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Graduate Student Journal of Psychology
2023, Vol. 20

Copyright 2023 by the Department of Counseling and Clinical Psychology
Teachers College, Columbia University

Autism Spectrum Disorder and Face Identity 
Recognition Deficit across Ages

 Autism spectrum disorder, or ASD, is a neurode-
velopmental disorder with symptoms such as difficul-
ties in social communication and social interaction, 
and restricted patterns in behaviors, interests, and 
activities (APA, 2022). According to the Centers for 
Disease Control and Prevention, around one in 44 
children has a diagnosis of ASD, and the prevalence 
has been continuously increasing (CDC, 2022). When 
the concept of autism was first brought up in 1908 by 
Swiss psychiatrist Eugen Bleuler, it was considered a 
cognition and behavior style that occurs in patients 
with schizophrenia (Evans, 2013), and was recognized 
as childhood-type schizophrenia in the second version 
of Diagnostic and Statistical Manual of Mental Dis-
orders, DSM-II (Kendhari et al., 2016). In DSM-III, 
published in 1980, autism was officially recognized and 
introduced as an isolated disorder of pervasive develop-
mental disorders. The diagnostic criteria were further 
specified in the later revised version of DSM-III, DSM-
III-R (Volkmar et al., 1988). In 1994, the fourth version 
of DSM specified autism as autistic disorder, Asperg-
er’s disorder, and pervasive developmental disorder, 
not otherwise specified (PDD-NOS), which were then 
all classified as autism spectrum disorders, ASD, in 
the current version of DSM (Harker & Stone, 2014).
 Although diagnostic labels and criteria change 
drastically for ASD, the core deficit presented in the 
disorder remains the same. Impaired social cognition, 
debilitated communication skills, and ritualized be-
havior patterns are core deficits commonly occurring 
in ASD (Faras et al., 2010). These deficits are included 
in the diagnostic manual for ASD, for their potential-
ly discriminative characteristics from other disorders, 
and can significantly impact daily functioning. Many 

aspects of manifestation of these deficits, including 
facial emotion recognition deficit (Uljarevic & Ham-
ilton, 2013; Lozier et al., 2014; Keung, 2022), delay in 
language development (Mitchell et al., 2006; Landa & 
Mayer, 206; Eigsti et al., 2010), reading comprehen-
sion deficit (Norbury & Nation, 2010; Ricketts, 2013) 
and other impairment have been extensively studied. 
They have shown different underlying mechanisms 
and developmental trajectories for these shortfalls, 
but all contributed to the dysfunction of the disorder. 
 Face perception, which is an essential part of social 
interaction and communication, is an innate ability 
that occurs as early as 9 minutes after birth. It is de-
fined as the ability to recognize, process, and integrate 
information from faces, which include direction of 
gazing, expression, identity, hostility, etc. (Ward & Ber-
nier, 2013; Palermo & Rhodes, 2006). Disruption in 
the systems, or unsuccessful face processing, can elicit 
prominent changes in social behaviors in some psychi-
atric disorders including ASD (Lopatina et al., 2018).
The social functioning deficit in people with diag-
nosed ASD may partially be explained by the impair-
ment in face perception, which manifested as un-
successful extraction of identification, emotion, and 
psychological information from faces during inter-
personal interaction (Todorv et al., 2012). The man-
ifestation of the deficit in face perception in the early 
stage of the face perception in ASD is the tendency of 
avoiding eye contact. This is also an early indication of 
children presenting symptoms of ASD, if they present 
an aversion to direct eye contact from caregivers and 
others. The avoidance of eye contact is also directly 
linked to socioemotional dysfunction in ASD (Klie-
mann et al., 2010). Two general models were proposed 

The purpose of this review was to assess the face identity recognition deficit and the developmental differ-
ence that manifested in autism spectrum disorder (ASD) compared to their typically developing (TD) peers. 
Based on the meta-analysis using a random-effect model of 94 studies, with 144 effect sizes, for both adult 
and pediatric subjects with simultaneous and delayed face identity recognition paradigms, the underper-
formance in ASD was significant and persistent across ages. In addition, a higher level of deficit was found in 
adult ASD when performing simultaneous face-matching tasks while other subgroups showed homoge-
nous effect sizes. This suggested a dissociation between the difficulties of the two mechanisms of face recogni-
tion: face perception (perceiving identity from the face with minimal memory load required) and face memory 
(recall of identity from the face that requires memory load), which was only shown in adults but not in chil-
dren. The result indicated the possibility of using face identity recognition deficit as a diagnostic trait for ASD.

Ye Song
Teachers College, Columbia University, Department of Clinical and Counseling Psychology



100

data on the face identity process. After duplicates were 
removed, full-text articles were screened for eligibility.
Inclusion Criteria
 The inclusion for the final meta-analysis: a) is an 
empirical study published in English. b) included an 
ASD group with diagnosed ASD, autism, Asperger, or 
PDD-NOS. c) include a typically developing, chrono-
logical age-matched, comparison group. d) include 
data on participants’ age. e) include a homogenous 
adult or pediatric group of participants or have separate 
data for different age groups (Categorization of adult 
and pediatric groups used an age cutoff of 18 years of 
ages). f) used static images with real human faces that 
are not the participants’ own faces. g)  include data on 
the types of tests performed, face identity recognition 
tasks or face identity discrimination tasks. h) reported 
accuracy data of participants’ performance on the tasks. 
Data Extraction
 Data was extracted from every paper that satisfied 
the inclusion criteria, and all data were input onto Mi-
crosoft Excel sheets. Results for studies with adult or 
pediatric participants were recorded separately, but the 
categories of data extracted were the same as follows: a) 
Authors and year the article was published. b) demo-
graphic data of ASD and TD control groups, includ-
ing sample size, gender distribution, mean age, and 
the standard deviation of age, Intelligence quotient, 
the standard deviation of IQ, and the diagnostic tool 
implemented. For studies that were carried out with 
multiple groups of participants, data were recorded 
independently and classified in accordance with their 
characteristics. c) Type of task implemented on face 
identity recognition or discrimination ability. The 
tasks implemented for each study were categorized 
into simultaneous or delayed categories. The simulta-
neous face identity recognition test, which is also cat-
egorized as the simultaneous face identity discrimina-
tion test, was a simultaneous match-to-sample test, in 
which, the target stimuli and test stimuli were present-
ed simultaneously. This type of task was adopted in 
the widely used face identification task, Benton facial 
recognition test. The simultaneous match-to-sample 
task did not require memory load to perform an accu-
rate matching of faces (Duchaine & Weidenfeld, 2002; 
Duchaine & Nakayama, 2004). On the other hand, a 
delayed match-to-sample task, which in Weigelt et al. 
(2013) and Griffin et al. (2021) was also identified as 
face discrimination, did require memory load and the 

amount of memory load required was directly related 
to the length of time delayed between the presentation 
of the stimulus (Anderson & Colombo, 2019). There-
fore, the rationale behind the categorization was the 
requirement of memory load. The simultaneous task 
demanded no memory load and the delayed task re-
quired at least some memory load to perform. Studies 
that used both types of tests were recorded individual-
ly in each section. d) Effect size of the difference in ac-
curacy performance between ASD and TD groups. If 
effect sizes were not provided, statistical data required 
to calculate the standardized mean difference were ex-
tracted. Sample sizes, means, and standard deviations 
of behavioral results for both groups were extracted 
for calculating the effect sizes. If these data were not 
provided, inferential statistics of comparison between 
groups were collected to estimate effect size. For studies 
that performed multiple experiments, the data for each 
experiment was recorded separately based on their par-
ticipants’ characteristics or the type of task performed.
 For studies that reported demographic informa-
tion and performance results individually for each 
participant, the mean and standard deviation data 
were calculated manually for pediatric and adult par-
ticipants groups. In some cases where neither the effect 
size nor specific data of results were textually available, 
but graphic representations of data were presented, the 
online application, WebPlotDigitizer was used to ex-
tract the necessary data. Numerous studies had shown 
consistent validity and reliability of numerical results 
extracted from graphic inputs using WebPlotDigi-
tizer (Drevon et al., 2016; Aydin & Yassikaya, 2022).
 The calculation of standardized mean differenc-
es was done manually by inputting equations with 
Excel functions. Most of the studies provided effect 
sizes in Cohen’s d value as the standardized effect 
size. It has been noticed that Cohen’s d values tend 
to overestimate the actual effect sizes when sample 
sizes are small. On the other hand, Hedges’ g removes 
the bias with a correction factor. (Lin & Aloe, 2021; 
Durlak, 2009) Most studies included in the current 
meta-analysis did not have large sample sizes. There-
fore, it might be prone to an upward bias if using Co-
hen’s d for calculating the effect sizes. On the other 
hand, Hedges’s g, which can easily be transformed 
from Cohen’s d, was more reliable in the current me-
ta-analysis. Therefore, for other studies that require 
manual calculation, Cohen’s d values were first calcu

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ASD AND FACE IDENTITY RECOGNITION DEFICIT

lated and then transformed into Hedge’s g together. 
 The formula for calculating the Cohen’s d was 
(Cohen, 1998; Lipsey, 2001):

where     and      were mean values and the pooled stan-
dard deviation, σpooled  is calculated as:

 
where N1 and N2 are sample sizes, and σ1 and σ2 
are standard deviations for each group. For stud-
ies that did not provide sample size, mean, or stan-
dard deviation, the equation used to convert val-
ues from f-test (3) or t-test (4) value to Cohen’s d 
were (Thalheimer & Cook, 2002; Lipsey, 2001): 

After all Cohen’s d were calculated, the calculated 
effect sizes were then converted into Hedges’ g value, 
along with the provided Cohen’s d effect sizes. The 
conversion formula used was (Hedges, 1981; Boren-
stein et al., 2011):                                                               

Where df = N1 + N2 - 2. The standard errors of Hedg-
es’ g (6; Hedges, 1981; NIST, 2018) were calculated for 
further analysis: 

Risk of Bias Evaluation and Quality Assessment
 An evaluation matrix of the studies’ design and 
methodology was adapted from previous meta-analyses 
(Griffin et al., 2021; Yeung, 2022; Tang et al., 2015). For 
every study, their evaluations were based on the quality 
of  participants’ selection procedures and the charac-
teristics of the instruments implemented. For assessing 
the reliability of the sample subjects representing the 
intended target population, the demographic charac-
teristics were assessed and compared to ensure the re-
sult performance data extracted were a representation 
of the group difference with minimal mediation from 
other properties. Whether data on participants’ age, 
gender, and IQ were provided for both ASD and TD 
groups, and whether these characteristics are matched 
to control for effects that can potentially bias the result, 
were significant determinants of the studies’ quality. 
 On the other hand, aside from many established 
tests targeting face identity recognition measurement: 

CFMT (Cambridge Face Memory Test), Benton Facial 
Recognition Test, NEPSY-II (Developmental Neuro-
psychological Assessment, Second Edition), face sub-
test, etc., many studies developed their own testing 
procedures evaluating the performance. Within estab-
lished tests, the materials used and courses of action 
varied largely from each other. There was not a gen-
eral consensus on which test was best in reliability and 
validity in rating the identity recognition ability spec-
ified in the ASD population (Duchaine & Weidenfeld, 
2002; Albonico et al., 2017). However, some materi-
al characteristics were preferred that tend to be more 
consistent in conveying reliable results. Compared to 
black-and-white, or grayscale photos of faces, colored 
photos had been shown to carry more information 
that was not related to faces. For instance, when pho-
tos of faces were presented in color, chunking areas of 
faces according to different tones or shades became 
possible. Instead of remembering and recognizing a 
person’s face from their facial features, the mechanism 
then became remembering patterns of color segments 
(Bindemann & Burton, 2009; Yip & Sinha, 2010; Bo-
bak et al., 2019). Similarly, photos of full faces, includ-
ing hair and clothing, provided excessive information 
that was not related to facial features when testing the 
identification ability. More significantly, when sub-
jects were not able to extract sufficient information 
from facial features alone, they were more likely to rely 
on external information, hairstyle, brow shapes, etc 
(Duchaine & Weidenfeld, 2003). In addition, different 
facial expressions also were shown to impact identity 
recognition (Chen et al., 2015). With the ASD pop-
ulation, whose recognition of facial affect is impaired, 
the ambivalent effect can lead to biased results. There-
fore, for achieving consistent reliable results, the meth-
od implemented with grayscale photos of inner face 
features alone with neutral expression was preferred. 
The evaluation is done in rating format. Each criteri-
on is marked as one point for each study on whether 
it provides the necessary information for each criteri-
on. The study’s quality is the sum of scores on each 
criterion and the maximum quality score is 12 points.
Statistical Analysis
 With the calculated Hedges’s g and standard error 
of Hedges’s g value, the data were input into SPSS v. 
28 for meta-analysis. Analysis was performed using a 
random effect model with the Hunter-Schmidt meth-
od (Hunter & Schmidt, 1990). Fixed-effect model 



102

hypothesized a universal effect size for all studies and 
proposed a similar methodology across studies in the 
meta-analysis (Field & Gillett, 2010). On the other 
hand, the random-effect model assumed that every 
study estimated a different inherent relation and ap-
praised both between-study and within-study vari-
ability (Kock, 2009; Tufanaru, 2015). The Hunt-
er-Schmidt method is a method using a random-effect 
model and it was shown to produce the most accurate 
and reliable estimates when heterogeneity exists in 
effect sizes (Cornwell & Ladd, 1993; Field, 2001). In 
addition, forest plots and funnel plots were produced 
with SPSS plotting functions for meta-analysis. Forest 
plots provide a vivid visual representation of the overall 
effect of the meta-analysis and effect size of individual 
studies used to generate the results. The meta-analyses 
were performed in accordance with these procedures.
 First, an overall meta-analysis of every study was 
performed to estimate the difference in accuracy per-
formance on face identity recognition between ASD 
and TD populations. Random-effect meta-analy-
sis was performed with all data included and for-
est plots were produced. In addition, evaluation of 
heterogeneity and homogeneity were carried out to 
inspect the variability across studies. Furthermore, 
an assessment of publication bias was also imple-
mented to further specify and solidify the results.
 Then, a meta-analysis of studies within each age 
group and a comparison of results across ages were 
inspected. Similar procedures that were executed 
for evaluating the overall effect size were performed 
for the pediatric group and the adult group. The 
comparison between groups was assessed with an es-
timation of the homogeneity of the two groups as a 
subgroup analysis of the overall effect. Additional-
ly, subgroup analyses of methodology effects within 
each age group were performed. This analysis exam-
ines whether the two kinds of face perception, with 
and without memory load, show different perfor-
mance between ASD and TD at different age stages.
 On the other hand, a hypothesis by Weigelt et al. 
(2013) was tested. Weigelt et al. (2013) proposed that 
face identity recognition deficit in ASD was specific 
to face memory deficit, in which the higher demands 
in memory load would lead to worse performance 
in ASD. For tests that did not require face memo-
ry, the performance between ASD and TD should 
be the same. Even though Griffin et al. (2021) had 

shown results opposite to this hypothesis, Griffin et 
al. (2021) studies examined the difference between 
face identity recognition and face identity discrimi-
nation tasks. The divergence between these two tasks 
was not clearly defined in either the Weigelt et al. 
(2012) or Griffin et al. (2021) study. As mentioned 
in Tang et al. (2015), the definition of face discrimi-
nation was ambiguous. Therefore, a dichotomy clas-
sification was used to be more robust and specific. 
Evaluation of Publication Bias
 To evaluate potential publication bias, a funnel 
plot and Egger’s regression were used. A funnel plot is 
a visual representation of comparing the sizes of trials 
to their effect size. Usually, studies without publica-
tion biases would produce a plot that is symmetric and 
shaped like a funnel. If the resulting plot was signifi-
cantly asymmetric, this indicated a potential publica-
tion bias (Lee & Hotopf, 2012; Simmonds, 2015). In-
terpretation from graphics alone can be unreliable so 
egger’s test is also used.  Egger’s regression test is a test 
based on a linear regression model comparing the in-
tercept, which evaluates the asymmetricity of the fun-
nel plot. Egger’s test examines the hypothesis of zero 
linear intercept, which represents a symmetric funnel 
plot with no publication biases (Egger et al., 1997).

Results
Study Selection
 The literature selection and screening process was 
shown in the flow diagram, Figure 1. An initial data-
base and references search gave 7,432 results, includ-
ing 1,975 from PubMed, 5,345 from PsycINFO, and 
112 from Griffin et al. (2021) references list. With the 
PsycINFO filter, 299 articles that were not written 
in English and 1,151 articles that were not empirical 
research studies were removed. Then, a total of 5,602 
studies were eliminated because they did not include 
information on ASD or face processing. After remov-
ing 82 duplicate papers, 298 unique papers related to 
autism spectrum disorder and face processing were re-
viewed in full-text screening. 204 papers, in total, were 
eliminated based on inclusion and exclusion criteria: 
a) 15 articles were not empirical research papers. b) 30 
papers were not studying face processing in the ASD 
population or did not include participants with diag-
nosed ASD. c) 13 studies did not have a comparison 
group or did not compare to the typically developing 
population. d) Seven papers did not provide informa

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ASD AND FACE IDENTITY RECOGNITION DEFICIT

tion on participants’ age range, and e) 19 papers have 
a heterogenous age that include a mix of adult and pe-
diatric participants. f) Six studies did not use static real 
human faces, whereas three studies studied self-recog-
nition. i) 114 studies involved face processing in ASD 
but did not include behavioral results concerning their 
face identity recognition or discrimination ability. 
 A total of 94 studies satisfied all the inclusion cri-
terias and were included in current meta-analysis, with 
4,849 total number of individual participants, 2,351 
with ASD and 2,498 TD comparisons. The gener-
al characteristics of participants in the 94 studies are 
shown in Table 1. The overall average age of pediatric 
ASD participants was 10.99 (SD=2.51), and pediatric 
TD participants with mean age of 10.64 (2.93). The 
mean age between the ASD and TD groups did not 
differ significantly; t(144)=0.768, p=.444. The over-
all mean age of adult ASD participants was 28.48 
(SD=4.6) , ranging from 20.60 to 43.2; the mean age for 
adult TD subjects was 28.19 (SD=4.52), with a range 
of 21.6 to 44. The mean ages between the two groups 
did not differ significantly; t(58)=0.249, p=.8041.
 Studies were also categorized based on the test 
characteristics, either a delayed design or a simulta-
neous presentation design. The number of studies 
with different characteristics is shown in Table 2.
Overall Face Identity Recognition Ability
 First, the overall difference in face identity recogni-
tion was evaluated. Meta-analysis was performed with 
a total of 94 papers and 144 pairs of results of effect size 
between ASD and TD. All results were included to as-
sess the overall difference in facial identity recognition 
ability between the ASD and TD groups. Of the 144 ef-
fect sizes from studies, 17 reported a positive effect size, 
which indicates a comparatively higher performance 
in ASD than the TD control group. In addition, three 
studies reported an effect size of 0, which indicated an 
equal level of performance between the two subject 
groups. All other 124 results showed a lower level of per-
formance in ASD subjects than in TD control subjects.
 Figure 3 shows the forest plot representing the 
result of a random-effect meta-analysis on overall face 
Identity Recognition ability in ASD. The results show 
a large overall effect size, Hedges’s g = -.716, 95% CI 
[- .835, - .597], p<.0001. Indicating a significant overall 
deficit in ASD population on face identity recognition.
 On the other hand, the heterogeneity measures 
of all 144 effect sizes from the studies show a signif-

icant heterogeneity, τ2= .405, I2 = .803. The homo-
geneity test also confirmed the variances between the 
studies’ effect sizes, Q(143) = 731.35,  p = .00. These 
results show a large heterogeneity in effect sizes. In ad-
dition, the I2 result confirmed that 80.3% of varianc-
es can be attributed to the heterogeneity of studies. 
  The funnel plot in Figure 2 shows the studies are 
roughly symmetrical, which indicates no potential 
publication bias. In addition, Egger’s regression-based 
test also confirmed the absence of biases with an inter-
cept of 0.325, 95% CI [-0.151, 0.8], t = 1.348, p=.180.
Face Identity Recognition in Adult Samples
 A total of 39 studies from 30 papers, with statistics 
from total sample sizes of 1316 participants, were in-
cluded in the random effect meta-analysis on the adult 
group. The resulting overall negative effect size on face 
identity recognition performance between ASD and 
TD, Hedges’ g = -.753, showed a significant deficit 
in the identification ability in ASD subjects. Figure 
4 showed the forest plot displaying the effect sizes of 
each study, which presented an overall lower perfor-
mance in ASD than in TD. Furthermore, subgroup 
analysis on the type of test performed were also includ-
ed. The statistics and visual representations in the for-
est plot both indicated an outstanding negative effect 
size. Hedges’ g= -.753, 95% CI [-.931, -.575], p<.0001. 
 Heterogeneity tests indicated a substantial vari-
ation in effect sizes between studies. τ2 = .211, I2 = 
.682. The test of homogeneity also confirmed the 
disparity. Q(38)= 123.499, p<.001. The I2 connoting 
68.2% of heterogeneity explained by studies’ differ-
ences was lower than the overall heterogeneity in data 
with both children and adult data. Egger’s regression 
test of intercept = 0.070, 95% CI [-0.774, 0.915],  
p=.867, which suggested a high level of robustness.
 In addition, consistent results were shown in 
subgroup analysis for both delayed and simultaneous 
tests.  Of the 39 studies’ results, 29 studies were per-
formed with delayed recognition tasks, and 10 studies 
were implemented with simultaneous designs. Het-
erogeneity testing indicated that for both categories 
classified based on test procedure, the heterogeneity 
between studies was on a similar level. For delayed 
tests, τ2 = .185,  I2 = .678; for simultaneous design, 
τ2 = .308,  I2 = .674, which both indicated a high lev-
el, 67.8% and 67.4% of heterogeneity from variation 
between studies. However, publication biases were 
not significant in either design. For delayed tests, in



104

tercept = -0.451, 95% CI [-1.457, 0.555], p=.366; 
for the simultaneous test, intercept = 2.036, 95% CI 
[ -0.591, 4.664], p=.112. Therefore, no stud-
ies were excluded from the analysis. Figure 5 pre-
sented a funnel plot image for all studied among 
adult participants, and different methodologies 
used were labeled with different colored dots.
 For studies with adult samples and implemented 
delayed identity recognition tests, the overall Hedge’s g 
effect size was -.697, 95% CI [-.891, -.504], p<.001. For 
studies with simultaneous design methods, the over-
all effect size was Hedges’ g = -.954, 95% CI [-1.377, 
-.531], p<.001. Although the overall effect size for the 
simultaneous test, g=-.954, was larger than the delayed 
test, g=-.697, the Homogeneity test between these two 
subgroups shows an insignificant effect, Q(1)=1.17, 
p=0.28. On the other hand, both significantly negative 
effect sizes results suggested a deficit in both delayed 
and simultaneous face identity recognition ability in 
adult ASD compared to typically developing controls. 
Face Identity Recognition in Children Samples
 A total of 66 papers with 104 studies of children 
were included in the random-effect meta-analysis for 
identity recognition performance difference between 
ASD and TD. The overall result showed a similar level 
of effect sizes to the overall effect size with adult sub-
jects. Hedges’ g= -.701, 95% CI [-.851, -.551], p=.000. 
A subgroup analysis comparing homogeneity of over-
all effect sizes between ASD and TD for adult and chil-
dren subjects showed Q(1)=.187, p=.666. This result 
indicated no significant difference between the dis-
tribution of the two subgroups, children and adults.
 In addition, Egger’s regression test showed a 
large but insignificant publication bias. Intercept 
= 0.43, 95% CI [-0.15, 1.011], p=.144. On the oth-
er hand, similar to prior results, the heterogeneity 
across studies included in the analysis was still pro-
nounced. The resulting funnel plot is also shown in 
Figure 7. Heterogeneity measures show an overall 
82.6% of heterogeneity from variation between stud-
ies. τ2 = .482,  I2 = .826. The Homogeneity measure 
also confirmed the significance. Q(103)=601.67, 
p=.000. Figure 6 displayed all effect sizes includ-
ed in the analysis for the children subject group.
 Although publication was not significant in the 
overall analysis of studies, when subgroup analyses 
were performed for studies implementing delayed and 
simultaneous design within the children group, the 

publication bias estimated by Egger’s Regression test 
predicted a high likelihood of publication bias in the 
delayed condition, intercept=0.598, 95% CI [-0.067, 
1.263], p=.07. The result is not statistically significant 
with p=.05, but the borderline significant result indi-
cated a high likelihood of effect of biases from extreme 
data. After eliminating five sets of data with extreme 
effect sizes, the possibility of publication bias became 
minimal and thus the results were more robust and 
funnel plots are symmetric. Figure 8 displayed the fun-
nel plots before and after extreme data were removed. 
For delayed groups, Intercept = 0.141, p=.65; for si-
multaneous group, intercept = -0.349, p=.55; and for 
all studies with children, intercept = 0.005, p= .987.
  The resulting heterogeneity measures were 
smaller but still significant. For overall effect:  τ2 = .281,  
I2 = .742; delayed condition:  τ2 = .283,  I2 = .75; simul-
taneous condition: τ2 = .273,  I2 = .715. The estimat-
ed effect sizes for both conditions are similar and all 
negatively significant. For the delayed face recognition 
condition, the effect size was Hedges’ g = -.628, 95% CI 
[-.776, -.48], p=.000. For the simultaneous condition, 
the hedge’s g effect size was -.607, 95% CI [-.837, -.377], 
p<.001. The results indicated a noticeable shortfall in 
face identity recognition in children with ASD com-
pared to TD regardless of memory load requirement 
of tests, delayed or simultaneous. The subgroup ho-
mogeneity test also provided the result that the distri-
bution of performance for delayed and simultaneous 
conditions was highly similar. Q(1)=0.022, p=.881. 
 Furthermore, after removing extreme effect siz-
es, the evaluated effect size of overall performance 
for children became slightly less negative. Hedges’ g 
= -.622, 95%CI [-.746, -.498], p=0.000. Additionally, 
the result for subgroup homogeneity tests between 
children and adult subgroups, although still insignif-
icant, decreases, indicating a lower level of similarity 
of distribution across the two groups. Q(1) = 1.393, 
p=.238. Therefore, although removing outlier data 
increased the robustness of studies data included in 
the analysis, it did not change the overall underper-
formance in children with ASD, or the homogene-
ity in results between children and adult subgroups. 
Additional Analysis
 Meta-regression analysis weighting the mean 
age of each study on the heterogeneity of perfor-
mance for overall result data, and for delayed and 
simultaneous identity recognition tasks showed 

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105

small mediating effects. The largest mediating ef-
fect observed was in simultaneous design where 
of the 71% of heterogeneity, the mean age of par-
ticipants could account for 3.2%  of the variation. 
 In addition, subgroup analysis was performed 
on assessing the difference of age groups in differ-
ent methodology groups. The homogeneity test of 
studies with children and adult subjects on simulta-
neous face identity recognition test gave the result, 
Q(1)=1.998, p=.157. The test on the homogeneity 
of studies for both groups on the delayed face iden-
tity recognition test resulted in, Q(1)=0.100, p=.752.

Discussion
 The current study examined facial identity rec-
ognition ability in Autism Spectrum Disorder, ASD, 
whether there were changes across developmental stag-
es, and whether there was a difference depending on 
specific aspects of face recognition. The result from the 
meta-analysis indicated an overall underperformance 
in face identity recognition in ASD compared to typi-
cally developing control and the deficit was significant.
 The overall effect size (Hedges’ g = -.716) present-
ed a significantly lower performance in the ASD group 
on identifying faces. For studies that were performed 
with adult subjects or children participants, the defi-
cits were consistent in both groups. The effect sizes 
for adult and children groups respectively were Hedg-
es’ g = -.753, and Hedges’ g = -.622. Both indicate a 
significant underperformance in ASD children and 
adults compared to their TD control. Although the 
effect size for adults was larger than the effect size for 
the children’s group, which represented a higher level 
of deficit in the adult population than in children, the 
homogeneity test shows insignificant results. There-
fore, the difference between the results cannot be sta-
tistically interpreted as a noticeable change across ages. 
 Additional subgroup analysis on the interaction 
between age group and type of test showed a variation 
in effect size between delayed and simultaneous tests 
in adult samples but not in children. In addition, only 
the effect size of performance on simultaneous iden-
tity-matching tasks in adults was considered to pres-
ent a large effect size with Hedges’ g = -.954 (Cohen, 
1998). The effect sizes showing underperformance 
in other interactive groups showed similar results, 
which indicates an indistinguishable level of deficits. 
 The general result of an overall deficit in ASD 

compared to TD was consistent with results found in 
most research studies and concluding remarks from 
the prior meta-analysis (Weigelt et al., 2012; Tang et 
al., 2015; Griffin et al., 2021). Of the 144 pairs of ef-
fect sizes data extracted from 94 studies for the current 
meta-analysis, 20 results found either no difference 
or slightly better results in performance in the ASD 
group. The differences in findings can be a mixed ef-
fect from the differences in the subject’s selections and 
disparity in the quality of the experimental paradigm 
adopted by the studies. On the other hand, majorities 
of the studies concluded a deficit in ASD face recog-
nition performance, which is also aligned with results 
from the current meta-analysis. The consistent deficit 
suggested a case of developmental prosopagnosia that 
can potentially be considered as an endophenotype 
of ASD. There have been continual reports of cases 
of patients with ASD having difficulties in face recog-
nition (Kracke, 2008; Pietz et al., 2007). In addition, 
subsets of patients with developmental prosopagnosia 
also present significant levels of autistic traits (Min-
io-Paluello et al., 2020; Cook et al., 2015). Therefore, 
research may need to consider this co-occurrence of 
the two disorders and potentially the face recogni-
tion deficit as an intermediate phenotype of ASD.
 Subgroup analysis revealed subtle development 
changes in performance, which suggested a persistent 
deficit in face recognition in ASD across ages. The 
difference in effect sizes occurred only in adults on si-
multaneous face- matching tasks rather than delayed 
face recognition tasks, but was not found in children 
samples, which implied an isolated face perception 
and face recognition in adults but not in children. 
This difference could potentially explain the con-
traction found in the results for Weigelt et al. (2013) 
and Griffin et al. (2021). Weigelt et al. (2013) initially 
proposed the deficit depended on memory demand, 
and Griffin et al. (2021) challenged the hypothesis by 
showing a significant deficit in both face discrimina-
tion and face recognition tasks. Since most studies in-
vestigating ASD deficits were performed in children, 
the overall results with systematic studies would likely 
present persistent results since ASD children showed 
constituent deficits across tasks. Although the defi-
cit persists on average, the underlying mechanism of 
performance differs across ages. Similar results were 
presented in studies on developmental prosopagno-
sia, which presented a dissociation in performance 



106

between face perception and face memory in adults 
but not in children (Dalrymple et al., 2014). In addi-
tion, studies in typically developing populations on 
simultaneous face identity match-to-sample tasks also 
indicate a decrease in accuracy performance as age in-
creases (Megreya et al., 2015; Schretlen et al., 2001).
 On the other hand, the limitations of the accura-
cy of results for the current meta-analysis also need to 
be considered. The majority of studies on ASD were 
conducted with children for it is a neurodevelopmen-
tal disorder. The drastic modification of diagnostic 
criteria of ASD also made the selection and classifi-
cation of ASD participation complicated. Of the 144 
studies included, only 39 studies data were performed 
on adult participants, and only 10 pairs of data were as-
sessing the simultaneous face-matching ability in adult 
ASD. With a limited number of studies, the high effect 
size for adults on simultaneous tasks may be biased. 
In addition, the difference in studies results can also 
contribute to heterogeneities in studies results. For 
future studies, the implementation of a random-ef-
fect size model is necessary since the heterogeneity in 
studies was substantial. A possible resolution can be 
the inclusion of single design studies in the inclusion 
criteria, for instance, using only CFMT or Benton for 
assessing face memory and face perception. However, 
these limiting criteria would be prone to having a min-
imal sample size. Therefore, for future research stud-
ies, there should be a consideration of the material and 
procedure used to perform the studies to have reliabil-
ity and validity across studies and populations. Anoth-
er limitation of this study was that the study process, 
including literature search, review, and meta-analysis, 
was done by the author alone so the inter-review-
er reliability was not assessed for the current study.
Conclusion 
 Overall the result was significant in that ASD 
presented a significantly lower level of accuracy in 
face identity recognition than their typically devel-
oping peers. In addition, the deficit persists across 
age, which may imply potential comorbidity of ASD 
and developmental prosopagnosia. Nevertheless, the 
difference in results from the subgroups analysis 
showing a difference in performance on simultane-
ous face matching tasks and delayed face recognition 
tasks indicated a dissociation between face percep-
tion and face memory that was only manifested in 
adults but not children with ASD. However, more 

studies focusing on the adult ASD population is nec-
essary to specify the mechanism of this divergence. 
In general, studies on face identity recognition abil-
ity in ASD should consider these factors when de-
ciding on the studies’ participants and materials.

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Table 1

Demographic Characteristics of the Studies Included in the Current Meta-Analysis

Note. ASD = Autism Spectrum Disorder; TD = Typically Developing;  N = Number; Std = standard deviation. 

* = Two studies had both adult and children participation groups and were included in both categories.



116

Table 2

Design Characteristics of Studies Included in the Current Meta-Analysis 

Note. The number indicates the number of studies in each category.

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Figure 1

PRISMA 2020 Flow Diagram Showing the Literature Identification and Screening Process

Note. ASD = Autism Spectrum Disorder; TD = Typically Developing. 



118

Figure 2

Funnel Plot of Effect Sizes of all Studies over Standard Errors for Overall Face Identity Recognition

Note. Egger’s linear regression test result of t = 1.348, p = .180 indicated overall symmetricity of all studies used 

in the current meta-analysis. 

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Figure 3

Forest Plot of the Overall Effect Size of Face Identity Recognition Ability Difference Between ASD and TD Groups



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Figure 4

Forest Plot of Overall Face Identity Recognition Performance in Adult ASD and TD Groups

Note. Forest plot from the meta-analysis using a random-effect model on all studies with adult participants. 

Overall Hedge’s g value= -.76; delayed subgroup, Hedges’ g = -.70; simultaneous subgroup, Hedges’ g = -.95. 



122

Figure 5

Funnel Plot of Overall Studies with Adult Participants

Note. Egger’s Regression-based test with an overall result of t=0.169, p=.867, indicated an overall symmetric 

funnel plot. 

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Figure 6

Forest Plot of Overall Identity Recognition Performance in Children with ASD and TD



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Figure 7

Funnel Plot of Overall Studies with Children Participants 



126

Figure 8

Funnel Plot of  Studies on Children with Delayed Face Identity Recognition Test Before and After 

(Extreme Data were Removed)

Note. The plot on the left showed the funnel plot prior to modification. The plot on the right showed 

the funnel plot after five studies’ data with extreme effect sizes removed.

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