People excel at recognizing objects across changes in position, size, viewpoint, lighting and general form, whereas computer recognition systems perform poorly when faced with such variable, unpredictable situations. Exactly how the human brain solves the computational challenges of object recognition is not well understood. To recognize an object, one must integrate the features, contours, and parts of an object into an organized whole, and then match these representations of an object's shape to items stored in memory. Somehow, the brain can solve this computational problem by extracting the stable, invariant properties of objects while disregarding superficial variations in the retinal image. With support from the National Science Foundation, Dr. Frank Tong and his colleagues at Vanderbilt University will investigate the neural bases of object recognition using functional magnetic resonance imaging (fMRI) and novel pattern classification methods adapted from machine learning. These studies will determine what types of information about objects are represented by cortical activity patterns across the human visual pathway, ranging from low-level visual areas that respond best to basic features, to high-level areas that respond best to complex objects. Rather than focusing exclusively on the high-level object-selective areas, this project emphasizes a different approach to understand how invariant representations of objects are formed. Studies will characterize the neural representation of objects at each stage of the visual pathway, from the primary visual cortex to anterior inferotemporal areas, to determine how object representations are transformed from one processing stage to the next.
This research will help reveal how the brain solves the problem of object recognition, by transforming the raw retinal input into increasingly more flexible representations of the object through a process spanning many successive levels of the visual pathway. Results from these studies will provide inform current theories of object recognition. Understanding these neural bases is necessary to comprehend what can go wrong with object recognition in cases of learning disability, developmental disorder or brain injury (e.g., developmental or acquired dyslexia). It is likely that computer algorithms for recognizing objects will also be improved.
