SpeakerNoah Goodman
DateSep 25, 2015
Time11:00AM 12:30PM
LocationIF 4.31/4.33
TitleUncertainty in language and thought

Probabilisticmodels of human cognition have been widely successful at capturing theways that people represent and reason with uncertain knowledge. In thistalk I will explore how this approach extends to natural languagepragmatics and semantics. I will first describe how probabilisticprogramming languages provide a formal tool encompassing probabilisticuncertainty and compositional structure. I will use these tools toconstruct a framework for language understanding that views literalsentence meaning through probabilistic conditioning and pragmaticenrichment as recursive social reasoning. I'll show that uncertaintyabout the world and the speaker lead to quantity implicature effects,that predict experimental results from reference games. I will thenconsider the effects of uncertainty about the language itself---what ifthe listener is unsure about the meaning of words or the topic ofconversation? Lexical uncertainty leads to models of vague adjectives(``Bob is tall'') and generic language (``boys are tall''). Topicuncertainty leads to models of figurative speech (hyperbole and irony).Time permitting I'll touch on recent work on uncertainty about commonground. In all of these cases the models predict human judgements withhigh quantitative accuracy. Taken together this approach provides atheory of context in language understanding, a connection toquantitative behavioral data, and a bridge to our broader understandingof cognition.


Noah D. Goodman is Assistant Professor of Psychology,Linguistics (by courtesy), and Computer Science (by courtesy) atStanford University. He studies the computational basis of humanthought, merging behavioral experiments with formal methods fromstatistics and programming languages. He received his Ph.D. inmathematics from the University of Texas at Austin in 2003. In 2005 heentered cognitive science, working as Postdoc and Research Scientist atMIT. In 2010 he moved to Stanford where he runs the Computation andCognition Lab. CoCoLab studies higher-level human cognition includinglanguage understanding, social reasoning, and concept learning; the labalso works on applications of these ideas and enabling technologies suchas probabilistic programming languages.

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