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Proceedings of the National Academy of Sciences of the United States of America

Publication date: 2010-07
Volume: 107 Pages: 13503 -
ISSN: 0027-8424, 1091-6490 PMID: 20628009
DOI: 10.1073/pnas.1002506107
Publisher: National Academy of Sciences


Zhang, Jiaxiang
Kourtzi, Zoe


Brain, Eye Movements, Humans, Learning, Magnetic Resonance Imaging, Neuronal Plasticity, Neuropsychological Tests, Pattern Recognition, Visual, Psychomotor Performance, Task Performance and Analysis, Time Factors, Young Adult, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, contour integration, shape perception, brain imaging, visual cortex, PRIMARY VISUAL-CORTEX, CONTOUR INTEGRATION, NATURAL STATISTICS, ATTENTION, TOP, ORIENTATION, MODULATION, EXPERIENCE, NEURONS, SYSTEM


Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.