A flexible framework for simulating and fitting generalized drift-diffusion models.

Maxwell Shinn,John D Murray,Norman H Lam

doi:10.7554/elife.56938

Abstract

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

Highlights

The drift-diffusion model (DDM) is an important model in cognitive psychology and cognitive neuroscience, and is fundamental to our understanding of decision-making (Ratcliff, 1978; Bogacz et al, 2006)
We found that the mean squared error (MSE) of the generalized drift-diffusion model (GDDM) was lower than all other models (Figure 2c)
The GDDM framework provides a consistent description of model extensions, and allows researchers to efficiently simulate and fit a wide variety of extensions to the DDM

Summary

Introduction

The drift-diffusion model (DDM) is an important model in cognitive psychology and cognitive neuroscience, and is fundamental to our understanding of decision-making (Ratcliff, 1978; Bogacz et al, 2006). In its simplest form, the DDM includes four parameters: a drift rate, representing evidence; a diffusion constant or bound height representing noise or response caution; a starting position, representing side bias and often fixed at zero; and a non-decision time, representing afferent and efferent delays but external to the DDM process (Ratcliff, 1978). These models can be fit to choice and response time data

Methods

Results

Conclusion