Abstract: In the last decades, the diffusion model (Ratcliff, 1978) became one the most popular models in cognitive psychology. The model proposes a mathematical account of cognitive processes underlying fast binary decisions. The present project challenges one of the core assumptions of the diffusion model, that is, the postulation that information accumulation follows a Wiener diffusion process. This process is a composition of a constant drift rate (i.e., a constant rate of evidence accumu-lation over time) and normal distributed noise. In the new Lévy-Flight account (Voss, Lerche, Mertens, & Voss, in press), the Gaussian noise distribution is replaced by a so-called heavy-tailed distribution (e.g., a Cauchy distribution). This fundamentally changes the proposed evidence accumulation mechanism and the model's predictions. In contrast to the diffusion model, the new model assumes that evidence accumulation incorporates "jumps", that is, sudden large changes in the amount of accumulated evidence. This not only alters the shape of predicted response-time distributions, but also provides an explanation for the fact that in simple perceptual tasks errors are typically faster than correct responses (Luce, 1984). This project aims at (a) developing an efficient and user-friendly program for Lévy-Flight data analyses; (b) comparing results from diffusion model analyses and Lévy-Flight analyses and (c) applying the model to assess inter-individual differences rigid vs. flexible decision-making styles. The project will help to improve understanding of process-es underlying human decision-making.