Concept formation in neural networks: Implications for evolution of cognitive functions

Abstract

Parallel systems composed of many interconnected elements are both simple brain models and possible novel computer architectures. Potential advantages of such systems are massive parallelism with resulting speedup of computation as well as general ability to compute with noisy, corrupted, or missing data. Parallel, distributed, associative models have pronounced psychologies. Some ways of handling information are natural for them, and some things that we might want them to do are unnatural and quite difficult to do. A question of considerable interest is whether the models’ capabilities and limitations are features of human psychology. Such systems form categories based on the structure their inputs and display behavior that looks as if they form and use simple concepts. However, if noisy examples are learned, an initially stable concept structure may break up. One very simple function of names attached to categories — i.e. a rudimentary language — could be to stabilize a concept structure against fragmentation. In addition, if the statistical structure of the names reflects the statistical structure of the inputs, capacity and reliability of categorization and recognition is enhanced.