A1 Journal article (refereed)
Deducing self-interaction in eye movement data using sequential spatial point processes (2016)

Penttinen, A., & Ylitalo, A.-K. (2016). Deducing self-interaction in eye movement data using sequential spatial point processes. Spatial Statistics, 17, 1-21. https://doi.org/10.1016/j.spasta.2016.03.005

JYU authors or editors

Publication details

All authors or editors: Penttinen, Antti; Ylitalo, Anna-Kaisa

Journal or series: Spatial Statistics

ISSN: 2211-6753

eISSN: 2211-6753

Publication year: 2016

Volume: 17

Issue number: 0

Pages range: 1-21

Publisher: Elsevier BV

Place of Publication: Amsterdam

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.spasta.2016.03.005

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/49995

Abstract

Eye movement data are outputs of an analyser tracking the gaze when a person is inspecting a scene. These kind of data are of increasing importance in scientific research as well as in applications, e.g. in marketing and human-computer interface design. Thus the new areas of application call for advanced analysis tools. Our research objective is to suggest statistical modelling of eye movement sequences using sequential spatial point processes, which decomposes the variation in data into structural components having interpretation. We consider three elements of an eye movement sequence: heterogeneity of the target space, contextuality between subsequent movements, and time-dependent behaviour describing self-interaction. We propose two model constructions. One is based on the history-dependent rejection of transitions in a random walk and the other makes use of a history-adapted kernel function penalized by user-defined geometric model characteristics. Both models are inhomogeneous self-interacting random walks. Statistical inference based on the likelihood is suggested, some experiments are carried out, and the models are used for determining the uncertainty of important data summaries for eye movement data.

Keywords: gaze; eye movements; eye tracking; modelling (representation); data systems; stochastic processes

Free keywords: coverage; heterogeneous media; likelihood; self-interacting random walk; stochastic geometry

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Related projects

Ministry reporting: Yes

Reporting Year: 2016

JUFO rating: 1