  Many visual phenomena suggest that humans use top-down generative or
reconstructive processes to create visual percepts (e.g., imagery, object
completion, pareidolia), but little is known about the role reconstruction
plays in robust object recognition. We built an iterative encoder-decoder
network that generates an object reconstruction and used it as top-down
attentional feedback to route the most relevant spatial and feature information
to feed-forward object recognition processes. We tested this model using the
challenging out-of-distribution digit recognition dataset, MNIST-C, where 15
different types of transformation and corruption are applied to handwritten
digit images. Our model showed strong generalization performance against
various image perturbations, on average outperforming all other models
including feedforward CNNs and adversarially trained networks. Our model is
particularly robust to blur, noise, and occlusion corruptions, where shape
perception plays an important role. Ablation studies further reveal two
complementary roles of spatial and feature-based attention in robust object
recognition, with the former largely consistent with spatial masking benefits
in the attention literature (the reconstruction serves as a mask) and the
latter mainly contributing to the model's inference speed (i.e., number of time
steps to reach a certain confidence threshold) by reducing the space of
possible object hypotheses. We also observed that the model sometimes
hallucinates a non-existing pattern out of noise, leading to highly
interpretable human-like errors. Our study shows that modeling
reconstruction-based feedback endows AI systems with a powerful attention
mechanism, which can help us understand the role of generating perception in
human visual processing.
