About

Professor Laurence B. Leonard is a Rachel E. Stark Distinguished Professor at Purdue University in the College of Liberal Arts. He holds a Ph.D. from the University of Pittsburgh, obtained in 1973. His primary interests include normal and disordered child language, with a focus on language development and language disorders in children. Professor Leonard has taught courses and seminars in normal and disordered language development and has served as a thesis and dissertation advisor to graduate students working in these areas. His research has included the early phonological, lexical, and morphosyntactic development of children with specific language impairment (SLI). He also studies SLI across different languages and conducts research on the effectiveness of language treatment procedures. His publications include articles, chapters in edited volumes, and a book, reflecting his contributions to the field of child language development and disorders.

Research topics

• Linguistics
• Medicine
• Psychology
• Nursing
• Family medicine
• Cognitive psychology
• Neuroscience
• Statistics
• Immunology

Selected publications

• Sentence prediction in DLD (Kueser et al., 2026)

  figshare ASHA Publications · 2026-04-21

  otherOpen accessSenior author

  <b>Purpose: </b>Children with developmental language disorder (DLD) have difficulty making online predictions of language material following verbs (e.g., “The monkey eats a very delicious … [banana]”). We explore the contributions of lexicosemantic knowledge deficits and online processing deficits by comparing performance across offline lexicosemantic and online processing tasks in sentences with higher versus lower speed and complexity.<b>Method:</b> Participants included twenty-six 4- to 5-year-old children with DLD and 26 age-matched children with typical development (TD). In Experiment 1, participants’ lexicosemantic knowledge about verb–patient associates was assessed (e.g., “What do babies usually wear? A bib or a necklace?”) in an offline pointing task. Following the offline task, participants’ online predictive processing for the same verb–patient associates was assessed using eye tracking (e.g., “The baby is wearing a very special [bib vs. necklace]”). In Experiment 2, the same tasks were completed with simpler sentences spoken at a slower rate with a reduced number of words.<b>Results: </b>Across the two experiments, the quality of lexicosemantic knowledge impacted the quality of sentence prediction for children in both groups. In addition, children with DLD had poorer lexicosemantic knowledge compared to peers with TD. Yet even after accounting for item-level lexicosemantic knowledge, the children with DLD differed from their peers with TD in sentence prediction. Specifically, children with DLD showed similar sentence prediction to peers with TD in faster sentences, but in slower sentences, their predictions decayed over time.<b>Conclusions: </b>Children with DLD have sentence prediction deficits due to a combination of lexicosemantic knowledge deficits and problems with working memory decay and sustained attention. Sentence prediction deficits in DLD arise from both lexicosemantic and processing factors.<b>Supplemental Material S1.</b> Supplemental figures showing raw participant-level data and two supplemental statistical analyses.Kueser, J. B., Outzen, C., Borovsky, A., Deevy, P., &amp; Leonard, L. B. (2026). Sentence prediction deficits in developmental language disorder are a product of vocabulary knowledge and processing abilities. <i>Journal of Speech, Language, and Hearing Research, </i><i>69</i>(5), 2219–2242. https://doi.org/10.1044/2026_JSLHR-25-00121<br>

  PublisherDOI

• Sentence Prediction Deficits in Developmental Language Disorder Are a Product of Vocabulary Knowledge and Processing Abilities

  Journal of Speech Language and Hearing Research · 2026-04-21

  articleOpen accessSenior author

  PURPOSE: Children with developmental language disorder (DLD) have difficulty making online predictions of language material following verbs (e.g., "The monkey eats a very delicious … [banana]"). We explore the contributions of lexicosemantic knowledge deficits and online processing deficits by comparing performance across offline lexicosemantic and online processing tasks in sentences with higher versus lower speed and complexity. METHOD: Participants included twenty-six 4- to 5-year-old children with DLD and 26 age-matched children with typical development (TD). In Experiment 1, participants' lexicosemantic knowledge about verb-patient associates was assessed (e.g., "What do babies usually wear? A bib or a necklace?") in an offline pointing task. Following the offline task, participants' online predictive processing for the same verb-patient associates was assessed using eye tracking (e.g., "The baby is wearing a very special [bib vs. necklace]"). In Experiment 2, the same tasks were completed with simpler sentences spoken at a slower rate with a reduced number of words. RESULTS: Across the two experiments, the quality of lexicosemantic knowledge impacted the quality of sentence prediction for children in both groups. In addition, children with DLD had poorer lexicosemantic knowledge compared to peers with TD. Yet even after accounting for item-level lexicosemantic knowledge, the children with DLD differed from their peers with TD in sentence prediction. Specifically, children with DLD showed similar sentence prediction to peers with TD in faster sentences, but in slower sentences, their predictions decayed over time. CONCLUSIONS: Children with DLD have sentence prediction deficits due to a combination of lexicosemantic knowledge deficits and problems with working memory decay and sustained attention. Sentence prediction deficits in DLD arise from both lexicosemantic and processing factors. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.31999236.

