Hi! I'm Shane, an Assistant Professor in Linguistics at the University of Washington, where I direct CLMBR: the Computation, Language, and Meaning Band of Researchers.

My research uses computational, logical, and experimental methods to address questions at the foundations of natural language semantics and cognitive science. Much of my work has been driven by the following question: in what ways does human cognition shape the structure of the natural languages that we speak? To that end, I have pursued explanations of semantic universals and of compositionality in terms of their impact on the learnability of meaning systems. Related interests include animal communication and expressivism in the theory of communication.

Before coming to Washington, I was (after a brief visit at Institut Jean Nicod) a postdoc at the Institute for Logic, Language and Computation at Universiteit van Amsterdam, on the project Cognitive Semantics and Quantities. Before that, I did my PhD in Philosophy and Symbolic Systems at Stanford University. During Summer 2016, I was a research software engineering intern in the Research and Machine Intelligence division of Google. Before that, I studied Philosophy, Mathematics, and Computer Science at The Johns Hopkins University.

You can reach me via snail or electronic mail:

Guggenheim Hall, room 418D
Seattle, Washington 98195

shanest AT uw DOT edu


Journal Articles

Ease of Learning Explains Semantic Universals
Shane Steinert-Threlkeld and Jakub Szymanik, Cognition, vol 195, no. XX, pp. XX.
official preprint code

Semantic Expressivism for Epistemic Modals
Peter Hawke and Shane Steinert-Threlkeld (alphabetical order), Linguistics and Philosophy, forthcoming.
official (open access) preprint

Towards the Emergence of Non-trivial Compositionality
Shane Steinert-Threlkeld, Philosophy of Science, forthcoming.
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An Explanation of the Veridical Uniformity Universal
Shane Steinert-Threlkeld, Journal of Semantics, vol 37 no 1, pp. 129-144.
official (open access) preprint code

Learnability and Semantic Universals
Shane Steinert-Threlkeld and Jakub Szymanik, Semantics & Pragmatics, vol 12 issue 4.
early access code

Informational Dynamics of Epistemic Possibility Modals
Peter Hawke and Shane Steinert-Threlkeld, Synthese, vol 195 no 10, pp. 4309-4342.

Compositional Signaling in a Complex World
Shane Steinert-Threlkeld, Journal of Logic, Language, and Information, vol 25 no 3, pp. 379-397.
official code

Compositionality and Competition in Monkey Alert Calls
Shane Steinert-Threlkeld, Theoretical Linguistics, vol 42 no 1-2, pp. 159-171.
official local

Some Properties of Iterated Languages
Shane Steinert-Threlkeld, Journal of Logic, Language, and Information, vol 25 no 2, pp. 191-213.
official local

Iterating semantic automata
Shane Steinert-Threlkeld and Thomas F. Icard, III., Linguistics and Philosophy, vol 36 no 2, pp. 151-173.

ADC Method of Proof Search in Intuitionistic Propositional Natural Deduction
Grigori Mints and Shane Steinert-Threlkeld, Journal of Logic and Computation, vol 26 no 1, pp. 395-408.

Ontological Labels for Automated Location of Anatomical Shape Differences
Shane Steinert-Threlkeld, Siamak Ardekani, Jose L.V. Mejino, Landon Todd Detwiler, James F. Brinkley, Michael Halle, Ron Kikinis, Raimond L. Winslow, Michael I. Miller, and J. Tilak Ratnanather, Journal of Biomedical Informatics, vol 45 no 3, pp. 522-527.

Conference Proceedings

Complexity/informativeness trade-off in the domain of indefinite pronouns
Milica Denic, Shane Steinert-Threlkeld Jakub Szymanik, Proceedings of Semantics and Linguistic Theory (SALT 30)

Most, but not more than half is proportion-dependent and sensitive to individual differences
Sonia Ramotowska, Shane Steinert-Threlkeld, Leendert van Maanen, Jakub Szymanik, Proceedings of Sinn und Bedeutung (SuB 24)

Quantifiers in natural language optimize the simplicity/informativeness trade-off
Shane Steinert-Threlkeld, Proceedings of the 22nd Amsterdam Colloquium, eds. Julian J. Schlöder, Dean McHugh & Floris Roelofsen, pp. 513-522.
official preprint code poster

The emergence of monotone quantifiers via iterated learning
Fausto Carcassi, Shane Steinert-Threlkeld (co-first), and Jakub Szymanik, Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019).
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Complexity and learnability in the explanation of semantic universals
Iris van de Pol, Shane Steinert-Threlkeld, and Jakub Szymanik, Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019).
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Neural Models of the Psychosemantics of "Most"
Lewis O'Sullivan and Shane Steinert-Threlkeld, Proceedings of the 9th Workshop on Cognitive Modeling and Computational Linguistics (CMCL2019).
official poster code

Paying Attention to Function Words
Shane Steinert-Threlkeld, Emergent Communication Workshop @ 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).
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Some of them can Be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Sandro Pezzelle, Shane Steinert-Threlkeld, Raffaella Bernardi, Jakub Szymanik, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018).
official code

Uniform Definability in Assertability Semantics
Shane Steinert-Threlkeld, Proceedings of the 21st Amsterdam Colloquium, eds. Alexandre Cremers, Thom van Gessel, and Floris Roelofsen, pp. 445-454.
final preprint

Alternative Representations in Formal Semantics: A case study of quantifiers
Shane Steinert-Threlkeld, Gert-Jan Munneke, and Jakub Szymanik, Proceedings of the 20th Amsterdam Colloquium, eds. Thomas Brochhagen, Floris Roelofsen, and Nadine Thelier, pp. 368-379.
final preprint

Informational Dynamics of `Might' Assertions
Peter Hawke and Shane Steinert-Threlkeld, Proceedings of Logic, Rationality, and Interaction (LORI-V), eds. Wiebe van der Hoek, Wes Holliday, and Wen-fang Wang, pp. 143-155.
official local

Learning to Use Function Words in Signaling Games
Shane Steinert-Threlkeld, Proceedings of Information Dynamics in Artificial Societies (IDAS-14), eds. Emiliano Lorini and Laurent Perrussel.

On the Decidability of Iterated Languages
Shane Steinert-Threlkeld, Proceedings of Philosophy, Mathematics, Linguistics: Aspects of Interaction (PhML2014), ed. Oleg Prosorov, pp. 215-224.

Automata and Complexity in Multiple-Quantifier Sentence Verification
Jakub Szymanik, Shane Steinert-Threlkeld, Marcin Zajenkowski, and Thomas F. Icard, III., Proceedings of the 12th International Conference on Cognitive Modeling (ICCM).

Ontological Labels for Automated Location of Left-Ventricular Remodeling
Shane Steinert-Threlkeld, Siamak Ardekani, Jose L.V. Mejino, Landon Todd Detwiler, James F. Brinkley, Michael Halle, Ron Kikinis, Raimond L. Winslow, Michael I. Miller, and J. Tilak Ratnanather, Proceedings of 2011 Fifth IEEE International Conference on Semantic Computing, pp. 572- 573.

In Progress

Shane Steinert-Threlkeld, Emmanuel Chemla and Philippe Schlenker, paper on the evolution of general calls and scalar reasoning, under review.

Shane Steinert-Threlkeld and Zachary Steinert-Threlkeld, "Social Network Structure and the Repression-Dissent Puzzle", under review.

Nur Lan, Emmanuel Chemla, Shane Steinert-Threlkeld, paper on emergence of discrete signals, under review.

Jaap Jumelet, Milica Denic, Dieuwke Hupkes, Jakub Szymanik, Shane Steinert-Threlkeld, paper on language models' understanding of NPIs, under review.

Other Publications

Shane Steinert-Threlkeld, "Communication and Computation: New Questions About Compositionality", PhD Dissertation, Stanford University (reprinted as ILLC Dissertation Series 2017-05).

Shane Steinert-Threlkeld, "Lambda Calculi", Internet Encyclopedia of Philosophy.

Shane Steinert-Threlkeld and J Tilak Ratnanather, "Open standards, web-based mathlets: making interactive tutorials using the html5 canvas element, Loci/JOMA.



  • LING 571: Deep Processing Techniques for Natural Language Processing. [Aut '19, Aut '20]
  • LING 572: Advanced Statistical Methods for Natural Language Processing. [Win '20]
  • LING 575N: Deep Learning for NLP [Spr '21]
  • LING 575: Topics in Computational Linguistics

Summer Schools

Amsterdam + Stanford

  • Causality, Decision Making, and Games @ ILLC (with Robert van Rooij)
  • Neural network methods for quantifiers @ ILLC, Universiteit van Amsterdam
  • PHIL 152: Computability and Logic @ Stanford (syllabus)
  • PHIL 23A: Cognitive Science of Mathematics @ Stanford (syllabus)
  • PHIL 150e: Logic in Action @ Stanford (with Peter Hawke and Thomas F. Icard, III.)


  • Wouter Posdijk, MSc Logic: "Simplicity and informativeness trade-off in the semantic typology of quantifiers"
  • Lewis O'Sullivan, MSc Brain and Cognitive Sciences: "Neural models of context-dependent quantifier verification in the visual identification paradigm"
  • Two BS in AI theses: machine learning for semantic universals
  • Two BS in AI theses: probabilistic models of syllogistic reasoning



  • Journals: Semantics & Pragmatics (2); Mind; Noûs; Mind & Language; Erkenntnis (2); Synthese (2); The Review of Symbolic Logic (2); Journal of Logic, Language and Information (2); Journal of Semantics; Ergo; Frontiers in Psychology (Language Sciences); Theoria (2); Logic Journal of the IGPL
  • Conferences: ACL; EMNLP; ICLR; SALT; Amsterdam Colloquium; CogSci; Fourth Workshop on Logic, Rationality, and Interaction (LORI-IV)

Conference Organization

Program committee:

Organizing committee:

  • 1st, 2nd, 3rd CSLI Workshop on Logic, Rationality, and Intelligent Interaction
  • 1st, 2nd Prometheus Undergraduate Philosophy Conference


Expository Material / Resources

  • Tutorial introduction to neural networks: slides and Jupyter notebook, explaining and introducing neural networks. Includes a worked example with quantifiers (in PyTorch) and additional practical advice. (Largely supersedes the tutorial linked below, though that one can still be useful for understanding TensorFlow's estimators interfactor.)
  • Decision and Game Theory for AI: lecture notes (in the form of Jupyter Notebooks) for a mini-course on decision and game theory for undergraduates in AI. Includes simple implementations of things like CDT/EDT, replicator dynamic, reinforcement learning in signaling games.
  • Introduction to Neural Networks via TensorFlow and its Estimators library, with an eye to quantifiers: an interactive Jupyter notebook that introduces the basics of training neural networks with TensorFlow. The main example is designed with an eye towards training networks to learn quantifiers.
  • Generate Dot Arrays for Psycholinguistic Experiments: Python script for generating colored dot arrays, including the four stimulus type from Pietroski et al 2009, "Psychosemantics of `most'".
  • Replicator Dynamics Examples: this repository has simple Python code that makes it easy to run and visualize simulations of the replicator and replicator-mutator dynamics in evolutionary game theory. It includes matrices and plots for common examples.
  • Joyce's Argument for Probabilism: this is a Mathematica notebook that carries out the construction in the proof of Joyce's most general argument for probabilism from his 2009 "Accuracy and Coherence: Prospects for an Alethic Epistemology of Partial Belief"

Erdös Number

My Erdös Number is 4, via at least three paths:

  • Grigori Mints - Yuri Matiyasevitch - Richard Guy - Paul Erdös
  • Michael I Miller - Ulf Grenander - Oved Shisha - Paul Erdös
  • Raimond L Winslow - Daniel Q. Naiman - János Pach - Paul Erdös