About

Jeffrey Lidz is a Professor and Chair of the Department of Linguistics at the University of Maryland. His research expertise encompasses language acquisition, psycholinguistics, syntax, and computational linguistics. Lidz's work investigates how infants and children acquire syntactic dependencies, how they interpret nonlocal syntactic relations, and how they use syntactic and semantic information to infer word meanings. He has contributed to understanding the interaction between syntactic dependency acquisition and lexical development, as well as the mechanisms underlying children's ability to learn verb meanings through syntactic bootstrapping. Lidz's research also explores the cognitive and perceptual processes involved in event representation, such as how adults visually perceive complex social events like trading, and how linguistic framing influences change detection tasks. His work emphasizes the role of statistical inference, grammatical content, and thematic relations in language learning and perception. As a scholar, Lidz has advanced theories on the relationship between syntax, semantics, and cognition, providing computational models and experimental evidence to support his findings.

Research topics

• Computer Science
• Linguistics
• Artificial Intelligence
• Natural Language Processing
• Psychology
• Philosophy
• Sociology
• Cognitive psychology
• Mathematics
• Epistemology
• History
• Cognitive science

Selected publications

• The Power of Ignoring: Filtering Input for Argument Structure Acquisition

  Cognitive Science · 2022 · 11 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

  Learning in any domain depends on how the data for learning are represented. In the domain of language acquisition, children's representations of the speech they hear determine what generalizations they can draw about their target grammar. But these input representations change over development as a function of children's developing linguistic knowledge, and may be incomplete or inaccurate when children lack the knowledge to parse their input veridically. How does learning succeed in the face of potentially misleading data? We address this issue using the case study of "non-basic" clauses in verb learning. A young infant hearing What did Amy fix? might not recognize that what stands in for the direct object of fix, and might think that fix is occurring without a direct object. We follow a previous proposal that children might filter nonbasic clauses out of the data for learning verb argument structure, but offer a new approach. Instead of assuming that children identify the data to filter in advance, we demonstrate computationally that it is possible for learners to infer a filter on their input without knowing which clauses are nonbasic. We instantiate a learner that considers the possibility that it misparses some of the sentences it hears, and learns to filter out those parsing errors in order to correctly infer transitivity for the majority of 50 frequent verbs in child-directed speech. Our learner offers a novel solution to the problem of learning from immature input representations: Learners may be able to avoid drawing faulty inferences from misleading data by identifying a filter on their input, without knowing in advance what needs to be filtered.

  PublisherDOI

• On the Acquisition of Attitude Verbs

  Annual Review of Linguistics · 2021 · 16 citations

  Senior authorCorresponding
  • Sociology
  • Psychology
  • Linguistics

  Attitude verbs, such as think, want, and know, describe internal mental states that leave few cues as to their meanings in the physical world. Consequently, their acquisition requires learners to draw from indirect evidence stemming from the linguistic and conversational contexts in which they occur. This provides us a unique opportunity to probe the linguistic and cognitive abilities that children deploy in acquiring these words. Through a few case studies, we show how children make use of syntactic and pragmatic cues to figure out attitude verb meanings and how their successes, and even their mistakes, reveal remarkable conceptual, linguistic, and pragmatic sophistication.

  PublisherDOI

• The mental representation of universal quantifiers

  Linguistics and Philosophy · 2021 · 22 citations

  Senior authorCorresponding
  • Computer Science
  • Natural Language Processing
  • Artificial Intelligence
  PublisherDOI

• Determiners are "conservative" because their meanings are not relations: evidence from verification

  Proceedings from Semantics and Linguistic Theory · 2021 · 8 citations

  Senior authorCorresponding
  • Linguistics
  • Mathematics
  • Philosophy

  Quantificational determiners have meanings that are "conservative" in the following sense: in sentences, repeating a determiner's internal argument within its external argument is logically insignificant. Using a verification task to probe which sets (or properties) of entities are represented when participants evaluate sentences, we test the predictions of three potential explanations for the cross-linguistic yet substantive conservativity constraint. According to "lexical restriction" views, words like every express relations that are exhibited by pairs of sets, but only some of these relations can be expressed with determiners. An "interface filtering" view retains the relational conception of determiner meanings, while replacing appeal to lexical filters (on relations of the relevant type) with special rules for interpreting the combination of a quantificational expression (Det NP) with its syntactic context and a ban on meanings that lead to triviality. The contrasting idea of "ordered predication" is that determiners don't express genuine relations. Instead, the second argument provides the scope of a monadic quantifier, while the first argument selects the domain for that quantifier: the sequences with respect to which it is evaluated. On this view, a determiner's two arguments each have a different logical status, suggesting that they might have a different psychological status as well. We find evidence that this is the case: When evaluating sentences like every big circle is blue, participants mentally group the things specified by the determiner's first argument (e.g., the big circles) but not the things specified by the second argument (e.g., the blue things) or the intersection of both (e.g., the big blue circles). These results suggest that the phenomenon of conservativity is due to ordered predication.

  PublisherOA PDFDOI

• Eighteen-month-old infants represent nonlocal syntactic dependencies

  Proceedings of the National Academy of Sciences · 2021 · 30 citations

  Senior authorCorresponding
  • Computer Science
  • Natural Language Processing
  • Linguistics

  -phrase and the verb, but 2) younger infants do not. These results suggest that the second year of life is a period of active syntactic development, during which the computational capacities for representing nonlocal syntactic dependencies become evident.

  PublisherOA PDFDOI

• Linguistic meanings as cognitive instructions

  Annals of the New York Academy of Sciences · 2021 · 20 citations

  Senior authorCorresponding
  • Computer Science
  • Linguistics
  • Psychology

  Natural languages like English connect pronunciations with meanings. Linguistic pronunciations can be described in ways that relate them to our motor system (e.g., to the movement of our lips and tongue). But how do linguistic meanings relate to our nonlinguistic cognitive systems? As a case study, we defend an explicit proposal about the meaning of most by comparing it to the closely related more: whereas more expresses a comparison between two independent subsets, most expresses a subset-superset comparison. Six experiments with adults and children demonstrate that these subtle differences between their meanings influence how participants organize and interrogate their visual world. In otherwise identical situations, changing the word from most to more affects preferences for picture-sentence matching (experiments 1-2), scene creation (experiments 3-4), memory for visual features (experiment 5), and accuracy on speeded truth judgments (experiment 6). These effects support the idea that the meanings of more and most are mental representations that provide detailed instructions to conceptual systems.

  PublisherDOI

Recent grants

• Doctoral Dissertation Research: Behavioral and Computational Investigation of Transitivity in the Acquisition of Non-Basic Syntax

  NSF · $16k · 2018–2020

• Quantification the Syntactic Interfaces in Language Acquisition

  NSF · $188k · 2005–2009

• Doctoral Dissertation Research: Similarity based interference and the acquisition of adjunct control

  NSF · $17k · 2016–2018

• Doctoral Dissertation Research: The mental representation and acquisition of the universal quantifiers

  NSF · $15k · 2020–2022

• NIH Grant R03DC006829

  NIH · $210k · 2007

Frequent coauthors

• Anne Christophe

  École des hautes études en sciences sociales

  21 shared

• Justin Halberda

  Johns Hopkins University

  19 shared

• Valentine Hacquard

  University of Maryland, College Park

  16 shared

• Paul M. Pietroski

  Rutgers Sexual and Reproductive Health and Rights

  16 shared

• Tim B. Hunter

  15 shared

• Alex de Carvalho

  Laboratoire de Psychologie du Développement et de L’Education de l’enfant

  11 shared

• Tyler Knowlton

  University of Pennsylvania

  10 shared

• Laurel Perkins

  University of California, Los Angeles

  9 shared

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