About

Kathryn Schuler is an Assistant Professor in the Department of Linguistics at the University of Pennsylvania. She holds a Ph.D. from Georgetown University, obtained in 2017. Her professional contact information includes an email address (kschuler@sas.upenn.edu), phone number (215-898-6909), and office location in Room 314C on the third floor at 3401-C Walnut Street. Her office hours are available by appointment. The department is part of the College of Arts & Sciences, and she is involved in the graduate program, with her office situated within the department's facilities. The page does not provide specific details about her research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Linguistics
• Cognitive psychology
• Mathematics
• Mathematics education
• Psychology
• Developmental psychology
• Arithmetic

Selected publications

• 6-To-8 Year-Old Children Adjust Their Linguistic Generalizations Based on Distributional Cues

  Journal of Cognition and Development · 2026-01-16

  article1st authorCorresponding
  PublisherDOI

• The Role of Frequency and Optionality in the Regularization of Linguistic Variation

  2025-04-01

  preprintOpen access1st authorCorresponding

  Languages are widely known to become more regular over time, yet not all variation is regularized — some forms resist regularization and remain stable over many generations. A large body of work has explored why learners regularize unpredictable variation, often focusing on the cognitive mechanisms that underlie this behavior and how these mechanisms differ between adults and children. However, less attention has been given to investigating the factors that allow some variation to remain stable over time. In this paper, we propose that optionality may be a particularly stable form of linguistic variation, likely to resist cognitive pressures to regularize. To test this hypothesis, we conducted two artificial language learning experiments designed to investigate the conditions under which optionality remains stable or becomes regularized. In Experiment 1, we compared optional variation to variation involving two alternating overt forms, expecting optionality to show greater stability. In Experiment 2, we explored whether the frequency of the optional form would influence regularization, hypothesizing that rare optional forms might resist regularization more effectively than frequent ones. Our findings suggest that, while optionality is not inherently more stable, it is nonetheless special, revealing critical insights into cognitive mechanisms proposed to be responsible for regularization behavior in child and adult learners.

  PublisherDOI

• Distributional learning of recursive structures is constrained by structural representation

  2025-02-24

  preprintOpen accessSenior author

  This work investigates the mechanism of learning recursive structures. The ability of recursion is considered crucial for language and universally available, but there are considerable with- and cross-linguistic differences regarding the rules for recursive embedding, which poses a learnability problem. Previous research has shown that adults can learn the recursivity of linear structures in an artificial language from distributional cues in non-recursively embedded data. However, an important open question is participants’ structural representation of the grammar, which is considered crucial for linguistic recursion. In this study, we examined the hypothesis that representation of the structural head is necessary for the distributional learning of recursive structures. Adult participants were exposed to one of the two artificial languages which had identical linear orders but different heads. At test, participants were asked to rate test strings which examined their knowledge of the head and recursion. As predicted, we found that the learning mechanism is constrained: participants who learned the language with the correct head were more likely to allow recursive embedding. The findings suggest that human learners need structural representations beyond surface-level distributional cues to acquire recursive structures.

  PublisherOA PDFDOI

• Children’s distributional learning of recursive structures: Capacities and constraints

  2025-04-11

  preprintOpen accessSenior author

  This study investigates how children learn recursive structures, a core property of human language. While recursion is considered universally available, languages differ in the specific rules governing recursive embedding, which poses a learnability challenge. Previous research has shown that adults can learn the recursivity of a structure in an artificial language from distributional cues in non-recursively embedded data, and this learning mechanism is constrained by the high-level structural representation. However, it remains an open question whether children can do the same. We addressed this question in two artificial language learning experiments. In Experiment 1, we found that children, like adults, can use distributional cues in non-recursive input to acquire recursive structures. In Experiment 2, we found that while children also learned the headedness of the structure from distributional cues, only older children were able to integrate this information to constrain the licensing of recursion. These findings suggest that children can acquire complex grammatical knowledge through distributional learning, and that their distributional learning ability changes over the course of development, particularly the ability to integrate multiple sources of information.

  PublisherOA PDFDOI

• The Role of Frequency and Optionality in the Regularization of Linguistic Variation

  2025-05-27

  preprintOpen access1st authorCorresponding

  Languages are widely known to become more regular over time, yet not all variation is regularized — some forms resist regularization and remain stable over many generations. A large body of work has explored why learners regularize unpredictable variation, often focusing on the cognitive mechanisms that underlie this behavior and how these mechanisms differ between adults and children. However, less attention has been given to investigating the factors that allow some variation to remain stable over time. In this paper, we propose that optionality may be a particularly stable form of linguistic variation, likely to resist cognitive pressures to regularize. To test this hypothesis, we conducted two artificial language learning experiments designed to investigate the conditions under which optionality remains stable or becomes regularized. In Experiment 1, we compared optional variation to variation involving two alternating overt forms, expecting optionality to show greater stability. In Experiment 2, we explored whether the frequency of the optional form would influence regularization, hypothesizing that rare optional forms might resist regularization more effectively than frequent ones. Our findings suggest that, while optionality is not inherently more stable, it is nonetheless special, revealing critical insights into cognitive mechanisms proposed to be responsible for regularization behavior in child and adult learners.

