Simulating language-specific and language-general effects in a statistical learning model of Chinese reading

Abstract

Many theoretical models of reading assume that different writing systems require different processing assumptions. For example, it is often claimed that print-to-sound mappings in Chinese are not represented or processed sub-lexically. We present a connectionist model that learns the print to sound mappings of Chinese characters using the same functional architecture and learning rules that have been applied to English. The model predicts an interaction between item frequency and printto-sound consistency analogous to what has been found for English, as well as a language-specific regularity effect particular to Chinese. Behavioral naming experiments using the same test items as the model confirmed these predictions. Corpus properties and the analyses of internal representations that evolved over training revealed that the model was able to capitalize on information in "phonetic components" -sub-lexical structures of variable size that convey probabilistic information about pronunciation. The results suggest that adult reading performance across very different writing systems may be explained as the result of applying the same learning mechanisms to the particular input statistics of writing systems shaped by both culture and the exigencies of communicating spoken language in a visual medium. KeywordsReading; Computer Simulation; Cross Cultural Differences Over the past three decades, computational models have become increasingly sophisticated in accounting for a broad range of phenomena and specifying the mechanisms underlying skilled reading and its acquisition (see reviews in, e.g., Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2001;Plaut, 2005). The vast majority of this work has been done in English, and has thus focused on issues arising from the particularities of its writing system (Share, 2008). This has led to the construction of models that implement relatively writing system specific assumptions, such as the inclusion of distinct processing mechanisms for "sub-lexical" and "lexical" translation from spelling to sound (Coltheart, Curtis, Atkins, & Haller, 1993;Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001). An alternative approach has been to assume that reading skill is acquired by way of domain-general learning mechanisms that operate over distributed representations of basic levels of information, such as orthography, © 2009 Elsevier Inc. All rights reserved.Address for Correspondence: Jason D. Zevin, Sackler Institute, Weill Cornell Medical College, 1300 York Ave., Box 140, New York, NY, 10065, Tel: 212-746-3781, E-mail: jdz2001@med.cornell.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and a...

A psycholinguistic database for traditional Chinese character naming

“…The consistency score, defined by type, is a measure of the total number of friends to the total number of neighbors. Similar to the findings in English, several studies have shown consistency effects in naming Chinese characters (Fang et al, 1986;Hue, 1992;Yang, McCandliss, Shu, & Zevin, 2009). However, these studies have been inconsistent as to whether the consistency effect emerged in naming high-frequency characters.…”

“…Character frequency A considerable amount of evidence has indicated that character frequency is a powerful predictor of RTs in Chinese character recognition; frequency effects have been observed consistently in a wide range of tasks (Hue, 1992;Lee et al, 2005;Liu et al, 2007;Yang et al, 2009). Higher frequency characters tend to be processed more quickly than lower frequency characters during naming (Lee et al, 2005;Yang et al, 2009).…”

“…Higher frequency characters tend to be processed more quickly than lower frequency characters during naming (Lee et al, 2005;Yang et al, 2009). In the present study, the character frequency data was compiled from the ASBC (Huang & Chen, 1998).…”

“…In addition, behavioral naming RTs are provided to substantiate the variables. To further assess the database and demonstrate the importance of selecting ranges of lexical variables, additional multiple regression analyses were conducted to explore the effects of consistency and character frequency (Lee et al, 2005;Yang et al, 2009). The resulting database will be useful for researchers in the area of Chinese language processing, Chinese educators, or linguists interested in conducting cross-linguistic comparisons.…”

Literacy effects on language and vision: Emergent effects from an amodal shared resource (ASR) computational model

“…For instance, previous models of reading acquisition have made important contributions demonstrating an influence of orthographic transparency on phonological processing (Harm & Seidenberg, 1999;Yang, McCandliss, Shu, & Zevin, 2009) and semantic processing (Harm & Seidenberg, 2004;Yang, Shu, McCandliss, & Zevin, 2013). However, such models have tended to be trained on prototypical phonological representations in which substantial phonological structure is embedded, and then the processing of the phonological structure itself is investigated as both the input and output system.…”