A Neurodynamical Model of Context-Dependent Category Learning

The abstraction of patterns from data and the formation of categories is a hallmark of human cognitive ability. As such, it has been studied from many different perspectives by researchers, and these studies have led to several explanatory models. In this paper, we consider the inference of categorical representations for the purpose of producing task-specific responses. Task-relevant responses require a knowledge repertoire that is organized to allow efficient access to useful information. We present a neurodynamical system that infers functionally coherent categories from semantic inputs (or concepts) presented sequentially in different contexts, and encodes them as attractors in a two-dimensional topological feature space. The resulting category representations can then act as pointers in a larger system for semantic cognition. The system allows controlled hierarchical organization and functional segregation of the inferred categories.