The dataset is now available in Figshare!
Eye-tracking is a well-established methodology to study reading processes. Our gaze jumps from word to word, sampling information from text almost sequentially. The time spent on each word, along with patterns of skipping or revisiting, provides proxies for different 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 includes both long stories (3300 ± 747 words, 11 participants per item on average) and short stories (795 ± 135 words, 50 participants per item on average), providing extensive coverage of natural reading scenarios. This comprehensive resource offers valuable opportunities to investigate eye movement patterns during Spanish reading, explore language-specific cognitive processes, examine Spanish linguistic phenomena, and develop computational algorithms for reading research and natural language processing applications.
A word is defined as a sequence of characters between two blank spaces, except those characters that correspond to punctuation signs.
- Unfrequent word: its number of appearances in the latinamerican subtitles database from EsPal is less or equal to 100.
- Short sentence: less or equal to 5 words.
- Long sentence: greater or equal to 30 words.
- Long word: greater or equal to 10 chars.
- Unfrequent characters: ¿; ?; ¡; !; “; ”; —; «; (; ).
Twenty and ten self-contained short (avg. 795 (± 135), min. 680, max. 1220 words) and long (avg. 3300 (± 147), min. 1975, max. 4640 words) stories written in Latin American Spanish were selected. From the twenty short stories, five were extracted from online Argentinian blog posts, and the other fifteen were extracted from “100 covers de cuentos clásicos” (Casciari, 2021). These constitute classic stories that were simplified, translated (if needed) and re-written in Spanish by Hernán Casciari. The goal was to obtain diversity in literary style while maintaining both difficulty and slang constant. The long stories were extracted from diverse sources and were selected mainly due to their use in other eye-tracking during reading experiments.
- Minimize dialogue.
- Minimize short and long sentences.
- Minimize unfrequent words and characters.
- Self-contained.
- No written dates.
- Not shorter than four hundred words.
- Not longer than two thousand words.
There is a correlation between minimizing dialogues and minimizing unfrequent characters, as dialogues are usually characterized by such.
- Stimuli creation for the short stories (see
metadata/stimuli_config.mat):- Resolution: 1080x1920
- Font: Courier New. Size: 24. Color: black.
- Background color: grey.
- Linespacing: 55px.
- Max lines per screen: 14.
- Max chars per line: 99.
- Left margin: 280px.
- Top margin: 185px.
- Stimuli creation for the long stories:
- Resolution: 768x1020
- Font: Courier New. Size: 18. Color: black.
- Background color: grey.
- Linespacing: 50px.
- Max lines per screen: 10.
- Max chars per line: 99.
- Left margin: 150px.
- Top margin: 120px.
- The participant is told that, after reading each text, he/she will be evaluated with three comprehensive questions.
- Their reading skills are also inquired (short-stories only).
- Items are ordered pseudo-randomly for each participant, ensuring a similar number of trials per story.
- Once an item has been read, comprehension questions are answered.
- Additionally, unique common nouns are displayed (one by one) and the participant is asked to write the first word that comes to mind (short-stories only).
- The following item is displayed by pressing a button.
- Each item is a block. After each block, a one-minute break and eye-tracker calibration follows.
- Eye-tracker calibration is validated by the presentation of points positioned in the corners of where stimuli is displayed.
The experiment is coded in MATLAB 2015a using the Psychophysics Toolbox (http://psychtoolbox.org/). It is launched by running run_experiment.m.
Data processing was carried out entirely in Python 3.10. Necessary packages are listed in requirements.txt. There are four distinct steps:
- Data extraction: Raw EDF data was converted to ASCII using EDF2ASC version 4.2.762.0 Linux from the EyeLink Display Software.
- Data cleaning: Trials were manually inspected, where horizontal lines are drawn for delimiting text lines and fixations were corrected when needed (
edit_trial.py). Very short (50ms) and very long (1000ms) fixations were discarded in this step. - Fixation assignment: Fixations were assigned to words, using blank spaces as delimiters (
scripts/data_processing/assign_fixations.py). Return sweeps were discarded in this step. - Measures extraction: Eye-tracking measures (early, intermediate and late) were computed for each word, except the first and last words of each line or those following or preceding punctuation marks (
scripts/data_processing/extract_measures.py). The measures were:- Early measures: First fixation duration (FFD); single fixation duration (SFD); first pass reading time/gaze duration (FPRT); likelihood of skipping (LS).
- Intermediate measures: Regression path duration (RPD); regression rate (RR).
- Late measures: Total fixation duration (TFD); re-reading time (RRT); second pass reading time (SPRT); fixation count (FC); regression count (RC).
Data analysis consists of printing overall stats per trial, plotting several early measures as a function of known effects (i.e., word length and frequency) and performing mixed effects models analysis with such fixed effects (em_analysis.py). To run the Linear Mixed Models analysis, R must be installed with the following packages: lme4, lmerTest, and emmeans (see pymer4 installation).
This script also takes care of steps 3 and 4 of data processing by calling the corresponding functions from the aforementioned files.