SequentialSamplingModels

This package provides a unified interface for simulating and evaluating sequential sampling models (SSMs) in Julia. SSMs describe decision making as a stochastic and dynamic evidence accumulation process in which a decision is triggered by the option whose evidence hits a decision treshold first.

Feature Overview

A summary of the core features is provided below. Please see the documentation for more information.

Supported Models

The following SSMs are supported:

Single Choice Models

• Ex-Gaussian
• Shifted Log Normal
• Wald
• Wald Mixture

Multi-Choice Models

Single Attribute

• Attentional Drift Diffusion
• Leaky Competing Accumulator
• Drift Diffusion
• Linear Ballistic Accumulator
• Log Normal Race
• Poisson Race
• Racing Diffusion

Multi-Attribute

• Multi-attribute Attentional Drift Diffusion
• Multi-attribute Decision Field Theory
• Multi-attribute Linear Ballistic Accumulator

Alternative Geometries

• Circular Drift Diffusion

API

The core API consists of the following

• rand: generate simulated data
• pdf: evaluate the probability density of the data
• logpdf: evaluate the log probability density of the data
• simulate: generate samples from the internal evidence accumulation process

Ecosystem Integration

SSMs work with the following packages (and possibly more):

• Distributions.jl: functions for probability distributions
• Pigeons.jl: Bayesian parameter estimation and Bayes factors
• Plots.jl: extended plotting tools for SSMs
• Turing.jl: Bayesian parameter estimation

Installation

You can install a stable version of SequentialSamplingModels by running the following in the Julia REPL:

] add SequentialSamplingModels

Quick Example

In the example below, we instantiate a Linear Ballistic Accumulator (LBA) model, and generate data from it.

using SequentialSamplingModels

# Create LBA distribution with known parameters
dist = LBA(; ν=[2.75,1.75], A=0.8, k=0.5, τ=0.25)
# Sample 1000 random data points from this distribution
choice, rt = rand(dist, 1000)