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Any visual system, biological or artificial, must make a trade-off between the number of units used to represent the visual environment and the spatial resolution of the sampling array. Humans and some other animals are able to allocate attention to spatial locations to reconfigure the sampling array of receptive fields (RFs), thereby enhancing the spatial resolution of representations without changing the overall number of sampling units. Here, we examine how representations of visual features in a fully convolutional neural network interact and interfere with each other in an eccentricity-dependent RF pooling array and how these interactions are influenced by dynamic changes in spatial resolution across the array. We study these feature interactions within the framework of visual crowding, a well-characterized perceptual phenomenon in which target objects in the visual periphery that are easily identified in isolation are much more difficult to identify when flanked by similar nearby objects. By separately simulating effects of spatial attention on RF size and on the density of the pooling array, we demonstrate that the increase in RF density due to attention is more beneficial than changes in RF size for enhancing target classification for crowded stimuli. Furthermore, by varying target/flanker spacing, as well as the spatial extent of attention, we find that feature redundancy across RFs has more influence on target classification than the fidelity of the feature representations themselves. Based on these findings, we propose a candidate mechanism by which spatial attention relieves visual crowding through enhanced feature redundancy that is mostly due to increased RF density.

Spatial Attention Enhances Crowded Stimulus Encoding Across Modeled Receptive Fields by Increasing Redundancy of Feature Representations