Learning-dependent plasticity with and without training in the human brain

Abstract

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.T he ability to detect and identify meaningful targets in cluttered scenes is a fundamental skill for survival and social interactions. In fact, it is thought that the visual system is optimized through evolution and development for the detection of frequently occurring regularities that typically define shape contours in natural scenes (e.g., elements collinear to the contour path) (1-3). Previous studies have shown that human observers are indeed better at detecting collinear contours than contours defined by regularities (e.g., elements orthogonal to the contour path) that typically define discontinuities (e.g., texture boundaries) rather than coherent shape contours (4-6). However, recent computational approaches propose that experience with the statistics of natural environments in adulthood plays a critical role in enhancing our ability to interpret complex scenes (7,8). Our previous work showed that observers learn to integrate image discontinuities (i.e., orthogonal elements) for contour detection, suggesting that short-term training may alter the utility of image regularities (9, 10). Despite accumulating computational and behavioral evidence for the role of experience in the interpretation of complex scenes, the brain plasticity mechanisms that mediate learning of statistical regularities in natural images remain largely unknown.Here, we combine behavioral and functional MRI (fMRI) measurements to investigate...

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