Daniel Lassiter

Assistant Professor of Linguistics, Stanford University

Research areas:

Contact: danlassiter [at sign] stanford [dot] edu

Research overview

My research combines formal tools and experimental methods from linguistics and other areas of cognitive science to work toward a unified theory of language understanding as a cognitive phenomenon. 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 language understanding as a problem of Bayesian inference. 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.

Some writings [more here]

Epistemic : Deontic :: Additive : Intermediate — Modality, scale structure, and scalar reasoning
   To appear in Pacific Philosophical Quarterly.
Epistemic comparison, models of uncertainty, and the disjunction puzzle
   Journal of Semantics, 2014.
Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought (w/Noah Goodman)
   In press, Handbook of Contemporary Semantics — 2nd edition, ed. C. Fox & S. Lappin.
Adjectival modification and gradation
   In press, Handbook of Contemporary Semantics — 2nd edition, ed. C. Fox & S. Lappin.
Context, scale structure, and statistics in the interpretation of positive-form adjectives
   Semantics and Linguistic Theory (SALT) 23, 2013. (w/Noah Goodman)
Measurement and Modality: The Scalar Basis of Modal Semantics
    Ph.D. dissertation, NYU Linguistics, 2011 (supervisor: Chris Barker).
Vagueness as probabilistic linguistic knowledge (2011)
    In R. Nouwen et al. (eds.), Vagueness in Communication, Springer, 2011.
Gradable epistemic modals, probability, and scale structure (2010)
   Semantics and Linguistic Theory (SALT) 20, 2010.

CV

Full list of online work

Current teaching

Language understanding and Bayesian inference
     (NASSLLI 2014 @ U. Maryland)

Experimental methods for studying pragmatics on the web
     (ESSLLI 2014 @ U. Tübingen; with Mike Frank)

All courses

Tools