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Decision Making Impairment: A Shared Vulnerability in Obesity, Gambling Disorder and Substance Use Disorders?


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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 controls (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 conducted the IGT. Results: SUD, GD and OB present impaired decision making when compared to the HC in the overall task and task learning, however no differences are found for the overall performance in 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.
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Decision Making Impairment: A Shared
Vulnerability in Obesity, Gambling Disorder
and Substance Use Disorders?
Nuria Mallorquı
, Ana B. Fagundo
, Susana Jimenez-Murcia
, Rafael de la
, Rosa M. Baños
, Cristina Botella
, Felipe F. Casanueva
, Ana B. Crujeiras
Jose C. Ferna
, Jose M. Ferna
, Gema Fru
Roser Granero
, Amaia Rodrı
, Iris Tolosa-Sola
, Francisco J. Ortega
Francisco J. Tinahones
, Eva Alvarez-Moya
, Cristian Ochoa
, Jose M. Mencho
Fernando Ferna
1Department of Psychiatry, University Hospital of Bellvitge-IDIBELL, Barcelona, Spain, 2CIBER
´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.
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).
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.
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
Citation: Mallorquı
´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.
Editor: Aviv M. Weinstein, Ariel University, ISRAEL
Received: March 21, 2016
Accepted: September 18, 2016
Published: September 30, 2016
Copyright: ©2016 Mallorquı
´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
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.
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.
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[68]. 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”
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
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.
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
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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
) 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].
Table 1 includes the results of the χ
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
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.
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.
Decision Making in OB, SUD & GD
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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
pMean SD Mean SD Mean SD Mean SD F
pMD pMD pMD pMD pMD pMD pMean SD Mean SD F
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.
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.
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.
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
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.
Methodology: NMB ABF SJM RT JAM FFA.
Project administration: NMB ABF SJM FFA.
Supervision: SJM 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
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Decision Making in OB, SUD & GD
PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 11 / 11
... The high comorbidity between gambling disorder and substance abuse may be explained by common etiological and maintenance factors (Goudriaan et al., 2019;Mallorquí-Bagué et al., 2016), among which the self-regulation model proposes a common origin based on attachment trauma (Lafond Padykula & Conklin, 2010). According to this model, attachment figures serve as models for the control and management of behavior. ...
... Alexithymia is an affective regulatory characteristic that may explain the effects of attachment. It is a personality trait whereby an individual has difficulty identifying and expressing emotions, and being aware of the feelings and emotions others and one's self (Luminet et al., 2018). Alexithymia is strongly influenced by emotional learning from attachment figures. ...
... Alexithymia is strongly influenced by emotional learning from attachment figures. Hence, the lack of adequate emotional regulation modeling and suitable responses to the feelings of children can lead to difficulties in acquiring functional regulation skills (Lafond Padykula & Conklin, 2010;Luminet et al., 2018). ...
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Gambling disorder is a high comorbid disorder in substance abusers which conjunct appearance is related to worse symptomatology and evolution. Nevertheless, the research on the risk factors that may explain this comorbidity is scarce. We build of the self-regulation theory of attachment and addiction to examine if insecure attachment is related to gambling comorbidity in substance abuse disorder and the mediating role of alexithymia in this process. A cross-sectional study was carried out with 369 clinical patients with substance use disorder of which 69 presented comorbid gambling disorder diagnosed with the DSM-5 criteria. Results showed that insecure attachment was more prevalent in the group with comorbid gambling. In this group, the alexithymia levels were also higher and mediated the relationship in between attachment and gambling disorder comorbidity even controlling for several sociodemographic variables. This research indicates that insecure attachment enhances the risk of gambling comorbidity on substance abusers due to the detrimental effect on the self-regulation of emotion. Thus, interventions directed to increase the identification, expression and awareness of emotions might help to reduce comorbidity of gambling of substance use disorders.
... In terms of other psychological characteristics, GD has been associated with high levels of impulsivity Steward et al., 2017) deficits in decision-making, emotional regulation, harm avoidance and low self-direction (Canale, Vieno, Griffiths, Rubaltelli, & Santinello, 2015;Leeman & Potenza, 2013;Mallorquí-Bagué et al., 2016;Moragas et al., 2015;Wyckmans et al., 2019). Moreover, several comorbidities have been reported in GD as substance use disorders (SUD) (Grant & Chamberlain, 2020;Petry, Stinson, & Grant, 2005;Rash, Weinstock, & Van Patten, 2016), affective disorders (Dowling et al., 2015;Karlsson & Håkansson, 2018;Rodriguez-Monguio, Errea, & Volberg, 2017) and personality disorders (Dash et al., 2019;Petry et al., 2005;Vaddiparti & Cottler, 2017) which have been associated with worse treatment outcomes (Stevens, King, Dorstyn, & Delfabbro, 2019). ...
... It is known that few people with gambling problems seek professional treatment (Loy, Grüne, Braun, Samuelsson, & Kraus, 2018), so it is necessary to strengthen intervention and prevention programs. As a prevention, early selfexclusion strategy can avoid future financial, psychological and social problems related to GD. ...
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Background and aims: Self-exclusion in gambling disorder (GD) is considered a measure to decrease the negative consequences of gambling behavior. Under a formal self-exclusion program, gamblers request to be banned from accessing to the gambling venues or online gambling. The aims of the present study are: 1) to determine sociodemographic characteristics of a clinical sample of seeking-treatment patients with GD who are self-excluded before arriving at the care unit; 2) to identify personality traits and general psychopathology of this clinical population; 3) to analyze the response to treatment, in terms of relapses and dropouts. Methods: 1,416 adults seeking treatment for GD, who are self-excluded completed screening tools to identify GD symptomatology, general psychopathology, and personality traits. The treatment outcome was measured by dropout and relapses. Results: Self-exclusion was significantly related to female sex and a high sociodemographic status. Also, it was associated with a preference for strategic and mixed gambling, longest duration and severity of the disorder, high rates of general psychopathology, more presence of illegal acts and high sensation seeking rates. In relation to treatment, self-exclusion was associated with low relapse rates. Conclusions: The patients who selfexclude before seeking treatment have a specific clinical profile, including high sociodemographic status, highest severity of GD, more years of evolution of the disorder and high emotional distress rates; however, these patients’ presents better response to treatment. Clinically, it could be expected that this strategy could be used as a facilitating variable in the therapeutic process.
... En el caso del circuito cerebral de recompensa, responsable del autocontrol o la impulsividad, so observa una actividad reducida de la dlPFC izquierda y de la corteza cingulada anterior izquierda, causando que el usuario requiera de una mayor cantidad de estímulos alimenticios para obtener los mismos niveles de satisfacción que una persona sana Carnell et al. (2012). Las alteraciones cognitivas detectadas son similares a las que presentan otros trastornos que afectan al sistema de recompensa como las adicciones a sustancias o al juego Mallorquí-Bagué et al. (2016). Estas alteraciones también influyen en la capacidad de los usuarios a adherirse a los tratamientos tradicionales, donde la impulsividad y la falta de autocontrol favorecen que las personas incumplan los regímenes marcados Sutin et al. (2011). ...
... In addition, the results reported by reaffirmed the utility of SOGS with both DSM-IV and DSM-5 criteria. Fourth, given the frequent comorbidity between substance and behavioral addictions (e.g., Liu et al., 2009) and the data supporting impaired decision-making in alcohol (e.g., Galandra et al., 2018) and substance disorders (e.g., Mallorquí-Bagué et al., 2016), another limitation of the present study is not having controlled for the role of comorbidity in decision-making impairment. Moreover, it is not clear whether there may be other unmeasured personality factors that might account for the observed associations. ...
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Several studies have examined age differences in affective decision-making utilizing the Iowa Gambling Task (IGT). However, findings are mixed, with some studies reporting impairments due to aging and others showing no age-related differences. The few studies that have explored personality correlates of IGT performance suggest that underlying personality characteristics may impact performance on the IGT beyond aging. Therefore, the present study investigated the interplay between chronological age, temporal perspective, and gambling-related cognitions in affective decision-making while controlling for gambling severity. Through snowball sampling, 302 adults aged 18–75 years were recruited. They administered the South Oaks Gambling Screen (SOGS), Consideration of Future Consequences scale (CFC-14), Gambling-Related Cognitions Scale (GRCS), and IGT. Regression analysis showed that future orientation and gambling-related cognitions (namely gambling expectancies, illusion of control, and predictive control) predicted IGT performance. Gender, age, education, and gambling severity were not included in the final model. Path analysis showed that gambling expectancies positively impacted the performance, whereas illusion of control and predictive control were detrimental to decision-making. Being oriented toward the future mitigated the negative effects of the two cognitive biases on IGT performance. The present study shows that aging does not affect negatively IGT performance. The quality of performance appears to depend upon individual characteristics, such as future orientation and gambling-related cognitions, irrespective of gambling severity. These findings suggest that individual characteristics should be considered in the clinical evaluation of IGT performance.
