dc.contributor.author | Schwettmann, Sarah | |
dc.contributor.author | Tenenbaum, Joshua B | |
dc.contributor.author | Kanwisher, Nancy | |
dc.date.accessioned | 2021-10-27T20:36:23Z | |
dc.date.available | 2021-10-27T20:36:23Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/136637 | |
dc.description.abstract | © 2019, eLife Sciences Publications Ltd. All rights reserved. An intuitive understanding of physical objects and events is critical for successfully interacting with the world. 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. | |
dc.language.iso | en | |
dc.publisher | eLife Sciences Publications, Ltd | |
dc.relation.isversionof | 10.7554/ELIFE.46619 | |
dc.rights | Creative Commons Attribution 4.0 International license | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | eLife | |
dc.title | Invariant representations of mass in the human brain | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | |
dc.contributor.department | Center for Brains, Minds, and Machines | |
dc.contributor.department | McGovern Institute for Brain Research at MIT | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.relation.journal | eLife | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2021-03-26T15:59:20Z | |
dspace.orderedauthors | Schwettmann, S; Tenenbaum, JB; Kanwisher, N | |
dspace.date.submission | 2021-03-26T15:59:21Z | |
mit.journal.volume | 8 | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |