Early visual processing is sensitive to the simplest aspects of images, such as luminance contrast and edge orientations. Importantly, not all basic image-content is treated equally. For example, sensitivity differs between cardinal and oblique orientations. Moreover, sensitivity also differs based on the spatial frequency content of the image. As such, variations in image content can have a large influence on subsequent visual processing. That means that when we compare behavioral responses to different categories of images, such as their ability to attract attention, systematic or prototypical image-differences can have an unwanted effect on the data. However, is this really an unwanted effect or is it part of the visual information we use to quickly identify the category of the object? For example, can these image-differences help us quickly dissociate one category from another? I am currently working on separating these possibilities using both hypothesis and data driven approaches with a focus on emotional facial expressions.

The bulk of my lectures are current at University College Utrecht where I teach in the level 2 Cognitive Neuroscience Course. My main focus for these classes is on the mechanisms behind sensory processing and their similarities between sensory systems. In the Artificial Intelligence Master of Utrecht University, I mainly give lectures and computer practical’s that focus on modelling human data and the application of AI-methodology in vision science. In the Psychology bachelors of Utrecht University I mainly work behind the scenes. My focus here is on teaching the students to write research reports.