A Dynamic Field Theory of Visual Working Memory

The main objective of this chapter is to introduce concepts of dynamic field theory, a continuous attractor neural network, and its implementation of visual working memory. In dynamic field theory, working memory is an attractor state where representations are self-sustained through strong interactions between self-excitation and lateral inhibition. The chapter discusses a visual working memory model with fields represented by stabilized attractor states. Using this model, it demonstrates how encoding, consolidation, maintenance, and comparison occur in correct and incorrect, same and different trials in a change detection task. Further, the model captures accuracy and capacity limitations when visual working memory load is manipulated. Critically, the chapter reviews work from the authors’ research group by demonstrating how the model captures behavioural performance and makes haemodynamic predictions in early childhood, young adulthood, and older adulthood. Using the model, the chapter posits that developmental changes in visual working memory processing occur as a result of the modulation of strength and width of excitation and inhibition. Finally, the chapter describes how the dynamic field theory account compares with current views on a domain-general account and distributed nature of working memory processing.