Neural dynamic foundations of a theory of higher cognition: the case of grounding nested phrases

• Published in: Cognitive neurodynamics Vol. 18; no. 2; pp. 557 - 579
• Main Authors: Sabinasz, Daniel, Richter, Mathis, Schöner, Gregor
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Binding; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Circuits; Cognition; Cognition & reasoning; Cognitive ability; Cognitive Psychology; Computer programming; Computer Science; Coordinate transformations; Dynamical systems; Field theory; Information processing; Linguistics; Mathematical functions; Neural networks; Neurosciences; Productivity; Representations; Research Article; Concepts; Conceptual structure; Embodied cognition; Dynamic field theory; Higher cognition; Neural process model; Language grounding
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-10007-7

Because cognitive competences emerge in evolution and development from the sensory-motor domain, we seek a neural process account for higher cognition in which all representations are necessarily grounded in perception and action. The challenge is to understand how hallmarks of higher cognition, productivity, systematicity, and compositionality, may emerge from such a bottom-up approach. To address this challenge, we present key ideas from Dynamic Field Theory which postulates that neural populations are organized by recurrent connectivity to create stable localist representations. Dynamic instabilities enable the autonomous generation of sequences of mental states. The capacity to apply neural circuitry across broad sets of inputs that emulates the function call postulated in symbolic computation emerges through coordinate transforms implemented in neural gain fields. We show how binding localist neural representations through a shared index dimension enables conceptual structure, in which the interdependence among components of a representation is flexibly expressed. We demonstrate these principles in a neural dynamic architecture that represents and perceptually grounds nested relational and action phrases. Sequences of neural processing steps are generated autonomously to attentionally select the referenced objects and events in a manner that is sensitive to their interdependencies. This solves the problem of 2 and the massive binding problem in expressions such as “the small tree that is to the left of the lake which is to the left of the large tree”. We extend earlier work by incorporating new types of grammatical constructions and a larger vocabulary. We discuss the DFT framework relative to other neural process accounts of higher cognition and assess the scope and challenges of such neural theories.