HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

ABSTRACT: The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Pyth

SUBMITTER: Wiecki TV 

PROVIDER: S-EPMC3731670 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature