Personality Theory in Gestalt Theoretical Psychotherapy: Kurt Lewin’s Field Theory and his Theory of Systems in Tension Revisited

Summary With regard to the dynamics of human experience and behavior, Gestalt theoretical psychotherapy (GTP) relies mainly on Kurt Lewin’s dynamic field theory of personality. GTP is carried out by including a re-interpretation of Lewin’s theory in some aspects of psychotherapeutic practice in relation to critical realism. Human experience and behavior are understood to be functions of the person and the environment (including the other individuals therein) in a psychic field (life space), which encompasses both of these mutually dependent factors. The anthropological model of this approach is, therefore, not mono-personal but, a priori, structural and relational in nature. It does not one-sidedly focus on the “inner components” of a person, but on the interrelation of the individual and a given environment, which affects experience and behavior. After a brief introduction of these basic concepts, this lecture will focus especially on Lewin’s concept of tension-systems, which may be considered as the Gestalt theoretical counterpart of Freud’s drive theory. Further, we define the basic assumptions which underlie GTP and explain how the person moves through her/his life experience in terms of Gestalt psychology.

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.

A Dynamic Field Theory of visual working memory

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

Dynamic contextualization and comparison as the basis of biologically inspired action understanding

People exhibit a robust ability to understand the actions of others around them. In this work, we identify two biologically inspired mechanisms that we hypothesize to be central in the function of action understanding. The first module is a contextual predictor of the observed action, given the goal-directed movement towards objects, and the actions that are allowed to be performed on the object. The second module is a kinematic trajectory parser that validates the previous prediction against a set of learned templates.We model both mechanisms and link them to the environment using the cognitive framework of Dynamic Field Theory and present our first steps into integrating the aforementioned modules into a consistent framework for the purpose of action understanding. The two modules and the combined architecture as awhole are experimentally validated using a recording of an actor performing a series of intentional actions testing the ability of the architecture to understand context and parse actions dynamically. Our initial qualitative results show that action understanding benefits from the combination of the two modules, while any module alone would be insufficient to resolve ambiguity in the perceived actions.