  PublisherDOI

• Sentence prediction in DLD (Kueser et al., 2026)

  figshare ASHA Publications · 2026-04-21

  otherOpen accessSenior author

  <b>Purpose: </b>Children with developmental language disorder (DLD) have difficulty making online predictions of language material following verbs (e.g., “The monkey eats a very delicious … [banana]”). We explore the contributions of lexicosemantic knowledge deficits and online processing deficits by comparing performance across offline lexicosemantic and online processing tasks in sentences with higher versus lower speed and complexity.<b>Method:</b> Participants included twenty-six 4- to 5-year-old children with DLD and 26 age-matched children with typical development (TD). In Experiment 1, participants’ lexicosemantic knowledge about verb–patient associates was assessed (e.g., “What do babies usually wear? A bib or a necklace?”) in an offline pointing task. Following the offline task, participants’ online predictive processing for the same verb–patient associates was assessed using eye tracking (e.g., “The baby is wearing a very special [bib vs. necklace]”). In Experiment 2, the same tasks were completed with simpler sentences spoken at a slower rate with a reduced number of words.<b>Results: </b>Across the two experiments, the quality of lexicosemantic knowledge impacted the quality of sentence prediction for children in both groups. In addition, children with DLD had poorer lexicosemantic knowledge compared to peers with TD. Yet even after accounting for item-level lexicosemantic knowledge, the children with DLD differed from their peers with TD in sentence prediction. Specifically, children with DLD showed similar sentence prediction to peers with TD in faster sentences, but in slower sentences, their predictions decayed over time.<b>Conclusions: </b>Children with DLD have sentence prediction deficits due to a combination of lexicosemantic knowledge deficits and problems with working memory decay and sustained attention. Sentence prediction deficits in DLD arise from both lexicosemantic and processing factors.<b>Supplemental Material S1.</b> Supplemental figures showing raw participant-level data and two supplemental statistical analyses.Kueser, J. B., Outzen, C., Borovsky, A., Deevy, P., &amp; Leonard, L. B. (2026). Sentence prediction deficits in developmental language disorder are a product of vocabulary knowledge and processing abilities. <i>Journal of Speech, Language, and Hearing Research, </i><i>69</i>(5), 2219–2242. https://doi.org/10.1044/2026_JSLHR-25-00121<br>

  PublisherDOI

• Word Learning in Children With Developmental Language Disorder: The Use of Retrieval Practice During Shared Book Reading

  Journal of Speech Language and Hearing Research · 2025-06-17 · 1 citations

  articleOpen access

  PURPOSE: Children with developmental language disorder (DLD) benefit from the inclusion of retrieval practice during word learning. However, most studies reporting this positive effect have been conducted in controlled laboratory conditions. In this study, we take a step toward real-world application by matching the design details of a previous laboratory study and inserting them in a shared book reading activity. METHOD: = 57.07 months) learned eight novel words presented in two illustrated children's books. Half of the novel words appeared in a repeated spaced retrieval (RSR) condition, and half appeared in a repeated study (RS) condition. The children learned both the novel word forms (e.g., /bog/) and their arbitrarily assigned "meanings" (e.g., "likes rain") in two learning sessions. Five minutes after the second learning session and 1 week later, the children's ability to learn the novel words was assessed. RESULTS: Both groups of children showed better recall of the novel words in the RSR condition than in the RS condition. This was true for both the novel word forms and their meanings. Scores on a recognition test did not show a difference between the two conditions. The children with TD performed at a higher level than the children with DLD on the word form recall and recognition tests. Both groups showed only a slight decline in word form recall after 1 week. There were no interactions. CONCLUSIONS: The results indicate that incorporation of retrieval practice into shared book reading activities can produce benefits to children's word learning. These findings should encourage future retrieval practice studies with ever closer approximations to the everyday shared book reading experiences of children.

