A visual auditory model based on Growing Self-Organizing Maps to analyze the taxonomic response in early childhood

In this paper we present an extension of a visual auditory neural network model previously proposed by Mayor and Plunkett (2010) in order to explain the emergence of the taxonomic response in early childhood. The original model consists of two self-organizing maps (respectively, visual and acoustic) connected with Hebbian connections. With respect to the original model, our proposal adds two major features. First, our model follows a dynamic training regime, learning categories and word-object associations that evolve through time. Second, the visual and acoustic maps are Growing self-organizing maps that grow during training, when they are no longer able to consistently represent categories. With these two new characterizing features, our model replicates the performance of the original Mayor and Plunkett (2010)'s model, acquires psychological plausibility in the training regime, and avoids the risk of catastrophic interference.