Conspiracy Theory as Individual and Group Behavior: Observations from the Flat Earth International Conference.

Conspiratorial thinking has been with humanity for a long time but has recently grown as a source of societal concern and as a subject of research in the cognitive and social sciences. We propose a three-tiered framework for the study of conspiracy theories: (1) cognitive processes, (2) the individual, and (3) social processes and communities of knowledge. At the level of cognitive processes, we identify explanatory coherence and faulty belief updating as critical ideas.

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.

Investor memory of past performance is positively biased and predicts overconfidence.

We document a memory-based mechanism associated with investor overconfidence. In Studies 1 and 2, investors were asked to recall their most important trades in the recent past and then reported investing confidence and trading frequency. After the study, they looked up and reported the actual returns of these trades.

False polarization: Cognitive mechanisms and potential solutions.

Although political polarization in the United States is real, intense, and increasing, partisans consistently overestimate its magnitude. This 'false polarization' is insidious because it reinforces actual polarization and inhibits compromise. We review empirical research on false polarization and the related phenomenon of negative meta-perceptions, and we propose three cognitive and affective processes that likely contribute to these phenomena: categorical thinking, oversimplification, and emotional amplification.

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.

Extreme opponents of genetically modified foods know the least but think they know the most.

There is widespread agreement among scientists that genetically modified foods are safe to consume and have the potential to provide substantial benefits to humankind. However, many people still harbour concerns about them or oppose their use. In a nationally representative sample of US adults, we find that as extremity of opposition to and concern about genetically modified foods increases, objective knowledge about science and genetics decreases, but perceived understanding of genetically modified foods increases.

Political extremism is supported by an illusion of understanding.

People often hold extreme political attitudes about complex policies. We hypothesized that people typically know less about such policies than they think they do (the illusion of explanatory depth) and that polarized attitudes are enabled by simplistic causal models. Asking people to explain policies in detail both undermined the illusion of explanatory depth and led to attitudes that were more moderate (Experiments 1 and 2).

A quantitative causal model theory of conditional reasoning.

The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability of MP is a judgment of causal power, the probability that the antecedent cause is efficacious in bringing about the consequent effect.

When good evidence goes bad: the weak evidence effect in judgment and decision-making.

An indispensable principle of rational thought is that positive evidence should increase belief. In this paper, we demonstrate that people routinely violate this principle when predicting an outcome from a weak cause. In Experiment 1 participants given weak positive evidence judged outcomes of public policy initiatives to be less likely than participants given no evidence, even though the evidence was separately judged to be supportive.

Asymmetries in predictive and diagnostic reasoning.

In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability.

Neglect of alternative causes in predictive but not diagnostic reasoning.

People are renowned for their failure to consider alternative hypotheses. We compare neglect of alternative causes when people make predictive versus diagnostic probability judgments. One study with medical professionals reasoning about psychopathology and two with undergraduates reasoning about goals and actions or about causal transmission yielded the same results: neglect of alternative causes when reasoning from cause to effect but not when reasoning from effect to cause.

Self-deception requires vagueness.

The paper sets out to reveal conditions enabling diagnostic self-deception, people's tendency to deceive themselves about the diagnostic value of their own actions. We characterize different types of self-deception in terms of the distinction between intervention and observation in causal reasoning. One type arises when people intervene but choose to view their actions as observations in order to find support for a self-serving diagnosis.

Causal learning with local computations.

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined.