The Chinese Lexicon Project: A repository of lexical decision behavioral responses for 2,500 Chinese characters

Sze, Wei Ping; Liow, Susan J. Rickard; Yap, Melvin J.

doi:10.3758/s13428-013-0355-9

Cited by 66 publications

(83 citation statements)

References 38 publications

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“…In the English Lexicon Project (ELP), they collected naming times and lexical decision times for over 40, 000 English words from several hundred participants. Here we tested our new frequencies on English behavioral data coming from the ELP, on French behavioral data (the French Lexicon Project, or FLP; Ferrand et al, 2010), on Dutch (Keuleers, Diependaele, & Brysbaert, 2010), on Malay (the Malay Lexicon Project, or MLP; Yap et al, 2010), and on Chinese (Sze, Rickard Liow, & Yap, 2014). …”

Section: Megastudies In French English Dutch Malay and Chinesementioning

confidence: 99%

Worldlex: Twitter and blog word frequencies for 66 languages

Gimenes

New

2015

Behav Res

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Lexical frequency is one of the strongest predictors of word processing time. The frequencies are often calculated from book-based corpora, or more recently from subtitlebased corpora. We present new frequencies based on Twitter, blog posts, or newspapers for 66 languages. We show that these frequencies predict lexical decision reaction times similar to the already existing frequencies, or even better than them. These new frequencies are freely available and may be downloaded from http://worldlex.lexique.org.Keywords Word frequency . Cross-language frequency . Twitter . BlogsThe number of occurrences of a word within a corpus is one of the best predictor of word processing time (Howes & Solomon, 1951). High-frequency words are processed more accurately and more rapidly than low-frequency words, both in comprehension and in production (Baayen, Feldman, & Schreuder, 2006;Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004;Monsell, 1991;Yap & Balota, 2009). This word frequency effect was observed in different tasks such as lexical decision tasks (Andrews & Heathcote, 2001;Balota et al., 2004), perceptual identification tasks (Grainger & Jacobs, 1996;Howes & Solomon, 1951), pronunciation tasks (Balota & Chumbley, 1985;Forster & Chambers, 1973), and semantic categorization tasks (Andrews & Heathcote, 2001;Taft & van Graan, 1998). This word frequency effect is a robust effect, since it was found in many languages.For a long time, corpora were compiled from written texts, principally books (Kučera & Francis, 1967;Thorndike & Lorge, 1944 (Kučera & Francis, 1967). Some corpora were used despite their age and despite some critics (see Brysbaert & New, 2009, for a discussion of the Kučera & Francis corpus).More recently, another source of corpora was found to be reliable: movie subtitles. The subtitle-based frequencies were first computed in French by New, Brysbaert, Véronis, and Pallier (2007). The authors showed two main results. First, they showed that the subtitle-based frequencies were a better predictor of reaction times than the book-based frequencies. Second, the subtitle-based frequencies were complementary to book-based frequencies. For instance, typical words from spoken language in everyday life were much more frequent in the subtitle-based than in the book-based corpora. Because the book-based and subtitle-based frequencies were shown to be complementary in the analyses (they explained more variance together than separately), the authors concluded that bookbased frequencies could be good estimates of written language and that subtitle-based frequencies could be good estimates of spoken language. The subtitle-based frequencies were then

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Section: Megastudies In French English Dutch Malay and Chinesementioning

confidence: 99%

Worldlex: Twitter and blog word frequencies for 66 languages

Gimenes

New

2015

Behav Res

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“…Regarding Chinese, only recently have large-scale psycholinguistic norms for simplified Chinese characters been developed (Liu, Shu, & Li, 2007;Sze, Rickard Liow, & Yap, 2013). Liu et al provided the first large-scale database of simplified Chinese character naming.…”

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confidence: 99%

“…This results in generally fewer strokes and homophones in simplified than in traditional characters. It is worth noting that the two existing sets of Chinese norms (Liu et al, 2007;Sze et al, 2013) are based on simplified Chinese characters. The existing norms may not be directly applied in studies on traditional characters, in particular for word-form and frequencyrelated measures.…”

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A psycholinguistic database for traditional Chinese character naming

