mu_ssp

Provides the drift rate for the SSP model. That is, the sum of attention
attributed to the flankers and central target, scaled by the perceptual
input.

internal

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

mu_ssp function

<dl><dt>prms_model</dt>
<dd>the model parameters, containing p, sd_0, r, sign</dd>
<dt>prms_solve</dt>
<dd>solver settings</dd>
<dt>t_vec</dt>
<dd>time space</dd>
<dt>one_cond</dt>
<dd>one condition</dd>
<dt>ddm_opts</dt>
<dd>optional arguments attached to an object</dd></dl>

Arguments

Drift Rate for SSP — mu_ssp

<dl>

<dt>prms_model</dt>
<dd>the model parameters, containing p, sd_0, r, sign</dd>


<dt>prms_solve</dt>
<dd>solver settings</dd>


<dt>t_vec</dt>
<dd>time space</dd>


<dt>one_cond</dt>
<dd>one condition</dd>


<dt>ddm_opts</dt>
<dd>optional arguments attached to an object</dd>

</dl>

mu_ssp: Drift Rate for SSP

Description

Usage

Value

Arguments