Modelling fixation locations using spatial point processes

TL;DR

This paper advocates modeling eye fixation locations as spatial point processes, especially Poisson processes, to quantitatively analyze how image features influence fixation patterns across different images.

Contribution

It introduces a spatial point process framework for eye movement analysis, connecting image features with fixation locations and extending existing qualitative methods.

Findings

Point processes effectively model fixation spatial distributions.

Image feature predictability varies across images and can be quantified.

The framework clarifies and extends traditional analysis methods.

Abstract

Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate, and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space: this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.