Dataset Information

Human and computer estimations of Predictability of words in written language.

ABSTRACT: When we read printed text, we are continuously predicting upcoming words to integrate information and guide future eye movements. Thus, the Predictability of a given word has become one of the most important variables when explaining human behaviour and information processing during reading. In parallel, the Natural Language Processing (NLP) field evolved by developing a wide variety of applications. Here, we show that using different word embeddings techniques (like Latent Semantic Analysis, Word2Vec, and FastText) and N-gram-based language models we were able to estimate how humans predict words (cloze-task Predictability) and how to better understand eye movements in long Spanish texts. Both types of models partially captured aspects of predictability. On the one hand, our N-gram model performed well when added as a replacement for the cloze-task Predictability of the fixated word. On the other hand, word embeddings were useful to mimic Predictability of the following word. Our study joins efforts from neurolinguistic and NLP fields to understand human information processing during reading to potentially improve NLP algorithms.

SUBMITTER: Bianchi B

PROVIDER: S-EPMC7064512 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Human and computer estimations of Predictability of words in written language.

Bianchi Bruno B Bengolea Monzón Gastón G Ferrer Luciana L Fernández Slezak Diego D Shalom Diego E DE Kamienkowski Juan E JE

Scientific reports 20200310 1

When we read printed text, we are continuously predicting upcoming words to integrate information and guide future eye movements. Thus, the Predictability of a given word has become one of the most important variables when explaining human behaviour and information processing during reading. In parallel, the Natural Language Processing (NLP) field evolved by developing a wide variety of applications. Here, we show that using different word embeddings techniques (like Latent Semantic Analysis, Wo ...[more]

PMID: 32157161

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Project description:INTRODUCTION:It is well known that a difficulty in forming lexical representations is a strong predictor of reading and spelling difficulties even after controlling for the effects of other cognitive skills. Our study had two main interrelated aims. First, we wanted to examine whether the ability to learn new written words (lexical learning) varies as a function of the orthographic consistency of the language of the learner. Second, we wanted to evaluate the cognitive abilities involved in orthographic lexical learning and whether they differed as a function of language consistency. METHOD:163 Italian children and 128 English children performed a lexical learning task as well as tasks assessing several cognitive skills potentially related to the ability to establish orthographic representations. RESULTS:We found that children learning an orthographic inconsistent orthography (English) were better able to learn novel written words presented in association with pictures than children learning a consistent orthography (Italian). This was true for both younger and older primary school children and also when children were matched for school grade. Lexical learning may be better in English children because the many irregularities of this language promote storing in memory whole-word representations and processing larger orthographic units. In Italian, instead, reading can be accomplished successfully on the basis of grapheme-phoneme conversion rules and on processing smaller orthographic units. This interpretation was supported by the pattern of cognitive skills associated with lexical learning skills in the two languages. Variations in lexical learning were explained by spatial visual memory and phonological awareness tasks in both languages, but phonological STM explained further variance in Italian, while a task tapping visuo-attentional capacity explained further variance in English. CONCLUSION:Learning a language with inconsistent orthography is associated with better lexical learning skills in children at different stages of primary school; the pattern of cognitive skills associated with lexical learning skills is also partially modulated by orthographic consistency.

Dataset Information

Human and computer estimations of Predictability of words in written language.

Publications

Human and computer estimations of Predictability of words in written language.

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