Motion perception in multiclustered environments

The ability to recognise objects in the environment is a fundamental process for all living beings. The features of the objects (size, color, location, etc) in addition to their integration with the environment play a significant role in identifying the object. Despite the extensive evidence in psychology and neuropsychology, the exact contribution for various features to recognition is still inconclusive. Although it is an easy task for the brain to identify an object, 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. In this thesis, we focused on studying the effect of global and local features in a crowded scene of multiple clusters on detecting the target object. To achieve this, similar stimulus metrics (e.g. density, distance, size) are calculated globally for the entire scene and locally for the target object and/or cluster. We found a global effect of the density metrics (e.g. the number of clusters) on performance. We also found a significant effect on global and local aspects of eccentricity and size metrics. Keywords: Motion Perception , Ensemble Perception, Attention