When looking at visual images, the eyes move to the most salient and behaviourally relevant
objects. Saliency and semantic information significantly explain where people look. Less is …

In the past decade, a large number of computational models of visual saliency have been
proposed. Recently a number of comprehensive benchmark studies have been presented …

Pseudoneglect, that is the tendency to pay more attention to the left side of space, is typically
assessed with paper-and-pencil tasks, particularly line bisection. In the present study, we …

While attention has been an increasingly popular component in deep neural networks to
both interpret and boost performance of models, little work has examined how attention …

Unique stimuli stand out. Despite an abundance of competing sensory stimuli, the detection
of the most salient ones occurs without effort, and that detection contributes to the guidance …

Recent laboratory studies have found large, stable individual differences in the location
people first fixate when identifying faces, ranging from the brows to the mouth. Importantly …

Deep learning models learn to fit training data while they are highly expected to generalize
well to testing data. Most works aim at finding such models by creatively designing …

Eye movement is a new type of data for cartography and geographic information science
(GIS) research. However, previous studies rarely built eye movement datasets with …

Developments in machine learning interpretability techniques over the past decade have
provided new tools to observe the image regions that are most informative for classification …

Emotional images are processed in a prioritized manner, attracting attention almost
immediately. In the present study we used eye tracking to reveal what type of features within …