Literature DB >> 28523601

The Provo Corpus: A large eye-tracking corpus with predictability norms.

Steven G Luke1, Kiel Christianson2,3.   

Abstract

This article presents the Provo Corpus, a corpus of eye-tracking data with accompanying predictability norms. The predictability norms for the Provo Corpus differ from those of other corpora. In addition to traditional cloze scores that estimate the predictability of the full orthographic form of each word, the Provo Corpus also includes measures of the predictability of the morpho-syntactic and semantic information for each word. This makes the Provo Corpus ideal for studying predictive processes in reading. Some analyses using these data have previously been reported elsewhere (Luke & Christianson, 2016). The Provo Corpus is available for download on the Open Science Framework, at https://osf.io/sjefs .

Keywords:  Corpus study; Eyetracking; Predictability; Reading

Mesh:

Year:  2018        PMID: 28523601     DOI: 10.3758/s13428-017-0908-4

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  10 in total

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2.  GECO-CN: Ghent Eye-tracking COrpus of sentence reading for Chinese-English bilinguals.

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3.  Prediction as a basis for skilled reading: insights from modern language models.

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4.  The Beijing Sentence Corpus: A Chinese sentence corpus with eye movement data and predictability norms.

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Journal:  Behav Res Methods       Date:  2021-11-23

5.  Predicting eye-movement characteristics across multiple tasks from working memory and executive control.

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6.  Return sweeps in reading: Processing implications of undersweep-fixations.

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Journal:  Psychon Bull Rev       Date:  2019-12

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Journal:  Sci Data       Date:  2022-07-15       Impact factor: 8.501

8.  RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese.

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9.  Expanding horizons of cross-linguistic research on reading: The Multilingual Eye-movement Corpus (MECO).

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Journal:  Behav Res Methods       Date:  2022-02-02

10.  Algorithms for the automated correction of vertical drift in eye-tracking data.

Authors:  Jon W Carr; Valentina N Pescuma; Michele Furlan; Maria Ktori; Davide Crepaldi
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  10 in total

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