Reading in Russian early schoolchildren: eye-tracking and neuropsychological data

Korneev, A A; Akhutina, Tatiana V.; Matveeva, Elena Yu.

doi:10.1016/j.ijpsycho.2018.07.298

Cited by 3 publications

(4 citation statements)

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“…All materials were taken from the child version (Korneev et al 2017) of the Russian Sentence Corpus (Laurinavichyute et al 2019) and consisted of 30 sentences that were constructed to be read by primary-school typically developing Russian-speaking children (see Example (1) for illustration). Sentences represent diverse grammatical structures typical for Russian, namely, various word orders, active and passive voice constructions and various types of relative clauses.…”

Section: Methodsmentioning

confidence: 99%

“…For all analyses, we used previously collected eye-movement data (Parshina et al 2021b) freely available on the Open Science Framework project page (https://osf.io/ek827, accessed on 25 February 2022). The participants read the same 30 sentences from the child version (Korneev et al 2017) of the Russian Sentence Corpus (Laurinavichyute et al 2019) that we used for the child norming study described in Section 2.1 above. The data came from 90 participants distributed in 3 groups, with 30 participants in each group: (1) Englishdominant HSs of Russian, (2) English-dominant L2 learners of Russian and (3) monolingual second graders (see Table 1 for participant characteristics).…”

Section: Secondary Analysis Of Eye Movements From the Previously Publ...mentioning

confidence: 99%

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Can Heritage Speakers Predict Lexical and Morphosyntactic Information in Reading?

2022

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Ample evidence suggests that monolingual adults can successfully generate lexical and morphosyntactic predictions in reading and that correct predictions facilitate sentence comprehension. In this eye-tracking corpus reading study, we investigate whether the same is true for reading in heritage language. Specifically, we ask whether heritage speakers (HSs) of Russian are able to anticipate lexical and/or morphosyntactic information of the upcoming words in the sentence and whether they differ in the predictions from monolingual children and L2 learners. We are also interested in whether the literacy level (i.e., Russian literacy experience or reading fluency in English) influences lexical and morphosyntactic prediction. Our results indicate that HSs as well as other groups were able to anticipate the specific lexical item, and the ability was contingent on the Russian literacy experience and reading fluency in dominant English as evident in some of the early and late eye-tracking measures. Similar to children and L2 learners, the word class and the verb number predictability affected reading times in HSs, but HSs were the only group to anticipate the number of the upcoming noun. We discuss findings in respect to the utility account of the bilingual prediction and divergent attainment trajectory of the heritage language development.

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Section: Methodsmentioning

confidence: 99%

Section: Secondary Analysis Of Eye Movements From the Previously Publ...mentioning

confidence: 99%

Can Heritage Speakers Predict Lexical and Morphosyntactic Information in Reading?

2022

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“…Only the right eye movements were tracked [35]. Natural reading performance was measured: the participants silently read 30 different sentences from the child's version of the Russian sentence corpus [36,37]. The selected sentences were suitable for primary school children and had diverse grammatical structures typical for the readers.…”

Section: Apparatus and Stimulimentioning

confidence: 99%

Identifying dyslexia in school pupils from eye movement and demographic data using artificial intelligence

Shalileh,

Ignatov,

Lopukhina

et al. 2023

PLoS ONE

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This paper represents our research results in the pursuit of the following objectives: (i) to introduce a novel multi-sources data set to tackle the shortcomings of the previous data sets, (ii) to propose a robust artificial intelligence-based solution to identify dyslexia in primary school pupils, (iii) to investigate our psycholinguistic knowledge by studying the importance of the features in identifying dyslexia by our best AI model. In order to achieve the first objective, we collected and annotated a new set of eye-movement-during-reading data. Furthermore, we collected demographic data, including the measure of non-verbal intelligence, to form our three data sources. Our data set is the largest eye-movement data set globally. Unlike the previously introduced binary-class data sets, it contains (A) three class labels and (B) reading speed. Concerning the second objective, we formulated the task of dyslexia prediction as regression and classification problems and scrutinized the performance of 12 classifications and eight regressions approaches. We exploited the Bayesian optimization method to fine-tune the hyperparameters of the models: and reported the average and the standard deviation of our evaluation metrics in a stratified ten-fold cross-validation. Our studies showed that multi-layer perceptron, random forest, gradient boosting, and k-nearest neighbor form the group having the most acceptable results. Moreover, we showed that although separately using each data source did not lead to accurate results, their combination led to a reliable solution. We also determined the importance of the features of our best classifier: our findings showed that the IQ, gender, and age are the top three important features; we also showed that fixation along the y-axis is more important than other fixation data. Dyslexia detection, eye fixation, eye movement, demographic, classification, regression, artificial intelligence.

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“…Thus, they signal more effortful and/or conscious processing (e.g., lexical integration in reading)" (Conklin & Pellicer-Sánchez, 2016, p. 455). They correlate with reading rate and are highly informative due to their correlation with several linguistic item characteristics (for Russian, Korneev et al, 2017;Laurinavichyute et al, 2019). Both measures are considered to be strongly related to the mental lexicon and to display processes of lexical retrieval and subsequent contextual integration (Conklin et al, 2018;Radach et al, 2012).…”

Section: Aims and Issues Of The Present Studymentioning

confidence: 99%

Bilingual vs. Monolingual Readers: Insights from Eye-Tracking Data

Krause

Ritter

2022

Journal of Home Language Research

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The present eye-tracking study examines silent and oral text reading by Russian-German bilinguals of two age groups (9--10 and 15--16 years) and compares it with their monolingual peers. The bilingual sample represents so-called heritage speakers of Russian who know both the Latin and the Cyrillic script. The paper relies on two global eye movement measures -the averaged total fixation count and the averaged total fixation duration per token -and analyses the effects of three factors: language profile, age, and reading mode. In both age groups, bilinguals made a higher number of fixations per token and fixated the token longer than their peers. This observation holds for both languages and reading modes. However, the gap between bilinguals and their peers was much larger in Russian, and interindividual variation was greater in Russian than in German. The better results in German can be explained by its status as the primary medium of instruction and its dominance in everyday literacy, but the prolonged information processing in both languages suggests the activation of both linguistic systems and their mental lexicon, even if only one language has been triggered by the task. Notably, general language command, more specifically, lexical knowledge and reading experience play a decisive role in reading acquisition and behaviour. These insights should be considered by the educational practice at school as well as by scientific research in multilingualism.

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Reading in Russian early schoolchildren: eye-tracking and neuropsychological data

Cited by 3 publications

References 0 publications

Can Heritage Speakers Predict Lexical and Morphosyntactic Information in Reading?

Can Heritage Speakers Predict Lexical and Morphosyntactic Information in Reading?

Identifying dyslexia in school pupils from eye movement and demographic data using artificial intelligence

Bilingual vs. Monolingual Readers: Insights from Eye-Tracking Data

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