Kyle Mahowald

About

Kyle Mahowald is an Assistant Professor in the College of Liberal Arts at the University of Texas at Austin. His research focuses on computational linguistics, psycholinguistics, and quantitative methods in linguistics. He is also involved in natural language processing, large language models, and cognitive science, contributing to the understanding of language processing and computational modeling within these fields.

Research topics

• Computer Science
• Psychology
• Cognitive science
• Natural Language Processing
• Linguistics
• Artificial Intelligence
• Social psychology
• Cognitive psychology
• Machine Learning
• Philosophy

Selected publications

• Dissociating language and thought in large language models

  Trends in Cognitive Sciences · 2024 · 276 citations

  1st authorCorresponding
  DOI

• Dissociating language and thought in large language models

  arXiv (Cornell University) · 2023 · 91 citations

  1st authorCorresponding

  Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence -- knowledge of linguistic rules and patterns -- and functional linguistic competence -- understanding and using language in the world. We ground this distinction in human neuroscience, which has shown that formal and functional competence re…

  DOI

• Action anticipation based on an agent's epistemic state in toddlers and adults

  2021 · 31 citations

  Do toddlers and adults engage in spontaneous Theory of Mind (ToM)? Evidence from anticipatory looking (AL) studies suggests that they do. But a growing body of failed replication studies raised questions about the paradigm’s suitability. In this multi-lab collaboration, we test the robustness of spontaneous ToM measures. We examine whether 18- to 27-month-olds’ and adults’ anticipatory looks distinguish between two basic forms of an agent’s epistemic states: knowledge and ignorance. In toddlers…

  PublisherOA PDFDOI

• France or Spain or Germany or France: A Neural Account of Non-Redundant Redundant Disjunctions

  ArXiv.org · 2026-02-26

  articleOpen accessSenior author

  Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or Spain, or a mathematics program in Germany or France." While this phenomenon has typically been analyzed using symbolic formal representations, we aim to provide an account grounded in artificial neural mechanisms. We first present new behavioral evidence from humans and large language models demo…

  PublisherOA PDF

• Dissociating Direct Access from Inference in AI Introspection

  arXiv (Cornell University) · 2026-03-05

  articleOpen accessSenior author

  Introspection is a foundational cognitive ability, but its mechanism is not well understood. Recent work has shown that AI models can introspect. We study their mechanism of introspection, first extensively replicating Lindsey et al. (2025)'s thought injection detection paradigm in large open-source models. We show that these models detect injected representations via two separable mechanisms: (i) probability-matching (inferring from perceived anomaly of the prompt) and (ii) direct access to int…

  PublisherOA PDF

Recent grants

• CRII: RI: Using Linguistic Variation to Understand Deep Neural Models of Language

  NSF · $175k · 2021–2024

Frequent coauthors

• Edward Gibson

  46 shared

• Evelina Fedorenko

  Massachusetts Institute of Technology

  40 shared

• Richard Futrell

  30 shared

• Isabelle Dautriche

  Laboratoire de Psychologie Cognitive

  18 shared

• Tiago Pimentel

  14 shared

• Peter Graff

  14 shared

• Ryan Cotterell

  13 shared

• Jeremy Hartman

  13 shared

Similar researchers at University of Texas at Austin

• Don Millerh-36
• Harvey M. Sussmanh-33
• David I. Beaverh-32