title: Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model.
abstract: Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses.
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bitzer
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
authors.forename: Hame
authors.initials: H
authors.lastname: Park
authors.affiliation: Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin Berlin Berlin, Germany ; Neurocomputation and Neuroimaging Unit, Department of Education and Psychology, Freie Universität Berlin Berlin, Germany.
authors.forename: Felix
authors.initials: F
authors.lastname: Blankenburg
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Biomagnetic Center, Hans Berger Clinic for Neurology, University Hospital Jena Jena, Germany.
authors.forename: Stefan J
authors.initials: SJ
authors.lastname: Kiebel
keywords: Bayesian models; decision variable; drift diffusion model; parameter fitting; perceptual decision making; reaction time; uncertainty