Large-scale project shows robust and long-lasting effects to reduce causal illusions through high-schools

Please can you tell us about your study?

The illusion of causality is a cognitive bias that consists of believing that there is a causal relationship between events that are causally unrelated. This bias is associated with pseudoscience, stereotypes, ideological extremism and many other unjustified and harmful beliefs. Thus, it is important to develop educational interventions to reduce this illusion, ideally during the school years, in order to protect people against it. However, interventions designed to debias the illusion of causality are still scarce, and their robustness and duration are usually unknown. 

We designed this research in order to test the robustness and the duration of the effects of an intervention to reduce causal illusions in high-school students. It was conducted in collaboration with the Spanish Foundation for Science and Technology (FECYT). The intervention was tested in more than 40 schools within the Spanish educational system. The research included a pilot study (n = 287), a large-scale implementation (n = 1,668) and a six-month follow-up (n = 353).

What did the educational intervention look like?

As most people have difficulties in detecting their own biases, the intervention started with a bias-induction phase, which was designed to show the students that they are vulnerable to the causal illusion. The researchers showed the students a supposedly hi-tech ring and told them that they could wear it and it would increase their cognitive and physical abilities. Students were then asked to test its properties, but testing always occurred either without control conditions or under flawed controls conditions. 

After this bogus-testing demonstration was completed, the researchers explained to participants the basics on experimental research: confounding variables, experimental control of variables, and use of control conditions. They showed the students the mistakes that they made when supposedly testing the properties of the ring, and discussed with them what they should and could have done to find out whether the product was effective. In other words, the researchers prompted the students to be more critical and to learn to discriminate between good and bad tests of causal relationships. 

Meanwhile, the control group received a workshop on an unrelated scientific topic (nanotechnology), so that all participants were exposed to an extracurricular activity of the same duration and potential interest during the time of the intervention.

How did you assess the success of the intervention?

Once the intervention (or the corresponding control activity) was completed, the subsequent assessment phase took place for all participants. This phase was conducted through the internet and used a standard active causal illusion task in which participants could manipulate the occurrence of a potential cause (a fictitious drug that they could administer to fictitious patients) and observe its effects (a recovery -or not- of their patients). 

In this causal illusion task, the drug was actually programmed to have no effects on the recovery of the fictitious patients, so that recovery occurred at a high rate regardless of whether the participants administered the drug or not. That is, the effect (patients’ recovery) had been programmed beforehand to occur in a high percentage of trials, so the participants had no control on its occurrence. This high percentage of occurrence of the desired effect was chosen because this is one of the variables that most strongly promotes the development of causal illusions. 

What did the results suggest?

As expected, the control group administered the potential cause (the drug) in a high number of occasions, and developed an illusion of causality (due to the high percentage of cause-effect coincidences by mere chance), thereby replicating previous results on causal illusions. 

By contrast, the intervention group had learned that if they were to evaluate the degree of causality between the two events they should, first of all, introduce the potential cause in some trials but not others, so that they could learn whether the effects were actually being caused by the potential cause or whether they occurred even when the cause was not present. 

That is, the results showed that the participants in the intervention group introduced the potential cause less often than the control participants. As a consequence, they also showed a lower causal illusion when asked at the end of the study about the causal relationship between the drug and the recovery of the patients. That is, they were more accurate in their judgment of the effectiveness of the fictitious drug.

In the follow up study, conducted six months later, the context of the causal illusion task did not involve fictitious patients and fictitious drugs, but aliens and mutants. The results showed that the scientific thinking and methods that the participants had acquired during the intervention was still significant (in relation to a new control group) after six months and in this different context.

Thus, the intervention was successful in reducing the causal illusion, thereby replicating previous findings using this methodology, but this time the reduction (a) was obtained in a large-scale project through the national educational system, and (b) was shown to be still effective after six months. This is the first research showing the robustness and long-term effects of a debiasing intervention against causal illusions. 

What are the future implications of your research?

Importantly, the results show that the intervention can be applied on a large scale through the educational system, something that makes it an optimal tool to immunize populations at an early age and protect them against the hazards that can derive from this illusion.

This study also contributes to the literature on debiasing strategies by shedding light on the effectiveness of a training procedure that emphasizes the role of teaching scientific methods in reducing the causal illusion. Further exploration of the potential of teaching scientific thinking and methods as a debiasing strategy seems a promising path. Such efforts should contribute to reducing the illusion of causality, and quite possibly, other cognitive biases along with the problems associated with them. 

In summary, the intervention has shown beneficial, robust, and long-lasting effects in reducing causal illusions, which are one of the best-known causes of pseudoscientific and other harmful beliefs and practices. This research also shows that the intervention can be applied in schools if there exists a good collaboration between psychological scientists, policy makers, and the educational system to produce public good. 

What was your experience like publishing in Royal Society Open Science?

It was a very nice experience. It was fast, and we received highly constructive and insightful comments from the Editor and the reviewers, which helped us improve our manuscript. Moreover, this journal is an advocate of Open Science, and encourages preprints, preregistrations, replications, and the sharing of data and materials, and we strongly agree with this philosophy. So, we are very happy that we published our research in the Royal Society Open Science.

About the authors 

Naroa Martínez received her PhD from Deusto University in Bilbao, Spain, and was a postdoctoral researcher at the Labpsico research group at Deusto while conducting the study. She is currently an Assistant Professor at the University of Cantabria, Spain. Her research interests are educational interventions to reduce cognitive biases, particularly through debiasing strategies, addressing illusions of causality and unwarranted beliefs such as pseudoscience. 

Helena Matute is a Professor of Experimental Psychology at Deusto University in Bilbao, Spain, where she is the leader of the Labpsico (Experimental Psychology Laboratory) research group. Her research interests include cognitive biases, illusions of causality, pseudoscience, misinformation, and how artificial intelligence influences human decisions.  

Fernando Blanco is currently an Associate Professor of Social Psychology at the University of Granada, Spain. His major research topic is the human learning of contingencies and causality, and, more specifically, those factors leading to decisions and beliefs that we would consider irrational, such as superstitions, pseudoscience, etc. 

Itxaso Barberia is currently an Associate Professor of Psychology at the University of Barcelona, Spain. Her research interests are contingency learning and causal reasoning, and their implications for understanding the development of unwarranted beliefs.

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