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Perceptual Expertise Network (PEN)
James S. McDonnell funded collaborator network studying the visual system plasticity using perceptual expertise as a model
Lighting Invariance
Our visual system has the amazing ability to recognize thousands of individual faces, animals, and objects, successfully discounting irrelevant changes and using relevant ones to distinguish among known individual instances. How is this invariant representation for an individual acquired? What neural changes occur in the brain as invariance is learned? Researchers have investigated invariance in a number of domains: viewpoint invariance where recognition occurs despite rotations of the object, position invariance despite translations of the object, size invariance despite scaling of the object, etc. Here, we tackle the question using lighting invariance. Why? It is the one domain of invariance that has a computational framework for generalizing across lighting conditions after a few exemplars.
Multimodal Expertise
Lots of multimodal research have looked at the how one modality can bias another modality (such as the ventriloquist effect where the thrown voice is attibuted to the dummy since vision trumphs audition) or random pairings of modalities (number of perceived flashes can be biased by number of auditory beeps). Multimodal expertise seems to be of another flavor. Is the processing different when we hear the voice/see the face of our mother?
Facial feature segmentation
The role of facial structure in visual processing is well documented by behavioral studies investigating the influence of local and relational information in various tasks. Facial structure is also exploited by automatic face recognition algorithms as it may provide an advantage over the use of holistic information. Unfortunately a method for identifying and localizing features robustly, consistently and psychologically plausibly seems to be missing. We propose and explore such a method based on the idea of region-based image segmentation. Facial features in our account are segments obtained through appeal to both bottom-up and top-down category-specific information.
Structure and color in face recognition
Face recognition is sensitive to the featural and configural properties of human faces. We examine this sensitivity to structure for both human and automatic recognition in a variety of tasks and with a variety of methods. One aspect of structure less investigated is the variation of color, specifically hue, over face surfaces. Its diagnosticity for categorization, e.g. gender discrimination, and identification is examined and compared with other types of structural information.
Recognition of disguised faces
The impact on performance of changes in features and viewing conditions has been studied extensively in face recognition. However, not much is known about how recognition performance is affected by changes in non-stable facial features, such as hairstyle, facial hair, eye glasses, and makeup. The main goal of this project is to understand how such changes affect face processing in control subjects, both in terms of recognition performance and with regard to the specific processing strategies.
Individual differences in face recognition
Face recognition abilities have been assumed to be homogenous within the population of control subjects, and specifically individuals have been assumed to be all equally expert in this domain. The first step of this study is to re-examine this claim by gathering data on a series of behavioral tasks on face processing sensitive enough to profile each subject's abilities very precisely and to capture possible individual differences. Secondly we are also interested in looking at correlations between the patterns of behavioral data obtained and each individual's brain responses to faces.
Tarr dad to receive the Leonardo da Vinci Medal