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Neurocognitive components of gambling disorder: Implications for assessment, treatment
and policy
Juan F. Navas1,2, Joël Billieux3,4, Antonio Verdejo-García5, José C. Perales1,2
1. Department of Experimental Psychology, University of Granada, Spain
2. Mind, Brain, and Behavior Research Center (CIMCYC), University of Granada, Spain
3. Addictive and Compulsive Behaviours Lab. Institute for Health and Behaviour, University of
Luxembourg, Esch-sur-Alzette, Luxembourg
4. Centre for Excessive Gambling, Lausanne University Hospital, Switzerland
5. Monash Institute of Cognitive and Clinical Neurosciences & School of Psychological
Sciences, Monash University, Melbourne, Australia
To be published as: Navas, J. F., Billieux, J., Verdejo-García, A., & Perales, J. C. (In press).
Neurocognitive components of gambling disorder: implications for policy, prevention, and
treatment. In H. Bowden-Jones, C. Dickson, C. Dunand, & O. Simon (Eds.), Harm Reduction
for Problem Gambling: A Public Health Approach. Routledge.
Aims and scope
The present chapter aims to describe the psychobiological bases of gambling disorder (GD), and
to identify how neuroscience research could inform better prevention and treatment strategies.
In the first section, we describe the characteristics shared by patients with gambling
disorder (PGD), and revisit the literature showing that GD is in essence a disorder of learning.
Among vulnerabilities, we highlight factors incrementing the allure of gambling, making it more
rewarding, or strengthening its negatively reinforcing properties.
Second, we pinpoint the variables contributing to individual differences within the PGD
population, with a particular focus on emotion regulation. Dysregulation of automatic (model-
free) emotion regulation is suggested to be a complicating factor of GD, and a transdiagnostic
vulnerability factor for psychopathology beyond GD. Dysregulation of controlled (model-based)
emotion regulation strategies, along with gambling-related cognitive distortions, are hypothesised
to contribute to self-deceptive thinking in some gamblers.
Lastly, all these variables are integrated into a dimensional model (the Gambling Space
Model), aimed at updating previous cluster-based proposals to subtype PGD, by incorporating
recent neurocognitive evidence. The implications of the model are discussed, and we address its
implications on policy and regulation. Additionally, we discuss whether or not other putative
behavioural addictions should be ascribed the same consideration. Eventually, we analyse how
better understanding individual differences could contribute to better treatment and prevention
designs.
Homogeneity in gambling disorder: Incentive sensitisation as the mechanism of gambling
conditioning
Gambling research has flourished in recent years, and can be considered an example of integration
of knowledge from different disciplines. The recent reconceptualisation of GD as an addictive
disorder (American Psychiatric Association, 2013) and the ensuing translational advances would
have not occurred without such cross-talk.
In a joint attempt to define addictive disorders, animal and human research, behavioural
neuroscience and cognitive neuroscience have converged in stressing the importance of
progressive detachment of addictive behaviour from instrumental goals. According to some
etiological models, addictive behaviours are problematic because they become habit-driven or
compulsive (Everitt & Robbins, 2016). Other models stress that individuals with addictive
disorder cannot help craving their addictive substance or activity (Skinner & Aubin, 2010).
Beyond the subtleties of these two approaches, here we will stress their commonality; namely, the
fact that wanting (to gamble, to use the drug) and seeking behaviour, once a person meet criteria
for addictive disorder, have little to do with the hedonic properties of drug use/gambling
consequences.
Craving is thus best defined as a multifaceted construct, manifested by the urge to engage
in the addictive behaviour, automatic hijacking of attention and cognitive resources by cues
reminding or signalling the availability of the object of desire, and imperative approach responses
to such cues. With regard to its proximal and distal causes, there is also convincing evidence that
craving is cue-driven, and acquired through exposure to the addictive agent (Sayette, 2016).
In keeping with the importance of craving in substance use disorders (SUDs; Kober &
Mell, 2015) considerable efforts have been made to prove the existence of gambling craving
(Ashrafioun & Rosenberg, 2012). Additionally, recent evidence suggests that craving may involve
a common brain circuitry, with an important hub in the insula, independently of the type of
addictive disorder (Garavan, 2010; Limbrick-Oldfield et al., 2017). The association between this
area and cravings accords with its status as an important node in interoceptive representation, and
the use of such information in decision-making (Garavan, 2010).
From a practical point of view, however, the key question is how gambling becomes
compulsive, and wanting detaches from hedonic value. A leading model in accounting for drug
craving acquisition is the incentive sensitisation (IS) hypothesis. The IS model posits that all
addictive drugs directly or indirectly sensitise dopamine release in the mesolimbic system, which
is responsible for attributing incentive salience to cues signalling the availability of reward
(Robinson & Berridge, 1993).
Incentive salience is hypothesised to be the inner engine of craving acquisition. In normal
circumstances, when an unexpected reward is encountered, a mismatch between predicted and
experienced utility generates error signals in the mesocorticolimbic system, and particularly in
the ventral striatum (VS; Humphrey & Richard, 2014). However, rewards become more
predictable as instrumental learning progresses, so the magnitude of error signals decreases, and
incentive salience reaches asymptote. Drugs of abuse alter this system by producing supra-
threshold stimulation and precluding habituation, and thus causing “irrationally strong motivation
urges that are not justified by any memories of previous reward values (and without distorting
associative predictions)” (Berridge, 2012, p. 1124). In other words, substance use disorders
(SUDs) are normally accompanied by a subjective and behavioural dissociation between liking
and wanting the drug (Pool, Sennwald, Delplanque, Brosch, & Sander, 2016).
In view of the success of the IS hypothesis to account for some of the seemingly irrational
features of SUDs patients’ behaviour, the question arises whether the hypothesis can be also tested
in behavioural addictions (Rømer Thomsen, Fjorback, Møller, & Lou, 2014). That is, in the
absence of an external chemical agent, what misleads dopaminergic error signals?
This question can be addressed by revisiting the literature on reinforcement schedules.
According to recent analyses, most gambling behaviours are under random ratio (RR) schedules
(Haw, 2008). These are characterised by intermittent reward, such that the probability of reward
in any single trial does not depend on the previous density of rewards. Uncertainty in RR
schedules is irreducible, and the rates of responding they generate are particularly stable and free
of breaks after reward (Schoenfeld, Cumming, & Hearst, 1956).
Irreducible uncertainty in gambling scenarios can be regarded as a constant source of
prediction error for the mesocorticolimbic dopaminergic system to feed incentive salience.
Supporting that hypothesis, Anselme, Robinson, and Berridge (2013) have indeed shown that
increasing the uncertainty level in the relationship between a cue and a reward enhances incentive
salience of this cue, as measured by a sign-tracking response. Further evidence supports the
involvement of the incentive salience dopamine system in the effect of uncertainty on the ability
of contextual cues to behave as motivational magnets (Anselme & Robinson, 2013).
Sources of heterogeneity in gambling disorder
Differences in gambling disorder vulnerability
If IS resulting from RR schedules is the main learning mechanism underlying gambling
conditioning, any factors fuelling this mechanism will contribute to GD vulnerability. More
specifically, any factors increasing exposure to gambling or its rewarding properties will facilitate
transition from recreational to problematic gambling. Accordingly, research shows that early wins
have a particularly strong effect on behaviour under RR schedules and in gambling scenarios
(Haw, 2008).
Complementarily, people differ in the degree to which they are sensitive to the various
appetitive and aversive properties of different types of events. Gray’s (1994) psychobiological
model of personality proposes reward sensitivity (RS) and punishment sensitivity (PS; the overt
manifestations of two biological systems referred to as behavioural activation and behavioural
inhibitions systems) as the main foundation of motivation and personality. In the framework we
are starting to sketch here, RS and PS easily enter the equation as individual differences that
modulate IS. However, reinforcement-related sources of individual vulnerability could be less
general than PS and RS traits, and more circumscribed to the types of rewards that occur in
gambling scenarios.
There is evidence, for instance, that some gamblers experience gambling-triggered
arousal or uncertainty as intrinsically rewarding (Megias et al., 2017; Sharpe, Tarrier, Schotte, &
Spence, 1995), a result that converges with studies on the biological basis of individual differences
in risk proneness in animals (Fiorillo, 2011). Complementarily, individuals presenting high levels
of neuroticism and punishment sensitivity, or proneness toward negative mood, are more likely
to use gambling to cope with psychological distress (Balodis, Thomas, & Moore, 2014).
The role of basic emotion regulation mechanisms
A growing corpus of evidence suggests that craving management, that is, succeeding in keeping
IS below a given threshold, can be viewed as an instance of emotional regulation (Loewenstein,
1996) that can be implemented at different levels.
