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Zoe Kourtzi; Adaptive shape coding in the human visual brain. Journal of Vision 2014;14(10):1457. doi: https://doi.org/10.1167/14.10.1457.
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© ARVO (1962-2015); The Authors (2016-present)
In the search for neural codes, we typically measure responses to input stimuli alone without considering their context in space (i.e. scene configuration) or time (i.e. temporal history). However, accumulating evidence suggests an adaptive neural code that is dynamically shaped by experience. Here, we present work showing that experience plays a critical role in molding mid-level visual representations and shape perception. Combining behavioral and brain imaging measurements we demonstrate that learning optimizes the binding of local elements into shapes, and the selection of behaviorally relevant features for shape categorization. First, we provide evidence that the brain flexibly exploits image regularities and learns to use discontinuities typically associated with surface boundaries for contour linking and target identification. Specifically, 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. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training, is stimulus dependent, and enhances processing in intraparietal regions. Second, by reverse correlating behavioral and fMRI responses with noisy stimulus trials, we identify the critical image parts that determine the observersÂ’ choice in a shape categorization task. We demonstrate that learning optimizes shape templates by tuning the representation of informative image parts in higher ventral cortex. In sum, we propose that similar learning mechanisms may mediate long-term optimization through development, tune the visual system to fundamental principles of feature binding, and shape visual category representations.
Meeting abstract presented at VSS 2014
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