ORIGINAL ARTICLE

Facial Emotion Recognition Abilities in Women
Experiencing Eating Disorders
Andrea Wyssen, PhD, Junpeng Lao, PhD, Helen Rodger, MSc, Nadine Humbel, MSc, Julia Lennertz, MSc,
Kathrin Schuck, PhD, Bettina Isenschmid, MD, Gabriella Milos, MD, Stephan Trier, MD,
Katherina Whinyates, MSc, Hans-Jörg Assion, MD, Bianca Ueberberg, MD, Judith Müller, MSc,
Benedikt Klauke, PhD, Tobias Teismann, PhD, Jürgen Margraf, PhD, Georg Juckel, MD,
Christian Kossmann, MD, Silvia Schneider, PhD, Roberto Caldara, PhD, and Simone Munsch, PhD
ABSTRACT
Downloaded from http://journals.lww.com/psychosomaticmedicine by BhDMf5ePHKbH4TTImqenVBMHby7N9qoLOlMIyLYjeX1IPo6XfN28jI/ImDzo7b+E on 02/07/2019

Objective: Impairments in facial emotion recognition are an underlying factor of deficits in emotion regulation and interpersonal difficulties in mental disorders and are evident in eating disorders (EDs).
Methods: We used a computerized psychophysical paradigm to manipulate parametrically the quantity of signal in facial expressions ofemotion (QUEST threshold seeking algorithm). This was used to measure emotion recognition in 308 adult women (anorexia nervosa[n = 61], bulimia nervosa [n = 58], healthy controls [n = 130], and mixed mental disorders [mixed, n = 59]). The M (SD) age was
22.84 (3.90) years. The aims were to establish recognition thresholds defining how much information a person needs to recognize a facial
emotion expression and to identify deficits in EDs compared with healthy and clinical controls. The stimuli included six basic emotion
expressions (fear, anger, disgust, happiness, sadness, surprise), plus a neutral expression.
Results: Happiness was discriminated at the lowest, fear at the highest threshold by all groups. There were no differences regarding
thresholds between groups, except for the mixed and the bulimia nervosa group with respect to the expression of disgust(F(3,302) = 5.97, p = .001, η2 = .056). Emotional clarity, ED pathology, and depressive symptoms did not predict performance(R2Change ≤ .010, F(1,305) ≤ 5.74, p ≥ .079). The confusion matrix did not reveal specific biases in either group.
Conclusions: Overall, within-subject effects were as expected, whereas between-subject effects were marginal and psychopathology did
not influence emotion recognition. Facial emotion recognition abilities in women experiencing EDs compared with women experiencing
mixed mental disorders and healthy controls were similar. Although basic facial emotion recognition processes seems to be intact, dysfunctional aspects such as misinterpretation might be important in emotion regulation problems.
Clinical Trial Registration Number: DRKS-ID: DRKS00005709.
Key words: anorexia nervosa, bulimia nervosa, eating disorders, facial emotion recognition, women.

INTRODUCTION

threshold for fear is 97% signal strength, whereas it is only an average of 35% for happiness (5). In addition, there is evidence
underlining a large intraindividual variability in emotion recognition in adults, i.e., in terms of signal strength to correctly recognize
an emotion expression (e.g., (5)).

Recognizing and decoding emotions in the faces of others is acore characteristic of human cognition and a fundamental
feature of social interaction (1,2). Previous research has shownthat there are intra- and interindividual differences in observers'
perceptual thresholds for the discrimination of facial emotion expressions. Happiness, surprise, fear, anger, disgust, and sadness
are described as the six basic emotions, which cannot be broken
down into smaller segments (see e.g., (3)). Among these emotions,
happiness can be discriminated at the lowest threshold. Conversely, much more information is needed to correctly identify fear
(4). A recent study identified that the average recognition

AN = anorexia nervosa, ANCOVA = analysis of covariance,
BDI-II = Beck Depression Inventory, BED = binge-eating disorder,
BMI = body mass index, BN = bulimia nervosa, DERS = Difficulties
with Emotion Regulation Scale, DIPS = Diagnostisches Interview fürPsychische Störungen, ED = eating disorder, EDE-Q = Eating Disorder
Examination-Questionnaire, HC = healthy control, RMET = Reading
the Mind in the Eye Task

Supplemental Content
From the Departments of Clinical Psychology and Psychotherapy (Wyssen, Humbel, Munsch) and Visual and Social Neuroscience (Lao, Rodger,
Caldara), University of Fribourg, Fribourg, Switzerland; Faculty of Psychology, Mental Health Research and Treatment Center (Lennertz, Schuck,
Teismann, Margraf, Schneider), Ruhr University Bochum, Bochum, Germany; Kompetenzzentrum für Essverhalten (Isenschmid), Adipositas und Psyche
Spital Zofingen; Departement of Consulation-Liaison Psychiatry and Psychosomatic Medicine (Milos), University Clinic Zürich, Zürich, Switzerland;
Privatklinik Aadorf (Trier), Aadorf; Klinik Schützen (Whinyates), Rheinfelden, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatic
Medicine (Assion, Ueberberg), LWL-Klinik Dortmund; Department of Psychiatry, Psychotherapy, Psychosomatic and Preventive Medicine (Juckel, Kossmann),
LWL-Klinik Bochum, Ruhr University Bochum, Bochum, Germany; and Christoph-Dornier-Clinic for Psychotherapy (Müller, Klauke), Münster, Germany.
Address correspondence to Simone Munsch, PhD, Institute of Psychology, Department of Clinical Psychology and Psychotherapy, University of
Fribourg, Rue de Faucigny 2, 1700 Fribourg, Switzerland. E-mail: simone.munsch@unifr.ch
Caldara and Munsch shared last authorship.
Received for publication January 17, 2018; revision received October 31, 2018.
DOI: 10.1097/PSY.0000000000000664
Copyright © 2019 by the American Psychosomatic Society

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ORIGINAL ARTICLE

positive emotions and neutral states (28). According to the critiquethat responses of the RMET are simply coded as right or wrong,more subtle tasks have been developed, where emotion recognition is assessed by computerized distorted facial pictures such asmorphing or variations in signal strength (29). A small group ofpatients with AN (n = 28) was less accurate in identifying basic
emotions than HCs (n = 28) when applying a facial emotion recognition task with the six basic emotions, which were displayedusing morphs (from neutral to the full expression) (30). In contrast, no difference in emotion recognition was found between
HCs (n = 73) and patients with EDs (n = 49 AN, n = 16 BN)
when participants rated video clips displaying a discrete emotion (happy, sad, anger, neutral) (31). Using pictures of faces
portraying emotions with varying levels of ambiguity, less accurate recognition of disgust and an error response bias toward
anger in AN patients (n = 35) compared with HCs (n = 42) was
found (32). There is further evidence for reduced accuracy inrecognizing subtle emotional expressions in nonclinical participants with ED symptoms (n = 40) compared with HCs (n = 40)
in a facial emotion recognition test with static images of faces
each representing one of five emotions (happiness, sadness, anger, fear, and disgust) on different intensity levels (33). In addition, two studies investigated attention processes influencing
emotion recognition in terms of reaction times in patients with
AN and BN. They found a bias toward rejecting versus accepting
faces and difficulties to shift attention away from rejection faces
especially in individuals with a current or history of EDs compared with HCs in a dot-probe task (34). Moreover, AN patients
revealed an attention bias toward angry faces and a reduced capacity in processing of positive facial emotion expressions comparedwith HCs and obese individuals (35). This was in an experimental
task in which adjectives had to be rated regarding their positive or
negative value after a short priming with a schematic facial emotion expression (happy, sad, angry, neutral). However, recent studies in samples experiencing other mental disorders or HCs haverevealed rather limited psychometric values of the dot-probe paradigm in terms of internal consistency or test-retest reliability (36).
Brewer and colleagues (37) applied a novel facial emotion recognition task, in which they showed basic facial emotion expressions(happiness, sadness, surprise, fear, disgust, anger, and pain). They
used gray-scaled images with different levels of visual noise for
800 milliseconds in an adaptive stair case procedure to determine
the maximal level of noise at which an individual could still recognize
an emotion reliably. They found that in 21 women with EDs (AN andBN) and 21 healthy women, the level of co-occurring alexithymia
was predictive of poorer emotion recognition ability, whereas the degree of ED pathology was not. Lule and colleagues (38) reported thatin a sample of 15 female adolescents with AN and 15 matched HCs
using the facial expressed emotion labeling test (six basic emotions:anger, fear, sadness, surprise, disgust, and happiness, presented for
300 milliseconds), patients showed impairments in correctly identifying the emotion disgust, whereas they performed better in the recognition of happiness. However, after controlling for depression,group differences disappeared. Current evidence (32) refers to a possible influence of body mass index (BMI) and length of illness in
AN patients (n = 35) on recognition of disgust using pictures
depicting blended emotions with different levels of ambiguity.
No effects have been found for depression, obsessive-compulsive symptoms, alexithymia, and psychotropic medication (32).

