A Computational Model for simulating Korean Visual Word Recognition

Abstract

We propose the connectionist model of visual word recognition which reflects the theoretically presented linguistic processing mechanisms of Koreans' visual word processing. In applying the connectionist model, sets of orthographic units, inter-level hidden units and semantic units, were constructed. During the training phase, the weights on the connections between the units were modified using the back-propagation learning algorithm. To evaluate the model, we also conducted behavioral experiments to compare the results of the model performances with human performances. The results show that the proposed model closely simulates Korean visual word processing characteristics such as the lexical status effect, the word frequency effect, and the word similarity effect.