Shared texture-like representations underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) excel at predicting neural responses across the visual hierarchy,^1,2,3,4,5 a success widely interpreted as evidence of shared object recognition computations.^6,7 Yet improving DNN object recognition accuracy does not reliably increase neural predictivity,^8,9 and even untrained networks predict brain responses above chance.^9,10,11 This disconnect suggests that object recognition may not drive DNN-brain alignment. Texture-like statistics are represented in both DNNs and mid-level visual cortex (A.V. Jagadeesh and M. Livingstone, 2024, ICLR, presentation).^12,13,1416 In natural images, these statistics are carried by objects and backgrounds, shaping representations and recognition in both systems.^16,17,18,19 Does DNN-brain alignment reflect a shared sensitivity to object-related information or texture-like statistics? To dissociate these factors, we recorded electroencephalograms (EEGs) from 57 participants viewing natural scenes, texture-synthesized images preserving local statistics while disrupting global form, and object-only images with backgrounds removed. If alignment reflects texture-like statistics, then it should peak for texture-synthesized images. If it reflects object-related processing, then alignment should be strongest for natural and object-only conditions, which preserve object information. We compared EEG responses with DNN activations via weighted representational similarity analysis.^20,21 Texture-synthesized images yielded the strongest DNN-EEG alignment, peaking in early responses (<200 ms) and explaining up to ∼85% of noise-ceiling-normalized explainable variance versus ∼44% for natural and ∼55% for isolated objects. Crucially, object categories were more decodable for natural and object-only images than texture-synthesized images, yet these object-rich conditions showed weaker alignment. This dissociation reveals that DNNs capture the texture-statistical component of early visual responses while failing to explain later, object-related variance.