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RESEARCH ARTICLE
Decision Making Impairment: A Shared
Vulnerability in Obesity, Gambling Disorder
and Substance Use Disorders?
Nuria Mallorquı
´-Bague
´
1,2‡
, Ana B. Fagundo
1,2‡
, Susana Jimenez-Murcia
1,2,3
, Rafael de la
Torre
2,4
, Rosa M. Baños
2,5
, Cristina Botella
2,6
, Felipe F. Casanueva
2,7
, Ana B. Crujeiras
2,7
,
Jose C. Ferna
´ndez-Garcı
´a
2,8
, Jose M. Ferna
´ndez-Real
2,9
, Gema Fru
¨hbeck
2,10
,
Roser Granero
2,11
, Amaia Rodrı
´guez
2,10
, Iris Tolosa-Sola
1
, Francisco J. Ortega
2,9
,
Francisco J. Tinahones
2,8
, Eva Alvarez-Moya
1
, Cristian Ochoa
1
, Jose M. Mencho
´n
1,3,12
,
Fernando Ferna
´ndez-Aranda
1,2,3
*
1Department of Psychiatry, University Hospital of Bellvitge-IDIBELL, Barcelona, Spain, 2CIBER
Fisiopatologı
´a de la Obesidad y Nutricio
´n (CIBERobn), Instituto Salud Carlos III, Madrid, Spain,
3Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain,
4Integrated Pharmacology and Systems Neurosciences Research Group, Neuroscience Research
Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain, Pompeu Fabra University
(CEXS-UPF), Barcelona, Spain, 5Department of Psychological, Personality, Evaluation and Treatment of
the University of Valencia, Valencia, Spain, 6Department of Basic Psychology, Clinic and Psychobiology of
the University Jaume I, Castello
´, Spain, 7Endocrine Division, Complejo Hospitalario U. de Santiago,
Santiago de Compostela University, Santiago de Compostela, Spain, 8Department of Endocrinology and
Nutrition, Hospital Clı
´nico Universitario Virgen de Victoria, Ma
´laga, Spain, 9Department of Diabetes,
Endocrinology and Nutrition, Institut d’Investigacio
´Biomèdica de Girona (IdlBGi) Hospital Dr Josep Trueta,
Girona, Spain, 10 Department of Endocrinology and Nutrition, Clı
´nica Universidad de Navarra, University of
Navarra, Pamplona, Spain, 11 Departament de Psicobiologia i Metodologia, Universitat Autònoma de
Barcelona, Barcelona, Spain, 12 CIBER Salud Mental (CIBERsam), Instituto Salud Carlos III, Madrid, Spain
‡ These authors share first authorship.
*ffernandez@bellvitgehospital.cat
Abstract
Introduction
Addictions are associated with decision making impairments. The present study explores
decision making in Substance use disorder (SUD), Gambling disorder (GD) and Obesity
(OB) when assessed by Iowa Gambling Task (IGT) and compares them with healthy con-
trols (HC).
Methods
For the aims of this study, 591 participants (194 HC, 178 GD, 113 OB, 106 SUD) were
assessed according to DSM criteria, completed a sociodemographic interview and con-
ducted the IGT.
Results
SUD, GD and OB present impaired decision making when compared to the HC in the over-
all task and task learning, however no differences are found for the overall performance in
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 1 / 11
a11111
OPEN ACCESS
Citation: Mallorquı
´-Bague
´N, Fagundo AB,
Jimenez-Murcia S, de la Torre R, Baños RM,
Botella C, et al. (2016) Decision Making
Impairment: A Shared Vulnerability in Obesity,
Gambling Disorder and Substance Use Disorders?
PLoS ONE 11(9): e0163901. doi:10.1371/journal.
pone.0163901
Editor: Aviv M. Weinstein, Ariel University, ISRAEL
Received: March 21, 2016
Accepted: September 18, 2016
Published: September 30, 2016
Copyright: ©2016 Mallorquı
´-Bague
´et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper.
Funding: This manuscript was supported by grants
from Instituto Salud Carlos III (ISCIII; FIS PI14/
00290 and CIBERobn) and co-funded by Fondos
Europeos de Desarrollo Regional (FEDER) funds - a
way to build Europe, and AGAUR (2014 SGR
1672). This manuscript was co-funded by
Ministerio de Economı
´a y Competitividad
(PSI2015-68701-R). CIBERobn and CIBERSAM are
both initiatives of ISCIII. Jose C. Ferna
´ndez-Garcı
´a
is recipient of a research contract from Servicio
the IGT among the clinical groups. Results also reveal some specific learning across the
task patterns within the clinical groups: OB maintains negative scores until the third set
where learning starts but with a less extend to HC, SUD presents an early learning followed
by a progressive although slow improvement and GD presents more random choices with
no learning.
Conclusions
Decision making impairments are present in the studied clinical samples and they display
individual differences in the task learning. Results can help understanding the underlying
mechanisms of OB and addiction behaviors as well as improve current clinical treatments.
Introduction
Evidence based neurocognitive models of addiction propose that addiction-related behaviors
are the result of an imbalance of three neural systems: an impulsive neural system that pro-
motes habitual and salient behaviors, interoceptive processes that are involved in uncertain
risk and reward, and a reflective neural system for inhibitory control and decision-making[1]
[2]. Decision-making entails the cognitive process of making a choice after reflection on the
consequences of that choice, and it is a key component of addiction development and mainte-
nance in both substance use disorders (SUD) and behavioral addictions such as gambling
disorder (GD). The assessment of decision-making is usually conducted through the Iowa
Gambling Task (IGT), which simulates real life decision making strategies by factoring uncer-
tainty, reward and punishment. The IGT is a relatively complex task as it is difficultto decon-
struct into different cognitive constructs, however it measures decision-making with a high
ecologicalvalidity[3] in a wide range of clinical and non-clinical groups[4]. Specifically, impair-
ments in this task have been demonstrated in patients with ventromedialprefrontal cortex
lesions (VMPC;[5]) and in different psychopathological conditions including addictions
and eating disorders[6–8]. Distinctively, while clinical individuals with decision making
impairment fail to learn the contingencies and prefer the choices that lead to higherlong term
losses, healthy individuals present a gradual learning across the IGT [4][5]. According to the
Somatic Marker Hypothesis, individualswho perform poorlyon the IGT have weakerphysio-
logical cues to guide risky choices and present what is referred to as “myopia for the future”
[9].
Complementary, obesity (OB) is an increasing worldwide problem that shares similar pat-
terns to addictions[6]. Individuals with obesity frequently decide to overeat despite being
aware of its negative long-term health consequences and they usually put extra but unsuccess-
ful efforts into controlling their eating behaviour. Neuropsychological studies support the
hypothesis of an alteration on inhibitory control, emotion regulation and the executive func-
tion circuit in which one of the core cognitive traits appears to be decision making[10]. Accord-
ingly, recent data shows that individuals with obesity are characterized by the tendency to
engage in decisions that support a positive short-term reword even if it results in long term
negative outcomes[7]. Furthermore when assessed by the IGT, individuals with obesity present
significant decisionmaking impairments in the overall task performance as well as in learning
across the task [7,11,12]. Likewise GD and SUD individuals present a similar decision making
pattern, being the overall task performance and learningacross the task impaired[8]. For
instance, a recent study conducted with GD participants describes a strong preference for
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 2 / 11
Andaluz de Salud (SAS) (B-0033-2014). The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing Interests: I have read the journal’s
policy and one of the authors of this manuscript
have the following competing interests: Susana
Jimenez Murcia is an academic editor of this
journal.
choices featuring high rewards rather than higher losses during the IGT. The authors of this
study suggest that this might reflect an hypersensitivity of their reward systems[13]. Addition-
ally, GD and SUD are usually associated; being the decision-making patterns worse when both
diagnoses are present[14]. Similarly, OB and GD display neurocognitive and clinical associa-
tions. For instance OB is associated with decision making and sustained attention impairments
in gamblers, along with greater monetary losses due to gambling [15].
Direct comparisons of decision making profiles among SUD, GD and OB groups have yet
to be conducted. However, direct comparisons can provide a valuable insight into the similari-
ties and/or singularities of different addictive related behaviours.
Aims of the study
The present study aims to further explore the decision making profiles of Substance use disor-
der, Gamblingdisorder and Obesitywhen assessedby the Iowa gambling task and compares
them with healthy controls (HC). The specificaims of the study are the following: (1) compare
the overall performance of the three clinical groups and the healthy controls, (2) observe and
compare specific patterns of learning across the task in the three clinical conditions and the
healthy controls. It is hypothesized that the clinical samples will present poorer performances
on the IGT when compared to the HC group. Also, specificpatterns of learning across the task
will be observed in the four studied samples. The results have the potential of improving our
understanding of the specific executive profiles (namely decision making) underlying the asso-
ciation between obesity and addictive behaviours which in turn can also help improving cur-
rent obesity treatments.
Methods
Sample
The final sample consists of 591 participants(51.7% females) distributed as follow: 194 HC,
178 GD, 113 OB and 106 SUD individuals.GD and SUD diagnosticcriterionwere assessed by
an experienced clinician (according to the DSM-IV-TR), by means of SCID-I [16].
Seven centers, all involved in the CIBERobn Spanish Research Network, participated in the
study: the Eating Disorders Unit (Department of Psychiatry, University Hospital of Bellvitge-
IDIBELL, Barcelona), the Department of Endocrinology (University Hospital of Santiago, San-
tiago de Compostela); the Department of Diabetes, Endocrinology and Nutrition (Clinic Uni-
versity Hospital Virgen de Victoria, Malaga); the Department of Endocrinology and Nutrition
(University of Navarra, Pamplona); the Diabetes, Endocrinology and Nutrition Department,
(Biomedical Research Institute of Girona IdIBGi-Doctor Josep Trueta Hospital, Girona); the
Clinical Research Unit (Hospital del Mar Medical Research Institute-IMIM, Barcelona) and
the Department of Basic Psychology, Clinic and Psychobiology (University Jaume I, Castellón).
The GD, SUD and OB participants were patients that were consecutively referred to the clinics
mentioned above. Recruitment of the controls took place by means of word-of-mouth and
advertisements at local universities.. All participants gave written signedinformed consent and
received no additional compensation for being part of the study. In accordance with the Hel-
sinki Declaration of 1975 as revised in 1983, the Ethics Committee of all the institutions
involved in the project approved the study: Comissió Deontológica de la Universitat Jaume I,
Subcomisión Clínica del Hospital Universitario “Virgen de la Victoria”, Málaga, Comite Etic
de Investigacio Clinica Hospital Universitari de Girona Doctor Josep Trueta (048/10), Comite
Etico de Investigacion Clinica del Consorci Mar Parc de Salut de Barcelona-Parc de Salut Mar
(2010/3914/I), Comité de Etica de la Investigación Universidad de Navarra (110/2010) and
Comité Etico de Investigación Clínica del Hospital Universitari de Bellvitge (307/06)].
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 3 / 11
Exclusion criterion were: (1) history of chronic medical illness or neurological condition that
might affect cognitive function; (2) head trauma, learning disability or intellectual disabilities;
(3) individuals who have suffered a lifetime mental disorder according to DSM-IV-TR other
than the specificdisorder of study (including the following: no OB individual had a lifetime
eating disorder, GD participants had noSUD, no SUD individualhad GD); (4) age under18 or
over 65 (to discard neuropsychological deficits associated with age); (5) having diabetes melli-
tus. There was one extra exclusion criteria for all groups except from the SUD group: (6) his-
tory of substanceabuse in the previous 3 months and useof psychoactive medication or drugs.
The SUD individualshad not taken any drugs during the last 72 hours prior to explorations
(assessed by urine drug testing). Finally, in addition to the already mentioned criterion, for the
OB group (7) patients with obesity who had comorbid binge eating disorder (DSM-IV-TR)
were also excluded.
Neuropsychological assessment
For the purpose of this study all individuals were assessed with the IGT [4]. This computer task
evaluates decision-making, risk and reward as well as punishment value. The subject has to
select 100 cards from four decks (A, B, C and D). After each card selection an output is given:
gain or a gain and loss of money. Two decks (A and B) are not advantageous as the final loss is
higher than the final gain. Decks C and D, however, are advantageous since the punishments
are smaller. The final objective of the task is to win as much money as possible. Before complet-
ing the task, all participants were instructed to try to win as much money as possible and avoid
losing as much as money as possible and, they were also informed that some decks were worse
than others. This test is scored by subtracting the amount of cards selected from decks A and B
from the amount of cards selected from decks C and D. It provides information about task
learning (NET 1 to 5) as wellas an overall performancescore (NET_Total).
Statistical analysis
Analyses were carried out with SPSS20 for Windows. Chi-square (χ
2
) tests compared categori-
cal variables between the diagnostic subtypes. Analysis of Variance (ANOVA), adjusted by the
covariates age and years of education, compared the means for the cognitive measures (IGT
scores). The ANOVA procedures included the between-subjects factors sex (two levels: men
versus women) and diagnostic subtype (four levels: HC, GD, OB and SUD), as well as the inter-
action group-by-sex to explore the potential moderator effect of sex into the relationships
between diagnosis and cognition outcomes. The Holm-Bonferroni method, which is one of the
Familywise error rate stepwise procedures that offers more powerful tests than the classical
Bonferroni-correction,was usedto control Type-I error due to multiple comparisons[17].
Results
Table 1 includes the results of the χ
2
tests and the ANOVA to compare the sociodemographic
variables between groups. Descriptive variables of our sample (Table 1) show the inherent dif-
ferences in sex, age and education among clinical groups (GD, SUD and OB groups). Accord-
ingly and for the statistical aims of this study we controlled for the age and education level
variables and used them as covariates in the ANOVA when comparing IGT scores.
Table 2 includes the ANOVA model comparing the cognitive mean scores measured
through the IGT, adjusted by the covariates age and years of education. The first step of the
ANOVA procedure tested the interaction parameter ‘group-by-sex’. The absence of significant
moderation effects indicates no sex significant differences in the diagnostic subtypes and it also
indicates that differences between sexes were statistically equal among the diagnostic subtypes.
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 4 / 11
Table 1. Sociodemographic descriptive.
HC GD OB SUD Factor: group Pairwise comparisons: p-value
n = 194 n = 178 n = 113 n = 106
1
Stat df p HC-GD HC-OB HC-SUD GD-OB GD-SUD OB-SUD
Sex; n(%) Females 154 79.4% 17 9.6% 87 77.0% 32 30.2% 232.9 3 <.001 <.001 .623 <.001 <.001 <.001 <.001
Males 40 20.6% 161 90.4% 26 23.0% 74 69.8%
Civil status; n(%) Single 155 79.9% 26 14.6% 13 11.5% - - - - - - 217.7 4 <.001 <.001 <.001 - - - .269 - - - - - -
Married—in couple 35 18.0% 144 80.9% 90 79.6% - - - -- -
Divorced—separated 4 2.1% 8 4.5% 10 8.8% - - - - - -
Employment; n(%) Unemployed 133 68.6% 6 3.4% 16 14.2% - - - - - - 202.9 2 <.001 <.001 <.001 - - - .001 - - - - - -
Employed 61 31.4% 172 96.6% 97 85.8% - - - - - -
Education; n(%) Primary 56 28.9% 156 87.6% 19 16.8% 77 72.6% 243.9 6 <.001 <.001 <.001 <.001 <.001 <.001 <.001
Secondary 81 41.8% 18 10.1% 78 69.0% 8 7.5%
University 57 29.4% 4 2.2% 16 14.2% 21 19.8%
Age (years-old); Mean-SD 24.95 7.10 38.28 10.94 43.38 10.42 21.66 2.87 189.9 3;587 <.001 <.001 <.001 .002 <.001 <.001 <.001
Education (yrs); Mean-SD 14.53 3.73 12.17 2.89 12.87 3.95 11.69 2.85 21.8 3;587 <.001 <.001 <.001 <.001 .090 .245 .011
SD: standard deviation.
1
Statistic: χ2-statistic for proportion comparison and F-statistic for mean comparison.
- -- Not available for this group. HC: healthy control. GD: gambling disorder. SUD: substance use disorder. OB: obesity.
doi:10.1371/journal.pone.0163901.t001
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 5 / 11
Table 2. Comparison of the cognitive mean scores between diagnostic subtypes: ANOVA.
Interaction HC GD OB SUD Factor Pairwise comparisons Women Men Factor
group×sex n= 194 n= 178 n= 113 n= 106 group HCvs GD HC vs OB HC vs SUD GD vs OB GD vs SUD OB vs SUD n= 290 n= 301 Sex
F
3;581
pMean SD Mean SD Mean SD Mean SD F
3;584
pMD pMD pMD pMD pMD pMD pMean SD Mean SD F
1;584
pMD
NET_total 0.94 .419 16.07 27.7 2.14 23.8 4.33 22.8 6.45 21.7 6.51 .001 13.93 .001 11.74 .001 9.61 .003 -2.19 .518 -4.31 .231 -2.13 .615 3.02 26.3 11.48 23.8 10.76 .001 8.45
NET_1 0.28 .842 -1.69 6.3 -1.15 5.2 -1.25 5.3 -2.30 4.3 0.69 .559 -0.55 .493 -0.44 .582 0.61 .410 0.10 .895 1.15 .159 1.05 .272 -1.62 6.0 -1.58 5.0 0.01 .953 -0.03
NET_2 0.24 .868 2.25 7.3 0.83 6.6 -1.11 5.8 1.51 5.5 4.30 .016 1.42 .128 3.35 .001 0.74 .387 1.93 .032 -0.68 .479 -2.61 .020 0.53 7.0 1.21 6.2 0.97 .326 -0.67
NET_3 0.36 .784 5.00 8.1 0.80 7.7 1.39 7.0 2.05 6.3 6.42 .001 4.20 .001 3.61 .001 2.95 .003 -0.59 .569 -1.25 .258 -0.66 .611 1.51 7.7 3.11 7.5 4.07 .044 -1.59
NET_4 1.61 .185 5.17 9.3 2.12 8.1 2.50 7.3 2.23 7.2 3.56 .028 3.05 .010 2.67 .026 2.94 .007 -0.38 .740 -0.12 .924 0.26 .853 1.80 8.7 4.21 7.9 7.74 .006 -2.42
NET_5 0.75 .521 5.33 9.9 -0.20 8.8 2.84 8.0 2.88 8.1 6.29 .001 5.53 .001 2.49 .056 2.45 .039 -3.03 .014 -3.08 .020 -0.04 .978 0.86 9.1 4.57 9.1 15.56 .001 -3.72
SD: standard deviation. MD: mean difference. |d|: Cohen’s-dcoefficient. HC: healthy control. GD: gambling disorder. SUD: substance use disorder. OB: obesity.
Bold: significant comparison. p-values includes Bonferroni-Holm correction.
Results obtained in ANOVA adjusted by the participants’ age and years of education.
doi:10.1371/journal.pone.0163901.t002
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 6 / 11
Consequently, the interaction parameter term was excluded from the modeling and the main
effects for thefactors group and sex were obtainedand interpreted. Regardingthe group factor
analysis, differences among groups emerged in all NET scores but in NET-1. Additionally, HC
participants achieved higher mean scores in NET-total, NET-3 and NET-4 when compared to
the other clinical conditions (GD, OB and SUD). The HC participants also achieved a higher
mean score in NET-5 when compared to GD and SUD individuals and, GD participants
obtained a lower mean score compared to OB and SUD individuals. Concerning NET-2, the
OB group achieved thelowest mean score and it was statistically different whencompared to
the other clinical conditions. Finally, with reference to the sex factor analysis, differences
between men and women emergedin all IGT scores except inNET-1 andNET-2 ones.Specifi-
cally, men achieved significantly higher means than women. Fig 1, displays the comparison of
the mean scores in the cognitive learningacross the task scales between groups.
Discussion
This study compares decision making patterns when assessed by the IGT in three different
clinical samples (SUD, OB and GD) and aHC. Results display impairments in theoverall per-
formance and in the task learning process in all clinical samples when individually compared
to the HC.
Fig 1. Mean cognitive measures in learning across the task between groups.
doi:10.1371/journal.pone.0163901.g001
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 7 / 11
Results are in agreementwith previous studies reporting impaired decision makingin SUD
and GD individuals [13,14] as well as in individuals with OB[10,12] when independently
assessed and compared to a HC group. Additionally, each of the three clinical groups of our
sample present more impaired learning across the task than the HC group. Data shows how
the HC group presents a preference for the short term wins but after their first set of selections
they progressively learn to choose the decks that will lead to bigger long term wins. This is an
expected pattern in healthy individuals[4]. In line with previous studies, the decision-making
pattern of our HC sample cannot be extended to the clinical groups where learning starts later
and/or with a more erratic progress. For instance, OB individuals maintain negative scores
until the third set (i.e.: NET_3) where learning starts but to a lesser extent than in the HC
group. Previous studies have also reported similar impairments in OB[7,18]. The SUD partici-
pants show an earlier learning across the task but improvement progression is slower when
compared to the HC ones. The GD individuals display more erratic or random choices. The
learning effect is quite small and ends with a score similar to the initial one. Accordingly, previ-
ous studies demonstrate how SUD individuals tend to present an adaptive shift in decision-
making performance towards the end of the task[19,20] whereas GD individuals do not[21,22].
Therefore, the decision-making impairment of individuals with SUD is probably more associ-
ated to a learning delay strategy rather than an inability to learn from the task and this pattern
seems not to be extended to GD participants. Finally, no significant differences are found in
the overall performance among the clinical groups. All groups achieve low but still positive
scores. Results can reflect that to some extent the three clinical samples respond to the punish-
ment cues (although probably to a lesser extent than the HC group) and also that their deficit
could be more due to difficulties in reward processing.
Results also display no sex influenceamonggroups. However, some differences are observed
when comparing the IGT performance between men and women in the whole sample. Specifi-
cally, men present a better performance on the IGT. Previous studies report similar results and
suggest that men tend to have a better performance than women in general population[23] and
in SUD[24]. Although sex factor was controlled in our study, it would be interesting to explore
further in future studies to specifically explore sex differences in the studied samples.
Our data adds to the current literature more evidence of decision making patterns in SUD,
OB and GD by comparing the three clinical samples. Importantly, in the analyses conducted
none of the differencesamong the groups couldbe due to sex or other sociodemographicvari-
ables, which adds extra value to the findings reported here. Our study supports the hypothesis
that OB shares specific cognitive and neurobiological patterns with SUD and GD which are
known to suffer from impairments in dopaminergic pathways that regulate neural systems
associated with reward sensitivity and incentive motivation[25]. Accordingly, food addiction
has been associated with obesity in previous behavioral and neuroimaging studies[26] and the
food intake management difficulties observed in some of the individuals with obesity could be
associated with decision making impairments. The clinical implications of these results lie on
the identified cognitivepatterns that suggest the value of parallel therapeutic approaches
among OB, SUD and GD individuals[27,28]. Importantly, the SUD and OB groups display
some improvement in decision making; however, they are far from the HC group results (see
Fig 1). Hence, cognitive stimulation protocols (through executive function working tools)
could potentially benefit these individuals by enhancing their adaptive decision making strate-
gies when treating their disorder. Additionally, some specific differences among the clinical
groups could be considered for improving current treatments. For instance, OB individuals
take longer to learn the relationship between decks but once they do, their behavior conforms
to that of HC. Still, their learning pattern remains less adaptive than the HC one and it should
be considered for treatment approaches. Specifically, results may suggest that OB individuals
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 8 / 11
could probably benefit from an early treatment extra emphasis on gaining more adaptive deci-
sion making strategies. On the other hand, SUD tend to learn faster than OB individuals and
more similar to HC but they display very little improvement when compared to HC. This
could be due to more risky behaviors and thus more emphasis should be given to these difficul-
ties. Finally, GD individuals display even more risky behaviors and move faster towards bigger
rewards. These difficulties should be targeted in treatment. Future studies should test this
hypothesis and further explore these domains in order to (1) help disentangle to what extend
these differences are generalizable or constrained to our sample, (2) test the predictive or medi-
ating role of decision making impairments in a study which compares the here studied clinical
samples. Furthermore, future studies could further explore sex differences within the studied
groups.
The present study has some limitations. Firstly, there are sociodemographic differences con-
cerning education,sex and age across the groups which are representative of the studied disor-
ders. This is an expected result when working with a consecutive clinical sample referred to
GD, SUD and OB treatments. However, we have controlled these variables in all the statistical
analysis. Although in this study we have paid specific attention to one of the most relevant
neuropsychological factors (namely decision making), other cognitive functioning variables or
intelligence measures not assessed here may better explain specific differences (more than com-
monalities) among the clinical groups. Finally, although participants did not present with-
drawal symptoms or presented any life time mood-anxiety disorder or mental disorder that
could hinder the assessment, the analyses conducted do not explicitly control for the partici-
pants’ hunger, anxiety or sadness feelings and these could also play a role in the participants’
performance on the task.
To our knowledge, we present the first study that compares decision making in substance
and behavioral related addictions, obese individuals and healthy controls. Results show similar
impairments in decisionmaking in the three clinical groups. These impairments are statisti-
cally significant when compared to the healthy control group but not different among the three
clinical groups. Finally, the clinical groups present significant difficulties in learning across the
task when compared to the healthy controls and also some specificdifferences when compar-
ing clinical groups.
Author Contributions
Conceptualization:NMB ABF SJM FFA.
Formal analysis: NMB RG.
Funding acquisition: JM FFA RT CB FFC JCFG JMFR FJO.
Investigation: NMB ABF RMB CB FFC ABC JCFG JMFR GF AR ITS FJO FJT EAM CO.
Methodology: NMB ABF SJM RT JAM FFA.
Project administration: NMB ABF SJM FFA.
Resources: SJM FFA RT RMB CB FFC ABC JCFG JMFR GF FJO FJT EAM CO.
Supervision: SJM FFA.
Validation: SJM RT RMB CB FFC ABC FFA.
Visualization: NMB ITS.
Writing – original draft: NMB ABF RG.
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 9 / 11
Writing – review & editing: SJM RT RMB CB FFC ABC JCFG JMFR GF RG AR ITS FJO FJT
EAM CO JMM FFA.
References
1. Bechara A, Van Der Linden M. Decision-making and impulse control after frontal lobe injuries. Curr
Opin Neurol. 2005; 18: 734–9. Available: http://www.ncbi.nlm.nih.gov/pubmed/16280687 doi: 10.
1097/01.wco.0000194141.56429.3c PMID: 16280687
2. Noe
¨l X, Brevers D, Bechara A. A triadic neurocognitive approach to addiction for clinical interventions.
Front Psychiatry. 2013; 4: 1–14. doi: 10.3389/fpsyt.2013.00179 PMID: 24409155
3. Schonberg T, Fox CR, Poldrack RA. Mind the gap: bridging economic and naturalistic risk-taking with
cognitive neuroscience. Trends Cogn Sci. 2011; 15: 11–9. doi: 10.1016/j.tics.2010.10.002 PMID:
21130018
4. Bechara A. Deciding Advantageously Before Knowing the Advantageous Strategy. Science (80-).
1997; 275: 1293–1295. doi: 10.1126/science.275.5304.1293 PMID: 9036851
5. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following
damage to human prefrontal cortex. Cognition. 50: 7–15. Available: http://www.ncbi.nlm.nih.gov/
pubmed/8039375 doi: 10.1016/0010-0277(94)90018-3 PMID: 8039375
6. Volkow ND, Baler RD. NOW vs LATER brain circuits: implications for obesity and addiction. Trends
Neurosci. 2015; 38: 345–352. doi: 10.1016/j.tins.2015.04.002 PMID: 25959611
7. Fagundo AB, De La Torre R, Jime
´Nez-Murcia S, Agu¨ Era Z, Granero R, Ta
´Rrega S, et al. Executive
Functions Profile in Extreme Eating/Weight Conditions: From Anorexia Nervosa to Obesity. PLoS
One. 2012; 7. doi: 10.1371/journal.pone.0043382 PMID: 22927962
8. Krmpotich T, Mikulich-Gilbertson S, Sakai J, Thompson L, Banich MT, Tanabe J. Impaired Decision-
Making, Higher Impulsivity, and Drug Severity in Substance Dependence and Pathological Gambling.
J Addict Med. 9: 273–80. doi: 10.1097/ADM.0000000000000129 PMID: 25918968
9. Bechara A, Tranel D, Damasio H. Characterization of the decision-making deficit of patients with ven-
tromedial prefrontal cortex lesions. Brain. 2000; 123: 2189–2202. doi: 10.1093/brain/123.11.2189
PMID: 11050020
10. Smith E, Hay P, Campbell L, Trollor JN. A review of the association between obesity and cognitive
function across the lifespan: implications for novel approaches to prevention and treatment. Obes Rev.
2011; 12: 740–55. Available: http://www.ncbi.nlm.nih.gov/pubmed/21991597 doi: 10.1111/j.1467-
789X.2011.00920.x PMID: 21991597
11. Brogan A, Hevey D, Pignatti R. Anorexia, bulimia, and obesity: Shared decision making deficitson the
Iowa Gambling Task (IGT). J Int Neuropsychol Soc. 2010; 16: 711–715. doi: 10.1017/
S1355617710000354 PMID: 20406532
12. Brogan A, Hevey D, O’Callaghan G, Yoder R, O’Shea D. Impaired decision making among morbidly
obese adults. J Psychosom Res. 2011; 70: 189–96. doi: 10.1016/j.jpsychores.2010.07.012 PMID:
21262422
13. Brevers D, Koritzky G, Bechara A, Noe
¨l X. Cognitive processes underlying impaired decision-making
under uncertainty in gambling disorder. Addict Behav. 2014; 39: 1533–6. doi: 10.1016/j.addbeh.2014.
