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Brain Change in Addiction as Learning, Not Disease



The nature of addiction remains a matter of debate. One school of thought is that addictive behavior is learned and is not the result of a pathologic neurologic pathway. This article summarizes support for the learning model.
new england journal
n engl j med 379;16 October 18, 2018
Review Article
uring the past 30 years, the assumption that addiction is a dis-
ease or pathology has crystallized into the “brain disease model of addic-
This trend was driven by the convergence of 12-step thinking with
residential treatment approaches in the latter half of the 20th century,
the explo-
sion of neuroimaging technologies that began in the 1990s, and promotion by pro-
fessional organizations
and community groups.
According to the brain disease
model, addiction is a chronic disease brought about by changes in the brain sys-
tems that mediate the experience and anticipation of reward and in higher-order
systems that underlie judgment and cognitive control.
The proponents of the
model propose that these changes are driven by exposure to drugs of abuse or
alcohol, though links with behavioral addictions have also been explored.
The brain disease model is the most prevalent model of addiction in the western
world. Particularly in the United States, it dominates professional and public dis-
course on prevention, treatment, research agendas, and policy issues. Because the
disease model focuses on brain change, it has helped explain why persons with
addictions f ind it diff icult to change their thoughts and behaviors quickly or easily.
Because it focuses on biologic factors rather than moral arguments, it has helped
reduce the stigma faced by those with addictions and their families, at least in
some respects. (See Table 1 for a broader discussion of stigma.) The brain disease
model has also legitimized the role of doctors and other medical professionals in
addiction treatment and driven research on new drugs to combat addiction, and it
has been used to advocate for access to treatment and care rather than segregation
and punishment.
These aims and outcomes are well intended, and they have been benef icial in
some contexts, but the narrow focus of the disease model on the neurobiologic
substrates of addiction has diverted attention (and research funding) from other
Alternatives to the brain disease model often highlight the social and
environmental factors that contribute to addiction, as well as the learning pro-
cesses that translate these factors into negative outcomes.
11-1 5
For example, it has
been shown repeatedly that adverse experiences in childhood and adolescence in-
crease the probability of later addiction.
Also, exposure to physical, economic,
or psychological trauma greatly increases susceptibility to addiction.
14-1 7
models propose that addiction, though obviously disadvantageous, is a natural,
context-sensitive response to challenging environmental contingencies, not a dis-
Yet the brain disease model construes addictive learning in terms of patho-
logic brain changes triggered mainly by substance abuse. Learning models also
favor individual solutions for overcoming addiction, facilitated by cognitive modi-
fications and personal agency. (See Table 2 for a discussion of empowerment.)
Learning models can include multiple levels of analysis: societal, social, psycho-
logical, and biologic. According to experts both inside and outside the medical
From the Universit y of Toronto, Toronto.
Address reprint requests to Dr. Lewis at
Klingelbe ekseweg 24, 6812DH Arnh em, the
Netherlands, or at m . lewis@ psych . ru . nl.
N Engl J Me d 2018;379:1551- 60.
DOI: 10.1056/NEJMra1602872
Copyright © 2018 Massachusetts Medical Society.
Dan L. Longo, M.D., Editor
Brain Change in Addiction as Learning,
Not Disease
Marc Lewis, Ph.D.
n engl j med 379;16 October 18, 2018
new england journal
these levels of analysis should ideally be
integrated for a comprehensive understanding of
addiction. Unfortunately, however, the neural level
of analysis is almost always ignored by nondis-
ease models that emphasize learning. (Work by
Szalavitz is a notable exception.
) Rather than
ignore (or dispute) evidence of brain change in
addiction, the current learning model reinterprets
such evidence. Psychological change, develop-
ment, and indeed all learning involve brain
change. It is therefore unnecessary and perhaps
unreasonable for a learning model of addiction
to dismiss neural f indings.
In this review, I examine addiction within a
learning framework, informed by classic and
contemporary cognitive principles, which can
incorporate the brain changes seen in addiction
without reference to pathology or disease. In do-
ing so, I hope to connect neurobiologic and en-
vironmental accounts to make sense of addiction
with a degree of depth and precision that could
not be achieved by either one alone. I also inter-
pret key neurocognitive f indings from both learn-
ing and disease perspectives to highlight their
parallels as well as their disparities (Table 3).
Addic tion as Le arning
Psychologists have historically divided learning
into operant conditioning, by which animals
work to receive rewards predicted by specific
cues, and Pavlovian conditioning, by which ani-
mals respond automatically to the stimulus prop-
erties of cues themselves. Advances in cognitive
psychology reveal that learning also involves
planning, decision making, inhibitory control,
and strings of cues that eventually lead to pre-
dicted rewards. The contemporary view from
cognitive science has extended this understand-
ing with models of “embodied cognition,” which
propose that all cognitive activity (including
learning) results from iterative, self-perpetuat-
ing interactions (i.e., feedback) between the ani-
mal and the environment.
From this perspec-
tive, learning occurs when the animal’s neural
capacities become entrained with an environ-
mental context. Thus, learning is not just a re-
sponse to stimuli but active engagement with
meaningful aspects of the environment.
The brain disease model does not dismiss the
importance of learning but views this learning
as pathologic. Addictive behaviors are proposed
to begin as impulsive bids for highly motivating
rewards, consolidated through operant condi-
tioning, but to end up as automatic (Pavlovian)
responses that bypass intention, augmented by
a loss of inhibitory control and a capacity for
choice. This observation is consistent with mod-
els of “delay discounting,” which propose that
immediate payoffs are inflated in their perceived
value, whereas longer-term rewards are “dis-
counted” (devalued).
Psychologists view delay
discounting as an intrinsic cognitive bias, not
only in humans but in other mammals as well.
Yet delay discounting seems to be augmented in
addiction, with long-term rewards falling off the
radar almost entirely. “Dual process” models of
addiction may help to explain this phenomenon,
in that a cognitive “overseer” loses the capacity
to override impulsive choices.
Although none
Proponents of the brain disease model of addiction have consistently claimed
that the disease definition has major social benefits for people with addic-
tion. Before addiction was defined as a disease, it was mostly viewed as a
moral failure, and “addicts” were reviled as self-indulgent, weak, dirty, or
malicious. But if addiction is viewed as a disease (like any other disease),
then the behaviors of people with addiction should not be seen as their
fault. In this way, the disease model was proposed to reduce stigma, blame,
and the assumption that people with addiction should be punished or re-
moved from society. The disease model should be commended for even
partial success in achieving these humanitarian goals.
Yet the disease definition can replace one kind of stigma with another. The
notion of a mental illness or disease can hurt more than help those with
behavioral problems such as addiction, because it fuels discrimination
and alienation of another sort. The disease designation can reinforce the
belief that an inviolable or essentialist “badness” is built in and perma-
nent, resulting in a sense that one is fundamentally different from “normal”
people, with concomitant feelings of inferiority and shame.
The label can
also curtail attempts to improve one’s functioning without medical care.
Biogenetic explanations carry the implication that people with addictions
are not really trustworthy, now or in the future, because of a biologic pro-
clivity they cannot control.
Not only does this fuel one kind of stigmatiza-
tion; it also helps rationalize a long-standing policy of withholding employ-
ment benefits and positions of authority from anyone who has ever been
labeled an addict.
It is true that some people with addiction feel consoled by the disease label.
In fact, psychiatric classifications have provided people who have diverse
emotional and mental problems with a label and (sometimes) a hypotheti-
cal explanation for adversities that can otherwise seem indefinable, amor-
phous, and yet blameworthy. Distinct categories with concrete labels can
help provide closure, context, and even a sense of belonging (to a particu-
lar group).
Yet many people with addiction recoil from the disease label. Especially when
they are successful in galvanizing their willpower and rejigging their habits
(i.e., recovering), they often find it confusing and debilitating to be told they
are chronically ill. People with previous addictions (“recovered addicts”)
usually want to feel that they have developed beyond their addiction and
become better people as a result. Many would prefer respect for that
achievement over the pity bequeathed by the disease definition.
Table 1. Brain Disease Model and Stigma.
n engl j med 379;16 October 18, 2018
Brain Cha nge in Addiction as Learning, Not Disease
of these learning mechanisms are necessarily
unique, the brain disease model of addiction
views the progression of decreasing control as a
reflection of pathologic brain changes.
Addiction neuroscience explores these brain
changes. The shift from impulsive (operant,
reward-driven) actions to compulsive (automatic,
Pavlovian) associations is a case in point. When
drug taking is found to be highly rewarding, the
ventral striatum (including the nucleus accum-
bens) focuses attention on the desired goal, acti-
vates a behavioral sequence to achieve that goal,
and produces a motivational urge to energize
that behavior.
Over time, however, as behavior
becomes more compulsive and less impulsive
(less reward-driven), activation increases in the
dorsal striatum, the region most associated with
automatic responses.
This progression is
thought to eradicate willpower,
because con-
scious choice is no longer driving the behavior.
The neurotransmitter dopamine has often
been the focus of neural models of addiction.
But dopamine has many functions, both in the
striatum and in the prefrontal cortex, depending
partly on the receptor type absorbing it. For the
purposes of this discussion, we can think of
dopamine as activating synaptic activity and,
over time, synaptic change, both in the ventral
and dorsal striatum and in the prefrontal cortex
(partly through its effect on glutamate transmis-
sion). The release of dopamine to these and
other systems is triggered by the perception of
cues paired with anticipated rewards (in the case
of operant learning) or with automatic responses
(in the case of Pavlovian conditioning). Yet dopa-
mine metabolism also responds to the experi-
ence of rewards, increasing when rewards ex-
ceed expectations and decreasing when they fall
short. Addiction neuroscientists highlight the
long-lasting sensitization of the dopamine sys-
tem to addictive rewards or the cues that predict
them, resulting in craving and narrowed atten-
as well as the subsequent blunting of the
dopamine system over time.
Striatal systems engage in constant cross-talk
with regions of the prefrontal cortex. Prefrontal
activation (in the orbitofrontal cortex) determines
the attractiveness of potential rewards and also
(in the dorsolateral prefrontal cortex) the exer-
cise of judgment and perspective shifting. In fact,
disrupted activation of the lateral prefrontal cor-
tex has been shown to increase delay discount-
ing (i.e., the proportion of impulsive choices).
A key finding in support of the brain disease
model is that drug use reduces connectivity be-
tween the prefrontal cortex and striatum, and
long-term addiction corresponds with reduced
gray-matter density (synaptic loss) in several
prefrontal and related regions. Such changes are
hypothesized to underlie diminished capacities
for judgment and self-control, or “impaired re-
sponse inhibition,” in people with addictions.
According to the brain disease model, the
cognitive and neural changes characterizing ad-
Viewing addiction in terms of learning rather than disease may have direct
advantages for those who are struggling. If people think that their addic-
tion results from an underlying pathology, as implied by the brain disease
model, and that the pathology is chronic, as highlighted both by profes-
sional bodies and by the 12-step movement, then they are less likely to
believe they will ever be free of it, especially as a result of their personal ef-
This characterization of addiction flies in the face of research show-
ing that a majority of persons with addictions recover without professional
In fact, addiction workers generally agree that personal mo-
tivation, a sense of empowerment, and belief in one’s own agency are the
most important psychological resources for overcoming addiction. These
qualities would seem peripheral rather than mandatory if addiction were
indeed a disease.
In response to this argument, proponents of the brain disease model have
pointed out that defining something as a disease does not exempt patients
from responsibility for self-care (e.g., making lifestyle choices that improve
their prognosis). There is some truth to this counterargument; a sense
of empowerment can bolster self-care for patients with various medical
Yet viewing oneself as a patient implies that one’s primary duty is to follow
the instructions of knowledgeable professionals rather than examine one’s
own motivations, beliefs, and intuitions. Taking on the role of a patient
may be especially counterproductive in institutional settings, where people
with addictions tend to offload responsibility to treatment staff.
over, biogenetic explanations for psychological problems induce “prog-
nostic pessimism.”
People dealing with addiction will try to change only
that which they feel is within their power to change.
Thus, their own faith
in their recovery and the confidence of those around them are hampered
by the disease definition.
The choice of terminology suggests specific guidelines for treatment. If replac-
ing the disease nomenclature with an emphasis on motivation and self-
direction increases the probability of successful outcomes, then treatment
professionals (including doctors) should advise those seeking help that
they do not have a chronic disease. They should encourage people with ad-
diction not to strive for obedience to a set of rules or pharmaceutical sub-
stitutes (unless heroin use prioritizes the need for medication-assisted
treatment) but instead to seek counseling or psychotherapy to help them
organize and modify their own attentional and motivational habits. For ex-
ample, a psychotherapeutic technique called motivational interviewing has
been developed in which nonconfrontational counseling by the clinician
encourages increased awareness of one’s own motives, conscious choices
that are consistent with one’s long-term goals, and reduced ambivalence;
this approach is best known for its success in reducing substance use.
More conventional psychotherapies such as cognitive behavioral therapy
also show efficacy in overcoming addiction,
and cognitively oriented
group interventions such as Self-Management and Recovery Training
(SMART Recovery) are quickly gaining recognition.
Table 2. Learning Models and Empowerment.
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new england journal
diction are unique and pathologic. Some theories
highlight distinct phases or stages: drug taking
is driven by positive reinforcement at first, then
by negative reinforcement (underpinned by re-
duced dopamine signaling and blunted receptor
responses), and finally by the loss of prefrontal
A closely related theory suggests that
addictive urges are increasingly driven by the
brain’s rebound from drug stimulation — an
“antireward” effect resulting from an overactive
stress-response system, dopamine blunting, and
physical withdrawal symptoms.
These theories
emphasize repeated episodes of negative reinforce-
ment (learning to avoid an aversive outcome) and
positive reinforcement, plus changes in neuro-
chemistry and circuitry.
But are the neurocognitive processes that give
rise to addiction actually pathologic, or are they
constituents of normal learning with detrimen-
tal consequences? To help resolve this question,
I examine four neurocognitive changes central to
brain disease models. The first is the hypothe-
sized shift from impulsive behavior mediated by
the ventral striatum to compulsive responses
mediated by the dorsal striatum.
The second
change, which also supports the presumption of
involuntary behavior, is a reduction in functional
and structural connectivity between the striatum
and prefrontal cortex.
The third change is in-
creased and enduring sensitivity (i.e., sensitiza-
tion) to cues predicting addictive rewards, under-
pinned by mesolimbic dopamine.
The fourth
change is a decrease in sensitivity, not only to
alternative rewards but even to addictive rewards
I argue that these four neurocogni-
tive changes are not specif ic to addiction and do
not indicate a disease process.
the Neuroco gnitive Data
Role of Compulsive or Automatic Responses
According to the brain disease model, impulsive
drug seeking and use are linked with activation
of the ventral striatum or nucleus accumbens at
first, but these behaviors become compulsive and
automatic with activation of the dorsal striatum
over time.
Yet behavior generally becomes
more automatic with practice, as novelty is re-
placed by familiarity, and dorsal striatal (includ-
ing globus pallidus) involvement underlies this
automatization even in a simple finger-tapping
As Everitt and Robbins, acknowledged
experts on the ventral-to-dorsal shift, state, “There
is nothing aberrant or unusual about devolving
behavioural control to a dorsal striatal S-R [stimu-
lus–response or Pavlovian] ‘habit’ mechanism.”
They assert that this shift is to be expected in
Disease Model Learning Model Evidence for Learning
Addiction is characterized by a shift from
impulsive to compulsive processing,
loss of free will, and a shift of activation
to dorsal striatum.
All behavioral habits devolve to stimulus–
response mechanisms; automatization
is a normal outcome of learning.
Dorsal striatal activation or behavioral automati-
zation is seen with practice of even simple
(e.g., motor) tasks; for people with addiction,
operant contingencies facilitate the choice to
abstain from using drugs.
Functional connectivity between striatum
and PFC is lost, with reduced synaptic
density in specific PFC regions.
When planning and decision making are
bypassed, PFC demand is reduced; ex-
tended plasticity is normal; underused
synapses may be pruned.
Immediate or valued rewards lead to increased
striatal activation and decreased dorsolateral
PFC activation and cognitive control; synaptic
density in the PFC has been shown to rebound
with recovery.
Sensitization to drug cues is increased
(and enduring), mediated by increased
mesolimbic dopamine uptake.
Sensitization to valued rewards is normal;
an ongoing need or desire leads to on-
going sensitization (e.g., love, attachment,
wealth acquisition, religious practice).
Motivated goal pursuit leads to increased dopa-
mine, cue sensitization, and learning; high
emotional salience facilitates lasting synaptic
alterations (e.g., after trauma).
Ongoing drug use leads to loss of receptor
availability or sensitivity and reduced
pleasure (dopaminergic blunting).
Adversity, trauma (with or without drug
use), isolation, and overstimulation
lead to reduced dopamine-receptor
response or pleasure.
Loss of social status or trauma leads to reduced
D2 or D3 receptor availability; high levels of
mating behavior, eating, engagement with
pornography, and Internet use lead to a hypo-
dopaminergic system.
* PFC denotes prefrontal cortex.
Table 3. Comparison of Claims Made by Disease and Learning Models of Addiction and Sample Evidence for Learning.*
n engl j med 379;16 October 18, 2018
Brain Cha nge in Addiction as Learning, Not Disease
many aspects of our lives, including eating and
other habitual activities. “Automatisation of be-
haviour frees up cognitive processes,” Everitt and
Robbins continue, which explains why we can
talk, eat, and drive at the same time.
Not only is normal behavior partly automatic,
but also addictive behavior, even in its later stages,
remains partly operant (reward-driven).
ing evidence comes from numerous studies in
which the reward value of the addictive goal
(e.g., the amount of drug offered) shifts in rela-
tion to the reward value of an alternative goal
(e.g., money).
In fact, these studies show that
the probability of abstaining is proportional to
the relative reward value of the two choices; this
sensitivity to environmental contingencies is the
hallmark of operant learning. Contingency man-
agement programs, based on these principles,
have shown a consistent effect in the reduction
of drug use.
The ventral striatum continues
to be involved in reward seeking in later-stage
addiction, even when the dorsal striatum domi-
nates behavior control.
In sum, a combination
of deliberate and automatic neurobehavioral
mechanisms characterizes both addiction and
“normal” habitual behavior.
Loss of Prefrontal Connectivity
and Synaptic Pruning
Evidence of a functional and (in some studies)
structural disconnection between the prefrontal
cortex and striatum has been pivotal for defin-
ing addiction as a brain disease.
these f indings come from cross-sectional, not
longitudinal, research, so some cortical differ-
ences must precede rather than follow addiction,
as acknowledged by the researchers. Yet even
cortical changes that arise from (or with) addic-
tive drug use need not be considered pathologic.
When skills become streamlined with prac-
tice, they no longer engage conscious, reflective,
or effortful control. In fact, higher-order cogni-
tion is unnecessary once behavior becomes habit-
ual, as any professional musician or athlete can
demonstrate. Also, rewards perceived as both
immediate and valuable often bypass cognitive
control, as seen in the reduction of planning,
decision making, and concomitant prefrontal in-
volvement when it comes to sex, gambling, and
eating fast food.
Research points to an inverse
correlation between striatal activation and dorso-
lateral prefrontal engagement, both in delay dis-
and more generally in effortful reward
But would this loss of functional con-
nectivity normally lead to structural changes?
Indeed, the elimination, or “pruning,” of under-
used synapses is considered a key mechanism of
57, 58
Massive cortical pruning has tradi-
tionally been associated with adolescence,
most addictions develop. However, since pruning
makes the brain more efficient when new skills
are practiced and consolidated, it is now thought
to underpin learning over the lifespan.
57, 6 0
Synaptic density in certain prefrontal regions
decreases with the duration of drug use, but a
contrasting increase in synaptic density (in simi-
lar but not identical regions) correlates with the
number of weeks of abstinence.
In studies using
functional magnetic resonance imaging (fMRI),
“cocaine-dependent” participants who became
abstinent no longer differed from controls with
respect to the activation of inhibitory control
networks in the prefrontal cortex or the perfor-
mance of motor-inhibition tasks.
Thus, reduc-
tions in prefrontal involvement and synaptic
density appear to be restricted to the period of
habitual drug use, which may be followed by a
period of synaptic growth when a new skill —
abstinence — is learned. This two-way street in
frontal neuroplasticity is consistent with evidence
that most people with addiction recover,
most of those who recover do so without treat-
This finding would seem to be impos-
sible if prefrontal changes were permanent and
therefore pathologic.
Sensitization to Cues
People with drug addiction are highly sensitive
to drug-related cues, even after they quit using
drugs. To account for this sensitization, the brain
disease model points to a sharp rise in mesolim-
bic (reward-related) dopamine uptake.
The moti-
vational drive provided by mesolimbic dopamine
is essential for survival, because it ensures that
we prioritize eating, social relationships, and pro-
creation. Addiction neuroscientists acknowledge
that the levels of cue-triggered dopamine seen in
addiction can parallel those related to “natural”
Indeed, romantic relationships de-
pend on motivational dopamine uptake,
desire after romantic rejection matches the crav-
ing for cocaine.
Motivated pursuits (natural or
otherwise), including shopping, sports, religious
practice, wealth acquisition, gambling, binge eat-
n engl j med 379;16 October 18, 2018
new england journal
ing, romantic love, and pornography, correspond
with cue sensitization and increased activation
of striatal dopamine.
35,50-55,64- 66
Even a simple in-
crease in reward availability on a computer screen
is suff icient to increase mesolimbic dopamine,
with a concomitant increase in effort.
Proponents of the brain disease model empha-
size that cue sensitivity in addiction is not only
extreme but also prolonged, whereas cue sensitiv-
ity returns to normal levels in relation to natural
reinforcers, once the need has been met.
prolonged sensitization is seen as the cause of
37, 68
Yet prolonged sensitization also results
from normal learning of emotionally salient
associations, through synaptic alterations in re-
gions that process emotion, such as the amyg-
Stimuli associated with past triumphs or
traumas or even a once-loved song will reliably
trigger strong feelings. Because these cues refer
to still-meaningful experiences, dopamine uptake
remains adaptive (rather than pathologic) for
ongoing behavioral adaptations.
Perhaps the most parsimonious explanation
for enduring cue sensitivity is that, in addiction,
goal seeking remains unfulfilled. The drug or
activity that was pursued to satisfy emotional
needs may have lost its effect because of a short
duration of action, chemical tolerance, or habitu-
ation. The value of addictive rewards is always
determined by context, including both the strength
of aversive feelings and the effectiveness of drugs,
for example, in quelling them. Unresolved needs
can make drug taking relevant indef initely.
Desensitization to Drug-Related
and Natural Rewards
In parallel with cue sensitization and increased
levels of dopamine release, there is an appar-
ently paradoxical decrease in sensitivity to alter-
native rewards and even to drugs themselves.
This reward desensitization is thought to con-
tribute to increasing drug consumption. Brain
disease models ascribe this blunting to the down-
regulation (reduced availability or responsiveness)
of dopamine receptors (e.g., D2 and D3 recep-
tors), a pathologic process that may be mani-
fested as tolerance or withdrawal effects.
37, 42
many studies of addiction use psychostimulants
(e.g., cocaine and methamphetamine), serious-
ly confounding this observation.
The buildup
(e.g., delayed reuptake) of dopamine resulting
from psychostimulants may directly trigger a
chemical rebound effect, independent of addictive
But even if addictive learning results in dopa-
minergic blunting, it need not denote pathologic
brain change. Poverty, trauma, and diminished
social status reduce the availability of the D2 and
D3 dopamine receptors in humans and nonhu-
man primates.
In fact, a reduction in D2 or D3
receptor availability has been shown to corre-
spond with reduced social dominance or isola-
tion, driving drug or alcohol use as a means of
countering anxiety or distress.
73-7 6
As noted above,
early adversity and trauma are reliable predictors
of subsequent drug use.
However, social
adversity may also result from drug use itself.
Society responds to illicit drug use by excluding
or punishing users, which in turn leads to bro-
ken relationships and erosion of self-esteem.
Thus, social and psychological hardships may
result in dopaminergic blunting, which then en-
courages addictive activities, amplifying these
Dopaminergic blunting can also result from
nondrug rewards. Mating behavior in rats reduces
dopamine output in mesolimbic dopamine cir-
cuitry, leading to “a hypodopaminergic system,”
and identical changes result from prolonged ex-
posure to opiates.
In addition, obesity has been
linked to reduced dopamine receptivity, with the
hypothetical explanation that dopaminergic blunt-
ing leads to increased food consumption.
posure to other potentially habit-forming plea-
surable activities also leads to dopaminergic
blunting, as shown with pornography use
extensive Internet use.
Thus, it seems that dopa-
minergic blunting can result from frequent acti-
vation of the mesolimbic dopamine system by
any repetitive reward-seeking behavior rather than
by drug exposure itself. Kent Berridge, a renowned
addiction neuroscientist, views dopaminergic
suppression as a temporary effect of overstimu-
lation, which may result from drug addiction but
does not cause it.
