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Can lateral connectivity in the primary visual cortex account for the timedependence and intrinsic task difficulty of human contour detection? To answerthis question, we created a synthetic image set that prevents sole reliance oneither low-level visual features or high-level context for the detection oftarget objects. Rendered images consist of smoothly varying, globally alignedcontour fragments (amoebas) distributed among groups of randomly rotatedfragments (clutter). The time course and accuracy of amoeba detection by humanswas measured using a two-alternative forced choice protocol with self-reportedconfidence and variable image presentation time (20-200 ms), followed by animage mask optimized so as to interrupt visual processing. Measuredpsychometric functions were well fit by sigmoidal functions with exponentialtime constants of 30-91 ms, depending on amoeba complexity. Key aspects of thepsychophysical experiments were accounted for by a computational network model,in which simulated responses across retinotopic arrays of orientation-selectiveelements were modulated by cortical association fields, represented asmultiplicative kernels computed from the differences in pairwise edgestatistics between target and distractor images. Comparing the experimental andthe computational results suggests that each iteration of the lateralinteractions takes at least 37.5 ms of cortical processing time. Our resultsprovide evidence that cortical association fields between orientation selectiveelements in early visual areas can account for important temporal andtask-dependent aspects of the psychometric curves characterizing human contourperception, with the remaining discrepancies postulated to arise from theinfluence of higher cortical areas.

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception