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.

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

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."

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

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.

A therapeutic game as a measurement of ADHD decisions

DriftWatch is an intelligent ML drift detection and response system designed to help teams detect data shifts, understand their potential impact, and make cost-aware decisions about monitoring and intervention