Quantifying the predictability of visual scanpaths using active information storage

TL;DR

This paper introduces a method using active information storage (AIS) to better quantify the predictability of human gaze scanpaths by capturing longer-term dependencies than traditional entropy-based measures.

Contribution

The paper proposes applying AIS to gaze data, enabling the measurement of longer-range temporal dependencies in scanpaths, which improves predictability analysis over existing methods.

Findings

AIS can distinguish different observer states based on gaze behavior

AIS captures dependencies spanning multiple fixations, unlike GTE

Proposed method enhances understanding of human gaze predictability

Abstract

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of fixation transitions. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate a scanpath's actual predictability. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes' multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human-machine interaction.