A psycholinguistic database for traditional Chinese character naming

Abstract

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

Exploring orthographic neighborhood size effects in a computational model of Chinese character naming

“…Note that in Chinese, the syllable structure is relatively simple and most Chinese syllables can be mapped onto more than one character so the number of phonological neighbors for a given character is generally rather large. Between-items oneway ANOVA analyses confirmed that the condition groups did not differ significantly on any 1 According to the Chinese naming database by Chang et al (2016), the frequency scores in Chinese range from 1 to 60158 per million. To categorize the frequency scores into five levels (very low, low, medium, high and very high), we computed the percentiles of the frequency distribution in steps of 20.…”

“…For both naming latencies and accuracy data, the significance of individual and interaction factors was assessed using a likelihood ratio test to determine whether the model fit changed significantly between models with and without the factor or interaction of interest. To control for potential onset effects caused by the sensitivity of voice key to different onsets (Balota et al, 2004;Chang et al 2016;Liu et al, 2007), the initial phonemes were also included as a fixed effect in the linear mixed effects models. Following Balota et al (2004) both the random and main effects were included.…”

“…In contrast, Chinese has a deep orthographic system where the spelling-to-sound information is less reliable. According to the Chinese naming database by Chang et al (2016), about 89% of Chinese characters have pronounceable phonetic radicals.…”

“…of those measures all ps > .05. The measures of all the lexical factors used in stimulus selection were taken from or computed based on an online psycholinguistic naming database (Chang, Hsu, Tsai, Chen, & Lee, 2016). Table 1 shows the descriptive statistics along with a representative example of characters in each condition.…”

“…The initial weights were set to random values ranging from -0.1 to 0.1. The frequency of each character was taken from psycholinguistic norms (Chang et al, 2016), and the score was compressed by using a square-root function with a cutoff frequency of 2000. The frequency of character presentation was implemented by scaling the error derivatives during the backpropagation procedure based on the compressed frequency (Plaut et al, 1996).…”