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. At the level of the community of knowledge, we explore how conspiracy communities facilitate false belief by promoting a contagious sense of understanding, and how community norms catalyze the biased assimilation of evidence. We review recent research on conspiracy theories and explain how conspiratorial thinking emerges from the interaction of individual and group processes. As a case study, we describe observations the first author made while attending the Flat Earth International Conference, a meeting of conspiracy theorists who believe the Earth is flat. Rather than treating conspiracy belief as pathological, we take the perspective that is an extreme outcome of common cognitive processes.

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.

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. Finally, we review several interventions that have shown promise in mitigating these biases.

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. In both studies, investors were biased to recall returns as higher than achieved, and larger memory biases were associated with greater overconfidence and trading frequency. The design of Study 2 allowed us to separately investigate the effects of two types of memory biases: distortion and selective forgetting. Both types of bias were present and were independently associated with overconfidence and trading frequency. Study 3 was an incentive-compatible experiment in which overconfidence and trading frequency were reduced when participants looked up previous consequential trades compared to when they reported them from memory.

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.

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 consume1,2 and have the potential to provide substantial benefits to humankind3. However, many people still harbour concerns about them or oppose their use4,5. 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. Extreme opponents know the least, but think they know the most. Moreover, the relationship between self-assessed and objective knowledge shifts from positive to negative at high levels of opposition. Similar results were obtained in a parallel study with representative samples from the United States, France and Germany, and in a study testing attitudes about a medical application of genetic engineering technology (gene therapy). This pattern did not emerge, however, for attitudes and beliefs about climate change.

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). Although these effects occurred when people were asked to generate a mechanistic explanation, they did not occur when people were instead asked to enumerate reasons for their policy preferences (Experiment 2). Finally, generating mechanistic explanations reduced donations to relevant political advocacy groups (Experiment 3). The evidence suggests that people's mistaken sense that they understand the causal processes underlying policies contributes to political polarization.

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. Acceptability of AC is a judgment of diagnostic strength, the probability of the antecedent cause given the consequent effect. The model proposes that acceptability judgments are derived from a causal Bayesian network with a common effect structure in which the probability of the consequent effect is a function of the antecedent cause, alternative causes, and disabling conditions. In 2 experiments, the model was tested by collecting judgments of the causal parameters of conditionals and using them to derive predictions for MP and AC acceptability using 0 free parameters. To assess the validity of the model, its predictions were fit to the acceptability ratings and compared to the fits of 3 versions of Mental Models Theory. The fits of the causal model theory were superior. Experiment 3 provides direct evidence that people engage in a causal analysis and not a direct calculation of conditional probability when assessing causal conditionals. The causal model theory represents a synthesis across the disparate literatures on deductive, probabilistic, and causal reasoning. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

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. Experiment 2 ruled out a pragmatic explanation of the result, that the weak evidence implies the absence of stronger evidence. In Experiment 3, weak positive evidence made people less likely to gamble on the outcome of the 2010 United States mid-term Congressional election. Experiments 4 and 5 replicated these findings with everyday causal scenarios. We argue that this "weak evidence effect" arises because people focus disproportionately on the mentioned weak cause and fail to think about alternative causes.

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. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way.

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. The findings suggest that framing a problem as a diagnostic-likelihood judgment can reduce bias.

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. We hypothesized that such self-deception depends on imprecision in the environment that allows leeway to represent one's own actions as either observations or interventions. Four experiments tested this idea using a dot-tracking task. Participants were told to go as quickly as they could and that going fast indicated either above-average or below-average intelligence. Precision was manipulated by varying the vagueness in feedback about performance. As predicted, self-deception was observed only when feedback on the task used vague terms rather than precise values. The diagnosticity of the feedback did not matter.

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. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure.