ssp_synth_data

This dataset was simulated by using the Shrinking Spotlight Model
(see <code>ssp_dm()</code>) with parameter settings that are typical for a
Flanker task.

datasets

Fit and explore Drift Diffusion Models (DDMs),
a common tool in psychology for describing decision processes in simple
tasks. It can handle both time-independent and time-dependent DDMs. You
either choose prebuilt models or create your own, and the package takes
care of model predictions and parameter estimation. Model predictions
are derived via the numerical solutions provided by Richter, Ulrich, and
Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).

Valentin Koob

dRiftDM

Estimating (Time-Dependent) Drift Diffusion Models

Thomas Richter

Markus Janczyk

ssp_synth_data function

A data frame with 600 rows and 3 columns:<dl>
<dt>RT</dt>
<dd>Response Times</dd><dt>Error</dt>
<dd>Error Coding (Error Response = 1; Correct Response = 0)</dd><dt>Cond</dt>
<dd>Condition ('comp' and 'incomp')</dd>
</dl>

Format

A synthetic data set with two conditions — ssp_synth_data

A data frame with 600 rows and 3 columns:<dl>
<dt>RT</dt>
<dd>Response Times</dd>

<dt>Error</dt>
<dd>Error Coding (Error Response = 1; Correct Response = 0)</dd>

<dt>Cond</dt>
<dd>Condition ('comp' and 'incomp')</dd>


</dl>

ssp_synth_data: A synthetic data set with two conditions

Description

Usage

Arguments

Format