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| Paper IPM / Cognitive / 15143 |
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Several recent findings have indicated that the core object recognition is primarily solved through the feedforward sweep of visual information processing. On the other hand, while recurrent connections are ubiquitous in our visual system, their role in object-processing is not yet fully understood. Here, we investigated the contribution of recurrent processes in object recognition under a prevalent challenging condition, that is when objects are occluded by other natural or artificial occluders in the environment. To characterize neural dynamics of object recognition under occlusion, we acquired magnetoencephalography(MEG) data (N=15 subjects), while subjects were presented with images of objects with 0the behavioral data when objects were occluded; however, a CNN with local recurrent connections reached the human-level performance under occlusion, and partially explained the MEG data for occluded objects. Taken together, our empirical results suggest an essential role for recurrent processing when objects are occluded, and our computational model with local recurrent connections explains how our brain might be solving this problem.
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