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

Matute H, Blanco F, Yarritu I, Díaz-Lago M, Vadillo MA, Barberia I - Front Psychol (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto , Bilbao, Spain.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Results of a computer simulation of the four experimental conditions presented by Blanco et al. (2013, Experiment 1) using the Rescorla–Wagner learning algorithm. The simulation was conducted using the Java simulator developed by Alonso et al. (2012). For this simulation, the learning rate parameters were set to αcause = 0.3, αcontext = 0.1, βoutcome = β∼outcome = 0.8.
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Figure 1: Results of a computer simulation of the four experimental conditions presented by Blanco et al. (2013, Experiment 1) using the Rescorla–Wagner learning algorithm. The simulation was conducted using the Java simulator developed by Alonso et al. (2012). For this simulation, the learning rate parameters were set to αcause = 0.3, αcontext = 0.1, βoutcome = β∼outcome = 0.8.

Mentions: The effects discussed so far do not seem to bias only human judgments. Computer simulations show that the probabilities of the cause and the outcome can also bias machine-learning algorithms designed to detect contingencies. For example, Figure 1 shows the results of a computer simulation based on the popular Rescorla–Wagner learning algorithm (Rescorla and Wagner, 1972). The model tries to associate causes and outcomes co-occurring in an environment while minimizing prediction errors. Each of the four lines shown in Figure 1 denotes the behavior of the model when exposed to each of the four conditions used by Blanco et al. (2013). In this experiment, all participants were exposed to a sequence of trials where the contingency between a potential cause and an outcome was actually 0. The probability of the cause was high (0.80) for half of the participants and low (0.20) for the other half. Orthogonally, the outcome tended to appear with a large probability (0.80) for half of the participants and with low probability (0.20) for the other half.


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

Matute H, Blanco F, Yarritu I, Díaz-Lago M, Vadillo MA, Barberia I - Front Psychol (2015)

Results of a computer simulation of the four experimental conditions presented by Blanco et al. (2013, Experiment 1) using the Rescorla–Wagner learning algorithm. The simulation was conducted using the Java simulator developed by Alonso et al. (2012). For this simulation, the learning rate parameters were set to αcause = 0.3, αcontext = 0.1, βoutcome = β∼outcome = 0.8.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488611&req=5

Figure 1: Results of a computer simulation of the four experimental conditions presented by Blanco et al. (2013, Experiment 1) using the Rescorla–Wagner learning algorithm. The simulation was conducted using the Java simulator developed by Alonso et al. (2012). For this simulation, the learning rate parameters were set to αcause = 0.3, αcontext = 0.1, βoutcome = β∼outcome = 0.8.
Mentions: The effects discussed so far do not seem to bias only human judgments. Computer simulations show that the probabilities of the cause and the outcome can also bias machine-learning algorithms designed to detect contingencies. For example, Figure 1 shows the results of a computer simulation based on the popular Rescorla–Wagner learning algorithm (Rescorla and Wagner, 1972). The model tries to associate causes and outcomes co-occurring in an environment while minimizing prediction errors. Each of the four lines shown in Figure 1 denotes the behavior of the model when exposed to each of the four conditions used by Blanco et al. (2013). In this experiment, all participants were exposed to a sequence of trials where the contingency between a potential cause and an outcome was actually 0. The probability of the cause was high (0.80) for half of the participants and low (0.20) for the other half. Orthogonally, the outcome tended to appear with a large probability (0.80) for half of the participants and with low probability (0.20) for the other half.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto , Bilbao, Spain.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus