2023

Ylitalo, Anna-Kaisa

In this dissertation we employ point process statistics for eye tracking data in order to understand its spatial nature together with the temporal dynamics. The emphasis is in statistical inference on eye movements using existing point process statistics along with the new methods and models. The objective is to get understanding of eye movements as a temporally evolving process in space in four steps: first, secondorder characteristics of point processes are applied to describe features of the process. Second, new functional summary statistics are developed in order to evaluate the temporal nature of the eye movements. Third, likelihood-based modelling is used to assess the uncertainty related to these data summaries. Fourth, the developed models are used both for group comparisons and for distinguishing components in an eye movement sequence. The empirical results of this dissertation give new information on visual processing of paintings. The thesis is available at: http://urn.fi/URN:ISBN:978-951-39-7064-2