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has proposed the diffusion model as a form of data analysis for speeded binary decisions. The diffusion model assumes that binary decisions are based on a continuous process that fluctuates between two possible outcomes ( Figure 1). As soon as the process reaches a critical upper or lower value, a decision is made, and the corresponding response is executed. One main advantage of the diffusion model is that different components of the decision process (rate of information uptake, bias, conservatism, and motor component) are represented by different parameters of the model. Theoretically, distinct aspects of the decision process can be separated statistically. In a sense, the model allows inferences regarding hidden cognitive processes. Another advantage of the diffusion model data analysis is the high degree of information utilization. In contrast to conventional forms of data analysis, the diffusion model incorporates response times (RTs) for correct responses and errors, as well as the ratio of correct and erroneous responses.The diffusion model thus provides a powerful statistical tool that allows a very fine-grained analysis of the cognitive processes underlying simple response tasks. Previous applications of the diffusion model have included, for example, the analysis of retrieval processes (Ratcliff, 1978(Ratcliff, , 1988 and the identification of the factors underlying age-related slowing (Ratcliff, Spieler, & McKoon, 2000;Ratcliff, Thapar, & McKoon, 2001, 2003. These examples reveal that the diffusion model has a wide range of possible applications and can be used to decide between competing theoretical interpretations of experimental findings. However, although interpretations of the different parameters of the diffusion model seem fairly straightforward, it should be kept in mind that these interpretations are based mainly on theoretical reflections and presuppose the validity of the diffusion model as an adequate representation of the underlying cognitive processes. 1 The main aim of the present article, therefore, is to provide an empirical validation of the theoretical interpretation of the parameters of the diffusion model. To this end, we investigated the effects of different experimental manipulations on the parameter set in a diffusion model data analysis. The experimental manipulations were intended to specifically affect the different processing components of the model (rate of information uptake, setting of response criteria, duration of the motor response and bias; see Ratcliff, 2002;Ratcliff & Rouder, 1998;Ratcliff et al., 2001). Analyzing whether experimental manipulations of the different processing components that are specified in the model map uniquely onto the corresponding parameters of the model (and not Correspondence concerning this article should be addressed to A. Voss, Institut für Psychologie, Albert-Ludwigs-Universistät Freiburg, Engelbergstr. 41, D-79085 Freiburg im Breisgau, Germany (e-mail: andreas. voss@psychologie.uni-freiburg.de) or to K. Rothermund, Institut für Psy...

The authors investigated whether effects of the Implicit Association Test (IAT) are influenced by salience asymmetries, independent of associations. Two series of experiments analyzed unique effects of salience by using nonassociated, neutral categories that differed in salience. In a 3rd series, salience asymmetries were manipulated experimentally while holding associations between categories constant. In a 4th series, valent associations of the target categories were manipulated experimentally while holding salience asymmetries constant. Throughout, IAT effects were found to depend on salience asymmetries. Additionally, salience asymmetries between categories were assessed directly with a visual search task to provide an independent criterion of salience asymmetries. Salience asymmetries corresponded to IAT effects and also accounted for common variance in IAT effects and explicit measures of attitudes or the self-concept.

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