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Semantic Representations in Monolingual and Bilingual Connectionist Networks

A neural network model is presented which investigates the suggestion that being multilingual contributes to an individual’s level of cognitive reserve. Two versions of this model were produced, one which learnt the names of input representations in a single language and another model which learnt input representations in two languages. The languages in both models were split further into two semantic categories. The relationship between the representations of both semantic categories in the first language of each of the two versions was investigated. Further manipulations came in the form of changing the sensitivity of the artificial neurons in the network and varying the hidden layer size with respect to variable levels of brain reserve. Findings were not immediately interpretable in terms of age-related decline in the absence of a behavioral measure. However, the variance in trajectories of category separation provide a cautionary tale against the interpretation of any measures gained at discreet intervals.