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.
Interactions between the mediodorsal thalamus and the prefrontal cortex are critical for cognition. Studies in humans indicate that these interactions may resolve uncertainty in decision-making1, but the precise mechanisms are unknown. Here we identify two distinct mediodorsal projections to the prefrontal cortex that have complementary mechanistic roles in decision-making under uncertainty. Specifically, we found that a dopamine receptor (D2)-expressing projection amplifies prefrontal signals when task inputs are sparse and a kainate receptor (GRIK4) expressing-projection suppresses prefrontal noise when task inputs are dense but conflicting. Collectively, our data suggest that there are distinct brain mechanisms for handling uncertainty due to low signals versus uncertainty due to high noise, and provide a mechanistic entry point for correcting decision-making abnormalities in disorders that have a prominent prefrontal component2–6.
The drift-diffusion model (DDM) is an important decision-making model in cognitive 7 neuroscience. However, innovations in model form have been limited by methodological 8 challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for 9 building, simulating, and fitting DDM extensions, and provide a software package which 10 implements the framework. The GDDM framework augments traditional DDM parameters 11 through arbitrary user-defined functions. Models are simulated numerically by directly solving 12 the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater 13 speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum 14 likelihood on the full response time (RT) distribution. We show that a GDDM fit with our 15 framework explains a classic open dataset with better accuracy and fewer parameters than 16 several DDMs implemented using the latest methodology. Overall, our framework will allow for 17 decision-making model innovation and novel experimental designs. 18 19 82software packages for diffusion modeling. In addition to being the only package to support the 83 GDDM framework, we show PyDDM has a number of technical advantages, such as near-perfect 84 parallel efficiency, a graphical user interface for exploring model forms, and a software verification 85 system for ensuring reliable results. We hope that the GDDM framework will encourage innovative 86 experimental designs and lower the barrier for experimenting with new model mechanisms. Results 88 GDDM as a generalization of the DDM 89The classic DDM is a three parameter model which specifies fixed values for drift, non-decision time, 90 and either bound height or noise level (Figure 1). It assumes that evidence is constant throughout 91 the trial, and that the integration process does not directly depend on time. This form is mathe-92 matically accessible, and has been used to model choice and RT data (Ratcliff, 1978; Ratcliff et al., 93 2016). 94 In order to expand our knowledge of decision-making mechanisms, there is interest in using 95 the DDM to model a variety of experimental paradigms. To accommodate different paradigms, 96 the DDM itself must be extended. One key example is the case where a choice bias is induced 97 experimentally via a change in prior probability or reward magnitude. The DDM has been extended 98 to accommodate an experimentally-induced bias through either a shift in the starting position of 99 the integrator (Edwards, 1965; Laming, 1968; Ratcliff, 1985) or a constant drift bias (Mulder et al., 100 2012; Ratcliff, 1985; Ashby, 1983). 101 There is a set of influential yet insufficient extensions to the DDM called the "full DDM". This set 102 of extensions was developed to explain two specific discrepancies between the DDM and data (An-103 derson, 1960; Laming, 1968; Blurton et al., 2017). First, experimental data exhibited a difference 104 in mean RT between correct and error trials which could not be captured by the clas...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.