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Cognitive Biases in Human Causal Learning

Published online by Cambridge University Press:  10 April 2014

Antonio Maldonado ,
Andrés Catena ,
José César Perales  and
Antonio Cándido
Antonio Maldonado*
Affiliation:
Universidad de Granada
Andrés Catena
Affiliation:
Universidad de Granada
José César Perales
Affiliation:
Universidad de Granada
Antonio Cándido
Affiliation:
Universidad de Granada
*
Correspondence concerning this article should be addressed to: Antonio Maldonado López, Departamento de Psicología Experimental, Facultad de Psicologia, Universidad de Granada, Campus de la Cartuja. Granada- 18014 (Spain). E-mail: [email protected]
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Abstract

The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed “inattentional blindness” for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.

El objetivo de este trabajo fue la búsqueda de sesgos cognitivos en la inferencia de relaciones causales para descubrir qué procesos psicológicos modulan el aprendizaje causal. A partir del efecto de la frecuencia de juicio, este trabajo presenta investigación consecuente sobre competición entre claves (ensombrecimiento, bloqueo o súper-condicionamiento) para demostrar cómo la fuerza de las creencias previas y la evidencia sobre la covariación de cada causa contribuyen aditivamente en los juicios causales y en la toma de decisiones, siendo su fuerza relativa modulada por la fiabilidad otorgada a cada tipo de información. Nuevos datos muestran también la incapacidad para detectar relaciones causales incidentales preventivas, pero no generativas. Esta “ceguera inatencional” parece deberse a un fallo en la codificación o recuperación de la información. Todos estos datos revelan que una arquitectura cognitiva del aprendizaje causal debe basarse en tres niveles. El primer nivel sería responsable de la codificación de los eventos en cada ensayo. El segundo nivel computaría la nueva evidencia a partir de la información recibida del primer nivel. En el tercer nivel, el individuo debe interpretar e integrar toda esta información con su conocimiento causal previo. En suma, los modelos sobre juicios de causalidad y toma de decisiones normalmente se han centrado en el efecto exclusivo de las “creencias y conocimiento causal” o de la “experiencia directa y covariación” entre causas y efectos. Este trabajo demuestra que ambos tipos de información se requieren e interactúan cuando se trata de explicar la complejidad y flexibilidad que implica el aprendizaje y la inferencia de relaciones causales en humanos.

Keywords

causal learningcausal inferencecognitive biasbeliefaprendizaje causalinferencia causalsesgo cognitivocreencia
Type
Articles
Information
The Spanish Journal of Psychology , Volume 10 , Issue 2 , November 2007 , pp. 242 - 250
Copyright
Copyright © Cambridge University Press 2007

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