Profiling Language Skills in Minimally Verbal Autism: Findings From Greek-Speaking Preschoolers

Abstract

Background and Aims

Minimally verbal (MV) autistic children constitute a considerable portion of the autism spectrum, representing approximately one-third of the autistic individuals. Despite the urgency of understanding this population, relatively few studies have focused specifically on the language abilities of MV autistic children. This study aims to examine the language abilities of Greek-speaking preschool-aged MV children with autism prior to intervention. Specifically, we sought to identify the children's strengths and weaknesses across various language systems (receptive, expressive, and organizational) and modalities (phonological, semantic, and morphosyntactic), and also assess the influence of nonverbal intelligence (performance intelligence quotient [PIQ]) and age on their language performance.

Methods

Twenty-six MV autistic preschoolers (mean age = 5;3) from Greece participated in the study. They were assessed using Level I of the LaTo tool, a standardized battery for evaluating language in young Greek-speaking children. The tool comprises 10 subtests covering expressive, receptive, and organizational language within the phonological, semantic, and morphosyntactic modality. Standard scores were compared across language systems (receptive, expressive, and organizational) and modalities (phonological, semantic, and morphosyntactic). Linear regression models were also used to evaluate the influence of age and PIQ on the children's language performance.

Results

The children showed widespread difficulties across language systems and modalities. Performance was particularly low in expressive language and phonological awareness tests; however, MV children showed relatively better performance in receptive and organizational language, especially in tests that utilized visual support cues. Notably, performance in organizational language tests significantly exceeded performance in both expressive and receptive language tests. Regression analyses indicated a significant negative association between age and language performance across most domains, suggesting an age-related decline in the children's language abilities. No significant relationship was found between PIQ and language outcomes, indicating that nonverbal intelligence did not predict linguistic performance.

Conclusions

The study confirms that MV autistic children experience substantial but non-uniform language difficulties. While expressive and phonological skills were severely compromised, receptive and organizational language skills showed relative strengths, particularly when tests incorporated nonlinguistic knowledge and visual scaffolding. The negative effect of age on language performance underscores the risk of language deterioration over time for the specific population, while the lack of correlation between language performance and PIQ suggests that linguistic ability in MV autistic children is not tightly linked to their general cognitive function. The findings also reinforce the notion that language development in MV children is a heterogeneous process influenced by task effects.

Implications

The findings have significant implications for the design of language assessment and intervention strategies for MV children. Interventions that draw on semantic scaffolding and incorporate visual aids—such as picture-based communication tools—seem to be a promising approach to implement language treatment programs for the specific population. Also, the variability across language domains emphasizes the need for personalized intervention plans grounded in detailed language profiling rather than broad cognitive assessments. Finally, the results highlight the urgency of early identification and targeted support, and call for the development of standardized criteria for MV classification to facilitate cross-study comparability and improved clinical practices.

Keywords

Autism spectrum disorder minimally verbal preschool-aged children receptive language expressive language organizational language phonological modality semantic modality morphosyntactic modality

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by differences in social interaction, verbal and nonverbal communication, and the presence of repetitive and patterned behaviors (American Psychiatric Association, 2013). A core feature of ASD is the considerable heterogeneity observed in individuals’ language, communicative, and cognitive abilities (Andreou & Skrimpa, 2022; Andreou et al., 2020; Masi et al., 2017; Peristeri & Andreou, 2024; Peristeri et al., 2017; Tager-Flusberg & Joseph, 2003). Individuals with ASD differ not only from typically developing peers but also significantly from one another.

The term “autism spectrum” was introduced to capture the extensive variability in developmental trajectories among autistic individuals, particularly in domains such as language and cognitive functioning (Hodges et al., 2020; Ousley & Cermak, 2014). Earlier approaches to classification often relied on simplified functional labels to describe these differences; however, such terminology is now recognized as both imprecise and potentially stigmatizing (Bottema-Beutel et al., 2021). Contemporary frameworks support a multidimensional understanding of ASD, wherein cognitive ability (e.g., intelligence quotient [IQ]) and language competence represent important, but not exhaustive axes of variation (Baron-Cohen, 2002, 2008; Burack & Volkmar, 1992; Peristeri et al., 2024). Despite increased interest in language abilities within autism research over the past three decades, minimally verbal (MV) children with autism remain significantly underrepresented in the literature. This group, estimated to comprise approximately 25% to 35% of the autistic population (Tager-Flusberg et al., 2005), has historically received limited research attention. As Kapp (2023) notes, some MV autistic children have demonstrated intellectual abilities at or near the average range, challenging earlier assumptions that minimal speech necessarily reflects severe cognitive impairment. Due to this longstanding research gap, MV individuals have often been described as the “neglected end of the spectrum” (Tager-Flusberg & Kasari, 2013). In recent years, however, there has been a growing focus on children who, beyond the age of five, continue to exhibit severely limited or nonfunctional spoken language. Various terms have been used to characterize this population, including “preverbal” (Keen et al., 2016), “nonverbal” (Jónsdóttir et al., 2007; Norrelgen et al., 2015), and “minimally verbal” (Goods et al., 2013; Kasari et al., 2013; Posar & Visconti, 2021; Woynaroski et al., 2016), with the latter becoming the most widely adopted term in the literature (Keen et al., 2016; Schaeffer et al., 2023). A notable point of concern is the lack of consensus in the literature regarding the precise definition of MV autistic children, which further underscores the need for more research and diagnostic clarity in this area. This definitional inconsistency primarily stems from differing numerical criteria used to define the expected range of receptive and expressive vocabulary in MV children with ASD (Bal et al., 2016). Drawing from the existing literature, children may be classified as MV if their expressive language is limited to a complete absence of oral language, or the use of a very restricted repertoire of (approximately up to 30) single words and/or phrases (Chen et al., 2024; Kasari et al., 2013; Norrelgen et al., 2015; Plesa Skwerer et al., 2015; Tager-Flusberg & Kasari, 2013; Thurm et al., 2015). These linguistic units are typically employed to serve a narrow range of communicative functions (e.g., requesting or refusing), or may manifest as idiosyncratic speech patterns such as echolalia—the persistent and stereotyped repetition of words or phrases (Ryan et al., 2022).

Assessing the cognitive profile of MV children is challenging, likely due to difficulty conforming to behavioral requirements in existing testing methods, such as verbally loaded assessments, which often result in chronic floor performance in this group (Bacon et al., 2018; Kasari et al., 2013; Koegel et al., 1997; Tager-Flusberg et al., 2016). Therefore, there is limited knowledge on key areas of language functioning in MV children, which are foundational to their verbal communication skills (Kasari et al., 2013). The need to gain insight into the language abilities of this population is becoming increasingly relevant as recent evidence suggests that assessment of atypical communication characteristics can contribute to diagnostic information and subgroups within autism (Kang et al., 2019). Despite the urgency of understanding this population, relatively few studies have focused specifically on the language abilities of MV autistic children (Howlin et al., 2014; Kasari et al., 2013; Pickles et al., 2014; Rose et al., 2016; Tager-Flusberg & Kasari, 2013). These studies have shown that language development in these children often progresses slowly, even when their communication difficulties are the focus of early intervention and educational programs. For example, while a considerable proportion (60% to 70%) of preschool-aged MV children shows some gains in verbal ability, only a smaller subset (35% to 47%) ultimately attains fluent speech by or after school age (Mouga et al., 2020; Saul & Norbury, 2020; Wodka et al., 2013; Yoder et al., 2015), with many continuing to exhibit limited verbal output.

Research on the language profile of MV autistic children has predominantly examined expressive language, focusing on vocabulary (Butler et al., 2023; Haebig et al., 2021) and conversational skills, such as topic maintenance and turn-taking (DiStefano et al., 2016; La Valle et al., 2020; Pecukonis et al., 2019). These studies consistently report both substantial challenges and considerable variability among MV individuals. In contrast, receptive language has received relatively little attention, though existing findings suggest similarly marked impairments (Garrido et al., 2015; Maljaars et al., 2011; Plesa Skwerer et al., 2015). Kilili-Lesta et al.'s (2024) case-control study has addressed this gap by examining potential risk and prognostic factors associated with nonverbal or MV outcomes in autistic children in Cyprus. Their study involved 56 children aged 3 to 12 years, all diagnosed with ASD, divided into two groups: 22 MV children and 34 verbal peers, matched for age and gender. Retrospective data on familial, perinatal, and developmental risk factors were collected and used to compute corresponding risk scores. Particular emphasis was placed on early developmental milestones (Early Development Score [EDS]) and early gesture use (Early Gesture Score). The results indicated that early developmental delays, especially those reflected by a low EDS, were significantly associated with an MV outcome. Specifically, children with low EDS were 4.5 times more likely to remain MV than those with typical early development.

The findings so far underscore the predictive value of early developmental markers for long-term language outcomes and emphasize the need for early identification and intervention for MV autistic children. Accurate representation of language skills among MV children can be difficult as this group is often excluded from research studies, and is significantly underrepresented due to demographic barriers, such as poverty, minority status, and low maternal education (Bagner & Graziano, 2013; Pinborough-Zimmerman et al., 2012).

The present study aimed to investigate the language abilities of 26 Greek-speaking, preschool-aged MV autistic children, assessed prior to intervention using a standardized instrument widely employed to evaluate language development in young children with language difficulties. Specifically, the study pursued two objectives: (1) to identify performance patterns across key language domains (receptive, expressive, and organizational) and language modalities, including phonological, semantic, and morphosyntactic components; and (2) to examine the extent to which age and performance IQ (PIQ), serving as an estimate of nonverbal cognitive ability, predict individual variation in language performance. To our knowledge, this constitutes the first study to offer a comprehensive, domain-specific characterization of language abilities in Greek-speaking MV autistic children.

Method

Participants

The study included 26 Greek-speaking preschool-aged children with ASD (mean age: 5;3, age range: 5–5;9 years, standard deviation [SD]: 0.3, 18 males). The participants were recruited from the geographical region of Macedonia and were referred by the Centers for Interdisciplinary Evaluation, Counseling, and Support (KEDASY). These are the official state centers in Greece that are responsible for the investigation and assessment of educational needs or barriers to learning of preschool- and school-aged students, including students with disabilities or special educational needs. In addition, KEDASY has the responsibility to issue an evaluation report that defines the central axes of an individualized education program that is tailored to the educational needs of each student. Besides their diagnostic role, KEDASY is also responsible for delivering educational treatment and intervention programs, which are codesigned by a transdisciplinary team including teachers, speech pathologists, psychologists, child psychiatrists, and social workers with training in neurodevelopmental disorders.

In the current study, preschool-aged autistic children were recruited within the context of the ongoing project entitled “Language Phenotyping in Autism Using Machine Learning (Acronym: AmaLgAM)” (2024), which aims to develop objective, quantitative measurements of language competence in young autistic children using automated methods and software. The children who took part in the experimental procedures had received their first-time ASD diagnosis at KEDASY shortly before being recruited for the study, and none of them had received early intervention.

Following Institutional Review Board (IRB) approval, the two authors have visited the recruitment site (i.e., KEDASY) and worked closely with the director of the center and the transdisciplinary team to identify and contact parents whose child had a diagnosis of MV autism. Then, with the assistance of the center's social worker, the two authors invited the participation of 32 families with one or more children with MV autism, who were diagnosed at KEDASY and had interacted with the transdisciplinary team. Each parent was individually approached by the social worker and was introduced to the two authors and to the research project through two in-person meetings. Following experience-based good practice guides focused on conducting research with MV autistic participants (Tager-Flusberg et al., 2016), we provided the parents with detailed information in advance about the aims of the study, the content, demands, and duration of the tests, and the testing procedure. In the second meeting, the parents were encouraged to bring their child, so that the first author could interact with her/him, while the second author obtained consent from the parent (McKinney et al., 2021). Of those families invited, a total of 25 agreed to participate, resulting in the participation of 26 MV autistic children (i.e., one family had two MV autistic twins). Data collection took place at the children's home.

All the children had received a formal clinical diagnosis of ASD from a child psychiatrist or developmental specialist on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and International Statistical Classification of Diseases and Related Health Problems, 10th Revision Criteria (American Psychiatric Association, 2013; World Health Organization, 1993). The classification of the autistic children as MV was based on parental reports in the absence of standardized assessment measures in the evaluation of ASD in Greece. According to the parental reports collected at KEDASY during the time of testing, children were documented to be using fewer than eight spontaneous and functional words/word approximations, and also partially/sometimes using two-word phrases, which satisfy the classification criteria for MV children for the specific age range (Norrelgen et al., 2015; Tager-Flusberg & Kasari, 2013; Tager-Flusberg et al., 2009).

The children who participated in the study attended special kindergarten schools that function outside mainstream school units in Greece. Children's nonverbal/PIQ was estimated using the Greek version of the Wechsler Preschool and Primary Scales of Intelligence–Fourth Edition (WPPSI-IV: Wechsler, 2014; Greek version by Sideridis & Antoniou, 2015). All autistic children were found to score in the below-average PIQ range (mean PIQ: 59.5; SD: 5.7; range: 49–67). Besides intelligence measurements, the children were also assessed by the physiotherapist and the special education kindergarten teacher at KEDASY on their fine and gross motor skills, including ball skills, balance, motor imitation, fill-up-the-hole games, and load lifting, and no severe motor impairments were reported. Children with severe motor and sensory impairments reported by parents were excluded from the experimental procedures.

Regarding the process of acquiring the assent of the young MV autistic children, we first asked the parents to actually speak to their children about participating prior to their encounter with the experimenter (first author), who had extensive clinical experience with this population. The parents informed us about the children's communicative strategies and behaviors that prepared the experimenter for the first visit (Tager-Flusberg et al., 2016). Almost all children exhibited unique communication patterns during the experimental sessions, including longer than expected silences, shifts in eye gaze, using alternative forms of communication, such as pointing, nodding or gesturing to share their intent or desire, as well as more challenging behaviors such as turning the body away, leaving the room, being aggressive, shouting or crying to express distress. We encountered such behaviors during some sessions, and while strategies such as praise, access to preferred items, or offering a break were often helpful, they were not always effective in fully resolving the behavior (Chenausky et al., 2022). Also, parental guidance in such cases was of crucial importance to decide whether it would be best to continue with the testing procedure or come back another day (McKinney et al., 2021). Furthermore, within our research, the children's mothers were consistently asked to participate in the sessions, as they made the child feel more comfortable, and could effectively interpret requests and gestures that the experimenter was initially unfamiliar with. We should note that the mothers were particularly effective in inviting more elaborate responses from the children in the expressive language tests of the study. During the sessions, we frequently used visual cues representing “yes” and “no,” as we continuously invited the assent or dissent of the children. This further allowed us to confirm that we communicated with the children in a meaningful and responsive way, and also served to build rapport with child. Finally, over the course of the study, we regularly checked with the children and their parents to ensure that they were still feeling comfortable with our presence. The longer we engaged in the research, the more competent we became at interpreting the desires, intentions, and requests of the children. Overall, monitoring ongoing assent to participate was a dynamic and challenging procedure that demanded that we remained flexible with the varied ways these children communicated.

All study procedures were approved by the Aristotle University of Thessaloniki Institutional review board (IRB protocol number: 39928). The studies were conducted in accordance with the local legislation and institutional requirements.

LaTo Tool

In the current study, we employed Level I of the LaTo tool, which has been developed and standardized on 905 Greek-speaking monolingual typically developing children aged 4–7;11 years, and consists of 10 tests. LaTo (Level I) is one of the very few language assessment tools normed in the Greek language on preschool-aged children that covers a wide range of skills in both language production and comprehension. Internal consistency reliability coefficients for the LaTo Level I tool range from .86 to .94 for the language system composites (i.e., receptive, expressive, and organizational); from .87 to .94 for the three modalities (phonological, semantic, and morphosyntactic); and from .97 to .98 for the general language aptitude quotient (GlAQ; Tzouriadou et al., 2008), overall implying high validity and reliability for the language assessment tool. Below, we describe the materials and procedures followed in each of the 10 tests of the tool.

Picture Vocabulary

This test assesses the child's ability to understand common concepts represented by items. All items correspond to nouns, which are represented in simple, colored pictures. The child is not required to name any item but only pick (through pointing) two targets out of four pictures. The instructor shows the child a page with four pictures (e.g., baby, ball, ice cream, and tree) comprising two distractor illustrations and two target words. S/he names the first target word (e.g., show me the baby) and asks the child to pick the target picture. The same instruction is repeated for the second target item (e.g., show me the tree).

The subtest comprises 23 trials, and the child receives one point for each correct answer. The maximum raw score is 46.

Relational Vocabulary

This test assesses the child's ability to understand semantic relations between concepts. Semantic relations pertain to membership in a semantic category, specifically, the following: fruit, sports, toys, furniture, means of transportation, emotions, vegetables, means of transport, household appliances, sources of light, school supplies, musical instruments, and celestial bodies. The instructor shows the child a triplet of pictures on the top margin of the page that belong to the same semantic category (i.e., watermelon, apple, and orange). A quartet of colored pictures (e.g., peach, glass, ice cream, and cherry) also appears in the centre of the same page. Two pictures of the quartet (i.e., peach and cherry) share the same semantic membership with the triplet. The instructor shows the child the triplet of pictures at the top of the page and asks him/her to pick (by pointing) two pictures of the quartet that match the triplet. The subtest comprises 15 trials, and the child receives one point for each correct answer. The maximum raw score is 30.

Receptive Vocabulary

This test assesses the child's receptive vocabulary skills and her/his ability to understand concepts based on their definitions. The child is shown four colored pictures (e.g., shoes, bathing suit, ice cream, and coat). The instructor utters a definition (e.g., we wear this in the summer at the beach) and the child is asked to pick (by pointing) the picture that matches the definition (i.e., bathing suit). The test comprises 12 trials, and the child receives one point for each correct answer. The maximum raw score is 12.

Expressive Vocabulary

In each trial of this test, the child listens to the sound of the first syllable of two- or three-syllable words pronounced by the instructor (e.g., the syllable /fi/ in the word “filo,” leaf in English). The instructor also provides the child with a short definition (i.e., this is the part of the tree that is green), and the child is required to name the word. The test comprises 14 trials, and the child receives one point for each correct answer. The maximum raw score is 14.

Articulation

In this test, the child is presented with sentences missing their final word (e.g., “A bird makes its nest on a __”), and at the same time views a colored picture that represents the target word. The examiner reads each sentence aloud, points toward the picture, and asks the child to complete the sentence with a word that matches the meaning of the sentence. The child is required to name the target word with no articulatory errors. The test comprises 13 trials, and the child receives one point for each correct answer. The maximum raw score is 13.

Phonemic Blending

During this test, the instructor utters the individual sounds of two- or three-syllable words slowly and then directs the child to say the word (e.g., I'll say the sounds in a word slowly, then you say it fast. Listen, /hhhhh//iiiii//nnnnn//aaaaa/. Which word did I say? /hina/. Say it fast, /hina/, goose in English). The test comprises 30 trials, and the child receives one point for each correct answer. The maximum raw score is 30.

Phonemic Segmentation

During this test, the instructor provides the child with two- or three-syllable words and asks the child to utter the individual sounds in each word (e.g., We're going to say the sounds in the word /ɣala/ (milk in English) slowly: /ɣɣɣɣɣ//aaaaa//lllll//aaaaa/). The test comprises 30 trials, and the child receives 1 point for each correct answer. The maximum raw score is 30. The words in the phoneme segmentation test were different from the words in the phonemic blending test.

Phonemic Distinction

The child is orally presented with pairs of similar-sounding words (e.g., /ksino/ - /psino/, scratch-bake in English, /stala/ - /skala/, drop-stairs in English). The instructor also provides the child with a definition of the target word (i.e., “We do this to cook food” for /psino/, bake in English; “We are going down this,” for /skala/, stairs in English). The child is asked to say the word that matches the definition. The test comprises 14 trials, and the child receives one point for each correct answer. The maximum raw score is 14.

Morphosyntactic Comprehension

This is a sentence-picture matching test in which the child is presented with picture triplets, each appearing on a single page. The examiner utters a sentence, and the child is asked to point to the picture that best matches the meaning of the sentence-stimulus. The sentences target children's understanding of the semantics of verbs (i.e., go, sleep, catch, walk, and ride a horse), adjectives (i.e., sad, thoughtful, locked, hasty, angry, and half-finished), and quantifying expressions (i.e., few and only), and not the syntax of the sentences. As such, nontarget pictures across all trials included semantic distractors (i.e., Example 1/Target sentence: “George took the only piece of the cake.” Target picture: A boy takes the only piece of a cake at a plate on a table; Distractor picture 1: A boy takes the whole cake at a plate on a table; Distractor picture 2: A boy plays with a ball next to a table with an empty plate; Example 2/Target sentence: “Mary is hasty.” Target picture: A girl runs on the street; Distractor picture 1: A girl walks on the street; Distractor picture 2: A girl holds a flower). The subtest comprises 13 trials, and the child receives one point for each correct answer. The maximum raw score is 13.

Morphosyntactic Production

This is a sentence-continuation test in which the child is presented with pairs of pictures that differ in a single critical aspect. The examiner points to one picture and utters a sentence whose meaning corresponds to the visual content of the picture (e.g., this truck has many boxes). Then the examiner points to the second picture and says a sentence fragment (i.e., in this picture, this truck has…) that the child needs to continue in a way that is compatible with the second picture (…one box). Sentence continuation is this test targets (a) the marking of verbs with the appropriate person and tense feature (e.g., Example 1/First picture: “The child is climbing on the tree.” Second picture: The children….[are climbing]; Example 2/First picture: “The teacher is writing on the board.” Second picture: Yesterday, all day, the teacher….[was writing]), or the marking of nouns with plural (e.g., First picture: “Here is a seat.” Second picture: Here are many….[seats]), or singular number (e.g., First picture: “These dresses have many colors.” Second picture: This dress has one….[color]); (b) the production of genitive possessive forms (e.g., First picture: “This a notebook.” Second picture: This is the tag….[of the notebookGENITIVE]), and (c) the production of deverbal nouns (e.g., First picture: “This man paints.” Second picture: He is a ….[painter]) and participles (First picture: “The mother plants the flowers.” Second picture: These flowers are ….[planted]). The test comprises 13 trials, and the child receives one point for each correct answer. The maximum raw score is 13.

Each test included four familiarization items. During the familiarization phase, the experimenter modeled accurate responses and corrected the child's errors before moving to the next item. In the current study, Lato's tests were administered in five sessions that took place on different days at the children's home or school. Session 1 included the picture and the relational vocabulary tests. Session 2 included receptive and expressive vocabulary. Session 3 included articulation and phonemic blending. Session 4 included phonemic segmentation and phonemic distinction, while session 5 comprised the morphosyntactic comprehension and morphosyntactic production tests. Children's PIQ scores were obtained by a trained clinical psychologist at KEDASY using the Greek adaptation of the WPPSI-IV (Sideridis & Antoniou, 2015; Wechsler, 2014). These assessments were conducted during an additional session beyond the four sessions previously described, as part of the broader cognitive evaluation process.

Analysis Plan

We first provide the descriptive values (means) of children's performances in each test. Next, raw scores from picture vocabulary, receptive vocabulary, phonemic distinction, and morphosyntactic comprehension were summed into the composite score of receptive language. Similarly, raw scores in expressive vocabulary and articulation were summed into the composite score of expressive language, while scores from relational vocabulary, phonemic blending, phonemic segmentation, morphosyntactic comprehension, and morphosyntactic production were summed into the composite score of organizational language. Furthermore, scores from the articulation, phonemic blending, phonemic segmentation, and phonemic distinction tests were summed into the composite score of phonological modality. Raw scores from picture vocabulary, relational vocabulary, receptive vocabulary, and expressive vocabulary were summed into the semantic modality, while scores from the morphosyntactic comprehension and production tests were summed into the composite score of morphosyntactic modality. Composite scores across language domains and modalities were converted into individual standard scores, which were next summed into a single score (GlAQ) for each child.

