Deducing self-interaction in eye movement data using sequential spatial point processes

TL;DR

This paper introduces statistical models for analyzing eye movement sequences using sequential spatial point processes, capturing heterogeneity, contextuality, and self-interaction to improve understanding of gaze behavior.

Contribution

It proposes two novel inhomogeneous self-interacting random walk models for eye movement data, incorporating history-dependent transition rejection and kernel-based penalization.

Findings

Models effectively capture self-interaction in eye movements.

Likelihood-based inference enables uncertainty quantification.

Experiments demonstrate the models' applicability to real data.

Abstract

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 man-machine interface planning. 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.