Matsumoto et al. Journal of Eating Disorders (2015) 3:14
DOI 10.1186/s40337-015-0050-6

RESEARCH ARTICLE

Open AccessComparison in decision-making between bulimia
nervosa, anorexia nervosa, and healthy women:
influence of mood status and pathological eating
concerns
Junko Matsumoto1,2,3,4, Yoshiyuki Hirano1,2, Noriko Numata2,6, Daisuke Matzuzawa1,6, Shunichi Murano4,
Koutaro Yokote7, Masaomi Iyo5,8,9, Eiji Shimizu1,2,6 and Michiko Nakazato1,2,5*Abstract
Background: Decision-making is reported to be impaired in anorexia nervosa (AN) and bulimia nervosa (BN), but
the influence of mood status, pathophysiological eating, and weight concerns on the performance of decision-making
ability between AN and BN is still unclear. The aims of this study were to investigate differential impairments in thedecision-making process between AN, BN, and healthy controls (HC), and secondly, to explore the role of mood status,
such as anxiety, depression, pathological eating, and weight concerns, in decision-making ability.
Methods: Patients suffering from AN (n = 22), BN (n = 36) and age-matched HC (n = 51) were assessed for theirdecision-making abilities using the Iowa Gambling Task (IGT). Self-reported questionnaires including the Eating
Disorder Examination Questionnaire (EDE-Q), the Bulimia Investigatory Test, Edinburgh (BITE), the Eating Disorders
Inventory, the Maudsley Obsessive-Compulsive Inventory measuring obsessive-compulsive traits, the Hospital
Anxiety and Depression Scale, and the Toronto Alexithymia Scale were used to assess pathological eating concerns
and attitude to feelings.
Results: Significant differences in IGT performance were observed between BN and HC. Significant negative correlation
was found between IGT performance and the BITE symptom subscale in AN. In BN, there was a negative correlation
between the EDE-Q weight concerns subscale and IGT performance. It was also found that increased anxiety,depression, and eating/weight concerns predicted poorer decision-making.
Conclusion: Different patterns of association between pathological eating concerns/behaviors and performances
in decision-making ability were found between AN, BN, and HC. Anxiety, depressive mood status, and eating/weight
concerns were related to decision-making ability.
Keywords: Anorexia nervosa, Bulimia nervosa, Decision-making, Iowa Gambling Task, Weight-concern, Anxiety,Depressive mood

* Correspondence: michiko.nakazato@nifty.ne.jp
1
United Graduate School of Child Development, Osaka University, Kanazawa
University, Hamamatsu University School of Medicine, Chiba University andUniversity of Fukui, Suita, Japan
2
Research Center for Child Mental Development, Graduate School of
Medicine, Chiba University, Chiba, Japan
Full list of author information is available at the end of the article© 2015 Matsumoto et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

Background
Eating disorders (EDs) are severe and enduring psychiatricdisorders of eating behavior, including extreme, unhealthy
decreases in food intake as well as severe overeating, accompanied by feelings of distress or excessive concern
about body shape or weight [1]. Three types of eating disorders are recognized by the text revision of the fourth
edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM-IV-TR) [2]: anorexia nervosa (AN), bulimia nervosa (BN), and eating disorder not otherwise specified (EDNOS).
Decision-making is affected by the combination of emotional representations, sensitivity to immediate reward andlong-term outcome according to the somatic marker
hypothesis (SMH) [3-5]. Multiple cognitive functions
such as attention, memory, learning [6,7], risk-taking,and obsessive-compulsive traits [8] have been suggested
as being involved in performances in decision-making[9,10]. A previous study by Tchanturia et al. found impairment of emotional signal by skin conductance (SCR),
showing the lowest emotional signal by demonstrating alack of ability to be aware of emotional signal during
decision-making task [11]. In BN, a previous study showedno significant correlation between SCR and performancein decision-making [12]. For this reason, it is unclear
whether the AN and BN groups have different deficits inemotional skills during decision-making.
The role of emotion, specifically anxiety or worry, may
influence the decision-making process [13]. High levelsof worry may have expected consequences of future events
that influence the performances of decision-making [14].
The majority of people with EDs have high levels of anxiety[15], worry, a defining cognitive feature, and a maintenance factor of anticipatory anxiety [16]. Heightened
anxiety levels may affect the process of decision-making in
AN or BN patients. However, few studies have focusedon the effect of mood status on a prospect during thedecision-making process in AN compared with BN. Twostudies showed that decision-making in patients with AN
may be related to anxiety [17,18], whereas other studies
showed that they were unrelated [7,19]. Some studies
suggested significant associations between measures ofdepressive disorder and decision-making [20,21], but anumber of other studies have indicated that depressive
symptoms did not significantly influence decision-making
ability in patients with EDs [7,12,22-24].
Alexithymia is commonly described as consisting of fourfeatures: (1) difficulty identifying and describing subjective
feelings; (2) difficulty distinguishing between feelings andthe bodily sensations of emotional arousal; (3) lack of fantasy; and (4) an externally orientated cognitive style [25].
Previous studies demonstrated that patients with EDs
use maladaptive eating behaviors (e.g., binging, purging,or dietary restriction) as a way to avoid or cope with

Page 2 of 10

their emotions [26,27], with many clinical studies suggesting that eating disorder symptoms are associated withemotional dysfunction [28,29], with clear functional links
expressed between emotional states and both bulimic andrestrictive pathology. It has been suggested that patientswith EDs tend to show alexithymia, but only one study
has examined the relation in EDs in comparison withhealthy controls (HC), showing that alexithymia was unrelated to decision-making in AN [30].
The Iowa Gambling Task (IGT) is a neuropsychologicaltask that tests the decision-making ability to sacrifice immediate rewards in order to achieve long-term gain [3].
IGT assesses set-shifting ability, reaction to reward andpunishment, and learning ability to decide advantageousover disadvantageous choice under uncertainty [31]. IGT
is underpinned by SMH, a theory that, in essence, posits
that decision-making under uncertainties is guided byemotional responses to anticipated positive and negative consequences [5,6]. Neuroimaging findings suggestthat activation of the mesolimbic pathway during wins
and decreased activation of the inferior frontal gyrus
during losses lead to repeated selections in reward andpunishment in IGT [32]. IGT was developed for functional assessment, given that patients with ventromedial
prefrontal cortex (vmPFC) and limbic system dysfunction show severe impairments in decision-making.
Increasing evidence suggests neuropsychological traits
such as poor set-shifting ability [19], weak central coherence [19,33], a dysfunction of the reward circuit, including
a preference for immediate reward despite long-term adverse consequences [15], higher sensitivity to punishment[34], and poor insight into illness [35,36] in AN. In previous studies, memory function [37], skin conductance response [11], body mass index (BMI) [38], anxious mood[18] and impaired decision-making ability were indicatedin AN. Regarding the domain of decision-making ability,
several studies have reported that individuals with ANshow impaired decision-making ability as reflected bypoorer performance on IGT [11,22,39-41].
In BN, decision-making ability was impaired in somestudies [12,22,41], showing that obsessive-compulsive traits[42] and pathological eating symptoms may be related to
impaired decision-making ability, which in turn may leadto real-life risk-taking and immediate reward-seeking behavior such as binge eating and purging.
Thus, in total, relatively few studies have been conductedconcerning the decision-making ability between AN, BN
and HC [12,37,43,44]. In addition, it has remained unclear
whether the performances of decision-making are distinguished by pathological eating concerns/behaviors, moodstatus (anxiety, depression), and attitude to feelings such as
alexithymia between AN, BN, and HC.
The hypotheses of this study were: 1) decision-making
performances can be distinguished in AN, BN and HC,