Kret and Ploeger (6) proposed disrupted processing of emotion
expression in the faces of others as an important factor underlying
mental disorders, which could partly explain high comorbidity
rates. They envisaged two main steps of emotion processing: first,
emotion perception, described as the attention to emotional stimuli
in the environment, and the correct identification and adequate
response to the emotion. Second, emotion regulation defined as
managing one's own emotional state. To date, there is considerable evidence for the second step of the model, e.g., increased
levels of suppression or rumination in depressive or anxiety disorders (e.g., (7,8)). However, our understanding of similarities
and disorder-specific deficits related to the first part of the model(i.e., basic mechanisms as the processes underlying emotion recognition) is still incomplete. In the present study, we assess emotion
recognition via recognition thresholds as well as via confusion
rates. The basic assumption (following established theories, seee.g. (6)) is that recognition thresholds and confusion rates represent aspects of emotion recognition accuracy. The recognition
threshold is more related to impaired interpretation of emotional
facial stimuli, whereas the confusion rate is related to an attentional bias (e.g., a bias toward negative emotions).
There is growing evidence on impairments in facial emotion
recognition irrespective of task type in patients with borderline
personality disorder (9,10), schizophrenia (11), autism spectrum
disorders (12), bipolar disorder (13), major depressive disorder(14), and posttraumatic stress disorder (15). Impairment in emotion recognition in these groups of patients is pronounced and often includes a biased perception such as a heightened sensitivity
to negative emotions, (16) e.g., to fear and disgust in anxiety disorders (15,17), influences subsequent processes such as the interpretation of a situation, and thus is part of an overall problematicsocial cognitive functioning. Eating disorders (EDs) have also
been linked to emotion processing deficits (6,18) as well as toproblems in social cognition and social interactions (19). In their“cognitive-interpersonal maintenance model of anorexia nervosa,”
Treasure and Schmidt (18) describe an automatic attentional
bias toward negative emotions and an impaired interpretationof emotions related to problems in social interaction and social
anhedonia in anorexia nervosa (AN). Difficulties in social communication and the avoidance of close relationships are importantfeatures AN because they foster ED characteristic behaviors andbeliefs. This leads to social isolation, which is associated with lesssocial support, and fewer social activities, which connects to othercommon characteristics of patients with EDs such as inhibition,
shyness, and a tendency to internalize problems (18).
Recent studies investigating emotion recognition abilities in
EDs often applied the Reading the Mind in the Eye Task (RMET)(20). For each of the 36 gray-scale cropped photos of eyes, themost appropriate word among four mental state terms is selected
as the probable correct description of the person feelings whoseeyes are shown (21). The RMET represents not only an elementary
emotion recognition test but also a theory of mind test because it isnecessary to project one's thoughts into the mind of the person in
the picture to attribute the correct mental state (22). For ED patients, results on the RMET were ambiguous: some found reduced
emotion recognition in ED patients, especially in individuals withAN (23–25), whereas results for patients with BN are less clear.
Several authors report similar performances for BN patients andHCs (25–27), whereas others referred to difficulties, e.g. in reading

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Emotion Recognition in Eating Disorders

was part of a large cross-sectional and longitudinal multicenter study (42)
investigating moderators of the effect of media on body image in womenexperiencing EDs versus experiencing mixed mental disorders and HCs.
Data were collected between November 2012 and February 2017.
An inclusion criterion for all participants was age between 18 and35 years. In total, 67 participants dropped out during the assessment period
before the application of the emotion recognition task (26 HC, 6 AN, 13
BN, 22 mixed). Another three participants (2 AN, 1 HC) were excluded
from the analyses because of only providing “don't know” response during
the task. Of the remaining participants, 130 were healthy female students
who did not meet current or former criteria for any mental disorder according to the DSM-IV-TR (43). The three groups of patients included
61 women with AN, 58 with BN, and 59 women experiencing either froma depressive (n = 36) or from an anxiety disorder (n = 23). Comorbidity
rates in our ED samples were comparable with the literature (44,45), approximately 50% revealing one or more comorbid diagnosis. A total of
36.1% of AN patients experienced a comorbid depressive disorder,
19.7% from a comorbid anxiety disorder, whereas 43.1% of BN patients
presented a comorbid depressive disorder and 27.6% with a comorbid
anxiety disorder. For the mixed sample, the presence of an ED was an exclusion criterion. All mental disorders were assessed according to the
DSM-IV-TR criteria. In the patients' group, 59% were treated in an inpatient and 41% in an outpatient setting. Exclusion criteria for study participation were psychotic disorders and pregnancy as well as other mental or
physical conditions that would not have allowed the participation in thelaboratory experiment or the inability to give informed consent. Healthy
participants were bachelor or master students at the University of Fribourg(Switzerland) and had at least secondary school level. Patients were recruited from different psychiatric units in Switzerland and from psychiatricand psychotherapeutic units in Germany. Forty-seven percent of all participants were Swiss, 47% were German, and 6% belonged to anothernationality. Participants were compensated for their time with course credits(students) or money (patients). The study protocol was approved by the
local human ethics committees of the Department of Psychology of the
University of Fribourg (Protocol Number 2012_001), the canton of Fribourg(Protocol Number 023/12-CER-FR), Aargau (Protocol Number 2013/057),
Zürich (Protocol Number 2013-0457), and Thurgau (Protocol Number 2013/24), as well as by the local ethics committees of the Department of Psychology at the Faculty of Medicine of the Ruhr University Bochum(Protocol Number 142). Informed consent was obtained from all participants.
Table 1 displays characteristics of the present samples.

