Using Dynamic Field Theory to extend the embodiment stance toward higher cognition

Abstract

The embodiment stance emphasizes that cognitive processes unfold continuously in time, are constantly linked to the sensory and motor surfaces, and adapt through learning and development. Dynamic Field Theory (DFT) is a neurally based set of concepts that has turned out to be useful for understanding how cognition emerges in an embodied and situated system. We explore how the embodiment stance may be extended beyond those forms of cognition that are closest to sensorimotor processes. The core elements of DFT are dynamic neural fields (DNFs), patterns of activation defined over different kinds of spaces. These may include retinal space and visual feature spaces, spaces spanned by movement parameters such as movement direction and amplitude, or abstract spaces like the ordinal axis along which sequences unfold. Instances of representation that stand for perceptual objects, motor plans, or action intentions are peaks of activation in the DNFs. We show how such peaks may arise from input and are stabilized by intra-field interaction. Given a neural mechanism for instantiation, the neuronal couplings between DNFs implement cognitive operations. We illustrate how these mechanisms can be used to enable architectures of dynamic neural fields to perform cognitive functions such as acquiring and updating scene representations, using grounded spatial language, and generating sequences of actions. Implementing these DFT models in autonomous robots demonstrates how these cognitive functions can be enacted in embodied, situated systems.