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

Page 3 of 10

and 2) decision-making deficits are related to mood status such as anxiety, depression, alexithymia, and pathological eating symptoms.
The aims of this study were to investigate differential
impairments in the decision-making process between AN,
BN, and HC, and secondly, to explore the role of moodstatus such as anxiety, depression, attitudes to feelings,
and pathological weight concerns in decision-making
ability.

Methods
Participants

The patients of this study were 58 females recruited from
Chiba University Hospital, Japan (22 AN; 36 BN). They
were interviewed by a senior psychiatrist assessing criteria
for AN and BN as defined by DSM-IV [2]. In addition, the

M.I.N.I. International Neuropsychiatric Interview translated
into Japanese (M.I.N.I.) [45] was applied. Exclusion criteria
for patients with AN and BN were a history of brain injury,
epilepsy, psychosis or drug dependence. The AN group included restrictive (n = 9) and binge eating/purging (n = 13)
subtypes. The BN group included purging (n = 34) andnon-purging (n = 2) subtypes. A total of 7 females (2 AN
and 5 BN) had the following comorbidities: dysthymia (5%;
3 with BN), panic disorder (2%; 1 with BN), somatoform
disorder (2%; 1 with AN), anxiety disorder (2%; 1 with AN),and alcohol dependence (2%; 1 with BN). Seventeen
percent of all patients were taking serotonergic drugs(SSRIs) (Table 1).
HC (n = 51) were recruited through local advertisements
and a website from a potential pool of university students
and volunteers. Age-matched HC underwent an interview

Table 1 Demographic and clinical characteristics of anorexia nervosa patients (AN), bulimia nervosa patients (BN), andhealthy controls (HC)
Eating disorders

Healthy control

AN (n = 22)

BN (n = 36)

HC (n = 51)

Observed mean, M(SD)

Mean

SD

Mean

SD

Mean

Age (years)

25.77

6.26

25.94

5.81

23.82

5.58

Education (years)

13.23

2.20

14.07

1.87

14.00

0.91

1.24

0.30

n.s.

Duration of illness (only AN, BN)

7.24

6.47

7.15

5.80

−

−

0.00

0.96

n.s.

SD

F-valuep-value

1.71

0.19

Post hocn.s.

BMI (kg/m2)

15.87

2.62

19.76

2.38

20.99

1.71

76.83

0.00

HC>AN, HC>BN, BN>ANTAS-20

60.07

7.95

64.00

8.16

49.50

9.71

26.89

0.00

AN>HC, BN>HC

HADSa

11.06

4.29

12.00

3.87

4.61

3.39

42.55

0.00

AN>HC, BN>HC

HADSd

9.18

4.79

11.32

4.43

3.37

3.14

41.38

0.00

AN>HC, BN>HCEDE-Qg

3.23

1.60

3.97

1.26

1.07

0.89

59.89

0.00

AN>HC, BN>HCEDE-Qr

3.00

1.89

3.17

1.62

0.68

0.81

39.78

0.00

AN>HC, BN>HCEDE-Qe

3.39

1.70

3.72

1.63

0.47

0.64

75.27

0.00

AN>HC, BN>HCEDE-Qw

3.74

1.32

4.32

1.45

1.44

1.29

44.73

0.00

AN>HC, BN>HCEDE-Qs

3.96

1.23

4.56

1.23

1.70

1.25

50.96

0.00

AN>HC, BN>HCBITEss

8.75

6.32

11.57

5.56

1.37

1.18

67.76

0.00

AN>HC, BN>HCBITEsas

17.63

10.14

22.61

4.38

5.33

4.46

104.27

0.00

AN>HC, BN>HC

EDI-2

117.0643.93

139.83

37.19

60.64

29.77

52.29

0.00

AN>HC, BN>HC

MOCI

10.47

5.93

13.55

6.21

7.17

3.34

15.83

0.00

BN>HC

Comobidities; n=Dysthymia−

3

Panic disorder−1

Somatoform

1−

Anxiety disorder1−

Alcohol dependence

−

1

−

10

Medication; n=
SSRIs

BMI: body mass index; TAS-20: Toronto Alexithymia Scale; EDE-Qg: Eating Disorder Examination Questionnaire (global score); EDE-Qr: restricting; EDE-Qe: eating
concern; EDE-Qw: weight concern; EDE-Qs: shape concern; HADSa: Hospital Anxiety and Depression Scale (anxiety); HADSd: Hospital Anxiety and Depression Scale(depression); BITEsas: Bulimia Investigatory Test, Edinburgh: (symptom scale); BITEss: Bulimia Investigatory Test, Edinburgh (severity scale); MOCI: Maudsley
Obsessive-Compulsive Inventory; EDI-2: Eating Disorders Inventory 2; SSRIs: selective serotonin reuptake inhibitors.
In bold: p-value <0.05, n.s.: not significant.

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

by a senior psychiatrist using M.I.N.I. [45], and they were
determined to have no family history of psychiatric conditions, history of brain injury, epilepsy, psychosis, current
substance abuse or dependence, risk of suicide, mental
retardation, autistic spectrum disorders, comorbid depression and bipolar disorders, and that their BMI(body mass index) was between 19 and 25 kg/m2.

Page 4 of 10

and loss of control. EDE-Q includes four subscale scores,
Restricting (EDE-Qr), Eating concern (EDE-Qe), Shapeconcern (EDE-Qs) and Weight Concern (EDE-Qw), which
are included in this assessment, the response format ofwhich is a 7-point Likert-type scale (0: never; 6: everyday). The subscale scores are obtained by calculating the
average of the items forming each subscale, and the global
score (EDE-Qg) is the average of the four subscale scores.

Procedures

All participants, female native Japanese speakers, were
between age 18 and 38 (mean = 24.92, SD = 5.83 years).
After the study had been described to the participants,
their written informed consent was obtained. The ethics
committee of the Chiba University Graduate School of
Medicine approved the study protocol.
Measurements
Instruments
Toronto Alexithymia Scale (TAS-20)

The Toronto Alexithymia Scale [46,47], Japanese version
with established validity and reliability [48], is a 20-item
self report questionnaire measuring alexithymia. It includes
three subscales: difficulty identifying feelings, difficulty describing feelings, and externally oriented (concrete) thinking. Cut-off scores for TAS-20 are equal to or less than 51
for non-alexithymic individuals, and equal to or greaterthan 61 for alexithymia. Scores of 52–60 indicate possible
alexithymia.
Hospital Anxiety and Depression Scale (HADS)The Hospital Anxiety and Depression Scale [49], Japaneseversion established as valid and reliable [50], is a widelyused self-report scale developed to detect states of depression, anxiety and emotional distress among patients being
treated for a variety of clinical problems. The scale consists of eight questions assessing depression (HADS-d)and eight assessing anxiety (HADS-a). The optimal cut-off
point is said to be greater than or equal to 8 for the identification of suspicious cases and greater than or equal to 11
for safe cases on both subscales [49].
Eating Disorder Examination Questionnaire (EDE-Q)

The Eating Disorders Examination Questionnaire [35],Japanese version, which was established for its validity as
well as reliability [51], is a widely used 36-item self-reportquestionnaire that assesses the eating disorders-related
level of symptoms over the past 28 days. EDE-Q generates
two types of data. First, 22 scaled items plus one unscaled
item (items 1–15 and 29–36) provide subscale scoresreflecting the severity of aspects of the ED psychopathology. Second, 13 more items (items 16–28) provide dataon six key behavioral features of ED in terms of presence/
absence and frequency with which the behavior occurred,

Bulimia Investigatory Test, Edinburgh (BITE)

The Bulimia Investigatory Test, Edinburgh [52,53], Japaneseversion, recognized for its validity and reliability [54], isa 33-item self-report measure designed to identify individuals with symptoms of bulimia or binge eating. BITE
consists of two subscales: the symptom scale (BITE-sas),
which measures the degree of symptoms present, and theseverity scale (BITE-ss), which provides an index of theseverity of binging and purging behavior as defined by theirfrequency.
Eating Disorders Inventory-2 (EDI-2)

The Eating Disorder Inventory-2 contains 91 items andis a self-report questionnaire designed for use with those
aged 12 years or older. This measure assesses featurescommonly associated with anorexia nervosa and bulimia
nervosa but does not provide diagnoses for eating disorders [55]. EDI-2 consists of 11 subscales including bulimia,
body dissatisfaction, drive for thinness, perfectionism, ineffectiveness, interpersonal distrust, interoceptive awareness,maturity fears, asceticism, impulse regulation and social insecurity. The Japanese version of EDI-2, which has been
established as valid and reliable [56], was used to assess thepresence of eating disorders.
Maudsley Obsessive-Compulsive Inventory (MOCI)The Maudsley Obsessive-Compulsive Inventory [57],Japanese version, recognized as being valid and reliable[58], is a true-false format self-report questionnaire developed for evaluating obsessive-compulsive symptoms
to discriminate obsessive patients from other neurotic
patients and nonclinical individuals. The test is composedof 30 dichotomous items, such that the total score for aparticipant will range between 0 (absence of symptoms)
and 30 (maximum presence of symptoms).
The levels of psychopathology in eating disorders
were measured using the scores of BITE, total scores of
EDI-2, and EDE-Q subscores.
Neuropsychological assessment
Iowa gambling task [3,59]

Decision-making ability of participants was evaluated using
IGT, which is a neuropsychological task based on emotionguided evaluation. Participants are required to choose one
card at a time from four available decks of cards (100 trials)

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

in this task. The goal of the task is to win as much moneyas possible. To accomplish a task, participants have todetect, from a long-term perspective, which are the mostadvantageous decks. First, participants were given both thetask instructions and 200,000 Japanese yen (approximately
US$1,666) of play money. Each time participants choose acard, they will win some money; however, on turning overeach card they also will, seldom or sometimes, have to
pay a penalty according to a pre-programmed scheduleof reward and punishment. Gains and losses differ foreach card selected from the four decks. Decks A and B
are “bad decks (disadvantageous)”, and the other decks,
C and D, are “good decks (advantageous)”, because, inthe former, while participants receive 10,000 Japaneseyen (approximately US$83), the losses are also higher,such that these decks cost more in the long run. In
contrast, the latter will lead to overall gains in the longrun (receiving less money, but punishments are also
smaller). The 100 choices were divided into five blocksof 20 choices each. We calculated the number of advantageous cards (decks C and D) selected in total.
Statistical analyses

All statistical analyses were performed using SPSS 21.0
(IBM Corp., Armonk, NY). Demographic and clinicalvariables for ED and HC groups were compared using
one-way analyses of variance (ANOVAs).
IGT scores were defined as the number of choicesfrom the advantageous decks (C and D) minus the numberof choices from the disadvantageous decks (A and B) for
all 100 trials. This net score (decks[C + D] - decks[A + B])
calculated for each 20-choice time block enables the assessment of learning during the task. A total net score forthe 100 selections is also calculated. A score of <10 was
established by Bechara et al. as the threshold for deficit ofdecision-making on IGT, given the maximum net scoreachieved by vmPFC patients was <10 [9]. A 5 × 3 repeatedmeasures ANOVA was carried out with the net scores ofthe five blocks [C + D]-[A + B](1–20, 21–40, 41–60, 61–80,
81–100) as the repeated-measures variable and the three
diagnostic groups (AN, BN, and HC) as a between-subjects
variable. Effect size was calculated using Cohen’s d, withd = 0.2 regarded as a small effect, d = 0.5 as a medium effect, and d = 0.8 as a large effect [60]. Pearson’s correlations were used to examine the relationship between IGT
performance and demographic and clinical variables in
the whole sample and in each group, respectively. Finally,
multiple regression analysis was performed for all participants to detect the best predictors of IGT performance,
using IGT performance as the dependent variable and allquestionnaire scores and subscale scores showing significant relationships as independent variables. In all analyses,
the statistical significance level was set at p < 0.05 (2-tailed
tests).

Page 5 of 10

Results
Sample characteristics

Demographic and clinical characteristics are summarizedin Table 1. The three groups did not differ in terms ofage (F (2,106) = 1.71; p =0.19) and education (F (2, 65) =1.24; p = 0.30). In addition, no significant difference between the patient groups in terms of illness duration
was found (F (1, 52) = 0.003; p < 0.096). On the other
hand, significant differences were obtained for BMI andclinical self-report measures (TAS-20, EDE-Q, HADS,
BITE, MOCI, and EDI-2). Post hoc t tests revealed that
AN and/or BN differed from HC for most of the dimensional assessments, while no significant differences were
found with respect to the overall questionnaires between
AN and BN.
Decision-making performancesGroup comparisons in IGT total net scores [C + D]-[A + B](1–100 choices)

Results from IGT are presented in Table 2. The prevalence of decision-making impairment (IGT < 10, [9]) wasapproximately 45% in AN, 44% in BN patients, and 45%
in HC. No significant group differences were found in
the mean IGT total net scores (F (2,103) = 1.06; p = 0.35),
indicating that the decision-making abilities of the threegroups were quite similar.
Group comparisons in IGT block net scores [C + D]-[A + B]
(1–20, 21–40, 41–60, 61–80, 81–100)

Figure 1 shows the mean IGT scores for the threegroups over the five blocks of 20 trials each. A 5 (IGT
block) × 3 (group) repeated measures ANOVA was performed on net scores for all five blocks. Mauchly’s test indicated that the assumption of sphericity had been violated(χ2 (9) = 51.51, p < 0.0001), and therefore the degrees of
freedom were corrected using Greenhouse-Geisser estimates of sphericity (ε = 0.78). There was no significant
main effect of group (F (2, 103) = 1.06, p = 0.35, ηp2 =0.02), but there was a significant main effect of block (F(3.14, 57.48) = 14.53, p < 0.0001, ηp2 = 0.12), and a significant group × block interaction (F (6.28, 57.48) =2.63, p = 0.02, ηp2 = 0.05) over the IGT blocks. In the
HC group, IGT performance showed a gradual increase
across blocks. There was a significant task-related learning
effect, as performance improved during the task for BN
and HC (BN: F (4, 32) = 2.69; p = 0.04; HC: F (4, 47) =15.24; p < 0.0001). A post-hoc least significant difference
test indicated that there was a significant difference between the BN and HC groups in the final block [C + D][A + B](81–100), that is, performance in BN was significantly worse than in HC (p = 0.02). On the other hand,
although no significant difference was observed between
AN and HC in the final block [C + D]-[A + B](81–100),
performance in AN was marginally deficient compared toMatsumoto et al. Journal of Eating Disorders (2015) 3:14

Page 6 of 10

Table 2 Decision-making ability on the Iowa Gambling Task (IGT) in AN, BN, and HC
Eating disorders
AN (n=22)

Healthy control
BN (n=36)

HC (n=51)

IGT

Mean

SD

Mean

SD

Mean

SD

F-valuep-valuepost hoc

Block 1

-1.43

7.46

-2.00

6.01

-2.61

5.29

0.31

0.73

n.s.

Block 2

2.19

7.45

2.18

7.21

-0.31

6.96

1.62

0.20

n.s.

Block 3

3.14

7.47

1.82

7.68

4.14

7.85

0.92

0.40

n.s.

Block 4

3.90

7.50

3.15

8.82

6.49

7.44

2.03

0.14

n.s.

Block 5

2.67

10.11

2.29

8.73

7.35

9.29

3.73

0.03

BN<HC

Total net scores

10.48

25.53

7.50

27.09

15.06

20.99

1.05

0.35

n.s.

In bold: p-value < 0.05, n.s.: not significant.

HC (p = 0.054). The two clinical groups were not significantly different from each other in any other block. Effectsizes for between-groups differences in IGT net scores
were measured using Cohen’s d (block 1: AN vs. HC,
d = 0.20; BN vs. HC, d = 0.11; block 2: AN vs. HC, d =0.36; BN vs. HC, d = 0.36; block 3: AN vs. HC, d = 0.13;
BN vs. HC, d = 0.30; block 4: AN vs. HC, d = 0.35; BN
vs. HC, d = 0.42; block 5: AN vs. HC, d = 0.50; BN vs.
HC, d = 0.56).

Group comparisons controlling for covariates

We demonstrated the same group comparisons analysis,controlling for the use of SSRIs in the IGT block netscores [C + D]-[A + B](1–20, 21–40, 41–60, 61–80, 81–100).
We not only detected remaining significant differences in
the IGT net scores in the final [C + D]-[A + B](81–100)
choices (p = 0.01), but also found significant differences
between BN and HC in the fourth [C + D]-[A + B](61–80)
choices (p = 0.01).

Association between decision-making and clinicalvariables
Correlation analysis

We explored correlations among clinical measures including all scores such as TAS-20, EDE-Q, BITE, EDI-2,
HADS, MOCI and IGT performance (both IGT total netscores: [C + D]-[A + B](1–100 choices) and block netscores: [C + D]-[A + B](1–20, 21–40, 41–60, 61–80, 81–100) ) in AN and BN females, respectively. Performancein the first block [C + D]-[A + B](1–20) of the IGT was
negatively associated with BITE-sas in the AN group(r = −0.73, p = 0.04) (Figure 2). In the BN group, as shownin Figure 3, the IGT performance in the third block [C +D]-[A + B](41–60) was also negatively correlated with
EDEQ-w (r = −0.47, p = 0.02). Therefore, we detected different patterns of association between pathological eating
concerns/behaviors and the performances of decisionmaking ability between AN and BN.

Regression analysis
8.
AN

Net score (C+D)-(A+B)

6.

BN
HC

*p<0.054.

2.

0.
1 (1-20)

2 (21-40)

3 (41-60)

4 (61-80)

5 (81-100)

-2.

-4.

BlocksFigure 1 Strategy of Iowa Gambling Task, as total number of“Advantageous” minus “Disadvantageous” cards selected ineach block of 20 cards; anorexia nervosa (AN), bulimia nervosa(BN), and healthy control (HC). A significant difference between
BN and HC was indicated (p < 0.05).