To sum up, although there are indications of more pronounced
difficulties in, e.g., the RMET in AN, findings in BN are less clear(25–27). Applying more subtle methods assessing emotion recognition by computerized distorted facial pictures such as morphingor variations in signal strength or focusing on attentional patterns
did not reveal a clear line of results. Although some studies founddifferences between EDs and healthy participants, others did not(29). Additional findings of previous studies in samples of AN
and BN patients compared with HCs showed that comorbid depression, duration of illness, BMI, and alexithymia are associatedwith poorer emotion recognition performance.
This lack of consistent knowledge regarding the ability to identify correctly emotions is detrimental, influences social cognition,and plays an important role in the “cognitive-interpersonal maintenance model of anorexia nervosa” (18). Disturbed eating behavior(e.g., restriction of food intake, binge eating, and vomiting) isthought to be triggered by deficient emotion regulation after social
conflicts (18,25,39). A training of emotion processing, including
emotion recognition, could contribute to an efficacious treatment
because it addresses highly relevant maintaining factors (40).
The objectives of our study were therefore twofold: first, weaimed to assess individual facial emotion recognition thresholds
in a large sample of women with AN and BN and compare themwith a group of women with mixed mental disorders (depressive
and anxiety disorders) and healthy women. Based on preliminary
findings (30,32), we assumed impairments in terms of a higher signal threshold in emotion recognition in AN, but not in BN patients,
where we expected similar performance as in HCs. The mixed
control group of patients experiencing depressive and anxiety disorder was included to explore the question of similarity or specificity of emotion recognition deficits. Second, response profiles
for each facial expression, including their confusion with otherexpressions, were analyzed to identify biases for certain emotions.
Based on existing evidence (28,34,35), we attempted to validate
previous findings of a more pronounced biases toward negative/
rejecting facial emotion expressions (such as angry faces) in patients
experiencing AN and BN.
In our analyses, we controlled for age and examined the roleof potential patient-related factors such as BMI, emotional clarity(i.e., the extent to which an individual understands the emotions
that it is experiencing), depressiveness, and degree of ED pathology. Whereas many previous studies have used the RMET or prototypical pictures of faces that show an emotion expression very
intensely, we favored the novel psychophysical method QUEST,
an adaptive staircase procedure (originally developed by (41)) tomeasure recognition thresholds for expression discrimination ina signal detection paradigm. The sensitivity of this procedure allows
the detection of subtle emotion recognition difficulties, which
may nevertheless impair social functioning and contribute to themaintenance of ED pathologies (33). It is the first time that the
QUEST paradigm has been used in a large sample of patients
experiencing AN, BN, and mixed mental disorders and comparedwith an HC group.

Instruments and Materials
In the following, measures included in this substudy are introduced. For an
introduction to the overall assessment, refer to Munsch (42).

Diagnostic Interview
The Diagnostisches Interview für psychische Störungen (DIPS) (46) is a
structured diagnostic interview based on the DSM-IV-TR (43) with good
reliability and validity. Ten percent of the interviews in the present study
were coded twice. Interrater reliability was satisfying with .80, .85 forprimary diagnosis, and .90 for comorbidity (Fleiss κ). The interview was
conducted in a face-to-face inpatient settings. For all other participants,
the DIPS was applied via telephone. The validity of telephone-based interviews is comparable to face-to-face interviews when screening for diagnoses (47). The interviews were conducted by trained and supervised masterstudents in clinical psychology or postgraduate psychologists.

Body Mass Index

METHOD

Weight and height were measured at the end of the experiment (for a
detailed description see (42)) on an electronic balance scale (Seca) and by
a stadiometer (Seca) with participants wearing clothing without shoes.
BMI was calculated as weight in kilograms divided by the square of heightin meters.

Participants
Altogether 381 adult women from the age of 18 to 35 years were eligible toparticipate in the study. The assessment of emotion recognition capacities

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ORIGINAL ARTICLETABLE 1. Sample Characteristics (N = 308)
Statisticsa

HC (n = 130)

AN (n = 61)

BN (n = 58)

Mixed (n = 59)

M (SD)

M (SD)

M (SD)

M (SD)

Age

21.53 (2.18)

22.87 (4.57)

23.16 (3.96)

25.29 (4.79)

11.46 *** Mixed > HC***, AN**, BN**; HC < AN*, BN**BMI

22.01 (2.63)

17.05 (1.63)

22.64 (2.56)

24.32 (6.45)

39.61 ***

BDI-II

3.89 (4.71)

24.50 (11.15)

25.78 (11.63)

25.48 (9.16)

117.85 ***HC < AN, BN, mixed***DERS total

67.32 (16.68) 110.73 (24.13) 114.19 (24.30) 113.94 (23.32) 107.06 ***HC < AN, BN, mixed***F

Bonferroni Post hocbpAN < HC, BN, mixed***, mixed > HC***, BN*

DERS clarity

8.72 (2.62)

16.07 (4.91)

16.43 (5.29)

15.33 (4.55)

62.67 ***HC < AN, BN, mixed***

EDE-Q

0.76 (0.70)

3.53 (1.23)

4.06 (1.31)

1.50 (1.28)

142.59 ***HC < AN, BN, mixed***; AN < BN*;
mixed < AN, BN***; HC < mixed***HC = healthy controls; AN = anorexia nervosa; BN = bulimia nervosa; Mixed = mixed mental disorders; BMI = body mass index; BDI-II = Beck Depression Inventory;
EDE-Q = Eating Disorder Examination-Questionnaire; DERS = Difficulties in Emotion Regulation Scale.
* p < .05.
** p < .01.
*** p < .001.
aOne-way ANOVA between diagnostic groups, df age (3,190.86), df BMI (3,100.72), df BDI-II (3,182.77), df DERS total (3,212.10), df DERS awareness (3,232.34), df DERS
clarity (3,192.75), and df EDE-Q (3,197.27); because analyses showed a violation of the assumption of homogeneity of variances, the results of the Brown-Forsythe test, which
provides good robustness, are reported.
b

Bonferroni post hoc test, only significant relationships are reported.

Questionnaires
Standardized questionnaires in German were presented via an online survey platform (LimeSurvey).

expression of emotions across groups. For more information see Text S1and Figure S1, Supplemental Digital Content, http://links.lww.com/
PSYMED/A531.

Depressive Symptoms (BDI-II)

Procedure

The “Beck Depression Inventory” (BDI-II) was used to assess depressive
symptoms. It consists of 21 items and measures the severity of depressive
symptoms during the previous 2 weeks (48). The instrument has good validity and reliability (48,49). Cronbach's α in this sample was .96.

The “Eating Disorder Examination-Questionnaire” (EDE-Q; German
Version by (50)), is a self-report version of the Eating Disorder Examination (EDE (51)) assessing ED behaviors and symptoms during the past
28 days. It consists of 28 items; 22 items address attitudinal aspects of
ED pathology. These items are rated on a scale from 0 to 6 and can besummarized into a global score as well as the following four subscales:
restraint eating, eating concern, shape concern, and weight concern. Six
additional items assess the frequency of key ED behaviors. The EDE-Q
has good psychometric properties and is suitable to detect symptoms of
EDs in individuals of the general population (52). In the present sample,
the values were between .90 and .97.

This work is part of a multicenter study (for a detailed description of theoverall study protocol, see (42)).
Participants were introduced to the computerized emotion recognition
task and told that they would see a series of facial expressions displaying
a specific emotion. They were asked to indicate as accurately as possible
which emotional expression they recognized by pressing the corresponding
key on a keyboard. The participants were also told that the responsetime was not important and that they could take as much time as needed
to give their response. The facial expression would be shown only briefly(500 milliseconds). Notice that the 500-millisecond presentation time
was chosen because it allows sufficient time for emotion recognition to
occur according to previous studies (55,56) and we do not expect an effectof presentation time. Event-related potential studies show that differencesamong emotional expressions are processed from 140 milliseconds (55).
The experiment took approximately 10 to 15 minutes to complete.
For more information, see Text S2, Supplemental Digital Content,
http://links.lww.com/PSYMED/A531.

Emotion Regulation

Data Analyses

Eating Disorder Pathology

The “Difficulties with Emotion Regulation Scale” (DERS (53)) (German
version by (54)) is a 36-item self-report questionnaire assessing six dimensions of emotion regulation (none acceptance of emotions, difficulties inengaging in goal directed behavior, impulse control difficulties, lack ofemotional awareness, limited access to emotion regulation strategies, andlack of emotional clarity) on a scale from 1 to 5. All scales showed goodinternal consistencies and validity (53,54). Cronbach's α of the DERS
global score in the present sample was .97.

Threshold Detection
The standard implementation of the QUEST procedure is provided in thePsychtoolbox Version 3 ((41)) as a toolbox in a subdirectory. It contains acollection of functions that extracts different quantity related to the threshold from the psychometric curved. Here, we use one of the MATLAB
function (QuestMean.m) from the toolbox to compute the mean of the
QUEST posterior probability density function (pdf ) as the threshold foreach participant. For more information, see Text S3, Supplemental DigitalContent, http://links.lww.com/PSYMED/A531.

Emotion Recognition
The QUEST threshold seeking algorithm was implemented in MATLAB
7.10.0. QUEST is a Bayesian adaptive staircase procedure for establishingan observer's threshold sensitivity to some physical attributes of a stimulus(41), providing a measure of how effectively an observer can discriminate
a stimulus. Here, we investigated threshold sensitivity for signal of facial

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Threshold Differences Across Groups
To test whether emotion recognition thresholds were different in the fourgroups, a repeated measures analysis of covariance (ANCOVA) was performed with the within-subject factor emotion and the between-subject

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Emotion Recognition in Eating Disorders

factor group (7  4); age was entered as covariate. We computed multiple regression models to qualify whether the expected differences inemotion recognition was the result of the intervening variables (BMI,
depressiveness, ED pathology, and emotional clarity). Therefore, we
predicted emotion recognition threshold with age (step 1) and the respective intervening variable (step 2). This analysis was performed
for each emotion separately.

HC group and the mixed group. The HC group significantly differed from the patients groups on depressive symptoms (BDI-II)
and emotion regulation (DERS). The AN group showed significantly lower values on BMI than all other groups.

Facial Emotion Expression Recognition Thresholds
Across Groups
The repeated measures ANCOVA (covariate age) revealed significant between- and within-subject effects (dependent variable recognition threshold): F(3,302) = 3.15, p = .025, η2 = .030,
F(4.65,1404.66) = 21.89, p < .001, η2 = .068. The interaction between the within-subject factor (emotion) and the covariate (age)was not significant (F(4.65,1404.66) ≤ 1.12, p = .35, η2 = .004).
The interaction group by emotion (F(13.95,1404.66) = 1.83,
p = .030, η2 = .018) was significant. Huynh-Feldt's correction
was applied to the within-subject factor, because Mauchly's testof sphericity was significant. As displayed in Figure 1, all groups
performed best for the emotion happiness, which had the lowest
perceptual threshold.
The one way ANCOVA (controlling for age) revealed no significant effect of the between-subject factor group on the recognition of
the emotion happiness (F(3,302) = 0.32, p = .814, η2 = .003), surprise (F(3,302) = 0.11, p = .952, η2 = .001), fear (F(3,301) = 1.42,
p = .238, η2 = .014), sadness (F(3,302) = 0.85, p = .465,
η2 = .008), anger (F(3,302) = 1.56, p = .198, η2 = .015), and neutral(F(3,302) = 1.51, p = .212, η2 = .015). A significant effect was
found for the emotion disgust (F(3,302) = 5.97, p = .001,
η2 = .056). Contrast analyses (contrast 1: HC versus AN; contrast
2: HC versus BN; contrast 3: HC versus mixed; contrast 4: AN versus BN; contrast 5: AN versus mixed; contrast 6: BN versus mixed)
showed that the mixed group needed significantly more information
to correctly identify disgust than the HC group, t(85.51) = −3.12,
p = .002, d = 0.49, and the AN group, t(106.28) = −3.26,
p = .002, d = 0.59. Moreover, the BN group performed worse thanthe HCs, t(89.76) = −2.20, p = .030, d = 0.35, and the AN group,t(109.66) = −2.43, p = .017, d = 0.40. Bonferroni correction wasapplied because of alpha error accumulation with multiple testing.

Confusion Matrix Analysis
Although the QUEST procedure is efficient in estimating the desired
threshold, misclassification from the participant is not well represented ina multinomial setting such as the current experiment. Thus, to explore fully
the individual response profile during each task, we also analyzed the confusion matrix of participant's response regardless of the presented stimuli
signal level. For each participant, we constructed a confusion matrix where
each row displays one of the six presented facial expressions plus neutral,and each column represents the response given by the observers. The
values in the matrix thus show the frequency of response, conditioned on
the presented stimulus. Ideally, the diagonal of the matrix, which represents
the correct identification, should be at 75% by design of the task. However,this is not necessarily the case because some expressions could not be correctly identified at 75% even at the maximum signal level and we applied astopping rule. Moreover, we did not consider Don't Know response as confusion, and therefore, the sum of each row could be less than 100%. Nonetheless, the resulting matrix quantifies the common confusion made by theparticipant during the task. We then computed the average confusion matrix
for each group to compare the response profile.
Moreover, to explore the (dis)similarity between and within eachgroup, we computed full representational similarity matrix (57) by calculating the Pearson correlation of the confusion matrix between each uniquepair of participants. Similarities within and between the groups are compared using a bootstrapped 95% confidence interval.

RESULTS
Sample Characteristics
Participant's characteristics are presented in Table 1. The mixed
group was significantly older than the other groups. As expected,
AN and BN patients had higher ED pathology values than the

FIGURE 1. Mean emotion recognition thresholds across groups (N = 308). Notes: Mean values of thresholds were reported. Covariates in
the model were calculated with age = 22.82. Color image is available only in online version (www.psychosomaticmedicine.org).

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ORIGINAL ARTICLETABLE 2. Mean Emotion Recognition Thresholds (Signal
Strength Where Expression Is Predicted to be Discriminatedon 75% of Trials) Across Groups (N = 308)

The mean values and standard deviation for each emotion expression divided into the diagnostic groups were presented in Table 2.

The Influence of Depressiveness and ED Pathology on
Emotion Recognition Thresholds

HC (n = 130) AN (n = 61) BN (n = 58) Mixed (n = 59)
ThresholdM (SD)

M (SD)

M (SD)

M (SD)

Fear

1.04 (0.23)

1.07 (0.21)

1.03 (0.24)

1.09 (0.18)

Anger

0.53 (0.13)

0.55 (0.13)

0.56 (0.13)

0.60 (0.19)

DisgustHappiness

0.68 (0.20)
0.32 (0.10)

0.66 (0.20)
0.33 (0.04)

0.76 (0.24)
0.34 (0.07)

0.81 (0.28)
0.32 (0.10)

Neutral0.49 (0.13)

0.49 (0.11)

0.49 (0.11)

0.54 (0.15)

Sadness0.69 (0.20)

0.71 (0.20)

0.73 (0.21)

0.72 (0.20)

Surprise0.51 (0.16)

0.51 (0.16)

0.53 (0.16)

0.53 (0.16)

Statistical tests with multiple linear regression analyses showedthat age has a small but significant effect on the recognition threshold of the emotion anger (R2Change = .027, F(1,305) = 8.45,
p = .004) and surprise (R2Change = .015, F(1,305) = 4.55,
p = .034). None of the intervening variables (BMI, emotional
clarity, depressiveness, ED pathology) explained additionalvariance in either of the emotion recognition thresholds: fear
(R2Change ≤ .004, F(1,305) ≤ 0.62, p ≥ .269), anger(R2Change ≤ .006, F(1,305) ≤ 5.74, p ≥ .085), disgust(R2Change ≤ .010, F(1,305) ≤ 1.98, p ≥ .079), happiness(R2Change ≤ .002, F(1,305) ≤ 0.94, p ≥ .366), sadness(R2Change ≤ .005, F(1,305) ≤ 0.82, p ≥ .202), and surprise(R2Change ≤ .008, F(1,305) ≤ 3.45, p ≥ .112). The only significant

HC = healthy controls; AN = anorexia nervosa; BN = bulimia nervosa; mixed = mixed
mental disorders. Lower threshold indicate more effective discrimination.
Lower threshold indicate more effective discrimination.

