Dynamical Systems thinking has been influential in the way psychologists, cognitive scientists, and neuroscientists think about sensori-motor behavior and its development. The initial emphasis on motor behavior was expanded when the concept of dynamic activation fields provided access to embodied cognition. Dynamical Field Theory (DFT) offers 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 modeled with Dynamic Neural Fields, deriving testable predictions and providing quantitative accounts of behavior.

One obstacle for researchers wishing to use DFT has been that the mathematical and technical skills required to make these concepts operational are not part of the standard repertoire of cognitive and developmental scientists. The goal of this tutorial is 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. 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.

A published book on DF modeling, Dynamic Thinking: A Primer on Dynamic Field Theory, covers these topics and more, with interactive simulators available to give hands-on experience to readers. We will take participants through the process of building and simulating models to illustrate key concepts in the case studies we describe in the tutorial.

Suggested Readings

(available online)

1. Schöner, G., Spencer, J.P. & the DFT Research Group (2016). Dynamic Thinking: A Primer on Dynamic Field Theory. New York: Oxford University Press.
2. Bhat, A., Spencer, J.P. & Samuelson, L.K. (2021). Word-Object Learning via Visual Exploration in Space (WOLVES): A Neural Process Model of Cross-Situational Word Learning. Psychological Review, https://doi.org/10.1037/rev0000313.
3. Spencer, J. P. (2020). The development of working memory. Current Directions in Psychological Science, doi/10.1177/0963721420959835.

Target Audience

No specific prior knowledge of the mathematics of dynamical systems models or neural networks is required as the mathematical and conceptual foundations will be provided during the tutorial. An interest in formal approaches to cognition and development is an advantage.

Schedule of Material Covered in the Tutorial

1. Conceptual foundations of Dynamical Systems Thinking and Dynamical Field Theory (DFT) – 30 minutes: embodied and situated cognition; stability as a necessary property of embodied cognitive processes; distributions of population representation as the basis of spatially and temporally continuous neural representations.
2. Dynamical Systems and Dynamic Field Theory Tutorial – 90 minutes: concept of dynamical system; attractors and stability; input tracking; detection, selection, and memory instabilities in discrete neuronal dynamics; Dynamical Fields and the basic instabilities: detection, selection, memory, boost-driven detection; learning dynamics; categorial vs. graded mode of operation.
3. Hands-on introduction – 60 minutes: practical implementation of DFT in simulators; interactive simulation using CEDAR ‘drag-and-drop’ model building; introduction to COSIVINA framework for simulations in MATLAB and PYTHON.
4. Case study using DFT to understand visual working memory and its development – 90 minutes: visual and spatial working memory in infants, children, and adults; spatial precision hypothesis as a developmental mechanism supporting visuospatial cognition; scaling up WM architectures to include attention, change detection, and binding of object features.
5. Case study using DFT to understand early word learning – 90 minutes: cross-situational word learning as an exemplary case; using and implementing large-scale neural architectures; WOLVES model and simulation of adult and developmental data.

To register for the tutorial, see the ICDL website

https://icdl2022.qmul.ac.uk/

Or you can register to join the tutorial on-line (on Teams) using the link on this page.