Multiple regression analysis was performed for all participants using demographic and clinical scores such as TAS20, EDE-Q (four subscales: restricting, eating concern,shape concern, weight concern), HADS (depression andanxiety), EDI-2, MOCI, and BITE (symptoms and severity)
as independent variables and IGT net scores [C + D]-[A +B] (1–20, 21–40, 41–60, 61–80, 81–100) as dependent
variables. As shown in Table 3, the analyses revealed sevenpredictive factors for the third block of IGT [C + D]-[A +B](41–60): years of education (β = 0.77, p = 0.0001),
EDEQ-r (β = 1.58, p = 0.0001), HADS-a (β = −0.69, p =0.01), HADS-d (β = 1.44, p = 0.0001), EDI-2 (β = −0.81,
p = 0.01), BITE-ss (β = −0.51, p = 0.03), and BITE-sas(β = −1.80, p = 0.0001). In contrast, no significant predictive factor was highlighted for the AN and BN groups,
suggesting that mood status (anxiety or depression), in
addition to the pathological eating/weight concerns forthe prospect of decision-making were detected.

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

Page 7 of 10

Table 3 Multiple regression analysis with Iowa Gambling
Task net scores (third block 41-60 within 100 trials) as
the dependent variable in all participants

40

BITE-sas

30

AN

Variables

Beta

t

p

BN

education (years)

0.77

4.40

0.00

HC

EDE-Qr

1.58

4.45

0.00

R² = 0.54

AN

HADS (anxiety)

-0.69

-2.69

0.01

p = 0.04

BN

HADS (depression)

1.44

4.65

0.00

20
10
0

-30

-20

-10

0

10

20

30

HC

BITEss

-0.51

-2.33

0.03

-10

BITEsas

-1.8

-5.01

0.00

IGT (1-20)

EDI-2

-0.81

-2.62

0.01

Figure 2 Scatter plot shows the scores of the first block on IGT(1–20 within 100 trials) and the bulimia investigatory test,
edinburgh symptom subscale (BITE-sas) for AN, BN, and HC.
Negative correlation was found in AN (r = −0.73; p = 0.04).

n = 109; R2 = 0.556; adjusted R2 = 0.388; SE of estimate = 6.036.
Results showed seven variables predicting performance on the IGT.
SE: standard error; EDE-Qr: Eating Disorder Examination Questionnaire restricting
subscale; HADS: Hospital Anxiety and Depression Scale; BITE: Bulimia Investigatory
Test, Edinburgh; EDI-2: Eating Disorder Inventory-2.

Discussion
In the present study, we found different profiles in IGT
performance between BN, AN, and HC. As shown in
Figure 1, a comparison of the performance curves of
the three groups revealed that the individuals with AN
and BN, as opposed to HC, failed to learn advantageousdecision-making until the end of the task. Although nosignificant difference between AN and HC was observed,a difference between BN and HC (BN < HC, p = 0.02)
was detected in the final block [C + D]-[A + B](81–100).
Regarding total net scores, the prevalence of decisionmaking impairment (IGT < 10) was reported to be approximately 61% in AN and 77% in BN by Brogan et al.
[44], but our data showed lower percentages. Secondly,
only in the BN group, there was a significant negative correlation between the weight concern subscales and theperformances of decision-making ability. These findings
may be strongly confirmed by the fact that in the BNgroup, pathological weight concern affected the impaired
decision-making ability.
A previous study has reported that patients with BN
were significantly different from the HC group in blocks

3 [C + D]-[A + B](41–60) and 4 [C + D]-[A + B](61–80)[44]. In contrast, we found that BN made fewer advantageous choices than HC in the final block [C + D]-[A + B]
(81–100) of the task. This would suggest that pathologicalconcerns affect ignored long-term negative consequences,which may have led to impaired decision-making ability inthe final block [C + D]-[A + B](81–100) in the current
study. There is a striking resemblance between the IGT
performance of the patients and their real-life pathologicalbehaviors, in which they have a tendency to reduce theirfood intake and/or refuse to eat, or in contrast to thispattern, repetitively overeating and purging, ignoring
long-term negative consequences. In a previous study,
BN subjects failed to learn an advantageous decisionmaking strategy by choosing immediate rewards (highgains) despite the long-term negative consequences (lossof money) as compared to HC, showing that sensitivity togains affect these findings [23], results consistent withthe current study. Boeka and Lokken [22] suggested that
there are links between decision-making, weight, and eating
concerns/restricting behavior in BN, and thus the authors
argued that the severity of bulimic symptoms as measured
by the Bulimia Test-Revised [61] and the severity of EDE-Q(restraint, eating concerns and weight concerns) contributeto decision-making ability. These data were consistent withthe findings in the BN group in the current study. Brand
et al. suggested that performance in decision-making was
related to executive functioning but not to other neuropsychological functions, personality, or disease-specificvariables in the BN group [41]. Regarding the task, incomparison to HC, the patients with BN tended tochoose disadvantageous alternatives more frequently,
possibly due to a tendency to fail to learn from the anterior half of the task, which might be linked to real-life
pathological behaviors.
On the other hand, although performance in AN was
marginally deficient compared to HC (p = 0.05) in the

7
6AN

EDE-Qw

5

BN

4
3

R² = 0.22
p = 0.02

2

HC

0
-10

0
IGT (41-60)

AN
BN

1

-20

HC

10

20

30

Figure 3 Scatter plot shows the scores of the third block on IGT(41–60 within 100 trials) and the eating disorder examinationquestionnaire weight concern subscale (EDE-Qw) for AN, BN, and
HC. Negative correlation was found in BN (r = −0.47; p = 0.02).

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

last trial [C + D]-[A + B](81–100) of the IGT, the current
study does not support results from other studies[11,39,40], showing that AN patients failed to reach asignificant difference in decision-making compared to
HC. One explanation for this is the small sample sizeof the current study. Additionally, the fewer comorbidities(AN, 2 with comorbidities; BN, 5 comorbidities) in AN
might have led to better decision-making compared to
BN. Interestingly, in the AN group, there was a significant
correlation between bulimic symptomatology measured
by the BITE symptom subscale, which measures the degree of present symptoms, and the poor performanceof IGT in the first block [C + D]-[A + B](1–20). 59.1%(13/22) of the AN group had binge eating/purging subtype, which may have affected the poor performance of
IGT. Thus, our first hypothesis, that the AN and BN
groups present a different pattern in decision-making
ability, was confirmed.
The second aim of this study was to explore the links
between decision-making ability and mood status, weight/
eating concerns of pathological symptoms. Using multiple
regression analysis, we found that EDI-2 and BITE-ss
measures predicted decision-making. These data are inline with previous investigations concerning this subject[18,22].
Both the states of anxiety and depressive mood were
found to be predictors of better decision-making. These
data suggest that emotional states may impact decisionmaking in EDs [11] as well as in HC [62-64]. Zeecket al. reported that the urge to eat is significantly higher
under negative-emotional states; negative emotions suchas sadness or disappointment correlated significantly with
the number of binges, whereas positive emotions did not[65]. Thereby, the ‘Network Theory of Affect’ [66], that
is, affective nodes (central units), can be semantic (withstraightforward meaning) or affective (with emotional
meaning), which may confirm the findings of the previous study. One recent study of binge eating disorders
was in line with this view, proposing that the emotional
state may have a direct experience that is similar to its
emotion [67].
Alexithymia, as measured by TAS-20, did not affectdecision-making ability in the current study, although ahigher level of alexithymia compared with HC was observed.
Miyake et al. reported that there was no correlation
with decision-making ability using emotional decisionmaking task and alexithymia in EDs [30], a result consistent with the finding of our study.
In the current study, controlling for the use of SSRIs
as covariance, we detected significant difference in IGT
performances between BN and HC in the fourth block[C + D]-[A + B](61–80) and the final block [C + D]-[A + B]
(81–100), which suggested the influence of the serotonin system in decision-making. In the previous study