FIGURE 2. Confusion matrix: the group average response errors during the QUEST procedure for each facial expression of emotion(N = 308). Notes: SU = surprise, SA = sadness, HA = happiness, DI = disgust, AN = anger, AF = fear, NE = neutral. Each row
displays one of the six presented facial expressions plus neutral, whereas each column shows the average confusion response given by
the observers. The color map is correspondent to the number displayed in the matrix, with dark blue tones indicating low confusion,
whereas yellow color indicates high confusion. Color image is available only in online version (www.psychosomaticmedicine.org).

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Emotion Recognition in Eating Disorders

mental disorders (depressive and anxiety disorder). We assumed
impairments in patients with AN, whereas patients experiencing
BN would perform similarly to HCs. The mixed mental disordergroup was included as a clinical control group, because studies
comparing EDs with other mental disorders are lacking. We additionally considered person-related factors (i.e., BMI, emotional clarity, severity of depressiveness, and ED pathology), which might
influence the threshold of correct identification of facial emotion
expressions and applied a new paradigm, which assesses an individual's emotion recognition threshold for six basic facial emotion expressions and a neutral expression.
Our findings in a large healthy and clinical sample, where in
total just three persons were excluded from analyses (delivering only
“don't know” answers), confirm the practicability of the paradigm
presented in the initial study (5) in a clinical group. The paradigm
established individual thresholds of facial emotion recognition,
which were similar to the Rodger and colleagues (5) study. Thesefindings confirm the reliability and validity of the paradigm.
With respect to our first study objective to detect thresholds ofindividual emotion recognition and related differences amonggroups, we found strong within-subject effects regarding particularemotions. As expected, facial expression of happiness was discriminated at the lowest threshold by all groups. Much stronger
signals were required to identify correctly fear in facial expressions. In line with this, fear was the most commonly confounded
expression in all groups. Most difficulties were shown in discriminating fear from surprise. This supports the finding of Rodger andcolleagues (5), who reported that children at the age of 5 years
showed the same emotion recognition thresholds as healthy adults
for the emotions happiness and fear. In contrast to our expectations,

result was found for BMI increasing the threshold for the neutral expression (R2Change = .022, F(1,305) = 4.97, p = .009), allother intervening variables were not significant (R2Change ≤ .002,
F(1,305) ≤ 1.92, p ≥ .359).

Response Biases for Emotion Expressions
To explore response biases, confusion matrix for each emotion
expression separately for each diagnostic group was calculated.
As shown in Figure 2, fear was the most frequently confounded
expression in all groups with a confusion rate approximately
50% for all groups, and the most commonly miscategorized as
surprise (21%–24%). Happiness was rarely confounded with a
confusion rate of 3% to 7%. The second most common confusion
rates were between the expressions disgust and anger (17%–22%)and between sadness and neutral (14%–18%).
Moreover, as shown in Figure 3A, the similarity of confusion
matrix among participants is rather high regardless of groups.
The average between-subject correlation of response pattern is at
0.901 [0.734, 0.972] (bracket shows .025 and .975 quantiles ofthe distribution). We also compared the within-group correlation(orange dots and line in Figure 3B) with the between-group correlation (compare with HCs as baseline, blue dots and line in Figure 3B).
No significances were found using the random sample permutation test comparing the distribution, indicating that the participantsfrom all groups show a similar response profile.

DISCUSSION
The scope of the present study was to shed light on facial emotion
recognition thresholds and biases in women experiencing AN andBN compared with healthy women and women experiencing mixed

FIGURE 3. Single-subject representational similarity matrix (N = 308). A, Correlation of the confusion matrix between each unique pairof participants, organized by different groups. As shown in the figure, participants make similar confusions (i.e., high correlation). B,
Summary of each cell in the similarity matrix as shown in Figure 3A. The orange color dots show the similarity for each pair ofparticipants within the same group, whereas blue color dots show the similarity for between the clinical group and HCs. Error bars
show the interval where 95% of the data point falls in. Color image is available only in online version (www.psychosomaticmedicine.org).

