Abstract
Several drift-diffusion models have been developed to account for the performance in conflict tasks. Although a common characteristic of these models is that the drift rate changes within a trial, their architecture is rather different. Comparative studies usually examine which model fits the data best. However, a good fit does not guarantee good parameter recovery, which is a necessary condition for a valid interpretation of any fit. A recent simulation study revealed that recovery performance varies largely between models and individual parameters. Moreover, recovery was generally not very impressive. Therefore, the aim of the present study was to introduce and test an improved fit procedure. It is based on a grid search for determining the initial parameter values and on a specific criterion for assessing the goodness of fit. Simulations show that not only the fit performance but also parameter recovery improved substantially by applying this procedure, compared to the standard one. The improvement was largest for the most complex model.
Highlights
The ability to act in a goal-oriented manner is an essential characteristic of human performance
To investigate involved mental processes, several so-called conflict paradigms have been developed, such as the Stroop task (Steinhauser & Hübner, 2009; Stroop, 1935), the Eriksen flanker task (Eriksen & Eriksen, 1974), and the Simon task (Hübner & Mishra, 2013; Proctor, 2011; Simon, 1969), where irrelevant stimulus features produce response conflicts that are reflected by congruency effects
Because parameter recovery depends on the model as such and on the applied fit procedure, the aim of the present study was to investigate to what extent recovery performance of conflict DDMs can be improved by using a fit procedure that is more sophisticated than those usually applied
Summary
The ability to act in a goal-oriented manner is an essential characteristic of human performance. Conflict DDMs (drift diffusion models) have been proposed that, based on response-time (RT) distributions and accuracy data, model the dynamics of the performance in conflict tasks. The first of these models was the Dual-Stage Two-Phase (DSTP) model (Hübner, Steinhauser, & Lehle, 2010), followed by the Shrinking Spotlight (SSP) model (White, Ratcliff, & Starns, 2011). Both models were first applied to the flanker task.
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