Mind the Gap: Executive Function is Associated With the Discrepancy Between Cognitive and Adaptive Functioning in Autistic Children without Cognitive Delay

Abstract

Purpose

Adaptive functioning is central to supporting autistic individuals’ independence and well-being. However, autism spectrum disorder (ASD) is associated with poor adaptive functioning, even in the absence of cognitive delays or deficits. This study examined how age and executive function associate with adaptive functioning –– particularly the gap between cognitive and adaptive functioning.

Methods

We addressed our research questions separately for a school-age (N = 101 ages 7-12) cohort and a preschool (N = 48 ages 2 and 4) cohort of autistic children without cognitive delays. Both cohorts of parents reported on their children’s adaptive and executive functioning skills. The difference between adaptive and cognitive skills was computed for each participant. For each cohort, we evaluated whether adaptive skills decline with age. Next, we measured, in each cohort, whether children’s executive function corresponded with this gap between their adaptive and cognitive skills.

Results

Adaptive functioning did not decline relative to cognitive ability in the younger cohort, but the gap was present in the school-age cohort. Yet, reduced executive function consistently corresponded with a greater cognitive-adaptive gap in socialization domains for both preschool and school-age children.

Conclusions

Targeting EF, specifically emotional control, during preschool years may support both adaptive functioning and social connectedness for autistic children without cognitive delays.

The Cognitive-Adaptive Gap in Autism

Adaptive function (AF) is a set of skills (i.e., communication, socialization, and daily living skills) that describes one’s personal independence and social responsibility relative to age- and socioculturally-matched peers (American Psychiatric Association, 2013; Tassé et al., 2012). Better AF, as measured by the Vineland Adaptive Behavior Scales (VABS, Sparrow et al., 2005; Sparrow et al., 2016) or Adaptive Behavior Assessment System (ABAS), is associated with positive quality of life outcomes including increased independence and opportunities for employment in adults with developmental disabilities (Farley et al., 2009; Woolf et al., 2010).

For neurotypical (NT) individuals, AF and cognitive abilities are strongly aligned. However, autistic individuals often experience challenges with AF, as measured with the VABS or ABAS, even in the absence of cognitive delays (Duncan & Bishop, 2015; Farley et al., 2009; Klin et al., 2007; Wang et al., 2023). Investigations using the VABS demonstrate autistic individuals require more support for socialization and daily living skills (DLS), whereas communication skills, more closely correspond with cognitive ability (Carter et al., 1998; Kanne et al., 2011; Liss et al., 2001; Pugliese et al., 2015; Tillmann et al., 2019). The degree to which AF falls behind cognition, relative to age-matched peers, increases across childhood and adolescence for autistic individuals (Meyer et al., 2018). Cross-sectional studies demonstrate larger discrepancies in AF relative to age-matched peers for older versus younger autistic individuals ranging from three- to 23-year-olds; findings are particularly pronounced among autistic samples with higher cognitive ability (Kanne et al., 2011; Kenworthy et al., 2010; Klin et al., 2007; Pugliese et al., 2015; Wang et al., 2023).

Although this cognitive-adaptive “gap” in autism is well documented, few studies have investigated what characteristics relate with the degree of AF delay relative to cognitive ability in autistic individuals without cognitive delays. Better understanding of these correlates may provide important clues for shrinking the cognitive-adaptive “gap.” In one cross-sectional study, Duncan & Bishop (2015) found that cognition, age, sex, and maternal education explained only 10% of variance in membership in a group with below average DLS but average cognitive ability. Additionally, Ashwood and colleagues (2015) found that co-occurrence of ADHD and ASD correlated with the size of the cognitive-adaptive gap in 6- to 16-year-old boys. These limited findings underscore the critical need to identify factors linked to the cognitive-adaptive gap throughout development for autistic children, as persistent challenges in AF affect mental health, ability to live independently, and quality of life (Farley et a., 2009; Woolf et al., 2010).

Executive Function in Autism

One factor that has not yet been examined as a correlate of the cognitive-adaptive gap for autistic individuals at any age is executive function (EF). EF is a set of higher-order cognitive skills that emerge in toddlerhood and continue to develop during childhood and adolescence. Better EF, measured using lab-based behavioral tasks, is associated with optimal mental health outcomes (Fairchild et al., 2009; Taylor Tavares et al., 2007), increased physical health (Miller et al., 2011), and academic success (Borella et al., 2010; Duncan et al., 2007). This link to positive outcomes can be understood by the role EF plays in supporting organized and deliberate behavior and cognition via working memory, flexible thinking, inhibitory control, planning, and directed attention (Diamond, 2013). EF is often conceptualized as having two domains: “cool” EF, or metacognition, and “hot” EF, or behavioral/emotional regulation (Zelazo & Carlson, 2012) both of which support self-control and engagement in complex tasks across a variety of settings.

Compared to NT individuals, autistic children and adolescents generally demonstrate poorer overall EF skills as measured using the Behavior Rating Inventory of Executive Function (BRIEF; Demetriou et al., 2018; Gioia et al., 2000; 2002; Granader et al., 2014). Autistic children and teens without co-occurring developmental delay or intellectual disability (ID) often exhibit challenges with EF, as measured by parent-report and lab-based behavioral tasks which include measures of flexibility, organization, and planning (Barron-Linnankoski et al., 2015; Kenworthy et al., 2005; McClain et al., 2022; Valeri et al. 2020).

Executive and Adaptive Function in Autism

EF, measured by BRIEF parent report, is associated with AF, measured by the VABS, in autistic preschoolers, school-age children, and adolescents (Gilotty et al., 2002; Pugliese et al., 2015; Powell et al., 2022, White et al., 2017). In particular, metacognitive EF skills have been concurrently associated with AF in autistic children, adolescents, and young adults without cognitive delay. Specifically, organizational ability has been associated with daily living (Gardiner & Iarocci, 2018; Pugliese et al., 2015) and socialization skills (Pugliese et al., 2015), while monitoring has been associated with communication and socialization skills (Gardiner & Iarocci, 2018). Working memory and initiation were also associated with daily living and socialization skills (Pugliese et al., 2015), but other studies have not found these associations (e.g., Gardiner & Iarocci, 2018).

In addition to concurrent correlational studies, longitudinal studies have demonstrated that EF predicts later AF in autistic children and adolescents without cognitive delay. Global EF, as well as the specific EF skills of monitoring and inhibition, predicted socialization and daily living skills at later follow-up among three- to 14-year-olds whereas monitoring also predicted communication, and shifting predicted socialization (Pugliese et al., 2016). Inhibition, planning, and cognitive flexibility assessed using lab-based behavioral tasks also predicted global AF 12 years later (Kenny et al., 2019) in a different sample of five-year-olds. Finally, EF problems measured using the BRIEF as early as 3 years of age predicted later AF measured with the VABS in autistic preschoolers (Powell et al., 2022).

No studies have examined whether EF skills are associated with the gap between cognitive and AF in autistic children without cognitive delays. Additionally, only one study has examined the association between EF and AF in toddlers and preschool-aged autistic children who do not have cognitive delays (Powell et al., 2022). Evaluating whether EF contributes to the gap between cognitive ability and AF early in development may inform targeted intervention strategies that are critical for effectively supporting positive outcomes for children by reducing the lag in AF relative to cognitive ability.

The Current Study

In the current study, we evaluated the association between AF and age and assessed whether EF skills were associated with both AF and the gap between cognition and AF in two separate, developmentally distinct, cross-sectional cohorts. First, to replicate the current literature examining AF and EF in predominantly school age children, we evaluated a sample of seven- to 12-year-old autistic children without co-occurring intellectual disability (“School-Age Cohort”). Next, to extend the literature and explore whether these patterns were consistent at a younger age, we evaluated a sample of two- and four-year-old children without co-occurring developmental delay (“Preschool Cohort”).

We investigated the following research questions separately within each distinct cohort:

1. Do adaptive function (AF) levels differ with age in cross sectional samples?

2. Is the gap between cognitive ability and AF larger for older children?

3. Are specific domains of executive function (EF) associated with AF?

4. Is EF associated with the gap between cognitive ability and AF?

We hypothesized that AF scores would be lower relative to same-age peers for older autistic children and that the gap between cognitive ability and AF would be larger. Further, we hypothesized EF would be associated with both the degree of AF delay and the gap between cognitive ability and AF within both the school-age and preschool cohorts. We did not make specific predictions about which EF subdomains would correspond with EF or the cognitive-adaptive gap given the limited research in this area.

Methods

Participants

Children were recruited either at Boston Children’s Hospital or the University of Washington using recruitment registries, community events, community advocacy groups, clinics serving autistic children, and word of mouth. Exclusionary criteria included a history of seizures or seizure medication, medical disorders or medications impacting the central nervous system, inability to complete questionnaires or testing in English, prolonged prenatal substance exposure and significant sensory or motor impairments or major physical abnormalities which limited ability to complete the testing battery. The Human Subjects Division or Institutional Review Board at each institution approved all study procedures and all parents provided written and verbal consent for their children to participate. Table 1 provides demographics, and table 2 provides measure characteristics, for each cohort.

Table 1.

Participant Demographics

	School Age Cohort	Preschool Cohort
		2-Year-Olds	4-Year-Olds
	N (%) or M (SD)	N (%) or M (SD)
Number of Participants	101	22	26
Sex Assigned at Birth
Male	90 (89.1%)	17 (77.3%)	21 (80.8%)
Female	11 (10.9%)	5 (22.7%)	5 (19.2%)
Race
American Indian/Alaskan Native	0 (0.0%)	0 (0.0%)	0 (0.0%)
Asian	5 (5.0%)	3 (13.6%)	1 (3.8%)
Hawaiian/Pacific Islander	0 (0.0%)	0 (0.0%)	0 (0.0%)
Black/African American	4 (4.0%)	2 (9.1%)	0 (0.0%)
White/Caucasian	79 (78.2%)	14 (63.6%)	21 (80.8%)
More than one Race	9 (8.9%)	1 (4.5%)	3 (11.5%)
Other	0 (0.0%)	2 (9.1%)	1 (3.8%)
Unknown	4 (4.0%)	0 (0.0%)	0 (0.0%)
Ethnicity
Non-Hispanic/Latine	91 (90.1%)	20 (90.1%)	22 (84.6%)
Hispanic/Latine	10 (9.9%)	2 (9.1%)	4 (15.4%)
Parental Education Level
Some high school (no diploma)	0 (0.0%)	0 (0.0%)	0 (0.0%)
GED diploma	2 (2.0%)	1 (4.5%)	1 (3.8%)
High School Graduate	3 (3.0%)	0 (0.0%)	2 (7.7%)
Associate Degree (2yr Degree)	15 (14.9%)	1 (4.5%)	0 (0.0%)
Some College	24 (23.8%)	3 (13.6%)	1 (3.8%)
BA/BS Degree (4yr Degree)	45 (44.6%)	4 (18.2%)	5 (19.2%)
Graduate/Professional Degree)	10 (9.9%)	8 (36.4%)	13 (50.0%)
Unknown/Missing	2 (2.0%)	5 (22.7%)	4 (15.4%)
Age (Months)	110.2 (16.6)	31.3 (3.5)	54.7 (3.5)
Range	84 - 144	25 - 36	48 - 60
Average Household Income
< $20,000	3 (3.0%)
$21,000 - $35,000	8 (7.9%)
$36,000 - $50,000	6 (5.9%)
$51,000 - $65,000	5 (5.0%)
$66,000 - $80,000	12 (11.9%)
$81,000 - $100,000	17 (16.8%)
$101,000 - $130,000	13 (12.9%)
$131,000 - $160,000	9 (8.9%)
> $160,000	23 (22.8%)
Unknown/Missing	5 (5.0%)
Average Household Income
< $20,000		1 (4.5%)	0 (0.0%)
$20,000 - $40,000		1 (4.5%)	1 (3.8%)
$40,000 - $60,000		1 (4.5%)	0 (0.0%)
$60,000 - $80,000		2 (9.1%)	1 (3.8%)
$80,000 - $100,000		2 (9.1%)	0 (0.0%)
$100,000 - $120,000		3 (13.6%)	1 (3.8%)
$120,000 - $140,000		1 (4.5%)	3 (11.5%)
$140,000 - $180,000		1 (4.5%)	4 (15.4%)
$180,000 - $200,000		0 (0.0%)	0 (0.0%)
> $200,000		2 (9.1%)	7 (26.9%)
Unknown/Missing		8 (36.4%)	9 (34.6%)

Notes: M = mean; SD = standard deviation. Due to expected rounding errors, cumulative percentages are between 99 and 101.

Table 2.

Cognitive, Adaptive Behavior, and Executive Function Descriptive Data

	School Age Cohort	Preschool Cohort
		2-Year-Olds	4-Year-Olds
	M(SD)	M(SD)
Cognitive Level	WASI-2 Full	MSEL Early Learning
	Scale IQ (FSIQ)	Composite (ELC)
	105.9 (14.3)	89.18 (10.2)	92.19 (14.5)
Vineland Adaptive Behavior Scales	Second Edition	Third Edition
Adaptive Behavior Composite (ABC)	85.0 (8.4)	77.50 (9.9)	82.31 (10.4)
Communication	90.9 (10.4)	74.54 (10.9)	81.00 (12.4)
Socialization	80.8 (9.9)	85.82 (11.7)	91.62 (14.3)
Daily Living Skills (DLS)	88.9 (9.6)	78.18 (18.5)	81.08 (10.9)
Gap Scores
(FSIQ or ELC) - ABC	20.9 (16.1)	11.7 (11.3)	9.9 (14.0)
(FSIQ or ELC) - Communication	15.0 (15.2)	11.0 (18.0)	11.1 (16.4)
(FSIQ or ELC) - Socialization	25.1 (18.6)	3.4 (11.8)	0.6 (11.7)
(FSIQ or ELC) - DLS	17.0 (16.9)	14.6 (14.1)	11.2 (18.3)
Behavior Rating Inventory of Executive Function (BRIEF)
Behavior Regulation Index	67.1 (12.3)
Initiate	64.3 (10.3)
Emotional Control	62.4 (12.2)
Shift	68.2 (13.0)
Metacognition Index	67.1 (10.6)
Inhibit	64.8 (12.4)
Organize/Plan	65.7 (11.5)
Organization of Materials	59.3 (9.8)
Working Memory	67.0 (10.3)
Monitor	64.1 (10.6)
BRIEF-Preschool Edition
Inhibitory Self Control Index		63.0 (14.0)	67.3 (12.8)
Inhibit		65.3 (14.9)	66.2 (13.2)
Emotional Control		57.0 (12.5)	63.6 (13.6)
Flexibility Index		61.3 (14.9)	63.1 (12.6)
Shift		63.0 (15.6)	60.3 (11.6)
Emotional Control		57.0 (12.5)	63.6 (13.6)
Emergent Metacognition Index		66.5 (13.0)	68.8 (15.0)
Working Memory		67.3 (11.8)	69.3 (14.6)
Plan/Organize		63.0 (12.9)	64.4 (13.4)
ADOS Calibrated Severity Score	8.50 (1.68)
Range	4-10

Notes: M = mean; SD = standard deviation; FSIQ is computed with four subtests; Gap scores were created by subtracting adaptive functioning domains from WASI-2 FSIQ or MSEL ELC

Participants - School-Age Cohort

Participants were 101 seven- to 12-year-old autistic children (90 male) without co-occurring intellectual disability, originally recruited into a larger study of executive function (EF) development in middle childhood at Boston Children’s Hospital and the University of Washington. Parent report measures, collected as part of the larger project, were used for the current study (7 year n = 24; 8 year n = 22; 9 year n = 21; 10 year n = 20; 11 year n = 13; 12 year n = 1).

All children had cognitive ability scores of 80 or above as assessed by the Wechsler Abbreviated Scale of Intelligence-2 (WASI-II; Wechsler, 2011) and were verbally fluent. All children had existing diagnoses of ASD, which were confirmed using the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al., 2012) and the Autism Diagnostic Interview-Revised (ADI-R; Rutter et al., 2003) according to Collaborative Programs of Excellence in Autism (CPEA) (Sung et al., 2005) and DSM-5 criteria (American Psychiatric Association, 2013) based on expert clinical judgment.

Participants - Preschool Cohort

Participants were 48 autistic children (38 male) without co-occurring developmental delay aged two years (25 to 36 months) or four years (48 to 60 months) at their visit. Participants were originally recruited into an ongoing larger study of EF development in early childhood; Parent report measures, collected as part of the larger project, were used for the current study (2 year n = 22; 4 year n = 26).

All preschoolers had cognitive ability scores of 70 or above as assessed by the Early Learning Composite (ELC) on the Mullen Scales of Early Learning (MSEL; Mullen, 1995). All preschoolers entered with existing or suspected ASD diagnoses. Diagnosis was determined using the ADOS-2 (Lord, Luyster, et al., 2012, Lord, Rutter et al., 2012) and the ADI-R (Kim & Lord, 2012; Rutter et al., 2003) and DSM-5 criteria (American Psychiatric Association, 2013) based on expert clinical judgment. For children who participated during the COVID-19 pandemic, PPE use prevented valid scoring of the ADOS. Tasks were still administered to inform DSM-5 decision making and the Childhood Autism Rating Scale, Second Edition (CARS-2) was additionally administered.

Measures - School-Age Cohort

Cognitive Ability, WASI-II: Cognitive ability was measured using the Full-Scale IQ (FSIQ) derived from the Wechsler Abbreviated Scales of Intelligence-Second Edition (WASI-II; Weschler 2011). The WASI-II FSIQ is derived from four subtests: Vocabulary, Block Design, Similarities, and Matrix Reasoning.

Adaptive Function (AF), VABS-2: AF was measured using the Vineland Adaptive Behavior Scales, Second Edition (VABS-2). The VABS is a structured parent interview that assesses several domains of AF and has good psychometric properties (Salekin et al., 2018; Sparrow et al., 2005). The VABS is a norm-based instrument, where a standard score of 100 reflects the mean adaptive level for a typical population of the same age. Standard scores for the Communication, Daily Living Skills (DLS), and Socialization domains, as well as the Adaptive Behavior Composite (ABC), were used as measures of AF for this study.

Executive Function (EF), BRIEF: EF was measured via parent report using the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al. 2000). The BRIEF includes two indices which allow for an interpretation of distinct EF domains: the Behavioral Regulation Index (BRI; comprised of the Inhibit, Shift, and Emotional Control clinical scales) and Metacognition Index (MCI; comprised of the Initiate, Working Memory, Monitor, Plan/Organize, and Organization of Materials clinical scales).

Measures - Preschool Cohort

Early Cognitive Ability, MSEL: Cognitive ability was measured using the Early Learning Composite (ELC) standard score derived from the Mullen Scales of Early Learning (MSEL; Mullen, 1995). The MSEL is a standardized developmental measure that assesses nonverbal and verbal developmental level (Mullen, 1995; Swineford, Guthrie, & Thurm, 2015).

Adaptive Function (AF), VABS-3: AF was measured via the Vineland Adaptive Behavior Scales, Third Edition (VABS-3; Sparrow et al. 2016). As with the School-Age Cohort, standard scores for VABS-3 Communication, DLS, and Socialization domains and the ABC were analyzed for this study.

Executive Function (EF), BRIEF-P: EF was measured via parent report using the Behavior Rating Inventory of Executive Function, Preschool Edition (BRIEF-P; Gioia et al., 2003). The BRIEF-P includes three indices which allow for an interpretation of distinct EF domains: Inhibitory Self-Control (ISCI: comprised of the Inhibit and Emotional Control clinical scales), Flexibility (FI: Shift and Emotional Control clinical scales), and Emergent Metacognition (EMI: Working Memory and Plan/Organize clinical scales).

Procedure

All parents first completed the VABS-2 or VABS-3 via a clinician-administered interview. Parents and their children then visited the lab for an ASD diagnostic evaluation using the ADOS-2, which included either the WASI-II or MSEL. Finally, parents completed the BRIEF or BRIEF-P.

Analytic Plan

To examine whether children’s AF standard scores varied with age in the school-age cohort (Question 1), four separate linear regression models tested the relation between age in months and each dependent variable–VABS Adaptive Behavior Composite score (ABC), Communication, Socialization, and DLS–while controlling for cognitive ability. In the preschool cohort, because participants were tested at either age two or four, ANCOVAs controlling for cognitive ability examined whether VABS ABC and domain scores differed between the two- and four-year old subgroups.

To evaluate whether the gap between cognitive and adaptive functioning differed with age (Question 2), we first computed four “gap” scores by subtracting the ABC, Communication, Socialization, and DLS scores from cognitive ability, consistent with prior work (Pugliese et al., 2015; Tillmann et al., 2019) ¹. Next, we conducted a series of linear regressions in the school-age cohort to test whether age in months corresponded with the gap between cognitive ability and AF. In the preschool cohort, we conducted univariate ANOVAs to test whether gap scores differed between the two- and four-year-old subgroups.

The analytic plan for Questions 3 and 4 were identical for both cohorts. Within the preschool cohort, each research question was examined within two- and four-year-old subgroups separately. To assess whether EF related to AF (Question 3), separate hierarchical regression models examined whether EF indices corresponded with VABS ABC, Communication, Socialization, and DLS scores, controlling for age and cognitive ability. To assess whether EF related to the cognitive-adaptive gap (Question 4), separate hierarchical regression models examined whether EF indices corresponded with the gap between cognitive ability and each VABS score, controlling for age. For both Question 3 and Question 4, when EF indices were significant, planned post-hoc analyses explored whether the corresponding BRIEF clinical subscales were associated with each outcome.

Outliers were identified using Cook’s distance and by assessing an undue influence they had on each model; no outliers were identified. Child sex assigned at birth, amount of autism features², and parental income and education were assessed as covariates in all models but were removed when they were non-significant. As such, only the final model examining the relation between EF and the gap between cognitive and AF Socialization scores (Question 4, School-Age Cohort) included these variables. The statistical significance criterion was set at alpha = .05, in line with similar research on the cognitive-adaptive gap (Pugliese et al. 2015; Kenworthy et al. 2010)

Results

School-Age Cohort

Question 1: For school age children, does adaptive function (AF) differ with age?

A significant linear relation between AF and age was detected (Figure 1) relative to same-age peers. Specifically, older children tended to have lower global AF relative to age expectations, (β = −.42, p < .001) (Figure 1), as well as lower scores on each AF domain: Communication, (β = −.38, p < .001), Socialization, (β = −.38, p < .001), and DLS, (β = −.33, p = .001), relative to age expectations.

Figure 1. For School-Aged Children, Older Age Corresponded With Lower Adaptive Skills.

AF = Adaptive Function; SS = Standard Score

Question 2: For school age children, does the gap between cognitive ability and AF vary with age?

Gap scores were computed by subtracting the standard scores of the adaptive functioning domains on the VABS 2 from the WASI-2 FSIQ. The gap between cognition and AF also significantly related to age (Figure 2). Older children tended to have greater gaps between their cognitive level and global AF, (β = .30, p = .004), as well as between their cognitive level and each AF domain: Communication, (β = .32, p = .001), Socialization, (β = .28, p = .006), and DLS, (β = .26, p = .01).

Figure 2. For School-Aged Children, Older Age Corresponded With a Larger Gap Between Overall Adaptive Skills and Cognition.

AF = Adaptive Function; SS = Standard Score

Question 3: For school age children, do EF domain scores relate to AF scores?

Two significant relations were detected between EF and AF levels. First, the BRIEF MCI was negatively associated with adaptive communication skills, such that lower levels of metacognition problems related to higher adaptive communication levels, controlling for cognitive ability. Post-hoc analyses revealed that children with increased working memory difficulties within the MCI (BRIEF Working Memory clinical subscale) had decreased communication skills. Second, the BRI was negatively associated with adaptive socialization skills, such that fewer behavioral regulation problems related with higher adaptive socialization levels. Post-hoc analyses revealed that children with increased emotional control difficulties (BRIEF Emotional Control clinical subscale) had decreased socialization skills (Table 3).

Table 3.

Working Memory is Associated With Everyday Communication Skills, While Emotional Control is Associated With Everyday Socialization Skills, for School-Age Autistic Children

	Adaptive Behavior Composite	Communication	Socialization	Daily Living Skills
	β (p)	β (p)	β (p)	β (p)
MCI	−.27 (.02)*	−.35 (.002)**	−.13 (.22)	−.20 (.10)
Working Memory	−.24 (.07)	−.38 (.004)**	–	–
BRI	−.01 (.92)	.19 (.10)	−.32 (.006)**	.08 (.52)
Emotional Control			−.30 (.02)*	–

Notes:

^*p < .05;

^**p < .01.

VABS-2 standard scores were used for each AF domain. EF indices and clinical subscales were from the BRIEF. Each cell reports the beta of interest from a linear regression model using the listed EF variable as predictor of interest and the listed AF variable as outcome of interest (controlling for age in months and cognitive ability). Full results from each regression model are available from the second author upon request.

Question 4: For school-age children, is EF associated with the gap between cognitive ability and AF?

Behavioral regulation (BRI), but not metacognitive (MCI), EF skills were significantly associated with the gap between cognition and AF (Table 4). Specifically, greater BRI problems corresponded with larger gaps between cognition and adaptive socialization skills, controlling for parental income. Post-hoc analyses revealed the emotional control subscale related to the gap between cognition and AF socialization skills, controlling for parental income. For children with greater emotional control difficulties, the gap between cognition and adaptive socialization skills was greater.

Table 4.

Behavioral Regulation EF Skills, Specifically Emotional Control, Associates With the Gap between Cognition and Everyday Socialization Skills in School-Age Autistic Children

	Cognition - AF ABC Gap	Cognition - AF Communication Gap	Cognition - AF Socialization Gap	Cognition - AF Daily Living Skills Gap
	β (p)	β (p)	β (p)	β (p)
MCI	.01 (.91)	.13 (.29)	−.08 (.49)	−.01 (.93)
BRI	.26 (.03)*	.10 (.41)	.44 (.001)*	.21 (.11)
Emotional Control	.17 (.23)		.26 (.04)*

Notes:

^*p < .05.

Cognition for the School-Age Cohort is taken from the WASI-II FSIQ standard score (SS). VABS-2 SS were used for each AF domain. A “gap” score was created by subtracting AF from cognitive ability. EF indices and clinical subscales were from the BRIEF. Each cell reports the beta of interest from a linear regression model using the listed EF variable as predictor of interest and the listed cognitive-adaptive gap variable as outcome of interest (controlling for age, and for parent income for models with AF socialization as outcome).

In contrast, BRI levels were not related to the gap between cognition and AF communication skills or DLS. The MCI did not correspond with the gap between cognition and any of the AF domains.

Preschool Cohort

Questions 1-3

Adaptive function standard scores were not significantly lower for four-year-olds compared to two-year-olds (.24 < Fs < 3.23 ps >.07). Further, gap scores were computed by subtracting the standard scores of the adaptive functioning domains on the VABS 3 from the Mullen ELC. The gap between cognition and any domain of AF was not larger in the four-year-olds compared to two-year-olds (.001 < Fs < .67 ps > .41). EF indices (ISCI, FI, and EMI) were not significantly associated with any domain of AF (−.53 < β < .09, ps >.13).

Question 4: During preschool, is EF associated with the gap between cognitive ability and AF?

EF problems were significantly associated with the gap between cognition and AF socialization skills for both 2-year-olds and 4-year-olds (Table 5, Figure 3). Specifically, greater EF problems corresponded with larger gaps between AF socialization skills and cognition. Different aspects of EF related to the cognition-socialization gap depending on age. Greater metacognition problems (EMI) were associated with a larger cognition-socialization gap for two-year-olds, though post-hoc analyses examining individual clinical subscales of the EMI were non-significant. For four-year-olds, greater inhibition and flexibility problems (ISCI and FI) were associated with a larger cognition-socialization gap. In addition, four-year-olds who had greater challenges with flexibility (FI) had larger gaps between DLS and cognitive ability. For four-year-olds, the only clinical subscale that significantly related to any gap scores was the Emotional Control clinical subscale. Specifically, the Emotional Control scale, a subscale of both the ISCI and FI, related to both the cognition-socialization and cognition-DLS gap.

Table 5.

EF Problems are associated with a greater Cognition-Socialization Gap for 2- and 4-Year-Olds, with Effects Driven by Emotional Control

	BRIEF Index	Cognition-Communication Gap	Cognition-Socialization Gap	Cognition-Daily Living Skills Gap
		β (p)	β (p)	β (p)
2-year-olds	EMI	.38 (.20)	.53 (.04)*	.25 (.46)
4-year-olds	ISCI	.20 (.40)	.57 (.007)*	.40 (.06)
	Emotional Control		.54 (.01)*
	FI	.30 (.20)	.59 (.01)*	.47 (.02)*
	Emotional Control		.53 (.03)*	.53 (.03)*

Notes:

^*p < .05.

Controlling for age in months. Cognition for the Preschool Cohort is taken from the MSEL Early Learning Composite. VABS-3 SS were used for each AF domain. A “gap” score was created by subtracting AF from cognitive ability. EF indices and clinical subscales were from the BRIEF-P. Each cell reports the beta of interest from a linear regression model using the listed EF variable as predictor of interest and the listed cognitive-adaptive variable as outcome of interest. The emotional control subscale is a component of both the ISCI and FI. We report significant outcomes from both regressions, where Emotional Control was included as a predictor along with either the Inhibit or Shift subscale.

Figure 3. For Both Two- and Four-year-olds, as EF Problems Increased, the Gap by Which Their Everyday Socialization Skills Lagged Cognition Increased.

SS = Standard Score

Discussion

Summary of Results

Within two distinct cohorts, a school-age cohort of autistic children without intellectual disability (ID) and a preschool cohort of autistic children without developmental delays, we examined whether executive function (EF) explained differences in adaptive functioning (AF) and the “gap” between cognition and AF. Even within the average range of cognitive functioning, we found that older school-age children had lower adaptive behavior skills relative to age norms (Question 1), and larger gaps between cognitive and adaptive skills (Question 2) (Kanne et al. 2011). Furthermore, we replicated prior work by demonstrating EF and AF levels were related above and beyond cognitive ability (Pugliese et al. 2016) (Question 3) and added to this body of work by finding that better EF skills, specifically better emotional control and behavioral regulation, corresponded with smaller gaps between cognition and AF socialization levels (Question 4).

In the preschool cohort, we did not find age related differences in AF relative to same-age peers (Question 1) or in the cognitive-adaptive skills gap (Question 2). Although EF was not correlated with AF among autistic preschoolers (Question 3), EF challenges related to a larger gap between cognition and adaptive skills in the socialization domain for two-year-olds and greater EF challenges, specifically emotional control problems, related to greater cognition-socialization and cognition-DLS gaps for four-year-olds (Question 4). Our cross-sectional samples provide a unique opportunity to examine EF and AF in autistic children of varying ages, both during preschool and, separately, across middle childhood. We will therefore discuss our results for both the older and younger cohort for each cross-cutting research question.

Relations between Age and Adaptive Functioning

Findings from our school-age cohort replicated previous work suggesting global and domain-specific AF decreases relative to age expectations during middle childhood, above and beyond cognitive ability (Chatham et al. 2018, Klin et al. 2007). This further underscores a need to examine child and family factors related to AF, given the importance of AF for independence and later success in life (Farley et al., 2009; Woolf et al., 2010). Our preschool cohort results suggest adaptive skills do not decrease with age during the preschool period. This finding is consistent with work by Hodge and colleagues (2021), who found AF did not decrease relative to age expectations in a cross-sectional cohort of two- to six-year-olds. Taken together, this evidence suggests that adaptive skills in autistic children without ID may not decline until later in childhood, consistent with research by Meyer and colleagues (2018) in a longitudinal sample of one-to 33-year-olds.

Relations between Age and the Cognitive-Adaptive Gap

Within our school-age cohort, we replicated previous findings that the cognitive-adaptive gap exists across communication and socialization domains for autistic children without ID, and increases with age (Tillman et al. 2019, McQuaid et al. 2021). We found the cognitive-DLS gap increases with age, whereas Tillman and colleagues (2019) did not find an increase, and McQuaid and colleagues (2021) found an increase only in boys but not girls. Of note, Tillman and colleagues (2019) did not constrain their sample to individuals without ID which may have obscured the relationship between age and the cognitive-DLS gap for children without cognitive delay. Furthermore, gender was not related to AF in our analyses, although our sample was predominantly comprised of boys. Given the particular importance of DLS for autonomy, these conflicting findings suggest more work is necessary to understand why, and for whom, the cognitive-DLS gap widens as children transition from early school age to adolescence.

In contrast to our school-age cohort, we found the cognitive-adaptive gap does not increase with age within the preschool cohort. Notably, the smaller and non-continuous age range in our preschool cohort as compared with the school-age cohort might have contributed to lower likelihood of detecting an association between the cognitive-adaptive gap and age. Therefore, future work with a larger continuous or longitudinal sample from four to seven years of age could potentially pinpoint whether age begins to relate to the cognitive-adaptive gap as children enter school (Tillman et al., 2019). Interestingly, Bradshaw and colleagues (2018) reported the cognitive-adaptive gap increases from 12-36 months in a sample of siblings at increased likelihood of autism, although these developmental patterns may not generalize to a community-based sample of ASD (Sacrey et al. 2017, Frazier et al. 2015). More research in younger autistic children including prospective work with children who later receive an autism diagnosis is needed to better understand the origin and developmental trajectory of the cognitive-adaptive gap and, therefore, the optimal age for intervention delivery.

Relations between Executive Function and Adaptive Function

We found that EF, specifically emotional control, relates to socialization skills in both 4-year-old and school-age children. This finding adds to literature linking peer relationships and emotion regulation broadly (Berkovits et al., 2017; Reyes et al. 2020, Nader-Grosbois & Mazzone, 2014). Many autistic children require different levels of support for emotion regulation compared to NT children (Jahromi et al., 2013), so future research should investigate the amount and type of support needed to foster sufficient emotional control in social contexts. Taken together, our cross-sectional findings suggest the relation between emotional control and a core domain of autism–social skills–emerges as early as the preschool period and persists through mid-childhood. Thus, developing and evaluating interventions that target emotional control may have an early and lasting impact on everyday social connectedness among autistic children, especially those without co-occurring developmental delay.

Relations Between Executive Function and the Cognitive-Adaptive Gap

Finally, results from both cohorts demonstrate a novel and consistent relation between EF and the gap between socialization skills and cognitive ability, which further underscores the possibility that implementing EF interventions beginning in preschool, before socialization and other adaptive skills begin to lag, may lead to more optimal adaptive and social outcomes for autistic preschoolers without developmental delay. Longitudinal work will be critical in determining whether EF supports subsequent AF development and, therefore, represents an important intervention target.

Within the broad array of EF skills, emotional control may be especially relevant to the cognitive-socialization gap. Effective self-regulation of emotions during stressful social interactions and conflicts with peers could support relationship formation. In the school-age cohort, the relation between emotional control and AF was specific and constrained; child emotional control skills did not relate to the gap between other adaptive domains (DLS or communication skills). In the preschool cohort, emotional control began to correspond with the cognition-socialization gap by four-years-old. Though no other work to our knowledge has examined EF as a correlate of the cognitive-socialization gap, our current results, and literature investigating predictors of socialization skills (Reyes et al. 2020, Davico et al. 2022), suggest targeting emotional control in preschool years may support autistic children to achieve individual social goals and allow for more meaningful community engagement.

Limitations

First, assessment of EF was limited to parent report, and results suggest some lack of continuity in this measurement of EF across cohorts. While unique EF domains were significantly associated with various domains of AF in our school-age cohort and four-year-old sample, EF was less strongly associated with adaptive skills in our 2-year-old sample. One explanation for this finding could be that global EF may differentiate into unique subdomains after the preschool period (Wiebe et al. 2011, Powell et al. 2022). It is also possible that the BRIEF-P may be less robust in measuring specific EF domains for autistic toddlers. Given that lab-based behavioral measures of EF have successfully categorized EF domains in preschoolers (Carlson et al., 2005; Edmunds et al., 2021), future work should use adapted lab-based measures of EF to explore more precisely whether specific EF skills are associated with AF in early childhood.

Second, the sample was not representative of the larger population in terms of race, ethnicity, SES, and sex assigned at birth. Although studies report fewer girls among autistic samples with higher IQ (Baio et al. 2018), future work would benefit from a sample with greater diversity.

Further, within our preschool cohort, we were limited by a small sample size and lack of a continuous age range. A priori power analyses were not conducted and these results are exploratory given the small sample size. Within our school age sample (N = 101), we were powered to detect medium or larger effects as significant (i.e., r > .26). Within our preschool sample (N = 48 2-year-olds and Y 4-year-olds), we were only powered to find large effects as statistically significant (i.e., r > .47) (Cohen, 1992). Future work with a larger, continuous sample is necessary to clarify our findings and to examine subgroups (e.g., girls) or demographic factors (e.g., parent education).

Our investigation combined datasets from two different studies, and differences in study design may also have impacted our findings. First, there was variability in the version of the VABS used across studies. The VABS-3 generally produces lower scores compared to the VABS-2 (Farmer et al., 2020). Second, IQ cutoffs were lower in the preschool (ELC >= 70) compared to the school-age (FSIQ >= 80) cohort. Notably, the present study did not make any comparisons between participants in the toddler and school-age cohorts. Nevertheless, future studies would benefit from using identical instruments and inclusion criteria to mitigate potential confounding factors.

Finally, due to the cross-sectional design, no conclusions can be made about causal or predictive relationships between EF and AF. To fully investigate how EF may contribute to AF and the cognitive-adaptive gap, future work should follow cohorts longitudinally, study intervention effects, and use mixed-methods measurement. Studies of evidence-based interventions for AF in autistic children (Gillespie-Lynch et al., 2012) should collect longitudinal measures of EF, AF, and intervention history to allow researchers to analyze how such interventions moderate improvements in EF and AF. A mixed-methods approach to measuring AF would center the narratives of stakeholders and provide a more nuanced understanding of the cognitive-adaptive gap, and its predictors (Matthews et al. 2021). Combining parent-report with observational measures of EF will also capture a more holistic picture of EF.

To our knowledge there are no EF interventions designed for autistic preschoolers. Future research studying the mechanisms or pathways by which EF relates to functional adaptive outcomes in NT versus autistic participants will elucidate whether already existing or newly created interventions will best benefit autistic preschool-aged children.

Conclusions

Despite these limitations, our study provides novel insights. Our findings suggest that adaptive functioning does not begin to decline relative to age expectations within the preschool period, and the gap between children’s cognitive and adaptive ability does not widen until after age four. Furthermore, although our study does not use a longitudinal design, we show a consistent relationship between EF and the cognition-AF gap for our two distinct cross sectional cohorts wherein children’s EF is associated with their socialization-specific adaptive functioning at as early as two years. To our knowledge, our study was the first to examine whether EF relates to the cognition-AF gap and our results suggest it may provide important information about the development of the lag between social functioning and cognition and potential directions (e.g., emotional control) for intervention to reduce the lag. Our cross sectional design also provides information about the potential developmental course of these relations between AF and EF.

Although evidence suggests that adaptive functioning is critical for autistic children’s later autonomy and quality of life, factors that contribute to greater adaptive skills are not well-understood. The current study suggests executive functioning may play a key role in improved adaptive functioning as autistic children grow. Interventions that directly address or support mechanistic predictors of adaptive functioning early in development are crucial. Our findings highlight that interventions targeting early executive function may have the potential to improve adaptive functioning and, consequently, quality of life for autistic children.

Data Availability Statement

The datasets analyzed for the school age cohort can be found in the National Database for Autism Research (NDAR reference number C2761 and C2030). The datasets analyzed for the pre-school cohort are available from the corresponding author upon reasonable request.