... One of the most widely used tasks to evaluate decision-making is the Iowa Gambling Task (IGT) (Bechara et al., 1994). This task has frequently been used to assess decision-making strategies in people with obesity (Brogan et al., 2010(Brogan et al., , 2011Davis et al., 2010;Koritzky et al., 2012;Mallorquí-Bagué et al., 2016), and two recent meta-analyses (Rotge et al., 2017;Yang et al., 2018) concluded that adults with obesity performed worse on the IGT than healthy weight participants, especially in decisions under risk. These results suggest that excessive intake of highly palatable foods might be underpinned by an inability to successfully weigh the risks of reward and punishment (Fagundo et al., 2012). ...
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Impairments in decision-making have been suggested as a predisposing factor to obesity development. Individuals with excess weight display riskier decisions than normal weight people. Furthermore, adolescence is a period of life in which risky behavior may increase. We aimed to investigate decision making applying the Outcome-Representation-Learning (ORL) model to the Iowa Gambling Task (IGT) in adolescents with excess weight. Twenty-nine excess weight and twenty-eight normal weight adolescents, classified according to their age-adjusted body mass index (BMI) percentile, participated in the study. Decision-making was measured using the IGT. A Bayesian computational ORL model was applied to assess reward learning, punishment learning, forgetfulness, win perseverance and deck perseverance. The IGT net score was lower in excess weight than normal weight adolescents (β = 2.85; p < .027). Reward learning (95% HDI [0.011, 0.232]) was higher, while forgetfulness (95% HDI [− 0.711, − 0.181]) and deck perseverance (95% HDI [− 3.349, − 0.203]) were lower, in excess weight than normal weight adolescents. Excess weight adolescents seemed better at learning the most rewarding choices and showed a random strategy based on reward and novelty seeking. Consequently, excess weight adolescents made more disadvantageous selections, and performed worse in the IGT.
... Regarding GD, impulsivity has been positively associated with GD severity (Brevers et al., 2012;Krueger et al., 2005). Individuals with GD often exhibit disadvantageous decision-making (Mallorquí-Bagué et al., 2016;Navas et al., 2016;Potenza, 2009), with tendencies to discount rewards steeply (Grecucci et al., 2014;Petry, 2001), and they frequently demonstrate impairments in delaying or interrupting inappropriate behavioral responses (Kräplin et al., 2014). GD also involves continued behavioral engagement despite adverse consequences (Potenza, 2007). ...
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Impulsivity and self-control are often inversely related, yet little is known regarding their concurrent role in gambling disorder (GD). Therefore, we aimed to explore self-control and impulsivity with respect to GD severity, gender, and age in an adult sample with GD. The secondary aim of this study was to consider the roles of these factors by means of path analysis. One hundred and twelve adults who met criteria for GD completed the Barratt Impulsiveness Scale (BIS-11) and the Self-Control Scale (SCS). Structural equation modeling (SEM) evaluated direct and indirect effects of impulsivity and self-control on GD severity. Impulsivity and self-control were negatively correlated. The SEM identified a direct positive relationship between impulsivity and GD severity. Impulsivity also mediated a relationship between age and GD severity. High impulsivity levels are associated with greater severity of GD. Future studies should examine treatments targeting impulsivity and self-control in individuals with GD.
... These structures are particular important to executive functioning, learning, and memory, which was found to be associated with BMI in the current study. Deficits in this neurocognitive process underly poor decision-making and impulse control, lack of goal-directed behavior, and impaired independent function of individuals with obesity (41). While the focus of this study was not on the role of specific obesity-related mechanisms and underlying pathophysiological process, it does provide support of the relationship between BMI and neurocognitive (dys) function based on the MoCA scores. ...
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Objective: Recent evidence suggests that obesity is increasing worldwide and may negatively impact neurocognition. Local studies on the association of weight status with neurocognitive function are sparse. This study is aimed at examining the association between body mass index (BMI) and neurocognitive functioning scores in a cohort of adult women. Methods: A convenience sample of 175 women aged 18 to 59 years (28.03 ± 8.87) recruited in a community-based quantitative study completed the Montreal Cognitive Assessment (MoCA). The BMI metric was used to measure body fat based on weight and height and was stratified as high BMI (overweight or obese) or low BMI (normal weight). The Beck Depression Inventory (BDI) was used to assess depression. Pearson's correlation analysis and the student's t-test analysis were performed. Results: We observed a significant inverse association between BMI and performance on MoCA (r(173) = -0.32, p < 0.001). Performance on subtest of attention, memory, constructive abstraction, and executive functions significantly and inversely correlated with BMI. Significantly lower scores on the MoCA were found in women with a high BMI compared to women with a low BMI (23 ± 4 vs. 26 ± 3), t(173) = 4.12, p < 0.0001). Conclusions: BMI and MoCA were inversely associated on both global and domain-specific neurocognitive test of attention, memory, and executive function; key neurocognitive control; and regulatory functions underlying behavior and decision-making. The findings provide a rationale for further research into the long-term effects of BMI on neurocognition.
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Pathological gambling is the most widespread and severe form of non-chemical addiction. It is challenging to categorize pathological gambling into just one category, ie. into a disorder characterized by impulsivity or into behavioral addiction, since there are obvious overlaps. With the above in mind, the changes within the latest Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the eleventh revision of the International Classification of Diseases (ICD-11) are not surprising. Although not listed in the diagnostic criteria, impulsivity and neuropsychological deficits are an integral part of gambling disorder. For this reason, they are essential for a more complete understanding of the profile of pathological gamblers. The strongest arguments in favor of the reclassification of pathological gambling under the category of addiction are: similarities with the diagnostic characteristics of addiction to psychoactive substances (PAS); high degree of comorbidity between these two disorders; their common features including aspects related to the reward system; findings that the same brain structures are involved in both disorders. There are similarities in the way gambling disorders are reclassified within DSM-5 and ICD-11. As in DSM-5, pathological gambling is recognized as a form of addiction. In ICD-11, it was renamed gambling disorder and classified as behavioral addictions. The latest revisions of both classifications (DSM and ICD) have the same development path and essentially the same foundations, and a change in the perception of gambling within diagnostics is clearly visible. Pathological gambling is a very complex disease that is accompanied by neuropsychological deficits and impulsive behavior, both characteristic of addicts and people with impulse control disorders. Reclassification is significant for several reasons. First, there are similarities with the diagnostic characteristics of chemical addiction. Second, there is a high degree of comorbidity between impulse control disorders and addiction. Third, both involve the reward system and activate the same parts of the brain. It is assumed that these similarities led to the reclassification in both DSM-5 and ICD-11. It is still not entirely clear how this change in the perception of gambling within diagnostics will affect the actual treatment of pathological gambling.
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Substance abuse among young adults increased during the COVID-19 pandemic. Although pre-pandemic data indicate non-Hispanic White adults had higher levels of substance use disorder (SUD), Black adults suffered more serious consequences. The COVID-19 pandemic has introduced new stressors that may contribute to SUD, especially among Black young adults, including employment as essential workers, which may be related to victimization distress associated with the coronavirus (i.e., coronavirus victimization distress). The current study administered an anonymous, cross-sectional, online survey to a national sample of 132 Black and 141 non-Hispanic White adults 18 - 25 years to assess the relationship between health, economic disparities, employment, coronavirus victimization distress, and substance use during the first wave of the pandemic. Controlling for COVID-19 health risks and income, structural equation models indicated that coronavirus victimization distress fully accounted for the positive association between employment and SUD risk, and this association was more pronounced among Black young adults. Findings underscore the urgency of considering disease-related victimization in SUD interventions involving employed young adults during infectious disease pandemics.
Background and aims Emotion regulation (ER) and impulsivity impairments have been reported in patients with gambling disorder (GD). However, both constructs have not been studied in depth jointly in clinical samples. Therefore, the aim of this study was to analyze ER and impulsive tendencies/traits in a sample of n=321 treatment-seeking individuals with GD by differentiating them according to their gambling preference (n=100 strategic; n=221 non-strategic). Methods Our sample was assessed through the DERS (ER), the UPPS-P (impulsivity), and the DSM-5 (GD severity). Results The non-strategic group included a higher proportion of women and reported greater ER impairments, and more impulsive traits/tendencies compared to strategic gamblers. GD severity was associated with all DERS subscale (except for awareness) and with urgency dimensions of the UPPS-P. Discussion and Conclusions Our findings confirm that strategic and non-strategic gamblers differ in their ER processes and impulsive tendencies, showing the first clinical group a more adaptive profile. These results suggest the relevance of assessing these ER and impulsivity in order to tailor better treatment approaches.
