Object vision and population-code representation

Recognising the objects we see around us is effortless. Seeing is much easier for us than high-level cognitive tasks like playing chess. This belies the computational complexity of object recognition. Computers have surpassed us at chess, but object recognition is an open problem of artificial intelligence. In this programme, we study how the brain achieves visual object recognition so effortlessly and efficiently. The visual images are converted into neural signals in the retina and send to the visual cortex. At early stages of processing the visual system recognises edges and other local features. The visual information is then transformed through a sequence of representational stages that lead to recognition. We present real-world images to people while scanning their brain activity. By analysing the brain-activity patterns representing the stimuli at each stage of processing, we investigate how the brain performs the feat of recognition so swiftly and without requiring us to make any conscious effort. We also study how our goals (e.g. looking for a particular kind of object, such as food) affect the process of visual perception and how our brain representations give rise to the unique way each of us perceives the relationships between objects in the world.

Technical Summary

The objective of this programme is to understand the computational and neural mechanisms of visual object perception in health and disease and their variation across individuals.

Object recognition is effortless for humans and animals, and yet it is one of the unsolved problems of artificial intelligence. Our central challenge is to understand the transformation of representations along the ventral visual stream, from the early visual representation to the level at which natural categories and semantic dimensions are represented, and the computational mechanisms that enable the brain to perform object recognition so swiftly and more reliably than current computer vision systems.

We study representations of objects, places, and faces and higher-level semantic content in healthy volunteers and also in selected clinical populations, starting with autism. Our main methodology is high-resolution functional magnetic resonance imaging combined with pattern-information analyses.