run_condition

This function runs multiple trials for a given condition using the specified

internal

Simulation-based evidence accumulation models for analyzing responses
and reaction times in single- and multi-response tasks. The package includes
simulation engines for five representative models: the Diffusion Decision
Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator
(LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and
extends these frameworks to multi-response settings. The package supports
user-defined functions for item-level parameterization and the incorporation
of covariates, enabling flexible customization and the development of new
model variants based on existing architectures. Inference is performed using
simulation-based methods, including Approximate Bayesian Computation (ABC)
and Amortized Bayesian Inference (ABI), which allow parameter estimation
without requiring tractable likelihood functions. In addition to core
inference tools, the package provides modules for parameter recovery,
posterior predictive checks, and model comparison, facilitating the study of
a wide range of cognitive processes in tasks involving perceptual decision
making, memory retrieval, and value-based decision making. Key methods
implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>,
Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008)
<doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020)
<doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020)
<doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012)
<doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019)
<doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale,
Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.

Guang Yang

Evidence Accumulation Models

Guangyu Zhu

run_condition function

<dl><dt>condition_setting</dt>
<dd>A list of named values representing the condition
settings</dd>
<dt>between_trial_formulas</dt>
<dd>A list of formulas defining the between-trial
parameters</dd>
<dt>item_formulas</dt>
<dd>A list of formulas defining the item parameters</dd>
<dt>n_trials</dt>
<dd>The number of trials to simulate</dd>
<dt>n_items</dt>
<dd>The number of items per trial</dd>
<dt>max_reached</dt>
<dd>The threshold for evidence accumulation</dd>
<dt>max_t</dt>
<dd>The maximum time to simulate</dd>
<dt>dt</dt>
<dd>The step size for each increment</dd>
<dt>noise_mechanism</dt>
<dd>The noise mechanism to use ("add" or "mult")</dd>
<dt>noise_factory</dt>
<dd>A function that takes condition_setting and returns a
noise function with signature function(n, dt)</dd>
<dt>backend</dt>
<dd>The backend implementation to use ("ddm", "ddm-2b", or "lca-gi")</dd>
<dt>trajectories</dt>
<dd>Whether to return full output including trajectories.</dd></dl>

Arguments

Run a given condition with multiple trials — run_condition

<dl>

<dt>condition_setting</dt>
<dd>A list of named values representing the condition
settings</dd>


<dt>between_trial_formulas</dt>
<dd>A list of formulas defining the between-trial
parameters</dd>


<dt>item_formulas</dt>
<dd>A list of formulas defining the item parameters</dd>


<dt>n_trials</dt>
<dd>The number of trials to simulate</dd>


<dt>n_items</dt>
<dd>The number of items per trial</dd>


<dt>max_reached</dt>
<dd>The threshold for evidence accumulation</dd>


<dt>max_t</dt>
<dd>The maximum time to simulate</dd>


<dt>dt</dt>
<dd>The step size for each increment</dd>


<dt>noise_mechanism</dt>
<dd>The noise mechanism to use ("add" or "mult")</dd>


<dt>noise_factory</dt>
<dd>A function that takes condition_setting and returns a
noise function with signature function(n, dt)</dd>


<dt>backend</dt>
<dd>The backend implementation to use ("ddm", "ddm-2b", or "lca-gi")</dd>


<dt>trajectories</dt>
<dd>Whether to return full output including trajectories.</dd>

</dl>

run_condition: Run a given condition with multiple trials

Description

Usage

Value

Arguments