A crucial question in vision science is how do we recognize objects? It seems trivial, easy, and self-evident, but taking a cursory look at today's artificial intelligence research (or at face detection software in cameras) is enough to suggest how incredibly complex the process is. I have been working on uncovering the mechanisms underlying object recognition, using visual crowding as a tool. Currently, I am working on determining the neural processes underlying attention and visual short term memory (VSTM) using a combination of psychophysics and EEG.
Object recognition takes place in two steps. First, the features (orientation, color, etc) of an object are detected independently. Then these features are put together to form the representation of that object. Crowding is a breakdown of the second step. When a target object is flanked closely by other objects, the features of the target and the flankers get mixed up leading to a jumbled percept. This is crowding. It offers a direct window in how feature integration occurs, and hence serves as a handy tool in investigating object recognition.
Crowding is illustrated below. Fixate (keep your eyes focused on) the black square in the center. If you try to read the central letter of the triplet on the right, you will find it very hard. The same letter at the same distance on the left is extremely easy to identify. It is the presence of the the flanking letters on the right that makes the target letter unidentifiable.
Visual attention directs the limited resources of the visual system to the currently relevant input. I am interested in how attention is deployed in space in order to select objects. Currently, I am applying multivariate classifiers to EEG data obtained in attentional tasks to determine the shifts in spatial attention as a function of time; can we tell where attention is on a millisecond-by-millisecond basis? I am also applying similar techniques to determine how the brain represents various features of objects, such as location and motion.
- Neural oscillations in attention, awareness and VSTM
- Neural representation of object location
- Time perception