Deducing self-interaction in eye movement data using sequential spatial point processes
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
Published inSpatial Statistics
© 2016 Elsevier B.V. This is a final draft version of an article whose final and definitive form has been published by Elsevier. Published in this repository with the kind permission of the publisher.
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. ...
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThe second author has been financially supported by the Finnish Doctoral Programme in Stochastic and Statistics and by the Academy of Finland (Project number 275929).
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