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      Single- and Dual-Process Models of Biased Contingency Detection

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          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 references 141

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          Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

          Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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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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              Pragmatics of measuring recognition memory: applications to dementia and amnesia.

              This article has two purposes. The first is to describe four theoretical models of yes-no recognition memory and present their associated measures of discrimination and response bias. These models are then applied to a set of data from normal subjects to determine which pairs of discrimination and bias indices show independence between discrimination and bias. The following models demonstrated independence: a two-high-threshold model, a signal detection model with normal distributions using d' and C (rather than beta), and a signal detection model with logistic distributions and a bias measure analogous to C. C is defined as the distance of criterion from the intersection of the two underlying distributions. The second purpose is to use the indices from the acceptable models to characterize recognition memory deficits in dementia and amnesia. Young normal subjects, Alzheimer's disease patients, and parkinsonian dementia patients were tested with picture recognition tasks with repeated study-test trials. Huntington's disease patients, mixed etiology amnesics, and age-matched normals were tested by Butters, Wolfe, Martone, Granholm, and Cermak (1985) using the same paradigm with word stimuli. Demented and amnesic patients produced distinctly different patterns of abnormal memory performance. Both groups of demented patients showed poor discrimination and abnormally liberal response bias for words (Huntington's disease) and pictures (Alzheimer's disease and parkinsonian dementia), whereas the amnesic patients showed the worst discrimination but normal response bias for words. Although both signal detection theory and two-high-threshold discrimination parameters showed identical results, the bias measure from the two-high-threshold model was more sensitive to change than the bias measure (C) from signal detection theory. Three major points are emphasized. First, any index of recognition memory performance assumes an underlying model. Second, even acceptable models can lead to different conclusions about patterns of learning and forgetting. Third, efforts to characterize and ameliorate abnormal memory should address both discrimination and bias deficits.

                Author and article information

                Exp Psychol
                Exp Psychol
                Experimental Psychology
                Hogrefe Publishing
                March 29, 2016
                : 63
                : 1
                : 3-19
                [ 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@
                © 2016 Hogrefe Publishing

                Distributed under the Hogrefe OpenMind License

                Theoretical Article


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