IS is subject to influences from same-level learning mechanisms (e.g. extinction, counter-
conditioning, cue-interaction; Kober et al., 2010). Etkin, Büchel, and Gross (2015) have recently
proposed that, at this level, emotion regulation proceeds in a model-free, automatic manner. This
could be the case for loss-related learning processes necessary to compensate IS. Supporting this
idea, a recent study has showed casino gamblers to underestimate how much money they spend
on gambling in the long run. And, that their gambling expenditures could be reduced just by
providing them with a player account with their personal spend (Wohl, Davis, & Hollingshead,
2017). Interestingly, behaviour changed with limited or no awareness. Accordingly, manipulations
that reduce loss awareness increase wagering, in a similar, mostly automatic way (Monaghan,
2009).
Etkin and colleagues (2015) have identified the ventromedial prefrontal cortex (vmPFC)
and the ventral anterior cingulate (vACC) as the main regions in the circuit for model-free emotion
regulation, although their review mostly focuses on fear regulation and it is unclear whether these
would also constitute the most important structures for craving regulation. A discussion on the
exact brain implementation of model-free emotion regulation goes beyond the scope of the present
chapter. Medial and ventral parts of the PFC, the insula, and their connections with the amygdala
and the VS are, however, the most frequently mentioned structures (Phillips, Ladouceur, &
Drevets, 2008).
With regard to the model-free regulation of craving in GD, the evidence to date remains
indirect. For example, Contreras-Rodríguez et al. (2016) found a common pattern of
hyperconnectivity in PGD and cocaine-dependent individuals, mostly between the orbitofrontal
cortex (OFC) and VS, and between the insula and the amygdala. Complementary evidence comes
from studies showing that PGD perform worse than controls on the Iowa Gambling Task, in which
successful performance is known to depend on balanced emotion-driven learning (Buelow &
Suhr, 2009).
Malfunctioning of basic, model-free emotion regulation will be subjectively experienced
as a pervasive influence of craving on behaviour, and if such malfunctioning is extensive enough,
as a disproportionate impact of emotions in other areas of decision and action. This resonates with
similar findings in the SUDs literature that craving correlates with negative urgency (NU), namely
the tendency to act rashly under the influence of strong negative emotions (Cyders et al., 2014;
Doran, Cook, McChargue, & Spring, 2009).
Higher-order emotion regulation mechanisms
Model-free emotion regulation is complemented by model-based emotion regulation strategies.
These form a category of learned goal-directed responses through which people act upon their
own emotional processes. Not surprisingly, then, specific cerebral areas involved in this type of
emotion regulation (lateral PFC, pre-supplementary and supplementary motor areas [pSMA,
SMA], and parts of the parietal cortex) overlap with those involved in model-based instrumental
behaviour (O’Doherty, Cockburn, & Pauli, 2017).
Emotion regulation strategies are varied. The emotion regulation questionnaire (ERQ;
Gross & John, 2003) distinguishes between expressive suppression (suppressing the external
manifestations of emotion), and reappraisal (reprocessing of the causes of the emotion), with use
of the latter being considered adaptive and the former maladaptive. The more comprehensive
cognitive emotion regulation questionnaire (CERQ, Garnefski & Kraaij, 2007) identifies nine
cognitive strategies to deal with negative affect (blaming oneself, blaming others, acceptance,
rumination, positive refocusing, refocus on planning, positive reappraisal, putting into
perspective, and catastrophising).
In psychobiological terms, reappraisal has been shown to downregulate the activity of the
VS and the amygdala, altering the balance in favour of either continuing or interrupting gambling
(Kober et al., 2010; Sokol-Hessner et al., 2009). Accordingly, studies on craving regulation have
focused on this cognitive strategy (Giuliani & Berkman, 2015), and have observed that successful
downregulation of craving is associated with increased activity of the lateral and dorsomedial
PFC, and dampened activity of the ventral striatum, subgenual cingulate, amygdala, and ventral
tegmental area (Kober et al., 2010).
GD can progress with malfunctioning of the basic mechanisms necessary to regulate
craving and other undesirable emotions, and this can have an influence on how model-based
strategies operate. In a recent study, we tested the hypothesis that regulation of negative emotions
in PGD imposes an extra burden on cognitive control mechanisms, relative to healthy controls
(Navas et al., 2017b). Downregulation of emotions triggered by negative pictorial stimuli
activated the control network in controls and PGD, but the latter showed further hyperactivation
of an area comprising parts of the premotor cortex and the dlPFC. Additionally, activation of
dlPFC correlated with NU. In a separate sample, NU significantly correlated with the proneness
to use expressive suppression as a (maladaptive) strategy to regulate negative affect.