06.004 PMID: 24980287
14. Tanabe J, Krmpotich T, Mikulich S, Sakai J, Thompson L, Reynolds J, et al. Decision-making, impulsiv-
ity, and drug severity in co-occurring substance dependence and pathological gambling. Drug Alcohol
Depend. 2015; 146: e115–e116. doi: 10.1016/j.drugalcdep.2014.09.681
15. Grant JE, Derbyshire K, Leppink E, Chamberlain SR. Obesity and gambling: neurocognitive and clini-
cal associations. Acta Psychiatr Scand. 2015; 131: 379–86. doi: 10.1111/acps.12353 PMID:
25346399
16. First Michael B., Spitzer Robert L, Gibbon Miriam, and Williams JB. Structured Clinical Interview for
DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). New York: Biometrics
Research,New York State Psychiatric Institute,; 2002.
17. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979; 6: 65–70.
18. Wu M, Brockmeyer T, Hartmann M, Skunde M, Herzog W, Friederich H-C. Reward-related decision
making in eating and weight disorders: A systematic review and meta-analysis of the evidence from
neuropsychological studies. Neurosci Biobehav Rev. 2015; 61: 177–196. doi: 10.1016/j.neubiorev.
2015.11.017 PMID: 26698021
19. 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.
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 10 / 11
Neuropsychologia. 2001; 39: 376–89. Available: http://www.ncbi.nlm.nih.gov/pubmed/11164876 doi:
10.1016/S0028-3932(00)00136-6 PMID: 11164876
20. Verdejo-Garcı
´a A, Rivas-Pe
´rez C, Vilar-Lo
´pez R, Pe
´rez-Garcı
´a M. Strategic self-regulation, decision-
making and emotion processing in poly-substance abusers in their first year of abstinence. Drug Alco-
hol Depend. 2007; 86: 139–46. doi: 10.1016/j.drugalcdep.2006.05.024 PMID: 16806737
21. Wiehler A, Peters J. Reward-based decision making in pathological gambling: the roles of risk and
delay. Neurosci Res. 2015; 90: 3–14. doi: 10.1016/j.neures.2014.09.008 PMID: 25269860
22. Goudriaan AE, Oosterlaan J, de Beurs E, van den Brink W. Decision making in pathological gambling:
a comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome,
and normal controls. Brain Res Cogn Brain Res. 2005; 23: 137–51. doi: 10.1016/j.cogbrainres.2005.
01.017 PMID: 15795140
23. van den Bos R, Homberg J, de Visser L. A critical review of sex differences in decision-making tasks:
Focus on the Iowa Gambling Task. Behav Brain Res. 2013; 238: 95–108. doi: 10.1016/j.bbr.2012.10.
002 PMID: 23078950
24. Fridberg DJ, Gerst KR, Finn PR. Effects of working memory load, a history of conduct disorder, and
sex on decision making in substance dependent individuals. Drug Alcohol Depend. 2013; 133: 654–
60. doi: 10.1016/j.drugalcdep.2013.08.014 PMID: 24011986
25. Volkow ND, Wang G-J, Tomasi D, Baler RD. Pro v Con Reviews: Is Food Addictive? Obesity and
addiction: neurobiological overlaps. Obes Rev. 2013; 14: 2–18. doi: 10.1111/j.1467-789X.2012.01031.
xPMID: 23016694
26. Avena NM, Rada P, Hoebel BG. Sugar and fat bingeing have notable differences in addictive-like
behavior. J Nutr. 2009; 139: 623–8. doi: 10.3945/jn.108.097584 PMID: 19176748
27. Gearhardt AN, Yokum S, Orr PT, Stice E, Corbin WR, Brownell KD. Neural correlates of food addiction.
Arch Gen Psychiatry. 2011; 68: 808–16. doi: 10.1001/archgenpsychiatry.2011.32 PMID: 21464344
28. Gearhardt AN, Corbin WR. The role of food addiction in clinical research. Curr Pharm Des. 2011; 17:
1140–2. Available: http://www.ncbi.nlm.nih.gov/pubmed/21492090 PMID: 21492090
Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 11 / 11
