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

Wang, Dong; Mulvey, Fiona B. LU ; Pelz, Jeff B. and Holmqvist, Kenneth LU (2016) In Behavior Research Methods
Abstract

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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Artificial eye, Data quality, Eye movements, Eye-tracker noise, Power-spectral density, Precision
in
Behavior Research Methods
pages
13 pages
publisher
The Psychonomic Society
external identifiers
  • scopus:84978062591
  • wos:000404245700010
ISSN
1554-351X
DOI
10.3758/s13428-016-0755-8
language
English
LU publication?
yes
id
681d0ed1-467b-4872-ab83-7cb4c5a65d5f
date added to LUP
2017-01-12 14:25:38
date last changed
2017-09-18 11:36:34
@article{681d0ed1-467b-4872-ab83-7cb4c5a65d5f,
  abstract     = {<p>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.</p>},
  author       = {Wang, Dong and Mulvey, Fiona B. and Pelz, Jeff B. and Holmqvist, Kenneth},
  issn         = {1554-351X},
  keyword      = {Artificial eye,Data quality,Eye movements,Eye-tracker noise,Power-spectral density,Precision},
  language     = {eng},
  month        = {07},
  pages        = {13},
  publisher    = {The Psychonomic Society},
  series       = {Behavior Research Methods},
  title        = {A study of artificial eyes for the measurement of precision in eye-trackers},
  url          = {http://dx.doi.org/10.3758/s13428-016-0755-8},
  year         = {2016},
}