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How reliable is eye movement data?

Mulvey, Fiona LU ; Pelz, Jeff ; Simpson, Sol ; Cleveland, Dixon ; Wang, Dong ; Latorella, Kara ; Joos, Markus ; Borah, Josh ; Mulligan, Jeff and Morimoto, Carlos , et al. (2018) American Association for Applied Linguistics conference, 2018
Abstract
Eye data quality varies greatly with the eyetracking system, the individual characteristics of participants, the recording environment, and the operator. The quality of the signal has important repercussions for which eye movement measures are valid, and what conclusions can be drawn from them. Reading research often has high demands in terms of spatial precision and accuracy. Reliably detecting events (e.g., fixations, saccades, pursuit, blinks) depends strongly on intrinsic instrument noise and the correct application of parsing algorithms. We define standard measures of data quality across commercial systems and screen area, as well as system robustness to individual variation, from a large data collection. We show differences in the... (More)
Eye data quality varies greatly with the eyetracking system, the individual characteristics of participants, the recording environment, and the operator. The quality of the signal has important repercussions for which eye movement measures are valid, and what conclusions can be drawn from them. Reading research often has high demands in terms of spatial precision and accuracy. Reliably detecting events (e.g., fixations, saccades, pursuit, blinks) depends strongly on intrinsic instrument noise and the correct application of parsing algorithms. We define standard measures of data quality across commercial systems and screen area, as well as system robustness to individual variation, from a large data collection. We show differences in the results from parsing algorithms with varying eye data quality, and infer predictive models of data quality as a function of employed systems, operator, and characteristics of the recorded eye across 12 tower mounted and remote eyetracking systems, and 194 participants. We discuss these results in terms of stimulus constraints in reading research. This work is output of the Eye Data Quality Standardization Committee – a collaboration of manufacturers and researchers towards unbiased measures, and the provision of open-source tools for transparency of eye data quality. (Less)
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organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
American Association for Applied Linguistics conference, 2018
conference location
Chicago, United States
conference dates
2018-03-23 - 2018-03-27
project
Eye Data Quality Standardisation
language
English
LU publication?
yes
id
f000fd9d-6bac-4b92-b800-a737ee48e267
date added to LUP
2018-06-01 17:15:41
date last changed
2018-11-21 21:40:08
@misc{f000fd9d-6bac-4b92-b800-a737ee48e267,
  abstract     = {{Eye data quality varies greatly with the eyetracking system, the individual characteristics of participants, the recording environment, and the operator. The quality of the signal has important repercussions for which eye movement measures are valid, and what conclusions can be drawn from them. Reading research often has high demands in terms of spatial precision and accuracy. Reliably detecting events (e.g., fixations, saccades, pursuit, blinks) depends strongly on intrinsic instrument noise and the correct application of parsing algorithms. We define standard measures of data quality across commercial systems and screen area, as well as system robustness to individual variation, from a large data collection. We show differences in the results from parsing algorithms with varying eye data quality, and infer predictive models of data quality as a function of employed systems, operator, and characteristics of the recorded eye across 12 tower mounted and remote eyetracking systems, and 194 participants. We discuss these results in terms of stimulus constraints in reading research. This work is output of the Eye Data Quality Standardization Committee – a collaboration of manufacturers and researchers towards unbiased measures, and the provision of open-source tools for transparency of eye data quality.}},
  author       = {{Mulvey, Fiona and Pelz, Jeff and Simpson, Sol and Cleveland, Dixon and Wang, Dong and Latorella, Kara and Joos, Markus and Borah, Josh and Mulligan, Jeff and Morimoto, Carlos and Babcock, Jason and Mardanbegi, Diako and Hayhoe, Mary}},
  language     = {{eng}},
  month        = {{03}},
  title        = {{How reliable is eye movement data?}},
  year         = {{2018}},
}