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      Single- and Dual-Process Models of Biased Contingency Detection

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          Abstract

          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.

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          Most cited references125

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          Measuring individual differences in implicit cognition: the implicit association test.

          An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect & pleasant) share a key. This performance difference implicitly measures differential association of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects).
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            Associative and propositional processes in evaluation: an integrative review of implicit and explicit attitude change.

            A central theme in recent research on attitudes is the distinction between deliberate, "explicit" attitudes and automatic, "implicit" attitudes. The present article provides an integrative review of the available evidence on implicit and explicit attitude change that is guided by a distinction between associative and propositional processes. Whereas associative processes are characterized by mere activation independent of subjective truth or falsity, propositional reasoning is concerned with the validation of evaluations and beliefs. The proposed associative-propositional evaluation (APE) model makes specific assumptions about the mutual interplay of the 2 processes, implying several mechanisms that lead to symmetric or asymmetric changes in implicit and explicit attitudes. The model integrates a broad range of empirical evidence and implies several new predictions for implicit and explicit attitude change.
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              Statistical learning by 8-month-old infants.

              Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
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                Author and article information

                Contributors
                Journal
                Exp Psychol
                Exp Psychol
                zea
                Experimental Psychology
                Hogrefe Publishing
                1618-3169
                March 29, 2016
                2016
                : 63
                : 1
                : 3-19
                Affiliations
                [ 1 ]Primary Care and Public Health Sciences, King’s College London, UK
                [ 2 ]Department of Experimental Psychology, University College London, UK
                [ 3 ]Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto, Bilbao, Spain
                Author notes
                Miguel A. Vadillo, Primary Care and Public Health Sciences, King’s College London, Addison House, Guy's Campus, London SE1 1UL, UK, Tel. +44 207 848-6620, Fax +44 207 848-6652, E-mail miguel.vadillo@ 123456kcl.ac.uk
                Article
                10.1027/1618-3169/a000309
                4901994
                27025532
                75b519bf-41c9-4cb2-b1dd-a8e5bf8a834c
                © 2016 Hogrefe Publishing

                Distributed under the Hogrefe OpenMind License http://dx.doi.org/10.1027/a000001

                History
                : February 3, 2015
                : September 16, 2015
                : September 17, 2015
                Categories
                Theoretical Article

                associative models,cognitive biases,contingency learning,cue-density bias,dual-process models,illusory correlations,outcome-density bias,propositional models

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