Page 8 of 10

by Tchanturia et al. [11], 44% of AN patients were taking
SSRIs, but no difference between medicated and nonmedicated patients was found. Emerging data have suggested that dysregulation of serotonin circuits in corticaland limbic structures are related to anxiety, eating behaviors and body image symptoms [68]. Alterations of thissystem may influence mood status and decision-making
process in EDs, which may lead to insights into potential
treatment approaches. The question of whether cognitive
impairment in EDs is an endophenotype and risk factor orwhether it is a correlate of illness remains unclear fromthe findings in the current study. It may be suggested thatthe relationship of symptomatology and emotional functioning to decision-making performance improves withrecovery of illness.
There are some limitations to this study. First, it should
be noted that a single task such as IGT is limited in examining decision-making impairments comprehensively, andthis is true for the other clinical scales as well. Second, theresults are generalizable for females only, and the sample
sizes were not large enough compared to previous studies[12,37,43,44], indicating that a replication with a larger
group that includes males is desirable. Finally, othervariables such as impulsivity, central coherence, set-shifting,and inhibitory control-confounding factors associated withdecision-making deficits or emotional dysregulation should
be considered in the future.
Future study should evaluate how some different emotional variables such as sadness and fear may influence
the decision-making process in patients with EDs. A longitudinal study is required to investigate changes in decisionmaking ability in accordance with emotional states andrecovery of symptomatology of illness.

Conclusions
In conclusion, we found different profiles in IGT performance between BN, AN, and HC. Different patterns of association between pathological eating concerns/behaviors
and the performances of decision-making ability were
found between AN, BN, and HC. Individuals with BN,
compared to HC, have a different processing pattern ofdecision-making ability that may be linked to pathologicaleating/weight concerns. Anxiety, depressive mood status,and pathological eating/weight concerns are linked todecision-making ability.
Abbreviations
AN: Anorexia nervosa; ANOVA: One-way analyses of variance; BITE: Bulimia
Investigatory Test, Edinburgh; BITEsas: Bulimia Investigatory Test, Edinburgh
symptom scale; BITEss: Bulimia Investigatory Test, Edinburgh severity scale;
BMI: Body Mass Index; BN: Bulimia nervosa; DSM-IV-TR: Diagnostic andStatistical Manual of Mental Disorders; EDs: Eating disorders; EDE-Q: Eating
Disorder Examination Questionnaire; EDE-Qe: Eating Disorder Examination
Questionnaire eating concern; EDE-Qg: Eating Disorder Examination
Questionnaire global score; EDE-Qr: Eating Disorder Examination Questionnaire
restricting; EDE-Qs: Eating Disorder Examination Questionnaire shapeconcern; EDE-Qw: Eating Disorder Examination Questionnaire weightMatsumoto et al. Journal of Eating Disorders (2015) 3:14

concern; EDI-2: Eating Disorders Inventory; HADS: Hospital Anxiety andDepression Scale; HADS-a: Hospital Anxiety and Depression Scale anxiety;
HADS-d: Hospital Anxiety and Depression Scale depression; HC: Healthy
controls; IGT: Iowa Gambling Task; MOCI: Maudsley Obsessive-Compulsive
Inventory; SMH: Somatic Marker Hypothesis; SSRI: selective serotonin
reuptake inhibitor; TAS-20: Toronto Alexithymia Scale; vmPFC: Ventromedial
prefrontal cortex.
Competing interests
The authors do not have any competing interests to report.
Author contributions
Conceived and designed the experiments: JM, MN, SM, KY, MI, ES. Performed
the experiments: JM, MN, YH, NN. Analyzed the data: JM, MN, YH. Contributed
reagents/materials/analysis tools: JM, MN, NN, DM, MI. Wrote the paper: JM, MN,
YH. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank the Research Center for Child Mental Development
and the Department of Psychiatry, Chiba University Hospital, for their help
with recruitment. The authors are also grateful to the participants for theirco-operation in this study. A summary of this study was presented at the43rd Annual Congress European Association for Behavioral and Cognitive
Therapies, 2013.
Author details
1
United Graduate School of Child Development, Osaka University, Kanazawa
University, Hamamatsu University School of Medicine, Chiba University andUniversity of Fukui, Suita, Japan. 2Research Center for Child Mental
Development, Graduate School of Medicine, Chiba University, Chiba, Japan.
3
Department of Regional Disaster Medicine, Graduate School of Medicine,
Chiba University, Chiba, Japan. 4Tochigi Shimotsuga General Hospital, Tochigi
Medical Center, Tochigi, Japan. 5Department of Child Psychiatry, Graduate
School of Medicine, Chiba University, Chiba, Japan. 6Department of Cognitive
Behavioral Physiology, Graduate School of Medicine, Chiba University, Chiba,
Japan. 7Department of Clinical Cell Biology and Medicine, Graduate Schoolof Medicine, Chiba University, Chiba, Japan. 8Department of Psychiatry,
Graduate School of Medicine, Chiba University, Chiba, Japan. 9Center for
Forensic Mental Health, Chiba University, Chiba, Japan.
Received: 14 November 2014 Accepted: 8 March 2015