  PublisherOA PDFDOI

• Evaluating Start-up Success Factors in Modern versus Traditional Industries in India: Insights from Poisson and Machine learning Models

  2024-12-20

  article1st authorCorresponding

  This study explores the success of startups in modern industries (FinTech, EdTech, AI) compared to traditional sectors (manufacturing, textiles, agriculture) in India, focusing on factors such as funding, location, and industry type. Using a Poisson regression model, which shows strong overall significance with a p-value of 1.40e-50, the research identifies Total Funding, CB Rank, and South India as key predictors of financing rounds. Although the Poisson model struggles with startups having 12 or more funding rounds, the Random Forest model provides improved fit and prediction accuracy, with an RMSE of 0.427. The study finds no significant difference in success between technological and traditional startups, nor does it confirm that startups with 3 to 5 founders are more successful. However, it confirms that higher total funding correlates with greater success and that startups in South India outperform those in Central India. These findings offer valuable insights into startup success factors and suggest that future research could benefit from larger datasets and additional variables.

  PublisherDOI

• Word learning by children with developmental language disorder: Identifying gaps in our understanding of spaced retrieval effects

  Autism & Developmental Language Impairments · 2024-01-01 · 2 citations

  articleOpen access1st authorCorresponding

  Background and aims: Current evidence shows that children with developmental language disorder (DLD) benefit from spaced retrieval during word learning activities. Word recall is quite good relative to recall with alternative word learning procedures. However, recall on an absolute basis can be improved further; many studies report that fewer than two-thirds of the words are learned, even with the assistance of spaced retrieval during the learning activities. In this article we identify details of spaced retrieval that are less well understood in an effort to promote more effective learning through retrieval practice. Main contribution: We discuss the importance of factors such as: (a) integrating immediate retrieval with spaced retrieval trials; (b) determining whether gradual increases in spacing have more than short-term benefits relative to equal spacing; (c) discovering the number of successful retrievals sufficient to ensure later recall; (d) using spaced retrieval to avoid erosion of phonetic details on later recall tests; and (e) whether the well-documented difficulties with learning word forms might be tied to a particular subgroup of children with DLD. We also speculate on some of the possible reasons why spaced retrieval is beneficial in the first place. Conclusions: Although many children with DLD make gains in word learning through procedures that incorporate spaced retrieval, there are numerous details involved in the process that can alter its success. Until we have a better understanding of the boundaries of spaced retrieval's effectiveness, we will not be taking full advantage of this promising addition to word learning procedures. Implications: Spaced retrieval activities can be an important addition to the resources that clinicians and educators have available to assist children in their word learning. With a deeper understanding of the issues discussed here, we should be able to put spaced retrieval to even greater use.

  PublisherDOI

• A further look at two grammatical measures from children’s language samples and their contribution to the diagnostic process

  Clinical Linguistics & Phonetics · 2024-02-13 · 1 citations

  articleOpen accessSenior authorCorresponding

  Previous research has identified two measures derived from language sample analysis as having a high level of diagnostic accuracy for developmental language disorder (DLD): a verb-based measure, the Finite Verb Morphology Composite (FVMC) and a more comprehensive grammatical measure, the Sentence Point. In this study, we evaluated the sensitivity and specificity of these two measures using a new group of children with DLD. To determine whether these measures would likely add to diagnostic decision making if used in conjuncion with other tests of language, we also examined the relationship between scores on these two measures and scores on a standardized test with a grammatical emphasis. In Study 1, FVMC and Sentence Point scores were computed from the language samples of 22 four- and five-year-olds with DLD and 22 age-matched typically developing peers. Both measures showed very good sensitivity and specificity. In Study 2, we analyzed the FVMC and the Sentence Point correlations with the SPELT-P2 for the 22 children wtih DLD from Study 1 and for a larger group of 60 children with DLD. All correlations were very low and non-significant. Results suggest that the FVMC and Sentence Point could be part of a diagnostic battery for DLD as these measures demonstrate good sensitivity and specificity. Furthermore, the findings of very low correlations between these measures and the SPELT-P2 suggest that they can contribute unique information to the diagnostic process even when used in concert with standardized tests of a grammatical nature.

  PublisherOA PDFDOI

• A <i>Streptococcus suis</i> targeted microbial solution in gestation and lactation diets affected the microbiome and <i>S suis</i> populations in sow herds and their piglets

  AASV Annual Meeting · 2024-02-11

  article
  PublisherDOI

• Verb Vocabulary Supports Event Probability Use in Developmental Language Disorder

  Journal of Speech Language and Hearing Research · 2024-04-04 · 4 citations

  articleOpen accessSenior author

  Purpose: Children with developmental language disorder (DLD) tend to interpret noncanonical sentences like passives using event probability (EP) information regardless of structure (e.g., by interpreting “The dog was chased by the squirrel” as “The dog chased the squirrel”). Verbs are a major source of EP information in adults and children with typical development (TD), who know that “chase” implies an unequal relationship among participants. Individuals with DLD have poor verb knowledge and verb-based sentence processing. Yet, they also appear to rely more on EP information than their peers. This paradox raises two questions: (a) How do children with DLD use verb-based EP information alongside other information in online passive sentence interpretation? (b) How does verb vocabulary knowledge support EP information use? Method: We created novel EP biases by showing animations of agents with consistent action tendencies (e.g., clumsy vs. helpful actions). We then used eye tracking to examine how this EP information was used during online passive sentence processing. Participants were 4- to 5-year-old children with DLD ( n = 20) and same-age peers with TD ( n = 20). Results: In Experiment 1, children with DLD quickly integrated verb-based EP information with morphosyntax close to the verb but failed to do so with distant morphosyntax. In Experiment 2, the quality of children's sentence-specific verb vocabulary knowledge was positively associated with the use of EP information in both groups. Conclusion: Depending on the morphosyntactic context, children with DLD and TD used EP information differently, but verb vocabulary knowledge aided its use. Supplemental Material: https://doi.org/10.23641/asha.25491805

  PublisherDOI

• Learning Verbs in Sentences: Children With Developmental Language Disorder and the Role of Retrieval Practice

  Journal of Speech Language and Hearing Research · 2024-10-03 · 4 citations

  articleOpen access1st authorCorresponding

  PURPOSE: Retrieval practice has been shown to assist the word learning of children with developmental language disorder (DLD). Although this has been true for learning new verbs as well as new nouns and adjectives, these children's overall verb learning has remained quite low. In this preregistered study, we presented novel verbs in transitive sentences with varying subjects/agents and objects/patients to determine if recall could be improved and if retrieval practice continued to be facilitative. METHOD: Fourteen children with DLD aged 4-5 years and 13 same-age peers with typical language development (TD) learned eight novel verbs over two sessions. Half of the novel verbs were presented with spacing between study and retrieval trials, and half were presented with the same frequency in study trials without the opportunity for retrieval. All novel verbs were presented in sentences such as, "The woman is deeking the shoe." Children's ability to recall and use the novel verbs in the same sentence structure was tested after the second session and 1 week later. The children were also required to use the novel verbs in bare-stem form in a new structure, as in, "That woman likes to deek the towel." RESULTS: Both groups of children showed increased recall relative to a previous novel verb study. The children with TD showed the expected advantages of spaced retrieval over repeated study and could use the novel verbs in the new morphological form and sentence structure. The children with DLD, however, showed an advantage for spaced retrieval only shortly after the learning period. These children had great difficulty changing the novel verbs to a bare stem and using them in a new structure. CONCLUSION: Although spaced retrieval assists children's novel verb recall, children with DLD in particular require additional help using these verbs with morphological and syntactic flexibility.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01DC004544

  NIH · $2.5M · 2006

• Communicative Disorders

  NIH · $6.1M · 1991–2027

• NIH Grant R01NS022395

  NIH · $174k · 1988

• NIH Grant R01DC000458

  NIH · $7.2M · 2011

• Retrieval-Based Word Learning in Developmental Language Disorder

  NIH · $5.2M · 2016–2026

Frequent coauthors

• Patricia Deevy

  Purdue University West Lafayette

  76 shared

• S Ervin-Tripp

  74 shared

• Michael Garman

  74 shared

• L Bloom

  Wilfrid Laurier University

  74 shared

• Melissa Bowerman

  74 shared

• Martin D. S. Braine

  Universidade Federal de Pernambuco

  74 shared

• Elaine Clark

  74 shared

• Colin Garvey

  Stanford University

  73 shared

Awards & honors

• Rachel E. Stark Distinguished Professor