Itxaso Barberia

Illusions of causality: how they bias our everyday thinking and how they could be reduced.

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion.

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

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.

The lack of side effects of an ineffective treatment facilitates the development of a belief in its effectiveness.

Some alternative medicines enjoy widespread use, and in certain situations are preferred over conventional, validated treatments in spite of the fact that they fail to prove effective when tested scientifically. We propose that the causal illusion, a basic cognitive bias, underlies the belief in the effectiveness of bogus treatments. Therefore, the variables that modulate the former might affect the latter. For example, it is well known that the illusion is boosted when a potential cause occurs with high probability. In this study, we examined the effect of this variable in a fictitious medical scenario. First, we showed that people used a fictitious medicine (i.e., a potential cause of remission) more often when they thought it caused no side effects. Second, the more often they used the medicine, the more likely they were to develop an illusory belief in its effectiveness, despite the fact that it was actually useless. This behavior may be parallel to actual pseudomedicine usage; that because a treatment is thought to be harmless, it is used with high frequency, hence the overestimation of its effectiveness in treating diseases with a high rate of spontaneous relief. This study helps shed light on the motivations spurring the widespread preference of pseudomedicines over scientific medicines. This is a valuable first step toward the development of scientifically validated strategies to counteract the impact of pseudomedicine on society.

Choosing optimal causal backgrounds for causal discovery

In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context. For generative causes this would be a context with a low base rate of effects generated by other causes and for preventive causes a context with a high base rate. In the following experiments, we used probabilistic and/or deterministic target causes and contexts. In each experiment, participants observed several contexts in which the effect occurred with different probabilities. After this training, the participants were presented with different target causes whose causal status was unknown. In order to discover the influence of each cause, participants were allowed, on each trial, to choose the context in which the cause would be tested. As expected by inferential theories, the participants preferred to test generative causes in low base rate contexts and preventative causes in high base rate contexts. The participants, however, persisted in choosing the less informative contexts on a substantial minority of trials long after they had discovered the power of the cause. We discuss the matching law from operant conditioning as an alternative explanation of the findings.

Maybe this old dinosaur isn't extinct: What does Bayesian modeling add to associationism?

We agree with Jones & Love (JL but we do not believe in the potential of Bayesianism to provide insights into psychological processes. We discuss the advantages of associative explanations over Bayesian approaches to causal induction, and argue that Bayesian models have added little to our understanding of human causal reasoning.

From reading numbers to seeing ratios: a benefit of icons for risk comprehension

Promoting a better understanding of statistical data is becoming increasingly important for improving risk comprehension and decision-making. In this regard, previous studies on Bayesian problem solving have shown that iconic representations help infer frequencies in sets and subsets. Nevertheless, the mechanisms by which icons enhance performance remain unclear. Here, we tested the hypothesis that the benefit offered by icon arrays lies in a better alignment between presented and requested relationships, which should facilitate the comprehension of the requested ratio beyond the represented quantities. To this end, we analyzed individual risk estimates based on data presented either in standard verbal presentations (percentages and natural frequency formats) or as icon arrays. Compared to the other formats, icons led to estimates that were more accurate, and importantly, promoted the use of equivalent expressions for the requested probability. Furthermore, whereas the accuracy of the estimates based on verbal formats depended on their alignment with the text, all the estimates based on icons were equally accurate. Therefore, these results support the proposal that icons enhance the comprehension of the ratio and its mapping onto the requested probability and point to relational misalignment as potential interference for text-based Bayesian reasoning. The present findings also argue against an intrinsic difficulty with understanding single-event probabilities.

Slower Reacquisition after Partial Extinction in Human Contingency Learning.

Extinction is a very relevant learning phenomenon from a theoretical and applied point of view. One of its most relevant features is that relapse phenomena often take place once the extinction training has been completed. Accordingly, as extinction-based therapies constitute the most widespread empirically validated treatment of anxiety disorders, one of their most important limitations is this potential relapse. We provide the first demonstration of relapse reduction in human contingency learning using mild aversive stimuli. This effect was found after partial extinction (i.e., reinforced trials were occasionally experienced during extinction, Experiment 1) and progressive extinction treatments (Experiment 3), and it was not only because of differences in uncertainty levels between the partial and a standard extinction group (Experiment 2). The theoretical explanation of these results, the potential uses of this strategy in applied situations, and its current limitations are discussed.

When is a cause the "same"? Incoherent generalization across contexts.

A theory or model of cause such as Chengs power ( p ) allows people to predict the effectiveness of a cause in a different causal context from the one in which they observed its actions. Liljeholm and Cheng demonstrated that people could detect differences in the effectiveness of the cause when causal power varied across contexts of different outcome base rates, but that they did not detect similar changes when only the cause–outcome contingency, ∆p, but not power, varied. However, their procedure allowed participants to simplify the causal scenarios and consider only a subsample of observations with a base rate of zero. This confounds p , ∆p, and the probability of an outcome (O) given a cause (C), P(O|C). Furthermore, the contingencies that they used confounded p and P(O|C) in the overall sample. Following the work of Liljeholm and Cheng, we examined whether causal induction in a wider range of situations follows the principles suggested by Cheng. Experiments 1a and 1b compared the procedure used by Liljeholm and Cheng with one that did not allow the sample of observations to be simplified. Experiments 2a and 2b compared the same two procedures using contingencies that controlled for P(O|C). The results indicated that, if the possibility of converting all contexts to a zero base rate situation was avoided, people were sensitive to changes in P(O|C), p , and ∆p when each of these was varied. This is inconsistent with Liljeholm and Chengs conclusion that people detect only changes in p . These results question the idea that people naturally extract the metric or model of cause from their observation of stochastic events and then, reasonably exclusively, use this theory of a causal mechanism, or for that matter any simple normative theory, to generalize their experience to alternative contexts.

A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students.

A comparator-hypothesis account of biased contingency detection

Our ability to detect statistical dependencies between different events in the environment is strongly biased by the number of coincidences between them. Even when there is no true covariation between a cue and an outcome, if the marginal probability of either of them is high, people tend to perceive some degree of statistical contingency between both events. The present paper explores the ability of the Comparator Hypothesis to explain the general pattern of results observed in this literature. Our simulations show that this model can account for the biasing effects of the marginal probabilities of cues and outcomes. Furthermore, the overall fit of the Comparator Hypothesis to a sample of experimental conditions from previous studies is comparable to that of the popular Rescorla-Wagner model. These results should encourage researchers to further explore and put to the test the predictions of the Comparator Hypothesis in the domain of biased contingency detection.