Process components of the Implicit Association Test: A diffusion-model analysis.

Abstract

The authors present a diffusion-model analysis of the Implicit Association Test (IAT). In Study 1, the IAT effect was decomposed into 3 dissociable components: Relative to the compatible phase, (a) ease and speed of information accumulation are lowered in the incompatible phase, (b) more cautious speed-accuracy settings are adopted, and (c) nondecision components of processing require more time. Studies 2 and 3 assessed the nature of interindividual differences in these components. Construct-specific variance in the IAT relating to the construct to be measured (such as implicit attitudes) was concentrated in the compatibility effect on information accumulation (Studies 2 and 3), whereas systematic method variance in the IAT was mapped on differential speed-accuracy settings (Study 3). Implications of these dissociations for process theories of the IAT and for applications are discussed.

Hierarchical diffusion models for two-choice response times.

“…In particular, when studying higher level cognitive processes or emotions the stimulus material may simply not allow for the generation of hundreds of trials or for presenting stimuli more than once (see e.g., Brysbaert, Van Wijnendaele, & De Deyne, 2000;Klauer et al, 2007). Often, however, there are many participants in the sample.…”

“…More recently, however, the diffusion model has been applied to study individual differences (see e.g., Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Wagenmakers et al, 2007). The typical approach in such cases is to run multistep analyses: In a first step a specific model is fitted to data from each individual, and then inferences regarding individual differences are made on the basis of summary measures of the parameter estimates.…”

“…The typical approach in such cases is to run multistep analyses: In a first step a specific model is fitted to data from each individual, and then inferences regarding individual differences are made on the basis of summary measures of the parameter estimates. An example of this approach can be found in Klauer et al (2007), in which individual participants' parameter estimates are subjected to second-stage analysis using ANOVA.…”

“…The typical approach in such cases is to run multistep analyses: In a first step a specific model is fitted to data from each individual, and then inferences regarding individual differences are made on the basis of summary measures of the parameter estimates. An example of this approach can be found in Klauer et al (2007), in which individual participants' parameter estimates are subjected to second-stage analysis using ANOVA.However, data do not always allow for separate analyses per individual: Estimating the diffusion model's parameters typically requires a large number of data points (Wagenmakers, 2009), and in many experimental contexts it may be impractical or even impossible to obtain many data points within each participant. In particular, when studying higher level cognitive processes or emotions the stimulus material may simply not allow for the generation of hundreds of trials or for presenting stimuli more than once (see e.g., Brysbaert, Van Wijnendaele, & De Deyne, 2000;Klauer et al, 2007).…”

“…An example of this approach can be found in Klauer et al (2007), in which individual participants' parameter estimates are subjected to second-stage analysis using ANOVA.…”