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Lexical Development From The Perspectives Of Artificial Neural Network Models And Dynamical Systems Theory

2355 words - 9 pages

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 neural brain functioning (Poveda & Vellido, 2006). The ANN consists of large number of units or nodes, which could be thought of as the neurons. These are organised and determine activation of certain levels as well as consequent spread of information through the network of layers. The activation of units depends on the total weighted input in their connections which resembles the strength of a synapse, either excitatory or inhibitory (Krogh, 2008). However, in order for an ANN model to simulate the processes in real neurons, the weights and threshold of units’ activation have to be set. This can be done by training the ANN using different algorithms and adjusting the weights after presentation of new information (Krogh, 2008).
With regard to models of lexicon and language development, using these supervised learning algorithms has been considered a limitation in explanatory power and biological plausibility because children do not get constant feedback and error correction during learning (Li, Farkas, & MacWhinney, 2004). In order to account for previous limitations and failure to model the gradual expansion of lexical growth, Li et al. (2004) developed a self-organising model of lexical development, the DevLex. Its...

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