https://puma.ub.uni-stuttgart.de/user/llis/ACT-RPUMA RSS feed for /user/llis/ACT-R2026-04-14T00:57:01+02:00https://puma.ub.uni-stuttgart.de/bibtex/26967bd47c956f6850bf08f6450e36dbd/llisllis2023-09-23T09:31:13+02:00ACT-R cognition learning llis load modeling myown <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Wirzberger" itemprop="url" href="/person/198e735899df4a406d1a1a7e03566c8ab/author/0"><span itemprop="name">M. Wirzberger</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jelmer P Borst" itemprop="url" href="/person/198e735899df4a406d1a1a7e03566c8ab/author/1"><span itemprop="name">J. Borst</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Josef F Krems" itemprop="url" href="/person/198e735899df4a406d1a1a7e03566c8ab/author/2"><span itemprop="name">J. Krems</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Günter Daniel Rey" itemprop="url" href="/person/198e735899df4a406d1a1a7e03566c8ab/author/3"><span itemprop="name">G. Rey</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Trends in Neuroscience and Education</span>, </em> </span>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Sat Sep 23 09:31:13 CEST 2023Trends in Neuroscience and Education100139Memory-related cognitive load effects in an interrupted learning task: A model-based explanation2020ACT-R cognition learning llis load modeling myown Background The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners’ working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood. Method We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions. Results The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory. Conclusions Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level.