Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.

ABSTRACT: Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing the processing of real-world size from other visual properties, like depth, has been challenging. Using the THINGS EEG2 dataset with human EEG recordings and more ecologically valid naturalistic stimuli, our study combines human EEG and representational similarity analysis to disentangle neural representations of object real-world size from retinal size and perceived depth, leveraging recent datasets and modeling approaches to address challenges not fully resolved in previous work. We report a representational timeline of visual object processing: object real

SUBMITTER: Lu Z 

PROVIDER: S-EPMC10473678 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature