A Scanner Deeply: Predicting Gaze Heatmaps on Visualizations Using Crowdsourced Eye Movement Data

Visual perception is a key component of data visualization. Much prior empirical work uses eye movement as a
proxy
to understand human visual perception. Diverse apparatus and techniques have been proposed to collect eye movements, but there is still no optimal approach. In this paper, we review 30 prior works for collecting eye movements based on three axes: (1) the
tracker
technology used to measure eye movements; (2) the
image stimulus
shown to participants; and (3) the
collection methodology
used to gather the data. Based on this taxonomy, we employ a webcam-based eyetracking approach using task-specific visualizations as the stimulus. The low technology requirement means that virtually anyone can participate, thus enabling us to collect data at large scale using crowdsourcing: approximately 12,000 samples in total. Choosing visualization images as stimulus means that the eye movements will be specific to perceptual tasks associated with visualization. We use these data to propose a S
canner
D
eeply
, a virtual eyetracker model that, given an image of a visualization, generates a gaze heatmap for that image. We employ a computationally efficient, yet powerful convolutional neural network for our model. We compare the results of our work with results from the DVS model and a neural network trained on the Salicon dataset. The analysis of our gaze patterns enables us to understand how users grasp the
structure
of visualized data. We also make our stimulus dataset of visualization images available as part of this paper's contribution.