Here's a bold statement: Our brains effortlessly perceive the real-world size of objects, but how this complex process unfolds remains a mystery. But here's where it gets controversial: while previous studies have explored this phenomenon, disentangling the processing of real-world size from other visual properties like depth has proven challenging. In this study, we tackle this issue head-on by combining human EEG recordings with artificial neural networks and naturalistic stimuli. We reveal a fascinating timeline of visual object processing: real-world depth is processed first, followed by retinal size, and finally, real-world size. And this is the part most people miss: our findings suggest that real-world size is a stable, higher-level dimension in object space, incorporating both visual and semantic information. This research not only advances our understanding of object representation in the brain but also has implications for developing more brain-like visual models. However, the question remains: how do these findings translate to real-world scenarios, and what are the limitations of using 2D images to study 3D object perception? We invite readers to share their thoughts and engage in a discussion about the complexities of visual processing and the potential controversies surrounding the use of artificial neural networks in cognitive neuroscience research.
