News

• I've accepted a position as an Assistant Professor of Linguistics at Stanford University, starting September 2013.
• New book: Measurement and Modality: The Scalar Basis of Modal Semantics, to appear with Oxford University Press.

Representative writings

Communicating with epistemic modals in stochastic λ-calculus (2012)
   (Draft, with Noah Goodman. Comments welcome, but please don't cite.)
How many kinds of reasoning? Inference, probability, and natural language semantics (2012)
   Proceedings of the 34th Annual Conference of the Cognitive Science Society. (with Noah Goodman)
Presuppositions, provisos, and probability (2012)
   Semantics & Pragmatics 5(2): 1-37.
Quantificational and modal interveners in degree constructions (2012)
   Semantics and Linguistic Theory (SALT) 22.
Measurement and Modality: The Scalar Basis of Modal Semantics (2011)
    Ph.D. dissertation, NYU Linguistics (supervisor: Chris Barker).
Vagueness as probabilistic linguistic knowledge (2011)
    In Nouwen et al. eds., Vagueness in Communication, Springer.
Nouwen's puzzle and a scalar semantics for obligations, needs, and desires (2011)
   Semantics and Linguistic Theory (SALT) 21.
Gradable epistemic modals, probability, and scale structure (2010)
   Semantics and Linguistic Theory (SALT) 20.
Semantic Externalism, Language Variation, and Sociolinguistic Accommodation (2008)
    Mind and Language 23(5): 607-633.

Research overview

My research combines formal tools and experimental methods from linguistics, psychology, philosophy, and computer science to work toward a unified theory of uncertainty in the cognitive science of meaning. Uncertain reasoning and inference play a crucial role in linguistic semantics and pragmatics as well as in the study of reasoning, concepts, and other areas of cognitive science, and a diverse variety of formalisms have been employed in each field. I work to combine methods and insights of formal model-theoretic semantics and Gricean pragmatics with probabilistic and decision-theoretic models, which are widely considered to be the best available tools for constructing precise, semantically sound, and flexible models of uncertain inference and decision-making. Using methods from computational cognitive science, I work to construct precise models which make both qualitative and quantitative predictions, and conduct behavioral experiments to test them.

The ultimate goal is to construct a broad-coverage probabilistic theory of meaning and reasoning which

• exhibits both systematicity and variability (like real people)
• makes precise and testable quantitative predictions about the interaction between world knowledge and linguistic interpretation
• can be used to bring existing insights from formal linguistics and cognitive science to bear on one another.

Toward this end, I've worked on a variety of topics such as the semantics of modals and degree expressions, the pragmatics of vagueness and presupposition, inductive vs. deductive reasoning, and models of various pragmatic phenomena which treat speech interpretation as inverse planning (formalized in precise Bayesian terms). I've argued in various domains that combining logical and probabilistic models not only achieves a desirable theoretical unification but also improved empirical coverage and new theoretical insights.