title: Fast and accurate Monte Carlo sampling of first-passage times from Wiener diffusion models.
abstract: We present a new, fast approach for drawing boundary crossing samples from Wiener diffusion models. Diffusion models are widely applied to model choices and reaction times in two-choice decisions. Samples from these models can be used to simulate the choices and reaction times they predict. These samples, in turn, can be utilized to adjust the models' parameters to match observed behavior from humans and other animals. Usually, such samples are drawn by simulating a stochastic differential equation in discrete time steps, which is slow and leads to biases in the reaction time estimates. Our method, instead, facilitates known expressions for first-passage time densities, which results in unbiased, exact samples and a hundred to thousand-fold speed increase in typical situations. In its most basic form it is restricted to diffusion models with symmetric boundaries and non-leaky accumulation, but our approach can be extended to also handle asymmetric boundaries or to approximate leaky accumulation.
authors: University of Geneva, Department of Basic Neuroscience, 1211 Geneva, Switzerland.
authors: Jan
authors: J
authors: Drugowitsch
mesh_terms: D000465:Algorithms; D000818:Animals; D002755:Choice Behavior; D003657:Decision Making; D004058:Diffusion; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D013997:Time Factors