

How Well Do COP22 Attendees Understand Graphs on Climate Change Health Impacts from the Fifth IPCC Assessment Report?

Graphs are prevalent in the reports of the Intergovernmental Panel on Climate Change (IPCC), often depicting key points and major results. However, the popularity of graphs in the IPCC reports contrasts with a neglect of empirical tests of their understandability. Here we put the understandability of three graphs taken from the Health chapter of the Fifth Assessment Report to an empirical test. We present a pilot study where we evaluate objective understanding (mean accuracy in multiple-choice questions) and subjective understanding (self-assessed confidence in accuracy) in a sample of attendees of the United Nations Climate Change Conference in Marrakesh, 2016 (COP22), and a student sample. Results show a mean objective understanding of M = 0.33 for the COP sample, and M = 0.38 for the student sample. Subjective and objective understanding were unrelated for the COP22 sample, but associated for the student sample. These results suggest that (i) understandability of the IPCC health chapter graphs is insufficient, and that (ii) particularly COP22 attendees lacked insight into which graphs they did, and which they did not understand. Implications for the construction of graphs to communicate health impacts of climate change to decision-makers are discussed.

Making Sense of Dynamic Systems: How Our Understanding of Stocks and Flows Depends on a Global Perspective.

Stocks and flows (SF) are building blocks of dynamic systems: Stocks change through inflows and outflows, such as our bank balance changing with withdrawals and deposits, or atmospheric CO2 with absorptions and emissions. However, people make systematic errors when trying to infer the behavior of dynamic systems, termed SF failure, whose cognitive explanations are yet unknown. We argue that SF failure appears when people focus on specific system elements (local processing), rather than on the system structure and gestalt (global processing). Using a standard SF task (n = 148), SF failure decreased by (a) a global as opposed to local task format; (b) individual global as opposed to local processing styles; and (c) global as opposed to local perceptual priming. These results converge toward local processing as an explanation for SF failure. We discuss theoretical and practical implications on the connections between the scope of attention and understanding of dynamic systems.

Climate change and human health: what are the research trends? A scoping review protocol.

INTRODUCTION: For 28 years, the Intergovernmental Panel on Climate Change (IPCC) has been assessing the potential risks associated with anthropogenic climate change. Although interest in climate change and health is growing, the implications arising from their interaction remain understudied. Generating a greater understanding of the health impacts of climate change could be key step in inciting some of the changes necessary to decelerate global warming. A long-term and broad overview of the existing scientific literature in the field of climate change and health is currently missing in order to ensure that all priority areas are being adequately addressed. In this paper we outline our methods to conduct a scoping review of the published peer-reviewed literature on climate change and health between 1990 and 2015. METHODS AND ANALYSIS: A detailed search strategy will be used to search the PubMed and Web of Science databases. Specific inclusion and exclusion criteria will be applied in order to capture the most relevant literature in the time frame chosen. Data will be extracted, categorised and coded to allow for statistical analysis of the results. ETHICS AND DISSEMINATION: No ethical approval was required for this study. A searchable database of climate change and health publications will be developed and a manuscript will be complied for publication and dissemination of the findings. We anticipate that this study will allow us to map the trends observed in publications over the 25-year time period in climate change and health research. It will also identify the research areas with the highest volume of publications as well as highlight the research trends in climate change and health.