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Many studies have shown that words learned early in life are read better than words learned later and are less vulnerable to brain damage. In the first part of this research, primary school students in grade 5 read word groups learned initially in one of the previous grades. The words used in the experiments were 327 Farsi monosyllable words matched on other factors involved in Farsi word naming. The subjects were 25 students in grade 5. The analysis of covariance (the consistency and frequency as covariates) on the data showed that, other factors being equal, words learned in earlier grades are read better than words learned later, showing the known Age of Acquisition (AoA) effect. In the second part of this study, a number of simulations were carried out to simulate AoA in word naming by a neural network model developed earlier based on connectionist approach. While previous studies have used random patterns, in this research words from primary school books were used. Like human data, words learned early by the model were read better than words learned later. However, there was a failure in replicating previous simulation of AoA in English reading by an algorithm called Quick prop here for Farsi. In addition, the model was lesioned by removing some hidden units to see its effect on word reading. As a result, words learned earlier were less vulnerable to damage compared to ones learned later. These effects are explained by considering the nature of learning in neural networks trained by error back-propagation.

نوع مطالعه: Original | موضوع مقاله: Computational Neuroscience
دریافت: ۱۳۹۳/۴/۲۸ | پذیرش: ۱۳۹۶/۱۱/۳ | انتشار: ۱۳۹۷/۲/۲۳

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