Scientists indeed show all the strengths and illusions of other humans. Hence, we should expect mistakes understanding a procedure as complex and unnatural as linear regression. Scientists, like other humans, are not designed to work in a vacuum. They work best with other people, preferably in an adversarial process. Adversarial processes can turn errors due to bias and illusion into features that enable progress.

We compare two methods to elicit graphs from people that represent the causal structure of common artifacts. One method asks participants to focus narrowly on local causal relations and is based on the “make-a-difference” view of causality, specifically on an interventional theory of causality and so we call it “Intervention.” It asks subjects to answer a series of counterfactual questions. The second method draws directly from the graphical aspect of Causal Bayesian Networks and allows people to consider causal structure at a more global level. It involves drawing causal graphs using an online interface called “Loopy.” This method does not depend on a definition of causal relatedness. We use signal detection theory to analyze the likelihoods of people generating correct and incorrect causal relations (hit rates and false alarm rates, respectively) using each method. The results show that the intervention method leads people to generate more accurate causal models.

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.

In this retrospective honoring the exemplary psychologist Daniel Kahneman (1934-2024), the authors present a curated selection of quotes from the academic community reflecting on his ideas. These submissions, gathered from a wide range of scholars, highlight Kahneman's contributions to fields spanning attention, judgment, decision-making, and well-being. From his exploration of cognitive biases to his groundbreaking work on prospect theory, Kahneman's research revolutionized researchers' understanding of human behavior and decision-making. Beyond his research, many quotes also emphasize Kahneman's thoughts on what it means to be a behavioral scientist-focusing on a commitment to criticism, transparency, and adversarial collaboration; showcasing the dynamic nature of scientific inquiry across disciplinary divides; and highlighting his dedication to advancing the greater good. Together, these reflections paint a portrait of a visionary thinker whose theoretical and meta-scientific contributions have left an indelible mark on psychology and other social sciences.

Mechanisms play a central role in how we think about causality, yet not all causal explanations describe mechanisms. Across four 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 (appealing to as few causes as necessary to explain an effect), the previous literature is divided over whether people adhere to this simplicity principle. Our findings suggest that the presence of mechanisms in an explanation is one factor that moderates this preference for simplicity. Without mechanisms, people typically exhibit a preference for simple explanations, consistent with probabilistic accounts. This preference is significantly reduced or even reversed when explanations contain mechanisms, suggesting that mechanisms afford a different way of evaluating explanations. Rather than focusing on probability, complex explanations that contain mechanisms may be preferred because they provide a fuller account of the underlying causal network, promoting a greater sense of understanding.

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. COVID-19 in the United States offered a natural experiment in collective-knowledge development because a novel phenomenon arrived at a moment of intense division in political partisanship. We review evidence from the pandemic showing that the thought leaders of the two partisan groups sent radically different messages about COVID, which were, in turn, reinforced by close community members (family, friends, etc.). We show that although actual understanding of the individual plays a role in a key COVID-mitigation behavior (vaccination), it plays a smaller role than perceived understanding of thought leaders and beliefs about COVID-related behaviors of close community members. We discuss implications for theory and practice when all communities are in the same epistemic circumstance-relying on the testimony of others.

We evaluate whether people will outsource their opinion on public policy to consensus conference participants. The ideal consensus conference brings together a representative sample of citizens and introduces them to the range of perspectives and evidence related to some policy. The sample is given the opportunity to ask questions of experts and to deliberate. Attitudes about each policy are queried before and after the conference to see if the event has changed minds. In general, such conferences do produce opinion shifts. Our hypothesis is that the shift can be leveraged by simply communicating conference results – absent substantive information about the merits of the policies discussed – to scale up the value of conferences to the population at large. In five studies, we tell participants about the impact of a consensus conference on a sample of citizens’ opinions for a range of policies without providing any new information about the inherent value of the policy itself. For several of the policies, we see a shift in opinion. We conclude that the value of consensus conferences can be scaled up simply by telling an electorate about its results. This suggests an economical way to bring evidence and rational argument to bear on citizens’ policy attitudes.