The Gambling Space Model
So far, we have suggested a number of psychobiological processes (1) to account for the transition
from recreational to problem gambling, (2) to facilitate that transition and contribute to GD
vulnerability, and (3) to underlie individual differences in PGD. In the next section these
constructs are integrated into a coherent model (Table 1), and their contribution to the behavioural
manifestations and clinical implications of disordered gambling are explicated.
Table 1. The Gambling Space Model
Construct
Sensitivity to
positively
reinforcing
properties of
gambling
Sensitivity to
negatively
reinforcing
properties of
gambling
Generalized
affect
dysregulation
Cognitive
elaboration and
self-deception
Psychobiological
basis
Reward system,
uncertainty-
sensitive
dopaminergic
projections
Fronto-
amygdalar
systems of
escape and
avoidance
Model-free
emotion
regulation
systems
Model-based
emotion
regulation
system, cognitive
control structures
Behavioural
manifestations
Positive motives
for gambling,
reward seeking
Negative
motives, poor
mood,
neuroticism,
boredom
Affect-driven
impulsivity,
disinhibition,
deficits in
decision making
Exaggerated
expectancies,
interpretative
biases, motivated
reasoning
Clinical
implications
Vulnerability to
risk gambling,
low motivation to
quit gambling,
dropout risk
Emotional
vulnerability,
internalizing
comorbidity, risk
of relapse
Low problem
awareness,
externalizing
comorbidity,
dropout risk
Cognitive
distortions,
preference for
skill-based
games, low
change
motivation,
treatment
reluctance
Common
construct
Incentive sensitization driven by random ratio schedules
The first construct in the Gambling Space Model (sensitivity to appetitive properties of
gambling) is related to reward sensitivity. The relationship between RS and gambling has been
lingering in the literature for decades, yet it has been difficult to identify it as a strong and
independent predictor of disordered gambling behaviour (Goudriaan, van Holst, Veltman, & van
den Brink, 2013). More consistently, gamblers have been found to differ from non-gamblers in
how they respond to the different sources of reward present in gambling scenarios (Sescousse,
Barbalat, Domenech, & Dreher, 2013). Still, reward sensitivity can interact with gambling
features in shaping individual gambling preferences. In a recent article, Navas et al. (2017a) found
that recreational and disordered gamblers preferring card, skill and casino games show higher RS
scores than those preferring slot machines, lotteries, and bingo. These gamblers are more strongly
motivated by positive reinforcers, and also more sensitive to the positive features of the gambling
experience.
The second putative construct relies on the negatively reinforcing properties of gambling.
Negative trait emotions can interact with sensitivity to the mood-modifying properties of
gambling. In practical terms, gambling-to-cope has been observed to correlate with comorbid
depression and relapse risk (Ledgerwood & Petry, 2006; Lister, Milosevic, & Ledgerwood, 2015)
The third construct, generalised emotional dysregulation, captures deterioration of
model-free emotion regulation mechanisms. Weakness of low-level regulation mechanisms
necessary to limit gambling conditioning are hypothesised to characterise all disordered gamblers.
However, extensive malfunctioning of basic emotion regulation mechanisms is likely to be
responsible for differences among PGD. Unfortunately, to date, there is a dearth of reliable and
psychometrically sound neurobehavioural tasks that could be used as tools to assess the extent
and severity of this type of dysregulation. Provisionally, we propose NU as the most promising
available proxy to evaluate it across disorders (Berg, Latzman, Bliwise, & Lilienfeld, 2015).
Finally, the fourth construct has to do with the use of strategic, model-based emotion
regulation. Recent evidence shows the existence of a subgroup of PGD who effectively use
putatively adaptive forms of emotion regulation (Navas, Verdejo-García, López-Gómez,
Maldonado, & Perales, 2016). However, in these patients, such strategies correlate directly with
gambling severity and gambling-related cognitive distortions. In a similarly counterintuitive
fashion, gamblers with high dispositional optimism have been found to be more prone to maintain
positive expectations and remain motivated to gamble after negative outcomes (Gibson &
Sanbonmatsu, 2004). In our model, gamblers’ use of model-based emotion regulation strategies
in combination with certain gambling-related cognitive distortions forms part of a self-deceptive
reasoning style. This ego-protective mechanism has been established as a factor contributing to
drug use perseverance, and to reluctance to treatment (Martínez-González, Vilar López, Becoña
Iglesias, & Verdejo-García, 2016). Here, we posit that self-deception has an emotional regulation
function, in line with models of motivated reasoning (Kunda, 1990).
