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European Child & Adolescent Psychiatry
https://doi.org/10.1007/s00787-025-02647-3
RESEARCH
Emotion processing difficulties inADHD: aBayesian meta‑analysis
study
Ana‑MaríaSoler‑Gutiérrez1,2 · AlbertoJ.Sánchez‑Carmona3· JacoboAlbert4 · JoséAntonioHinojosa5,6,7 ·
SamueleCortese8,9,10,11,12,13 · AlessioBellato8,9,14,15,16 · JuliaMayas1
Received: 1 August 2024 / Accepted: 9 January 2025
© The Author(s) 2025
Abstract
We investigated whether there is an emotional processing deficit in ADHD and whether this only applies to specific emo-
tional categories. In this PRISMA-compliant systematic review based on a pre-registered protocol (https:// osf. io/ egp7d), we
searched MEDLINE, PsycINFO, ERIC, Scopus and Web of Science databases until 3rd December 2023, to identify empirical
studies comparing emotional processing in individuals meeting DSM (version III to 5-TR) or ICD (version 9 or 10) criteria
for Attention Deficit/Hyperactivity Disorder (ADHD) and in a non-psychiatric control group. Study quality was assessed
with the Appraisal tool for Cross-Sectional Studies (AXIS). Eighty studies were included and meta-analysed (encompass-
ing 6191 participants and 465 observations). Bayesian meta-analyses were conducted to compare individuals with ADHD
and non-psychiatric controls on overall emotional processing measures (meta-analysis 1) and across emotional categories
(meta-analysis 2). The type of stimulus employed, outcome measurement reported, age, sex, and medication status were
analysed as moderators. We found poorer performance in both overall emotion processing (g = − 0.65) and across emotional
categories (anger g = − 0.37; disgust g = − 0.24; fear g = − 0.37; sadness g = − 0.34; surpr ise g = − 0.26; happiness/positive
g = − 0.31; negative g = − 0.20; neutral g = − 0.25) for individuals with ADHD compared to non-psychiatric controls. Scales
items and accuracy outcome being the most effective moderators in detecting such differences. No effects of age, sex, or
medication status were found. Overall, these results show that impaired emotional processing is a relevant feature of ADHD
and suggest that it should be systematically assessed in clinical practice.
Keywords ADHD· Emotional processing· Emotion recognition· Meta-analysis
Introduction
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neu-
rodevelopmental disorder characterized by developmentally
inappropriate, persistent and impairing inattention and/or
hyperactivity/impulsivity [1]. These symptoms may be asso-
ciated with poor quality of life, and risk of premature mortal-
ity if not properly identified and treated [2, 3]. ADHD is one
of the most prevalent childhood-onset disorders, affecting
around 5% of children and adolescents [4] and impairing
symptoms persist into adulthood in up to 70% of those diag-
nosed in childhood [5]. ADHD is a complex and heterogene-
ous disorder, both etiologically and phenotypically, and its
causal mechanisms are not fully understood [6, 7]. Current
evidence suggests that some individuals with ADHD may
experience difficulties in inhibitory control [8], working
memory [9], and emotional functioning [10, 11] (see [7] for
a review). Difficulties in emotion regulation, processing, and
recognition are likely to negatively impact social relation-
ships and quality of life of people with ADHD.
The mechanisms underlying emotional dysfunction in
ADHD are still unclear [12–14]. There is evidence of altered
activation of the limbic system (including amygdala) and
prefrontal systems (including the medial prefrontal cortex)
underlying emotion processing [15, 16] in ADHD. Associa-
tions between difficulties in emotion regulation and altered
autonomic functioning (especially, reduced parasympathetic
vagal control) have also been reported, but these are not
specific to ADHD as they can characterise people with other
psychiatric or neurodevelopmental disorders [17, 18]. Some
studies found that inattention is specifically associated with
Alessio Bellato and Julia Mayas have equally contributed as senior
authors.
Extended author information available on the last page of the article
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European Child & Adolescent Psychiatry
difficulties in emotion recognition [19, 20], particularly in
relation to anger and sadness [21, 22]. However, other stud-
ies did not find evidence supporting these findings [23], or
reported associations between other ADHD symptoms (e.g.,
impulsivity) and emotional functioning deficits [24]. Yet
other studies found no correlation with ADHD symptoms
[19]. Based on this body of evidence, the nature and extent
of emotional functioning deficits in ADHD is unclear.
Emotional processing engages multiple neural networks
to identify important stimuli and influence emotional states
and behaviors. It consists of three main subprocesses: 1)
identification, which recognizes emotional cues and assesses
their significance; 2) reaction, which activates psychological
and behavioral responses based on the stimulus' valence;
and 3) modulation, which applies strategies to regulate emo-
tional reactions to reach specific goals [25]. Most studies
investigating emotional processing focus on the first step,
understanding the processing as emotional detection and
recognition. While some studies found poorer performance
in emotion processing tasks in both children (e.g., [26–28])
and adults (e.g., [29, 30]) with ADHD, other studies failed to
replicate these findings (e.g., [31–34]). Nevertheless, a meta-
analysis of 77 studies (published up to 2015) in children and
adolescents with ADHD found evidence of an emotional
information processing deficit contributing to socio-emo-
tional functioning difficulties independent of co-occurring
conduct or cognitive problems [13]. Data on adults are more
limited but findings are in the same direction (emotion pro-
cessing deficit in ADHD), with only six studies (up to 2019)
investigating emotion processing in adults with ADHD [35].
Difficulties in emotion recognition have also been detected
in individuals with ADHD. Bora and Pantelis [36] meta-
analysed 44 studies up to 2015 and found that people with
ADHD, regardless of age and sex, showed difficulties in rec-
ognizing emotions during social cognition tasks or based
on face- or voice-stimuli. This was corroborated by another
meta-analysis of 21 studies (up to 2022) on vocal emotion
recognition tasks [37], which found evidence of vocal emo-
tion recognition deficits in ADHD, regardless of the emotion
analysed.
A wide range of tools and outcome measures have been
used to study emotion processing and recognition in ADHD,
including various types of emotional stimuli that differ in
terms of the type of emotion/valence they report on (e.g.,
discrete emotions or dimensional categories). Neverthe-
less, none of the previously discussed meta-analyses tried
to disentangle the nature of emotion recognition/processing
deficits in ADHD by investigating whether specific types of
stimuli (e.g., faces, eyes, scenes, voices, or words) or out-
come measures (e.g., performance accuracy, reaction time
(RT), or other measures) modulate the differences found
across studies between people with ADHD and controls. The
present study therefore aimed to fill this gap by assessing
whether there is an emotion processing deficit in ADHD
and if such deficit is modulated by type of emotion assessed,
as well as the type of stimulus used, and outcome measure
collected. This is of relevance to better understand emotion
functioning in ADHD, informing more personalised strat-
egies to support the development of emotion recognition/
processing skills tailored to specific subgroups of individu-
als with ADHD.
We used Bayesian meta-analysis, which allows to quan-
tify the evidence in favour of both the null and the alterna-
tive hypothesis, and monitor evidence as data accumulate
[38], therefore providing more robust results than traditional
meta-analyses. The main objectives were: (a) investigating
whether people with ADHD show alterations in overall emo-
tion recognition/processing compared to neurotypical con-
trols (meta-analysis 1, MA1), (b) exploring whether these
differences are more evident for specific types of emotion
assessed (e.g., happiness, sadness, fear, anger, disgust, sur-
prise; positive, negative and neutral categories) (MA2), and
(c) assessing whether variables such as sex, age, medication
status, ADHD symptom severity, co-occurring conditions or
diagnoses, type of outcome reported (accuracy, reaction time
or other), or type of stimuli used (faces, voices, eyes, scenes,
words, and scales) moderated the results (both for MA1 and
MA2). Based on the reviewed literature, we expected to
observe altered emotion processing and recognition in peo-
ple with ADHD, compared to neurotypical controls, while
we could not make any predictions regarding type/category
of emotion investigated or other variables potentially mod-
erating these effects.
Methods
The reporting of this systematic review/meta-analysis fol-
lowed the most updated PRISMA guidelines [39]. The pro-
tocol for this study was pre-registered on the OSF website,
where the dataset is also available: https:// osf. io/ egp7d. The
PRISMA checklist is included in Supplement 1.
Search strategy andselection criteria
A systematic search was conducted on 3 December 2023
in MEDLINE, PsycINFO, ERIC, Scopus and Web of Sci-
ence with the following pre-specified strategy, adapted for
each database and limited to English language: (ADHD
OR ADD OR “attention deficit hyperactivity disorder” OR
“attention-deficit/hyperactivity disorder” OR “attention defi-
cit disorder” OR “hyperkinetic disorder” OR “hyperkinetic
syndrome”) AND (emotion* OR labil* OR affect* OR nega-
tive* OR irritability OR frustration OR “theory of mind”
OR empathy). References from retrieved systematic reviews/
meta-analyses were hand-searched to detect any relevant
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reference possibly missed with the electronic search. See
Supplement 2 for a detailed search strategy description.
We included (a) original primary studies, (b) compar-
ing people of any age meeting ADHD criteria according
to DSM (II to 5-TR) or ICD (9,10) and a neurotypical non-
psychiatric control group, and (c) reporting, either in the
main text or supplementary materials, relevant information
(e.g., means and standard deviations) of any available emo-
tion recognition/processing measure derived from a task or
a self-reported questionnaire/scale. Studies with unspeci-
fied ADHD diagnostic criteria, cohort studies without a
control group, control groups including people with other
psychiatric disorders, or emotion-induction experiments
were excluded.
Data extraction andoutcomes
Records were screened based on title and abstract, first, and
based on full text, then. Screening and data extraction was
carried out by one author (AMSG). Queries were resolved
by expert judgement (JM, JA, and JAH). We extracted
relevant raw data (mean and standard deviations) includ-
ing accuracy scores, reaction times, or other performance
measures such as arousal-valence ratings and psychophysi-
ological measurements, for the ADHD and control groups.
As can be seen,it was possible to identify the presence of
various effect sizes within each study. Thus, in order not to
introduce any bias in the selection of any particular measure,
all information was incorporated into the analysis. However,
this measure raised the need to take into account the possible
dependency between measures, integrating a new layer into
the structure of the meta-analysis. Consequently, effect sizes
were first nested within individual studies (level 2), and then
aggregated together to form an overall effect size (level 3).
AMSG used a Microsoft Excel spreadsheet for data extrac-
tion. Data from indirect measurements including emotion
recognition/processing-relevant outcomes from cognitive
tasks (e.g., n-back, Go/no-Go, Stroop, and continuous per-
formance tasks), as well as self-report questionnaires/scales
(e.g., Self-Assessment Manikin, and Toronto Alexithymia
Scale), and direct measurements, such as tasks in which
the type of emotion displayed must be explicitly recog-
nised by the participant (e.g., Reading the Mind in the Eyes
Test, RMET; Diagnostic Analysis of Nonverbal Behavior,
DANVA; facial emotion recognition tasks), were extracted.
Each outcome was classified by the type of emotional stimuli
used (face, eyes, voice, scale/questionnaire), as well as by
the type of emotion (happiness/positive, neutral, negative,
sadness, angry, fear, disgust, and surprise). The categories
“positive” and “negative” were used for studies where emo-
tional categories (based on valence, e.g., positive or nega-
tive), but not a specific set of emotions, were used. We also
extracted information about variables that might moderate
the association between ADHD and emotion recognition/
processing, such as age, sex, co-occurring conditions, medi-
cation status, and ADHD symptom severity. Study quality
was assessed by AMSG using the Appraisal tool for Cross-
Sectional Studies (AXIS; Supplement 3).
Statistical analyses
All analyses were carried out using the metafor [40] (version
3.4–0), brms [41] (version 2.18.0) and bayestestR [42] (ver-
sion 0.13.1) packages for the statistical software program R
[43] (version 4.1.3). Hedge’s G (Standardised Mean Differ-
ences) were calculated (ADHD data vs control group data)
to estimate differences between ADHD and non-ADHD
groups on emotion processing outcomes; hence, negative
effect sizes indicate poorer emotion processing in ADHD
compared to the control group. Before fitting each model,
an influence analysis (based on the criteria of Cook’s dis-
tance, hatvalues and dfbetas) was performed to detect pos-
sible outliers with respect to their role in the pooled effect
size [44, 45].
Two Bayesian multilevel meta-analyses (MAs) were con-
ducted to study differences in emotion recognition/process-
ing between ADHD patients and non-psychiatric controls
(MA1 was focused on overall measures of emotional rec-
ognition/processing, while MA2 focused on discrete emo-
tions and valence dimensions). Effect sizes were first nested
within individual studies, and then pooled together to form a
global effect size. Publication bias was assessed by visually
exploring the symmetry of the funnel plots and quantita-
tively by constructing a regression of the individual effect
sizes on their corresponding standard errors [46]. Hetero-
geneity—associated with both the difference in true intra-
cluster effect size and with inter-cluster variation, because
of the multilevel nature of the analysis—was investigated via
the I2 parameter [47]. Moderation analyses were also con-
ducted, with the same Bayesian multilevel procedure used
but including moderator variables as predictors in the mod-
els. Specifically, Age (mean), Sex (% males), Medication
status (under medication, without medication/drug-naïve,
washout period), Type of emotional stimuli (scales, scenes,
faces, eyes, words, and voices) and Outcome measure used
(accuracy, RTs, and other) were analysed as moderators
for MA1. Type of stimuli (faces, eyes, voices, words, and
scenes) and Outcome measurement (accuracy, RTs and
other) were analysed as moderators for MA2.
Considering we adopted a Bayesian approach, a weakly
informative prior was chosen given the lack of specific prior
information, incorporating the possibility that certain val-
ues are more credible than others, but maintaining a general
character that allows it to be applied to multiple contexts
[48]. Concretely, the following parameters were chosen:
𝜇∼ℵ(0,1)
;
𝜏∼HC(0,0.5
). In any case, to eliminate the
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European Child & Adolescent Psychiatry
presence of any bias and to test the robustness of the results
obtained, a sensitivity analysis was performed. Thus, the
results of the above analysis were compared with those asso-
ciated with two different priors. Specifically, each model
was evaluated twice more, but starting from a weak prior
(
𝜇∼ℵ(0,10)
and from a vague prior (
𝜇∼ℵ(0,100)
.
Bayesian models were interpreted in terms of different
factors. Firstly, the confidence intervals that contained the
true value of the parameter with a 95% probability (high
density interval, HDI) were reported. In addition, we exam-
ined what percentage of the posterior distribution of the
parameter was compatible with the hypothesis that it dif-
fered from zero (credibility). We also provided the evidence
ratios associated with this hypothesis, which quantify the
evidence provided by the estimate in favour of the effect
versus the alternative interpretations. It was concluded that
there was indeed a difference between groups if this HDI
differed from the criterion (zero). However, this procedure
would only allow the rejection of the null parameter, but not
its acceptance. Therefore, to complete the decision making
on effects, the procedure based on the region of practical
equivalence (ROPE) was used [49]. This procedure consists
of setting a range of values around the null value, which,
in practical terms, would reflect the absence of effects. In
our case, the ROPE was set between − 0.1 and + 0.1 around
a zero value, on the scale of the standardised mean differ-
ence. Thus, the zero value was rejected if the 95% HDI does
not overlap at all with the ROPE region. Conversely, if the
95% HDI fell within the ROPE region, the zero value was
accepted. In any other case, the decision would be unde-
cided. Beyond the criteria used to reject the null hypothesis,
each analysis was accompanied by an assessment of the level
of precision achieved. Concretely, the width of each confi-
dence interval was compared with a practical threshold set
at 80% of the ROPE region (0.16) [50, 51]. This precision
assessment allowed for a proper weighting of the relevance
of each conclusion, especially for the moderation analyses
where smaller samples of studies were used. For modera-
tion analyses, decision making regarding the null hypothesis
(no differences with respect to the intercept or other levels
of the moderator variable) was based on the assessment of
the degree of overlap between the HDI and ROPE regions.
Importantly, the scale of the continuous variables was
adjusted to the standardised mean different scale, because
of its impact on decision making based on ROPE region.
Results
Of 1380 references initially screened, 161 full texts were
assessed for eligibility (Fig.1). A total of 80 studies (6191
participants in total, 53% with ADHD, 77% children/
adolescents) met the inclusion criteria, from which 465
observations (effect sizes) were obtained. Table1 provides
detailed data about the studies included and Table2 sum-
marises the main characteristics of the studies. Sample
sizes ranged from 20 to 364 participants, with the majority
focusing on children and adolescents, and some covering
age ranges as wide as 6 to 18 years. Only 53% of the studies
specified the ADHD subtype/presentation, with the com-
bined subtype/presentation being the most prevalent (65%).
Furthermore, an under-representation of women was also
observed (72% male participants) in line with the sex ratio
seen in clinical practice, possibly accounted for, at least in
part, by referral bias. In 74% of the studies, the presence or
absence of comorbidity was reported. Additionally, behav-
ioural problems, including conduct disorder (CD) and oppo-
sitional defiant disorder (ODD), were reported in 28.75% of
the studies. In terms of ADHD medication status, 59% of
studies indicated that a 24–48-h washout period required
before the testing session, while 23% did not provide infor-
mation about participants taking medication.
Meta‑analysis 1: overall emotion processing
A summary of the data for the studies included in MA1 is
presented in Supplement 4. As shown in Table3, we found
that people with ADHD perform significantly poorer on
measures of emotion processing than controls (large effect
size). In the assessment of the probability that the parameter
is less than zero (i.e., that there really are differences), 100%
of its posterior distribution would be compatible with this
statement, and the probability of that result with respect to
its complementary (parameter greater than zero) is much
higher (Bayes Factor > > 100). In addition, the comparison
of the HDI + ROPE regions showed a null overlap between
the two, which would allow us to reject the value zero. These
results show moderate to high levels of heterogeneity at the
within-study level (I2 = 48.81%), but low heterogeneity at the
between-study level (I2 = 33.29%). There was a high publica-
tion bias r isk (b = − 2.99, se = 0.93, 95% CrI [-4.55, − 1.47],
Credibility = 99%, Evidence Ratio > > 100). The influence
analysis reported no significant results for any effect size.
Bayesian forest plot with the distributions of the individual
studies is shown in Fig.2.
The moderation analyses showed larger effect sizes
reported in studies using scales compared to scenes, and
using scales compared to faces (see Table4). This indicates
that deficits in emotion processing in ADHD vs controls
were more evident in studies using questionnaires/scales
compared to those implementing emotional scenes or faces.
However, only four studies reported scale outcomes; this
probably led to a level of precision below the established
threshold (CrI width of scene-scale comparison = 1.19, CrI
width of face-scale comparison = 1.04). We did not find any
statistically significant effects for other stimuli, i.e., eyes,
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European Child & Adolescent Psychiatry
scenes, and voices, suggesting that these stimuli are similar
in detecting differences between people with ADHD and
non-psychiatric controls. Lastly, significant differences
between the ADHD and control groups were found for all
type of stimuli (except words), showing a global emotional
processing deficit in ADHD compared to controls.
In terms of the reported outcome measurement, we found
larger effect sizes (i.e., differences between ADHD and
control groups) for accuracy than RTs, or other outcome
measures. However, only for the latter the difference was
statistically significant. Specifically, although the 98% of the
posterior density distribution supported the presence of the
differences between accuracy and RT and despite observing
a notable evidence ratio, the HDI and ROPE regions showed
an overlap of 5.8%. It should be noted that the precision of
the estimations was once again lower than desirable (CrI
width of accuracy-RT contrast = 0.54, and CrI width of accu-
racy-other contrast = 0.57). Moreover, the high magnitude
of the differences observed between RTs and Other meas-
ures, despite not reaching the significance criterion, is also
noteworthy (b = 0.39, se = 0.22, CrI [0.03, 0.75], evidence
ratio = 26.62, credibility = 96%, 6.7% overlap HDI-ROPE).
Conclusively, this implies that accuracy is the most sensitive
outcome measure to identify differences between individuals
with ADHD and non-psychiatric controls. The other mod-
eration analyses showed that age, sex, and medication sta-
tus had no significant effect, indicating that the differences
were not due to age, sex nor ADHD medication intake. Due
to the heterogeneity of the collected data on comorbidity
and ADHD symptom severity, they could not be used in the
moderation analyses.
Meta‑analysis 2: specific emotion processing
A description of studies included in MA2 is shown in
Supplement 5. MA2 found results in line with MA1 (see
Table5), albeit with smaller effect sizes. Specifically, we
found that ADHD participants performed significantly worse
on emotion recognition/processing tasks/measures across
all emotional categories, except in relation to “negative
Fig. 1 PRISMA flowchart.
Notes. ER: Emotion Recogni-
tion/Processing
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Table 1 Characteristics of studies included in the systematic review/meta-analysis
1st author and year Sample size Developmental stage Emotion recognition/processing tool Main findings reported
Albayrak 2022 [52] 41 ADHD; 43 controls Children and adolescents RMET Scores: ADHD < Controls
Alperin 2017 [53] 49 ADHD; 60 controls Adolescents Emotional Faces Go/no-Go Task Accuracy: ADHD < Controls; fear < neutral = happy
RT: no effect group
Andrade 2012 [54] 39 ADHD; 25 controls Children Social information processing vignettes Control group detected a significantly larger proportion of
positive, negative, and neutral cues, after adjusting for
conduct problems
Ayaz 2013 [55] 64 ADHD; 69 controls Adolescents RMET Correct responses: ADHD < Controls
Balogh 2017 [56] 26 ADHD; 14 controls Adults Emotional Go/no-Go Task Commission errors: ADHD > Controls for neutral and nega-
tive stimuli. No differences for positive valence. RT: no
group differences
Basile 2018 [31] 39 ADHD; 42 controls Children Emotion Recognition Task Accuracy: no group differences. ER confidence:
ADHD > Controls. ER gamma resolution index (discrimi-
nating correct from incorrect responses): ADHD < Controls
Berenguer 2018 [57] 35 ADHD; 37 controls Children Emotion recognition (NEPSY-II) ER scores: ADHD < Controls
Berggren 2016 [19] 32 ADHD; 32 controls Children and adolescents Facial affect recognition (FAR) Overall FAR face accuracy: no group differences. Overall
FAR eyes accuracy: ADHD < Controls. Overall RT: no
group differences
Blaskey 2008 [58] 71 ADHD; 45 controls Children Chimeric Faces Test In happy-neutral condition (for left-handers), control children
showed the usual left-visual hemispace (LVH) bias, but
ADHD combined subtype did not. Right-handers (controls
and ADHD) showed the usual LVH bias in all conditions
Boakes 2008 [59] 24 ADHD; 24 controls Children Facial Affect Interpretation Scores: ADHD < Controls interpretating disgust and fear
No group differences regarding happiness, anger, and sad-
ness
Bolat 2017 [60] 69 ADHD; 69 controls Children and adolescents Comprehension Test (CT) CT scores: ADHD < Controls. ADHD-I = ADHD-C < Con-
trols
No ADHD subtype groups differences
Brotman 2010 [61] 18 ADHD; 37 controls Children and adolescents Facial emotion recognition No differences rating subjective fear
Cadesky 2000 [62] 86 ADHD; 27 controls Children DANVA Accuracy: ADHD < Controls on all emotions except anger.
Analysis of the pattern of errors showed that ADHD made
more errors but in a random manner, like the control group
Chronaki 2014 [63] 25 ADHD; 25 controls Children Vocal emotion recognition task Accuracy: ADHD < Controls for angry voices
ADHD tended to miscategorise angry voices as neutral
Conzelmann 2009 [64] 197 ADHD; 128 controls Adults IAPS rating No differences between the control group and ADHD in time
viewing or in valence and arousal ratings. ADHD showed a
reduced emotional responding to pleasant stimuli
Corbett 2000 [65] 37 ADHD; 37 controls Children POFA &
Prosody test
Accuracy: ADHD < Controls in both ER test. 85% of the
variance was explained by POFA
Cortez-Carbonell 2017 [66] 30 ADHD; 30 controls Adults Facial emotion recognition Accuracy: ADHD < Controls for the three facial expressions
used (happiness, anger and neutral). RT: ADHD > Controls
for anger, but not for happiness or neutral
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Table 1 (continued)
1st author and year Sample size Developmental stage Emotion recognition/processing tool Main findings reported
Da Fonseca 2009 [26] 27 ADHD; 27 controls Children and adolescents Emotion recognition tasks ER accuracy Experiment 1: ADHD < Controls. ER accuracy
Experiment 2: ADHD < Controls. Object recognition
Experiment 2: no group differences
Dan 2018 [67] 15 ADHD; 16 controls Adults Facial emotional expression morph task ER threshold at baseline: no group differences. After sleep
deprivation ADHD experienced an increased threshold for
emotion recognition, while controls did not
Dan 2015 [68] 45 ADHD; 46 controls Adolescents Facial emotion recognition Ratings: ADHD (combined) < Control (happy and neutral).
RT: no group differences. Variability of RT and ratings:
ADHD > Controls
Demirci 2016 [69] 60 ADHD; 60 controls Children and adolescents RMET RMET accuracy: ADHD < Controls. ADHD-HI < ADHD-
I = ADHD-C. Benton Face Recognition Test:
ADHD < Controls
Demurie 2011 [70] 13 ADHD; 18 controls Adolescents RMET Score: no group differences
Dini 2020 [71] 24 ADHD; 25 controls Children Facial emotion recognition Accuracy and RT: no group differences
Variance of RT: ADHD > Controls
Downs 2004 [72] 16 ADHD; 10 controls Children Emotional Understanding Total correct: ADHD < Controls
Dyck 2001 [73] 35 ADHD; 36 controls Children and adolescents Facial cues test & Comprehensive Test Empathic ability index (Facial cues & Comprehensive tests
included): ADHD < Controls
Friedman 2003 [74] 31 ADHD; 32 controls Adults Emotional sensitivity subscale (SSI), Social
context films & TAS-20
Emotional Sensitivity: no group differences. TAS-20:
ADHD > Controls
Social Context: ADHD < Controls in using affect-related
words (unrelated to vocabulary skills or number of words
to describe scenes). No group differences in Benton Test
Gonzalez-Gadea 2013 [32] 22 ADHD; 21 controls Adults RMET RMET: no group differences
Grabemann 2013 [75] 20 ADHD; 20 controls Adults Florida Affect Battery Correct responses: ADHD < Controls naming affects (incon-
gruent condition). No differences in congruent condition
Greco 2021 [76] 20 ADHD; 21 controls Children Morphing Task—Human Faces Latency: ADHD > Controls for happiness, anger, and disgust
Greenbaum 2009 [33] 30 ADHD; 34 controls Children MNTAP No group difference in any subtest
Helfer 2021 [77] 43 ADHD; 46 controls Adults Facial emotion recognition Accuracy: no group differences
RT: ADHD > Controls (except for surprise)
Herrmann 2009 [78] 32 ADHD; 32 controls Adults View pictures IAPS while EEG EPN amplitudes: ADHD < controls for positive stimuli
condition
No group differences for negative stimuli condition
Ibáñez 2014 [29] 16 ADHD; 41 controls Adults RMET ADHD showed a trend toward reduced ER abilities com-
pared to controls
Ibáñez 2011 [79] 10 ADHD; 10 controls Adults RMET ADHD showed a trend toward reduced ER abilities com-
pared to controls
Imanipour 2021 [80] 25 ADHD; 25 controls Children RMET Correct responses: ADHD < Controls. In ADHD group,
RMET was associated with biological motion discrimina-
tion
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Table 1 (continued)
1st author and year Sample size Developmental stage Emotion recognition/processing tool Main findings reported
Kılınçel 2021 [81] 42 ADHD; 41 controls Adolescents Child Eyes Test Scores: ADHD < Control
Kis 2017 [30] 28 ADHD; 29 controls Adults Tübinger Affect Battery (TAB) TAB naming & discrimination: ADHD < Controls, particu-
larly angry statements. TAB conflicting & matching: no
group differences
Krauel 2009 [82] 18 ADHD; 15 controls Children and adolescents Perceptual and semantic task No group differences in any perceptual or semantic task with
neutral or emotional stimuli. RT variability: ADHD > Con-
trols
Lee 2009 [83] 42 ADHD; 45 controls Children Facial emotion recognition Accuracy: no group differences
Levy 2022 [84] 236 ADHD; 128 controls Children and adolescents RMET Correct responses: no group differences
In ADHD, high irritability predicted lower RMET accuracy
López-Martín 2013 [85] 20 ADHD; 20 controls Children Emo-distractors Error rates: no group differences
López-Martín 2015 [86] 24 ADHD; 24 controls Children Emo-distractors (Go/no-Go) No effect group in any measure
Maire 2018 [21] 40 ADHD; 40 controls Children Facial emotion recognition ER scores: ADHD < Controls, only for full sadness. No
group differences in geometric recognition. Inattention
predicted lower anger recognition score
Manassis 2000 [87] 15 ADHD; 16 controls Children Emotional Word Test No group differences for emotion words
Mauri 2020 [88] 20 ADHD; 25 controls Children and adolescents emo-CPT RT: ADHD < Controls
RT variability and false alarms: ADHD > Controls
Miller 2011 [89] 33 ADHD; 18 controls Adults DANVA Fearful errors: ADHD-I > Controls. No differences between
ADHD-I and ADHD-C, nor ADHD-C and controls. Inat-
tention was associated with more errors
Miranda 2017 [27] 35 ADHD; 39 controls Children Affect Recognition (NEPSY-II) Scores: ADHD < Controls. Affect recognition significantly
correlated with Inhibit, Shift, Emotional control, and
Behavioural Regulation Index of the BRIEF
Noordermeer 2020 [90] 82 ADHD; 82 controls Adolescents Facial and vocal emotion recognition No group differences in any measurement
Özbaran 2018 [91] 100 ADHD; 100 controls Children and adolescents Faces Test & RMET Face test and RMET scores: ADHD < Controls
Parke 2018 [92] 25 ADHD; 25 controls Children Affect Recognition (NEPSY-II) Scores: ADHD < Controls
Passarotti 2010 [93] 14 ADHD; 19 controls Children and adolescents Facial emotion recognition ADHD showed a nonsignificant trend (p = 0.06) for lower
accuracy compared with controls. RT: no group differences
Passarotti 2010 [94] 15 ADHD; 14 controls Children and adolescents Emo-Stroop Task Accuracy: no group differences
RT: ADHD > Controls
Pelc 2006 [95] 30 ADHD; 30 controls Children Facial emotion recognition Accuracy: ADHD < Controls for anger (70% intensity) and
sadness (all intensities). ADHD showed significantly lower
awareness of errors of anger and disgust compared with
controls
Pitzianti 2017 [34] 23 ADHD; 20 controls Children and adolescents Emotion recognition (NEPSY-II) No group differences
Plecevic 2021 [96] 31 ADHD; 29 controls Children GEES Speech Emotional Expression and Attitude accuracy:
ADHD < Controls for all emotions, except for joy
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Table 1 (continued)
1st author and year Sample size Developmental stage Emotion recognition/processing tool Main findings reported
Rapport 2002 [97] 28 ADHD; 28 controls Adults Tachistoscope affect recognition & DANVA Accuracy: ADHD < Controls for happy, angry, and fearful.
RT: ADHD > Controls
DANVA: ADHD < Controls for all measures
Saeedi 2014 [98] 30 ADHD; 30 controls Children and adolescents RMET Score: ADHD < Controls
Sahin 2018 [28] 24 ADHD; 26 controls Children RMET Score: ADHD < Controls
Schwenck 2013 [99] 56 ADHD; 28 controls Children and adolescents Morphing Task Accuracy, RT, and RT variability: no group differences,
included comparison between ADHD with and without
medication
Semrud-Clikeman 2010 [100] 153 ADHD; 113 controls Children and adolescents CASP emotion cues Scores: ADHD < Controls. ADHD symptoms predicted
CASP emotional cues performing, but no CASP nonverbal
cues
Serrano 2015 [101] 19 ADHD; 26 controls Children POFA & scene images Face RT: Moderate to large effect sizes (ADHD > Controls).
Face accuracy: moderate effect sizes for total and disgust
(ADHD < Controls). Situations RT: moderate to large effect
size, except for happy (ADHD > Controls). Situations accu-
racy: moderate for total and happy (ADHD < Controls)
Seymour 2015 [102] 25 ADHD; 25 controls Children and adolescents Emo Go/no-Go (CANTAB) Commission errors: ADHD > Controls. ADHD made more
errors on negative vs positive words compared to controls
and showed a bias toward positive emotional stimuli. RT:
no group differences
Seymour 2013 [103] 38 ADHD; 41 controls Children and adolescents DANVA Errors: ADHD > Controls for total and fearful child faces. No
group differences for adult faces
Shin 2008 [20] 42 ADHD; 27 controls Children and adolescents Emotion Recognition Test Facial emotion recognition: no group differences
Contextual understanding score: ADHD < Controls
Sinzig 2008 [104] 30 ADHD; 29 controls Children and adolescents Facial affect recognition (FEFA) Total score faces and eyes: ADHD < Controls. Significant
effect for joy (eyes)
Sjöwall 2013 [11] 102 ADHD; 102 controls Children Facial emotion recognition Scores: ADHD < Controls for anger, sadness, fear, happi-
ness, and surprise recognition. No sex differences. Emotion
regulation and emotion recognition showed independent
effects beyond neuropsychological impairment
Sjöwall 2019 [105] 52 ADHD; 72 controls Children Emotion Recognition Task Errors: ADHD > Controls
Taskiran 2017 [106] 28 ADHD; 20 Controls Children Emotion recognition (pictures) Valence and arousal ratings: No group differences. ADHD
with emotion dysregulation (ED) rated unpleasant stimuli
as more negative than ADHD without ED
Tatar 2015 [24] 40 ADHD; 40 controls Adults POFA Accuracy: ADHD < Controls for overall outcome and neutral
expressions. No difference group in Benton Test. In ADHD
group, CPT commissions were associated with erroneously
identified emotions and the error rate identifying anger and
fear
Tatar 2020 [23] 40 ADHD; 40 controls Adults RMET Correct answers: ADHD < Controls. Mental flexibility meas-
ured with the TMT-B predicted performance on the RMET
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Table 1 (continued)
1st author and year Sample size Developmental stage Emotion recognition/processing tool Main findings reported
Tehrani-Doost 2016 [22] 28 ADHD; 27 controls Children Facial emotion recognition Accuracy: ADHD < Controls for anger, happiness, and
sadness. No group differences for neutral faces. RT:
ADHD > Controls only for happiness
Thoma 2020 [107] 19 ADHD; 20 controls Adults TAS-20 Scores: ADHD > Controls, indicating difficulties identifying
and describing feelings
Thoma 2020 [108] 19 ADHD; 25 controls Adults TAS-20 & RMET TAS-20 scores: ADHD > Controls. RMET: no group differ-
ences
Van Cauwenberge 2015 [109] 29 ADHD; 38 controls Children and adolescents SAM rating pictures Arousal and valence ratings: no group differences
RT Emotional n-back: ADHD > Controls
Vetter 2018 [110] 25 ADHD; 25 controls Children and adolescents Perceptual discrimination task RT: no group differences
Accuracy: ADHD < Controls
Viering 2021 [111] 61 ADHD; 51 controls Adolescents and adults Facial emotion match RT: ADHD > Controls. Accuracy: ADHD < Controls
No group differences in non-emotional condition
Villemonteix 2017 [112] 33 ADHD; 24 controls Children Emotional n-back Accuracy: ADHD < Controls
RT: ADHD > Controls in the presence of negative distractors
Walter 2023 [113] 52 ADHD; 24 controls Adults Emotional Word Fluency Test No differences group
Yuill 2007 [114] 19 ADHD; 19 controls Children Emotion matching task Emotional situation-matching: ADHD < Controls for all
emotions
No differences between ADHD with and without ODD
Yuill 2007 [114] 17 ADHD; 13 controls Children Emotion matching task (scaffolding) Emotional situation-matching with scaffolding:
ADHD < Controls
Zhu 2021 [115] 30 ADHD; 20 controls Children and adolescents Emo-Stroop Task RT: ADHD > Controls for positive and negative congruent
condition and for positive incongruent condition
Notes: ADHD, Attention Deficit Hyperactivity Disorder; ADHD-C, Attention Deficit Hyperactivity Disorder, combined subtype; ADHD-I, Attention Deficit Hyperactivity Disorder, inatten-
tive subtype; ADHD-HI, Attention Deficit Hyperactivity Disorder, hyperactive/impulsive subtype; RT; reaction time; RMET, Reading The Mind In The Eyes Test; ER, emotion recognition;
NEPSY-II, Developmental Neuropsychological Assessment, second edition; DANVA, Diagnostic Analysis Of Nonverbal Behavior; IAPS, International Affective Picture System; POFA, Pic-
tures of Facial Affect; TAS-20, Toronto Alexitimia Scale; MNTAP, Minnesota Test of Affective Processing; emo-CPT, emotional Continuous Performance Test; BRIEF, Behavior Rating Inven-
tory of Executive Function; GEES, Govorna emocionalna ekspresija i stavovi; CASP, Child and Adolescent Social Perception Measure; CANTAB, Cambridge Neuropsychological Test Auto-
mated Battery; TMT-B, Trail Making Test, form B; SAM, Self-Assessment Manikin
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Table 2 Summary of the key
characteristics of the included
studies
ADHD, Attention Deficit Hyperactivity Disorder
N % Sample size Mean Range
Total 80 6191
ADHD 53 3257 40.2 10–236
Controls 47 2934 36.2 10–128
Participant age 15.9
Children/adolescents (< 18) 60 77 4766 10.7 4–18
Adults (18 +) 20 23 1425 31.9 18 <
ADHD presentations 42 53
Inattentive (%) 28.4 0–87.5
Hyperactive/impulsive (%) 6.8 0–100
Combined (%) 64.6 0–100
Male participants (%) 80 72 41.9–100
ADHD medication status 53 0–100
Without medication 17 21
Washout period 46 58
Active medication 7 9
Presence of co-occurring diagnoses (%) 59 74 23.6 0–100
Number of observations
Emotion processing tasks
Indirect measures 30 37.5
Direct measures 55 68.75
Type of emotional stimuli
Face 39 48.75 228
Eye 17 21.25 39
Scale 3 3.75 4
Scene 20 25 123
Voice 10 12.5 35
Wor d 4 5 36
Emotional category
Overall 55 68.75 100
Happiness/positive 38 47.5 91
Negative 15 18.75 43
Anger 23 28.75 48
Fear 17 21.25 36
Disgust 13 16.25 28
Sadness 20 25 40
Neutral 22 27.5 57
Surprise 9 11.25 22
Outcome measure
Accuracy/score 67 83.75 259
Reaction Time 22 27.5 131
Other 12 15 75
Table 3 MA1 statistical results
CrI, credibility interval; ER, evidence ratio; HDI, high density interval; ROPE, region of practical equivalence
Outcome gCrI Within variability Between variability Credibility (p < 0) % overlap HDI + ROPE ER
Overall − 0.65 − 0.79, − 0.51 0.31 [0.10, 0.48] 0.41 [0.29, 0.53] 100% 0% > > 100
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emotions” (8.2 overlap between the HDI and ROPE regions,
nevertheless above the established criterion). This indicates
that individuals with ADHD, compared to controls, show a
general difficulty in processing emotional cues, regardless
of the type of emotion involved. As for MA1, the influence
analysis reported no significant results for any effect size,
for any discrete emotion. Bayesian forest plots are shown
in Fig.3a–h.
Moderation analyses for MA2 showed that type of stim-
uli and outcome measures acted as significant moderators
of global effect sizes. The use of words as emotional stim-
uli was associated with more negative effect sizes than
other stimuli used in relation to neutral emotions, indicat-
ing that neutral words are more difficult to identify as such
for people with ADHD vs. controls. Regarding happiness,
eyes and face stimuli were associated with more nega-
tive effects than scenes. (Table6), indicating that people
with ADHD struggle more to identify positive emotions,
compared to controls, when happy faces and eyes stimuli
Fig. 2 Bayesian forest plot (MA1). Graphs are in different colours to
differentiate one study from another. The Bayesian approach allows
for an estimation of the full distribution of parameters, rather than
providing a point data of average and variability. The curves represent
these full distributions of effect sizes. The points within each curve
reflect the multilevel nature of the design, where each point is associ-
ated with the number of effect sizes included in each study
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are displayed. Only a few effect sizes could be computed
for the word and eyes categories (4 and 3, respectively).
In addition to type of stimuli, more negative effect sizes
were observed for accuracy than for other measures in rela-
tion to both negative emotions and happiness (Table7). In
relation to neutral emotions, a statistically significant differ-
ence was observed between accuracy and other measures,
and between Accuracy and RTs, with more negative effect
sizes for accuracy. This indicates that, at least for negative,
neutral and positive emotions, accuracy is more sensitive
to detect differences between those with ADHD and non-
psychiatric controls, with more difficulties observed in those
with ADHD, in line with MA1 results. Finally, in relation to
surprise, more negative effect sizes were reported for RTs
compared to Other measures. A post-hoc comparison across
emotional categories was conducted to explore whether dif-
ferences between ADHD and controls were either equal
or different in magnitude depending on emotion. Analysis
showed no significant differences, suggesting a global emo-
tion processing deficit in ADHD.
Sensitivity analysis
Both the estimates of the main effects and those derived
from the moderation analyses remained stable irrespective
of the prior distribution used (vague or weak) for both MA1
and MA2. See Supplement 6 for more detailed information
(Tables S5 to S13).
Table 4 Significant moderation effects (MA1)
RT, reaction time; CrI, credibility interval; ER, evidence ratio; HDI, high density interval; ROPE, region of practical equivalence
Moderator Subtype Effect size g Contrast Signification
Type of stimuli Scenes − 0.50 (se = 0.12, CrI [− 0.77, − 0.26])
Scales − 1.32 (se = 0.30, CrI [− 1.92, − 0.73]) Scenes > Scales b = 0.82 (se = 0.31, CrI [0.31, 1.33], credibil-
ity = 99%, ER > > 100, 0% overlap between
HDI-ROPE)
Faces − 0.75 (se = 0.31, CrI [− 1.27, − 0.23]) Scales < Faces b = − 0.75 (se = 0.31, CrI [− 1.27, − 0.23], cred-
ibility = 99%, ER > 100, 0% overlap between
HDI and ROPE)
Outcome measures Accuracy − 0.72 (se = 0.06, CrI [− 0.85, − 0.59])
RT − 0.39 (se = 0.15, CrI [− 0.69, − 0.09]) Accuracy < RT b = − 0.33 (se = 0.16, CrI [− 0.60, − 0.06], cred-
ibility = 98%, ER = 46.62, 5.8% overlap between
HDI and ROPE)
Other − 0.08 (se = 0.17, CrI [− 0.33, 0.33]) Accuracy < Other b = − 0.72 (se = 0.17, CrI [− 1.01, − 0.44] cred-
ibility = 100%, ER > > 100, 0% overlap between
HDI and ROPE)
Table 5 MA2 statistical results
CrI, credibility interval; ER, evidence ratio; HDI, high density interval; ROPE, region of practical equivalence
Outcome gCrI Within variability Between variability Credibility
(p < − 0.1)
% overlap
HDI + ROPE
ER
Anger − 0.37 − 0.53, − 0.22 0.12
[0.00, 0.33]
0.37
[0.22, 0.52]
100% 0 > > 100
Disgust − 0.24 − 0.39, − 0.1 0.12
[0.00, 0.33]
0.13
[0.01, 0.30]
98% 0 39.49
Fear − 0.37 − 0.54, − 0.22 0.17
[0.01, 0.438]
0.21
[0.05, 0.35]
100% 0 > > 100
Sadness − 0.34 − 0.49, − 0.19 0.10
[0.00, 0.29]
0.30
[0.14, 0.48]
99% 0 > > 100
Surprise − 0.26 − 0.43, − 0.11 0.09
[0.00, 0.28]
0.13
[0.01, 0.32]
98% 0 45.08
Happiness/ Positive − 0.31 − 0.44, − 0.20 0.25
[0.09, 0.39]
0.24
[0.08, 0.37]
100% 0 > > 100
Negative − 0.20 − 0.38, − 0.04 0.20
[0.02, 0.43]
0.25
[0.05, 0.42]
89% 8.2 8.70
Neutral − 0.25 − 0.43, − 0.09 0.29
[0.07, 0.48]
0.22
[0.05, 0.39]
97% 0.7 30.75
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Fig. 3 Bayesian forest plot
(MA2). Graphs are in differ-
ent colours to differentiate
one study from another. The
Bayesian approach allows for an
estimation of the full distribu-
tion of parameters, rather than
providing a point data of aver-
age and variability. The curves
represent these full distribu-
tions of effect sizes. The points
within each curve reflect the
multilevel nature of the design,
where each point is associated
with the number of effect sizes
included in each study
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Discussion
We conducted a systematic review with Bayesian meta-anal-
ysis to meta-analytically determine for the first time whether
individuals with ADHD have difficulties in processing emo-
tions, compared to non-psychiatric controls, and to identify
what factors may influence these mechanisms. We found
evidence of lower accuracy in processing/recognising emo-
tions in people with ADHD, particularly on self-reported
questionnaires/scales, supporting the assumption of a global
deficit in emotional processing in ADHD. Importantly, we
found that individuals with ADHD exhibit difficulties in
processing all emotional categories, showing a worse per-
formance regardless of their valence (positive or negative).
To our knowledge, it is the first meta-analysis exploring
the effect of the type of stimulus used and the outcome
recorded in research comparing emotional processing
functioning of individuals with ADHD and non-psychi-
atric controls. Our results highlight the relevance of tak-
ing such variables into account, given that the accuracy
measurement, as well as the scales items, seem to be more
sensitive in detecting differences between these groups.
Our findings are consistent with, and extend, previous
meta-analyses conducted on this topic [13, 36]. A gen-
eral emotion processing deficit in ADHD was observed
independently of age, sex, and medication status. Indeed,
prior research did not find any effects of sex [11, 30, 65,
91, 101], or age [36] on emotion processing mechanisms
in ADHD. Interestingly, medication also did not appear
to play a significant role either, although some previous
studies [69, 110] found a trend towards normalization of
these mechanisms following pharmacological treatment,
but this was only tested on small samples. Likewise, a
Table 6 Significant stimulus type moderators for MA2
CrI, credibility interval; ER, evidence ratio; HDI, high density interval; ROPE, region of practical equivalence
Emotion Stimulus Effect size g Contrast Signification
Neutral Word − 0.93 (se = 0.26, CrI [− 1.46, − 0.4])
Face − 0.19 (se = 0.10, CrI [− 0.39, 0.02]) Face > Word b = 0.744 (se = 0.29, CrI [1.21, 0.27], credibility = 99.4%,
ER > > 100, 0% overlap between HDI-ROPE)
Eyes − 0.29, se = 0.24, CrI [− 0.76, 0.19]) Word < Eyes b = − 0.64 (se = 0.36, CrI [− 1.24, − 0.05], credibility = 96%,
ER = 26.14, 4.1% overlap between HDI and ROPE)
Scene − 0.14 (se = 0.10, CrI [− 0.35, 0.06]) Word < Scene b = − 0.79 (se = 0.29, CrI [− 1.26, − 0.32], credibility = 99.6%,
ER > > 100, 0% overlap between HDI and ROPE)
Happiness Face − 0.39 (se = 0.08, CrI [− 0.54, − 0.24])
Scene − 0.11 (se = 0.10, CrI [− 0.31, 0.08]) Face < Scene b = − 0.28 (se = 0.12, CrI [− 0.48, − 0.07], credibility = 98.6%,
ER = 72.8, 0.05% overlap between HDI and ROPE)
Eyes − 0.59 (se = 0.24 CrI [− 1.07, − 0.11]) Eyes < Scene b = − 0.48 (se = 0.26, CrI [− 0.91, − 0.05], credibility = 96.6%,
ER > > 100, 0.5% overlap between HDI and ROPE)
Table 7 Significant outcome measure moderators for MA2
RT, reaction time; CrI, credibility interval; ER, evidence ratio; HDI, high density interval; ROPE, region of practical equivalence
Emotion Measure Effect size g Contrast Signification
Negative Accuracy − 0.42 (se = 0.13, CrI − 0.68, − 0.17]
Other − 0.07 (se = 0.11, CrI [− 0.31, 0.15] Accuracy < Other b = − 0.35 (se = 0.16, CrI [− 0.61, − 0.08], credibility = 98%,
ER = 60.54, 4% overlap HDI-ROPE)
Neutral Accuracy − 0.46 (se = 0.08, CrI [− 0.63, − 0.29]
RT − 0.14 (se = 0.1, CrI [− 0.34, 0.06] Accuracy < RT b = − 0.32 (se = 0.11, CrI [− 0.5, − 0.14], credibility = 99.6%,
ER = > > 100, 0.2% overlap HDI-ROPE)
Other 0.06 (se = 0.12, CrI [− 0.2, 0.3] Accuracy < Other b = − 0.52 (se = 0.14, CrI [− 0.75, − 0.28], credibility = 100%,
ER = > > 100, 0% overlap HDI-ROPE)
Happiness Accuracy − 0.43 (se = 0.07, CrI [− 0.58, − 0.29]
Other − 0.14 (se = 0.10, CrI [− 0.35, 0.07] Accuracy < Other b = − 0.29 (se = 0.12, CrI [− 0.49, − 0.1], credibility = 99%,
ER = > > 100, 3.1% overlap HDI-ROPE)
Suprise RT − 0.41, (se = 0.14, CrI [− 0.7, − 0.14]
Other 0.12, (se = 0.25, CrI [− 0.39, 0.64] RT < Other b = 0.53 (se = 0.29, CrI [0.07, 1], credibility = 97%,
ER = 31.79, 3.8% overlap HDI-ROPE)
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meta-analysis of randomised clinical trials in adults with
ADHD suggests a small effect of ADHD medication on
the bottom-up mechanisms underlying emotion regulation
[116]. Importantly, only 9% of the studies included in our
systematic review had participants with ADHD on current
medication, while in 60% of the studies a 24–48-h washout
period was used.
In MA1, we found that differences between ADHD and
control groups on overall emotion processing were more
marked when self-reported questionnaires/scales were used,
while word stimuli were less sensitive to detect between-
group differences. Of note, prior evidence has shown a pro-
cessing advantage for both emotional scenes and faces over
words with affective content [117–119]. It might be that dif-
ferences between people with ADHD and controls are less
evident for those stimuli that elicit less intense emotions
(i.e., words). Although there were only three studies using
scales, findings based on the Toronto Alexitimia Scale-20
(TAS-20) suggested that people with ADHD may have a
lack of self-awareness in their emotional competence [74,
107, 108]. Taken together, these results suggest that there is
a global impairment in emotion processing in ADHD affect-
ing emotion recognition, appraisal, and expression. In terms
of the outcome measures reported in the studies, we found
that accuracy was more sensitive than reaction times or other
measures (i.e. arousal, valence and psychophysiological) to
detect between-group differences on overall emotional pro-
cessing. Indeed, most studies found higher accuracy in the
control group compared to those with ADHD [11, 24, 57,
62, 81, 105], or no significant differences [31, 32, 71, 77,
83, 93]. No studies found individuals with ADHD perform-
ing more accurately than controls. In contrast, results for
reaction time (RT) were mixed [19, 66, 85, 88, 97]. Other
measures, such as valence and arousal ratings, showed no
differences between ADHD and control groups [64, 86, 106,
109], suggesting similar emotional perception intensity.
When emotional processing was examined across the
specific emotions in MA2, significant differences were
found between ADHD and controls across all emotion cat-
egories. Numerous studies have previously reported differ-
ences between ADHD and control groups in processing of
positive emotions, as assessed by behavioural [11, 54, 62,
66, 68, 97, 104], neural [78, 86] or psychophysiological
measures [64]. These differences cannot be attributed to a
lack of knowledge or problems retrieving emotional labels,
as both groups seem to exhibit similar proficiency in emo-
tional word fluency [113]. Studies that failed to find differ-
ences in positive emotions proposed several explanations,
such as the potential ceiling effect [59], methodological
differences [21], a bias towards positive stimuli [102], and
a high variability in emotional responses [53]. Another
possible explanation lies in the assumption that positive
emotions are seen as a global mood like positive affect or
happiness, whereas negative emotions tend to involve a
wider range of discrete emotions like anger, fear, sadness,
or disgust [120]. In our study, not all discrete negative
emotions provide the same differences between people
with ADHD and non-psychiatric controls. This could be
also happening regarding positive emotion, as Shiota etal.
[120] claim in their model of discrete positive emotions.
According to this model, the positive dimension would
contain a set of discrete emotions each with their neu-
ral, cognitive, behavioural, and functional implications,
that are based on the neural reward system. Indeed, recent
studies have reported differences in the assessment of sev-
eral positive emotions like awe, contentment, amusement,
excitement, serenity, relief, or pleasure [121, 122].
In this second MA, the type of stimuli (i.e., face, eyes,
scene, voice and word) and the outcome measures (i.e.,
accuracy, RT and others) were analysed as moderators of
the emotional categories processing. In terms of the type
of stimuli, faces were the stimuli that best discriminate
between the ADHD and control groups. However, it should
be noted that this type of stimulus is the most common in
emotional processing research. An important limitation of
existing research is that some emotional categories do not
include all the types of stimuli considered (e.g., disgust only
includes a register of words and does not include voice). In
line with MA1, the moderator outcome measures yielded
similar results, with accuracy being associated with larger
effect sizes than other outcome measures. This was espe-
cially true for happiness, negative and neutral categories.
Despite reporting the same tasks, accuracy is more sensitive
than RT and other outcome measures in detecting between-
group differences in emotional processing. Results related to
type of stimuli and outcome measures moderators are more
controversial, with the reviewed literature showing greater
heterogeneity. When assessing emotional processing, labo-
ratory tasks are commonly used, which differ greatly from
ecological contexts. Thus, our results are probably under-
estimating the actual emotional processing impairment in
ADHD. For example, Basile etal. [31] found no significant
differences between the groups in emotion recognition per-
formance, but they noted that easy items were intention-
ally selected. However, in more complex tasks involving
social scenes, individuals with ADHD identified fewer rel-
evant cues compared to controls [54, 100]. In this regard,
Friedman etal. [74] found that adults with ADHD used less
emotional vocabulary to describe interactions between two
characters they viewed in a film. However, ADHD group
did not differ from the control group in their use of non-
emotional vocabulary to describe the scenes, suggesting a
specific difficulty in emotional functioning. When faced with
a dynamic emotion recognition task, ADHD also exhibited
more errors and a greater tendency to confuse emotions than
controls [76].
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We observed that inattention was linked to a higher
number of errors in people with ADHD during emotion
recognition tasks [89], and it has been suggested that this
symptomatologic domain might underlie failures in emo-
tion processing [69], resulting in missing emotional cues.
Nevertheless, some studies have not found differences
between ADHD and control groups in attentional tasks
unrelated to emotion recognition, such as face recognition
[24, 74], gender recognition [77], geometric recognition
[21], or object recognition tasks [26], so emotional pro-
cessing differences could not be fully explained by inat-
tention. Conversely, impulsivity can lead to hurried iden-
tification based on incomplete data, potentially resulting in
misinterpretation of emotions and maladaptive regulatory
responses, which are common in ADHD [13, 14, 35]. Even
though it remains unclear how core symptoms of ADHD
are related to impairments in emotional processing, our
results suggest that, despite the high variability in task
performance among individuals with ADHD probably due
to fluctuations in attention focus, the general difficulty in
emotion processing extends beyond the core symptoms of
the disorder and cannot be completely explained by them.
Overall, the results of our study highlight the relevance
of emotional processing assessment in individuals with
ADHD in clinical practice, as this appears to be a critical
feature of the disorder. The emotional difficulties observed
go beyond the ADHD core symptoms and pharmacological
treatment does not seem to have a relevant effect on this
regard, hence the need to address this aspect specifically
to impact on social relationships and quality of life for
people with ADHD.
The findings of this study should be considered in the
light of some limitations. Studies in which emotional
stimuli have been used in different ways were analysed
jointly. While we have found information that converges
into robust evidence, further research is needed regard-
ing the complexity of emotional stimuli in ecological
contexts. Furthermore, due to limitations in funding, we
limited the search to articles English language. Despite
potential methodological limitations that may exclude rel-
evant studies, this study's extensive inclusion of papers
and use of Bayesian methodology ensure robust results.
Future research should explore ADHD's impact on emo-
tion processing using dynamic tasks resembling real-life
interactions, across different time points and while con-
trolling for attention, impulsivity, and symptom sever-
ity. It remains uncertain whether the observed emotion
processing deficits in our study are primary or second-
ary to attentional and executive function impairments in
ADHD. While some suggest these deficits relate to work-
ing memory failures in ADHD [123, 124], further research
is needed. Additionally, investigating positive emotions in
ADHD may shed light on variability in results in this area.
Conclusions
This study indicates that individuals with ADHD show
impairments in recognizing and processing emotions, which
appear consistent across age, sex, and pharmacological con-
ditions. These impairments span all basic emotions, suggest-
ing a widespread deficit with notable variability. Therefore,
assessing emotion processing in ADHD using composite
scores across various ecological contexts and time points
could help establish a specific profile for improved detection
and diagnosis in clinical practice.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00787- 025- 02647-3.
Acknowledgements This study was supported by projects
PID2021-125677OB-I00 and PID2022-141420NB-I00 from Minis-
terio de Ciencia e Innovación of Spain, and by an FPI-UNED grant
from Universidad Nacional de Educación a Distancia given to AMSG.
Samuele Cortese, NIHR Research Professor (NIHR303122) is funded
by the NIHR for this research project. The views expressed in this
publication are those of the author(s) and not necessarily those of
the NIHR, NHS or the UK Department of Health and Social Care.
Samuele Cortese is also supported by NIHR grants NIHR203684,
NIHR203035, NIHR130077, NIHR128472, RP-PG-0618-20003 and
by grant 101095568-HORIZONHLTH- 2022-DISEASE-07-03 from
the European Research Executive Agency.
Author contributions A.M.S.G. had the idea for the study; J.M., J.A.
and J.A.H. contributed to the study conception and design; A.M.S.G.
performed the literature search and the data extraction; A.J.S.C. per-
formed data analysis; A.M.S.G. and A.J.S.C. wrote the first draft of the
manuscript; A.B., S.C. and J.M. supervised, and all authors critically
reviewed the work.
Funding Open Access funding provided thanks to the CRUE-CSIC
agreement with Springer Nature. The funders had no role in the study
design, data collection and analysis, the decision to publish, or the
preparation of the manuscript.
Data availability Data is provided within the manuscript or supple-
mentary information files.
Declarations
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
European Child & Adolescent Psychiatry
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Authors and Aliations
Ana‑MaríaSoler‑Gutiérrez1,2 · AlbertoJ.Sánchez‑Carmona3· JacoboAlbert4 · JoséAntonioHinojosa5,6,7 ·
SamueleCortese8,9,10,11,12,13 · AlessioBellato8,9,14,15,16 · JuliaMayas1
* Julia Mayas
jmayas@psi.uned.es
1 Faculty ofPsychology, Universidad Nacional de Educación
aDistancia (UNED), Despacho 2.36 bis, Calle Juan del
Rosal, 10, 28040Madrid, Spain
2 Escuela Internacional de Doctorado de la UNED
(EIDUNED), Universidad Nacional de Educación aDistancia
(UNED), Madrid, Spain
3 Centro Neuromottiva, 28016Madrid, Spain
4 Faculty ofPsychology, Universidad Autónoma de Madrid,
Madrid, Spain
5 Instituto Pluridisciplinar, Universidad Complutense de
Madrid, Madrid, Spain
6 Faculty ofPsychology, Universidad Complutense de Madrid,
Madrid, Spain
7 Centro de Investigación Nebrija en Cognición (CINC),
Universidad Nebrija, Madrid, Spain
8 School ofPsychology, University ofSouthampton,
Southampton, UK
9 Centre forInnovation inMental Health, University
ofSouthampton, Southampton, UK
10 Solent NHS Trust, Southampton, UK
11 Clinical andExperimental Sciences (CNS andPsychiatry),
Faculty ofMedicine, University ofSouthampton,
Southampton, UK
12 Hassenfeld Children’s Hospital atNYU Langone, New York
University Child Study Center, NewYork, NY, USA
13 DiMePRe-J-Department ofPrecision andRigenerative
Medicine-Jonic Area, Università degli Studi di Bari “Aldo
Moro”, Bari, Italy
14 Institute forLife Sciences, University ofSouthampton,
Southampton, UK
15 School ofPsychology, University ofNottingham, Semenyih,
Malaysia
16 Mind andNeurodevelopment (MiND) Research Group,
University ofNottingham, Semenyih, Malaysia
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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5.
6.
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