title: Single- and Dual-Process Models of Biased Contingency Detection.
abstract: Decades of research in causal and contingency learning show that people's estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.
authors.affiliation: 1 Primary Care and Public Health Sciences, King's College London, UK.|2 Department of Experimental Psychology, University College London, UK.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Vadillo
authors.affiliation: 3 Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto, Bilbao, Spain.
authors.forename: Fernando
authors.initials: F
authors.lastname: Blanco
authors.affiliation: 3 Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto, Bilbao, Spain.
authors.forename: Ion
authors.initials: I
authors.lastname: Yarritu
authors.affiliation: 3 Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto, Bilbao, Spain.
authors.forename: Helena
authors.initials: H
authors.lastname: Matute
mesh_terms: D001245:Association Learning; D015982:Bias; D003198:Computer Simulation; D006801:Humans; D008960:Models, Psychological; D011337:Probability Learning
keywords: associative models; cognitive biases; contingency learning; cue-density bias; dual-process models; illusory correlations; outcome-density bias; propositional models