The role of causal models in multiple judgments under uncertainty

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.     

The effect of constant outcome value on judgments and decision making given linguistic probabilities

Two experiments were performed to determine whether judgments of the relative chances of two independent events occurring are biased by constant outcome values contingent on the events when the uncertainties are specified by linguistic expressions (e.g. doubtful). In Experiment 1, subjects directly judged the relative chances of the two events, of which one was represented by a spinner and the other by a linguistic probability expression. In Experiment 2, only linguistic probability expressions were used to describe the two events and a betting procedure was used. A bias was evident in both studies, such that the relative judgments tended to favour the event with the positive rather than the negative contingent outcome. The bias was smaller for the low- than for the high-probability phrases. Individual differences were great, with the bias appearing strongly in only about one-third of the population. Theoretical implications of the present and related results are discussed.     

Expertise and gambling: Using Type 2 signal detection theory to investigate differences between regular gamblers and nongamblers

This paper presents an experimental investigation into how individuals make decisions under uncertainty when faced with different payout structures in the context of gambling. Type 2 signal detection theory was utilized to compare sensitivity to bias manipulations between regular nonproblem gamblers and nongamblers in a novel probability-based gambling task. The results indicated that both regular gamblers and nongamblers responded to the changes of rewards for correct responses (Experiment 1) and penalties for errors (Experiment 2) in setting their gambling criteria, but that regular gamblers were more sensitive to these manipulations of bias. Regular gamblers also set gambling criteria that were more optimal. The results are discussed in terms of an expertise-transference hypothesis.     

Locomotion, assessment, and regulatory fit: Value transfer from “how” to “what”

Regulatory fit is experienced when people pursue a goal in a manner that sustains their regulatory orientation. Previous research on promotion and prevention orientations has found that regulatory fit increases people’s perception that a decision they made was “right,” which in turn transfers value to the decision outcome, including being willing to pay more for a product than those who chose the same product without regulatory fit (Higgins, 2000; Higgins et al., in press). We predicted that the effect of regulatory fit on monetary value could be generalized to locomotion and assessment orientations. Participants were willing to pay over 40% more for the same book-light when it was chosen with a strategy that fit their regulatory orientation (assessment/“full evaluation”; locomotion/“progressive elimination”) than when it was chosen with a non-fit strategy.     

Separating the confident from the correct: Leveraging member knowledge in groups to improve decision making and performance

Groups often struggle to distinguish expert members from others who stand out for various reasons but may not be particularly knowledgeable (Littlepage & Mueller, 1997). We examined an intervention designed to improve group decision making and performance through instructing group members to search for information they already possessed that was relevant to a problem. Participants estimated values and expressed their confidence in their estimates individually and then a second time either individually or in a group. This was done with or without the intervention. Results indicated that: (1) groups were more confident than, and out-performed, individuals, (2) group decision making was best captured by models predicting more influence for more accurate members when the intervention was used and more influence for more confident members in its absence, and (3) groups that received the intervention out-performed groups that did not.