Quantifying the predictability of visual scanpaths using active information storage
arXiv:2012.11447 · doi:10.3390/e23020167
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.
19 pages, 3 figures
References in corpus (5)
- Estimating Mutual Information
- Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction
- Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
- IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
- Breakdown of local information processing may underlie isoflurane anesthesia effects