Deepfakes are an effective method of media manipulation because of their realism and also because truth is not a priority when people are consuming and sharing content online. Consumers are more focused on creating their own reality that aligns with their desires, opinions, and values. We explain how deepfakes differ from other sources of information. Their realism and vividness makes them unusually effective at depicting alternative facts, including fake news. Deepfakes are difficult to detect and will be even harder to detect in the future. However, people share deepfakes not necessarily because they believe them but because they want to reinforce their own identity and social position. The threat posed by deepfakes is that they can radicalize people by sowing chaos and confusion. They rarely change minds. We review the consequences of deepfakes in both the social sphere and private lives. We suggest potential solutions to reduce their negative consequences.

While the scientific community documents environmental degradation and develops scenarios to identify the operational margins of system Earth, less attention is given to how decisions are made that steer the system in one direction or the other. We propose to use strategy games for this purpose, increasing the representation of human agency in scenario development and creating spaces for deliberation between different worldviews. Played by the right people, strategy games could help break free from established norms and support more transparent democratic dialogues, responding to the human and social limitations of current decision-making. The question is, who gets to play? Scholars develop scenarios to identify the operational margins of system Earth, but focus less on how decisions are made that affect the system one way or another. Strategy games can help increase the representation of human agency in scenario development, allowing for deliberation among diverse worldviews.

At least two fundamental types of evidence feature in attempts to persuade: Anecdotal and generalized (Baesler & Burgoon, 1994). Experimental research has found anecdotal evidence more effective at changing attitudes in issues marked by personal significance and health-relevance (Freling et al., 2020). These apply to marijuana legalization, where a massive shift in American attitudes (from 35% to 67% in favor during 2008-2019; Pew Research Center, 2019) was followed by rapid legalization. However, no research to date has examined whether the movement benefited from anecdotal framing. Since the attitude shift coincided with the rise of social media, we developed the largest corpus of marijuana legalization discussions from Reddit to address this gap (more than 3M comments from 2008-2019, comprising more than 300M words). The dataset is the first to separate marijuana legalization discourse from general mentions of cannabis (e.g., product advertisements) across an entire popular platform. We then developed neural network models to distinguish anecdotal from generalized text in the dataset based on three clause-level features derived from linguistic theory: Whether a clause is about a generic kind rather than specific instances, whether it presents a reliable state or an event, and whether events are bounded in time. Principal Components Analysis provided a reliable composite score of the three features, treated as a measure of the degree to which major themes of discourse are anecdotal versus generalized. We combined topic modeling (Latent Dirichlet Allocation; Blei et al., 2003) with hierarchical clustering and smoothed polynomial regressions to track themes’ prominence over time and bin them into broader categories. Anecdotal themes were less prevalent but present in most comments. We trained separate neural networks on human annotations of attitude and persuasion attempt. Within non-argumentative discourse, anecdotes became more prominent only later in time, presumably as a consequence of softening societal attitudes. But they played a more prominent role throughout in arguments favoring legalization, suggesting that they were actively used to persuade others. Were such anecdotal arguments timed in a way that benefitted legalization ballot initiatives? To answer, we inferred user locations and compared the rate of anecdotal themes before and after legalization in comments from pioneering states. Despite the experimental evidence favoring anecdotal argumentation, we found that the 2012 and 2016 legal milestones followed short-term increases in generalized arguments instead. The particular content, however, varied between the two periods. Character judgments were prominent in 2012, while crime and politics took center-stage in 2016. The generalized precedents of legalization in leading states were argumentative and moralistic but had distinctive clause-level profiles. Meanwhile, legal and medical arguments were sidelined, meaning the novel consensus was not informed by much of the relevant information, anecdotal or otherwise. Together, our results show that while the emerging consensus probably benefited from anecdotal argumentation, the legalization movement’s success happened despite its reliance on less effective generalized discussions with less concrete information content. Addressing this discrepancy between experimental research and the direction of societal discourse may help bring about more informed discussions while better enabling the changing of attitudes.

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.

chapter Share on Causal Bayes Nets as Psychological Theory Author: Steven A. Sloman Brown University Brown UniversitySearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 853–866https://doi.org/10.1145/3501714.3501758Online:04 March 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In September 2021, the"One Hundred Year Study on Artificial Intelligence"project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled"Gathering Strength, Gathering Storms,"answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.

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.

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.