Neural Networks - Special issue: automatic target recognition
Object recognition in man, monkey, and machine
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Visual Selective Behavior Can Be Triggered by a Feed-Forward Process
Journal of Cognitive Neuroscience
Masking Interrupts Figure–Ground Signals in V1
Journal of Cognitive Neuroscience
The Time Course of Visual Processing: From Early Perception to Decision-Making
Journal of Cognitive Neuroscience
The Neurophysiology of Backward Visual Masking: Information Analysis
Journal of Cognitive Neuroscience
Masking Disrupts Reentrant Processing in Human Visual Cortex
Journal of Cognitive Neuroscience
Neural Computation
Journal of Cognitive Neuroscience
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Everyday vision requires robustness to a myriad of environmental factors that degrade stimuli. Foreground clutter can occlude objects of interest, and complex lighting and shadows can decrease the contrast of items. How does the brain recognize visual objects despite these low-quality inputs? On the basis of predictions from a model of object recognition that contains excitatory feedback, we hypothesized that recurrent processing would promote robust recognition when objects were degraded by strengthening bottom-up signals that were weakened because of occlusion and contrast reduction. To test this hypothesis, we used backward masking to interrupt the processing of partially occluded and contrast reduced images during a categorization experiment. As predicted by the model, we found significant interactions between the mask and occlusion and the mask and contrast, such that the recognition of heavily degraded stimuli was differentially impaired by masking. The model provided a close fit of these results in an isomorphic version of the experiment with identical stimuli. The model also provided an intuitive explanation of the interactions between the mask and degradations, indicating that masking interfered specifically with the extensive recurrent processing necessary to amplify and resolve highly degraded inputs, whereas less degraded inputs did not require much amplification and could be rapidly resolved, making them less susceptible to masking. Together, the results of the experiment and the accompanying model simulations illustrate the limits of feedforward vision and suggest that object recognition is better characterized as a highly interactive, dynamic process that depends on the coordination of multiple brain areas.