  PublisherOA PDFDOI

• Structure and Sound: How Similarity and Consistency Jointly Shape Language Learning and Change

  2025-06-05

  preprintOpen accessSenior author

  This study investigates how two core pressures in language change—structural consistency and phonological similarity—interact to shape learners’ representations of linguistic systems. Using a 2x2 artificial language learning experiment, 80 adult participants were exposed to one of four spoken artificial languages that varied in the consistency of plural marker conditioning (consistent vs inconsistent) and the phonological similarity of the alternating markers (similar vs. distinct). Results revealed that learners’ encoding and reproduction of linguistic input are jointly shaped by perceptual constraints and structural cues. Learners were less accurate in repetition when markers were similar, particularly when they were also inconsistently conditioned, suggesting that structural consistency protects against confusability. When producing the markers themselves, participants regularized more in the similar-marker conditions, regardless of consistency, yet, crucially, they discovered and maintained consistent input structures more readily when markers were distinct, suggesting that perceptual similarity can erode structural learning. In addition, participants’ repetition error patterns aligned with attested pathways of sound change, including a bias toward devoicing and diphthong shifts analogous to those found in natural language sound changes. We argue that language change cannot be fully understood without considering how input properties interact with cognitive biases. Overall, this study highlights the value of experimental paradigms for isolating mechanisms of change while also pointing to the complex interplay of structure and sound in shaping language evolution.

  PublisherOA PDFDOI

• Acquisition of a rare variant: ne-realization in the negative utterances of French children and their caregivers

  Language Learning and Development · 2024-08-28 · 1 citations

  articleSenior author
  PublisherDOI

• Acquiring recursive structures through distributional learning

  Language Acquisition · 2023-03-22 · 3 citations

  articleOpen accessSenior author

  Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a single language, some structures allow infinite self-embedding while others are more restricted. For example, when expressing ownership relation, English allows infinite embedding of the prenominal genitive -s, whereas the postnominal genitive of is much more restricted. How do speakers learn which specific structures allow infinite embedding and which do not? The distributional learning proposal suggests that the recursion of a structure (e.g., X1’s-X2) is licensed if the X1 position and the X2 position are productively substitutable in non-recursive input. The present study tests this proposal with an artificial language learning experiment. We exposed adult participants to X1-ka-X2 strings. In the productive condition, almost all words attested in X1 position were also attested in X2 position; in the unproductive condition, only some were. We found that, as predicted, participants from the productive condition were more likely to accept unattested strings at both one- and two-embedding levels than participants from the unproductive condition. Our results suggest that speakers can use distributional information at one-embedding level to learn whether or not a structure is recursive.

  PublisherOA PDFDOI

• Acquiring recursive structures through distributional learning

  2023-02-20 · 2 citations

  preprintOpen accessSenior author

  Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a single language, some structures allow infinite self-embedding while others are more restricted. For example, when expressing ownership relation, English allows infinite embedding of the prenominal genitive -s, whereas the postnominal genitive of is much more restricted. How do speakers learn which specific structures allow infinite embedding and which do not? The distributional learning proposal suggests that the recursion of a structure (e.g., X1’s-X2) is licensed if the X1 position and the X2 position are productively substitutable in non-recursive input. The present study tests this proposal with an artificial language learning experiment. We exposed adult participants to X1-ka-X2 strings. In the productive condition, almost all words attested in X1 position were also attested in X2 position; in the unproductive condition, only some were. We found that, as predicted, participants from the productive condition were more likely to accept unattested strings at both one- and two-embedding levels than participants from the unproductive condition. Our results suggest that speakers can use distributional information at one-embedding level to learn whether or not a structure is recursive.

  PublisherOA PDFDOI

• Adults regularize variation when linguistic cues suggest low input reliability

  Proceedings of the Linguistic Society of America · 2022-05-05

  articleOpen accessSenior author

  Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation.

  PublisherOA PDFDOI

Frequent coauthors

• Elissa L. Newport

  Georgetown University

  24 shared

• Alison Austin

  Georgetown University

  9 shared

• Peter E. Turkeltaub

  Georgetown University Medical Center

  9 shared

• Mackenzie E. Fama

  George Washington University

  9 shared

• Sarah Furlong

  University of North Carolina at Chapel Hill

  3 shared

• Daoxin Li

  University of Pennsylvania

  3 shared

• Charles Yang

  Walter Reed National Military Medical Center

  2 shared

• Jaclyn E. Horowitz

  1 shared

Labs

• Department of Linguistics, University of PennsylvaniaPI

Education

• Ph.D., Language Acquisition

  Georgetown University

  2017