Radical Collective Intelligence and the Reimagining of Cognitive Science.

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information.

Communities of Knowledge in Trouble.

The community-of-knowledge framework explains the extraordinary success of the human species, despite individual members' demonstrably shallow understanding of many topics, by appealing to outsourcing. People follow the cues of members of their community because understanding of phenomena is generally distributed across the group. Typically, communities do possess the relevant knowledge, but it is possible in principle for communities to send cues despite lacking knowledge-a weakness in the system's design.

Not so simple! Causal mechanisms increase preference for complex explanations.

Mechanisms play a central role in how we think about causality, yet not all causal explanations describe mechanisms. Across five experiments, we find that people evaluate explanations differently depending on whether or not they include mechanisms. Despite common wisdom suggesting that explanations ought to be simple in the sense of appealing to as few causes as necessary to explain an effect, the literature is divided over whether people adhere to this principle.

Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues.

Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much they think they know. Across seven critical issues that enjoy substantial scientific consensus, as well as attitudes toward COVID-19 vaccines and mitigation measures like mask wearing and social distancing, results indicate that those with the highest levels of opposition have the lowest levels of objective knowledge but the highest levels of subjective knowledge.

Causal reasoning without mechanism.

Unobservable mechanisms that tie causes to their effects generate observable events. How can one make inferences about hidden causal structures? This paper introduces the domain-matching heuristic to explain how humans perform causal reasoning when lacking mechanistic knowledge. We posit that people reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones.

Is political extremism supported by an illusion of understanding?

Polarization is rising in most countries in the West. How can we reduce it? One potential strategy is to ask people to explain how a political policy works-how it leads to consequences- because that has been shown to induce a kind of intellectual humility: Explanation causes people to reduce their judgments of understanding of the issues (their "illusion of explanatory depth"). It also reduces confidence in attitudes about the policies; people become less extreme.

How Do We Believe?

My first 30-odd years of research in cognitive science has been driven by an attempt to balance two facts about human thought that seem incompatible and two corresponding ways of understanding information processing. The facts are that, on one hand, human memories serve as sophisticated pattern recognition devices with great flexibility and an ability to generalize and predict as long as circumstances remain sufficiently familiar. On the other hand, we are capable of deploying an enormous variety of representational schemes that map closely onto articulable structure in the world and that support explanation even in unfamiliar circumstances.

Walking the party line: The growing role of political ideology in shaping health behavior in the United States.

Objective: To assess the extent to which political ideology affects COVID-19 preventive behaviors and related beliefs and attitudes in the U.S.

Methods: Two surveys, one using a convenience sample and another using a nationally representative sample, were conducted in September and November 2020, respectively.

Cognitive Neuroscience Meets the Community of Knowledge.

Cognitive neuroscience seeks to discover the biological foundations of the human mind. One goal is to explain how mental operations are generated by the information processing architecture of the human brain. Our aim is to assess whether this is a well-defined objective.

Evidence against risk as a motivating driver of COVID-19 preventive behaviors in the United States.

Does an individual's risk profile predict their social distancing and mask wearing in the U.S. during the COVID-19 pandemic? Common sense and some health behavior theories suggest that as a perceived threat increases, an individual should be more likely to take preventive measures.

Regular and random judgements are not two sides of the same coin: Both representativeness and encoding play a role in randomness perception.

The overalternating bias is that people rate sequences with an excess of alternation as more random than prescribed by information theory. There are two main explanations: the representativeness heuristic (Kahneman & Tversky Cognitive Psychology, 3, 430-454, 1972) and the implicit encoding hypothesis (Falk & Konold Psychological Review, 104, 301-318, 1997). These hypotheses are associated with different reaction times predictions.

Managing evidence in food safety and nutrition.

Evidence ('data') is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on 'Managing evidence' of EFSA's third Scientific Conference 'Science, Food, Society', current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it.

Individual Representation in a Community of Knowledge.

