Algorithms of contour integration and their verification through human errors in psychophysical experiments.

Contour integration is an important step in the decomposition of a visual scene into distinct objects (figure-ground segmentation). During this process, oriented and aligned edge elements are bound together to form a coherent contour.

Psychophysical experiments revealed that contour integration in macaque monkeys and human observers is both very efficient and astonishingly fast. This high performance challenges computational algorithms of contour integration, and opens the question about the relevant neurophysiological mechanisms underlying this specific part of neural information processing.

Several algorithms for contour integration have been proposed, ranging from probabilistic algorithms with multiplicative, directed interactions up to neural networks with long-ranging additive, undirected horizontal couplings. We investigate these different model classes by requiring that a suitable, neurophysiologically plausible model should not only reproduce the performance of human observers, but also systematic errors made during the integration of contours. Our analysis of these errors predicts that contour integration in the brain is mediated by multiplicative and directed interactions, as opposed to current models employing additive and non-directed interactions.