oberauer_lewandowsky_2019_e1

Raw data of 40 subjects that completed a verbal memory recall task in three different
conditions using different types of distractor words.

dataset

Fit computational and measurement models using full Bayesian
inference. The package provides a simple and accessible interface by
translating complex domain-specific models into 'brms' syntax, a
powerful and flexible framework for fitting Bayesian regression models
using 'Stan'. The package is designed so that users can easily apply
state-of-the-art models in various research fields, and so that
researchers can use it as a new model development framework.
References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.

Vencislav Popov

Easy and Accessible Bayesian Measurement Models Using 'brms'

Gidon T. Frischkorn

Chenyu Li

Paul-Christian Bürkner

oberauer_lewandowsky_2019_e1 function

<div class="section" id="oberauer-lewandowsky-e-">
<h3><code>oberauer_lewandowsky_2019_e1</code></h3>
A data frame with 120 rows and 10 columns:<dl>
<dt>ID</dt>
<dd>Integer uniquely identifying each subject</dd><dt>cond</dt>
<dd>Factor sperating the three experimental conditions: <code>new distractors</code> refers to
new words being used as distractors, <code>old reordered</code> refers to the to be remembered words
being the distractors, but reordered relative to the serial position, <code>old same</code> refers
to the to be remebered words being the distractors, and appearing in the same order as
the to be remembered words.</dd><dt>corr</dt>
<dd>The frequency a subject recalled the correct item</dd><dt>other</dt>
<dd>The frequency a subject recalled one of the other to be remebered words</dd><dt>dist</dt>
<dd>The frequency a subject recalled one of the distractors</dd><dt>npl</dt>
<dd>The frequency a subject recalled a not-presented lure (NPL), that is a word
that was not presented during a trial</dd><dt>n_corr, n_other, n_dist, n_npl</dt>
<dd>The number of candidataes in each of the response categories</dd>
</dl></div>

Format

Data from Experiment 1 reported by Oberauer &amp; Lewandowsky (2019) — oberauer_lewandowsky_2019_e1

<div class='section' id='oberauer-lewandowsky-e-'>
<h3><code>oberauer_lewandowsky_2019_e1</code></h3>


A data frame with 120 rows and 10 columns:<dl>
<dt>ID</dt>
<dd>Integer uniquely identifying each subject</dd>

<dt>cond</dt>
<dd>Factor sperating the three experimental conditions: <code>new distractors</code> refers to
new words being used as distractors, <code>old reordered</code> refers to the to be remembered words
being the distractors, but reordered relative to the serial position, <code>old same</code> refers
to the to be remebered words being the distractors, and appearing in the same order as
the to be remembered words.</dd>

<dt>corr</dt>
<dd>The frequency a subject recalled the correct item</dd>

<dt>other</dt>
<dd>The frequency a subject recalled one of the other to be remebered words</dd>

<dt>dist</dt>
<dd>The frequency a subject recalled one of the distractors</dd>

<dt>npl</dt>
<dd>The frequency a subject recalled a not-presented lure (NPL), that is a word
that was not presented during a trial</dd>

<dt>n_corr, n_other, n_dist, n_npl</dt>
<dd>The number of candidataes in each of the response categories</dd>


</dl>

</div>

oberauer_lewandowsky_2019_e1: Data from Experiment 1 reported by Oberauer & Lewandowsky (2019)

Description

Usage

Arguments

Format

`oberauer_lewandowsky_2019_e1`