Gaze Behaviour during Space Perception and Spatial Decision Making

A series of four experiments investigating gaze behavior and decision making in the context of wayfinding is reported. Participants were presented with screen-shots of choice points taken in large virtual environments. Each screen-shot depicted alternative path options. In Experiment 1, participants had to decide between them in order to find an object hidden in the environment. In Experiment 2, participants were first informed about which path option to take as if following a guided route. Subsequently they were presented with the same images in random order and had to indicate which path option they chose during initial exposure. In Experiment 1, we demonstrate (1) that participants have a tendency to choose the path option that featured the longer line of sight, and (2) a robust gaze bias towards the eventually chosen path option. In Experiment 2, systematic differences in gaze behavior towards the alternative path options between encoding and decoding were observed. Based on data from Experiments 1 & 2 and two control experiments ensuring that fixation patterns were specific to the spatial tasks, we develop a tentative model of gaze behavior during wayfinding decision making suggesting that particular attention was paid to image areas depicting changes in the local geometry of the environments such as corners, openings, and occlusions. Together, the results suggest that gaze during a wayfinding tasks is directed toward, and can be predicted by, a subset of environmental features and that gaze bias effects are a general phenomenon of visual decision making

Expected exponential loss for gaze-based video and volume ground truth annotation

Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data. In the context of object segmentation, pixel-wise annotations are extremely expensive to collect, especially in video and 3D volumes. To reduce this annotation burden, we propose a novel framework to allow annotators to simply observe the object to segment and record where they have looked at with a \$200 eye gaze tracker. Our method then estimates pixel-wise probabilities for the presence of the object throughout the sequence from which we train a classifier in semi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho