Sentence Prediction in Developmental Language Disorder
Phase: NA
Conditions: Developmental Language Disorder
Sponsor: Father Flanagan's Boys' Home

Title: “Sentence Prediction in Developmental Language Disorder” 
Protocol ID: R21DC022371-01 
Date: 2026-02-24 
 
 

Study Information 
Hypotheses 
This study aims to (1) characterize the effect of sentence- and participant-level properties on 
sentence prediction, (2) assess the linguistic skills underlying sentence prediction, and (3) 
measure the event processing that contributes to sentence prediction in 5-7-year-old school-age 
children with developmental language disorder (DLD) and same-age peers with typical language 
development (TD). First, the investigators will use eye-tracking procedures to measure how 
sentence complexity affects sentence prediction under conditions of varying semantic competition 
among potential completions. Second, the investigators will measure verb-patient and agent-verb 
cooccurrence knowledge and verb semantic feature knowledge. Last, the investigators will use 
pattern-recognition and attention tasks to measure children’s processing of event participants. 
Across these Aims, the investigators will uncover in detail how sentence prediction is affected in 
children with DLD and how underlying linguistic and event-processing skills contribute to sentence 
prediction.  
The investigators hypothesize: (1a) children with DLD’s sentence prediction ability will be worse 
in higher-complexity sentences compared to lower-complexity sentences, (1b) compared to 
children with TD, children with DLD will rely on less specific prediction strategies, using broad 
agent/patient proto-roles over specific prediction strategies, (1c) high processing speed and 
strong working memory will serve as positive compensatory factors when children with DLD 
interpret the most challenging sentences, (2a) children with DLD have some statistical verb 
cooccurrence knowledge but that knowledge will not be not as well-developed as their peers’, 
(2b) children with DLD have poorer verb semantic feature knowledge than peers and this 
knowledge will be more impacted than statistical verb cooccurrence knowledge, and (3) the 
sentence-prediction deficits of children with DLD are undergirded in some part by disturbances in 
event processing.  
 
Design Plan 
The investigators will preregister in detail all hypotheses, data collection procedures, and 
analytical plans on the Open Science Foundation (OSF) prior to data analysis and link to these 
plans in resultant manuscripts (see https://osf.io/b9wd2/overview for project repository). Below 
the investigators provide a brief summary of our anticipated plans and the investigators point the 
reader to the OSF repository for the most up-to-date and detailed information. 
 
Study type 
Experiment 
 
Blinding 
No blinding at the level of participant group is involved in this study as the participants will either 
have DLD or TD and the examiner will be aware of their diagnoses. However, participants will 
be not be aware of experimental manipulations as described in Hypotheses above. 
 
 

 
Study design 
The overall goal of this proposal is to characterize participant- and sentence-level factors that 
influence sentence prediction (Aim 1) and measure robustness of the linguistic knowledge (Aim 
2) and event-processing skills (Aim 3) that underlie sentence prediction. 
 
Randomization 
Participants will either have DLD or TD and therefore will not be randomly assigned to groups. 
However, participants will be randomly assigned to one of four counterbalances, which will 
alternate the order in which tasks and trials are presented and completed.  
 
Sampling Plan 
Existing data 
This study does not rely on existing data to fulfil any of the study aims with the exception of verb 
semantic features from Kueser et al. (in prep.) used to develop stimuli for Aim 2. 
 
Explanation of existing data 
The verb semantic feature dataset consists of the semantic features associated with early-
learned verbs. 
 
Data collection procedures 
Participants include children with DLD and TD ages 5;0 to 7;11 will be included in this study. 
Participants will be recruited via word-of-mouth, social media campaigns, Boys Town National 
Research Hospital (BTNHR) Recruitment Core activities (e.g., community screenings), and the 
BTNRH participant registry. For each of these recruitment methods, interested caregivers and 
adults will be directed to the project website. This site includes the researcher’s contact 
information and a link to an interest form. A member of the research team will contact each 
caregiver who completed the interest form to: 1) discuss the study, 2) verify eligibility, 3) enroll 
child in study, and 4) schedule a first in-person session to determine eligibility. In-person data 
collection will primarily take place in the Emerging Language Knowledge Lab at Boys Town 
National Research Hospital. Data collection for Aims 1, 2, and 3 begin in Year 1 and occur 
simultaneously throughout the three-year project. Child participants will be compensated at the 
rate of $20 cash per hour. Adult participants will complete surveys to measure properties of the 
stimuli. 
 
Sample size 
Children. The investigators will have a final sample size of 80 participants (40 DLD, 40 
TD) for Aims 1-3 (88 total participants before anticipated 10% attrition).  

Adults. The investigators will collect norming data from 100 adults representative of US 
demographics through online platforms; adults will be English-speaking, live in the United States, 
and have no known hearing, neurological, or language disorder. 
Sample size rationale  
Given our planned sample size, a power analysis shows that the investigators will be able to 
detect an effect size of 0.4 (small-medium) for the main effect of Group and 0.4 (small-medium) 
for the main effect of Complexity. The investigators will be able to detect an effect size of 0.3 
(small-medium) for the two-way interaction between Group and Complexity. 
 
Stopping rule 
The investigators will terminate data collection when the investigators have data sets from 40 
children with DLD and 40 with TD. Note that any given child can participate in more than one 
Aim if desired if they meet enrollment criteria. 
 
Variables 
Diagnostic group (DLD, TD) is a categorial variable based on a cut-point of 92 on the Test of 
Narrative Language 2 (TNL-2; Gillam & Pearson, 2017).  
 
Aim 1 
• 
Sentence complexity model (agents): Proportion time spent looking at the target image 
from verb onset to final noun onset divided by looking to any image (e.g., “Something was 
kicked by the boy”) 
• 
Sentence complexity model (patients): Proportion time spent looking at the target image 
from verb onset to final noun onset divided by looking to any image (e.g., “Someone is 
kicking the ball”) 
• 
Semantic competition model (agents): Proportion of (Target + Specific distractor looking) 
/ (Target + Specific + Broad + Unrelated distractor looking) 
• 
Semantic competition model (patients): Proportion of (Target + Specific distractor looking) 
/ (Target + Specific + Broad + Unrelated distractor looking) 
• 
Cognitive processing factors model (agents): Proportion time spent looking at the target 
image 
• 
Cognitive processing factors model (patients): Proportion time spent looking at the target 
image 
 
Aim 2 
• 
Cooccurrence statistics task accuracy: Accuracy of judgement (0/1) 
• 
Cooccurrence statistics task reaction time: Reaction time 
• 
Semantic feature task accuracy: Accuracy of judgement (0/1) 
• 
Semantic feature task reaction time: Reaction time 
 
Aim 3 

• 
Event participant pattern recognition (accuracy): Accuracy of for item (0/1)  
• 
Event participant pattern recognition (eye gaze): Proportion looks to target area of interest 
(AOI) out of both AOIs from 0-4000 ms  
 
Analysis Plan 
Statistical models  
The investigators will use Bayesian mixed-effects models to test our hypotheses. In all models, 
the investigators will include random intercepts of Item (corresponding to trial) and Participant. All 
models include fixed effects for Group (TD/DLD) and Sex (Male/Female). Specific models will 
vary in their inclusion of other terms and interactions based on hypotheses. Models with 
continuous outcomes will use linear mixed-effects regression; models with proportion outcomes 
will use beta mixed-effects regression; models with binary outcomes will use logistic mixed-effects 
regression. Our power analyses simulated standardized effect sizes (mean difference divided by 
standard deviation) ranging from d = 0.0 through d = 2.0 in steps of 0.1. The investigators ran 
1,000 simulations for each possible effect size. Above, the investigators reported the smallest 
detectable effect size given our planned sample size at alpha = 0.05 and power ≥ 0.80. The 
investigators ran models with 80 participants (our planned final sample size after accounting for 
attrition). 
 
Transformations 
The investigators will take transformations as necessary to meet modeling assumptions and to 
aid model fitting. 
 
Inference Criteria 
When making directional predictions, the investigators will use one-tailed tests; otherwise, the 
investigators will use two-tailed tests. Where appropriate, the investigators will correct for multiple 
comparisons. 
 
Data exclusion 
The investigators will exclude data if a child does not provide sufficient data for analysis for if a 
child has not been exposed to English since birth or has a diagnosis of permanent hearing loss, 
autism, or intellectual disability. The investigators anticipate that this would require us to exclude 
no more than 10% of the data.  
 
Missing data 

The investigators take steps to reduce this risk by designing tasks that are enjoyable with varying 
difficulty so that all participants can contribute data. Our use of mixed-effects models ensures that 
the investigators can flexibly handle missing data (Baayen et al., 2008). In addition, the 
investigators are well-powered to detect small-medium effect sizes.  
 
References 
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed 
random effects for subjects and items. Journal of Memory and Language, 59(4), 390–
412. https://doi.org/10.1016/j.jml.2007.12.005 
Gillam, R. B., & Pearson, N. A. (2017). Test of Narrative Language: Second Edition. Western 
Psychological Services. 
Kueser, J. B., Horvath, S., & Borovsky, A. (In prep.). Semantic feature norms for early-learned 
English verbs. 
 

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