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. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem-solving. Other papers focus on the cultural and institutional practices that make radical CI possible.

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. Our findings suggest that the presence of causal mechanisms in an explanation is one factor that reduces adherence. While competing explanations are often judged based on their probability of being correct, mechanisms afford a different way of evaluating explanations: They describe the underlying nature of causal relations. Complex explanations (appealing to multiple causes) contain more causal relations and thus allow for more mechanistic information, providing a fuller account of the causal network and promoting a greater sense of understanding.

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. Implications for scientists, policymakers, and science communicators are discussed.

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. Some attempts to replicate this reduction of polarization have been unsuccessful. Is the original effect real or is it just a fluke? In this paper, we explore the effect using more timely political issues and compare judgments of issues whose attitudes are grounded in consequentialist reasoning versus protected values. We also investigate the role of social proof. We find that understanding and attitude extremity are reduced after explanation but only for consequentialist issues, not those based on protected values. There was no effect of social proof.

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. When thinking about a cause, people tend to think about possible effects that participate in the same domain, and vice versa. To explore the specific domains that people use, we asked people to cluster artifacts. The analyses revealed three commonly employed mechanism domains: the mechanical, chemical, and electromagnetic. Using these domains, we tested the domain-matching heuristic by testing adults' and children's causal attribution, prediction, judgment, and subjective understanding. We found that people's responses conform with domain-matching. These results provide evidence for a heuristic that explains how people engage in causal reasoning without directly appealing to mechanistic or probabilistic knowledge.

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. We explore this hypothesis using survey responses collected from 1114 U.S. adults during April and October 2020, and find that neither perceived nor actual risk predicted these preventive behaviors. Instead, being an essential worker, partisanship, and believing compliance was important were more reliable predictors. These results provide guidance for better pandemic response policies and challenge models of health behavior.

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. The contrasting ways of modeling such processes involve, first, more and more sophisticated associative models that capture progressively higher-order statistical structure and, second, more powerful representational languages for other sorts of structure, especially compositional and causal structure. My efforts to rectify these forces have taken me from the study of memory to induction and category knowledge to causal reasoning. In the process, I have consistently appealed to dual systems of thinking. I have come to realize that a key reason for our success as cognizers is that we rely on others for most of our information processing needs; we live in a community of knowledge. We make use of others both intuitively-by outsourcing much of our thinking without knowing we are doing it-and by deliberating with others.

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. Our contention will be that it is not because the information processing of any given individual is not contained entirely within that individual's brain. Rather, it typically includes components situated in the heads of others, in addition to being distributed across parts of the individual's body and physical environment. Our focus here will be on cognition distributed across individuals, or on what we call the "community of knowledge," the challenge that poses for reduction of cognition to neurobiology and the contribution of cognitive neuroscience to the study of communal processes.

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. According to the encoding hypothesis, reaction times should increase as the complexity of the sequence increases, whereas the representativeness heuristic predicts fast reaction times only for more complex sequences that appear more random. We asked participants to guess the generating source of pairs of sequences of dichotomous elements in two different conditions: selecting the string generated by a random source or selecting the string generated by a nonrandom source. Results suggest that both the encoding strategy and the representativeness heuristic have a role in randomness perception and that the two criteria may have a different weight when determining the randomness versus the regularity of a string.

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. Belief polarization depends on the web of epistemic dependence and is greatest for those who know the least, plausibly due to extreme conflation of others' knowledge with one's own.

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. The upshot is that each perspective captures part of the dynamics of opinion change, but the underlying processes operate in an integrated way. Individuals' affective reactions are conditioned by the social world, and therefore reflect their community norms. They often precede processes of reasoning, but not always. In turn, reasoning is generally motivated in the service of transmitting beliefs acquired from citizens' communities of belief. Cognition is largely a filter for attending to and sharing community norms.

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. In contrast, the discussion of certain consequentialist topics (the impact of politicians' stance and same-sex marriage as a matter of policy) showed the opposite pattern. Our results reinforce the meaningfulness of protected values and consequentialism as relevant dimensions for describing public discourse and highlight the usefulness of unsupervised machine-learning methods in tackling questions about social attitude change.

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. Experiment 2 shows that these intuitions persist when the community has no additional, relevant featural information, so the label amounts to a mere name for the explanandum. Experiment 3 finds a similar effect when the unentrenched label is not widely used, but is defined by a group of experts and the recipient of the explanation is herself an expert familiar with the topic. The effect also obtains for categories that lack a coherent causal structure. Experiment 4 further demonstrates the domain generality of the entrenchment effect and provides evidence against several interpretations of the results. A majority of participants in Experiments 3 and 4 could not report the impact of entrenchment on their judgments. We argue that this reliance on community cues arose because the community often has useful information to provide about categories. The common use of labels as conduits for this communal knowledge results in reliance on community cues even when they are uninformative. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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. In Experiment 3, we further ruled out two classes of alternative explanations (one appealing to task demands and the other proposing that judgments were mediated by inferences about a phenomenon's understandability). In Experiment 4, we ruled out the possibility that the effect could be attributed to a pragmatic inference.

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. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.

Causal explanation in the face of contradiction.

Previous work has shown that predictions can be mediated by mechanistic beliefs. The present study shows that such mediation only occurs in the face of contradictory, and not corroborative, evidence. In four experiments, we presented participants with causal statements describing a common-cause structure (E1 ← C → E2). Then we informed them of the states of C and E1 and asked them to judge the likelihood of E2. In Experiments 1 and 2, we manipulated whether the mechanisms supporting the two effects were the same or different, and whether the evidence presented confirmed or contradicted the participants' expectations. The relation between the mechanisms only influenced predictions when evidence contradicted the expectations, but not when it was consistent. In Experiments 3 and 4, we used a common-cause structure with identical mechanisms. We manipulated the order in which predictions were made. When confirmatory predictions were made before contradictory predictions, mechanistic modulation was not observed in the confirmatory case. In contrast, the modulation was found when confirmatory predictions were made after contradictory ones. The results support the contradiction hypothesis that causal structure is revised during prediction, but only in the face of unexpected evidence.

Mechanistic beliefs determine adherence to the Markov property in causal reasoning.

What kind of information do people use to make predictions? Causal Bayes nets theory implies that people should follow structural constraints like the Markov property in the form of the screening-off rule, but previous work shows little evidence that people do. We tested six hypotheses that attempt to explain violations of screening off, some by asserting that people use mechanistic knowledge to infer additional latent structure. In three experiments, we manipulated whether the causal relations among variables within a causal structure were supported by the same or different mechanisms. The experiments differed in the type of causal structures (common cause vs. chain), the way that causal structures were presented (verbal description vs. observational learning), how the mechanisms were presented (explicit description vs. implicit description vs. visual hint), and the number of predictions requested (2 vs. 24). The results revealed that the screening-off rule was violated more often when the mechanisms were the same than when they were different. The findings suggest that people use knowledge about underlying mechanisms to infer latent structure for prediction.

Counterfactuals and causal models: introduction to the special issue.

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent. Pearl refers to Bayes nets as oracles for intervention, and interventions can tell us what the effect of action will be or what the effect of counterfactual possibilities would be. Counterfactuals turn out to be necessary to understand thought, perception, and language. This selection of papers tells us why, sometimes in ways that support the Bayes net framework and sometimes in ways that challenge it.