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dc.contributor.authorGibson, Edward A.
dc.contributor.authorBergen, Leon
dc.contributor.authorPiantadosi, Steven T.
dc.date.accessioned2014-07-10T20:32:19Z
dc.date.available2014-07-10T20:32:19Z
dc.date.issued2013-05
dc.date.submitted2012-09
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/88255
dc.description.abstractSentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Indeed, like many cognitive systems, language processing mechanisms may even be “well designed”–in this case for the task of recovering intended meaning from noisy utterances. In particular, comprehension mechanisms may be sensitive to the types of information that an idealized statistical comprehender would be sensitive to. Here, we evaluate four predictions about such a rational (Bayesian) noisy-channel language comprehender in a sentence comprehension task: (i) semantic cues should pull sentence interpretation towards plausible meanings, especially if the wording of the more plausible meaning is close to the observed utterance in terms of the number of edits; (ii) this process should asymmetrically treat insertions and deletions due to the Bayesian “size principle”; such non-literal interpretation of sentences should (iii) increase with theperceived noise rate of the communicative situation and (iv) decrease if semantically anomalous meanings are more likely to be communicated. These predictions are borne out, strongly suggesting that human language relies on rational statistical inference over a noisy channel.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant 0844472, from the Linguistics Program)en_US
dc.language.isoen_US
dc.publisherNational Academy of Sciences (U.S.)en_US
dc.relation.isversionofhttp://dx.doi.org/110.1073/pnas.1216438110en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourcePNASen_US
dc.titleRational integration of noisy evidence and prior semantic expectations in sentence interpretationen_US
dc.typeArticleen_US
dc.identifier.citationGibson, E., L. Bergen, and S. T. Piantadosi. “Rational Integration of Noisy Evidence and Prior Semantic Expectations in Sentence Interpretation.” Proceedings of the National Academy of Sciences 110, no. 20 (May 14, 2013): 8051–8056.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Linguistics and Philosophyen_US
dc.contributor.mitauthorGibson, Edward A.en_US
dc.contributor.mitauthorBergen, Leonen_US
dc.relation.journalProceedings of the National Academy of Sciencesen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGibson, E.; Bergen, L.; Piantadosi, S. T.en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-1013-1461
dc.identifier.orcidhttps://orcid.org/0000-0002-5912-883X
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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