new_simulation_config.chunk_size.heuristic

Heuristic to calculate optimal chunk size for simulation configuration

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

new_simulation_config.chunk_size.heuristic function

<dl><dt>n_conditions</dt>
<dd>Total number of conditions to simulate</dd>
<dt>n_trials_per_condition</dt>
<dd>Number of trials per condition</dd>
<dt>n_items</dt>
<dd>Number of items per trial</dd>
<dt>parallel</dt>
<dd>Whether to run in parallel</dd>
<dt>n_cores</dt>
<dd>Number of cores for parallel processing</dd></dl>

Arguments

Heuristic to calculate optimal chunk size for simulation configuration — new_simulation_config.chunk_size.heuristic

<dl>

<dt>n_conditions</dt>
<dd>Total number of conditions to simulate</dd>


<dt>n_trials_per_condition</dt>
<dd>Number of trials per condition</dd>


<dt>n_items</dt>
<dd>Number of items per trial</dd>


<dt>parallel</dt>
<dd>Whether to run in parallel</dd>


<dt>n_cores</dt>
<dd>Number of cores for parallel processing</dd>

</dl>

new_simulation_config.chunk_size.heuristic: Heuristic to calculate optimal chunk size for simulation configuration

Description

Usage

Value

Arguments