The database of eye-movement measures on words in Chinese reading

Abstract

Eye movements are one of the most fundamental behaviors during reading. A growing number of Chinese reading studies have used eye-tracking techniques in the last two decades. The accumulated data provide a rich resource that can reflect the complex cognitive mechanisms underlying Chinese reading. This article reports a database of eye-movement measures of words during Chinese sentence reading. The database contains nine eye-movement measures of 8,551 Chinese words obtained from 1,718 participants across 57 Chinese sentence reading experiments. All data were collected in the same experimental environment and from homogenous participants, using the same protocols and parameters. This database enables researchers to test their theoretical or computational hypotheses concerning Chinese reading efficiently using a large number of words. The database can also indicate the processing difficulty of Chinese words during text reading, thus providing a way to control or manipulate the difficulty level of Chinese texts.

An interpretable measure of semantic similarity for predicting eye movements in reading

“…For example, we used these semantic similarity metrics to predict the newly released large-scale eye-movement data on reading Chinese sentences (Zhang, Yao, & et al 2022 ), and achieved similar results to English. Moreover, after the same semantic metrics having been applied in predicting the eye-movement data on reading in 13 languages (Siegelman et al, 2022 ), we received the same results.…”

An interpretable semantic similarity for predicting eye movements in reading: Approach comparison

“…This study used data from the dataset of eye-movement measures on words in Chinese reading, which is a corpus of eye-tracking data that includes predictability norms (Zhang et al, 2022). There are other several reasons why this corpus was chosen.…”

“…Additionally, to facilitate a comprehensive comparison, we computed "word surprisal" for the stimuli text for the corpus of eye-tracking data on Chinese reading (Zhang et al, 2022). The two state-of-the-art large language models (LLMs) were taken to estimate word surprisal: Chinese BERT (Cui et al, 2021) and multilingual GPT (Shliazhko et al, 2022).…”

“…However, as for Chinese reading, we believe that "word stroke count" is taken as a control predictor better than "word length". Using the eye-movement measures on words in Chinese reading (Zhang et al, 2022), we applied GAMM fittings to compare which factor performs better in impacting on Chinese word reading, and the result shows that " word stroke count" remarkably outperformed "word length". The present study took "word stroke count" as a control predictor in GAMM fittings.…”

“…We are interested in exploring whether stroke count of word may help better predict the word processing in reading Chinese naturalistic discourse, compared to stroke count of character and word length. To this end, we fitted three groups of GAMM models (nine models) to analyze the effect of stroke count of word as predictors of three dependent variables first duration, gaze duration and total duration from the same eye-movement dataset on Chinese reading (Zhang et al, 2022). The first group uses the GAMM fittings including word stroke count and log(word frequency), as well as experiment as a random effect.…”