To address the study's first goal, that is, to identify performance patterns in the MV autistic group, we ran paired t-tests on standard scores among language systems (receptive, expressive, and organizational) and modalities (phonological, semantic, and morphosyntactic). To address the second goal, that is, to explore the effects of age and PIQ on MV autistic children's language performance, we ran a series of linear regression models. Each set included age and PIQ (as a proxy for nonverbal intelligence) as the predictors, and standard composite scores in each language system and modality, as well as the children's GlAQs, as the dependent variables. For the analyses, we considered a p value of .05 to be statistically significant.

Results

Table 1 shows the mean raw scores across LaTo's individual tests, while Table 2 presents composite (raw and standard) scores across language systems and modalities. Figures 1 –3 display the distribution of MV autistic children's performances in the receptive, expressive, and organizational language system of the Lato tool, based on the children's typical scores.

Figure 1.

Distribution of Minimally Verbally Autistic Children's Performances in the Receptive Language System of the LaTo Tool.

Figure 2.

Distribution of Minimally Verbally Autistic Children's Performances in the Expressive Language System of the LaTo Tool.

Figure 3.

Distribution of Minimally Verbally Autistic Children's Performances in the Organizational Language System of the LaTo Tool.

Table 1.

Autistic Children's Mean Raw Scores (and Standard Deviations) Across LaTo's Individual Tests.

Picture Vocabulary(max.: 46)	Relational Vocabulary(max.: 30)	Receptive Vocabulary(max.: 12)	Expressive Vocabulary(max.: 14)	Articulation(max.: 13)	Phonemic Blending (max.: 30)	Phonemic Segmentation(max.: 30)	Phonemic Distinction(max.: 14)	Morphosyntactic Comprehension(max.: 13)	Morphosyntactic Production(max.: 13)
34.8 (2.0)	20.7 (2.2)	9.9 (1.4)	2.8 (1.1)	1.1 (0.9)	1.3 (1.5)	0.5 (0.5)	2.7 (0.8)	6.6 (0.9)	2.4 (1.1)

Note. max. = maximum raw score.

Table 2.

Autistic Children's Mean Raw and Standard Scores Across LaTo's Language Systems and Modalities, and Children's Mean GlAQ (and SDs and Ranges).

	Language Systems	Modalities	GlAQ
	Receptive	Expressive	Organizational	Phonological	Semantic	Morphosyntactic
Raw scores	56.3 (2.5) 53–65	5.2 (1.7) 0–7	31.6 (2.2) 28–37	7.6 (2.1) 4–12	68.3 (3.7) 64–78	9.1 (1.4) 6–11	27.6 (7.8) 15–37
Standard scores	3.1 (1.5) 1–5	2.7 (2.2) 1–5	6.1 (0.8) 6–7	1.8 (0.7) 1–3	6.2 (2.0) 2–7	3.6 (1.4) 1–5	66.4 (8.2) 53–76

Note. GlAQ = general language aptitude quotient; SD = standard deviation.

Our first research goal was to identify performance patterns across language systems and modalities in the MV autistic children, as well as classify their language competence in terms of LaTo's GlAQ index. According to the standard score ratings across language systems, MV children's performance in both receptive and expressive language ranged from very low to poor, with mean typical scores in the receptive system being slightly higher than the expressive system (see Figures 1 and 2). On the other hand, MV autistic children's mean performance in the organizational language system fell within the below-average range and was better compared to receptive and expressive language. For example, according to the distribution plot of children's performances in the organizational system of the Lato tool in Figure 3, seven children (i.e., ≈27% of the sample) scored 5 (Poor), while the rest, that is, 19 children (i.e., ≈73% of the sample), scored 6 to 7, which corresponds to below-average performance.

Paired t-tests on standard scores among language systems showed that performance in organizational language was significantly better than both receptive, t(25) = 30.200, p < .001, and expressive language, t(25) = 17.959, p < .001. Though MV children's performance in receptive language was better than expressive language (i.e., 3.1 > 2.7), the difference failed to reach significance, t(25) = 1.814, p = .123.

Regarding modalities, children's phonological skills were classified as being very low, while their morphosyntactic skills ranged from very low to poor. Children's performance profile in the semantic modality was rather mixed, ranging from very low to below average.

Paired t-tests on standard scores among language modalities showed that performance in semantic tests was significantly better than both phonological, t(25) = 14.879, p < .001, and morphosyntactic tests, t(25) = 12.933, p < .001. Also, MV children's performance in tests that tapped into phonological skills was significantly lower than their performance in LaTo's morphosyntactic tests, t(25) = 9.092, p < .001.

Regarding children's GlAQ, it ranged from very poor to poor. As one can see in the distribution plot in Figure 4, eight children's GlAQ (i.e., ≈31% of the sample) was <60 (very poor), while for the rest of the participants (i.e., ≈69%) GlAQ ranged from 63 to 76, thus, their language competence was characterized as being poor.

Figure 4.

Distribution of Minimally Verbally Autistic Children's General Language Aptitude Quotients (GlAQs).

The second goal of the study was to investigate the effects of age and PIQ on children's performance in language systems and modalities, and their GlAQs. For language systems, age was significantly negatively related to MV children's both receptive and expressive language, while there was no significant effect for organizational language (see Table 3). For language modalities, age was significantly negatively related to all three domains, that is, phonological, semantic, and morphosyntactic processing skills (see Table 4). Also, there was a highly significant negative effect of age on MV children's GlAQ (see Table 5). Figure 5 displays the distribution of the MV autistic children's GlAQs across ages.

Figure 5.

Distribution of Minimally Verbally Autistic Children's General Language Aptitude Quotients (GlAQs) Across Age.

Table 3.

Summary of Linear Regression Models Exploring Effects of Age and PIQ in LaTo's Language Systems.

Fixed Effects	Coefficient Estimate	SE	df	t	p value
Receptive language
Intercept	63.677	26.25	21.70	2.42	.024*
Age	−11.23	4.91	21.57	−2.28	.032*
PIQ	0.68	.44	21.74	1.56	.132
Age × PIQ	0.12	.08	21.28	1.56	.133
Expressive language
Intercept	99.79	43.35	20.75	2.30	.032*
Age	−17.44	8.04	20.99	−2.17	.042*
PIQ	1.07	0.72	19.54	1.47	.156
Age × PIQ	.19	.13	20.23	1.40	.176
Organizational language
Intercept	30.74	14.03	19.07	2.19	.041*
Age	−4.17	2.61	19.02	−1.60	.126
PIQ	.16	.23	19.91	.71	.486
Age × PIQ	.033	.05	29.17	.75	.460

Note. PIQ = performance intelligence quotient; SE = standard error; df = degrees of freedom.

*p < .05.

Table 4.

Summary of Linear Regression Models Exploring Effects of Age and PIQ in LaTo's Language Modalities.

Fixed Effects	Coefficient Estimate	SE	df	t	p value
Phonological
Intercept	47.46	19.95	18.55	2.37	.028*
Age	−8.43	3.70	19.29	−2.27	.034*
PIQ	.65	.33	18.63	1.96	.065
Age × PIQ	.12	.06	18.72	1.95	.066
Semantic
Intercept	92.39	27.58	21.39	3.35	.003**
Age	−15.87	5.17	21.48	−3.06	.006**
PIQ	.92	.47	19.52	1.98	.062
Age × PIQ	.17	.08	21.11	2.03	.06
Morphosyntactic
Intercept	64.46	26.17	20.30	2.46	.023*
Age	−11.11	4.86	20.57	−2.28	.033*
PIQ	.68	.43	18.42	1.57	.133
Age × PIQ	.12	.08	21.07	1.53	.140

Note. PIQ = performance intelligence quotient; SE = standard error; df = degrees of freedom.

*p < .05; **p < .01.

Table 5.

Summary of Linear Regression Models Exploring Effects of Age and PIQ in LaTo's GlAQ.

Fixed Effects	Coefficient Estimate	SE	df	t	p value
Phonological
Intercept	445.14	103.39	21.77	4.0	<.001***
Age	−69.80	19.28	21.67	−3.62	.002**
PIQ	4.22	1.71	21.77	2.45	.062
Age × PIQ	.77	.32	21.11	3.40	.126

Note. PIQ = performance intelligence quotient; GlAQ = general language aptitude quotient; SE = standard error; df = degrees of freedom.

**p < .01; ***p < .001.

Discussion

Though almost one-third of the autistic individuals remain MV (Hughes et al., 2023; Naples et al., 2022; Rose et al., 2016), little is known about the language abilities of this specific population, as until recently they have not been the focus of scientific investigations. In typically developing children, there are clear benchmarks for defining stages of language development with respect to the number of words used, composition of vocabulary, and how these relate to emerging grammatical abilities evident in word combination and early morphosyntax. It remains unknown how late these benchmarks apply to the language development of children with MV status. In the current study, we aimed to investigate the language profiles of Greek-speaking preschool-aged MV autistic children using a standardized language assessment tool (LaTo). Importantly, the LaTo tool assesses children on tests tapping into discrete language systems (expressive, receptive, and organizational) and language skills (phonological, semantic, and morphosyntactic), which allowed us to gain access to subtle, fine-grained information about MV children's language ability. The main finding was that MV children's performances across language systems and skills exhibited variability, ranging from very low to below average, while this heterogeneity seemed to be driven selectively, first, by children's very low performance in expressive language tests, especially those that measured phonological abilities, and, second, their comparatively better performance in measures of receptive language skills, especially those that included visual stimuli. Also, MV children seemed to perform better (i.e., at below-average level) in LaTo's organizational system (as compared to the tool's expressive and receptive language system) that conflated structural language and general cognitive knowledge, as in the relational vocabulary test that grounded linguistic meaning in nonlinguistic experience, and the morphosyntactic comprehension test that mainly tested children's ability to understand event structures. Finally, age seemed to be significantly inversely related to performance accuracy across all language systems and skills, since older children performed worse than younger ones. The overall results show that major language impairment was not reported across the board for the MV children, but was mainly spotted in tests that assessed expressive language “divorced” from world knowledge grounding and/or visual support cues.

More specifically, our first research goal was to identify MV autistic children's performance patterns across the standardized tool's language systems and modalities, and detect possible asymmetries among them. Though the GlAQs of the children ranged from very poor to poor, further implying a major delay in the language development of the specific population, children's fluctuation of performances across tests suggests that this delay was not uniform across systems and modalities. More specifically, in line with previous research (Chen et al., 2024; Chenausky et al., 2019; Kasari et al., 2013), MV children faced greater challenges with tests measuring expressive (vs. receptive) language skills, though the difference was not statistically significant. Also, the children scored significantly better in LaTo's organizational system as compared to both the expressive and receptive systems. Notably, elevated performance in the organizational language system was primarily driven by the children's performances in two receptive tests, namely, relational vocabulary and morphosyntactic comprehension (see Table 1 for the raw scores). Specifically, relational vocabulary asked children to detect (via pointing to pictures) semantic associates of target concepts (e.g., picking “gloves” and “boots” as semantic associates of the concepts “snowman,” “knit cap,” and “scarf”; distractors being a “short-sleeve T-shirt” and a “bathing suit”). As such, relational vocabulary seemed to heavily rely on children's nonlinguistic experience along with lexical semantics. Furthermore, the syntactic comprehension test of the LaTo tool did not assess children's parsing decisions based on morphosyntactic cues, but rather evaluated children's grasp of the verbs’ event semantics and adjectives’ meaning in descriptive sentential contexts (e.g., Target picture: “The bicycle is locked,” the two distractor-pictures being “The bicycle is unlocked” and “A girl is crying”). Elevated performance in the two tests of the organizational system (as compared to the expressive and receptive system) suggests that the MV children may have benefited from reliance on semantic information across the two measures. Haebig et al. (2020) have found substantial overlap between MV autistic and vocabulary-size-matched typically developing toddlers in word learning, suggesting similarities in lexical knowledge development between the two populations. Besides tapping into nonlinguistic experience and lexical semantics, both tests also heavily relied on visual support cues, which likely supported MV children’s language performance. Visual support has been demonstrated to help MV children scaffold language-mediated activities, including shared reading (Mucchetti, 2013) and communication (Howlin et al., 2007), and may have contributed to the children's performance in the two tests of the current study. Increasing the use of visual aids and reducing verbal content are widely recognized strategies for supporting learning and communication in autistic children. Tools such as visual schedules, picture exchange systems, and symbol-based communication boards are commonly used in classrooms and at home to help children anticipate daily routines, make requests, and express preferences. One example of a more structured approach is facilitated communication (FC), a form of augmentative and alternative communication that combines visual prompts with physical support to assist MV autistic individuals in typing or pointing to communicate (for a review, see Mostert, 2001). While FC has been helpful for some individuals in initiating communicative attempts, it does not eliminate communication challenges entirely. Independent communication (i.e., without the physical presence or support of a facilitator) can remain difficult to achieve and requires time, trust, and tailored support (e.g., Biklen & Kliewer, 2006; Simpson & Myles, 1995). For example, some children may use symbol boards or tablets with voice-output functions to choose snacks, indicate discomfort, or participate in shared reading at school, but may still rely on a familiar adult nearby to prompt or encourage their engagement.

Regarding MV children's language skills, performance in phonological tests was significantly poorer as compared to the semantic and morphosyntactic tests. An inspection of LaTo's phonological tests, that is, phonemic blending, phonemic segmentation, and phonemic distinction, shows that the specific measures tapped into the ability to manipulate speech sounds, which is not a linguistic process per se but a metalinguistic process informed by phonotactic regularities (Laure & Armon-Lotem, 2024; Rehfeld et al., 2022). The high cognitive demands of phonemic awareness tests (Magnusson, 2018; Nagy & Anderson, 1995) may explain MV children's (almost) floor effect performance in the specific tests.

The second goal of the study was to investigate the role of PIQ (as a proxy for nonverbal cognition) and age on MV children's performances across language tests. We found no significant relation between PIQ and any of the language systems and/or modalities comprising the LaTo tool. This finding seems to agree with Haebig et al.'s (2020) study of vocabulary development in MV toddlers, which found no relation between children's nonverbal mental age and the proportion of verbs produced in the MV autistic group. The limited role of PIQ in MV children's language performance in the current study implies that nonverbal mental age did not scaffold MV children's language processing ability across a range of skills (phonological, semantic, and morphosyntactic). This finding seems to agree with Bal et al.'s (2016) large-scale study with 1,470 MV autistic children that found that a notable cluster of MV children (14.3%) failed to develop spoken language, though having an above-average PIQ score (>70). The overall evidence provides empirical support for the theoretical view of language modularity (Fodor, 1983), in the sense that the performance of a subgroup of MV autistic children exhibits a dissociation of language from nonverbal cognitive skills (see also Peristeri & Andreou, 2024; Peristeri et al., 2022 for similar findings with verbally able autistic children).

The findings of the current study also showed that older MV children tended to perform worse than their peers across all language systems (except for the organizational) and modalities. We should note that the MV sample that has been recruited for the purposes of the current study has not yet received any intervention or language treatment program. In a recent study, Brady et al. (2021) showed that intensive language intervention seemed to contribute significantly to improving language production in MV autistic children aged 5 and above. Research (Tager-Flusberg & Kasari, 2013) with verbally able autistic individuals also found that children's language developmental trajectories tended to be marked by plateauing or even regression in the absence of timely intervention. Lack of treatment in the MV children of the current study implies that better performance in the younger children cannot be attributed to early intervention or treatment factors. Since data collection took place in 2025, we can only speculate that older children performed worse than the younger ones due to the impact of the COVID-19 pandemic and its detrimental effects on the older children's critical milestones of cognitive development, as compared to the younger children.

The overall findings of the current study underscore the heterogeneity of language abilities in MV autistic children. Although the overall GlAQ confirmed the presence of a major language impairment in the group, distinct patterns among language domains have emerged: children demonstrated relatively stronger performance in the organizational language system compared to the expressive and receptive systems. This finding may stem from the visual context and the receptive nature of the organizational language tests that supported MV children's responses; however, this benefit may also be related to the organizational language system's interface with nonlinguistic experiences and semantic knowledge that scaffolded children's language performance. Notably, MV children's relatively better performance in the organizational language system (as compared to the expressive and receptive system) seemed to be partially driven by performance in the syntactic comprehension subtest, in which the mean accuracy score was 6.6 out of a maximum score of 13 (see the Results section, Table 1). Importantly, Greek has a rich morphological system reflected in explicit suffixation marking on nouns and verbs that clearly demarcates the thematic roles of the agent (i.e., syntactic subject) and the patient (i.e., syntactic object) across sentences. This further suggests that morphology in Greek might have played a compensatory role in areas of syntactic comprehension difficulty in the MV autistic group, in the sense that the explicit morphological marking of syntactic features may have facilitated the comprehension of the sentences. These resources can be used to inform intervention planning and direct treatment goals to improve language development in MV autistic children. Children with MV autism in Greece are placed in generic or autism special schools, while the very few studies that have been conducted on the educational policies designed to support this population in Greece underscore teachers’ insufficient knowledge and/or misconceptions about the cognitive profile of MV autistic children (Mavropoulou & Padeliadu, 2000), as well as the pertinent need for more specialized training in interventions designed for the specific population (Kalyva, 2010). The findings of the current study could inform efforts to develop language intervention programs for special education teachers to optimize the educational procedure for MV autistic children.

While this study provides insights into the language profiles of MV autistic preschoolers, several limitations should be acknowledged. First, the relatively small sample size (n = 26) introduces certain limitations. While this number is considerable for studies involving MV populations, particularly within the Greek context, where access to early diagnostic and support services has only recently expanded, it nevertheless narrows the scope for broader conclusions. The relatively small sample reduces the precision of interpretation and may not fully reflect the diversity within this population. In addition, participant characteristics, such as the presence of some expressive vocabulary, may limit the extent to which the findings apply to the wider MV population. The cultural and linguistic specificity of the sample, drawn from Greek-speaking children in one geographic area, also further constrains generalizability. Even so, this study offers a valuable starting point for understanding language development in MV autistic children in Greece. It provides initial data on a population that has received limited attention in both national and international research. Much of what is currently known about language and development in autism is based on studies with verbally fluent children, and these findings are often extended to MV individuals, despite key differences in needs and developmental pathways. By focusing on this underrepresented group, the present study contributes context-relevant evidence that can support more inclusive research efforts moving forward. Moreover, as already mentioned, MV autistic individuals often exhibit challenging behaviors that make data collection difficult (Tager-Flusberg et al., 2016). The current research has tried to limit confounds that could have affected the reliability of the data reflecting children's language ability, such as excluding participants with severe motor and/or sensory impairment, providing brief verbal instructions, modeling accurate responses in the familiarization items and using corrective feedback in case the response was not correct, providing breaks and planning the testing sessions on specific days and times indicated by the children's parents and therapists to minimize discomfort and anxiety effects for the children, and closely collaborating with the parents during the testing procedure. Though we cannot exclude the possibility that performance could have been affected by poor understanding of the instructions or the pragmatics of the testing situation, children's differential performance across tests with similar instructions implies that the LaTo tool was efficient at capturing variability in the children's language competence. For example, though the phonemic blending and segmentation tests had similar instructions (see the Method section), the MV group performed slightly better in the former than in the latter test. Similarly, though the vocabulary (i.e., picture, relational, and receptive) and the morphosyntactic comprehension and production tests included visual aids, children's performance was worse in morphosyntax than in the vocabulary tests. This pattern suggests that visual cues, though important in supporting MV children's performance, were not the sole determinant of their language performance. Of course, the development and validation of more comprehensive and multimodal approaches (e.g., Plesa Skwerer et al., 2016) for the collection of data in MV autistic populations is warranted. Another limitation of the current study concerns the lack of universally accepted, standardized criteria for MV classification, which further complicates cross-study comparisons and may introduce variability in participant characterization. In the current study, MV children were documented to be using no more than eight spontaneous and functional words, which seems to agree with the MV classification criteria of previous research (Rogers et al., 2006; Schreibman & Stahmer, 2014).

Future research should prioritize longitudinal designs to better understand language development over time in MV autistic children. Firm findings in large, well-characterized, MV autistic cohorts longitudinally are essential in order to better capture the language developmental trajectories in this population. This would further enable us to identify the appropriate timing for interventions and the most effective intervention types that would target the most vulnerable language areas in these individuals. Also, future studies should incorporate larger and more diverse cohorts and adopt standardized diagnostic frameworks to enhance the robustness, comparability, and external validity of investigations into this underrepresented population.

Conclusions

The study confirms that language development in MV autistic preschoolers is characterized by pervasive impairments, with particularly low performance in phonemic awareness and expressive language. These areas appear to be the most challenging for MV children, likely due to their high cognitive and metalinguistic demands. However, a contrasting pattern emerged in receptive language tasks, where performance was comparatively better—especially in those tasks leveraging the children's semantic knowledge and nonlinguistic experiences. The use of visual cues in these assessments may have also provided critical scaffolding that supported understanding and response accuracy. Regression analyses revealed a significant negative association between age and language performance, suggesting a decline in language skills as MV children grow older in the absence of intervention. In contrast, nonverbal IQ showed no significant relationship with language outcomes, indicating that general cognitive ability does not necessarily predict linguistic proficiency in this population. Altogether, the findings underscore that language development in MV autistic children is not uniform but shaped by task type and modality. Crucially, the data point to the importance of alternative cognitive resources—such as lexical semantics and experiential knowledge—as potential foundations for supporting and enhancing language acquisition in MV children.

Implications

The study's findings carry important implications for assessment and intervention strategies targeting MV children. First, tools and intervention programs that utilize semantic scaffolding and visual supports—such as picture-based communication and conceptually grounded vocabulary training—may be more effective than approaches that focus heavily on phonological instruction. Second, the heterogeneity observed across language domains underscores the importance of individualized treatment plans based on fine-grained language assessments rather than global IQ estimates. Third, the results advocate for early identification and intervention, as age was negatively linked to performance. Finally, establishing standardized criteria for MV classification and including culturally diverse populations in future research are essential for improving diagnostic precision and generalizability. Collectively, these directions can guide more inclusive and effective educational and clinical practices for MV children with autism. Specifically, in Greece, there is a growing number of MV autistic students enrolling in preschool education (Petinou et al., 2024); yet, despite the growing demand for diversity, equity, inclusion, and belonging in early educational settings, the norms are still set by “traditional” preschoolers, that is, those children who identify as verbally able and neurotypical (Fyssa & Vlachou, 2015). Our results about language competence in MV autistic children provide insight into how language assessment in MV autism can be extended and enhanced in the next phase of research. This work could help address existing barriers in access to diagnostic evaluations that underlie ongoing disparities in the Greek educational system. Utilizing an equity approach to understand the language abilities of preschool-aged MV children has the potential to transform the evaluation process for traditionally underserved populations.

Footnotes

Acknowledgments

We would like to thank the participants for their unfailing interest in our study.

ORCID iDs

Eleni Peristeri

Maria Andreou

Ethical Considerations

Parents and/or legal guardians of all children released their written informed consent for their participation in the study and the treatment of the data in accordance with the Declaration of Helsinki. The study was approved by the IRB (or Ethics Committee) of the Aristotle University of Thessaloniki (IRB protocol number: 39928; date of approval: 20 February 2024).

Consent for Publication

The authors of the study confirm that written informed consent for publication was provided by the legally authorized representatives of the study's participants.

Author Contributions

Conceptualization: EP and MA. Methodology: EP and MA. Software: EP and MA. Formal analysis: EP and MA. Data curation: EP and MA. Writing—original draft preparation: EP and MA. Writing— review and editing: EP and MA. Supervision: EP. Project administration: EP and MA.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Hellenic Foundation for Research & Innovation—H.F.R.I., within the research project entitled “Language Phenotyping in Autism Using Machine Learning,” which is implemented in the framework of H.F.R.I. called “Basic Research Financing (Horizontal Support of All Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union—NextGenerationEU (H.F.R.I. Project Number: 14864), Principal Investigator: Eleni Peristeri.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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