According to the present model, certain types of cognitive distortions thus reflect spared
cognitive control, rather than cognitive dysfunction. Indeed, cognitive distortions are more
frequently encountered in young, educated, skill-game gamblers (Myrseth, Brunborg, & Eidem,
2010). Furthermore, and importantly, they are not systematically accompanied by signs of
cognitive/non-planning impulsivity or lack of conscientiousness (Navas et al., 2017a). This
relationship between elaborate distorted cognitions and planning abilities could partially account
for the inconsistency of findings regarding the link between GD and executive tasks (Goudriaan,
Yücel, & van Holst, 2014). In self-deceptive gamblers, preserved executive function would
contribute to false mastery, whereas in patients with less elaborated gambling beliefs weaker
executive functions would contribute to inflexible behaviour and unconscientious gambling.
Figure 1. A simplified depiction of the mapping of gamblers subtypes onto a dimensional model.
It is worth noting that there are important connections between our Gambling Space
Model and the Pathways Model (Blaszczynski & Nower, 2002). Figure 1 displays a simplified
depiction of the mapping of the Pathways Model onto the Gambling Space Model. In this space,
all PGD are conditioned gamblers, and subtypes would arise from the combination of
conditioning processes with sources of heterogeneity. In individuals with high levels of
neuroticism, poor mood or susceptibility to boredom, the negatively reinforcing properties of
gambling would give rise to the emotionally vulnerable gambler, whereas in cognitively spared
individuals, motivated reasoning and elaborated emotion regulation could give rise to self-
deceptive gamblers. The latter are not specifically considered in the Pathways Model, but are
easily identifiable in emerging profiles (Griffiths, Wardle, Orford, Sproston, & Erens, 2009).
With regard to the impulsive-antisocial gambler type, our model depicts a slightly more
complex scenario. Given the partial overlap between RS and impulsivity (Knezevic-Budisin,
Pedden, White, Miller, & Hoaken, 2015), reward-sensitive GD patients have remained partially
confounded with impulsive-antisocial ones. RS, however, reflects the hyper-reactivity of the
behavioural activation system to potential sources of, whereas other relevant aspects of emotion-
driven impulsivity reflect a more generalised regulatory dysfunction. Hence, it would be possible
to distinguish between predominantly reward- or sensation-seeking impulsive gamblers, and
gamblers with high levels of urgency, with the latter presenting a higher incidence of problematic
behaviours (Vachon & Bagby, 2009).
What has neuroscience ever done for us? Summary and implications
According to a recent opinion article by Markowitz (2016), “there is such a thing as too much
neuroscience” in psychopathology and psychotherapy research. In the context of GD research, it
is true that clear-cut biomarkers are still lacking, drug trials have yielded inconclusive, mixed or
unspecific results (Alexandris, Smith, & Bowden-Jones, 2015; Yip & Potenza, 2014), and other
manipulations of the brain (e.g. transcranial magnetic stimulation, neurofeedback) are still matters
of ongoing research (Goudriaan et al., 2014). Still, neuroscientific research has contributed to
change the way we conceptualise GD, and such a change is having consequences on the way we
deal with it.
Implications of conceptualising gambling disorder as an addictive disorder
Conceptualising GD as an addictive disorder implies endorsing the dissociation between wanting
to gamble and liking gambling, and thus the view of gambling as economically inconsistent.
Individuals with nicotine use disorder, for example, can invest considerable effort and money in
purchasing tobacco and trying to quit smoking (Reith, 2007). The liking/wanting dissociation thus
provides ethical ground for some degree of political paternalism. Given that likes also belong to
individuals, consideration of likes beyond and above wants actually sanctions what has been
called liberal paternalism (Camerer, 2006).
