Evidence Accumulation Model Selector

What problem does it solve?

Analyzing choice-response time data often requires selecting among competing evidence accumulation models (DDM, LBA, EZ-diffusion, and racing diffusion). This skill guides researchers in choosing the appropriate model based on experimental design and data properties.

Core Features & Use Cases

• Model guidance: recommends when to use full DDM, EZ-diffusion, LBA, or race models depending on trial counts and bias considerations.
• Planning protocol: provides a structured decision framework for planning model fitting, evaluation, and parameter recovery checks.
• Use Case: when you have multi-alternative RT data with distributional information, this skill helps you select the suitable model and evaluation approach.

Quick Start

Run the Evidence Accumulation Model Selector on your choice-RT dataset to determine the appropriate model.