A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence

TL;DR

This study combines neurophysiological, psychophysical, and computational methods to demonstrate that recurrent processing in the ventral visual stream is crucial for recognizing partially occluded objects, with delays and disruptions impairing recognition.

Contribution

It provides direct neurophysiological evidence and computational modeling insights showing the importance of recurrent processing in object completion from partial information.

Findings

Selective responses persist with only 9-25% of the object visible

Recurrent processing delays are ~100 ms in higher visual areas

Disrupting recurrent processing impairs recognition of partial objects

Abstract

Recognition of objects from partial information presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. We combined neurophysiological recordings in human cortex with psychophysical measurements and computational modeling to investigate the mechanisms involved in object completion. We recorded intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to measure the timing and selectivity of responses along human visual cortex to whole and partial objects. Responses along the ventral visual stream remained selective despite showing only 9-25% of the object. However, these visually selective signals emerged ~100 ms later for partial versus whole objects. The processing delays were particularly pronounced in higher visual areas within the ventral stream, suggesting the involvement of additional recurrent processing. In separate psychophysics experiments, disrupting this recurrent computation with a backward mask at ~75ms significantly impaired recognition of partial, but not whole, objects. Additionally, computational modeling shows that the performance of a purely bottom-up architecture is impaired by heavy occlusion and that this effect can be partially rescued via the incorporation of top-down connections. These results provide spatiotemporal constraints on theories of object recognition that involve recurrent processing to recognize objects from partial information.