Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-18T10:44:16.446Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Catena, A., Maldonado, A., & Cándido, A. (1998). The effect of the frequency of judgment and the type of trials on covariation learning. Journal of Experimental Psychology: Human Perception and Performance, 24, 481–495.Google Scholar
Catena, A., Maldonado, A, Megias, J., & Frese, B. (2002). Judgement frequency, belief revision and serial processing of causal information. Quarterly Journal of Experimental Psychology, 55B, 267–281.CrossRefGoogle Scholar
Catena, A., Perales, J.C., & Maldonado, A. (2004). Previous noncontingency and frequency effects in generative and preventative causal learning. Psicológica, 25, 67–85.Google Scholar
Catena, A., Maldonado, A., Perales, J.C., Candido, A., & Herrera, A. (2007). Previous beliefs and cue predictive value interactions in covariation-based causal induction. Manuscript submitted for publication.Google Scholar
Cheng, P.W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.CrossRefGoogle Scholar
Collins, D.J., & Shanks, D.R (2002). Momentary and integrative response strategies in causal judgment. Memory & Cognition, 30, 1138–1147.CrossRefGoogle ScholarPubMed
De Houwer, J., & Beckers, T. (2002). A review of recent developments in research and theories on human contingency learning. Quarterly Journal of Experimental, 55B, 289–310.Google Scholar
Fugelsang, J.A., Stein, C.B., Green, A.E., & Dunbar, K.N. (2004). Theory and data interactions in the scientific mind: Evidence from molecular and the cognitive laboratory. Canadian Journal of Experimental Psychology, 58, 86–95.CrossRefGoogle ScholarPubMed
Fugelsang, J.A., & Thompson, V.A. (2003). A dual-process model of belief and evidence interactions in causal reasoning. Memory & Cognition, 31, 800–815.CrossRefGoogle ScholarPubMed
García-Retamero, R., Maldonado, A., Catena, A., Hoffrage, U., Herrera, A., & Candido, A. (2007). The influence of causal beliefs and empirical evidence on decision making and causal attribution. Manuscript submitted for publication.Google Scholar
Kao, S.F., & Wasserman, E.A. (1993). Assessment of information integration of contingency judgment with examination of subjective cell importance and method of information presentation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 1363–1386.Google Scholar
Kruschke, J. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812–863.CrossRefGoogle Scholar
Lien, Y., & Cheng, P.W. (2000). Distinguishing genuine from spurious causes: A coherence hypothesis. Cognitive Psychology, 40, 87–137.CrossRefGoogle ScholarPubMed
Mackintosh, N. (1975). A theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276–298.CrossRefGoogle Scholar
Maldonado, A, Catena, A., Cándido, A., & Garcia, I. (1999). The belief revision model: Asymmetrical effects of noncontingency on human covariation learning. Learning and Behavior, 27, 168–180.CrossRefGoogle Scholar
Maldonado, A., Herrera, A., Jiménez, I., Perales, J.C., & Catena, A. (2006). Inattentional blindness for negative relationships in human causal learning. Quarterly Journal of Experimental, 59, 457–470.Google ScholarPubMed
Matute, H., Vegas, S., & Marez, P. (2002). Flexible use of recent information in predictive and causal judgments. Journal of Experimental Psychology: Learning, Memory and cognition, 28, 714–725.Google Scholar
Pennington, N., & Hastie, R. (1992). Explaining the evidence: Tests of the story model for juror decision making. Journal of Personality and Social Psychology, 62, 189–206.CrossRefGoogle Scholar
Perales, J.C., & Catena, A. (2006). Human causal induction: A glimpse at the whole picture. The European Journal of Cognitive Psychology, 18, 277–320.CrossRefGoogle Scholar
Perales, J.C., Catena, A., & Maldonado, A. (2002). Aprendizaje de relaciones de causalidad: hacia un análisis integral del aprendizaje desde una perspectiva computacional. Cognitiva, 14, 15–41.CrossRefGoogle Scholar
Perales, J.C., Catena, A., & Maldonado, A. (2004). Inferring non-observed correlations from causal scenarios: The role of causal knowledge. Learning and Motivation, 35, 115–135.CrossRefGoogle Scholar
Perales, J.C., Catena, A., Maldonado, A., & Cándido, A. (2007). The role of mechanism and covariation information in causal belief updating. Cognition (available online).Google Scholar
Perales, J.C., & Shanks, D. (2003). Normative and descriptive accounts of the influence of power and contingency on causal judgment. Quarterly Journal of Experimental Psychology, 56A, 977–1007.CrossRefGoogle Scholar
Sloman, S.A., & Hagmayer, Y. (2006). The causal psycho-logic of choice. Trends in Cognitive Science, 10, 407–412.CrossRefGoogle ScholarPubMed
Waldmann, M.R. (2000). Competition among causes but not effects in predictive and diagnostic learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 53–76.Google Scholar