Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features

Yarbus' pioneering work in eye tracking has been influential to methodology and in demonstrating the apparent importance of task in eliciting different fixation patterns. There has been renewed interest in Yarbus' assertions on the importance of task in recent years, driven in part by a greater capability to apply quantitative methods to fixation data analysis. A number of recent research efforts have examined the work has raised a number of interesting questions, with some investigations calling for closer examination of the validity of Yarbus' claims, and subsequent efforts revealing some of the nuances involved in carrying out this type of analysis including both methodological, and data related considerations. In this paper, we present an overview of prior efforts in task prediction, and assess different types of statistics drawn from fixation data, or images in their ability to predict task from gaze. We also examine the extent to which relatively general task definitions (free-viewing, object-search, saliency-viewing, explicit saliency) may be predicted by spatial positioning of fixations, features co-located with fixation points, fixation dynamics and scene structure. This is accomplished in considering the data of Koehler et al. (2014) [30] affording a larger scale, and qualitatively different corpus of data for task prediction relative to existing efforts. Based on this analysis, we demonstrate that both spatial position, as well as local features are of value in distinguishing general task categories. The methods proposed provide a general framework for highlighting features that distinguish behavioural differences observed across visual tasks, and we relate new task prediction results in this paper to the body of prior work in this domain. Finally, we also comment on the value of task prediction and classification models in general in understanding facets of gaze behaviour. & 2016 Published by Elsevier B.V.