Predicting artificial visual field losses: a gaze-based inference study

Erwan David 1 Pierre Lebranchu 1 Matthieu Perreira da Silva 1 Patrick Le Callet 1
1 IPI - Image Perception Interaction
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Visual field defects are a world-wide concern, and the proportion of the population experiencing vision loss is ever increasing. Macular degeneration and glaucoma are among the four leading causes of permanent vision loss. Identifying and characterising visual field losses from gaze alone could prove crucial in the future for screening tests, rehabilitation therapies and monitoring. In this experiment, 54 participants took part in a free-viewing task of visual scenes while experiencing artificial scotomas (central and peripheral) of varying radii in a gaze-contingent paradigm. We studied the importance of a set of gaze features as predictors to best differentiate between artificial scotoma conditions. Linear mixed models were utilised to measure differences between scotoma conditions. Correlation and factorial analyses revealed redundancies in our data. Finally, hidden Markov models and recurrent neural networks were implemented as classifiers in order to measure the predictive usefulness of gaze features. The results show separate saccade direction biases depending on scotoma type. We demonstrate that the saccade relative angle, amplitude and peak velocity of saccades are the best features on the basis of which to distinguish between artificial scotomas in a free-viewing task. Finally, we discuss the usefulness of our protocol and analyses as a gaze-feature identifier tool that discriminates between artificial scotomas of different types and sizes.
Document type :
Journal articles
Complete list of metadatas
Contributor : Erwan David <>
Submitted on : Monday, September 16, 2019 - 1:47:47 PM
Last modification on : Tuesday, September 17, 2019 - 1:18:21 AM


  • HAL Id : hal-02289190, version 1



Erwan David, Pierre Lebranchu, Matthieu Perreira da Silva, Patrick Le Callet. Predicting artificial visual field losses: a gaze-based inference study. Journal of Vision, Association for Research in Vision and Ophthalmology, In press. ⟨hal-02289190⟩



Record views