Moving towards accurate and early prediction of language delay with network science and machine learning approaches

Longitudinal changes in consonant production in infant-directed speech and infants' early speech production from 6 to 12 months

Previous research suggests that acoustic features of infant-directed speech (IDS) might be beneficial for infants' language development. However, consonants have gained less attention than vowels and prosody. In the current study, we examined voice onset time (VOT) - a distinguishing cue for stop consonant contrasts - in IDS and adult-directed speech (ADS), and its relation to infants' speech production. We used a longitudinal sample of 48 Norwegian parent-infant dyads. Parents' IDS and ADS were recorded in-lab at three timepoints (infants' age: 6, 9, 12 months), and the VOTs of the stop consonants /b-p/, /d-t/, and /g-k/ were measured. In addition, at each timepoint, parents reported their infants' production of the same consonants, as well as their babbling. Hypotheses were preregistered, and we used full-null model comparisons to minimise type I-errors in the analyses. Our results demonstrate that, while controlling for speaking rate, in IDS, parents' VOTs were longer in voiceless stops, but shorter in voiced stops, resulting in overall less distinct consonant contrasts compared to ADS. Further, VOTs in IDS approached ADS values with infants' age. However, we found no relationship between parents' VOTs and infants' consonant production or babbling. Consonants, like vowels, appear to be less distinct in IDS than ADS, thus reinforcing the interpretation that IDS may serve an attentional and/or affective aim, rather than a didactic purpose.

Using machine-learning methods to identify early-life predictors of 11-year language outcome

BACKGROUND

Language is foundational for neurodevelopment and quality of life, but an estimated 10% of children have a language disorder at age 5. Many children shift between classifications of typical and low language if assessed at multiple times in the early years, making it difficult to identify which children will have persisting difficulties and benefit most from support. This study aims to identify a parsimonious set of preschool indicators that predict language outcomes in late childhood, using data from the population-based Early Language in Victoria Study (n = 839).

METHODS

Parents completed surveys about their children at ages 8, 12, 24, and 36 months. At 11 years, children were assessed using the Clinical Evaluation of Language Fundamentals 4th Edition (CELF-4). We used random forests to identify which of the 1990 parent-reported questions best predict children's 11-year language outcome (CELF-4 score ≤81 representing low language) and used SuperLearner to estimate the accuracy of the constrained sets of questions.

RESULTS

At 24 months, seven predictors relating to vocabulary, symbolic play, pragmatics and behavior yielded 73% sensitivity (95% CI: 57, 85) and 77% specificity (95% CI: 74, 80) for predicting low language at 11 years. [Corrections made on 5 May 2023, after first online publication: In the preceding sentence 'motor skills' has been corrected to 'behavior' in this version.] At 36 months, 7 predictors relating to morphosyntax, vocabulary, parent-child interactions, and parental stress yielded 75% sensitivity (95% CI: 58, 88) and 85% specificity (95% CI: 81, 87). Measures at 8 and 12 months yielded unsatisfactory accuracy.

CONCLUSIONS

We identified two short sets of questions that predict language outcomes at age 11 with fair accuracy. Future research should seek to replicate results in a separate cohort.

Sensitivity to Semantic Relationships in U.S. Monolingual English-Speaking Typical Talkers and Late Talkers

PURPOSE

Late talkers (LTs) are a group of children who exhibit delays in language development without a known cause. Although a hallmark of LTs is a reduced expressive vocabulary, little is known about LTs' processing of semantic relations among words in their emerging vocabularies. This study uses an eye-tracking task to compare 2-year-old LTs' and typical talkers' (TTs') sensitivity to semantic relationships among early acquired words.

METHOD

U.S. monolingual English-speaking LTs (n = 21) and TTs (n = 24) completed a looking-while-listening task in which they viewed two images on a screen (e.g., a shirt and a pizza), while they heard words that referred to one of the images (e.g., Look! Shirt!; target-present condition) or a semantically related item (e.g., Look! Hat!; target-absent condition). Children's eye movements (i.e., looks to the target) were monitored to assess their sensitivity to these semantic relationships.

RESULTS

Both LTs and TTs looked longer at the semantically related image than the unrelated image on target-absent trials, demonstrating sensitivity to the taxonomic relationships used in the experiment. There was no significant group difference between LTs and TTs. Both groups also looked more to the target in the target-present condition than in the target-absent condition.

CONCLUSIONS

These results reveal that, despite possessing smaller expressive vocabularies, LTs have encoded semantic relationships in their receptive vocabularies and activate these during real-time language comprehension. This study furthers our understanding of LTs' emerging linguistic systems and language processing skills.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.23303987.

Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure

In adults, nouns and verbs have varied and multilevel semantic interrelationships. In children, evidence suggests that nouns and verbs also have semantic interrelationships, though the timing of the emergence of these relationships and their precise impact on later noun and verb learning are not clear. In this work, we ask whether noun and verb semantic knowledge in 16-30-month-old children tend to be semantically isolated from one another or semantically interacting from the onset of vocabulary development. Early word learning patterns were quantified using network science. We measured the semantic network structure for nouns and verbs in 3,804 16-30-month-old children at several levels of granularity using a large, open dataset of vocabulary checklist data. In a cross-sectional approach in Experiment 1, early nouns and verbs exhibited stronger network relationships with other nouns and verbs than expected across multiple network levels. Using a longitudinal approach in Experiment 2, we examined patterns of normative vocabulary development over time. Initial noun and verb learning was supported by strong semantic connections to other nouns, whereas later-learned words exhibited strong connections to verbs. Overall, these two experiments suggest that nouns and verbs demonstrate early semantic interactions and that these interactions impact later word learning. Early verb and noun learning is affected by the emergence of noun and verb semantic networks during early lexical development.

Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review

In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.

Drivers of Lexical Processing and Implications for Early Learning

Understanding words in unfolding speech requires the coordination of many skills to support successful and rapid comprehension of word meanings. This multifaceted ability emerges before our first birthday, matures over a protracted period of development, varies widely between individuals, forecasts future learning outcomes, and is influenced by immediate context, prior knowledge, and lifetime experience. This article highlights drivers of early lexical processing abilities while exploring questions regarding how learners begin to acquire, represent, and activate meaning in language. The review additionally explores how lexical processing and representation are connected while reflecting on how network science approaches can support richly detailed insights into this connection in young learners. Future research avenues are considered that focus on addressing how language processing and other cognitive skills are connected.

Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28-30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.

Lexico-semantic structure in vocabulary and its links to lexical processing in toddlerhood and language outcomes at age three

Toddlerhood is marked by advances in several lexico-semantic skills, including improvements in the size and structure of the lexicon and increased efficiency in lexical processing. This project seeks to delineate how early changes in vocabulary size and vocabulary structure support lexical processing (Experiment 1), and how these three skills together (vocabulary size, structure, and lexical processing) relate to later language outcomes at age 3 (Experiment 2). Experiment 1 explored how the size and semantic structure of toddlers' vocabulary from 18 to 24 months (N = 61) predicted performance on two lexical processing tasks (semantically related and semantically unrelated trials). Denser semantic connectivity (i.e., global level connectivity between near and far neighbors) positively associated with semantic interference during semantically related lexical processing, whereas denser category structure (i.e., lower-level connectivity between near neighbors) facilitated lexical processing in semantically unrelated trials. In Experiment 2, a subset of the same children (N = 49) returned at age 36 months and completed a comprehensive assessment of their language skills using the Clinical Evaluation of Language Fundamental, Preschool 2 (CELF-P2). Here, earlier measures of lexico-semantic connectivity and lexical processing best predicted age 3 language skill. The findings support accounts that early vocabulary structure and lexical processing skills promote continued growth in language. (PsycInfo Database Record (c) 2022 APA, all rights reserved).