In other words, understanding the centrality of IS, its key role in the development of
craving, and how loss-based learning fails to compensate it, justifies product- and offer-centred
interventions regarding pervasiveness of gambling-triggering cues, and product design aimed at
reducing features that enhance their addictive potential (Parke, Parke, & Blaszczynski 2016). And
the other way round, if evidence does not support the consideration of a putative addictive
disorder as a genuine one, there would be less ethical ground to justify intervention. If we adopt
the same ‘addiction’ model for hypersexuality, dysregulated food intake or excessive video
gaming, there would be no reason not to implement similar rules in those markets. Previous
attempts to define addictive disorders based on the analogy between the excessive behaviour in
question and a previously accepted addictive disorder, based on the application of DSM diagnostic
criteria to the new putative ‘addictions’, has led to overdiagnosis and overtreatment. As stated by
Billieux, Schimmenti, Khazaal, Maurage, and Heeren (2015), behavioural addiction research
should shift “from a mere criteria-based approach toward an approach focusing on the
psychological processes involved” (p. 119). As reviewed in this chapter, neuroscience definitely
has a role in defining such processes.
Implications of a psychobiological approach to heterogeneity among patients with gambling
disorder
Complementarily to the coexisting ways in which gamblers’ heterogeneity has been approached
to date, neuroscientific work can already provide a set of core dimensional constructs with
practical use.
Individual treatments are likely to benefit from the reviewed evidence. First, in
accordance with the Gambling Space Model, gambling motives should be assessed in order to
draw a profile of the reinforcement sources that patients find in gambling, which could become
targets of intervention. The identification of reinforcement sources linked to gambling could be
useful to implement individual and process-oriented psychological interventions aimed, for
instance, at developing skills to cope with high relapse-risk situations (anxiety, low mood, money-
related thoughts, or boredom; Ledgerwood & Petry, 2006).
Second, gamblers with deficits in basic emotional dysregulation have been found as
especially refractory to treatment attempts. For these cases, a better prospect is provided by
studies in which mindfulness-based training has shown promising results in comorbid addictive
and emotion disorders (Hoppes, 2006), and positive effects on decision-making
neuropsychological tasks linked with basic emotion regulation (Alfonso, Caracuel, Delgado-
Pastor, & Verdejo-García, 2011).
Third, intervening on planning executive functions is likely to benefit gamblers in the low
end of the elaboration-self deception continuum (as it has been shown with SUDs patients,
Verdejo-Garcia, 2016), whereas people in the high end would probably benefit more from
metacognitive training skills aimed at making them aware of the connection between their
dysfunctional beliefs and their motives to gamble (see Lindberg, Fernie, & Spada, 2011).
Complementarily, secondary prevention efforts in community populations could also be
enriched with this dimensional-psychobiological vision, through the implementation of screening
techniques aimed at identifying high-risk profiles (although we are aware that extra measures
must be taken to avoid stigmatisation and stereotyping; O’Leary-Barrett et al., 2013).
Neurobiologically-informed risk profiling has already gone a step further than traditional
personality profiling, in delineating a common vulnerability factor for externalising problems in
early adolescence, and dissociating it from other factors with differential loadings in separate
disorders (Castellanos-Ryan et al., 2014). Prevention programs could thus be directed to
individuals in general populations (not necessarily current gamblers) identified to have poorer
basic emotion regulation. These individuals could benefit from interventions aimed at improving
general emotional regulation and self-control, and thus see their risk of externalising problems,
including disordered gambling, reduced.
Final remarks
The sociodemographic and behavioural map of gambling is changing rapidly. New gambling
opportunities and media (e.g., mobile gambling) are generating new gambler profiles, so
understanding the mechanisms that generate the evolving variability of vulnerabilities, symptoms,
and outcomes is necessary to be proactive at providing the best possible clinical and political
response to eventually diminish the public health burden of disordered gambling.
As depicted in the current chapter, a combined psychobiological and behavioural-
cognitive framework has shown some capacity to capture at least some of these sources of
variability. The four proposed constructs are not necessarily exhaustive but are grounded in
sufficient evidence to have clear implications for policy, prevention and psychological
interventions. Still, further evidence should be gathered to help delineate or reconfigure this set
of dimensions and evaluate its predictive power.
In parallel, it is important to acknowledge that theories of psychopathology have
important, and potentially negative, consequences in real life. Biological approaches to
psychopathology are often accused of crystallising abnormal behaviours that would be better
understood as dynamically evolving and distributed in a continuum. As we have tried to illustrate
here, psychobiological models can be learning-based and dimensional and, simultaneously, able
to incorporate biological factors. At the same time, such models must be discriminative enough
to allow identifying genuine addictive disorders. So, the risk of overpathologisation and
psychiatrisation actually exists, in particular for many putative behavioural addictions. Probably,
misleading and overinclusive definitions are already creating more harm than good.
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