Sensitivity to Evidential Dependencies in Judgments Under Uncertainty.

According to Bayesian models of judgment, testimony from independent informants has more evidential value than dependent testimony. Three experiments investigated learners' sensitivity to this distinction. Each experiment used a social version of the balls-and-urns task, in which participants judged which of two urns was the most likely source of evidence presented by multiple informants. Informants either provided independent testimony based solely on their own observations or dependent-sequential testimony that considered the testimonies of previous informants. Although participants updated their beliefs with additional evidence, this updating was generally insensitive to evidential dependency (Experiments 1 and 2). A notable exception was when individuals were separated according to their beliefs about the relative value of independent and sequential evidence. Those who viewed independent evidence as having greater value subsequently gave more weight to independent testimony in the balls-and-urns task (Experiment 3), in line with the predictions of a Bayesian model. Our findings suggest that only a minority of individuals conform to Bayesian predictions in the relative weighting of independent and dependent evidence in judgments under uncertainty.

Getting to the source of the illusion of consensus.

Consensus between informants is a valuable cue to a claim's epistemic value, when informants' beliefs are developed independently of each other. Recent work (Yousif et al., 2019) described an illusion of consensus such that people did not generally discriminate between the epistemic warrant of true consensus, where a majority claim is supported by multiple independent sources, and false consensus arising from repetition of a single source's claim. Four experiments tested a novel account of the illusion of consensus; that it arises when people are unsure about the independence of the primary sources on which informant claims are based. When this independence relationship was ambiguous we found evidence for the illusion. However, when steps were taken to highlight the independence between data sources in the true consensus conditions, and confidence in a claim was measured against a no consensus baseline (where there was an equal number of reports supporting and opposing a claim), more weight was given to claims based on true consensus than false consensus. These findings show that although the illusion of consensus is prevalent, people do have the capacity to distinguish between true and false consensus.

Adding Types, But Not Tokens, Affects Property Induction.

The extent to which we generalize a novel property from a sample of familiar instances to novel instances depends on the sample composition. Previous property induction experiments have only used samples consisting of novel types (unique entities). Because real-world evidence samples often contain redundant tokens (repetitions of the same entity), we studied the effects on property induction of adding types and tokens to an observed sample. In Experiments 1-3, we presented participants with a sample of birds or flowers known to have a novel property and probed whether this property generalized to novel items varying in similarity to the initial sample. Increasing the number of novel types (e.g., new birds with the target property) in a sample produced tightening, promoting property generalization to highly similar stimuli but decreasing generalization to less similar stimuli. On the other hand, increasing the number of tokens (e.g., repeated presentations of the same bird with the target property) had little effect on generalization. Experiment 4 showed that repeated tokens are encoded and can benefit recognition, but appear to be given little weight when inferring property generalization. We modified an existing Bayesian model of induction (Navarro, Dry, & Lee, 2012) to account for both the information added by new types and the discounting of information conveyed by tokens.

Correct Me if I'm Wrong: Groups Outperform Individuals in the Climate Stabilization Task.

Avoiding dangerous climate change requires ambitious emissions reduction. Scientists agree on this, but policy-makers and citizens do not. This discrepancy can be partly attributed to faulty mental models, which cause individuals to misunderstand the carbon dioxide (CO2) system. For example, in the Climate Stabilization Task (hereafter, "CST") (Sterman and Booth-Sweeney, 2007), individuals systematically underestimate the emissions reduction required to stabilize atmospheric CO2 levels, which may lead them to endorse ineffective "wait-and-see" climate policies. Thus far, interventions to correct faulty mental models in the CST have failed to produce robust improvements in decision-making. Here, in the first study to test a group-based intervention, we found that success rates on the CST markedly increased after participants deliberated with peers in a group discussion. The group discussion served to invalidate the faulty reasoning strategies used by some individual group members, thus increasing the proportion of group members who possessed the correct mental model of the CO2 system. Our findings suggest that policy-making and public education would benefit from group-based practices.