drift-diffusion-model

A unified interface for simulating and evaluating sequential sampling models in Julia.

spatial_attenNCM (Spatial Attention Neuro-Cognitive Modeling) used some hierarchical neuro-cognitive models to find out the spatial attention effect on perceptual decision making.

A Julia library for fitting DDMs to pulse-based evidence accumulations task data

A Community for Evidence Accumulation Models

The repository describing, simulating and fitting the nl-DDM to different datasets.

J-DriftX

Analyses scripts used for Algermissen, J. & den Ouden, H. E. M. (2024). High stakes slow responding, but do not help overcome Pavlovian biases in humans. Learning & Memory.

Development of an R package for Bayesian estimation of drift diffusion item response theory models

We provide a semi-automated Python workflow for reproducible BIDS-EEG and computational modelling analysis. It includes preprocessing, optimized ICA, spectral and ERP/IAF analyses, and hierarchical drift-diffusion modelling (HSSM) linking neural, experimental, and clinical measures to latent decision processes, all configured in one JSON file.

Computational modelling of intertemporal choice using the classical Drift Diffusion Model across 12 ITC Database studies, evaluating choice prediction, response-time distributions, parameter recovery, cross-validation, and model limitations.

Analysis notebooks for behavioral, HDDM, and MRI analyses of Stroop and Go/No-Go data from 106 adults

Code for "Risk and loss aversion and attitude to COVID and vaccines in anxious individuals" by Ferrari, Alexander and Seriès (2023).

Drift-diffusion model (DDM) simulator with EZ-diffusion and simulation-based parameter recovery — Rust core, Python bindings

Browser-based cognitive tasks (Stroop, Flanker, N-back) + a Python pipeline that fits drift-diffusion models to separate processing speed from decision threshold.

Replication data and code for "How Carbon Labels Enter Food Choice: Eye Tracking and Computational Modeling."

🏁 Official code for “The Neural Race Model” (NeurIPS 2026 Spotlight): a stimulus-conditional neural SDE where one accumulator per alternative races to a learnable threshold, trained end-to-end on choices and reaction times.

Code, derived results, reporting tables, figure assets, and validation materials accompanying a study of cross-fitted reinforcement-learning states, behavioral history, and censored diffusion modeling in probabilistically cued Go/NoGo behavior.

VNS Course Project

Ex-Gaussian, drift-diffusion & spectral models of reaction-time variability, with parameter/model recovery as verifier.

Modular computational psychiatry platform: SDE-based modules of mental functions with parameter modulation couplings