![PRISMA flow diagram outlining study selection [81].](https://www.researchgate.net/publication/354889582/figure/fig1/AS:11431281375774034@1744649940393/PRISMA-flow-diagram-outlining-study-selection-81_Q320.jpg)
Access to this full-text is provided by MDPI.
Content available from Brain Sciences
This content is subject to copyright.
brain
sciences
Systematic Review
Effects of Cognitive Training Programs on Executive Function
in Children and Adolescents with Autism Spectrum Disorder:
A Systematic Review
Angela Pasqualotto 1, 2, *,† , Noemi Mazzoni 1 ,3 ,† , Arianna Bentenuto 1, Anna Mulè1, Francesco Benso 1
and Paola Venuti 1
Citation: Pasqualotto, A.; Mazzoni,
N.; Bentenuto, A.; Mulè, A.; Benso, F.;
Venuti, P. Effects of Cognitive
Training Programs on Executive
Function in Children and Adolescents
with Autism Spectrum Disorder: A
Systematic Review. Brain Sci. 2021,11,
1280. https://doi.org/10.3390/
brainsci11101280
Academic Editor: Antonino Vallesi
Received: 31 July 2021
Accepted: 23 September 2021
Published: 27 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1
Laboratory of Observational, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive
Science, University of Trento, 38068 Rovereto, Italy; noemi.mazzoni@unitn.it (N.M.);
arianna.bentenuto@unitn.it (A.B.); anna.mule@alumni.unitn.it (A.M.); francesco.benso@unitn.it (F.B.);
paola.venuti@unitn.it (P.V.)
2Faculty of Psychology and Educational Sciences, University of Geneva, 1205 Geneva, Switzerland
3
Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems,
Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
*Correspondence: a.pasqualotto.1@unitn.it
† These authors equally contributed.
Abstract:
Background. Autism Spectrum Disorder is often associated with deficits in executive
functions (EFs), which is contributing significantly to individuals with ASD’s difficulties in con-
ducting an independent life, particularly considering social skills. Technologies offer promising
opportunities to structure EF intervention programs for children on the autistic spectrum. Methods.
This study aimed to review the effectiveness of randomized controlled trials or quasi-experimental
studies of EF interventions delivered to children and young people (up to 23 years old) with a
diagnosis of ASD. A special focus was dedicated to document the effectiveness of computerized and
non-computerized cognitive training on (1) EFs and on (2) ASD symptomatology and social skills. Of
2601 studies retrieved, 19 fulfilled the inclusion criteria. Results. Most of the interventions identified
were effective in enhancing EFs and reducing symptoms in children and young people with ASD.
Limited evidence is available on their generalization to untrained skills (i.e., social abilities) as well
as long-term effects. Conclusions. There is growing evidence for overall effectiveness of EF training,
particularly when computerized. However, caution should be taken when interpreting these findings
owing to methodological limitations, the minimal number of papers retrieved, and a small samples
of included studies.
Keywords:
Autism Spectrum Disorder; executive functions; social skills; cognitive training; comput-
erized intervention
1. Introduction
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by
socio-communicative impairment and the presence of a repetitive and restrictive pattern
of behaviors and interests (DSM-5 [
1
]) that has a significant negative impact on children’s
development [2].
Children with ASD represent a heterogeneous group, given the variability of their
symptoms and the presence/absence of comorbidities [
3
–
5
]. Indeed, the core symptom
intensity and severity could vary remarkably among individuals with ASD and could
be associated with different atypicalities in the sensory domain, such as hyper- or hypo-
sensitivity [
1
]. As a result, individuals with ASD are likely to struggle in multiple areas,
including language, adaptive behavior, and academic performance [6].
Moreover, it is well-known that ASD is frequently associated with various types of
cognitive difficulties, which manifest in different ways, such as, for example, executive
Brain Sci. 2021,11, 1280. https://doi.org/10.3390/brainsci11101280 https://www.mdpi.com/journal/brainsci
Brain Sci. 2021,11, 1280 2 of 26
functioning deficits (for a review, see [
7
]). Executive functions (EFs)—which, in more gen-
eral and less reductionist theories, overlap with the concept of Working Memory Capacity
and Executive Attention [
8
]—include different processes that are necessary for individuals
to control and update their behaviors [
9
,
10
]. EFs have an important role in the adaptation
to new environmental stimuli, especially when these require the development of a new
behavior in order to be successful. These skills are crucial in mental and physical well-being,
academic achievement, and social, psychological and cognitive development [11]. Due to
their strong relationship with behavioral regulation, EFs are particularly of relevance for
ASD symptomatology and have been increasingly studied in this clinical population [12].
Given the negative impact of executive deficits on the daily functioning of the individu-
als, especially concerning autonomy, it is important to target executive functioning directly
through evidence-based interventions [
13
]. This is in line with a growing body of research
that advocates for an approach of early intervention and that considers neurodiversity and
focuses on a variety of cognitive and social domains [14,15].
The goal of the present review was, thus, to systematically analyze the effectiveness of
EF interventions in individuals with Autism Spectrum Disorder from an evidence-based
perspective. In the next paragraphs, we present a brief overview of the link between
EFs and ASD, before reporting our systematic review of the studies on the effectiveness
of cognitive training of EFs on children and adolescents with ASD (including RCTs and
quasi-experimental studies).
2. Executive Functions and Autism Spectrum Disorder
2.1. EF Heterogeneity in ASD
Executive functions (EFs) is an umbrella term used to refer to a family of top-down and
high-level mental processes that are active when there is the need to concentrate and pay
attention, in other words when automatic or instinctual responses would be not desirable
or would be insufficient [11,16–18].
Traditionally, the study of EF in ASD has focused on discrete EFs domains [
7
,
19
–
24
].
These executive processes usually include planning, working memory, attention, inhibitory
control, cognitive flexibility, self-monitoring, and self-regulation and are mainly subtended
by frontal lobes [
25
]. However, such approaches are often unsatisfactory since they isolate
single executive processes using complex and multifaceted tasks [
26
–
29
]. Indeed, the
main criticism to the validity and reliability of EF measures arises from the “task impurity
issue”, according to which the neuropsychological tests that are commonly used for EF
assessment would measure both multiple EF and non-EF processes [
30
,
31
]. In other terms,
a multicomponential system is reduced to a simple function [
13
,
32
]. For example, the
term “working memory” has been incorrectly and reductionistically used to describe
one of several executive functions, such as updating (e.g., [
9
]). Therefore, in order to
reduce theoretical-methodological misunderstandings, we prefer more general and less
reductionist theories, such as Executive Attention Theory [
29
,
33
–
35
], which, according to
some authors, is expressed in working memory capacity—WMC [
36
]. In this theoretical
framework, the executive system consists of synergistic interactions between executive,
attentive, and memory processes [37].
Conversely, as a result of the reductionist approach predominant in the literature, most
of the research on EFs in ASD is characterized by a high heterogeneity of the results [
38
],
encompassing different executive domains [
7
,
20
]. Thus, for methodological prudence,
the results on EFs and ASD should be reconverted to avoid dangerous statements on the
single function, defining instead a more general involvement between different executive
processes in interaction [13].
In addition, other factors that may have increased the variability in research on EF
performance in ASD include: participants’ age (particularly, in case of mixed age groups),
differences in intellectual ability and in their measures (verbal, nonverbal, full scale), as
well as different eligibility criteria across studies. In this regard, it has been reported that
comorbidity with ADHD affects inhibition abilities in ASD [
39
], and similarly, comorbidity
Brain Sci. 2021,11, 1280 3 of 26
with stress and anxiety is negatively correlated with inhibition, mental flexibility, and
shifting [40].
Moreover, the type of EF assessment that may drive to different results, such as
psychometric tests, experimental tasks, and behavioral rating scales, seem to be under-
pinned by different cognitive mechanisms [
41
–
43
]. Specifically, behavioral rating scales
have been demonstrated to predict better ASD phenotypes and difficulties compared to
neuropsychological and experimental measures (e.g., [
28
,
31
]) and, thus, seem to have a
greater ecological validity [
44
]. Finally, the format used to administer (computerized vs.
traditional), to present the testing materials (visual vs. verbal), and to collect participants
responses (verbal vs. motor) may affect EF performance. For instance, ASD participants
seem to take advantage of computerized tests [44,45], and visual perceptual tasks [46].
Altogether, these factors may have contributed to the heterogeneity in EF findings
in ASD. Nonetheless, recent reviews and meta-analyses have increasingly highlighted
the broader influence of executive processes on the ASD behavioral, cognitive, and social
phenotype [
7
]. Interestingly, it has also been hypothesized [
47
,
48
] that in ASD individuals,
a desynchronization of the Salience Network (SN) system could be responsible for a lack of
activation of the Central Executive Network (CEN) in a timely and adaptive manner.
Finally, executive processes have been shown to be associated with social cognition
abilities, such as theory of mind [
49
,
50
], and with adaptive behavior in daily living skills
and socialization domains [
51
]. It is noteworthy that in adults, difficulties in EFs have been
linked to disability [41], mental health [52], and functioning outcomes later in life [53].
Altogether, these findings consistently reported a broad deficit of executive processes
in ASD that seems to be stable across development and across ASD phenotypes [
11
,
54
–
56
].
This suggests the importance of measuring multiple executive domains using a variety of
tasks and tools, as well as of paying attention to their ecological validity, either in research
or in clinical settings [
13
]. Indeed, a careful evaluation of the attentive–executive system
in ASD assessment is crucial to support treatments targeted at improving adaptive skills
and—more in general—quality of life.
2.2. EFs and Social Skills
Research has suggested that EF alterations may explain some core characteristics of
ASD [
57
,
58
]. For example, it has been suggested that particularly relevant for the social
and interpersonal skills are the so-called Hot EFs [
59
]. Due to their strong relation with
behavioral regulation, ‘Hot EFs’ are particularly of relevance for ASD symptomatology
and have been increasingly studied in this clinical population [
12
,
52
]. According to these
authors, Hot EFs operate and guide the behavior when the context or the situation is
motivationally and/or emotionally characterized [
60
]. However, the usefulness of the
distinction between hot and cold executive process is still debated as it is not fully supported
by neuroscientific and neurophysiological evidence, e.g., [61].
Nonetheless, understanding whether the executive components may contribute to
the ASD symptomatology is relevant for developing effective interventions. Indeed, it has
been reported that some EF components have an effect on social competence and underlie
essential skills for adequate social interactions, such as emotional and cognitive regulation.
Fong and Iarocci [
62
] found that in children with ASD the difficulties in social inferencing
and social knowledge were predicted by lower self-monitoring skills, suggesting a different
role of EFs in regulating social behavior in ASD subjects. Leung et al. 2016 [
63
] found that in
ASD children the social function was associated with initiation, working memory, planning,
organization, and monitoring (i.e., the metacognitive executive processes), suggesting
a different relation between metacognitive executive function and social skills in ASD
compared to TD children and adolescents. In line with this, metacognitive EFs, such as
initiation and working memory [
64
], as well as initiation and cognitive flexibility [
65
], were
found to play a role also in adaptive social skills (measured by the Vineland Adaptive
Behavior Scales).
Brain Sci. 2021,11, 1280 4 of 26
The relation between EFs and social functioning was supported by the study of
Freeman and colleagues, which highlighted that poorer EFs (initiation, working memory,
planning, and organization) were associated with reduced engagement with peers and
higher playground isolation.
Performance in some specific executive tasks (particularly, the one assessing inhibitory
control and cognitive flexibility), emerged to be related to social verbal communication
in school-aged children [
50
]. The relationship between Theory of Mind (ToM) and EFs
was reported also in adolescents with ASD [
49
]. Besides ToM abilities, in adolescents
with ASD, higher EFs were found to be associated also with higher empathy (as rated
by parent and teacher) [
66
]. However, further research is needed, as Zimmerman and
colleagues [
12
] found that the impairments in emotion recognition and ToM in adults
with high-functioning ASD were not related to deficits in working memory and response
initiation and suppression.
Altogether, these studies suggest that specific EFs seem to contribute to different
aspects of social competence at different ages, highlighting the importance of targeting spe-
cific EF skills to improve the personalization and, hence, the effectiveness of interventions
in ASD.
3. Cognitive Training of EFs in ASD
Cognitive training exercises have been used in several studies to improve children’s
performance thanks to repeated practice on executive tasks [
67
–
70
] (for a review, see [
71
]).
In recent years, a great deal of attention has been paid to improving executive processes, ac-
celerating their development, stopping or slowing declines, and/or addressing deficits [
71
].
Many methods have been explored and, thus, it has to be noted that the term “cognitive
training” is used as an umbrella term for a variety of interventions that often does not
provide an accurate or shareable definition. Even within a single treatment, variability
of approaches seems common. Nowadays, there are various cognitive training methods
available, including downloadable tools, logical games, pencil-and-paper exercises and
virtual reality simulations on attention and working memory, to name a few.
Moreover, cognitive training studies with individuals with ASD are very new, and
their impact is still an object of discussion [
72
–
76
]. Indeed, evidence on cognitive training of
EFs is fairly limited and mixed, particularly around the aspects of transfer possibilities, the
type of treatment chosen for analyses, and the methodology employed [
13
]. For this reason,
it is important to account for, as much as possible, the numerous variables that might
affect behavior and brain plasticity [
75
,
77
]. These include participants’ characteristics (e.g.,
age, comorbid condition) and training characteristics (e.g., duration, intensity, treatment
type, setting). Another important feature is the use of technology to support the training
activities with ASD children. In fact, Grynszpan et al.’s meta-analysis [
78
] suggested
an overall effectiveness of technology-based training. Children with ASD were found
to enjoy the gamification of tasks (i.e., serious games) that provided a safe and secure
environment, and in which they commit errors with minor consequences and therefore
involve less social anxiety and shame [
79
]. Furthermore, it has been shown that there might
be a galvanizing effect towards technology for ASD children and a steep upper curve in
learning though the use of computers [
80
]. Since then, a growing number of studies have
confirmed that the development and evaluation of systems and applications for users with
ASD is very promising [
79
,
80
]. As technological advancements such as virtual agents,
artificial intelligence, virtual reality, and augmented reality become more prevalent, they
provide an enjoyable environment for individuals with ASD.
However, studies on the impact of technology-based cognitive training of executive
functions have often failed to provide clear and shareable results [
76
], which has indicated
the need for further research. The purpose of this systematic review is, therefore, to
document the effectiveness of computerized and non-computerized cognitive training
interventions for people with autism by comparing their impact on EFs and on core ASD
Brain Sci. 2021,11, 1280 5 of 26
symptoms, as well as by assessing their methodological quality. Finally, recommendations
for future research will be formulated.
3.1. Methods
A review of the literature was carried out in accordance with the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses—PRISMA [81].
3.2. Eligibility Criteria
The research question was formulated using the PICO model [
82
] and the eligibility
criteria were established a priori (see Table 1).
Table 1. Inclusion and exclusion criteria.
Eligibility Criteria
Population Individuals (up to 23 years of age) diagnosed with Autism Spectrum Disorder (ASD).
Intervention
Computerized and non-computerized interventions aimed to target executive functions. The training
could be administered individually or in a small group, with different durations and frequencies, in
different settings (home, clinics, community, school). In addition, it could be delivered with the support
of different figures (psychologists, teachers, parents, speech therapists, health care professionals).
Comparisons Other types of intervention which did not target executive functions, other interventions that were
considered “treatment as usual”, waiting list, no intervention group.
Outcomes Primary outcomes: Improvement in executive function domains, measured with standardized tests.
Secondary outcomes: A core feature of ASD o related cognitive domains/social skills.
Settings Any setting (e.g., home, clinics, community, school)
Study design
Randomized control trials (RCTs). If no RCTs were available: quasi-experimental studies or single-group
studies were also included. We considered only systematic reviews (SR) or meta-analyses that (1) were
included in at least one database (e.g., PubMed); (2) reported the participants inclusion criteria;
(3) conducted quality or risk of bias
assessment on included studies; and (4) provided a list and synthesis
of included studies.
Limits The SR focused on studies published between 2000 and 2020 with no publication language limit.
Exclusion criteria
Individuals with traumatic brain injuries, primary disorders (sensory, neurological, psychiatric).
Editorials, Opinions and Commentaries.
Qualitative studies.
Single case studies.
3.3. Search
Source of Data and Search Terms
To gather the peer-reviewed literature included in the current systematic review, we
conducted research using the following online databases from September 2020 to April
2021: PubMed (Title/Abstract); Psyndex, ERIC and Medline via PubPsych (All Text); Web
of Science (Title and Topic); Science Direct (Title, Abstract or Author-specified Keywords).
The reference collection procedure was initiated using a search strategy based on the
combination of keywords. The full query string is presented below:
(“training” OR “intervention” OR “instruction” OR “program” OR “treatment” OR
“remediation” OR “rehabilitation” OR “therapy”) AND (“executive functions” OR “execu-
tive function” OR “working memory” OR “inhibition” OR “attention” OR “attentional” OR
“attentive” OR “flexibility” OR “planning” OR “shifting”) AND (“children” OR “child” OR
“kids” OR “kid” OR “school age” OR “students” OR “student”) AND (“Autism Spectrum
Disorder” OR “Autism Spectrum Disorders” OR “ASD” OR “autistic”).
In addition, experts in the field indicated further potentially relevant studies. Finally,
a relevant source of information was the reviews and the meta-analysis on the topic, from
which the relevant citations to our topic were collected manually.
Brain Sci. 2021,11, 1280 6 of 26
This initial search yielded 2601 results from academic journals, dissertations, books,
reports, conference materials, and reviews. The PRISMA diagram in Figure 1describes the
search process and provides the rationale behind the exclusion of articles.
Brain Sci. 2021, 11, x FOR PEER REVIEW 6 of 26
(“training” OR “intervention” OR “instruction” OR “program” OR “treatment” OR
“remediation” OR “rehabilitation” OR “therapy”) AND (“executive functions” OR “exec-
utive function” OR “working memory” OR “inhibition” OR “attention” OR “attentional”
OR “attentive” OR “flexibility” OR “planning” OR “shifting”) AND (“children” OR
“child” OR “kids” OR “kid” OR “school age” OR “students” OR “student”) AND (“Au-
tism Spectrum Disorder” OR “Autism Spectrum Disorders” OR “ASD” OR “autistic”)
In addition, experts in the field indicated further potentially relevant studies. Finally,
a relevant source of information was the reviews and the meta-analysis on the topic, from
which the relevant citations to our topic were collected manually.
This initial search yielded 2601 results from academic journals, dissertations, books,
reports, conference materials, and reviews. The PRISMA diagram in Figure 1 describes
the search process and provides the rationale behind the exclusion of articles.
Figure 1. PRISMA flow diagram outlining study selection [81].
3.4. Data Selection and Extraction
Titles, abstracts, and full text screening were performed by three independent re-
viewers.
Disagreements were resolved through discussion with the first author. A consensus
was reached among the authors on 72 papers to be excluded, as they did not meet the
eligibility criteria, leaving 19 papers for further analysis and coding.
Figure 1. PRISMA flow diagram outlining study selection [81].
3.4. Data Selection and Extraction
Titles, abstracts, and full text screening were performed by three independent reviewers.
Disagreements were resolved through discussion with the first author. A consensus
was reached among the authors on 72 papers to be excluded, as they did not meet the
eligibility criteria, leaving 19 papers for further analysis and coding.
Included studies were then coded for sample characteristics, study design, type of
control group, intervention type, intervention target, intervention duration, type of setting
and trainer involved, type of pre-/post-measures used, results.
Sample characteristics. The main characteristics of the participants were coded as
follows: Sample size: number of participants; Attrition rate: percentage of sample which
did not conclude the intervention; Age: mean age (and standard deviation) of the par-
ticipants, in months. When this was not available, age range was reported; Female rate:
percentage of participants that were female, whenever it was reported; Comorbidity:
additional diagnosis.
Study design. Randomized controlled trial (RCT); Quasi-experimental (QE).
Intervention type. Intervention approaches used in the article that were coded for
categorization in “Computerized” or “Non-Computerized” if the intervention was (or not)
primarily delivered on a computer or a tablet.
Brain Sci. 2021,11, 1280 7 of 26
Outcome. Each result was categorized as Primary or Secondary. Primary: Execu-
tive processes. Secondary: a core feature of ASD (i.e., social-communication abilities,
restricted/repetitive patterns of behaviors and sensory processing) or a related outcome
(i.e., cognitive domains/linguistic/social skills). If outcomes were reported at different
time points, follow-up/s were also reported.
3.5. Quality Assessment
Finally, an evaluation of the quality of the results of the reviewed studies was per-
formed (see Figures 2and 3for a summary of randomized studies and Figures 4and 5
for non-randomized studies), according to the Cochrane Collaboration’s criteria [
83
,
84
].
Specifically, the following domains were rated: random sequence generation, allocation
concealment, blinding of outcome assessment, incomplete outcome data, and selective
reporting. For non-randomized studies, a differential code have been used for pre or
at-intervention features, covering the following parameters: confounding bias, selection of
participants into the study bias, classification of interventions bias. We refer the readers to
the Supplementary Information for further details on the parameters used.
Brain Sci. 2021, 11, x FOR PEER REVIEW 7 of 26
Included studies were then coded for sample characteristics, study design, type of
control group, intervention type, intervention target, intervention duration, type of setting
and trainer involved, type of pre-/post-measures used, results.
Sample characteristics. The main characteristics of the participants were coded as fol-
lows: Sample size: number of participants; Attrition rate: percentage of sample which did
not conclude the intervention; Age: mean age (and standard deviation) of the participants,
in months. When this was not available, age range was reported; Female rate: percentage
of participants that were female, whenever it was reported; Comorbidity: additional di-
agnosis.
Study design. Randomized controlled trial (RCT); Quasi-experimental (QE).
Intervention type. Intervention approaches used in the article that were coded for
categorization in “Computerized” or “Non-Computerized” if the intervention was (or
not) primarily delivered on a computer or a tablet.
Outcome. Each result was categorized as Primary or Secondary. Primary: Executive
processes. Secondary: a core feature of ASD (i.e., social-communication abilities, re-
stricted/repetitive patterns of behaviors and sensory processing) or a related outcome (i.e.,
cognitive domains/linguistic/social skills). If outcomes were reported at different time
points, follow-up/s were also reported.
3.5. Quality Assessment
Finally, an evaluation of the quality of the results of the reviewed studies was per-
formed (see Figures 2 and 3 for a summary of randomized studies and Figures 4 and 5 for
non-randomized studies), according to the Cochrane Collaboration’s criteria [83,84]. Spe-
cifically, the following domains were rated: random sequence generation, allocation con-
cealment, blinding of outcome assessment, incomplete outcome data, and selective report-
ing. For non-randomized studies, a differential code have been used for pre or at-inter-
vention features, covering the following parameters: confounding bias, selection of par-
ticipants into the study bias, classification of interventions bias. We refer the readers to
the Supplementary Information for further details on the parameters used.
Figure 2. Risk of bias in randomized control trials studies (RCTs) included in this review (green:
low bias risk; yellow: some concerns; red: high bias risk).
Figure 2.
Risk of bias in randomized control trials studies (RCTs) included in this review (green: low
bias risk; yellow: some concerns; red: high bias risk).
Brain Sci. 2021, 11, x FOR PEER REVIEW 8 of 26
Figure 3. Percentages of risk bias in randomized control trials (RCTs, green: low bias risk; yellow: some concerns; red:
high bias risk).
Figure 4. Risk of bias in non-randomized control trials (green: low bias risk; yellow: moderate bias
risk; red: serious bias risk; brown: critical bias risk; blue: no information).
Figure 5. Percentages of risk bias in non-randomized control trials (green: low bias risk; yellow: moderate bias risk; red:
serious bias risk; brown: critical bias risk; blue: no information).
Figure 3.
Percentages of risk bias in randomized control trials (RCTs, green: low bias risk; yellow: some concerns; red: high
bias risk).
Brain Sci. 2021,11, 1280 8 of 26
Brain Sci. 2021, 11, x FOR PEER REVIEW 8 of 26
Figure 3. Percentages of risk bias in randomized control trials (RCTs, green: low bias risk; yellow: some concerns; red:
high bias risk).
Figure 4. Risk of bias in non-randomized control trials (green: low bias risk; yellow: moderate bias
risk; red: serious bias risk; brown: critical bias risk; blue: no information).
Figure 5. Percentages of risk bias in non-randomized control trials (green: low bias risk; yellow: moderate bias risk; red:
serious bias risk; brown: critical bias risk; blue: no information).
Figure 4.
Risk of bias in non-randomized control trials (green: low bias risk; yellow: moderate bias
risk; red: serious bias risk; brown: critical bias risk; blue: no information).
Brain Sci. 2021, 11, x FOR PEER REVIEW 8 of 26
Figure 3. Percentages of risk bias in randomized control trials (RCTs, green: low bias risk; yellow: some concerns; red:
high bias risk).
Figure 4. Risk of bias in non-randomized control trials (green: low bias risk; yellow: moderate bias
risk; red: serious bias risk; brown: critical bias risk; blue: no information).
Figure 5. Percentages of risk bias in non-randomized control trials (green: low bias risk; yellow: moderate bias risk; red:
serious bias risk; brown: critical bias risk; blue: no information).
Figure 5.
Percentages of risk bias in non-randomized control trials (green: low bias risk; yellow: moderate bias risk; red:
serious bias risk; brown: critical bias risk; blue: no information).
4. Results
4.1. Results of the Search
The systematic gathering procedure performed in the databases returned a total of
2601 papers. Duplicates were excluded, reducing the total number from 2601 to 2170.
Initial screening using title and abstract as filter parameters identified 91 studies whose
characteristics met the criteria predetermined in the PICO question [
82
]. Nine among them
were identified by manual searching the reference lists of 44 articles, including Reviews,
Meta-Analysis, Editorials and Dissertations. In accordance with the inclusion criteria, a
total of 19 studies were eligible and were included in the qualitative synthesis.
The characteristics of the included studies are presented in Table 2. Due to the
heterogeneity of the studies, a meta-analysis of the outcomes was not appropriate. However,
narrative results are presented.
Brain Sci. 2021,11, 1280 9 of 26
Table 2. Characteristics of the included studies.
First Author
and Year of
Publication
Sample
Characteristics/
Attrition Rate
Age
(Years)
Study
Design Intervention Type Intervention Target Intervention
Duration Setting/
Trainer
Outcomes Pre- and
Posttest Findings
Primary Secondary
Milajerdi, 2021 n= 60 6–10 RCT Computerized
“Kinect” motor skills, EFs
tot hrs = 14
schedule = 3
sess. x week
for 8 weeks
N/A/
research
assistant
cognitive
flexibility N/A WCST Positive
Macoun, 2020
n= 23 (2 with
ADHD, 2 with
tics/sensory)/
13%
6–12 RCT Computerized
“Caribbean Quest”
WM, inhibitory
control, selective
attention, and
sustained attention
tot hrs = 12
schedule = 3
sess. x week
for 8 weeks
school/
research
assistant
attention;
visual and
verbal WM; EF
daily life
academic
achievement;
behavioral
symptoms;
social skills
KiTAP; SSP
and DS; ORF;
WJ-III; BRIEF;
CRS-3; BERS-2;
SSRS; GARS-2
Mixed
Meng-Ting Chen,
2020 n= 25 (9 with
ADHD) 6–12 RCT
Computerized
Comprehensive
Attention Training
System
sustained attention,
sensory selection,
response selection
and control,
attention
tot hrs = 6.6
schedule =
once sess. per
week for 8
weeks
clinic/
graduate
student
cognitive
flexibility
social
adaptability
WCST; TMT;
VABS Positive
Ridderinkhof,
2020 n= 100 8–23 RCT
Non
Computerized
mindfulness-
based
program
focused and
sustained attention tot hrs = 15
scheduleNA
N/A/
mindfulness
trainer
attention N/A ANT Null
Juliano, 2020
n= 29 (18 with
ADHD, 11
with Anxiety
Disorder, 3
with Sensory
Processing
Disorder, 1
with Language
Disorder)/
6.8%
10–17 QE
Non
Computerized
mindfulness-
based
program
attention, inhibition
tot hrs = 8
schedule = 2
sess. x week
for 8 weeks
school/
educator
attention;
inhibition N/A
CWIT; W/DW;
CN Positive
Yerys, 2019 n= 19 (with
ADHD
symptoms)
9–13 QE Computerized
“Project EVO”
attention, cognitive
control
tot hrs = 8.3
schedule = 5
sess. x week
for 4 weeks
home/
parents
attention;
impulsivity;
spatial WM;
EF daily life
social skills;
ADHD
symptoms
TOVA;
CANTAB;
ADHD-RS-IV;
BRIEF-2; SSIS
Mixed
Saniee, 2019 n= 24 5–7 QE
Computerized
“Tatka” + Non
Computerized
home tasks
set-shifting ability
tot hrs = 56
schedule = 4
sess. x day for
2 months
home/
parents
cognitive
flexibility
autism
symptoms MCS; BFRS-R;
GARS; ATEC Positive
Phung, 2019 n= 34 8–11 RCT
Non
Computerized
Mixed Martial Arts
behavioral inhibition,
WM, cognitive
fexibility
tot hrs = 19.5
schedule = 2
sess. x week
for 13 weeks
school/
instructor
behavioral
inhibition,
WM, cognitive
fexibility; EF
daily life
N/A HFT; BRIEF Positive
Brain Sci. 2021,11, 1280 10 of 26
Table 2. Cont.
First Author
and Year of
Publication
Sample
Characteristics/
Attrition Rate
Age
(Years)
Study
Design
Intervention
Type
Intervention
Target Intervention
Duration Setting/
Trainer
Outcomes Pre- and Posttest Findings
Primary Secondary
Hajri, 2019 n= 24/
33.3% 6–21 QE Non
Computerized
CRT
cognitive
flexibility, WM,
planning
tot hrs = 18
schedule = once sess.
per week for
5–6 months
clinic/
therapist
cognitive
flexibility; WM
non verbal
intelligence;
autism
symtomps;
academic
results
SVFT; DSF and DSB;
CARS; CMP; academic
results
Positive
Hajri, 2018 n= 25/
36% 6–21 QE Non
Computerized
CRT
cognitive
flexibility, WM,
planning
tot hrs = 13.5–18
schedule = once sess.
per week for
6 months
clinic/
therapist
cognitive
flexibility;
WM; planning;
inhibition
academic
results
SVFT; DSF and DSB;
ROCF; HSCT; CAAT;
academic results Mixed
Kerns, 2017 n= 23/
26% 6–13 QE
Computerized
“Caribbean
Quest”
WM, attention
tot hrs = 12–18
schedule = 2–3 sess.
x week for
8–12 weeks
school/
educator
attention; WM;
EF daily life
academic
results;
behavioral
symptoms
KiTAP, WMTB-C; SSP
and DS; AIMSweb;
BRIEF; CRS-3; BERS-2 Positive
Hajri, 2016 n= 25/
36% 6–21 QE Non
Computerized
CRT
Cognitive
flexibility, WM,
planning
tot hrs = 18
schedule = once sess.
per week for
6 months
clinic/
therapist
cognitive
flexibility;
WM;
non verbal
intelligence;
autism
symptoms;
academic
results
SVFT; DSF and DSB;
CARS; CPM; academic
results
Positive
de Vries, 2015 n= 121/
26% 8–12 RCT Computerized
“Braingame
Brian”
cognitive
flexibility, WM
tot hrs = NA
schedule = 25 sess.
per 6 weeks
home/
parents
cognitive
flexibility;
WM;
inhibition;
attention; EF
daily life
social skills;
ADHD
symptoms
Task-Switching;
Corsi-BTT; N-back;
Stop task; SART;
BRIEF; CSBQ;
DBDRS-ADHD
Mixed
Farrelly, 2015 n= 20/
10% 11–13 RCT
Non
Computerized
cognitive
flexibility
intervention
cognitive
flexibility
tot hrs = 1.5
schedule = 3 sess.
per 3 weeks
school/
investigator
cognitive
flexibility N/A TMT Positive
Hilton, 2015 n= 17/
5.5 % 8–18 QE Computerized
“Makoto Arena” motor skills, EFs
tot hrs = 0.5
schedule =
3 sess. x week for
5 weeks
school/
graduate
student
EF daily life N/A BRIEF Positive
Brain Sci. 2021,11, 1280 11 of 26
Table 2. Cont.
First Author
and Year of
Publication
Sample
Characteristics/
Attrition Rate
Age
(Years)
Study
Design
Intervention
Type
Intervention
Target Intervention
Duration Setting/
Trainer
Outcomes Pre- and Posttest Findings
Primary Secondary
Hilton, 2014 n= 8/
12.5% 6–13 QE Computerized
“Makoto Arena” motor skills, EFs tot hrs = 0.5
schedule = 3 sess. x
week for 5 weeks
school/
graduate
student
EF daily life N/A BRIEF Positive
Kenworthy, 2014 n= 67/
5% 7–11 RCT
Non
Computerized
“Unstuck and
On Target”
physical/mental
flexibility, goal
setting, planning
tot hrs = 14–18.6
schedule = 28 sess.
during 1 school-year
school/
teacher, parent,
interventionist
cognitive/behavioral
flexibility;
planning; EF
daily life
social skills WBD; CT; BRIEF; SRS Positive
Anderson-
Hanley,
2011
n= 24/
8.3% 8–21 QE
Computerized
“Cybercycling”
or “Dance Dance
Revolution”
EFs, exercise
behaviors tot hrs = 0.3
schedule = NA N/A
WM;
switching;
inhibition
repetitive and
stereotyped
behaviors
DSF and DSB; CTT;
Stroop
task; videotapes
according to the
RBS of the
GARS-2
Mixed
Fisher, 2005 n= 27 6–15 RCT
Non
Computerized
EF training
programme
inhibition,
set-shifting
tot hrs = 2–4
schedule = one sess.
for 5–10 days
school/
investigator
set-shifting; EF
daily life ToM
CST; TMT; ToM and EF
questionnaire; FB tasks Mixed
Brain Sci. 2021,11, 1280 12 of 26
4.2. Characteristics of Included Studies
4.2.1. Methodological Quality and Risk of Bias
The methodological quality of the included studies was considered problematic in
some domains, mainly related to confounding bias, blinding and follow-up data. Even
in RCT, many authors did not specify the way in which randomisation was performed,
although in most cases the risk of selection bias was never considered critical as allocation
concealment was preserved and the characteristics of the participants did not imbalance
between the groups. Assessment of the risk of selection bias in non-randomised studies is
associated with the presence of confounding factors that may influence treatment decisions.
Examples of baseline confounders detected are the level of symptom severity, presence or
absence of associated intellectual impairment, socio-economic status, presence of comor-
bidities, use of medication, and interference of other active interventions during the study.
In the majority of the studies, confounding was controlled by restricting the eligibility
criteria to subjects who had the same value and type of confounding variables.
Drop out in non-randomised studies was a relevant phenomenon, occurring in 55%
of cases. The percentage of subjects lost to follow-up includes subjects who dropped
out of the study before the end of the intervention for reasons such as illness, school
requirements, scheduling difficulties, or subjects who did not follow the treatment protocol
correctly. Furthermore, in some studies, the low number of returned questionnaires led to
the exclusion of this information from the analysis. Among the randomised studies, only
one reported a high attrition rate, which was treated within the analysis by adopting the
intention-to-treat principle [68].
Another qualitative feature that can influence the results and generate questionable in-
terpretations of efficacy is the blinding of participants and outcome assessors, which can be
partly remedied by the use of non-subjective instruments and allocation concealment in ran-
domised trials. Of the three cases with the highest risk of bias, two studies did not include
enough information about the blinding of the evaluators [
85
,
86
], and one study in which
the trainer of the intervention coincides with the evaluator of the outcome measures [
87
].
Overall, in five studies, the authors report the non-blindness of the assessors [
67
,
87
–
90
]
and in ten studies being unclear in their reporting.
4.2.2. Participant Characteristics
The 19 qualitatively assessed studies altogether collected data from 705 subjects with
an age ranging from 4 to 20 years. It is worth stating that in four studies, no specific
information regarding gender was reported. When reported, the proportion of males
within the sample is higher than that of females in all studies with a Male/Female ratio of
5:1, resulting in a percentage of males higher than 80% [
68
,
91
,
92
]. The size of the sample
analyzed ranged from 8 subjects [85] up to 100 [68,93].
A total of four studies used a sample size greater than 50 subjects [
68
,
90
,
93
,
94
]; 50% of
the studies reported participants who terminated the intervention before the hypothesized
end [
67
,
68
,
85
,
86
,
92
,
95
–
100
] with drop-out percentages ranging from 5,5% [
86
] to 45,8% [
96
].
Data lost during the study were in some cases analyzed by applying the principle of
Intention To Treat (ITT) that resulted in a percentage of data lost ranging from 6% [
94
] to
26% [68].
All the participants of the studies included in this review received a certified diagnosis
of Autism/Autism spectrum disorder according to the DSM-IV/DSM-V criteria that was
confirmed using standardized instruments such as ADOS-1 [
88
–
90
,
93
,
94
], ADI-R [
67
,
68
,
95
],
GARS-2 [
67
,
92
,
95
,
96
], SCQ [
88
,
89
], SRS [
68
] and CARS [
97
–
99
]. In 60% of the studies, the
average verbal/nonverbal intelligence index was reported, it was measured by WASI/WASI-
II (Wechsler Abbreviated Scale Intelligence), WISC-III/WISC-IV (Wechsler Intelligence Scale
for Children), DAS-II (Differential Ability Scales), WRIT (Wide Range Intelligence Test),
K-BIT (Kaufman Brief Intelligence Test), Leiter-R (Leiter International Performance Scale)
and CPM Raven (Coloured Progressive Matrices).
Brain Sci. 2021,11, 1280 13 of 26
4.2.3. Study Characteristics
This review focused on interventions that directly train executive functions through-
out several types of behavioral and computerized activities. In this regard, a variety of
clinical approaches have been adopted within the various studies, including the cognitive-
behavioral model, restorative techniques of the cognitive remediation therapy, mindfulness
practice conveyed in martial arts exercise, and cognitive enhancement programs. In addi-
tion to this, in light of the most recent innovations in the therapeutic field, we focused on
technological advancements that supported training activities with ASD children. There-
fore, interventions were categorized into Non-Computerized [
89
–
91
,
93
,
97
–
101
] and Com-
puterized training programs [
67
,
68
,
85
–
88
,
92
,
94
–
96
]. A summary of the main characteristics
of each study is shown in Table 2.
4.3. Characteristics of Non-Computerized Trainings
With “non-computerized” modality we refer to interventions that used cognitive-
behavioral techniques such as modelling, reinforcement, scaffolding and
self-instruction [
90
,
91
,
101
], cognitive stimulation interventions using Cognitive Reme-
diation Therapy [
97
–
99
], mindfulness-based programs [
93
,
100
], sometimes in combination
with martial arts exercises [89].
Most of the interventions were conducted in the school environment, mainly by
experimenters or instructors of specific practices such as mindfulness and martial arts;
other figures involved were teachers and in one case also parents. Cognitive remediation
therapy [97–99] was implemented by a therapist in a hospital setting.
The number of sessions falls in a range of 9–28, delivered over a period from 3 to
22 weeks. Exceptions to this include Kenworthy’s study [
90
], in which 28 training sessions
were distributed across a school year, and Fisher’s study [
91
] in which one session per day
was provided for 4–10 days of training in total. The minimum duration of an intervention
was 30 min for one session and 1.5 h for the entire intervention—as estimated in Farrelly’s
study [101]—while the maximum duration was 19.5 h [89].
4.4. Effects on Non-Computerized Training Outcomes
4.4.1. Primary Outcomes
The category of cognitive-behavioral therapies includes several studies that, through
a psycho-educational approach, aimed at the gradual and conscious acquisition of func-
tional behaviors [
90
,
91
,
101
]. The application of perspective-taking techniques characterized
Fisher’s pilot study [
91
], in which a group of children involved in set-shifting training (i.e.,
a EF training) was compared with a ToM training group and a control group that had no
intervention. Data were collected after the intervention and at follow-up (6–12 weeks) and
included responses to the Card Sort test [
91
], a simplified version of the WCST that mea-
sures cognitive flexibility, and responses of teachers to questionnaires that measure changes
related to executive functioning in real life. The results do not reveal any differences
between the groups in the questionnaires completed by the teachers. The performance on
the Card Sort task showed a similar improvement in the ToM group and in the control
group, which was not observed in the EF group.
Promising results came from Kenworthy’s study [
90
] that evaluated an intervention
on EF (Unstock and On Target—UOT) in an ecological context and compared it with a
training on social skills. The UOT training includes lessons for resolving concrete experi-
ments, videos, and discussion of different situations to help children use self-regulatory
scripts. The lessons of social—communication skills focus on specific skill (e.g physical
distance) that are first presented by a didactic class and followed by activities of role-plays
and/or games. Both interventions were delivered in the school context by teachers during
the school year and reinforced at home through parental support. All the participants
were primary school children with ASD without intellectual disability, and were randomly
assigned to either the EF intervention group or a social skills’ intervention group. Re-
sults showed a significantly greater improvement (medium effect size) in flexibility and
Brain Sci. 2021,11, 1280 14 of 26
problem-solving scores in the UOT group compared to the social skill group, calculated
respectively with the WASI Block Design (WASI; Wechsler, 1999) and with the Challenge
Task [
97
]. Furthermore, a greater improvement in the UOT group was also observed in
classroom observations and information collected through the BRIEF [
102
] by teachers and
by parents. The classroom-based intervention proposed by Farrelly [
101
] integrates training
aimed at enhancing cognitive flexibility into the school curriculum. Twenty children aged
11–13 years
with a diagnosis of ASD were randomly allocated to either a control group
or the experimental group. The subjects in the control group did not take part in any
intervention, while those in the experimental group participated in three sessions of 30 min
each over three weeks. One part of the sessions focused on the social aspects of flexibility
using perspective-taking techniques, while another part focused on the cognitive aspects
of flexibility using executive tasks such as the WCST [
103
] and the Stroop Test [
104
]. One
aspect of cognitive flexibility targeted was the social aspect to address deficient perspective-
taking and empathy skills. In order to illustrate flexible versus inflexible thinking, the
researchers created fictional characters. Then, participants were encouraged to discuss
how they might respond to typical social scenarios (shown through cards) in a flexible or
inflexible way. Participants were assessed before and after the intervention using the Trail
Making Test—TMT [
105
], an instrument extremely sensitive to cognitive flexibility and
with good ecological validity. To minimize the practice effect, the layout of the TMT was
modified in the second evaluation. The influence of the intervention in the experimental
group results in a larger decrease in time to complete both part A and part B of the TMT
than the control group.
Another intervention targeted at EF impairments is cognitive remediation therapy,
which was used in Hajri’s studies to replicate in an ASD sample the positive effects
that were previously documented in other clinical populations [
106
,
107
]. This type of
training consists of simple paper-and-pencil cognitive exercises that focus on specific
functions such as cognitive flexibility, working memory and planning, with the intention
of helping the person to develop the processing strategies. Analysis of differences in
pre-post measures showed significant improvements in working memory [
97
,
99
] and
cognitive flexibility, measured with the Digit Span task [
108
,
109
] and the verbal and
semantic Fluency task [
110
,
111
] respectively. Moreover, post intervention responses to
the Hayling Test [
112
] showed an increase in thinking time that, although not significant,
suggests better management of impulsivity. Ridderinkhof [
93
] used mindfulness with a
group of high-functioning ASD subjects to train attentional and inhibitory control. A group
of ASD subjects participated in the “MyMind” programme, whose sessions included the
teaching of psycho-education and mindfulness techniques, and were compared with a
group of TD subjects who had not received any intervention. Before and after the training
all participants underwent the Attention Network Test—ANT [
113
]—prior to the start
of the intervention, within two weeks after and two months later. The training lasted
9 weeks and any session had a duration of 1.5 h. The results showed no significant group
differences in the Attention Network Test either at baseline or post intervention. Although
not significant, the authors reported that ASD subjects showed lower accuracy on executive
attention tasks at baseline that improved immediately after the intervention and reached
the TD level. At follow-up the observed non-significant improvements were limited only
to orienting attention tasks. Alerting attention did not change pre and post treatment.
Together with martial arts, mindfulness offered benefits on emotional regulation, working
memory and cognitive flexibility [89].
4.4.2. Secondary Outcomes
A few articles considered secondary outcome measures, which reported positive
changes following the intervention. Specifically, the use of cognitive remediation therapy
helped to maximize participants’ school performance [
97
–
99
] and decrease their scores on
the CARS [
97
,
99
]. In this training program, the majority of the activities were conducted
individually with the support of paper and pencil materials. In Fisher ’s study [
91
], subjects
Brain Sci. 2021,11, 1280 15 of 26
assigned to the experimental EF condition performed well on ToM tasks at follow-up and
showed positive effects in social skills, as indicated by the responses to the items in the
teachers’ ad hoc questionnaires created by the researchers to measure EF changes in real
life. The results of Kenworthy’s study of 2015 [
90
] revealed no differences between the two
experimental groups, as demonstrated by parent and teacher reports on the SRS [
114
]. This
suggests that EF training led to similar gains compared to specific social skills training,
demonstrating the importance of training flexibility to help, for example, the child to better
manage his frustration and to interact socially and appropriately with others. These results
reflect the benefits of an EF intervention in delivering a trickle-down effect on social skills.
In summary, these results highlighted an increase in EFs, in particular with regard
to cognitive flexibility, problem-solving and emotion regulation after non-computerized
training. Interestingly, those studies showed that even when the EF training did not have a
direct effect on EF improvements, they seemed to have positive indirect effects on social
skills in ASD.
4.5. Characteristics of Computerized Trainings
Four studies focused on the investigation of intervention programs that combined
both motor and cognitive components by engaging participants in the activities of an
exergame [
85
,
86
,
92
,
94
]. In the context of our study, exergames are computer games run on
commercial consoles such as the Wii and the Kinect console and that are controlled with
body movements. The remainder have investigated the use of serious games and gamified
environments to train specific executive abilities [67,68,87,88,95,96].
Most of the interventions were made accessible directly from participants’ homes [
68
,
88
,
96
]
or were offered in the school setting [
67
,
85
,
86
,
95
], where parents or teachers were often
actively involved to support performance. In total, five training programs were assisted
by an adult [
67
,
87
,
95
,
96
]; however, each one differed in the level of involvement of the
adult within the program. In Saniee’s study [
96
], for example, computer-based training
was integrated with daily life activities using help and promption from mothers. Both
Macoun’s [
95
] and Kerns’s [
67
] studies involved an research assistant or educator to attend
training to learn appropriate metacognitive strategies for supporting and encouraging the
young participants during the interventions. Only one study was based in a clinical setting
where the training was conducted by a graduate student in clinical psychology under the
supervision of a psychologist [87].
Training programs had a varying range of intervention delivery period, number
of sessions and total training duration. The range of sessions planned varies between
8 and 24 within 8 to 12 weeks except for Saniee’s study [
96
], which distributes 4 daily
sessions over two months. The duration of training sessions ranged from
2 min [85,86]
,
to
20–30 min [67,88,94,95]
; to a maximum of 50–60 min [
87
,
96
]. The majority of the in-
terventions lasted between 6 and 12 h in total [
67
,
87
,
88
,
95
], while three studies focused
on extremely brief trainings with a duration of no more than 30 min [
85
,
86
,
92
], and one
study [96] has a total of 56 h of training.
4.6. Effects on Computerized Training Outcomes
4.6.1. Primary Outcomes
The intent of much training that uses the virtual tool is to take action on multiple
executive domains. Sometimes, the integration of activities of daily living and the use of
metacognitive strategies allowed for the transfer of skills learned by means of the com-
puter [
67
,
95
,
96
]. Two studies examined the benefits of a serious game on performance in
working memory tasks (both in the spatial and verbal domains) and attentional control
(i.e., WISC-IV; The Working Memory Test Battery; KiTAP, Test of Attentional Performance
for Children). Specifically, Macoun’s study [
95
] reported significant near-transfer effects
in selective attention and visual working memory for the experimental groups compared
to a waiting-list group (distractibility attention task; ‘Colored Boxes’ visual-spatial WM
task) and in Kerns’ study [
67
] the ASD sample shows near-transfer improvements in di-
Brain Sci. 2021,11, 1280 16 of 26
vided attention and verbal working memory (distractibility and divided attention task; the
Counting Recall and Listening Recall tasks) compared to sample of subjects with Fetal Al-
cohol Spectrum Disorders. Another interactive serious game (i.e., Project EVO) was found
to be beneficial in improving executive skills in a sample of ASD subjects with ADHD
symptomatology [
88
]. Significant improvements (with medium to large effect sizes) in
the TOVA [
115
], an instrument for screening inhibitory control and attention, in the group
who trained with the experimental activity (i.e., Project EVO) compared to the control
condition involving an educational word-generation intervention. In Anderson-Hanley’s
study [
92
], participants’ executive skills—assessed through three tasks tapping, i.e., work-
ing memory, cognitive flexibility, inhibition—showed significant increases compared to
the control only in verbal working memory (i.e., Digit Span Backward [
116
]). Milajerdi
and collaborators [
94
] examined improvement effects on EF using the WCST [
117
], whose
results confirmed the hypothesis that Kinect training offers greater benefits than tradi-
tional physical activity training and a Treatment-As-Usual control group. Intervention
programs that focused explicitly on one specific executive function were examined in the
other studies, although it is important to emphasize the known possibility of involving
other executive processes in accordance with the type of task [
13
]. Among these works is
Chen [
87
], who proposed a sequence of attentional exercises graded over several sessions
in increasingly difficult levels, which required cognitive flexibility to be completed. At the
end of the eight-week intervention, participants—compared with a social skills control
group—significantly reduced the number of perseverative responses in tests of cognitive
flexibility (Trail-Making Test [
118
]; Wisconsin Card Sorting Test [
103
]). Saniee et al.’s [
96
]
set-shifting puzzle game not only decreased the production of perseverative responses,
but also generalized the skills acquired at the behavioral level, encouraging the subject
to shift attention between activities of his/her interest. Interestingly, the improvements
were maintained even one month after the end of the intervention. In the study of de
Vries and collaborators [
68
], children who were asked to perform activities prevalent on
WM or cognitive flexibility undergo only marginal near-transfer effects compared to the
control group (i.e., all the training tasks remained at the low, non-adaptive level) and do
not obtain any generalization to untrained skills or maintenance effect in the long term.
Benefits offered in terms of generalization emerged in Hilton’s studies [
85
,
86
], in which
the effectiveness of an exergame was analyzed in a sample of children aged 6 to 18 years.
The results showed an improvement in almost all scales of the BRIEF [
102
], reaching sig-
nificance levels at the working memory scale and the metacognition index. Cognitive
flexibility and inhibitory control skills required during play were transferred to contexts
other than that of the intervention (i.e., BRIEF responses).
4.6.2. Secondary Outcomes
More than half of the analyzed interventions assessed the generalization of training
improvements to domains other than those directly trained. Specifically, four studies
investigated far-transfer effects on social skills [
68
,
87
,
88
,
95
], finding significant long-term
benefits in one case only [
87
]. Of these, two found no significant differences between
the groups [
68
,
88
]. Specifically, in Yerys’s study the SSIS was used (The Social Skills
Improvement System [
119
]), while in de Vries’s study the CSBQ (The Children’s Social
Behavior Questionnaire; [
120
]). Six studies reported behavioral changes related to ASD
symptomatology [
67
,
92
,
95
,
96
] and ADHD symptoms [
68
,
88
]. Critically, those studies
measured the symptomatology using a variety of different tools, and this may have led to
different results. For instance, Yerys used the ADHD-RS-IV [
121
] to measure symptoms of
inattention, hyperactivity and impulsivity. In line with the results of the TOVA test, the
parent reports confirmed a significant reduction in ADHD symptoms in the experimental
group compared to the control group. In the case of Macoun [
95
] and Kerns [
67
], the low
returns rating data (i.e., BERS-2 [
122
]; CRS-3 [
123
]; GARS-2 [
124
]) did not allow the analysis
to be completed. Nevertheless, the interviews with parents, teachers and EAs show an
improvement in self-esteem, a greater tolerance of frustration and adequate emotional
Brain Sci. 2021,11, 1280 17 of 26
regulation in response to mistakes. On the other hand, WM-training seems to contribute
in de Vries’s study [
68
] to a slight reduction in ADHD behavior, as can be seen from the
scores on the The Disruptive Behavior Disorders Rating Scale (DBDRS, [
125
]). Participants
who trained with the experimental tool in the Saniee and Anderson-Hanley studies [
92
,
96
]
had better scores on the repetitive and restricted behaviour scale of the GARS [
124
,
126
]
compared to the control groups.
Finally, two studies reported positive impacts on school performance [
67
,
95
]. Sim-
ilarly to the symptomatology assessment, the measurement of school performance has
also been collected using different tools—such as Reading Fluency Curriculum Based
Measure [
127
,
128
] and Woodcock Johnson III- Math Fluency (WJ-III [
129
])—leading to a
possible heterogeneity in results.
To sum up, the results showed an overall significant improvement in the performance
of some executive tasks after a computerized training of EFs. Specifically, the experimental
groups improved more than the control groups in the following executive components: at-
tention (both on divided and sustained attention), working memory (verbal and visual) and
inhibitory control (decreased number of perseverative responses) through the application
of computerized training.
5. Discussion
The main aim of this systematic review was to address the emergence in the scientific
literature of cognitive training tools that target executive functions in children and ado-
lescents with Autism Spectrum Disorder. An individual with ASD may exhibit a variety
of executive deficits and these problems can negatively impact the child’s development,
making early identification and targeted intervention crucial [
130
,
131
]. Increasing execu-
tive skills can positively affect children with ASD’s ability to cope with adult life. Indeed,
a timely and effective diagnosis can assist with the planning of targeted rehabilitation
interventions in an age group in which significant improvement is most likely to occur.
Our systematic review included randomized controlled trials (RCTs) and quasi-
experimental studies and provides up-to-date information on the existent literature on
EFs interventions delivered to children and young people with ASD, either through the
use of technology (i.e., “Computerized training”) or via more classical paper-and-pencil
and behavioral activities (i.e., “Non-Computerized training”). Despite the difficulty in
treating ASD comprehensively, several interventions have proved beneficial in improving
executive attention, which in turn positively impacts on the quality of life of children with
autism and their families. In general, most EF interventions were effective in improving
children’s executive skills when compared to control activities or treatment as usual, with
some indirect outcomes also over social skills. In a few studies, the evidence indicates that
intensive interventions can also be effective in reducing comorbid ADHD symptoms in
children and young people with ASD.
The studies on non-computerized training suggests that the greater improvement of
the executive functions in ASD are produced by training performed in ecological envi-
ronments, such as in the school and home—which produced positive effects on shifting,
flexibility, problem-solving, and ecological EF measures assessed with BRIEF—and cog-
nitive remediation therapy—which led to improvement in working memory and verbal
and semantic fluency [
90
,
97
,
99
,
101
]. Conversely, interventions based on mindfulness and
perspective-taking techniques seem to lead to no or little EF improvement, neither in set-
shifting and cognitive flexibility, nor in executive, orienting, and alerting attention [
91
,
93
].
Notwithstanding, it is worth noticing that the EF training seemed to have positive indirect
effects on social skills in ASD [
90
,
91
]. Moreover, the use of cognitive remediation therapy
helped to maximize participants’ school performance [
97
–
99
] and decrease the symptoma-
tology [
97
–
99
]. Altogether, these results suggest the importance of non-computerized
EF training, especially when performed in ecological contests, in supporting the chil-
dren with ASD to better manage the frustration and to interact with others using socially
appropriate strategies.
Brain Sci. 2021,11, 1280 18 of 26
The extent to which improvements in EF tasks translate to learning is an important
issue for educational policies and clinical guidelines [
132
,
133
]. So far, little evidence exists
on how training effects in specific EFs are transferred into untrained academic skills (e.g.,
Math grades). Future research should hence investigate this aspect, as it can inform clinical
and educational practices.
Regarding the intervention duration, in most of the reviewed studies the training
took place over a period of a few weeks (most trials: 2–8 weeks) and ensured a significant
improvement in the “quality of life”, suggesting that significant results can be achieved
with brief interventions, reducing monetary costs. Notably, most of the studies focused on
the analysis of short-term training effects (i.e., 1 week–1 month after the end of the training).
Follow-up evaluations after the end of the intervention are pivotal to inform about the
maintenance of training gains over time. Therefore, future research should include both
immediate and follow-up measures.
Concerning the cost-effectiveness aspect, the use of technology (e.g., virtual agents,
artificial intelligence, virtual reality, and augmented reality) seems to support the interven-
tion of ASD children. Computerized training of EFs employed one or more technologies
(e.g., tablets, computers, virtual reality) for delivering intervention. The interventions are
designed to take advantage of the special interest—often found in the literature [
78
,
134
]—
that many individuals with ASD have in computer technology. Children with ASD are
attracted to digital technologies because they are often predictable and offer clearly defined
tasks [
135
], which minimizes sensory stimulation distractions, provide self-paced usage
and require less social interaction [
136
,
137
]. Additionally, visual and auditory feedback,
such as lights and sounds, provide clear and timely reinforcements [138].
Although digital technologies may be appealing, they are often not thought to produce
long-lasting improvements in behavioral performance in children’s real-life activities [
139
],
and some of them can even worsen social behavior. In this regard, results of the studies
included in this work suggested that the two types of training may impact individuals’
every day functioning differently: computerized tools produced significant improvements
in tasks assessing WMC/executive attention [
8
,
29
,
140
], while non-computerized techniques
proved to be more beneficial, even on far transfer measures and, in particular, cognitive
flexibility and emotional regulation. Indeed, while technology is a valuable support that
should be available to individuals with ASD, EF interventions that are mediated solely by
technology may result in limited improvement of social communication or social emotional
functioning for children with ASD. In these contexts, the lack of human interaction may
result in reduced generalization of the training tools to untrained skills (e.g., social and
communicative skills). Although technological supports embed characteristics that make
them particularly useful for children with autism, these supports would be more effective
if they were integrated into interpersonal interactions, which might include computer-
mediated interpersonal interactions, versus replacing interaction partners entirely. This
may be especially true when the targeted developmental achievements relate to social
abilities. On the other hand, computer-based trials which involved well-trained personnel
to support children have been proven to be effective in enhancing not only the trained
executive functions, but also in ameliorating self-regulation in daily activities [
68
,
92
,
95
,
96
].
As a result of strengthening the attentive–executive system, participants were able to
sustain longer their focus of attention on the tasks (compared with baseline), often leading
to improved planning and organization skills in everyday life [
67
,
92
]. Indeed, expert
trainers play a major role, especially when dealing with potential emotional or behavioral
issues that arise during the intervention. Thus, future research on the effectiveness of
computer-based interventions paired with the involvement of expert trainers is not only
needed, but also timely. Besides this, greater attention should be paid to the usability and
design of technology and computerized training, as this can limit the possible use and
access to this type of intervention by individuals with low technological skills [
135
,
138
,
139
].
Although the results of most of the reviewed interventions were associated with a
positive improvement in EFs, several limitations characterized the reviewed studies and
Brain Sci. 2021,11, 1280 19 of 26
might impede the results to be straightforwardly applicable in the clinical practice. The
major issues are related to the lack of methodological rigor and scarce replication. First,
the heterogeneity of the training protocols and outcome measures greatly differ among the
studies and prevent us from drawing firm conclusions on training efficacy, as well as the
absence of replication studies. Indeed, the results of previous studies might be driven by
the number and the durations of the sessions, by the different EF subcomponents that have
been trained, or by the used assessment tools which may be too sensitive, or not sensitive
enough, to detect EF-related changes. All these limitations impede the clinicians to trust
the effectiveness of one or another EFs training and to adopt it within or besides the clinical
interventions. In light of that, it would be desirable that future studies sought to replicate
the existing findings using more standardized protocols and clinically relevant assessment
tools. The improvement in methodological aspects should also regard the increase of
the sample size numerosity, analysis of missing data, randomization of subjects, and a
comparison of adequate control groups. In addition, statistical analyses should include
measures of effect size, corrections for multiple comparisons when multiple measures are
used, and the estimate should be published by researchers before beginning recruitment to
avoid the phenomenon of “fishing”. Importantly, replication is needed.
Secondly, in addition to standardized EF measures, the outcome measures should
consistently include ecological EFs and social skills measures that inform clinicians of the
secondary effect of EF training on those capabilities, which are pivotal for self-regulation
in daily life and for social interactions.
Third, future research should include ASD individuals with intellectual disabilities, as
they represent a great percentage of the ASD population. Indeed, 31% of children with ASD
have an intellectual disability (intelligence quotient [IQ] < 70), 25% are in the borderline
range (IQ 71–85), and 44% have IQ scores in the average to above average range (i.e.,
IQ > 85). In addition, the development of interventions for the adult population with ASD
also remains necessary. Seltzer and collaborators [
141
] found that most adults still have
difficulty in dealing with the demands of their environment, regardless of their level of
impairment. Thus, despite the greater impact of early interventions on ASD symptoms,
interventions would also be beneficial for older individuals.
Finally, as mentioned earlier, the functioning profile of children and adolescents with
EFs is characterized by a profound heterogeneity (e.g., executive deficits are present in a
part of the ASD population). Research should therefore take into account the efficacy of the
various interventions on the basis of specific cognitive profiles and severity of symptoms,
especially considering the presence of restricted and repetitive behaviors and interests
of the participants included in the study. It is likely that the heterogeneity of the results
obtained in the studies included in this systematic review could be, in part, explained by
the heterogeneity in ASD symptomatology and cognitive profile.
Clinical Implications
Despite considering the several limitations that emerged from this review, it is impor-
tant to consider the implications of the results at a clinical level.
Above all, it must be stressed how complex and difficult it can be to carry on training
with children and adolescents with ASD. In fact, the use of a computer may improve the
sustained attention of the subject, but the repetition of similar exercises may affect the
expression of restricted interests. Considering the non-computerized training, the most
difficult part is to activate the motivation and interest of the person with ASD; generally, this
is possible only after building a positive relationship between the clinician and him/her.
Some results regarding the effectiveness of the training, especially in the first part of
intervention, may be affected by characteristics of the subjects with ASD, in particular the
social difficulties and the activation of the motivation and not for the specific impairment in
the EFs. With this in mind, the reviewed studies inform us about the importance of realizing
the training in ecological contexts, such as the school and home, and also involving people
that are significant for the child, such as parents and teachers. Indeed, besides the therapist,
Brain Sci. 2021,11, 1280 20 of 26
those people well know the strength and the difficulties that characterize the child and, thus,
can adapt the training accordingly, maximizing its efficacy. In line with this, a great amount
of research has reported the importance of involving parents in the intervention of children
with ASD [
142
–
144
]. Additionally, a wide consensus is present concerning the intensity of
intervention necessary to support the development of social-communication and cognitive
abilities, and different studies have underlined that parents and school educators have the
potential to guarantee more intensive stimulation in the child’s daily life than therapists
alone [
145
,
146
]. Furthermore, parent-mediated interventions, in which the parent learns
appropriate strategies to support the social and communicative development of the child
with the therapist during sharing activities [
144
,
147
], contribute to better generalization
and maintenance of acquired skills. This could be even achieved thanks to participation in
the school context. Consistent with this evidence, our review suggests that the positive
effect of parents and teachers involved in the treatment may also encompass the EFs.
Moreover, the greater effect of training provided in naturalistic contests, such as in the
school and home, highlight the fact that the intervention efficacy is maximized when it is
administered not exclusively in therapeutic settings, but also in daily life contexts. In addi-
tion, another important result emerging from this review is that the EFs could be improved
also at a younger age (i.e., kindergarten children [
148
,
149
]). Crucially, the improvement
of these functions is positively reflected on the social skills and self-regulation in daily
activities, with great potential for ameliorating the adaptation to the life environments and
the life quality of both the individuals with ASD and their caregivers. These aspects have
a pivotal relevance at the clinical level and should be carefully considered by therapists.
Indeed, rehabilitating both the cognitive and the social aspects will offer ASD children the
opportunity to train EFs by learning directly from the familiar environment that, in turn,
would lead to a higher generalization of positive outcomes. Finally, digital technologies
seem to fit well with the ASD characteristics, allowing them to access the computerized
training autonomously and to exercise every day without the need of being constantly
supported by the presence of a therapist in any session. This is reflected in a decrease in
clinical costs and efforts, as well as in an increase in independence and self-monitoring in
ASD patients that would promote the generalization of positive training gains. However,
briefing and debriefing sessions provided by an expert trainer might be necessary to carry
over and generalize skills learned with a computerized tool to real life.
6. Conclusions
Most of the interventions reviewed here were associated with a positive improvement
in EFs. However, given the publication bias in favor of positive results, intervention studies
with negative results are less likely to be published [
150
]. Therefore, it is possible that
the studies included in this review are not a fully comprehensive representation of the
EF interventions developed since 2000. In general, although most of the interventions
identified have obtained significantly positive results, a suboptimal methodology for
many studies and a lack of replication of several training programs impose caution in
interpreting the results and make it difficult to draw ultimate conclusions. Although the
proof of EF training effectiveness emerging fromthis review is still inconclusive, the existing
body of research supports the importance of conducting timely assessments of executive
function and, whenever appropriate, providing targeted interventions [
18
]. Indeed, the
main challenge for the remediation of this disorder is not only to find the most effective
remediation programs, but also to precisely select a personalized program for each child
with ASD. In fact, the results presented in this review showed a great variability in the
effectiveness of training both for the specific EFs and for different groups of children
with ASD. For this reason, starting from a careful evaluation of the functioning of the EFs
(considering strengths and weaknesses), a clinician will define times, modes and training
more suitable for the specific characteristics of the subject with ASD. Nevertheless, we
believe that this systematic review could be relevant for researchers and clinicians since
it highlights the lack of controlled and randomized clinical trials directly targeting EFs in
Brain Sci. 2021,11, 1280 21 of 26
children and adolescents with ASD and gives important suggestions for improving future
research in the field.
Supplementary Materials:
Supplementary Information are available online at https://www.mdpi.
com/article/10.3390/brainsci11101280/s1.
Author Contributions:
Conceptualization and Methodology: A.P., P.V. Formal Analysis and Data
Curation: A.P., A.M. Writing—Original Draft Preparation: A.P., N.M., A.M. Writing—Review and
Editing: A.B., P.V., F.B. Supervision: P.V. and A.B. with a focus on Autism Spectrum Disorder; F.B.
with a focus on training of Executive Functions. All authors have read and agreed to the published
version of the manuscript.
Funding: N.M. is founded by the Municipality of Rovereto and Fondazione Caritro.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments:
We gratefully acknowledge the help of F.P., M.M. and B.Z. in the initial phases
of the literature search.
Conflicts of Interest: We declare to not have any conflict of interest.
References
1.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association:
Washington, DC, USA, 2013; ISBN 9780890425558.
2. Lai, M.-C.; Lombardo, M.V.; Baron-Cohen, S. Autism. Lancet 2014,383, 896–910. [CrossRef]
3.
Happé, F.; Frith, U. Annual Research Review: Looking Back to Look Forward-Changes in the Concept of Autism and Implications
for Future Research. J. Child. Psychol. Psychiatry 2020,61, 218–232. [CrossRef] [PubMed]
4.
Pender, R.; Fearon, P.; Heron, J.; Mandy, W. The Longitudinal Heterogeneity of Autistic Traits: A Systematic Review. Res. Autism
Spectr. Disord. 2020,79, 101671. [CrossRef]
5.
Wolfers, T.; Floris, D.L.; Dinga, R.; van Rooij, D.; Isakoglou, C.; Kia, S.M.; Zabihi, M.; Llera, A.; Chowdanayaka, R.;
Kumar, V.J.; et al
. From Pattern Classification to Stratification: Towards Conceptualizing the Heterogeneity of Autism Spectrum
Disorder. Neurosci. Biobehav. Rev. 2019,104, 240–254. [CrossRef] [PubMed]
6.
Friedman, L.; Sterling, A. A Review of Language, Executive Function, and Intervention in Autism Spectrum Disorder. Semin.
Speech Lang. 2019,40, 291–304. [CrossRef] [PubMed]
7.
Demetriou, E.A.; Lampit, A.; Quintana, D.S.; Naismith, S.L.; Song, Y.J.C.; Pye, J.E.; Hickie, I.; Guastella, A.J. Autism Spectrum
Disorders: A Meta-Analysis of Executive Function. Mol. Psychiatry 2018,23, 1198–1204. [CrossRef] [PubMed]
8. Engle, R.W. Working Memory Capacity as Executive Attention. Curr. Dir. Psychol. Sci. 2002,11, 19–23. [CrossRef]
9.
Miyake, A.; Friedman, N.P.; Emerson, M.J.; Witzki, A.H.; Howerter, A.; Wager, T.D. The Unity and Diversity of Executive
Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognit. Psychol.
2000
,41,
49–100. [CrossRef]
10.
Miyake, A.; Friedman, N.P. The Nature and Organization of Individual Differences in Executive Functions: Four General
Conclusions. Curr. Dir. Psychol. Sci. 2012,21, 8–14. [CrossRef]
11. Diamond, A. Executive Functions. Annu. Rev. Psychol. 2013,64, 135–168. [CrossRef]
12.
Zimmerman, D.L.; Ownsworth, T.; O’Donovan, A.; Roberts, J.; Gullo, M.J. Independence of Hot and Cold Executive Function
Deficits in High-Functioning Adults with Autism Spectrum Disorder. Front. Hum. Neurosci. 2016,10, 24. [CrossRef]
13.
Benso, F.; Moretti, S.; Bellazzini, V.; Benso, E.; Ardu, E.; Gazzellini, S. Principles of Integrated Cognitive Training for Executive
Attention: Application to an Instrumental Skill. Front. Psychol. 2021,12, 647749. [CrossRef]
14.
Brown, H.M.; Stahmer, A.C.; Dwyer, P.; Rivera, S. Changing the Story: How Diagnosticians Can Support a Neurodiversity
Perspective from the Start. Autism 2021,25, 1171–1174. [CrossRef] [PubMed]
15.
Leadbitter, K.; Buckle, K.L.; Ellis, C.; Dekker, M. Autistic Self-Advocacy and the Neurodiversity Movement: Implications for
Autism Early Intervention Research and Practice. Front. Psychol. 2021,12. [CrossRef] [PubMed]
16.
Miller, E.K.; Cohen, J.D. An Integrative Theory of Prefrontal Cortex Function. Annu. Rev. Neurosci.
2001
,24, 167–202.
[CrossRef] [PubMed]
17.
Espy, K.A. Using Developmental, Cognitive, and Neuroscience Approaches to Understand Executive Control in Young Children.
Dev. Neuropsychol. 2004,26, 379–384. [CrossRef]
18.
Burgess, P.; Simons, J. 18 Theories of Frontal Lobe Executive Function: Clinical Applications. In The Effectiveness of Rehabilitation
for Cognitive Deficits; Halligan, W.P., Wade, D.T., Eds.; Oxford University Press: Oxford, UK, 2005; pp. 211–231. [CrossRef]
19. Hill, E.L. Executive Dysfunction in Autism. Trends Cogn. Sci. 2004,8, 26–32. [CrossRef] [PubMed]
Brain Sci. 2021,11, 1280 22 of 26
20.
Lai, C.L.E.; Lau, Z.; Lui, S.S.Y.; Lok, E.; Tam, V.; Chan, Q.; Cheng, K.M.; Lam, S.M.; Cheung, E.F.C. Meta-Analysis of Neuropsy-
chological Measures of Executive Functioning in Children and Adolescents with High-Functioning Autism Spectrum Disorder.
Autism Res. 2017,10, 911–939. [CrossRef] [PubMed]
21.
Dawson, G.; Meltzoff, A.N.; Osterling, J.; Rinaldi, J. Neuropsychological Correlates of Early Symptoms of Autism. Child. Dev.
1998,69, 1276–1285. [CrossRef]
22.
Dawson, G.; Munson, J.; Estes, A.; Osterling, J.; McPartland, J.; Toth, K.; Carver, L.; Abbott, R. Neurocognitive Function and
Joint Attention Ability in Young Children with Autism Spectrum Disorder versus Developmental Delay. Child. Dev.
2002
,73,
345–358. [CrossRef]
23.
McEvoy, R.E.; Rogers, S.J.; Pennington, B.F. Executive Function and Social Communication Deficits in Young Autistic Children.
J. Child. Psychol. Psychiatry 1993,34, 563–578. [CrossRef] [PubMed]
24.
Griffith, E.M.; Pennington, B.F.; Wehner, E.A.; Rogers, S.J. Executive Functions in Young Children with Autism. Child. Dev.
1999
,
70, 817–832. [CrossRef]
25. Goldstein, S.; Naglieri, J.A. Handbook of Executive Functioning; Springer: New York, NY, USA, 2014; ISBN 978-1-4614-8105-8.
26.
Rabbitt, P. Introduction: Methodologies and Models in the Study of Executive Function. In Methodology of Frontal and Executive
Function; Routledge: London, UK, 1997; pp. 1–38.
27.
Bernstein, J.H.; Waber, D.P. Executive Capacities from a Developmental Perspective. In Executive Function in Education: From
Theory to Practice, 1st ed.; Guilford Press: New York, NY, USA, 2007; pp. 39–54.
28.
Engle, R.W.; Kane, M.J. Executive Attention, Working Memory Capacity, and a Two-Factor Theory of Cognitive Control. Psychol.
Learn. Motiv. 2004,44, 145–199.
29.
McCabe, D.P.; Roediger, H.L.; McDaniel, M.A.; Balota, D.A.; Hambrick, D.Z. The Relationship between Working Memory
Capacity and Executive Functioning: Evidence for a Common Executive Attention Construct. Neuropsychology
2010
,24, 222–243.
[CrossRef] [PubMed]
30.
Pennington, B.F.; Ozonoff, S. Executive Functions and Developmental Psychopathology. J. Child. Psychol. Psychiatry
1996
,37,
51–87. [CrossRef]
31.
Snyder, H.R.; Miyake, A.; Hankin, B.L. Advancing Understanding of Executive Function Impairments and Psychopathology:
Bridging the Gap between Clinical and Cognitive Approaches. Front. Psychol. 2015,6, 328. [CrossRef]
32.
Morra, S.; Panesi, S.; Traverso, L.; Usai, M.C. Which Tasks Measure What? Reflections on Executive Function Development and a
Commentary on Podjarny, Kamawar, and Andrews (2017). J. Exp. Child. Psychol. 2018,167, 246–258. [CrossRef]
33.
Posner, M.I.; DiGirolamo, G.J. Executive Attention: Conflict, Target Detection, and Cognitive Control. In The Attentive Brain; The
MIT Press: Cambridge, MA, USA, 1998; pp. 401–423. ISBN 0262161729.
34.
Engle, R.W.; Kane, M.J.; Tuholski, S.W. Individual Differences in Working Memory Capacity and What They Tell Us About
Controlled Attention, General Fluid Intelligence, and Functions of the Prefrontal Cortex. In Models of Working Memory: Mechanisms
of Active Maintenance and Executive Control; Cambridge University Press: New York, NY, USA, 1999; pp. 102–134. ISBN 052158325X.
35.
Rueda, M.R.; Rothbart, M.K.; McCandliss, B.D.; Saccomanno, L.; Posner, M.I. Training, Maturation, and Genetic Influences on the
Development of Executive Attention. Proc. Natl. Acad. Sci. USA 2005,102, 14931–14936. [CrossRef]
36.
Kane, M.J.; Engle, R.W. The Role of Prefrontal Cortex in Working-Memory Capacity, Executive Attention, and General Fluid
Intelligence: An Individual-Differences Perspective. Psychon. Bull. Rev. 2002,9, 637–671. [CrossRef]
37.
Repovs, G.; Baddeley, A. The Multi-Component Model of Working Memory: Explorations in Experimental Cognitive Psychology.
Neuroscience 2006,139, 5–21. [CrossRef]
38.
Geurts, H.; Sinzig, J.; Booth, R.; Happe, F. Neuropsychological Heterogeneity in Executive Functioning in Autism Spectrum
Disorders. Int. J. Dev. Disabil. 2014,60, 155–162. [CrossRef]
39.
Wallace, G.L.; Yerys, B.E.; Peng, C.; Dlugi, E.; Anthony, L.G.; Kenworthy, L. Assessment and Treatment of Executive Function
Impairments in Autism Spectrum Disorder: An Update. In International Review of Research in Developmental Disabilities; Hodapp,
R.M., Fidler, D.J., Eds.; Elsevier Academic Press Inc.: San Diego, CA, USA, 2016; Volume 51, pp. 85–122. ISBN 9780128051771.
40.
Hollocks, M.J.; Jones, C.R.G.; Pickles, A.; Baird, G.; Happé, F.; Charman, T.; Simonoff, E. The Association between Social Cognition
and Executive Functioning and Symptoms of Anxiety and Depression in Adolescents with Autism Spectrum Disorders. Autism
Res. 2014,7, 216–228. [CrossRef] [PubMed]
41.
Demetriou, E.A.; Song, C.Y.; Park, S.H.; Pepper, K.L.; Naismith, S.L.; Hermens, D.F.; Hickie, I.B.; Thomas, E.E.; Norton, A.; White,
D.; et al. Autism, Early Psychosis, and Social Anxiety Disorder: A Transdiagnostic Examination of Executive Function Cognitive
Circuitry and Contribution to Disability. Transl. Psychiatry 2018,8, 200. [CrossRef]
42.
Toplak, M.E.; West, R.F.; Stanovich, K.E. Practitioner Review: Do Performance-Based Measures and Ratings of Executive Function
Assess the Same Construct? J. Child. Psychol. Psychiatry 2013,54, 131–143. [CrossRef] [PubMed]
43.
Toplak, M.E.; Bucciarelli, S.M.; Jain, U.; Tannock, R. Executive Functions: Performance-Based Measures and the Behavior
Rating Inventory of Executive Function (BRIEF) in Adolescents with Attention Deficit/Hyperactivity Disorder (ADHD). Child
Neuropsychol. 2009,15, 53–72. [CrossRef]
44.
Kenworthy, L.; Yerys, B.E.; Anthony, L.G.; Wallace, G.L. Understanding Executive Control in Autism Spectrum Disorders in the
Lab and in the Real World. Neuropsychol. Rev. 2008,18, 320–338. [CrossRef]
45.
Ozonoff, S. Reliability and Validity of the Wisconsin Card Sorting Test in Studies of Autism. Neuropsychology
1995
,9,
491–500. [CrossRef]
Brain Sci. 2021,11, 1280 23 of 26
46.
Joseph, R.M.; Keehn, B.; Connolly, C.; Wolfe, J.M.; Horowitz, T.S. Why Is Visual Search Superior in Autism Spectrum Disorder?
Dev. Sci. 2009,12, 1083–1096. [CrossRef]
47.
Di Martino, A.; Ross, K.; Uddin, L.Q.; Sklar, A.B.; Castellanos, F.X.; Milham, M.P. Functional Brain Correlates of Social and
Nonsocial Processes in Autism Spectrum Disorders: An Activation Likelihood Estimation Meta-Analysis. Biol. Psychiatry
2009
,
65, 63–74. [CrossRef] [PubMed]
48.
Uddin, L.; Supekar, K.; Menon, V. Reconceptualizing Functional Brain Connectivity in Autism from a Developmental Perspective.
Front. Hum. Neurosci. 2013,7, 458. [CrossRef]
49.
Jones, C.R.G.; Simonoff, E.; Baird, G.; Pickles, A.; Marsden, A.J.S.; Tregay, J.; Happé, F.; Charman, T. The Association between
Theory of Mind, Executive Function, and the Symptoms of Autism Spectrum Disorder. Autism Res.
2018
,11, 95–109. [CrossRef]
50.
Kouklari, E.-C.; Tsermentseli, S.; Auyeung, B. Executive Function Predicts Theory of Mind but Not Social Verbal Communication
in School-Aged Children with Autism Spectrum Disorder. Res. Dev. Disabil. 2018,76, 12–24. [CrossRef]
51.
Pugliese, C.E.; Anthony, L.G.; Strang, J.F.; Dudley, K.; Wallace, G.L.; Naiman, D.Q.; Kenworthy, L. Longitudinal Examination of
Adaptive Behavior in Autism Spectrum Disorders: Influence of Executive Function. J. Autism Dev. Disord.
2016
,46, 467–477.
[CrossRef] [PubMed]
52.
Zimmerman, D.; Ownsworth, T.; O’Donovan, A.; Roberts, J.; Gullo, M.J. Associations between Executive Functions and Mental
Health Outcomes for Adults with Autism Spectrum Disorder. Psychiatry Res. 2017,253, 360–363. [CrossRef] [PubMed]
53.
Wallace, G.L.; Budgett, J.; Charlton, R.A. Aging and Autism Spectrum Disorder: Evidence from the Broad Autism Phenotype.
Autism Res. 2016,9, 1294–1303. [CrossRef] [PubMed]
54.
Leung, R.C.; Zakzanis, K.K. Brief Report: Cognitive Flexibility in Autism Spectrum Disorders: A Quantitative Review. J. Autism
Dev. Disord. 2014,44, 2628–2645. [CrossRef] [PubMed]
55.
Wang, Y.; Zhang, Y.; Liu, L.; Cui, J.; Wang, J.; Shum, D.H.K.; van Amelsvoort, T.; Chan, R.C.K. A Meta-Analysis of Working
Memory Impairments in Autism Spectrum Disorders. Neuropsychol. Rev. 2017,27, 46–61. [CrossRef]
56.
Van den Bergh, S.F.W.M.; Scheeren, A.M.; Begeer, S.; Koot, H.M.; Geurts, H.M. Age Related Differences of Executive Func-
tioning Problems in Everyday Life of Children and Adolescents in the Autism Spectrum. J. Autism Dev. Disord.
2014
,44,
1959–1971. [CrossRef]
57. Hill, E.L. Evaluating the Theory of Executive Dysfunction in Autism. Dev. Rev. 2004,24, 189–233. [CrossRef]
58.
Russell, J. How Executive Disorders Can Bring About an Inadequate “Theory of Mind”. In Autism as an Executive Disorder; Oxford
University Press: New York, NY, USA, 1997; pp. 256–304. ISBN 9780198523499.
59.
Perone, S.; Almy, B.; Zelazo, P.D. Toward an Understanding of the Neural Basis of Executive Function Development. In The
Neurobiology of Brain and Behavioral Development; Academic Press: Cambridge, MA, USA, 2018; pp. 291–314.
60.
Zelazo, P.D.; Carlson, S.M. Hot and Cool Executive Function in Childhood and Adolescence: Development and Plasticity. Child.
Dev. Perspect. 2012,6, 354–360. [CrossRef]
61.
Lewis, M.D.; Todd, R.M. The Self-Regulating Brain: Cortical-Subcortical Feedback and the Development of Intelligent Action.
Cogn. Dev. 2007,22, 406–430. [CrossRef]
62.
Fong, V.C.; Iarocci, G. The Role of Executive Functioning in Predicting Social Competence in Children with and without Autism
Spectrum Disorder. Autism Res. 2020,13, 1856–1866. [CrossRef]
63.
Leung, R.C.; Vogan, V.M.; Powell, T.L.; Anagnostou, E.; Taylor, M.J. The Role of Executive Functions in Social Impairment in
Autism Spectrum Disorder. Child. Neuropsychol. 2016,22, 336–344. [CrossRef] [PubMed]
64.
Gilotty, L.; Kenworthy, L.; Sirian, L.; Black, D.O.; Wagner, A.E. Adaptive Skills and Executive Function in Autism Spectrum
Disorders. Child. Neuropsychol. 2002,8, 241–248. [CrossRef] [PubMed]
65.
Pugliese, C.E.; Anthony, L.; Strang, J.F.; Dudley, K.; Wallace, G.L.; Kenworthy, L. Increasing Adaptive Behavior Skill Deficits
from Childhood to Adolescence in Autism Spectrum Disorder: Role of Executive Function. J. Autism Dev. Disord.
2015
,45,
1579–1587. [CrossRef]
66.
Cascia, J.; Barr, J.J. Associations among Vocabulary, Executive Function Skills and Empathy in Individuals with Autism Spectrum
Disorder. J. Appl. Res. Intellect. Disabil. 2017,30, 627–637. [CrossRef]
67.
Kerns, K.A.; Macoun, S.; MacSween, J.; Pei, J.; Hutchison, M. Attention and Working Memory Training: A Feasibility Study in
Children with Neurodevelopmental Disorders. Appl. Neuropsychol. Child. 2017,6, 120–137. [CrossRef]
68.
De Vries, M.; Prins, P.J.M.; Schmand, B.A.; Geurts, H.M. Working Memory and Cognitive Flexibility-Training for Children with
an Autism Spectrum Disorder: A Randomized Controlled Trial. J. Child. Psychol. Psychiatry 2015,56, 566–576. [CrossRef]
69.
Sohlberg, M.M.; Mateer, C.A. Cognitive Rehabilitation: An Integrative Neuropsychological Approach; Guilford Press: New York, NY,
USA, 2001; ISBN 9781572306134.
70.
Takeuchi, H.; Sekiguchi, A.; Taki, Y.; Yokoyama, S.; Yomogida, Y.; Komuro, N.; Yamanouchi, T.; Suzuki, S.; Kawashima, R. Training
of Working Memory Impacts Structural Connectivity. J. Neurosci. 2010,30, 3297–3303. [CrossRef]
71.
Diamond, A.; Ling, D.S. Conclusions about Interventions, Programs, and Approaches for Improving Executive Functions That
Appear Justified and Those That, Despite Much Hype, Do Not. Dev. Cogn. Neurosci. 2016,18, 34–48. [CrossRef]
72.
Au, J.; Sheehan, E.; Tsai, N.; Duncan, G.J.; Buschkuehl, M.; Jaeggi, S.M. Improving Fluid Intelligence with Training on Working
Memory: A Meta-Analysis. Psychon. Bull. Rev. 2015,22, 366–377. [CrossRef]
73.
Melby-Lervåg, M.; Hulme, C. Is Working Memory Training Effective? A Meta-Analytic Review. Dev. Psychol.
2013
,49,
270–291. [CrossRef]
Brain Sci. 2021,11, 1280 24 of 26
74.
Karbach, J.; Verhaeghen, P. Making Working Memory Work: A Meta-Analysis of Executive-Control and Working Memory
Training in Older Adults. Psychol. Sci. 2014,25, 2027–2037. [CrossRef]
75.
Schwaighofer, M.; Fischer, F.; Bühner, M. Does Working Memory Training Transfer? A Meta-Analysis Including Training
Conditions as Moderators. Educ. Psychol. 2015,50, 138–166. [CrossRef]
76.
Melby-Lervåg, M.; Redick, T.S.; Hulme, C. Working Memory Training Does Not Improve Performance on Measures of Intelligence
or Other Measures of “Far Transfer”: Evidence from a Meta-Analytic Review. Perspect. Psychol. Sci.
2016
,11, 512–534. [CrossRef]
77.
Schubert, T.; Strobach, T.; Karbach, J. New Directions in Cognitive Training: On Methods, Transfer, and Application. Psychol. Res.
2014,78, 749–755. [CrossRef]
78.
Grynszpan, O.; Weiss, P.L.T.; Perez-Diaz, F.; Gal, E. Innovative Technology-Based Interventions for Autism Spectrum Disorders:
A Meta-Analysis. Autism Int. J. Res. Pract. 2014,18, 346–361. [CrossRef]
79.
Kapp, K. The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education; Pfeiffer: San
Francisco, CA, USA, 2012; ISBN 978-1-118-09634-5.
80.
Lin, C.-S.; Chang, S.-H.; Liou, W.-Y.; Tsai, Y.-S. The Development of a Multimedia Online Language Assessment Tool for Young
Children with Autism. Res. Dev. Disabil. 2013,34, 3553–3565. [CrossRef]
81.
Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.;
Moher, D. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care
Interventions: Explanation and Elaboration. PLoS Med. 2009,6, e1000100. [CrossRef]
82.
Methley, A.M.; Campbell, S.; Chew-Graham, C.; McNally, R.; Cheraghi-Sohi, S. PICO, PICOS and SPIDER: A Comparison
Study of Specificity and Sensitivity in Three Search Tools for Qualitative Systematic Reviews. BMC Health Serv. Res.
2014
,14,
579. [CrossRef]
83.
Sterne, J.A.; Hernán, M.A.; Reeves, B.C.; Savovi´c, J.; Berkman, N.D.; Viswanathan, M.; Henry, D.; Altman, D.G.; Ansari, M.T.;
Boutron, I.; et al. ROBINS-I: A Tool for Assessing Risk of Bias in Non-Randomised Studies of Interventions. BMJ
2016
,355,
i4919. [CrossRef]
84.
Sterne, J.A.C.; Savovi´c, J.; Page, M.J.; Elbers, R.G.; Blencowe, N.S.; Boutron, I.; Cates, C.J.; Cheng, H.-Y.; Corbett, M.S.; Eldridge,
S.M.; et al. RoB 2: A Revised Tool for Assessing Risk of Bias in Randomised Trials. BMJ 2019,366, l4898. [CrossRef]
85.
Hilton, C.L.; Cumpata, K.; Klohr, C.; Gaetke, S.; Artner, A.; Johnson, H.; Dobbs, S. Effects of Exergaming on Executive Function
and Motor Skills in Children with Autism Spectrum Disorder: A Pilot Study. Am. J. Occup. Ther. 2014,68, 57–65. [CrossRef]
86.
Hilton, C.; Attal, A.; Best, J.; Reistetter, T.; Trapani, P.; Collins, D. Exergaming to Improve Physical and Mental Fitness in Children
and Adolescents with Autism Spectrum Disorders: Pilot Study. Int. J. Sports Exerc. Med. 2015,1, 017. [CrossRef]
87.
Chen, M.-T.; Chang, Y.-P.; Marraccini, M.E.; Cho, M.-C.; Guo, N.-W. Comprehensive Attention Training System (CATS): A
Computerized Executive-Functioning Training for School-Aged Children with Autism Spectrum Disorder. Int. J. Dev. Disabil.
2020, 1–10. [CrossRef]
88.
Yerys, B.E.; Bertollo, J.R.; Kenworthy, L.; Dawson, G.; Marco, E.J.; Schultz, R.T.; Sikich, L. Brief Report: Pilot Study of a Novel
Interactive Digital Treatment to Improve Cognitive Control in Children with Autism Spectrum Disorder and Co-Occurring
ADHD Symptoms. J. Autism Dev. Disord. 2019,49, 1727–1737. [CrossRef]
89.
Phung, J.N.; Goldberg, W.A. Promoting Executive Functioning in Children with Autism Spectrum Disorder Through Mixed
Martial Arts Training. J. Autism Dev. Disord. 2019,49, 3669–3684. [CrossRef]
90.
Kenworthy, L.; Anthony, L.G.; Naiman, D.Q.; Cannon, L.; Wills, M.C.; Luong-Tran, C.; Werner, M.A.; Alexander, K.C.; Strang, J.;
Bal, E.; et al. Randomized Controlled Effectiveness Trial of Executive Function Intervention for Children on the Autism Spectrum.
J. Child. Psychol. Psychiatry 2014,55, 374–383. [CrossRef]
91.
Fisher, N.; Happé, F. A Training Study of Theory of Mind and Executive Function in Children with Autistic Spectrum Disorders.
J. Autism Dev. Disord. 2005,35, 757–771. [CrossRef]
92.
Anderson-Hanley, C.; Tureck, K.; Schneiderman, R.L. Autism and Exergaming: Effects on Repetitive Behaviors and Cognition.
Psychol. Res. Behav. Manag. 2011,4, 129–137. [CrossRef]
93.
Ridderinkhof, A.; de Bruin, E.I.; van den Driesschen, S.; Bögels, S.M. Attention in Children with Autism Spectrum Disorder and
the Effects of a Mindfulness-Based Program. J. Atten. Disord. 2020,24, 681–692. [CrossRef]
94.
Rafiei Milajerdi, H.; Sheikh, M.; Najafabadi, M.G.; Saghaei, B.; Naghdi, N.; Dewey, D. The Effects of Physical Activity and
Exergaming on Motor Skills and Executive Functions in Children with Autism Spectrum Disorder. Games Health J.
2021
,10,
33–42. [CrossRef]
95.
Macoun, S.J.; Schneider, I.; Bedir, B.; Sheehan, J.; Sung, A. Pilot Study of an Attention and Executive Function Cognitive
Intervention in Children with Autism Spectrum Disorders. J. Autism Dev. Disord. 2020,51, 2600–2610. [CrossRef] [PubMed]
96.
Saniee, S.; Pouretemad, H.R.; Zardkhaneh, S.A. Developing Set-Shifting Improvement Tasks (SSIT) for Children with High-
Functioning Autism. J. Intellect. Disabil. Res. 2019,63, 1207–1220. [CrossRef] [PubMed]
97.
Hajri, M.; Abbes, Z.; Ben Yahia, H.; Boudali, M.; Bouden, A.; Mrabet, A.; Amado, I. Cognitive Remediation Therapy in Autism
Spectrum Disorder: Tunisian Experience. Tunis. Med. 2019,97, 795–801. [PubMed]
98.
Hajri, M.; Abbes, Z.; Ben Yahia, H.; Boudali, M.; Bouden, A.; Mrabet, A.; Amado, I. Remédiation cognitive et fonctionnement
scolaire chez les enfants avec trouble du spectre autistique. Neuropsychiatr. Enfance Adolesc. 2018,67, 19–24. [CrossRef]
99.
Hajri, M.; Abbes, Z.; Ben Yahia, H.; Boudali, M.; Halayem, S.; Othman, S.; Bouden, A. Effects of Cognitive Remediation Therapy
on Mental Flexibility in Children with Autism Spectrum Disorder. Eur. Child. Adolesc. Psychiatry 2015,24, S249.
Brain Sci. 2021,11, 1280 25 of 26
100.
Juliano, A.C.; Alexander, A.O.; DeLuca, J.; Genova, H. Feasibility of a School-Based Mindfulness Program for Improving
Inhibitory Skills in Children with Autism Spectrum Disorder. Res. Dev. Disabil. 2020,101, 103641. [CrossRef]
101.
Farrelly, K.; Mace, S. An Intervention to Enhance Cognitive Flexibility in Boys Aged 11–13 with Autism Spectrum Disorder.
Surrey Undergrad. Res. J. 2015,1, 1–19.
102.
Gioia, G.; Isquith, P.; Guy, S.C.; Kenworthy, L. Behavior Rating Inventory of Executive Function; Psychological Assessment Resources:
Lutz, FL, USA, 2000.
103.
Heaton, R.; Chelune, C.; Talley, J.; Kay, G.; Curtiss, G. Wisconsin Card Sorting Test Manual-Revised and Expanded; Psychological
Assessment Resources: Lutz, FL, USA, 1993.
104. Stroop, J.R. Studies of Interference in Serial Verbal Reactions. J. Exp. Psychol. 1935,18, 643–662. [CrossRef]
105. Partington, J.E.; Leiter, R.G. Partington’s Pathways Test. Psychol. Serv. Cent. J. 1949,1, 11–20.
106. Wykes, T.; Reeder, C.; Corner, J.; Williams, C.; Everitt, B. The Effects of Neurocognitive Remediation on Executive Processing in
Patients with Schizophrenia. Schizophr. Bull. 1999,25, 291–307. [CrossRef]
107.
Hamza, M.; Abbès, Z.; Yahyia, H.B.; Fakhfekh, R.; Amado, I.; Bouden, A. The Cognitive Remediation Therapy Program among
Children with ADHD: Tunisian Experience. Tunis. Med. 2018,96, 30–35.
108.
Azouz, O.B.; Dellagi, L.; Kebir, O.; Johnson, I.; Amado, I.; Tabbane, K. The Tunisian Cognitive Battery for Patients with
Schizophrenia. Tunis. Med. 2009,87, 674–679.
109.
Shebani, M.F.A.; Van De Vijver, F.J.R.; Poortinga, Y.H. A Strict Test of the Phonological Loop Hypothesis with Libyan Data. Mem.
Cognit. 2005,33, 196–202. [CrossRef]
110.
Heaton, R.K.; Gladsjo, J.A.; Palmer, B.W.; Kuck, J.; Marcotte, T.D.; Jeste, D.V. Stability and Course of Neuropsychological Deficits
in Schizophrenia. Arch. Gen. Psychiatry 2001,58, 24–32. [CrossRef] [PubMed]
111.
Gladsjo, J.A.; Schuman, C.C.; Evans, J.D.; Peavy, G.M.; Miller, S.W.; Heaton, R.K. Norms for Letter and Category Fluency:
Demographic Corrections for Age, Education, and Ethnicity. Assessment 1999,6, 147–178. [CrossRef] [PubMed]
112.
Wang, K.; Song, L.-L.; Cheung, E.F.C.; Lui, S.S.Y.; Shum, D.H.K.; Chan, R.C.K. Bipolar Disorder and Schizophrenia Share a Similar
Deficit in Semantic Inhibition: A Meta-Analysis Based on Hayling Sentence Completion Test Performance. Neuro-Psychopharmacol.
Biol. Psychiatry 2013,46, 153–160. [CrossRef] [PubMed]
113.
Rueda, M.R.; Fan, J.; McCandliss, B.D.; Halparin, J.D.; Gruber, D.B.; Lercari, L.P.; Posner, M.I. Development of Attentional
Networks in Childhood. Neuropsychologia 2004,42, 1029–1040. [CrossRef] [PubMed]
114. Constantino, J.; Gruber, C. Social Responsiveness Scale: SRS 2; Western Psychological Services: Torrance, CA, USA, 2012.
115.
Greenberg, L.; Kindschi, C.; Dupuy, T.; Corman, C. Test of Variables of Attention; Psychological Assessment Resources: Lutz, FL,
USA, 1996.
116.
Lezak, M.D.; Howieson, D.B.; Loring, D.W.; Hannay, H.J.; Fischer, J.S. Neuropsychological Assessment, 4th ed.; Oxford University
Press: New York, NY, USA, 2004; ISBN 9780195111217.
117.
Romine, C.B.; Lee, D.; Wolfe, M.E.; Homack, S.; George, C.; Riccio, C.A. Wisconsin Card Sorting Test with Children: A Meta-
Analytic Study of Sensitivity and Specificity. Arch. Clin. Neuropsychol. 2004,19, 1027–1041. [CrossRef]
118. Reitan, R.M. The Relation of the Trail Making Test to Organic Brain Damage. J. Consult. Psychol. 1955,19, 393–394. [CrossRef]
119.
Gresham, F.; Elliott, S.N. Social Skills Improvement System (SSIS) Rating Scales; Pearson Education Inc.: San Antonio, TX, USA, 2007.
120.
Hartman, C.A.; Luteijn, E.; Serra, M.; Minderaa, R. Refinement of the Children’s Social Behavior Questionnaire (CSBQ): An
Instrument That Describes the Diverse Problems Seen in Milder Forms of PDD. J. Autism Dev. Disord.
2006
,36, 325–342. [CrossRef]
121.
DuPaul, G.J.; Power, T.J.; Anastopoulos, A.D.; Reid, R. ADHD Rating Scale-IV: Checklists, Norms, and Clinical Interpretation; Guilford
Press: New York, NY, USA, 1998; ISBN 9781572304239.
122.
Epstein, M.H. The Behavioral and Emotional Rating Scale: A Strength-Based Approach to Assessment. Diagnostique
2000
,25,
249–256. [CrossRef]
123. Conners, K.C., III. Conners 3rd Edition Manual; Multi-Health Systems: New York, NY, USA, 2008.
124. Gilliam, J. Gilliam Autism Rating Scale, 2nd ed.; Pro-Ed: Austin, TX, USA, 2006.
125.
Oosterlaan, J.; Scheres, A.; Antrop, I.; Roeyers, H.; Sergeant, J.A. Handleiding Bij de Vragenlijst Voor Gedragsproblemen Bij Kinderen
VvGK; Swets Test Publishers: Amsterdam, The Netherlands, 2000.
126. Gilliam, J.E. GARS: Gilliam Autism Rating Scale; Pro-Ed: Austin, TX, USA, 1995.
127.
Hosp, M.K.; Hosp, J.L.; Howell, K.W. The ABCs of CBM: A Practical Guide to Curriculum-Based Measurement; Practical Intervention
in the Schools Series; Guilford Press: New York, NY, USA, 2007.
128.
Shinn, M.; Michelle, M. AIMSweb Training Workbook Administration and Scoring of Reading Curriculum-Based Measurement (R-CBM)
for Use in General Outcome Measurement; Edformation: Eden Prairie, MN, USA, 2002.
129.
Woodcock, R.W.; McGrew, K.S.; Mather, N. Woodcock-Johnson III Normative Update (NU) Tests of Achievement, Forms A and B;
Riverside Publishing: Rolling Meadows, IL, USA, 2007.
130.
Zwaigenbaum, L.; Bauman, M.L.; Choueiri, R.; Kasari, C.; Carter, A.; Granpeesheh, D.; Mailloux, Z.; Smith Roley, S.; Wagner, S.;
Fein, D.; et al. Early Intervention for Children with Autism Spectrum Disorder Under 3 Years of Age: Recommendations for
Practice and Research. Pediatrics 2015,136, 60–81. [CrossRef] [PubMed]
131.
Landa, R.J. Efficacy of Early Interventions for Infants and Young Children with, and at Risk for, Autism Spectrum Disorders.
Int. Rev. Psychiatry 2018,30, 25–39. [CrossRef] [PubMed]
Brain Sci. 2021,11, 1280 26 of 26
132.
Bierman, K.L.; Torres, M. Promoting the Development of Executive Functions through Early Education and Prevention Programs.
In Executive Function in Preschool-Age Children: Integrating Measurement, Neurodevelopment and Translational Research; Griffin,
J.A., McCardle, P., Freund, L.S., Eds.; American Psychological Association: Washington, DC, USA, 2016; pp. 299–326. ISBN
9781433818264.
133.
Traverso, L.; Viterbori, P.; Usai, M.C. Effectiveness of an Executive Function Training in Italian Preschool Educational Services
and Far Transfer Effects to Pre-Academic Skills. Front. Psychol. 2019,10, 2053. [CrossRef] [PubMed]
134.
Baron-Cohen, S.; Golan, O.; Ashwin, E. Educational Cognitive Neuroscience: Designing Autism-Friendly Methods to Teach
Emotion Recognition. In Neuroscience in Education: The Good, the Bad and the Ugly; Oxford University Press: Oxford, UK,
2012. [CrossRef]
135.
Swettenham, J. Can Children with Autism Be Taught to Understand False Belief Using Computers? J. Child. Psychol. Psychiatry
1996,37, 157–165. [CrossRef] [PubMed]
136. Moore, D.; Taylor, J. Interactive Multimedia Systems for Students with Autism. J. Educ. Media 2000,25, 169–177. [CrossRef]
137.
Murray, D. Autism and Information Technology: Therapy with Computers. In Autism and Learning; Routledge: London, UK,
1997; ISBN 185346421X.
138.
Fletcher-Watson, S.; Pain, H.; Hammond, S.; Humphry, A.; McConachie, H. Designing for Young Children with Autism Spectrum
Disorder: A Case Study of an IPad App. Int. J. Child-Comput. Interact. 2016,7, 1–14. [CrossRef]
139.
Barajas, A.O.; Al Osman, H.; Shirmohammadi, S. A Serious Game for Children with Autism Spectrum Disorder as a Tool for Play
Therapy. In Proceedings of the 2017 IEEE 5th International Conference on Serious Games and Applications for Health, Perth,
Australia, 2–4 April 2017; pp. 1–7.
140. Engle, R.W. Working Memory and Executive Attention: A Revisit. Perspect. Psychol. Sci. 2018,13, 190–193. [CrossRef]
141.
Seltzer, M.M.; Shattuck, P.; Abbeduto, L.; Greenberg, J.S. Trajectory of Development in Adolescents and Adults with Autism.
Ment. Retard. Dev. Disabil. Res. Rev. 2004,10, 234–247. [CrossRef] [PubMed]
142.
Wetherby, A.M.; Guthrie, W.; Woods, J.; Schatschneider, C.; Holland, R.D.; Morgan, L.; Lord, C. Parent-Implemented Social
Intervention for Toddlers with Autism: An RCT. Pediatrics 2014,134, 1084–1093. [CrossRef]
143.
Green, J.; Charman, T.; Pickles, A.; Wan, M.W.; Elsabbagh, M.; Slonims, V.; Taylor, C.; McNally, J.; Booth, R.; Gliga, T.; et al.
Parent-Mediated Intervention versus No Intervention for Infants at High Risk of Autism: A Parallel, Single-Blind, Randomised
Trial. Lancet Psychiatry 2015,2, 133–140. [CrossRef]
144.
Oono, I.P.; Honey, E.J.; McConachie, H. Parent-Mediated Early Intervention for Young Children with Autism Spectrum Disorders
(ASD). Evid. Based Child. Health Cochrane Rev. J. 2013,8, 2380–2479. [CrossRef]
145.
Rispoli, K.M.; Malcolm, A.L.; Nathanson, E.W.; Mathes, N.E. Feasibility of an Emotion Regulation Intervention for Young
Children with Autism Spectrum Disorder: A Brief Report. Res. Autism Spectr. Disord. 2019,67, 101420. [CrossRef]
146.
Fuller, E.A.; Oliver, K.; Vejnoska, S.F.; Rogers, S.J. The Effects of the Early Start Denver Model for Children with Autism Spectrum
Disorder: A Meta-Analysis. Brain Sci. 2020,10, 368. [CrossRef]
147.
Bradshaw, J.; Koegel, L.K.; Koegel, R.L. Improving Functional Language and Social Motivation with a Parent-Mediated Interven-
tion for Toddlers with Autism Spectrum Disorder. J. Autism Dev. Disord. 2017,47, 2443–2458. [CrossRef]
148.
Sandbank, M.; Bottema-Beutel, K.; Crowley, S.; Cassidy, M.; Dunham, K.; Feldman, J.I.; Crank, J.; Albarran, S.A.; Raj, S.; Mahbub,
P.; et al. Project AIM: Autism Intervention Meta-Analysis for Studies of Young Children. Psychol. Bull.
2020
,146, 1–29. [CrossRef]
149.
Perry, A.; Blacklock, K.; Dunn Geier, J. The Relative Importance of Age and IQ as Predictors of Outcomes in Intensive Behavioral
Intervention. Res. Autism Spectr. Disord. 2013,7, 1142–1150. [CrossRef]
150.
Fanelli, D. Negative Results Are Disappearing from Most Disciplines and Countries. Scientometrics
2012
,90, 891–904. [CrossRef]
Available via license: CC BY 4.0
Content may be subject to copyright.
