Eye tracking is a widely used method for recording eye movements, which are important
indicators of ongoing cognitive processes during the viewing of a target stimulus. Despite
the variety of applications, the analyses of eye movement data have been lacking of methods
that could take both the spatial and temporal information into account. So far, most of
the analyses are based on strongly aggregated measures, because eye movement data are
considered to be complex due to their richness and large variation between and within the
individuals. Therefore, the eye movement methodology needs new statistical tools in order to
take full advantage of the data.
This dissertation is among the first studies to employ point process statistics for eye movement data in order to understand its spatial nature together with the temporal dynamics.
Here, we consider eye movements as a realisation of a spatio-temporal point process. The emphasis is in statistical inference on eye movements using ex
isting point process statistics along
with the new methods and models introduced in this work. Our aim is to get understanding
of eye movements as a temporally evolving process in space. This objective is achieved in four
steps: First, we apply the second-order characteristics of point processes to describe features
of the process. Second, we develop new functional summary statistics in order to evaluate the
temporal nature of the eye movements. Third, we use likelihood-based modelling 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. We find evidence that the viewing process of one subject changes during the
inspection of the painting being an indication of learning. The behaviour of this learning
effect, however, varies between the individuals. We also study differences between novices
and non-novices in art viewing by comparing where they look at and for how long the gaze
typically stops. The latter distinguishes the two groups, whereas the former reveals minor
differences that are not statistically significant. Altogether, we hope that our results encourage
researchers to pay more attention to temporal dynamics in eye movement data, as well as to
the inevitable variation in the individual level.
The spatio-temporal analysis of eye movements presented here is novel and covers a wide
range of methods from functional summary statistics to the likelihood-based modelling. The
methods and tools presented are applicable to other eye movement data collected in a freeviewing condition, but we believe that the developed models, being rather simple but flexible,
could also be useful for the analysis of spatio-temporal sequences outside the field.
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