Addic tion as Organism–
Environment Entr ainment
Most alternatives to the brain disease model
of addiction share the view that explanations of
addiction should include societal, social, and fa-
milial factors that predict drug misuse. The brain
disease model has acknowledged these factors,
n engl j med 379;16 October 18, 2018
Brain Cha nge in Addiction as Learning, Not Disease
but its emphasis on brain pathology sidelines
their causal status and their relevance to preven-
tion and treatment efforts. Yet viewing addiction
solely as the product of environmental forces
tends to ignore the properties of the organism,
its nervous system, and its response proclivities.
A comprehensive, balanced model of addiction
needs to recognize that the organism and its
environment are connected at every level, from
perception to cognition to behavior, and interact
continuously as an open system.
I have presented arguments and evidence that
automatization, reduced neural flexibilit y, endur-
ing cue sensitization, and reward desensitization
are normal features of learning highly motivat-
ing, repetitive, and habitual behavioral patterns.
Thus, I dispute the idea that addiction is patho-
logic. Nevertheless, there is considerable poten-
tial for reconciliation between aspects of the brain
disease model and an environmental model of
addiction, given that both view a rigidified be-
havioral pattern as learned, and learned deeply.
Classic learning models have limited value for
this synthesis, since they view the learner as an
independent agent responding to a static environ-
ment. In contrast, principles of embodied cogni-
tion construe learning as a process of reciprocal
adjustments between the activities of the organ-
ism and meaningful features of the environment.
What is meaningful is assumed to be con-
strained by biologic antecedents and emerging
biologic sensitivities, as well as the stimulus
properties of the animal’s environment (i.e.,
features of the environment that afford or invite
specif ic actions, known as affordances).
For a young human, the range of potentially
meaningful environmental features can be vast,
at least until social, familial, and psychological
setbacks narrow it down to a small subset of
suboptimal rewards. For example, many children
grow up with an unpredictable, disengaged, or
violent parent. As adolescents, they may face
disruptions in education, employment, or rela-
tionships as a result of financial or other disad-
vantages. These persons tend to find increased
meaning in drugs that reduce stress or promote
feelings of security and well-being, especially
because these effects can be attained without
mediation by other people. As drug use pro-
gresses and becomes a more consistent focus of
attention and behavior, the properties of the in-
dividual and of the environment tend to become
synchronized through mutual adjustments. Be-
havioral outcomes continue to shape a social en-
vironment that progressively narrows behavioral
options. For example, the social environment may
become increasingly limited to people who can
supply drugs (dealers or doctors), people with
whom to take drugs, and “friends” who remain
apathetic and disengaged. Behavioral proclivities
will change accordingly. Besides the increasing
habit strength of drug pursuit itself, there is
likely to be increased lying to avoid rejection or
punishment, as well as disengagement from
romantic partners and family members, further
limiting the chance to feel connected and pro-
tected. These changes would be mediated by cog-
nitive modifications — changes in attentional
foci, belief systems, identity, and self-esteem — as
well as by immature habits of emotion regula-
tion (e.g., suppression or denial) more generally.
But how might this addiction spiral get
started? The embodied-cognition view encour-
ages us to look for biologic and environmental
vulnerabilities that amplify and reinforce each
other. The goal here is not to list organismic
(e.g., genetic) and environmental risk factors and
add them together, but instead to track the inter-
action of factors that reciprocally influence each
other. I suggest that the addiction spiral gets
started with early psychosocial adversity. First,
we already know that early adversity and trauma
are strong predictors of later addiction.
Second, developmental psychologists have shown
that early trauma (physical, emotional, or sexual)
leaves enduring effects on nervous system func-
tion, such as sympathetic or parasympathetic
overattunement (causing hyperreactivity or hypo-
reactivity), oversensitivity to threat based on ac-
celerated amygdala development, and hippocam-
pal damage resulting from excessive cortisol
levels. Third, in animal models, researchers have
pinpointed epigenetic changes (e.g., methylation
of a gene that tunes the glucocorticoid feedback
loop) that take place in utero or the f irst year of
life in response to inadequate nurturing. But
these neuropsychological insults do not emerge
in a vacuum. Both trauma and “stress methyla-
tion” can begin with overstressed parents and
even grandparents
in families challenged by
unemployment, marital discord, histories of abuse,
or alienation from the community, affecting the
stress response in childhood and throughout life.
From these beginnings, a narrowing spiral of
n engl j med 379;16 October 18, 2018
new england journal
ineffective coregulation emerges between devel-
oping children and their caregivers, leading even-
tually to entrainment between drug seeking and
its environmental concomitants. From the Rat
Park studies of the 1970s and 1980s, in which
even addicted rats avoided ingesting morphine
when allowed to socialize and play,
to contem-
porary evidence of the adverse consequences of
socioeconomic fragmentation,
Bruce Alexander
has shown that addiction emerges universally
as a response to the disruption of normal social
interactions. Therefore, models of addiction pred-
icated on embodied cognition should focus on
environments in which social stressors affect
early neuropsychological development, as a gate-
way to ongoing reciprocal adjustments between
disadvantageous organismic adaptations and nar-
rowing environmental opportunities.
In summary, the embodied-cognition frame-
work can help model the interaction between
neurobiologic and social-environmental contrib-
utors to addiction. Addictive activities are deter-
mined neither solely by brain changes nor solely
by social conditions. Although they indeed result
from and contribute to brain changes, addictive
activities also feed back to the social environ-
ment, further narrowing what are often already
limited opportunities for well-being, which in
turn further narrows cognitive and neural f lex-
ibility. It follows that the narrowing seen in ad-
diction takes place within the behavioral reper-
toire, the social surround, and the brain — all
at the same time. It also follows that growth
beyond addiction can be facilitated by improved
social support, extended behavioral opportuni-
ties, targeted pharmacologic interventions, or
some combination of these strategies.
Disclosure forms provided by the author are available w ith the
full text of t his article at
I thank Shaun Shelly (University of Pretoria, Department of
Family Medicine) for providing information and references sup-
porting the arguments reviewed here, as well as feedback on
previous versions of the manuscript.
1. Volkow ND, Koob GF, McLellan AT.
Neurobiologic advances from t he brain
disease model of addiction. N Engl J Med
2016; 374: 363-71.
2. Fletcher AM. Inside rehab: the sur-
prising truth about addiction treatment
— and how to get help that works. New
York: Penguin Books, 2013.
3. American Society of Addict ion Medi-
cine. Definition of addiction. April 12,
2011 (http://www .asam .org/ quality - pract ice/
definition - of - addict ion).
4. Narcotics Anonymous World Servic-
es. What is addiction? Narcot ics Anony-
mous Bulletin no. 16. 2017 (htt ps://na .org/
?ID=bulletins - bull17 - r).
5. Goldstein RZ, Volkow ND. Drug ad-
diction and its underlying neurobiologi-
cal basis: neuroimaging evidence for the
involvement of the frontal cortex. Am J
Psychiatr y 2002; 159: 1642-52.
6. Volkow ND, Baler RD, Goldstein RZ.
Addict ion: pulling at the neura l threads of
social behaviors. Neuron 2011; 69: 599-602.
7. Buchman D, Reiner PB. Stigma and
addiction: being and becoming. Am J Bio-
eth 2009; 9: 18-9.
8. Haslam N. Genet ic essentialism, neu-
roessent ialism, and stigma: comment ary
on Dar-Nimrod and Heine (2011). Psychol
Bull 2011; 137: 819-24.
9. Kvaale EP, Haslam N, Got tdiener WH.
The ‘side effects’ of medicalizat ion: a meta-
analytic revie w of how biogenetic expla-
nations affect stig ma. Clin Psychol Rev
2013; 33: 782-94.
10. Hall W, Carter A, Forlini C. The brain
disease model of addiction: is it support-
ed by the evidence and has it delivered
on its promises? Lancet Psychiatry 2015; 2:
105 -10 .
11. Alexander BK. The globalization of
addiction: a study in the povert y of spirit.
Oxford, England: Oxford Universit y Press,
12. Heilig M, Epstein DH, Nader MA,
Shaham Y. Time to connect: bringing so-
cial context into addiction neuroscience.
Nat Rev Neurosci 2016; 17: 592-9.
