An empirical pipeline to derive gaze prediction heuristics for 3D action games

Abstract

Gaze analysis and prediction in interactive virtual environments, such as games, is a challenging topic since the 3D perspective and variations of the viewpoint as well as the current task introduce many variables that affect the distribution of gaze. In this article, we present a novel pipeline to study eye-tracking data acquired from interactive 3D applications. The result of the pipeline is an importance map which scores the amount of gaze spent on each object. This importance map is then used as a heuristic to predict a user's visual attention according to the object properties present at runtime. The novelty of this approach is that the analysis is performed in object space and the importance map is defined in the feature space of high-level properties. High-level properties are used to encode task relevance and other attributes, such as eccentricity, which may have an impact on gaze behavior. The pipeline has been tested with an exemplary study on a first-person shooter game. In particular, a protocol is presented describing the data acquisition procedure, the learning of different importance maps from the data, and finally an evaluation of the performance of the derived gaze predictors. A metric measuring the degree of correlation between attention predicted by the importance map and the actual gaze yielded clearly positive results. The correlation becomes particularly strong when the player is attentive to an in-game task.