An individual's knowledge is collective in at least two senses: it often comes from other people's testimony, and its deployment in reasoning and action requires accuracy underwritten by other people's knowledge. What must one know to participate in a collective knowledge system? Here, we marshal evidence that individuals retain detailed causal information for a few domains and coarse causal models embedding markers indicating that these details are available elsewhere (others' heads or the physical world) for most domains. This framework yields further questions about metacognition, source credibility, and individual computation that are theoretically and practically important.

Think of the consequences: A decade of discourse about same-sex marriage.

Approaching issues through the lens of nonnegotiable values increases the perceived intractability of debate (Baron & Spranca in Organizational Behavior and Human Decision Processes, 70, 1-16, 1997), while focusing on the concrete consequences of policies instead results in the moderation of extreme opinions (Fernbach, Rogers, Fox, & Sloman in Psychological Science, 24, 939-946, 2013) and a greater likelihood of conflict resolution (Baron & Leshner in Journal of Experimental Psychology: Applied, 6, 183-194, 2000). Using comments on the popular social media platform Reddit from January 2006 until September 2017, we showed how changes in the framing of same-sex marriage in public discourse relate to changes in public opinion. We used a topic model to show that the contributions of certain protected-values-based topics to the debate (religious arguments and freedom of opinion) increased prior to the emergence of a public consensus in support of same-sex marriage (Gallup, 2017), and declined afterward.

Thought as a determinant of political opinion.

Recent political events around the world, including the apparently sudden rise of populism and decline of democratic zeal, have surprised many of us and offered a window onto how people form beliefs and attitudes about the wider world. Cognitive scientists have tended to view belief and attitude formation from one of three perspectives: as a process of deliberative reasoning, as a gut reaction modulated by feelings, or as a cultural phenomenon grounded in partisan relationships. This special issue on the cognitive science of political thought brings a variety of voices to bear on the issue.

Community appeal: Explanation without information.

Formal or categorical explanation involves the use of a label to explain a property of an object or group of objects. In four experiments, we provide evidence that label entrenchment, the degree to which a label is accepted and used by members of the community, influences the judged quality of a categorical explanation whether or not the explanation offers substantive information about the explanandum. Experiment 1 shows that explanations using unentrenched labels are seen as less comprehensive and less natural, independent of the causal information they provide.

Concreteness and abstraction in everyday explanation.

A number of philosophers argue for the value of abstraction in explanation. According to these prescriptive theories, an explanation becomes superior when it leaves out details that make no difference to the occurrence of the event one is trying to explain (the explanandum). Abstract explanations are not frugal placeholders for improved, detailed future explanations but are more valuable than their concrete counterparts because they highlight the factors that do the causal work, the factors in the absence of which the explanandum would not occur.

Evaluating everyday explanations.

People frequently rely on explanations provided by others to understand complex phenomena. A fair amount of attention has been devoted to the study of scientific explanation, and less on understanding how people evaluate naturalistic, everyday explanations. Using a corpus of diverse explanations from Reddit's "Explain Like I'm Five" and other online sources, we assessed how well a variety of explanatory criteria predict judgments of explanation quality.

Your Understanding Is My Understanding: Evidence for a Community of Knowledge.

In four experiments, we tested the community-of-knowledge hypothesis, that people fail to distinguish their own knowledge from other people's knowledge. In all the experiments, despite the absence of any actual explanatory information, people rated their own understanding of novel natural phenomena as higher when they were told that scientists understood the phenomena than when they were told that scientists did not yet understand them. In Experiment 2, we found that this occurs only when people have ostensible access to the scientists' explanations; the effect does not occur when the explanations exist but are held in secret.

Causality in thought.

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building.

Comments on quantum probability theory.

Quantum probability theory (QP) is the best formal representation available of the most common form of judgment involving attribute comparison (inside judgment). People are capable, however, of judgments that involve proportions over sets of instances (outside judgment). Here, the theory does not do so well.