Changing your mind about the data: Updating sampling assumptions in inductive inference.

When people use samples of evidence to make inferences, they consider both the sample contents and how the sample was generated ("sampling assumptions"). The current studies examined whether people can update their sampling assumptions - whether they can revise a belief about sample generation that is discovered to be incorrect, and reinterpret old data in light of the new belief. We used a property induction task where learners saw a sample of instances that shared a novel property and then inferred whether it generalized to other items.

Does explaining the origins of misinformation improve the effectiveness of a given correction?

Misinformation often has a continuing influence on event-related reasoning even when it is clearly and credibly corrected; this is referred to as the continued influence effect. The present work investigated whether a correction's effectiveness can be improved by explaining the origins of the misinformation. In two experiments, we examined whether a correction that explained misinformation as originating either from intentional deception or an unintentional error was more effective than a correction that only identified the misinformation as false.

What do our sampling assumptions affect: How we encode data or how we reason from it?

In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively distinct inferences on the basis of the same observations. Yet, relatively little is known about how and when these two sources of evidence are combined.

Who is sensitive to selection biases in inductive reasoning?

The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling).

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.

The rational continued influence of misinformation.

Misinformation has become an increasingly topical field of research. Studies on the 'Continued Influence Effect' (CIE) show that misinformation continues to influence reasoning despite subsequent retraction. Current explanatory theories of the CIE tacitly assume continued reliance on misinformation is the consequence of a biased process.

Widening Access to Bayesian Problem Solving.

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it.

Comparing the use of open and closed questions for Web-based measures of the continued-influence effect.

Open-ended questions, in which participants write or type their responses, are used in many areas of the behavioral sciences. Although effective in the lab, they are relatively untested in online experiments, and the quality of responses is largely unexplored. Closed-ended questions are easier to use online because they generally require only single key- or mouse-press responses and are less cognitively demanding, but they can bias the responses.