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This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal \u2018hub\u2019 in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.<\/jats:p>","DOI":"10.7554\/elife.66276","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T07:00:10Z","timestamp":1635318010000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.66276","source":"Crossref","is-referenced-by-count":57,"title":["Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6304-755X","authenticated-orcid":true,"given":"Timothy T","family":"Rogers","sequence":"first","affiliation":[{"name":"Department of Psychology, University of Wisconsin- Madison, Madison, United 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