The neural substrates of object-ensemble processing in the human brain

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The way in which we see the world is extremely important to our survival as human beings. For example, the ability to be able to find food to eat, friends to socialize with, and partners to mate with requires a visual system that can rapidly and accurately identify the objects and individuals in any given environment. Research in Dr. Jonathan Cant's laboratory explores these issues, in particular how the human brain processes objects and scenes. Previous research using a modern brain imaging technique known as functional magnetic resonance imaging (fMRI) has demonstrated that a region of the brain known as the parahippocampal place area (or PPA for short) plays a large role in scene perception, and does so by processing the 3D spatial structure, or geometry, of scenes. Dr. Cant has recently discovered however, that the PPA does more than just process the geometry of a scene. Specifically, he was the first to show that the PPA is involved in processing what is known as large collections, or ensembles, of multiple objects (e.g. all the leaves on a tree). While we do not know much about how the brain processes object ensembles, we do know that they are extremely important, as ensemble processing entails collapsing a lot of redundant visual information into a single summary feature (e.g. the average size of all the leaves on a tree), thereby saving the brain a lot of time and energy. We would simply not be able to perceive scenes efficiently if our visual system had to process every individual leaf on a tree one-by-one, or every individual blade of grass in a field! Thus, the goal of Dr. Cant's current research program is to understand how the brain processes ensembles of multiple objects, and how this type of processing relates to our ability to pick out and identify one object within the ensemble (that is, single-object perception). This research will not only help scientists understand how the visual brain is organized, but will help engineers develop better machine-vision systems, since many of the design principles used in computer vision systems are inspired by research on how humans perceive individual objects within complex scenes (e.g. the ability of digital cameras to recognize where people are standing in the scene you are taking a picture of).