Cuentos: A Large-Scale Eye-Tracking Reading Corpus on Spanish Narrative Texts

TL;DR

This paper introduces a large Spanish eye-tracking dataset to study how native speakers read narrative texts.

Contribution

The novel contribution is the creation of the largest publicly available Spanish eye-tracking reading corpus.

Findings

The dataset includes over 940,000 fixations from 113 native Spanish speakers.

It covers both long and short stories with extensive word and fixation coverage.

The resource enables research on Spanish-specific reading patterns and NLP applications.

Abstract

Eye-tracking is a well-established method for studying reading processes. Our gaze jumps word to word, sampling information almost sequentially. Time spent on each word, along with skipping or revisiting patterns, provides proxies for cognitive processes during comprehension. However, few studies have focused on Spanish, where empirical data remain scarce, and little is known about how findings from other languages translate to Spanish reading behavior. We present the largest publicly available Spanish eye-tracking dataset to date, comprising readings of self-contained stories from 113 native speakers (mean age 23.8; 61 females, 52 males). The dataset comprises both long stories (3300 ± 747 words, 11 readings per item on average) and short stories (795 ± 135 words, 50 readings per item on average), providing extensive coverage of natural reading scenarios with over 940,000 fixations covering close to 40,000 words (8,500 unique words). This comprehensive resource offers opportunities to investigate Spanish eye movement patterns, explore language-specific cognitive processes, examine Spanish linguistic phenomena, and develop computational algorithms for reading research and natural language processing applications.

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