The MathPsych and ICCM communities are commited to using precise mathematical concepts to understand human cognition. While this has been most succesful around specific cognitive competences such as decision making, a continuing challenge is to provide a set of theoretical principles that reach all facets of cognition. Cognitive architectures such as ACT-R, SOAR, and neural frameworks such as LISA and DORA address that challenge as does Dynamic Field Theory (DFT). DFT postulates that cognitive processes share properties with sensory-motor processes that both emerge from the dynamics of neural populations with strong recurrent connectivity. The goal of the workshop is to teach participants the principles of DFT and provide hands-on experience in building models that can be applied in their own research.

Structure of the tutorial:

Core concepts of DFT: Neural populations formalized as neural fields evolve according to integro-differential equations whose attractor states are the units of representation. Instabilities of these attractors are the basis for elementary cognitive functions: detection, selection, working memory, sequence generation.

Higher cognition: Binding different feature dimensions emerges from coupling neural fields that represent these feature spaces. A small set of mini-architectures provide the foundation of higher cognitive processes.

Hands-on modelling: Building neural dynamic cognitive architectures by combining mini-architectures using the programming framework PyCosivina. A model of visual attention and working memory will serve as a work example. Change detection paradigms will be modeled and predictions demonstrated in simulation.