13. Dube SR, Felitti VJ, Dong M, Chap-
man DP, Giles WH, Anda RF. Childhood
abuse, neglect, and household dysfunction
and the risk of illicit dr ug use: the adverse
childhood experiences study. Pediatrics
2003; 111: 564-72.
14. Enoch MA. The role of early life stress
as a predictor for alcohol and drug depen-
dence. Psychopharmacolog y (Berl) 2011;
214: 17-31.
15. Buchanan J. Understanding problem-
atic dr ug use: a medical matter or a social
issue? Br J Community Justice 20 06; 4: 47-61
(htt p://epubs .gly ndwr .ac .uk/ siru/ 18).
16. Valentine K, Fraser S. Trauma, da m-
age and pleasure: ret hinking problematic
drug use. Int J Drug Policy 2008; 19: 410-6.
17. McDevitt-Murphy ME, Murphy JG,
Monahan CM, Flood AM, Weathers FW.
Unique patterns of substance misuse asso-
ciated with PTSD, depression, and social
phobia. J Dual Diagn 2010; 6: 94-110.
18. Lewis M. Addiction and the brain: de-
velopment, not disease. Neuroethics 2017;
10: 7-18.
19. Heather N, Best D, Kawalek A, et al.
Challenging the brain disease model of
addiction: European launch of the Addic-
tion Theory Network. Addict Res Theory
2018; 26: 249-55.
20. Wiens TK, Walker LJ. The chronic dis-
ease concept of addiction: helpful or
harmful? Addict Res Theory 2015; 23: 309-
21. Hey man GM. Quitt ing drugs: quanti-
tative and qualitative features. Annu Rev
Clin Psychol 2013; 9: 29-59.
22. Lopez-Quintero C, Hasin DS, de Los
Cobos JP, et al. Probability and predictors
of remission from life-time nicot ine, alco-
hol, cannabis or cocaine dependence: re-
sults from the Nat ional Epidemiologic
Survey on Alcohol and Related Conditions.
Addiction 2011; 106: 657-69.
23. Bonnie RJ. Responsibility for addic-
tion. J Am Acad Psychiatry Law 2002; 30:
40 5-13 .
24. Pickard H, Ward L. Responsibilit y with-
out blame: philosophica l ref lect ions on
clinical practice. In: Fulford KWM, Davies
M, Gipps RGT, et al., eds. The Oxford
handbook of philosophy and psychiat ry.
Oxford, England: Oxford Universit y Press,
2013 (http://oxfordhandbooks .com/ view/
10 .1093/ oxfordhb/ 9780199579563 .001
.0001/ oxfordhb - 9780199579563 - e - 066).
25. Dunn C, Deroo L, Rivara FP. The use
of brief int erventions adapted from moti-
vational interviewing across behaviora l
domains: a systematic re view. Addiction
2001; 96: 1725-42.
26. Dutra L, Stathopoulou G, Basden SL,
n engl j med 379;16 October 18, 2018
Brain Cha nge in Addiction as Learning, Not Disease
Leyro TM, Powers MB, Otto MW. A meta-
analytic revie w of psychosocial inter ven-
tions for substance use disorders. Am J
Psychiatr y 20 08; 165: 179-87.
27. Satel S, Lilienfeld SO. Addiction and
the brain-disease fallac y. Front Psychiatry
2014; 4: 141 .
28. Szalavitz M. Squaring the circle: addic-
tion, disease and learning. Neuroethics
2017; 10: 83-6.
29. Cowart M. Embodied cognition. In-
ternet Encyclopedia of Philosophy, 2018
(https://www .iep .utm .edu/ embodcog/ ).
30. Wilson AD, Golonka S. Embodied cog-
nition is not what you think it is. Front
Psychol 2013; 4: 58.
31. Ainslie G. Précis of Breakdown of Will.
Behav Brain Sci 2005; 28: 635-50.
32. Bickel WK, Marsch LA. Toward a be-
havioral economic underst anding of drug
dependence: delay discounting processes.
Addiction 2001; 96: 73-86.
33. Wiers RW, Bartholow BD, van den
Wildenberg E, et al. Automatic and con-
trolled processes and the development of
addictive behaviors in adolescent s: a review
and a model. Pharmacol Biochem Behav
2007; 86: 263-83.
34. Stacy AW, Wiers RW. Implicit cogni-
tion and addiction: a tool for explaining
paradox ical behavior. Annu Rev Clin Psy-
chol 2010; 6: 551-75.
35. Everitt BJ, Robbins TW. From the ven-
tral to the dorsal st riat um: devolving
views of their roles in drug addict ion.
Neurosci Biobehav Rev 2013; 37: 9 Pt A:
36. Volkow ND, Fowler JS, Wang G-J,
Swanson JM, Telang F. Dopamine in drug
abuse and addiction: result s of imaging
studies and treatment implications. A rch
Neurol 2007; 64: 1575-9.
37. Robinson TE, Berridge KC. The neural
basis of dr ug craving: an incentive-sensi-
tization theory of addiction. Brain Res
Brain Res Rev 1993; 18: 247-91.
38. Volkow ND. Addiction is a disease of
free will. Bethesda, MD: Nat ional Insti-
tute on Dr ug Abuse, 2015 (https://www
.drugabuse .gov/ about - nida/ noras - blog/ 2015/
06/ addiction - disease - free - will).
39. Figner B, Knoch D, Johnson EJ, et al.
Lateral prefrontal cortex and self-control
in intertemporal choice. Nat Neurosci
2010; 13: 538-9.
40. Volkow ND, Fowler JS. Add iction, a dis-
ease of compu lsion and drive: involve-
ment of the orbitofront al cortex. Cereb
Cortex 2000; 10: 318-25.
41. Volkow ND, Morales M. The brain on
drugs: f rom reward to addiction. Cell 2015;
162: 7 12-25.
42. Koob GF. Neurobiological substrates
for the dark side of compu lsivity in addic-
tion. Neuropharmacolog y 2009; 56: Suppl 1:
43. Everitt BJ. Neural and psychological
mechan isms underlying compulsive drug
seeking habits and drug memories — in-
dicat ions for novel treatments of addic-
tion. Eur J Neurosci 2014; 40: 2163-82.
44. Lehéricy S, Benali H, Van de Moortele
P-F, et al. Distinct basa l ganglia territor ies
are engaged in early and advanced motor
sequence learning. Proc Nat l Acad Sci U S A
2005; 102: 12566-71.
45. Heather N. Is the concept of compul-
sion useful in the expla nation or descrip-
tion of addictive behaviour and experi-
ence? Addict Behav Rep 2017; 6: 15-38.
46. Cohen M, Liebson IA, Fail lace LA. The
role of reinforcement contingencies in
chronic a lcoholism: an experimental analy-
sis of one case. Behav Res Ther 1971; 9:
47. Hart C. High price: a neuroscientist’s
journey of self-discovery that challenges
everything you know about drugs and so-
ciety. New York: Harper, 2013.
48. Har t CL, Haney M, Foltin RW, Fisch-
man MW. Alternative reinforcers differ-
entially modify cocaine self-administra-
tion by humans. Behav Pharmacol 2000;
11: 87-91.
49. Menza TW, Jameson DR, Hughes JP,
Colfax GN, Shoptaw S, Golden MR. Con-
tingency management to reduce met ham-
phetamine use and sexual risk among
men who have sex with men: a random-
ized controlled trial. BMC Public Healt h
2010; 10: 774.
50. Fisher HE, Brown LL, A ron A, Strong
G, Mashek D. Rewa rd, addiction, a nd emo-
tion reg ulat ion systems associated with
reject ion in love. J Neurophysiol 2010; 104:
51-6 0.
51. Noori HR, Cosa Linan A, Spanagel R.
Largely overlapping neuronal subst rates
of reactivit y to dr ug, gambling, food and
sexua l cues: a comprehensive meta-analy-
sis. Eur Neuropsychopharmacol 2016; 26:
52. Rømer Thomsen K, Fjorback LO,
Møller A, Lou HC. Applying incentive sen-
sitization models to behavioral addiction.
Neurosci Biobehav Rev 2014; 45: 343-9.
53. Joyner MA, Kim S, Gearhardt A N.
Investigating an incentive-sensitization
model of eating behavior: impact of a
simulated fast-food laboratory. Clin Psy-
chol Sci 2017; 5: 1014-26.
54. Balod is IM, Kober H, Worhunsky PD,
Stevens MC, Pearlson GD, Potenza MN.
Diminished frontostriatal activity during
processing of monetary rewards and loss-
es in pathological gambling. Biol Psychia-
try 2012; 71: 749-57.
