Dynamic Field Theory: Conceptual Foundations and Applications in the Cognitive and Developmental Sciences

Background of the tutorial For the last 20 years, the concepts of Dynamical Systems Theory have been influential in the way psychologists, cognitive scientists, and neuroscientists think about sensori-motor behavior and embodied cognition. These concepts have had a particularly strong impact in developmental science, changing the way questions are asked, experiments are designed, and theoretical models are formulated. The initial emphasis on motor behavior was expanded when autonomous robotics was used as a tool to explore how the approach scaled to increasing behavioral and environmentel complexity. In this transition from an account of sensori-motor behavior to an account for cognition, a key step was to address how the concept of representation could be given meaning with Dynamical Systems thinking. Dynamical Field Theory (DFT) provided an answer to this question, offering a framework for thinking about representation-in-the-moment that is firmly grounded in both Dynamical Systems thinking and neurophysiology. Dynamic Neural Fields are formalizations of how neural populations represent the continuous dimensions that characterize perceptual features, movements, and cognitive decisions. Neural fields evolve dynamically under the influence of inputs as well as strong neuronal interaction, generating elementary forms of cognition through dynamical instabilities. The concepts of DFT establish links between brain and behavior, helping to define experimental paradigms in which behavioral signatures of specific neural mechanisms can be observed. These paradigms can be modelled with Dynamic Neural Fields, deriving testable predictions and providing quantitative accounts of behavior. Although Dynamical Systems thinking has had impact even when used at the level of metaphor, the full strength of these concepts is brought to bear when they are operationalized through mathematical modeling and quantitative analysis. This is particularly true of DFT, in which the classification of instabilities enables the systematic analysis of embodied cognitive systems. One obstacle for researchers wishing to use DFT has been that the mathematical and technical skills required at this operational level do not form part of the standard repertoire of cognitive scientists. The goal of this tutorial is, therefore, to provide the training and tools to overcome this obstacle. We will provide a systematic introduction to the central concepts of DFT and their grounding in both Dynamical Systems concepts and neurophysiology. We will discuss the concrete mathematical implementation of these concepts in Dynamic Neural Field models, giving all needed background and providing participants with some hands-on experience using interactive simulators in Matlab. We will review robotic implementations to make the ideas concrete and demonstrable. Finally, we will take participants through a number of selected, exemplary case studies in which the concepts and associated models have been used to ask questions about elementary forms of embodied cognition and their development. The interactive simulators will be available at the tutorial. We will also give participants access to generic productionlevel simulators of Dynamic Field Models that can be expaned by participants to simulate and evaluate Dynamic Field Models that they may develop in their own work.