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Balancing behaviors that provide a reward NOW versus behaviors that provide an advantage LATER is critical for survival. We propose a model in which dopamine (DA) can favor NOW processes through phasic signaling in reward circuits or LATER processes through tonic signaling in control circuits. At the same time, through its modulation of the orbitofrontal cortex, which processes salience attribution, DA also enables shifting from NOW to LATER, while its modulation of the insula, which processes interoceptive information, influences the probability of selecting NOW vs LATER actions on the basis of an individual's physiological state. Disruptions along these circuits contribute to diverse pathologies, including obesity and addiction. Published by Elsevier Ltd.
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According to the triadic neurocognitive model of addiction to drugs (e.g., cocaine) and non-drugs (e.g., gambling), weakened "willpower" associated with these behaviors is the product of an abnormal functioning in one or more of three key neural and cognitive systems: (1) an amygdala-striatum dependent system mediating automatic, habitual, and salient behaviors; (2) a prefrontal cortex dependent system important for self-regulation and forecasting the future consequences of a behavior; and (3) an insula dependent system for the reception of interoceptive signals and their translation into feeling states (such as urge and craving), which in turn plays a strong influential role in decision-making and impulse control processes related to uncertainty, risk, and reward. The described three-systems account for poor decision-making (i.e., prioritizing short-term consequences of a decisional option) and stimulus-driven actions, thus leading to a more elevated risk for relapse. Finally, this article elaborates on the need for "personalized" clinical model-based interventions targeting interactions between implicit processes, interoceptive signaling, and supervisory function aimed at helping individuals become less governed by immediate situations and automatic pre-potent responses, and more influenced by systems involved in the pursuit of future valued goals.
Substance use disorder is characterized by impaired decision making, impulsivity, and risk taking. Pathological gambling shares many of these characteristics, and having both diagnoses may be associated with greater problems than either diagnosis alone. We investigated whether among substance-dependent individuals, comorbid pathological gambling would be associated with worse decision making, greater impulsivity, risk taking, and drug severity. Ninety-six substance-dependent individuals were recruited from a residential treatment program and divided into 1 of the 2 groups depending on whether they met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria for pathological gambling (SDPG, n = 26) or not (SD, n = 70). Ninety-two controls were recruited from the community. Participants completed a decision-making task (modified Iowa Gambling Task), measures of impulsivity (Barratt Impulsivity Scale and Delay Discounting), and risk taking (Balloon Analog Risk Task). Decision making was analyzed using a computational model. We tested for group differences using analysis of covariance or Kruskal-Wallis and appropriate post-hoc tests. The groups differed in decision-making parameters (P < 0.001) and self-reported impulsivity (P < 0.001). All post-hoc comparisons were significant on these measures, and indicated stepwise changes in controls, followed by SD, followed by SDPG, with SDPG performing worse on decision making and being more impulsive. Compared with SD, SDPG had greater drug severity (P < 0.001). No group differences were observed in delay discounting or risk taking. Compared with individuals with substance dependence without pathological gambling, those with both disorders demonstrated worse decision making and significantly more drug-related symptoms. When evaluating patients with substance dependence, clinicians should consider diagnostic assessments for gambling, as the co-occurrence of both disorders may impact clinical characteristics.
Objective Research on health correlates in gamblers has found an association between gambling and obesity. The neurocognitive underpinnings of impulsivity may be useful targets for understanding and ultimately treating individuals with both gambling and obesity problems.Method207 non-treatment seeking young adults (18–29 years) with subsyndromal gambling disorder were recruited from the community. Subjects were grouped according to weight (‘normal weight’ BMI < 25, ‘overweight’ BMI ≥ 25; or ‘obese’ BMI ≥ 30). Measures relating to gambling behaviour and objective computerized neurocognitive measures were collected.ResultsOf the 207 subjects, 22 (10.6%) were obese and 49 (23.7%) were overweight. The obese gamblers consumed more nicotine (packs per day equivalent) and reported losing more money per week to gambling. Obese gamblers exhibited significant impairments in terms of reaction times for go trials on the stop-signal test (SST), quality of decision making and risk adjustment on the Cambridge Gamble Test (CGT), and sustained attention on the rapid visual information processing task (RVP).Conclusion Obesity was associated with decision making and sustained attention impairments in gamblers, along with greater monetary loss due to gambling. Future work should use longitudinal designs to examine the temporal relationship between these deficits, weight, other impulsive behaviour, and functional impairment.
