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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-world depth processed first, then retinal size, and finally, real-world size. Additionally, we input both these naturalistic images and object-only images without natural background into artificial neural networks. Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec). Moreover, our multi-modal representational comparison framework across human EEG and artificial neural networks reveals real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information. Our research provides a temporally resolved characterization of how certain key object properties \u2013 such as object real-world size, depth, and retinal size \u2013 are represented in the brain, which offers further advances and insights into our understanding of object space and the construction of more brain-like visual models.<\/jats:p>","DOI":"10.7554\/elife.98117","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T08:26:51Z","timestamp":1720686411000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.98117","source":"Crossref","is-referenced-by-count":1,"title":["Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images"],"prefix":"10.7554","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7953-6742","authenticated-orcid":true,"given":"Zitong","family":"Lu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00rs6vg23","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Psychology, The Ohio State University","place":["Columbus, United States"]},{"id":[{"id":"https:\/\/ror.org\/042nb2s44","id-type":"ROR","asserted-by":"publisher"}],"name":"McGovern Institute for Brain Research, Massachusetts Institute of Technology","place":["Boston, United States"]}],"role":[{"role":"author","vocabulary":"crossref"},{"role":"corresponding-author","vocabulary":"crossref"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3489-0702","authenticated-orcid":true,"given":"Julie","family":"Golomb","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00rs6vg23","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Psychology, The Ohio State University","place":["Columbus, United States"]}],"role":[{"role":"author","vocabulary":"crossref"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"4374","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"bib1","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s41593-021-00962-x","type":"journal-article","article-title":"A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence","volume":"25","author":"Allen","year":"2022","journal-title":"Nature Neuroscience"},{"key":"bib2","doi-asserted-by":"publisher","DOI":"10.31234\/osf.io\/6gjvp","type":"preprint","article-title":"Using deep neural networks to address the how of object recognition","volume-title":"PsyArXiv","author":"Ayzenberg","year":"2023"},{"key":"bib3","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s41586-020-2350-5","type":"journal-article","article-title":"A map of object space in primate inferotemporal cortex","volume":"583","author":"Bao","year":"2020","journal-title":"Nature"},{"key":"bib4","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1523\/JNEUROSCI.2314-15.2016","type":"journal-article","article-title":"Dissociations and associations between shape and category representations in the two visual pathways","volume":"36","author":"Bracci","year":"2016","journal-title":"The Journal of Neuroscience"},{"key":"bib5","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1093\/cercor\/bhw419","type":"journal-article","article-title":"Task context overrules object- and category-related representational content in the human parietal cortex","volume":"27","author":"Bracci","year":"2017","journal-title":"Cerebral Cortex"},{"key":"bib6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2022.105114","type":"journal-article","article-title":"How big should this object be? 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