Data-driven inference of cognitive models

We refine our machine learning technologies to massively improve the ability to infer mathematical models of complex systems from empirical data.

What do we mean by data-driven inference?

Mathematical models of dynamic complex systems primarily rely on differential or time-recursive equations. In cognitive sciences, however, the scenario is distinctly different from disciplines like physics and engineering that operate on foundational principles like Newton's laws or Maxwell's equations, facilitating the derivation and interpretation of these equations. In contrast, cognitive sciences necessitate an empirical approach due to the absence of such foundational principles. In essence, we are required to infer the governing equations of cognitive systems (behavioral and neural) based on what we can practically observe. However, these observations are inherently constrained. Given the lack of cognitive first principles, we face uncertainties regarding what to measure (i.e., state variables) and the duration (time scales and dimensionality) for which measurements should be taken. Prior research from our group and others indicates that any of these factors can compromise the entire machine learning process, resulting in models that lack replicability, explainability, predictability, and do not translate into traditional intervention mechanisms informed by control theory 


 The inherent challenge is to observe a minor fraction of the trajectory of the select measurable variables and extrapolate inferences about the entire trajectory of all pertinent state variables.

                            Tahmineh Koosha

Stefan Becker

Our Collaborators

  Medical Machine             Psychiatry                    Cognitive               Translational                 Learning                                                      Neuropsychiatry      Neuroimaging  

         Prof. Tim Hahn                  Prof. Tilo Kircher                 Prof. Igor Nenadić        Prof. Benjamin Straube


     Neuroimaging       Clinical Psychology         Language              Psychiatry and                                                                                         Technologies         Neurostimulation

    Prof. Andreas Jansen      Prof. Stefan G. Hofmann         Prof. Lucie Flek             Prof. Christoph Mulert


Dynamical Systems   Cognitive Modeling    Neurometabolic     Cell Models and                 Theory                                                              Circuitry             Parental Mental                                                                                                                                     Health

       Prof. Erfan Nozari         Prof. Marieke van Vugt   Dr. Sharmili E. Thanarajah       Prof. Sarah Kittel-                                                                                                                                                                Schneider



Imprint & Privacy Policy

Prof. Dr. Hamidreza Jamalabadi

Philipps-Universität Marburg
Klinik für Psychiatrie und Psychotherapie
Rudolf-Bultmann-Straße 8
35039 Marburg

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