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ORIGINAL ARTICLE

such as recognizing emotions from voices, where other studies
found impairments (for an overview, see, e.g., (19)). Future studies
could apply a stepwise procedure with additional tasks to identify
the step of emotion processing, where self-evaluation mechanisms
or emotional valence influences emotion recognition thresholds
and confusion rates in EDs compared with healthy or other clinicalgroups. Our study revealed that patients experiencing EDs are capable of recognizing basic emotion in the faces of others. This is not inaccordance with the assumption made in the cognitive-interpersonal
maintenance model of AN (18) assuming difficulties in emotion
recognition and a bias toward negative emotions to be an important part that contributes to difficulties in social processing andemotion avoidance. However, our study does not allow statements
regarding other emotion related processes that are relevant in social
interaction, such as the ability to correctly interpret or express one's
own emotions and intentions. Our findings further indicate that patients with EDs may rather fail in the interpretation process of emotions in complex scenarios including body movement and different
tonality of voices or with respect to signaling of emotions in social
interactions. The latter two processes are essential ingredients ofsocial processing traits. In addition, especially AN patients might
fail to functionally regulate the inner emotional response or state,
which may contribute to emotional avoidance (40,61).
Several limitations need to be considered when interpreting theresults. First, groups are not age matched, nor was the HC grouprepresentative because it was composed of students, whereas patient samples were more heterogeneous. Second, further factors
such as alexithymia known to influence emotion recognition (37)were not assessed in the present study. However, the inclusion ofemotional clarity of the DERS subscale as a proxy thereof did
not alter emotion recognition performance in our samples. Third,
the AN group was composed of 30 patients (49.2%) with the restrictive subtype and 28 patients (45.9%) with the binge/purging
subtype (for 3 patients [4.9%], the subtype is unknown). Althoughprevious studies refer to possible differences in emotion recognition between subtypes (e.g., 24), we did not run corresponding
analysis because subsamples were too small.
Nevertheless, the present study has several strengths such as
the large sample size and the inclusion of different clinical groups
identified by clinical interviews (DIPS) as well as an HC group,
which allowed to specify similarities and differences between thehealthy and mentally ill state as well as between different disordertypes. We applied the QUEST paradigm, which has shown to be afeasible, sensitive, and valid measure to identify basic competences in emotion recognition (5). Future studies should attempt
to increase the ecological validity of the stimulus material and include dynamic facial expressions as well as bodily or acoustic cues
not only in female but also in male participants (6). In addition, future paradigms should allow assessing not only basic information
processing during emotion recognition such as in QUEST but also
attentional processes related to the identification of emotional
faces and subsequent attributional and interpretation mechanisms.
These could provide additional information about the processingof emotional information (6).
To conclude, our findings indicate intact basic abilities in facial
emotion recognition in EDs. Thus, explanations for difficulties inemotion processing in EDs could not be found at this early stepof emotion processing and may rather relate to interpretation ofrecognized emotions under more complex circumstances. If ouranalyses in our sample only revealed marginal differences between
groups. The hypothesis that women experiencing AN would show
higher emotion recognition thresholds and higher confusion rates
was not confirmed. According to contrast analyses, the only difference we found was for the mixed and the BN group, who neededhigher signal strength to identify the emotion disgust than the HC
and AN group. This result was supported by a relatively high confusion rate. It may be speculated that our mixed and BN group revealed more problems when recognizing an emotional stimulus ofhigher complexity or ambiguity. However, these findings are rather
counterintuitive, because anxiety seems to be related with a disgust
proneness and thus an accelerated processing of disgust related information, whereas in depression, lower biases to distinguish positive signals in ambiguous faces have been found (17,58). In sum,
our findings suggest that basic abilities to correctly identify facial
emotion expressions do not differ between women experiencing
AN, BN, and mixed mental disorders nor HCs. Only minimal effects were found for age in respect of the emotions anger and surprise and for BMI in respect of the neutral expression. Emotional
clarity, the degree of depressiveness, and ED pathology did not alter the performance in the facial emotion recognition task, which isin line with the Brewer and colleagues (37) study but contradicts
the results of Ridout and colleagues (33) and Sharpe and colleagues(59), who found lower correct identification of emotion expressions in individuals with higher ED pathology.
According to our second study aim, we investigated whether
certain emotions were more frequently confused and whether confusion patterns differentiate between patients experiencing EDs
and the HC as well as the mixed group. In general, the confusion
rates in our sample correspond with the results of Rodger and colleagues (5) in a healthy population of different ages. In our sample,
fear and surprise were most often confused. This finding is in linewith literature, where this confusion is explained with their visual
similarity based on shared muscles activities especially in the eyeregion (60). Relatively high confusion rates between sad and neutral faces could partly be explained by the findings of a recent
meta-analysis in patients experiencing major depression. This revealed a bias toward sadness in that neutral, happy, or ambiguous
facial emotion expressions were more confused with sadness (16).
The authors explain these findings by an increased vigilance toward sadness cues, whereas there is evidence that general or emotion-specific recognition accuracy seems to be preserved.
In contrast to our expectations, we did not find specific or different confusion biases in any of our study groups. This result may
partly be related to the neutral and rather technical presentation of
the facial emotion expression stimuli in our study and the shortpresentation duration. Our stimuli neither were self-related nor
have they been associated with a previous emotion induction.
The emotion recognition task took place at the beginning of an experimental procedure, where participants were in a rather neutral
mood. Thus, our findings are unlikely to be influenced by information related to the self or emotion induction before the test.
To summarize, in our large sample of women experiencing EDs,
facial emotion recognition is preserved and confusion rates are comparable with the findings in the HC and the mixed mental disordergroup. All participants seem to successfully draw their attention toemotional stimuli (facial expressions) and identify them correctly
according to the Kret and Ploeger model (6). However, our study
does not allow statements regarding nonfacial emotional stimuli

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Emotion Recognition in Eating Disorders

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findings are replicated and new studies can identify concrete steps
where ED-related specific deficits occur, it would be helpful to focus on the development of trainings for specific stages of emotion
processing and regulation.

Source of Funding and Conflicts of Interest: This study was
supported by grants from the Research Fund of the University of
Fribourg, Switzerland (Grant Number 419), the Swiss Anorexia
Nervosa Foundation (Grant Number 22-12), the Swiss National
Science Foundation (Grant Number 100014L_149416/1), and theGerman Research Foundation (Grant Number SCHN 415/4-1).
The authors report no conflicts of interest.

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