Lexical Development from the Perspectives of Artificial Neural Network Models and Dynamical Systems Theory

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Word learning is a fundamental building block for early language acquisition. One controversial phenomenon associated with vocabulary growth is vocabulary spurt, usually characterised as a rapid increase in productive vocabulary in early child language. Despite that fact that initially, the word production starts slowly, it has been argued that after few months, children undergo a transition to a subsequent stage of faster vocabulary growth (Goldfield & Reznick, 1990). Several theories have attempted to account for this phenomenon. For instance, Plunkett (1993, as cited in Ganger & Brent, 2004) suggests that the acceleration results from linguistic advances such as word segmentation which allows children to pick up more words from speech stream; however, there is now a growing disagreement on its existence in all children (Goldfield & Reznick, 1990; Ganger & Brent, 2004). The aim of the present essay is to evaluate the ability of two theories, namely the Artificial Neural Network (ANN) and Dynamical Systems theory (DST), to explain the issues underlying the lexical development and vocabulary spurt. This essay provides an overview of both theories and compares their strengths and weaknesses in their explanation of lexical development supported by empirical evidence.

Both ANN and DST were formed in opposition to the symbol system view of cognition (Smith & Samuelson, 2003). Despite acknowledging that some of underlying mechanisms may be innate, they see lexical development as an emergent process resulting from early social interaction and exposure to linguistic input (Poveda & Vellido, 2006). The main aim of ANN is to construct computational models of various cognitive processes based on biological details of n...

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