A Localist Neural Network Model for Early Child Language Acquistion from Motherese

This paper presents a localist multimodal neural network that uses Hebbian learning to acquire one-word child language from child directed speech (CDS) comprising multiword utterances and queries in addition to one-word utterances. The model implements cross-situational learning between linguistic words used in child directed speech, the accompanying perceptual entities, conceptual relations and inferred communicative intentions. In 90 cases out of 117, the network successfully generates one-word utterances that may be viewed as being semantically equivalent to the CDS input used to train the network. The model also successfully emulates the one-word speech of a child in 12 out of 28 cases, despite its localist nature, thereby suggesting that Hebbian learning, as used in most models of cognitive development, is capable of crosssituational learning, a key component of multimodal temporal cognitive acquisition tasks, of which child language acquisition is one.