• Korean Journal of Cognitive Science
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• v.34 no.4
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• pp.277-297
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• 2023

This study attempted to find out how lexical decision of two-syllable words or pseudo-words is affected by syllabic information, such as syllable frequency, syllable (i.e. vowel) type, and presence of final consonant (i.e. batchim), through the analysis of the Korean Lexicon Project Database (KLP-DB). Hierarchical regression of RT data showed that lexical decision of words was influenced by the frequency of the first syllable, the syllable type of the first and second syllables, batchim for the first and second syllables, and also by the interaction of the two syllable types and the interaction of syllable frequency and batchim of the second syllable. For pseudo-words lexical decision was influenced by the frequency of the first and second syllables, syllable type of the first syllable, and batchim for the first and second syllables, and also by the interaction of the two syllable frequencies, the interaction of the two syllable types, and the interaction of syllable frequency and batchim of the first syllable. Word frequency had a strong effect on lexical decision of words, while syllabic information had a stable effect on the lexical decision of pseudo-words. These results indicate that syllabic information should be seriously considered in constructing word and pseudo-word lists and interpreting lexical decision time. Understanding the effect of syllabic information will also contribute to the understanding of word recognition process.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.41-49
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• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.