Encoding all the information coming through the environment at any given moment would be an impossible task for the human brain. Ensemble coding refers to the human ability to extract statistical summary information out of a group of objects with similar features. Although it is an easy task for the brain to accurately predict the average size of a group of objects, it remains difficult to identify an object in a crowded scene – a perceptual phenomenon where having similar flankers around a target object decreases the visual acuity of the target. The more the objects are similar to each other, the more difficult it is to identify the target object.
We focus on studying the effect of global and local features in a crowded scene of multiple clusters on detecting the target object. To achieve this, we investigate the effect of similar stimulus metrics (e.g. density, distance, size) for the entire scene and for the target object and/or cluster.