  Configural processing, the perception of spatial relationships among an
object's components, is crucial for object recognition. However, the teleology
and underlying neurocomputational mechanisms of such processing are still
elusive, notwithstanding decades of research. We hypothesized that processing
objects via configural cues provides a more robust means to recognizing them
relative to local featural cues. We evaluated this hypothesis by devising
identification tasks with composite letter stimuli and comparing different
neural network models trained with either only local or configural cues
available. We found that configural cues yielded more robust performance to
geometric transformations such as rotation or scaling. Furthermore, when both
features were simultaneously available, configural cues were favored over local
featural cues. Layerwise analysis revealed that the sensitivity to configural
cues emerged later relative to local feature cues, possibly contributing to the
robustness to pixel-level transformations. Notably, this configural processing
occurred in a purely feedforward manner, without the need for recurrent
computations. Our findings with letter stimuli were successfully extended to
naturalistic face images. Thus, our study provides neurocomputational evidence
that configural processing emerges in a na\"ive network based on task
contingencies, and is beneficial for robust object processing under varying
viewing conditions.