55. Robinson MJF, Fischer AM, Ahuja A,
Lesser EN, Maniates H. Roles of “want-
ing” and “liking” in motivati ng behav ior:
gambling, food, and drug addictions. Curr
Top Behav Neurosci 2016; 27: 105-36.
56. Westbrook A, Braver TS. Dopamine
does double dut y in mot ivating cognitive
effort. Neuron 2016; 89: 695-710.
57. Craik FIM, Bia lystok E. Cognit ion
through the li fespan: mechanisms of
change. Trends Cogn Sci 2006; 10: 131-8.
58. Peta njek Z, Judaš M, Šimic G, et a l.
Extraordinary neoteny of synapt ic spines
in the human prefrontal cortex. Proc Natl
Acad Sci U S A 2011; 108: 13281-6.
59. Selemon LD. A role for synaptic plas-
ticit y in the adolescent development of
executive function. Transl Psychiatry 2013;
3: e238.
60. Segal I, Korkotian I, Murphy DD. Den-
dritic spine formation and pruning: com-
mon cellular mechanisms? Trends Neuro-
sci 2000; 23: 53-7.
61. Connolly CG, Bell RP, Foxe JJ, Gara-
van H. Dissociated grey matter changes
with prolonged addiction and extended
abstinence in cocaine users. PLoS One
2013; 8(3): e59645.
62. Bell RP, Garavan H, Foxe JJ. Neural
correlates of craving and impulsivity in
abstinent former cocaine users: towards
biomarkers of relapse risk. Neuropharma-
cology 2014; 85: 461-70.
63. Dawson DA, Grant BF, Stinson FS,
Chou PS. Maturing out of alcohol depen-
dence: the impact of transit ional life
events. J St ud Alcohol 2006; 67: 195-203.
64. Olsen CM. Natur al rewards, neuroplas-
ticit y, and non-drug addict ions. Neuro-
pharmacology 2011; 61: 1109-22.
65. Fisher HE, Xu X, Aron A, Brown LL.
Intense, passionate, romantic love: a nat-
ural addiction? How the f ields that inves-
tigate romance and subst ance abuse can
inform each other. Front Psychol 2016; 7:
66. Sussman S, Lisha N, Griff iths M.
Prevalence of t he addictions: a problem of
the majority or the minority? Eval Health
Prof 2011; 34: 3-56.
67. Hamid AA, Pettibone JR, Mabrouk
OS, et al. Mesolimbic dopamine signals
the value of work. Nat Neurosci 2016; 19:
117-26 .
68. Berridge KC. Is addiction a brain dis-
ease? Neu roethics 2017; 10: 29-33.
69. Phelps EA, Lempert K M, Sokol-Hess-
ner P. Emotion and decision making: mul-
tiple modulator y neural circuits. A nnu
Rev Neurosci 2014; 37: 263-87.
70. Panno A, Lauriola M, Figner B. Emo-
tion reg ulat ion and risk taking: predict-
ing risk y choice in deliberative decision
maki ng. Cogn Emot 2013; 27: 326-34.
71. Nutt DJ, Lingford-Hughes A, Erritzoe
D, Stokes PRA. The dopamine theory of
addiction: 40 years of highs and lows. Nat
Rev Neurosci 2015; 16: 305-12.
72. Masten CL, Eisenberger NI, Borofsky
LA, et al. Neural correl ates of social exclu-
sion during adolescence: underst anding
the distress of peer rejection. Soc Cogn
Affect Neurosci 2009; 4: 143-57.
73. Czoty PW, Gage HD, Nader MA. Dif-
ferences in D2 dopamine receptor avail-
abilit y and reaction to novelt y in socially
housed male monkeys during abst inence
n engl j med 379;16 October 18, 2018
Brain Cha nge in Addiction as Learning, Not Disease
image s
clini cal
medic ine
The Journal welcomes consideration of new submissions for Images in Clinical
Medicine. Instructions for authors and procedures for submissions can be found
on the Journal’s website at At the discretion of the editor, images that
are accepted for publication may appear in the print version of the Journal,
the electronic version, or both.
from cocaine. Psychopharmacology (Berl)
2010; 208: 585-92.
74. Czoty PW, Nader MA. Ef fect s of dopa-
mine D2/D3 receptor ligands on food-
cocaine choice in socially housed male
cynomolg us monkeys. J Pharmacol Exp
Ther 2013; 344: 329-38.
75. Morgan D, Grant KA, Gage HD, et al.
Social dominance in monkeys: dopamine
D2 receptors and cocaine self-administra-
tion. Nat Neurosci 2002; 5: 169-74.
76. Blum K, Chen ALC, Chen TJH, et al.
Putat ive target ing of dopa mine D2 recep-
tor function in reward deficiency syn-
drome (RDS) by synaptamine complex
variant (KB220): clinical trial showing
anti-anxiety ef fect s. Gene Ther Mol Biol
2009; 13: 214-30.
77. Stein MD, Conti MT, Kenney S, et al.
Adverse childhood experience effects on
opioid use initiat ion, injection drug use,
and overdose among persons wit h opioid
use disorder. Drug Alcohol Depend 2017;
179: 325-9.
78. Pitchers KK, Coppens CM, Beloate LN,
et al. Endogenous opioid-induced neuro-
plasticity of dopaminergic neurons in t he
ventral tegmental area influences natural
and opiate reward. J Neurosci 2014; 34:
79. Volkow ND, Wang GJ, Telang F, et al.
Low dopamine striat al D2 receptors are
associated with prefrontal metabolism in
obese subjects: possible contributing fac-
tors. Neu roi mage 200 8; 42: 1537-43.
80. Stice E, Spoor S, Bohon C, Sma ll DM.
Relation between obesity and blunted
striatal response to food is moderated by
TaqIA A1 allele. Science 2008; 322: 449-
81. Kühn S, Gallinat J. Brain struct ure and
functional connectivity associated with
pornography consumption: the brain on
porn. JAMA Psychiatr y 2014; 71: 827-34.
82. Kim SH, Baik SH, Park CS, Kim SJ,
Choi SW, Kim SE. Reduced striatal dopa-
mine D2 receptors in people with Internet
addiction. Neuroreport 2011; 22: 407-11.
83. Meaney MJ. Maternal care, gene ex-
pression, and the transmission of individ-
ual differences in stress reactivity across
generations. Annu Rev Neurosci 2001; 24:
84. Keating DP. Born anxious: the life-
long impact of early life adversity — and
how to break the cycle. New York: St.
Mar ti n’s Press, 2017.
85. Alexander BK, Beyerstein BL, Hada-
way PF, Coambs RB. Effect of early and
later colony housing on oral ingestion of
morphine in rats. Pharmacol Biochem
Behav 1981; 15: 571-6.
Copyright © 2018 Massachusetts Medical Society.
... While depression or addiction would be problematic as cross-species concepts, the mechanisms of habit learning are largely conserved and consistent across mammalian species, not replaced by the higher cortical functions of the human brain (Wood & Rünger, 2016). Addiction has been understood as a learned behaviour (Lewis, 2018), and disorders of compulsivity demonstrate a common bias towards learning habits (Voon et al., 2015). To better understand such biases certain areas of behavioural research are of particular interest. ...
... Human cognition is premised on integrated sensorimotor units and as such habitual responses are represented by perceptual features and mental representations. In this way habit cues trigger not just a motor program, but a multimodal representation (thought experience) of the habitual response (Feldman & Friston, 2010;Lewis, 2018;Wood & Rünger, 2016). ...
... Habits strengthen slowly and incrementally; with repetition, changes occur in the neural substrates to procedural memory and by Hebbian learning cognitive network associations are made between the environment or other context cues and the habit response is strengthened such that context cues can automatically prompt habitual responses (Kometer et al., 2012;Lewis, 2018;Wood & Rünger, 2016;Woodward et al., 2015). Memory consolidation occurs at a synaptic level, and memory consolidation is the root of long-term learning. ...
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... This has recently been empirically shown to be important for behavioral addictions as well (Starcke et al., 2018). Lewis (2018) also postulated in his theory of addiction that addictive behavior can be significantly explained by very deep learning processes and mechanisms based on classical S-R learning and that the corresponding neural processes are involved in the development of addictions and contributes to compulsive, addictive behavior. ...
... In the light of the debate about the role of habits in describing and measuring addictive behaviors, this finding is important. It thus supports the assumption that habits indeed are closely related to addictive behaviors (Berridge, 2021;Lewis, 2018). ...