References
1. Fairburn CG, Harrison PJ. Eating disorders. Lancet. 2003;361:407–16.
2. American Psychiatric Association. Diagnostic and Statistical Manual of
Mental Disorders, 4th Edition, Text Revision. 4th ed. Washington, DC:
American Psychiatric Association; 2000.
3. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to futureconsequences following damage to human prefrontal cortex. Cognition.
1994;50:7–15.
4. Bechara A, Damasio H, Tranel D, Damasio AR. Deciding advantageously
before knowing the advantageous strategy. Science. 1997;275:1293–5.
5. Damasio AR. Descartes’ error and the future of human life. Sci Am.
1994;271:144.
6. Aloi M, Rania M, Caroleo M, Bruni A, Palmieri A, Cauteruccio M, et al.
Decision making, central coherence and set-shifting: a comparison between
Binge Eating Disorder, Anorexia Nervosa and Healthy Controls. BMC Psychiatry.
2015;15:6.
7. Adoue C, Jaussent I, Olié E, Beziat S, Van den Eynde F, Courtet P, Guillaume S.
A further assessment of decision-making in anorexia nervosa. Eur Psychiatry.
2014.
8. Martoni RM, Brombin C, Nonis A, Salgari GC, Buongiorno A, Cavallini MC,
Galimberti E, Bellodi L. Evaluating effect of symptoms heterogeneity on
decision making ability in obsessive compulsive disorder. Psychiatry Clin
Neurosci. 2014.
9. Bechara A, Dolan S, Denburg N, Hindes A, Anderson SW, Nathan PE.
Decision-making deficits, linked to a dysfunctional ventromedial prefrontal
cortex, revealed in alcohol and stimulant abusers. Neuropsychologia.
2001;39:376–89.

Page 9 of 10

10. Guillaume S, Jollant F, Jaussent I, Lawrence N, Malafosse A, Courtet P. Somatic
markers and explicit knowledge are both involved in decision-making.
Neuropsychologia. 2009;47:2120–4.
11. Tchanturia K, Liao P-C, Uher R, Lawrence N, Treasure J, Campbell IC. An
investigation of decision making in anorexia nervosa using the Iowa
Gambling Task and skin conductance measurements. J Int Neuropsychol
Soc. 2007;13:635–41.
12. Liao P-C, Uher R, Lawrence N, Treasure J, Schmidt U, Campbell IC, et al. An
examination of decision making in bulimia nervosa. J Clin Exp Neuropsychol.
2009;31:455–61.
13. Worthy DA, Byrne KA, Fields S. Effects of emotion on prospection during
decision-making. Front Psychol. 2014;5:591.
14. Loewenstein G, Lerner JS. The role of affect in decision making. In: Davidson RJ,
Scherer KR, Hill H, editors. Handbook of affective sciences. Volume xvii, Series in
Affective Science. New York, NY, US: Oxford University Press; 2003. p. 619–42.
15. Kaye WH, Fudge JL, Paulus M. New insights into symptoms and neurocircuit
function of anorexia nervosa. Nat Rev Neurosci. 2009;10:573–84.
16. Sternheim L, Startup H, Saeidi S, Morgan J, Hugo P, Russell A, et al.
Understanding catastrophic worry in eating disorders: process and content
characteristics. J Behav Ther Exp Psychiatry. 2012;43:1095–103.
17. Haegler K, Zernecke R, Kleemann AM, Albrecht J, Pollatos O, Brückmann H,
et al. No fear no risk! Human risk behavior is affected by chemosensory
anxiety signals. Neuropsychologia. 2010;48:3901–8.
18. Fornasari L, Gregoraci G, Isola M, Laura Negri GA, Rambaldelli G, Cremaschi S,
et al. Psychopathological and personality traits underlie decision making inrecent onset medication naïve anorexia nervosa: a pilot study. Psychiatry Res.
2014;216:89–96.
19. Danner UN, Sanders N, Smeets PAM, van Meer F, Adan RAH, Hoek HW, et al.
Neuropsychological weaknesses in anorexia nervosa: set-shifting, central
coherence, and decision making in currently ill and recovered women. Int
J Eat Disord. 2012;45:685–94.
20. Must A, Szabó Z, Bódi N, Szász A, Janka Z, Kéri S. Sensitivity to reward andpunishment and the prefrontal cortex in major depression. J Affect Disord.
2006;90:209–15.
21. Cella M, Dymond S, Cooper A. Impaired flexible decision-making in Major
Depressive Disorder. J Affect Disord. 2010;124:207–10.
22. Boeka AG, Lokken KL. The Iowa gambling task as a measure of decision
making in women with bulimia nervosa. J Int Neuropsychol Soc.
2006;12:741–5.
23. Garrido I, Subirá S. Decision-making and impulsivity in eating disorder patients.
Psychiatry Res. 2013;207:107–12.
24. Svaldi J, Brand M, Tuschen-Caffier B. Decision-making impairments inwomen with binge eating disorder. Appetite. 2010;54:84–92.
25. Nemiah JC, Freyberger H, Shifneos PE. Alexithymia; a view of thepsychosomatic process. In: Hill OW, O’Neill DF, editors. Modern Trends in
Psychosomatic Medicine. Volume 3. New York: Appleton-Century-Crofts;
1976. p. 430–9.
26. Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating
disorders: a “transdiagnostic” theory and treatment. Behav Res Ther.
2003;41:509–28.
27. Cooper MJ. Cognitive theory in anorexia nervosa and bulimia nervosa:progress, development and future directions. Clin Psychol Rev. 2005;25:511–31.
28. Abraham SF, Beumont PJ. How patients describe bulimia or binge eating.
Psychol Med. 1982;12:625–35.
29. Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating
disorders: a “transdiagnostic” theory and treatment. Behav Res Ther.
2003;41:509–28.
30. Miyake Y, Okamoto Y, Onoda K, Shirao N, Mantani T, Yamawaki S. Neuralcorrelates of alexithymia in response to emotional stimuli: a study ofanorexia nervosa patients. Hiroshima J Med Sci. 2009;58:1–8.
31. Bechara A, Damasio H, Tranel D, Damasio AR. The Iowa Gambling Task andthe somatic marker hypothesis: some questions and answers. Trends Cogn
Sci (Regul Ed). 2005;9:159–62. discussion 162–164.
32. Dong G, Lin X, Zhou H, Lu Q. How the win-lose balance situation affects
subsequent decision-making: functional magnetic resonance imaging
evidence from a gambling task. Neuroscience. 2014;272:131–40.
33. Lopez C, Tchanturia K, Stahl D, Treasure J. Weak central coherence in eating
disorders: a step towards looking for an endophenotype of eating disorders.
J Clin Exp Neuropsychol. 2009;31:117–25.
34. Harrison A, O’Brien N, Lopez C, Treasure J. Sensitivity to reward andpunishment in eating disorders. Psychiatry Res. 2010;177:1–11.

