Fiona Mulvey

Eye tracker data quality: what it is and how to measure it

Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported.

A study of artificial eyes for the measurement of precision in eye-trackers

The precision of an eye-tracker is critical to the correct identification of eye movements and their properties. To measure a system’s precision, artificial eyes (AEs) are often used, to exclude eye movements influencing the measurements. A possible issue, however, is that it is virtually impossible to construct AEs with sufficient complexity to fully represent the human eye. To examine the consequences of this limitation, we tested currently used AEs from three manufacturers of eye-trackers and compared them to a more complex model, using 12 commercial eye-trackers. Because precision can be measured in various ways, we compared different metrics in the spatial domain and analyzed the power-spectral densities in the frequency domain. To assess how precision measurements compare in artificial and human eyes, we also measured precision using human recordings on the same eye-trackers. Our results show that the modified eye model presented can cope with all eye-trackers tested and acts as a promising candidate for further development of a set of AEs with varying pupil size and pupil–iris contrast. The spectral analysis of both the AE and human data revealed that human eye data have different frequencies that likely reflect the physiological characteristics of human eye movements. We also report the effects of sample selection methods for precision calculations. This study is part of the EMRA/COGAIN Eye Data Quality Standardization Project.

Is visual content in textual search interfaces beneficial to dyslexic users

Dyslexia is a learning disability characterised by problems with accurate or fluent word recognition, poor decoding, and poor spelling abilities. Although several studies have addressed dyslexia and Web accessibility, less is known about how dyslexia affects information search. This study investigated whether the inclusion of icons in search user interfaces enhances performance among dyslexics. A total of 21 dyslexics and 21 controls completed 52 search tasks in 4 conditions: icons only, words only, and both icons and words in a grid layout and a list layout, while eye movements were recorded. Dyslexics took significantly longer than controls to locate targets in tasks containing text, but not in the icon-only condition. Dyslexics had longer fixation durations than controls in both icon and text based search arrays, suggesting higher mental load associated with search tasks generally. The addition of words to icon arrays led to faster search times within controls, but not dyslexics. Dyslexics also exhibited more fixations on dual-modality tasks, and longer scanpaths than controls in list layout. Both groups were fastest searching the list layout, with icons and words listed in columns. Results are discussed in terms of the design of accessible search interfaces for dyslexic users, taking into account mental load of dual-modality information display, and the arrangement of search items. Empirical data is provided for the design of accessible search results interfaces for dyslexics. HighlightsDyslexics are generally slower in visual search arrays containing text.Including icons in arrays seems distracting for both dyslexics and controls.Including icons in lists seems beneficial to performance generally.Content mode and layout interact to effect search performance in dyslexia.

Characterization and reconstruction of VOG noise with power spectral density analysis

Characterizing noise in eye movement data is important for data analysis, as well as for the comparison of research results across systems. We present a method that characterizes and reconstructs the noise in eye movement data from video-oculography (VOG) systems taking into account the uneven sampling in real recordings due to track loss and inherent system features. The proposed method extends the Lomb-Scargle periodogram, which is used for the estimation of the power spectral density (PSD) of unevenly sampled data [Hocke and Kämpfer 2009]. We estimate the PSD of fixational eye movement data and reconstruct the noise by applying a random phase to the inverse Fourier transform so that the reconstructed signal retains the amplitude of the original noise at each frequency. We apply this method to the EMRA/COGAIN Eye Data Quality Standardization projects dataset, which includes recordings from 11 commercially available VOG systems and a Dual Pukinje Image (DPI) eye tracker. The reconstructed noise from each VOG system was superimposed onto the DPI data and the resulting eye movement measures from the same original behaviors were compared.