Rational integration of noisy evidence and prior semantic expectations in sentence interpretation

Sentence 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 per...

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Bibliographic Details
Published in:	Proceedings of the National Academy of Sciences - PNAS 2013-05, Vol.110 (20), p.8051-8056
Main Authors:	Gibson, Edward, Bergen, Leon, Piantadosi, Steven T.
Format:	Article
Language:	eng
Subjects:	Bayes Theorem Bayesian analysis Biological and medical sciences Cognition Cognition & reasoning Cognitive linguistics Communication Comprehension Computational linguistics Daughters Fundamental and applied biological sciences. Psychology Grammatical constructions Humans Information Integration Language Language comprehension Mothers Natural Language Processing Production and perception of spoken language Psycholinguistics Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Reproducibility of Results Semantics Social Sciences Syntactic models Syntactics Syntax
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Summary:	Sentence 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 nonliteral interpretation of sentences should (iii) increase with the perceived 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.
ISSN:	0027-8424 1091-6490