Matsumoto et al. Journal of Eating Disorders (2015) 3:14

35. Arbel R, Koren D, Klein E, Latzer Y. The neurocognitive basis of insight into
illness in anorexia nervosa: a pilot metacognitive study. Psychiatry Res.
2013;209:604–10.
36. Arbel R, Latzer Y, Koren D. Revisiting poor insight into illness in anorexia
nervosa: true unawareness or conscious disagreement? J Psychiatr Pract.
2014;20:85–93.
37. Chan TWS, Ahn W-Y, Bates JE, Busemeyer JR, Guillaume S, Redgrave GW, et al.
Differential impairments underlying decision making in anorexia nervosa andbulimia nervosa: A cognitive modeling analysis. Int J Eat Disord. 2014;47:157–67.
38. Jáuregui-Lobera I. Executive functions in anorexia nervosa. Nutr Hosp.
2014;29:500–7.
39. Cavedini P, Bassi T, Ubbiali A, Casolari A, Giordani S, Zorzi C, et al.
Neuropsychological investigation of decision-making in anorexia
nervosa. Psychiatry Res. 2004;127:259–66.
40. Tchanturia K, Liao P-C, Forcano L, Fernández-Aranda F, Uher R, Treasure J,
et al. Poor decision making in male patients with anorexia nervosa. Eur Eat
Disord Rev. 2012;20:169–73.
41. Brand M, Franke-Sievert C, Jacoby GE, Markowitsch HJ, Tuschen-Caffier B.
Neuropsychological correlates of decision making in patients with bulimia
nervosa. Neuropsychology. 2007;21:742–50.
42. Tchanturia K, Morris RG, Anderluh MB, Collier DA, Nikolaou V, Treasure J. Set
shifting in anorexia nervosa: an examination before and after weight gain,in full recovery and relationship to childhood and adult OCPD traits. J
Psychiatr Res. 2004;38:545–52.
43. Guillaume S, Sang CNT, Jaussent I, Raingeard I, Bringer J, Jollant F, et al. Is
decision making really impaired in eating disorders? Neuropsychology.
2010;24:808–12.
44. Brogan A, Hevey D, Pignatti R. Anorexia, bulimia, and obesity: shareddecision making deficits on the Iowa Gambling Task (IGT). J Int
Neuropsychol Soc. 2010;16:711–5.
45. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al.
The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development
and validation of a structured diagnostic psychiatric interview for DSM-IV
and ICD-10. J Clin Psychiatry. 1998;59(20):22–33. quiz 34–57.
46. Bagby RM, Parker JD, Taylor GJ. The twenty-item Toronto Alexithymia Scale–I.
Item selection and cross-validation of the factor structure. J Psychosom Res.
1994;38:23–32.
47. Bagby RM, Taylor GJ, Parker JD. The Twenty-item Toronto Alexithymia
Scale–II. Convergent, discriminant, and concurrent validity. J Psychosom Res.
1994;38:33–40.
48. Komaki G, Maeda M, Arimura T, Nakata A, Shinoda H, Ogata I, et al. The
reliability and factorial validity of the Japanese version of the 20-item Toronto
Alexithymia Scale. Jpn J Psychosom Med. 2003;43:839–46.
49. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta
Psychiatr Scand. 1983;67:361–70.
50. Kitamura T, Zigmond AS, Snaith RP. Hospital anxiety and depression scale(HADS). In: Archives of psychiatric diagnostics and clinical evaluation.
Volume 4. Tokyo: Nippon Hyoron Sya Co, Ltd; 1993. p. 371–2.
51. Tachi T, Murakami K, Washizuka T, Ikuta N, Nishizono MA, Miyake Y.
Application of the Eating Disorder Examination (EDE) to Japanese Patientswith Eating Disorders : Reliability and Validity of the Japanese Version of
EDE. Jpn J Psychosom Med. 2005;45:785–92.
52. Fairburn CG, Beglin SJ. Assessment of eating disorders: interview or self-reportquestionnaire? Int J Eat Disord. 1994;16:363–70.
53. Henderson M, Freeman CP. A self-rating scale for bulimia. The “BITE”. Br J
Psychiatry. 1987;150:18–24.
54. Nakai Y, Hamagaki S, Takagi R. The validity of bulimic investigatory test of
Edinburgh (BITE) and the survey of bulimia nervosa. Clin Psychiatry.
1998;40:711–6 (in Japanese).
55. Garner DM. Eating Disorder Inventory-2. Odessa. 1991. [Professonal Manual]
56. Shimura M, Horie H, Kumano H, Sakano Y, Suematsu H. Reliability andvalidity of a Japanese version of the Eating Disorder Inventory. Psychol Rep.
2003;92:131–40.
57. Hodgson RJ, Rachman S. Obsessional-compulsive complaints. Behav Res
Ther. 1977;15:389–95.
58. Hosoba T, Uchida N, Seiwa H. Factor analysis of Japanese version of theObsessional-Compulsive Inventory. Memoirs of the Faculty of integrated
Arts and Sciences, Hiroshima University, IV, Science reports. 1992; 18:53–61
59. Kano M, Ito M, Fukudo S. Neural substrates of decision making as measured
with the Iowa Gambling Task in men with alexithymia. Psychosom Med.
2011;73:588–97.

Page 10 of 10

60. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed.
Hillsdale, N.J: Routledge; 1988.
61. Thelen MH, Farmer J, Wonderlich S, Smith M. A revision of the Bulimia Test:
The BULIT–R. Psychol Assess. 1991;3:119–24.
62. McDowell BD, Moser DJ, Ferneyhough K, Bowers WA, Andersen AE, Paulsen JS.
Cognitive impairment in anorexia nervosa is not due to depressed mood. Int J
Eat Disord. 2003;33:351–5.
63. Garon N, Moore C. Negative affectivity predicts individual differences indecision making for preschoolers. J Genet Psychol. 2006;167:443–62.
64. Adida M, Jollant F, Clark L, Besnier N, Guillaume S, Kaladjian A, et al. Trait-related
decision-making impairment in the three phases of bipolar disorder. Biol
Psychiatry. 2011;70:357–65.
65. Zeeck A, Stelzer N, Linster HW, Joos A, Hartmann A. Emotion and eating inbinge eating disorder and obesity. Eur Eat Disorders Rev. 2011;19:426–37.
66. Bower GH. Mood and memory. Am Psychol. 1981;36:129–48.
67. Danner UN, Evers C, Sternheim L, van Meer F, van Elburg AA, Geerets TAM,
et al. Influence of negative affect on choice behavior in individuals withbinge eating pathology. Psychiatry Res. 2013;207:100–6.
68. Kaye WH, Bailer UF, Frank GK, Wagner A, Henry SE. Brain imaging of serotonin
after recovery from anorexia and bulimia nervosa. Physiol Behav. 2005;86:15–7.

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