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.

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.

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

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.

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

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

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

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

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

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

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

A Streamlit-based interactive visualization of the Drift Diffusion Model (DDM)

Computational modeling pipeline integrating pupillometry, behavioral data, and drift diffusion models (DDM) to understand the relationship between brain arousal and decision-making processes.