From neural matter to rapid symbolic learning in brains and artificial neural networks: a brief overview and perspective

Abstract Advances in artificial neural networks (ANNs) have revolutionized the way we work, learn, and acquire information, achieving human-level capabilities. Yet, ANNs differ fundamentally from the human brain in how symbolic knowledge is acquired, typically requiring extensive training to form stable internal representations. In contrast, the human brain exhibits exceptional ability to instantaneously map new words to their referents, a process known as “fast mapping”, considered a fundamental mechanism underlying symbol acquisition in early ontogeny.

This review provides an overview of neurocognitive research on rapid symbolic learning and examines recent advances in computational modeling approaches aimed at replicating this capability. Models constrained by neurobiological principles known to exist in the human brain are discussed, providing a first step toward neural- and cortical-level explanations of rapid symbolic learning and opening new venues for identifying the neural mechanisms underpinning rapid word acquisition. Archiving these advances may be particularly relevant for guiding the development of sustainable, energy-efficient architectures.

A major desideratum from a linguistic and pragmatic perspective involves investigating the neural basis of fast mapping across diverse communicative and pragmatic contexts, an area where current models still fall short.