  Making inferences from partial information constitutes a critical aspect of
cognition. During visual perception, pattern completion enables recognition of
poorly visible or occluded objects. We combined psychophysics, physiology and
computational models to test the hypothesis that pattern completion is
implemented by recurrent computations and present three pieces of evidence that
are consistent with this hypothesis. First, subjects robustly recognized
objects even when rendered <15% visible, but recognition was largely impaired
when processing was interrupted by backward masking. Second, invasive
physiological responses along the human ventral cortex exhibited visually
selective responses to partially visible objects that were delayed compared to
whole objects, suggesting the need for additional computations. These
physiological delays were correlated with the effects of backward masking.
Third, state-of-the-art feed-forward computational architectures were not
robust to partial visibility. However, recognition performance was recovered
when the model was augmented with attractor-based recurrent connectivity. These
results provide a strong argument of plausibility for the role of recurrent
computations in making visual inferences from partial information.