... On the one hand, this emphasizes that measuring self-reported habits is an important and often neglected component of research on addictive behaviors (Sjoerds et al., 2014) and that the SRHI could be well used for this purpose. On the other hand, it emphasizes that habits are indeed likely to be important components in explaining behavior, in line with the assumptions of Lewis (2018) and Berridge (2021). Since habit formation can be explained with the help of classical S-R learning theories, corresponding processes of behavior change, e.g., through extinction learning, offer approaches to change addictive behavior again, to reduce excessive behavior, or to promote abstinence (e.g., Everitt, 2014). ...
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... In spite of their differences, most models converge on conceptualizing compulsivity as the hallmark of addiction progression and maintenance [1,10]. This view is supported by translational research showing that compulsive drug use corresponds to an extreme stage of otherwise functional learning and neuroadaptation processes [11,12]. The endpoint of this process could be either the formation of inflexible habits, or the abnormal valuation of addictive (relative to alternative) rewards. ...
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Background Compulsivity is the hallmark of addiction progression and, as a construct, has played an important role in unveiling the etiological pathways from learning mechanisms underlying addictive behavior to harms resulting from it. However, a sound use of the compulsivity construct in the field of behavioral addictions has been hindered to date by the lack of consensus regarding its definition and measurement. Here we capitalize on a previous systematic review and expert appraisal to develop a compulsivity scale for candidate behavioral addictions (the Granada Assessment for Cross-domain Compulsivity, GRACC). Methods The initial scale (GRACC90) consisted of 90 items comprising previously proposed operationalizations of compulsivity, and was validated in two panel samples of individuals regularly engaging in gambling and video gaming, using exploratory structural equation modeling (ESEM) and convergence analyses. Results The GRACC90 scale is unidimensional and structurally invariant across samples, and predicted severity of symptoms, lower quality of life, and negative affect, to similar degrees in the two samples. Additionally, poorer quality of life and negative affect were comparably predicted by compulsivity and by severity of symptoms. A shorter version of the scale (GRACC18) is proposed, based on selecting the 18 items with highest factor loadings. Conclusions Results support the proposal that core symptoms of behavioral addictions strongly overlap with compulsivity, and peripheral symptoms are not essential for their conceptualization. Further research should clarify the etiology of compulsive behavior, and whether pathways to compulsivity in behavioral addictions could be common or different across domains.
... In the case of psychological development, they have for instance been studied extensively in identity development (Lichtwarck-Aschoff et al., 2008) and selfesteem development (de Ruiter et al., 2017). The onset and remission of psychopathology may thus be explained through the same mechanisms as other changes in psychological development, which is in line with a developmental (in contrast to a disease) perspective on mental health (e.g., Lewis, 2018;Sameroff et al., 2000). ...
... Before and after transplant, we combine continued monitoring for alcohol use, with access to in house and community addiction treatment resources. We follow a learning model of addiction [34] and provide patients with resources to social work, psychology, psychiatry, addiction medicine and community treatment resources to learn new ways of living that don't rely on substance use. Transplant allows these patients the time they require, and a life altering event, that can facilitate making impactful changes. ...
... Second, it is precisely because humans can self-select their data and construct their own epistemic niche to reduce their uncertainty that they can end up in a reciprocal cycle in which a narrow world model leads to a narrow environment, which in turn further narrows cognition, which further narrows of the environments one tends to seek out (Lewis, 2018). Our account urges us to examine which contextual factors determine whether people deploy their epistemic agency to dig their own tunnels instead of broadening their horizons. ...
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The motto of the conspiracist, “Do your own research,” may seem ludicrous to scientists. Indeed, it is often dismissed as a mere rhetorical device that conspiracists use to give themselves the semblance of science. In this perspective paper, we explore the information-seeking activities (“research”) that conspiracists do engage in. Drawing on the experimental psychology of aha experiences, we explain how these activities, as well as the epistemic experiences that precede (curiosity) or follow (insight or “aha” experiences) them, may play a crucial role in the appeal and development of conspiracy beliefs. Aha moments have properties that can be exploited by conspiracy theories, such as the potential for false but seemingly grounded conclusions. Finally, we hypothesize that the need for autonomous epistemic agency and discovery is universal but increases as people experience more uncertainty and/or feel epistemically excluded in society, hence linking it to existing literature on explaining conspiracy theories. Public Abstract Recent events have made it painfully clear that conspiracy beliefs can tear deep rifts in society and that we still have not found an adequate, de-escalating response to this. To understand the appeal of conspiracy theories and find new, humanizing ways to talk about them, we propose in this perspective paper to start from the universal human need to autonomously make discoveries through personal knowledge-generating actions. Indeed, psychological research shows that the aha experiences that accompany subjective discoveries create confidence in and perceived ownership of ideas that may be exploited by conspiracy theories. We hypothesize that people experiencing more uncertainty and/or epistemic exclusion in society will especially feel the need to re-establish autonomous epistemic agency and discovery. While this explanation starts from shared human experiences and practices, it also illustrates the potential of those processes to lead to a narrowed world and ossified cognition.
... People suffering from drug addiction have a higher risk of unintentional injuries, accidents and domestic violence most especially adolescents. Lewis (2018) noted that drug addiction weakens the immune system, increases the risk of illness, and mental confusion and leads to brain damage which oftentimes affects the emotional well-being of adolescents especially those in rural areas. ...
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This study investigated drug addiction and its trauma on adolescents in Senior Secondary Schools in the Federal Capital Territory, Abuja. The study had three objectives, three research questions, and two null hypotheses, which guided it. The research design was a survey design. The population of the study was 3,547, out of which 346 students were selected randomly from nine schools within the study area. The instrument used to collect the data was a self-structured questionnaire entitled: "Drug Addiction and Trauma Questionnaire" (DATQ). The research questions were analysed, using simple percentages and mean scores, while all the hypotheses were tested using a t-test. All the hypotheses were tested at a 0.05 level of significance. The findings showed that drug addiction leads to complicated health challenges, such as depression, worries, loss of sense of belonging, anxiety, psychological disorder, and subsequently self-harm. It was also revealed that, drug addiction leads to trauma among adolescents. Results from the tested hypotheses revealed that drug addiction has no respect for gender and location. Students in rural areas were more involved and victims of drug addiction. It was recommended that stakeholders like counsellors, parents, social organisations, and religious bodies should use every available opportunity to assist students who are experiencing trauma as a result of drug addiction, both in urban and rural areas of the Federal Capital Territory, Abuja, Nigeria.
... In spite of their differences, most models converge on conceptualizing compulsivity as the hallmark of addiction chroni cation and maintenance [1,10]. This view is supported by translational research showing that compulsive drug use corresponds to an extreme stage of otherwise functional learning and neuroadaptation processes [11,12]. Regardless of whether the endpoint of this process is the formation of in exible habits or the abnormal valuation of addictive (relative to alternative) rewards, a precise and data-driven behavioral operationalization of compulsivity should provide, rst, a gateway to understanding the etiological mechanisms underlying loss of control in addictive processes. ...
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Background Compulsivity is the hallmark of addiction chronification and, as a construct, has played an important role in unveiling the etiological pathways from learning mechanisms underlying addictive behavior to harms resulting from it. However, a sound use of the compulsivity construct in the field of behavioral addictions has been hindered to date by the lack of consensus regarding its definition and measurement. Here we capitalize on a previous systematic review and expert appraisal to develop a compulsivity scale for candidate behavioral addictions (the Granada Assessment for Cross-domain Compulsivity, GRACC). Methods The initial scale (GRACC90) consisted of 90 items comprising previously proposed operationalizations of compulsivity, and was validated in two panel samples of individuals regularly engaging in gambling and video gaming, using exploratory structural equation modeling (ESEM) and convergence analyses. Results The GRACC90 scale is unidimensional and structurally invariant across samples, and predicted severity of symptoms, lower quality of life, and negative affect, to similar degrees in the two samples. Additionally, poorer quality of life and negative affect were comparably predicted by compulsivity and by severity of symptoms. A shorter version of the scale (GRACC18) is proposed, based on selecting the 18 items with highest factor loadings. Conclusions Results support the proposal that core symptoms of behavioral addictions strongly overlap with compulsivity, and peripheral symptoms are not essential for their conceptualization. Further research should clarify the etiology of compulsive behavior, and whether pathways to compulsivity in behavioral addictions could be common or different across domains.
