{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T13:41:05Z","timestamp":1792330865768,"version":"4.4.0"},"reference-count":43,"publisher":"MIT Press","issue":"9","funder":[{"DOI":"10.13039\/100000169","name":"Division of Behavioral and Cognitive Sciences","doi-asserted-by":"publisher","award":["CAREER BCS-1942438"],"award-info":[{"award-number":["CAREER BCS-1942438"]}],"id":[{"id":"10.13039\/100000169","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Responses to visually presented objects along the cortical surface of the human brain have a large-scale organization reflecting the broad categorical divisions of animacy and object size. Emerging evidence indicates that this topographical organization is supported by differences between objects in mid-level perceptual features. With regard to the timing of neural responses, images of objects quickly evoke neural responses with decodable information about animacy and object size, but are mid-level features sufficient to evoke these rapid neural responses? Or is slower iterative neural processing required to untangle information about animacy and object size from mid-level features, requiring hundreds of milliseconds more processing time? To answer this question, we used EEG to measure human neural responses to images of objects and their texform counterparts\u2014unrecognizable images that preserve some mid-level feature information about texture and coarse form. We found that texform images evoked neural responses with early decodable information about both animacy and real-world size, as early as responses evoked by original images. Furthermore, successful cross-decoding indicates that both texform and original images evoke information about animacy and size through a common underlying neural basis. Broadly, these results indicate that the visual system contains a mid-level feature bank carrying linearly decodable information on animacy and size, which can be rapidly activated without requiring explicit recognition or protracted temporal processing.<\/jats:p>","DOI":"10.1162\/jocn_a_01883","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T17:48:25Z","timestamp":1655315305000},"page":"1670-1680","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":36,"title":["Mid-level Feature Differences Support Early Animacy and Object Size Distinctions: Evidence from Electroencephalography Decoding"],"prefix":"10.1162","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0341-5020","authenticated-orcid":true,"given":"Ruosi","family":"Wang","sequence":"first","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Janini","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talia","family":"Konkle","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"2023080100052407200_bib1","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1523\/JNEUROSCI.2860-17.2017","article-title":"Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials","volume":"38","author":"Bae","year":"2018","journal-title":"Journal of Neuroscience"},{"key":"2023080100052407200_bib2","doi-asserted-by":"publisher","first-page":"e1003167","DOI":"10.1371\/journal.pcbi.1003167","article-title":"Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons","volume":"9","author":"Baldassi","year":"2013","journal-title":"PLoS Computational Biology"},{"key":"2023080100052407200_bib3","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s41586-020-2350-5","article-title":"A map of object space in primate inferotemporal cortex","volume":"583","author":"Bao","year":"2020","journal-title":"Nature"},{"key":"2023080100052407200_bib4","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1163\/156856897X00357","article-title":"The psychophysics toolbox","volume":"10","author":"Brainard","year":"1997","journal-title":"Spatial Vision"},{"key":"2023080100052407200_bib5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/13.10.1","article-title":"Representational dynamics of object vision: The first 1000 ms","volume":"13","author":"Carlson","year":"2013","journal-title":"Journal of Vision"},{"key":"2023080100052407200_bib6","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.neuroimage.2015.10.012","article-title":"Fast ventral stream neural activity enables rapid visual categorization","volume":"125","author":"Cauchoix","year":"2016","journal-title":"Neuroimage"},{"key":"2023080100052407200_bib7","doi-asserted-by":"publisher","first-page":"105114","DOI":"10.1016\/j.cognition.2022.105114","article-title":"How big should this object be? 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