A Data-Driven Mapping of Five ACT-R Modules on the Brain

In this paper we present a new, data-driven mapping of five ACT-R modules on the brain. In the last decade, many studies have been published that evaluated ACT-R models based on their ability to predict fMRI data in certain predefined brain regions. However, these predefined regions were based on a reading of the literature, and might not be optimal. Currently, we used the results of a model-based fMRI analysis of five datasets to define a new brain mapping for the problem state, declarative memory, manual, visual, and aural modules. Both the original and the new mapping were applied to data of an experiment that elicited differential activity in these five modules; the results were compared to model predictions. The new mapping performed slightly better for the problem state, declarative memory, aural, and manual modules, but not for the visual module. In addition, it provides a more principled way of validating ACT-R models. Although the mapping is ACT-R specific, the methodology can be use to map any cognitive architecture or model to the brain.

[1]  Leslie G. Ungerleider,et al.  Object vision and spatial vision: two cortical pathways , 1983, Trends in Neurosciences.

[2]  Niels Taatgen,et al.  Proceedings of the 12th International Conference on Cognitive Modeling , 2004, ICCM 2013.

[3]  Andrea Stocco,et al.  The Neural Correlates of Problem States: Testing fMRI Predictions of a Computational Model of Multitasking , 2010, PloS one.

[4]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[5]  John R. Anderson,et al.  A central circuit of the mind , 2008, Trends in Cognitive Sciences.

[6]  I. J. Myung,et al.  When a good fit can be bad , 2002, Trends in Cognitive Sciences.

[7]  John R. Anderson,et al.  Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network , 2013, Proceedings of the National Academy of Sciences.

[8]  I. J. Myung,et al.  The Importance of Complexity in Model Selection. , 2000, Journal of mathematical psychology.

[9]  Klaus Oberauer,et al.  Design for a working memory. , 2009 .

[10]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[11]  Scott D. Brown,et al.  Neural Correlates of Trial-to-Trial Fluctuations in Response Caution , 2011, The Journal of Neuroscience.

[12]  H Pashler,et al.  How persuasive is a good fit? A comment on theory testing. , 2000, Psychological review.

[13]  John R. Anderson.  How Can the Human Mind Occur in the Physical Universe , 2007 .

[14]  Niels Taatgen,et al.  Predicting interference in concurrent multitasking , 2013 .

[15]  Jean-Luc Anton,et al.  Region of interest analysis using an SPM toolbox , 2010 .

[16]  P. Dayan,et al.  Cortical substrates for exploratory decisions in humans , 2006, Nature.

[17]  J. Rieskamp,et al.  Deciding When to Decide: Time-Variant Sequential Sampling Models Explain the Emergence of Value-Based Decisions in the Human Brain , 2012, The Journal of Neuroscience.

[18]  John P O'Doherty,et al.  Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[19]  Karl J. Friston,et al.  CHAPTER 2 – Statistical parametric mapping , 2007 .

[20]  M. Just,et al.  From the Selectedworks of Marcel Adam Just the Organization of Thinking: What Functional Brain Imaging Reveals about the Neuroarchitecture of Complex Cognition , 2022 .

[21]  Julian N. Marewski,et al.  Using the ACT-R architecture to specify 39 quantitative process models of decision making , 2011, Judgment and Decision Making.

[22]  Niels Taatgen,et al.  Using a symbolic process model as input for model-based fMRI analysis: Locating the neural correlates of problem state replacements , 2011, NeuroImage.

[23]  Dario D. Salvucci.  Modeling Driver Behavior in a Cognitive Architecture , 2006, Hum. Factors.

[24]  Leslie G. Ungerleider,et al.  Dissociation of object and spatial visual processing pathways in human extrastriate cortex. , 1991, Proceedings of the National Academy of Sciences of the United States of America.