One important area for consideration especially in terms of combating the ongoing never ending opioid crisis, relates to novel newer assessments for all addictive behaviors both substance and non-substance behaviors (RDS). It is very important to identify early in one's life the possibility of, because of known DNA antecedents, the presence of pre-addiction. The development of the Genetic Addiction Risk Severity (GARS) test, Blum's group believes that this type of testing should be the "standard of care" following additional studies. Understandably that while polymorphisms in the Mu-Opioid receptor (MOR) is of real concern in terms of setting people up for predisposition to opioid dependence, the genetic and epigenetic status of dopaminergic function must be considered as well. While this sounds bold (which it is) the results should be protected by the G.I. N. A. law enacted in the USA in 2011. One avenue of further investigation, instead of providing powerful opioids for opioid dependence, is to seek out non-addictive alternatives. Accordingly, other non-addictive modalities including genetic guided KB220 (amino-acid-enkephalinase-N-acetylcysteine-NAD), non-invasive rTMS for psychiatry and pain, epigenetic remodeling, gene edits, non-invasive H-wave for pain management and enhanced functionality, brain spotting, cognitive behavioral therapy awarenesss integration therapy, NUCALM, trauma therapy, awareness tools, genograms, exercise, sports, fitness programs (one hour per day), light therapy and even laughing therapy as well as any other known modalities that can induce reward symmetry. While the short term use of opioids for opioid dependence to reduce harm is certainly acceptable, clinicians should consider a better long-term plan.
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The concept of compulsion, in which addictive behaviour is said to be carried out against the will, is central to the disease theory of addiction and ubiquitous in modern definitions. The aims of this article are: (i) to describe various meanings of compulsion in the literature; (ii) to compare the part thought to be played by compulsion in addiction with its suggested role in obsessive-compulsive disorder; (iii) to critically examine the place of compulsion in influential neurobiological accounts of addiction; (iv) to summarise the empirical evidence bearing on the usefulness of the compulsion concept, evidence that seems at first sight incompatible with the notion of compulsion. This is followed by a discussion of which possible meanings of compulsion can survive an empirical test and what role they might play in understanding addiction, paying particular attention to a distinction between strong and weak senses of compulsion. A conclusion is that addictive behaviour cannot be considered compulsive at the time it is carried out, though other possible meanings of compulsion as an explanation or description of addictive behaviour and experience are discussed. Among other conclusions, it is suggested that, although in some senses of the term it may seem arbitrary whether or not ‘compulsion’ should be retained, its use has important consequences for the public understanding of addiction, and is likely to deter people's attempts to overcome their addictions and their chances of success.
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I review the brain disease model of addiction promoted by medical, scientific, and clinical authorities in the US and elsewhere. I then show that the disease model is flawed because brain changes in addiction are similar to those generally observed when recurrent, highly motivated goal seeking results in the development of deep habits, Pavlovian learning, and prefrontal disengagement. This analysis relies on concepts of self-organization, neuroplasticity, personality development, and delay discounting. It also highlights neural and behavioral parallels between substance addictions, behavioral addictions, normative compulsive behaviors, and falling in love. I note that the short duration of addictive rewards leads to negative emotions that accelerate the learning cycle, but cortical reconfiguration in recovery should also inform our understanding of addiction. I end by showing that the ethos of the disease model makes it difficult to reconcile with a developmental-learning orientation.
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The history of ideas about addiction often comes down to a history of debates over the use and meaning of language (Levine et al. Journal of Studies on Alcohol 15:493–506, 1978). Nowhere is this more clear than in the interminable “Is addiction a ‘disease’?” debate. In Marc Lewis’ excellent Biology of Desire and in his paper that centers this issue, there is far more agreement between his work and mine than there is disagreement on the “disease” question. Here, however, I make a case for greater compatibility between the “disease” view and learning models of addiction than Lewis does, because I think the nuance is worth exploring. Indeed, if addiction science and ethics paid more attention to nuance in general, the whole field would be far better off.
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Where does normal brain or psychological function end, and pathology begin? The line can be hard to discern, making disease sometimes a tricky word. In addiction, normal ‘wanting’ processes become distorted and excessive, according to the incentive-sensitization theory. Excessive ‘wanting’ results from drug-induced neural sensitization changes in underlying brain mesolimbic systems of incentive. ‘Brain disease’ was never used by the theory, but neural sensitization changes are arguably extreme enough and problematic enough to be called pathological. This implies that ‘brain disease’ can be a legitimate description of addiction, though caveats are needed to acknowledge roles for choice and active agency by the addict. Finally, arguments over ‘brain disease’ should be put behind us. Our real challenge is to understand addiction and devise better ways to help. Arguments over descriptive words only distract from that challenge.
This resource shows that the social circumstances that spread addiction in a conquered tribe or a falling civilisation are also built into today's globalizing free-market society, and argues that the most effective response to a growing addiction problem is a social and political one, rather than an individual one.
The incentive-sensitization (IS) theory proposes that “wanting” (strong motivation), more so than “liking” (hedonic pleasure), drives compulsive consumption in both substance use and overeating. Importantly, “wanting” and “liking” are only distinct in the presence of relevant cues. Cues may also contribute to overeating by increasing feelings of hunger. We employed a simulated fast-food laboratory to test IS theory by examining the effect of environmental cues on eating behavior. We tested the following hypotheses: In a cue-rich compared to neutral environment, (a) wanting would be greater whereas liking would remain the same, (b) feelings of hunger would be greater, and (c) food consumption would be greater, a relationship mediated by elevated wanting and hunger. These hypotheses were supported in the current study. Thus, contextual cues may contribute to overconsumption through increased wanting and hunger. These findings have public health implications for overeating and obesity.
Introduction: Adverse childhood experiences are associated with the development of substance use disorders. With opioid use disorder, a growing concern in the United States, we were interested in examining the relationship between adverse experiences and three landmarks of opioid use: age of opioid initiation, injection drug use, and lifetime overdose. Methods: Between May and December 2015, we interviewed consecutive persons seeking inpatient opioid detoxification. Participants were asked about age of opioid initiation, last month injection drug use, and lifetime history of overdose, and completed the ten-item Adverse Childhood Experience (ACE) questionnaire. Results: Participants (n=457) averaged 32.2 (±8.64) years of age, 71.3% were male, and 82.5% were non-Hispanic White. The mean score on the ACE scale was 3.64 (±2.75). Mean age at time of initiating opioid use was 21.7 (±7.1) years, 68.7% had injected drugs within the past month, and 39.0% had overdosed. After adjusting for age, gender, and ethnicity, the ACE score was inversely associated with age of initiating opioid use (b=-0.50, 95% CI -0.70; -0.29, p<.001), and positively associated with recent injection drug use (OR=1.11, 95% CI 1.02; 1.20, p=0.014) and the likelihood of experiencing an overdose (OR=1.10, 95% CI 1.02; 1.20, p=0.015) in a graded dose response manner. Conclusion: Greater adverse childhood experiences are associated with three landmarks of opioid use risk. ACE screening may be useful in identifying high-risk subsets of opioid-using populations.
Cue reactivity to natural and social rewards is essential for motivational behavior. However, cue reactivity to drug rewards can also elicit craving in addicted subjects. The degree to which drug and natural rewards share neural substrates is not known. The objective of this study is to conduct a comprehensive meta-analysis of neuroimaging studies on drug, gambling and natural stimuli (food and sex) to identify the common and distinct neural substrates of cue reactivity to drug and natural rewards. Neural cue reactivity studies were selected for the meta-analysis by means of activation likelihood estimations, followed by sensitivity and clustering analyses of averaged neuronal response patterns. Data from 176 studies (5573 individuals) suggests largely overlapping neural response patterns towards all tested reward modalities. Common cue reactivity to natural and drug rewards was expressed by bilateral neural responses within anterior cingulate gyrus, insula, caudate head, inferior frontal gyrus, middle frontal gyrus and cerebellum. However, drug cues also generated distinct activation patterns in medial frontal gyrus, middle temporal gyrus, posterior cingulate gyrus, caudate body and putamen. Natural (sexual) reward cues induced unique activation of the pulvinar in thalamus. Neural substrates of cue reactivity to alcohol, drugs of abuse, food, sex and gambling are largely overlapping and comprise a network that processes reward, emotional responses and habit formation. This suggests that cue-mediated craving involves mechanisms that are not exclusive for addictive disorders but rather resemble the intersection of information pathways for processing reward, emotional responses, non-declarative memory and obsessive-compulsive behavior.