arXiv subjects

Timothy J. Slattery

Publications and source records attributed to Timothy J. Slattery.

3 recordsLinked to original sources

arXiv2024

GazeGenie: Enhancing Multi-Line Reading Research with an Innovative User-Friendly Tool

In the study of reading, eye-tracking technology offers unique insights into the time-course of how individuals extract information from text. A significant hurdle in using multi-line paragraph stimuli is the need to align eye gaze position with the correct line. This is made more difficult by positional noise in the eye-tracking data, primarily due to vertical drift, and often necessitates manual intervention. Such manual correction is labor-intensive, subjective, and limits the scalability of research efforts. As a result, automated solutions are desirable, especially those that do not require extensive technical skills and still allow close control over the outcome. To address this, we introduce GazeGenie: a comprehensive software solution designed specifically for researchers in eye-tracking studies on multi-line reading. Accessible via an intuitive web browser-based user interface and easily installed using Docker, GazeGenie streamlines the entire data processing pipeline from parsing fixations from raw data to calculation of word and sentence-based measures based on cleaned and drift-corrected fixations. The software's core features include the recently introduced Dual Input Stream Transformer (DIST) model and various classical algorithms all of which can be combined within a Wisdom of the Crowds (WOC) approach to enhance accuracy in fixation line-assignment. By providing an all-in-one solution for researchers, we hope to make automated fixation alignment more accessible, reducing researchers' reliance on manual intervention in vertical fixation alignment. This should lead to more accurate, efficient, and reproducible analyses of multi-line eye-movement data and pave the way to enabling larger scale studies to be carried out.

cs.HC↗

arXiv2023

Dual input stream transformer for vertical drift correction in eye-tracking reading data

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against eleven classical approaches on a comprehensive suite of nine diverse datasets. We demonstrate that combining multiple instances of the DIST model in an ensemble achieves high accuracy across all datasets. Further combining the DIST ensemble with the best classical approach yields an average accuracy of 98.17 %. Our approach presents a significant step towards addressing the bottleneck of manual line assignment in reading research. Through extensive analysis and ablation studies, we identify key factors that contribute to DIST's success, including the incorporation of line overlap features and the use of a second input stream. Via rigorous evaluation, we demonstrate that DIST is robust to various experimental setups, making it a safe first choice for practitioners in the field.

cs.CV↗

arXiv2021

Do readers use character information when programming return-sweep saccades?

Reading saccades that occur within a single line of text are guided by the size of letters. However, readers occasionally need to make longer saccades (known as return-sweeps) that take their eyes from the end of one line of text to the beginning of the next. In this study, we tested whether return-sweep saccades are also guided by font size information and whether this guidance depends on visual acuity of the return-sweep target area. To do this, we manipulated the font size of letters (0.29 vs 0.39 deg. per character) and the length of the first line of text (16 vs 26 deg.). The larger font resulted in return-sweeps that landed further to the right of the line start and in a reduction of under-sweeps compared to the smaller font. This suggests that font size information is used when programming return-sweeps. Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of under-sweeps increased compared to the short line condition. This likely reflects an increase in saccadic undershoot error with the increase in intended saccade size. Critically, there was no interaction between font size and line length. This suggests that when programming return-sweeps, the use of font size information does not depend on visual acuity at the saccade target. Instead, it appears that readers rely on global typographic properties of the text in order to maintain an optimal number of characters to the left of their first fixation on a new line.

q-bio.NC↗