Implementation and assessment of an intervention to debias adolescents against causal illusions

Abstract

Researchers have warned that causal illusions are at the root of many superstitious beliefs and fuel many people's faith in pseudoscience, thus generating significant suffering in modern society. Therefore, it is critical that we understand the mechanisms by which these illusions develop and persist. A vast amount of research in psychology has investigated these mechanisms, but little work has been done on the extent to which it is possible to debias individuals against causal illusions. We present an intervention in which a sample of adolescents was introduced to the concept of experimental control, focusing on the need to consider the base rate of the outcome variable in order to determine if a causal relationship exists. The effectiveness of the intervention was measured using a standard contingency learning task that involved fake medicines that typically produce causal illusions. Half of the participants performed the contingency learning task before participating in the educational intervention (the control group), and the other half performed the task after they had completed the intervention (the experimental group). The participants in the experimental group made more realistic causal judgments than did those in the control group, which served as a baseline. To the best of our knowledge, this is the first evidence-based educational intervention that could be easily implemented to reduce causal illusions and the many problems associated with them, such as superstitions and belief in pseudoscience.

Figure 1. Mean P(Cause) (top panel) and mean causal judgments (bottom panel).

The top panel represents the mean proportion of trials in which the participants from the control and experimental groups decided to administer the medicine in the zero contingency and positive contingency conditions. The bottom panel represents the mean judgments regarding the effectiveness of the medicines in the zero contingency and positive contingency conditions. The filled bars refer to the participants in the control group, and the striped bars refer to the participants in the experimental group. The error bars represent the standard error of the mean.

Figure 2. Histogram depicting the distribution of the judgments in the zero contingency condition, for the experimental group (top panel) and the control group (bottom panel).

Figure 3. Mediational structure underlying the experimental manipulation in the zero contingency condition.

The total effect of the intervention on the causal judgments, depicted as path c, is divided into two components: one indirect effect (paths a and b) through the P(Cause), and one direct effect (path c'), which is the result of discounting the indirect effect. The mediation analysis reveals that the intervention affected the judgments both directly and indirectly, via the P(Cause).