Pathological gambling (PG) is a non substance based addiction that shares many behavioral and neural features with substance based addictions. However, in PG behavioral and neural changes are unlikely to be confounded by effects of acute or chronic drug exposure. Changes in reward based decision-making in particular increases in impulsivity are hallmark features of addictions. Here we review studies in PG that applied three reward-related decision tasks: the Iowa Gambling Task, probability discounting and delay discounting. We discuss the findings and focus on the impact of addiction severity and the relation of effects to impulsivity measures. While there is evidence that PGs differ from healthy controls on all three tasks, there is only little support for a further modulation of impairments by addiction severity. Conceptually, delay discounting is related to impulsivity measures and findings in this task show a considerable correlation with e.g. questionnaire-based measures of impulsivity. Taken together, impairments in PG on these three tasks are relatively well replicated, although impairments appear to be largely uncorrelated between tasks. An important next step will be to conceptualize a process-based account of behavioral impairments in PG.
Substance dependence and antisocial psychopathology, such as a history of childhood conduct disorder (HCCD), are associated with impulsive or disadvantageous decision making and reduced working memory capacity (WMC). Reducing WMC via a working memory load increases disadvantageous decision making in healthy adults, but no previous studies have examined this effect in young adults with substance dependence and HCCD. Young adults with substance dependence (SubDep; n=158, 71 female), substance dependence and HCCD (SubDep+HCCD; n=72, 24 female), and control participants (n=152, 84 female) completed a test of decision making (the Iowa Gambling Task; IGT) with or without a concurrent working memory load intended to tax WMC. Outcomes were (i) net advantageous decisions on the IGT, and (ii) preferences for infrequent- versus frequent-punishment decks. SubDep+HCCD men made fewer advantageous decisions on the IGT than control men without a load, but there were no group differences among women in that condition. Load was associated with fewer advantageous decisions for SubDep+HCCD women and control men, but not for men or women in the other groups. Participants showed greater preference for infrequent-punishment, advantageous decks under load as well. There are gender differences in the effects of substance dependence, HCCD, and working memory load on decision making on the IGT. Decision making by control men and SubDep+HCCD women suffered the most under load. Load increases preferences for less-frequent punishments, similar to a delay discounting effect. Future research should clarify the cognitive and neural mechanisms underlying these effects.
Drug addiction and obesity appear to share several properties. Both can be defined as disorders in which the saliency of a specific type of reward (food or drug) becomes exaggerated relative to, and at the expense of others rewards. Both drugs and food have powerful reinforcing effects, which are in part mediated by abrupt dopamine increases in the brain reward centres. The abrupt dopamine increases, in vulnerable individuals, can override the brain's homeostatic control mechanisms. These parallels have generated interest in understanding the shared vul-nerabilities between addiction and obesity. Predictably, they also engendered a heated debate. Specifically, brain imaging studies are beginning to uncover common features between these two conditions and delineate some of the overlapping brain circuits whose dysfunctions may underlie the observed deficits. The combined results suggest that both obese and drug-addicted individuals suffer from impairments in dopaminergic pathways that regulate neuronal systems associated not only with reward sensitivity and incentive motivation, but also with conditioning, self-control, stress reactivity and interoceptive awareness. In parallel , studies are also delineating differences between them that centre on the key role that peripheral signals involved with homeostatic control exert on food intake. Here, we focus on the shared neurobiological substrates of obesity and addiction.
It has been observed that men and women show performance differences in the Iowa Gambling Task (IGT), a task of decision-making in which subjects through exploration learn to differentiate long-term advantageous from long-term disadvantageous decks of cards: men choose more cards from the long-term advantageous decks than women within the standard number of 100 trials. Here, we aim at discussing psychological mechanisms and neurobiological substrates underlying sex differences in IGT-like decision-making. Our review suggests that women focus on both win-loss frequencies and long-term pay-off of decks, while men focus on long-term pay-off. Furthermore, women may be more sensitive to occasional losses in the long-term advantageous decks than men. As a consequence hereof, women need 40-60 trials in addition before they reach the same level of performance as men. These performance differences are related to differences in activity in the orbitofrontal cortex and dorsolateral prefrontal cortex as well as in serotonergic activity and left-right hemispheric activity. Sex differences in orbitofrontal cortex activity may be due to organisational effects of gonadal hormones early in life. The behavioural and neurobiological differences in the IGT between men and women are an expression of more general sex differences in the regulation of emotions. We discuss these findings in the context of sex differences in information processing related to evolutionary processes. Furthermore we discuss the relationship between these findings and real world decision-making.