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.

Academic Appointments

  • Associate Professor, Linguistics

Program Affiliations

  • Symbolic Systems Program

2021-22 Courses

Stanford Advisees

All Publications

  • Complex sentential operators refute unrestricted Simplification of Disjunctive Antecedents SEMANTICS & PRAGMATICS Lassiter, D. 2018; 11

    View details for DOI 10.3765/sp.11.9

    View details for Web of Science ID 000438144600009

  • Embedded Implicatures as Pragmatic Inferences under Compositional Lexical Uncertainty JOURNAL OF SEMANTICS Potts, C., Lassiter, D., Levy, R., Frank, M. C. 2016; 33 (4): 755-802

    View details for DOI 10.1093/jos/ffv012

    View details for Web of Science ID 000393183900004

  • Must, knowledge, and (in)directness NATURAL LANGUAGE SEMANTICS Lassiter, D. 2016; 24 (2): 117-163
  • Epistemic Comparison, Models of Uncertainty, and the Disjunction Puzzle JOURNAL OF SEMANTICS Lassiter, D. 2015; 32 (4): 649-684

    View details for DOI 10.1093/jos/ffu008

    View details for Web of Science ID 000366631100003

  • How many kinds of reasoning? Inference, probability, and natural language semantics. Cognition Lassiter, D., Goodman, N. D. 2015; 136: 123-134


    The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments.

    View details for DOI 10.1016/j.cognition.2014.10.016

    View details for PubMedID 25497521


    View details for DOI 10.1111/papq.12045

    View details for Web of Science ID 000346790400003