Vygotsky Meets Backpropagation

Abstract

In this paper we revisit Vygotsky’s developmental model of concept formation, and use it to discuss learning in artificial neural networks. We study learning in neural networks from a learning science point of view, asking whether it is possible to construct systems that have developmental patterns that align with empirical studies on concept formation. We put the state-of-the-art Inception-v3 image recognition architecture in an experimental setting that highlights differences and similarities in algorithmic and human cognitive processes.