Journal of Eye Movement Research

16 pages, 564 KiB

Open AccessArticle

Eye-Tracking Deaf and Hearing Viewing of Sign Language Interpreted News Broadcasts

by Jennifer Wehrmeyer

J. Eye Mov. Res. 2014, 7(1), 1-16; https://doi.org/10.16910/jemr.7.1.3 (registering DOI) - 13 Mar 2014

Cited by 27 | Viewed by 75

In this study, the viewing habits of deaf and hearing adults are investigated using eye tracking while they watched interpreted news broadcasts. The study shows that deaf viewers primarily focus on the interpreter and secondarily access picture material, but make very little use of subtitles or lip-reading. In contrast, hearing viewers prioritise pictorial content but also spend significant proportions of time examining subtitles, lip-reading and even watching the interpreter. Viewing patterns are dependent on pictorial information density rather than comprehension. The study confirms the precedence of the interpreter as primary source for deaf viewers, but also questions the efficiency of subtitling as an alternative information source for deaf viewers if an interpreter is present. Full article

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14 pages, 666 KiB

Open AccessArticle

Tracking Eye Movements When Solving Geometry Problems with Handwriting Devices

by John J. H. Lin and Sunny S. J. Lin

J. Eye Mov. Res. 2014, 7(1), 1-14; https://doi.org/10.16910/jemr.7.1.2 (registering DOI) - 28 Feb 2014

Cited by 33 | Viewed by 61

Abstract

The present study investigated the following issues: (1) whether differences are evident in the eye movement measures of successful and unsuccessful problem-solvers; (2) what is the relationship between perceived difficulty and eye movement measures; and (3) whether eye movements in various AOIs differ when solving problems. Sixty-three 11th grade students solved five geometry problems about the properties of similar triangles. A digital drawing tablet and sensitive pressure pen were used to record the responses. The results indicated that unsuccessful solvers tended to have more fixation counts, run counts, and longer dwell time on the problem area, whereas successful solvers focused more on the calculation area. In addition, fixation counts, dwell time, and run counts in the diagram area were positively correlated with the perceived difficulty, suggesting that understanding similar triangles may require translation or mental rotation. We argue that three eye movement measures (i.e., fixation counts, dwell time, and run counts) are appropriate for use in examining problem solving given that they differentiate successful from unsuccessful solvers and correlate with perceived difficulty. Furthermore, the eye-tracking technique provides objective measures of students’ cognitive load for instructional designers. Full article

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10 pages, 1917 KiB

Open AccessArticle

EyeMMV Toolbox: An Eye Movement Post-Analysis Tool Based on a Two-Step Spatial Dispersion Threshold for Fixation Identification

by Vassilios Krassanakis, Vassiliki Filippakopoulou and Byron Nakos

J. Eye Mov. Res. 2014, 7(1), 1-10; https://doi.org/10.16910/jemr.7.1.1 (registering DOI) - 21 Feb 2014

Cited by 107 | Viewed by 101

Abstract

Eye movement recordings and their analysis constitute an effective way to examine visual perception. There is a special need for the design of computer software for the performance of data analysis. The present study describes the development of a new toolbox, called EyeMMV (Eye Movements Metrics & Visualizations), for post experimental eye movement analysis. The detection of fixation events is performed with the use of an introduced algorithm based on a two-step spatial dispersion threshold. Furthermore, EyeMMV is designed to support all well-known eye tracking metrics and visualization techniques. The results of fixation identification algorithm are compared with those of an algorithm of dispersion-type with a moving window, imported in another open source analysis tool. The comparison produces outputs that are strongly correlated. The EyeMMV software is developed using the scripting language of MATLAB and the source code is distributed through GitHub under the third version of GNU General Public License (link: https://github.com/krasvas/EyeMMV). Full article

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11 pages, 1304 KiB

Open AccessArticle

Mapping the Pupil-Glint Vector to Gaze Coordinates in a Simple Video-Based Eye Tracker

by Pieter Blignaut

J. Eye Mov. Res. 2014, 7(1), 1-11; https://doi.org/10.16910/jemr.7.1.4 (registering DOI) - 15 Mar 2013

Cited by 21 | Viewed by 81

Abstract

In a video-based eye tracker, the normalized pupil-glint vector changes as the eyes move. Using an appropriate model, the pupil-glint vector can be mapped to gaze coordinates. Using a simple hardware configuration with one camera and one infrared source, several mapping functions—some from literature and some derived here—were compared with one another with respect to the accuracy that could be achieved. The study served to confirm the results of a previous study with another data set and to expand on the possibilities that are considered from the previous study. The data of various participants was examined for trends which led to derivation of a mapping model that proved to be more accurate than all but one model from literature. It was also shown that the best calibration configuration for this hardware setup is one that contains fourteen targets while taking about 20 s for the procedure to be completed. Full article

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J. Eye Mov. Res., Volume 7, Issue 1 (March 2014) – 4 articles

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