ISSN : 1226-9654
In the present study, two experiments were conducted to examine how neighbor words affect Korean visual word recognition. Previous studies have shown that word frequency has a crucial role in word recognition. In addition, some researchers argue that neighbor words that are orthographically or phonologically similar to a target word also affect word recognition. In Korean, neighbor words can be defined as a group of words that share the first syllable with the target word. The type frequency of the syllabic neighbor words refers to the number of neighbor words and the token frequency refers to the accumulated word frequency of the neighbor words. Although previous studies on Korean visual word recognition have shown that the word frequency effect emerges, there are few studies on effects of the type or the token frequency using a factorial design. To this end, we conducted a lexical decision task, in which the type frequency was manipulated in Experiment 1 and the token frequency was manipulated in Experiment 2. The results showed that neither the type nor the token frequency affect response times of the lexical decision task. The results suggest the necessity to further discuss the nature and the characteristics on the effect of syllabic neighbor words in Korean visual word recognition.
Andrews, S. (1989). Frequency and neighborhood effects on lexical access: Activation or search?. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 802-814.
Andrews, S. (1992). Frequency and neighborhood effects on lexical access: Lexical similarity or orthographic redundancy?. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 234-254.
Andrews, S. (1997). The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4, 439-461.
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255-278.
Bates, D., Maechler, M., & Bolker, B (2012). lme4: Linear mixed-effects models using S4 classes (R package version 0.999999-0). Retrieved from http://CRAN.R-project.org/
Brysbaert, M., and Stevens, M. (2018). Power analysis and effect size in mixed effects models: a tutorial. Journal of Cognition, 2, 1-20.
Carrieras, M., Alvarez, J. C., & De Vega, M. (1993). Syllable frequency and visual word recognition in Spanish. Journal of Memory and Language, 32, 766-780.
Carreiras, M., Perea, M., & Grainger, J. (1997). Effects of orthographic neighborhood in visual word recognition comparisons. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 857-871.
Choi, W., Lee, C., Kang, J., & Nam, K. (2015). The lexical inhibition of the phonological information in Korean visual word recognition. The Korean Journal of Cognitive and Biological Psychology, 27, 561-581.
Coltheart, M., Davelaar, E., Jonasson, T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic, editor, Attention & Performance Ⅳ. Hillsdale. NJ: Erlbaum.
Conard, M., Carreiras, M., & Jacobs, A. M. (2008). Contrasting effects of token and type syllable frequency in lexical decision, Language and Cognitive process, 23, 296-326.
Conrad, M., & Jacobs, A. M. (2004). Replicating syllable-frequency effects in Spanish in German: One more challenge to computational models of visual word recognition. Language and Cognitive Processes, 19, 369-390.
Cutler, A. (1997). The syllable's role in the segmentation of stress languages. Language and Cognitive Processes, 12(5-6), 839-846.
Ferrand, L., & Segui, J. (1998). The syllable’s role in speech production: Are syllables chunks, schemas, or both?. Psychonomic Bulletin & Review, 5, 253-258.
Grainger, J., & Jacobs, A. M. (1996). Orthographic processing in visual word recognition: a multiple read-out model. Psychological Review, 103, 518-565.
Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59, 434-446.
Jarosz, A. F., & Wiley, J. (2014). What are the odds? A practical guide to computing and reporting Bayes factors. Journal of Problem Solving, 7, 1-9.
JASP Team (2018). JASP (Version 0.8.5) [Computer software].
Kang, B. M., & Kim, H. K. (2009). Token frequency of Korean: Analyze of 15 million words in Sejong corpus. Seoul: Hankookmunhwasa.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2013). lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version 2.0–3 [computer software].
Kwon, Y. (2012). The dissociation of syllabic token and type frequency effect in lexical decision task. The Korean Journal of Cognitive and Biological Psychology, 24, 315-328.
Kwon, Y. (2014). The syllable type and token frequency effect in naming task. Korean Journal of Cognitive Science, 25, 91-107.
Kwon, Y., Cho, H., Kim, C., & Nam, K. (2006). The neighborhood effect in Korean visual word recognition. The Korean Society of Phonetic Sciences and Speech Technology, 60, 29-45.
Kwon, Y., & Lee, C. (2017). The reason for the absence of the syllable frequency effect in Korean: Behavioral and ERP evidences from morphological syllable. Journal of the Korean Data Analysis Society, 19, 465-476.
Kwon, Y., Lee, C., Lee, K., & Nam, K. (2011). The inhibitory effect of phonological syllables, rather than orthographic syllables, as evidenced in Korean lexical decision tasks. Psychologia, 54, 1-14.
Kwon, Y., & Nam, K. (2011). The relationship between morphological family size and syllabic neighborhoods density in Korean visual word recognition. The Korean Journal of Cognitive and Biological Psychology, 23, 301-319.
Lee, S., & Lee, Y. (2018). The effect of the morphological characteristics on Korean spoken word recognition: Comparing simple words and compound words. The Korean Journal of Cognitive and Biological Psychology, 30, 35-51.
Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43, 679-690.
Mathey, S., Zagar, D, Doignon, N., & Seigneuric, A. (2006). The nature of the syllabic neighborhoods effect in French. Acta Psychology, 123, 372-393.
Mehler, J., Dommergues, J. Y., Frauenfelder, U., & Segui, J. (1981). The syllable's role in speech segmentation. Journal of Verbal Learning and Verbal Behavior, 20, 298-305.
R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing. Retrieved from http://www.r-project.org/
Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 111-163.
Snodgrass, J. G., & Poster, M. (1992). Visual-word recognition thresholds for screen-fragmented names of the Snodgrass and Vanderwart pictures. Behavior Research Methods, Instruments, & Computers, 24, 1-15.
Wagenmakers, E. J. (2007). A practical solution to the pervasive problems ofp values. Psychonomic Bulletin & Review, 14, 779-804.
Wilkinson, L., & Task Force on Statistical Inference, American Psychological Association, Science Directorate. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.
Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv preprint, arXiv:1308.5499.
Yi, K. (2011). The neighborhood effects in Hangul word recognition. The Korean Journal of Cognitive and Biological Psychology, 23, 639-651.