et al. 2015

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In this study, we aimed to provide a large-scale set of psycholinguistic norms for 3,314 traditional Chinese characters, along with their naming reaction times (RTs), collected from 140 Chinese speakers. The lexical and semantic variables in the database include frequency, regularity, familiarity, consistency, number of strokes, homophone density, semantic ambiguity rating, phonetic combinability, semantic combinability, and the number of disyllabic compound words formed by a character. Multiple regression analyses were conducted to examine the predictive powers of these variables for the naming RTs. The results demonstrated that these variables could account for a significant portion of variance (55.8 %) in the naming RTs. An additional multiple regression analysis was conducted to demonstrate the effects of consistency and character frequency. Overall, the regression results were consistent with the findings of previous studies on Chinese character naming. This database should be useful for research into Chinese language processing, Chinese education, or crosslinguistic comparisons. The database can be accessed via an online inquiry system (http://ball.ling.sinica.edu.tw/ namingdatabase/index.html).Keywords Naming . Chinese character naming . Psycholinguistic norm . Chinese visual word recognition Understanding how readers process words, which are fundamental units for reading, has been a core issue in psycholinguistic studies. Behavioral studies have utilized tasks such as naming, lexical decision, speech perception, and synonym judgment, with the aim of understanding how orthographic, phonological, and semantic information is processed during visual word recognition. These experiments have typically employed a factorial design by manipulating one or two lexical variables (e.g., word frequency and spelling-to-sound consistency) in a limited set of stimuli, while trying to control for other lexical variables (e.g., orthographic neighborhood size and acoustic properties of onsets) in order to prevent any potential confounds. Although this approach has provided fruitful evidence to advance our understanding of how various lexical properties affect visual word recognition, it has some limitations in terms of how to select a set of items that vary in only one dimension and how to generalize the findings to a larger set of stimuli.To overcome these limitations, a number of large-scale psycholinguistic databases have been made available for languages with alphabetic orthographies, including English (Balota et al

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“…Lexicon projects collecting behavioral data for thousands of words have been conducted in English (Balota et al, 2007), French (Ferrand et al, 2010), Dutch (Keuleers, Diependaele, & Brysbaert, 2010), Malay (Yap et al, 2010), British English , and Chinese (Sze, Rickard Liow, & Yap, 2014). The approach has also been used for research on second language processing (Lemhöfer et al, 2008) and priming (Hutchinson et al, 2013;Adelman, Johnson, et al, 2014).…”

Section: Background and Motivationmentioning

confidence: 99%

The Developmental Lexicon Project: A behavioral database to investigate visual word recognition across the lifespan

Schröter

Schroeder

2017

Behav Res

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With the Developmental Lexicon Project (DeveL), we present a large-scale study that was conducted to collect data on visual word recognition in German across the lifespan. A total of 800 children from Grades 1 to 6, as well as two groups of younger and older adults, participated in the study and completed a lexical decision and a naming task. We provide a database for 1,152 German words, comprising behavioral data from seven different stages of reading development, along with sublexical and lexical characteristics for all stimuli. The present article describes our motivation for this project, explains the methods we used to collect the data, and reports analyses on the reliability of our results. In addition, we explored developmental changes in three marker effects in psycholinguistic research: word length, word frequency, and orthographic similarity. The database is available online.Keywords Visual word recognition . Development . Mega studies . Lexical decision . NamingThere is an extensive body of research on the visual word recognition processes in skilled adults (e.g., Balota et al., 2007). On the basis of this research, several computational models have been developed that account for many of the benchmark effects observed in word processing tasks such as lexical decision (LD) or naming (e.g., Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001;Harm & Seidenberg, 2004;Perry, Ziegler, & Zorzi, 2007). Most of these models, however, only aim at explaining the reading behavior of proficient adults who have already acquired the ability to read. In recent years, some efforts have been made to bring interindividual differences into the picture (Andrews & Lo, 2012; Adelman, Sabatos-DeVito, Marquis, & Estes, 2014;Kuperman & van Dyke, 2013;Yap, Balota, Sibley, & Ratcliff, 2012). Arguably, however, the most pronounced differences between readers are intra-individual in nature: Children are not born with the ability to read but need years of extensive practice in order to learn it. And even during adulthood, profound changes take place in lexical and sublexical processing (Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004;Ratcliff, Perea, Colangelo, & Buchanan, 2004). Yet, developmental models of the visual word recognition process are still rather scarce (but see Pritchard, Coltheart, Marinus, & Castles, 2016;Ziegler, Bertrand, Lété, & Grainger, 2014). One of the main reasons for this is that very few studies have been conducted that investigate visual word recognition across the lifespan within a coherent framework. Thus, at present, the empirical data that are necessary to feed any computational modeling efforts are missing.The present article describes the Developmental Lexicon Project (DeveL), which provides a linguistic database for 1,152 German words including behavioral measures of how they are processed at different age groups across the lifespan. Extending the logic and methodology of existing mega studies on visual word recognition (Balota et al., 2007;Balota, Yap, Hutchison, & Cortese, 2012;...

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The Chinese Lexicon Project: A repository of lexical decision behavioral responses for 2,500 Chinese characters

Cited by 66 publications

References 38 publications

Worldlex: Twitter and blog word frequencies for 66 languages

Worldlex: Twitter and blog word frequencies for 66 languages

A psycholinguistic database for traditional Chinese character naming

The Developmental Lexicon Project: A behavioral database to investigate visual word recognition across the lifespan

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