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Does the brain achieve this understanding by running simulations in a mental physics engine, which represents variables such as force and mass, or by analyzing patterns of motion without encoding underlying physical quantities? To investigate, we scanned participants with fMRI while they viewed videos of objects interacting in scenarios indicating their mass. Decoding analyses in brain regions previously implicated in intuitive physical inference revealed mass representations that generalized across variations in scenario, material, friction, and motion energy. These invariant representations were found during tasks without action planning, and tasks focusing on an orthogonal dimension (object color). Our results support an account of physical reasoning where abstract physical variables serve as inputs to a forward model of dynamics, akin to a physics engine, in parietal and frontal cortex.<\/jats:p>","DOI":"10.7554\/elife.46619","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T08:00:18Z","timestamp":1576569618000},"source":"Crossref","is-referenced-by-count":72,"title":["Invariant representations of mass in the human brain"],"prefix":"10.7554","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6385-1396","authenticated-orcid":true,"given":"Sarah","family":"Schwettmann","sequence":"first","affiliation":[{"name":"Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua B","family":"Tenenbaum","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy","family":"Kanwisher","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, United States"},{"name":"McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4374","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"bib1","volume-title":"Advances in Psychological Science\u00a0Biological and Cognitive Aspects","volume":"2","author":"Baillargeon","year":"1998"},{"key":"bib2","article-title":"Humans predict liquid dynamics using probabilistic simulation","author":"Bates","year":"2015","unstructured":"Bates C, Battaglia P, Yildirim I, Tenenbaum JB. 2015. 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