A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Penttinen, Antti; Ylitalo, Anna-Kaisa

Lehti tai sarja: Spatial Statistics

ISSN: 2211-6753

eISSN: 2211-6753

Julkaisuvuosi: 2016

Volyymi: 17

Lehden numero: 0

Artikkelin sivunumerot: 1-21

Kustantaja: Elsevier BV

Kustannuspaikka: Amsterdam

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/49995

Tiivistelmä

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.

YSO-asiasanat: katse; silmänliikkeet; katseenseuranta; mallintaminen; tietojärjestelmät; stokastiset prosessit

Vapaat asiasanat:  coverage; heterogeneous media; likelihood; self-interacting random walk; stochastic geometry

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2016

JUFO-taso: 1