Configural processing as an optimized strategy for robust object recognition in neural networks

Configural processing, the perception of spatial relationships among an object's components, is crucial for object recognition, yet its teleology and underlying mechanisms remain unclear. We hypothesize that configural processing drives robust recognition under varying conditions. Using identification tasks with composite letter stimuli, we compare neural network models trained with either configural or local cues. We find that configural cues support robust generalization across geometric transformations (e.g., rotation, scaling) and novel feature sets. When both cues are available, configural cues dominate local features. Layerwise analysis reveals that sensitivity to configural cues emerges later in processing, likely enhancing robustness to pixel-level transformations. Notably, this occurs in a purely feedforward manner without recurrent computations. These findings with letter stimuli successfully extend to naturalistic face images. Our results demonstrate that configural processing emerges in a naíve network based on task contingencies, and is beneficial for robust object processing under varying viewing conditions.