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Brain and Cognition

journal homepage: www.elsevier.com/locate/b&c

Embodying addiction: A predictive processing account

Mark Millera,⁎, Julian Kiversteinb,e, Erik Rietveldb,c,d,e
a Department of Informatics, University of Sussex, Sussex House, Falmer, Brighton BN1

RH, United Kingdom
bAmsterdam University Medical Center, Department of Psychiatry, University of Amsterdam, Amsterdam, the Netherlands
c Department of Philosophy, Institute for Logic, Language and Computation, University of Amsterdam, the Netherlands
dDepartment of Philosophy, University of

wente, Enschede, the Netherlands
eAmsterdam Brain and Cognition Centre, University of Amsterdam, Amsterdam, the Netherlands

A B S T R A C T

In this paper we show how addiction can be thought of as the outcome of learning. We look to the increasingly influential predictive processing theory for an account
of how learning can go wrong in addiction. Perhaps counter intuitively, it is a consequence of this predictive processing perspective on addiction that while the brain
plays a deep and important role in leading a person into addiction, it cannot be the whole story. We’ll argue that predictive processing implies a view of addiction not
as a brain disease, but rather as a breakdown in the dynamics of the wider agent-environment system. The environment becomes meaningfully organised around the
agent’s drug-seeking and using behaviours. Our account of addiction offers a new perspective on what is harmful about addiction. Philosophers often characterise
addiction as a mental illness because addicts irrationally shift in their judgement of how they should act based on cues that predict drug use. We argue that predictive
processing leads to a different view of what can go wrong in addiction. We suggest that addiction can prove harmful to the person because as their addiction
progressively takes hold, the addict comes to embody a predictive model of the environment that fails to adequately attune them to a volatile, dynamic environment.
The use of an addictive substance produces illusory feedback of being well-attuned to the environment when the reality is the opposite. This can be comforting for a
person inhabiting a hostile niche, but it can also prove to be harmful to the person as they become skilled at living the life of an addict, to the neglect of all other
alternatives. The harm in addiction we’ll argue is not to be found in the brains of addicts, but in their way of life.

1. Introduction

Addiction has a devastating effect upon those whose life it afflicts.
Addicts find their life increasingly dominated by their addictive beha-
viours. The other pursuits they care about begin to be crowded out as
they devote increasing amounts of time and energy to servicing their
addiction. The undesirable outcomes of their addictive behaviours are
increasingly ignored by them, yet at the same time addicts feel com-
pelled to continue acting on their addictions often long after the ad-
dictive behaviour has ceased to bring any pleasure. Addiction can reach
a point in a person’s life where it seems all that matters to them is doing
what their addiction demands of them, yet at the same time this is also
often something they do not want. The director of the National Institute
on Drug Abuse, Nora Volkow, has observed “I’ve never come across a
single person that was addicted that wanted to be addicted” (Gugliotta,

Volkow’s claim she has never encountered a willing addict is per-
haps something of an exaggeration. Flanagan (201

) has shown
through a range of convincing examples how some addicts may rea-
sonably prefer, all things considered, to remain addicts. These in-
dividuals may legitimately be said to want their addictive lifestyles to
continue, more or less unchanged. This needn’t be because they

compare the costs of stopping using a substance with continuing, and
conclude the best course of action is to preserve the status quo. It maybe
because using a drug helps them numb physical or emotional pain, or to
have the novel and meaningful experiences they can only achieve
through the use of a substance, or because using a substance gives them
a sense of group belonging (Flanagan, 2017: p.

). More generally, an
individual’s cultural and economic circumstances may allow them to
avoid incurring the personal, social and economic costs addiction ty-
pically incurs. They may not experience any of the shame, depression
and loss addicts often suffer.

What does seem right in Volkow’s observation however is that many
addicts see the harm that a continued use of a substance will do to
themselves and those around them, and desire more than anything to
change. Yet they also feel a strong compulsion to continue using a drug
or to drink, and find themselves again doing what their addiction de-
mands of them. They are unwilling addicts; their lifestyle is one that
causes them to suffer yet they feel powerless to change the life they
lead. This raises the central questions that will occupy us in this paper:
When does the behaviour of an addict cross the line from contributing
to the person’s well-being to being harmful to them? When should the
habits an addict develops be thought of as bad habits?

In what follows we will argue addiction should be thought of as a self-

https://doi.org/

.1016/j.bandc.2019.10

95
Received 16 September 2019; Received in revised form 8 November 2019; Accepted 13 November 2019

⁎ Corresponding author.
E-mail addresses: m.d.miller@sussex.ac.uk (M. Miller), j.d.kiverstein@amsterdamumc.nl (J. Kiverstein), d.w.rietveld@amsterdamumc.nl (E. Rietveld).

Brain and Cognition 138 (2020) 105495

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mailto:m.d.miller@sussex.ac.uk

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organising process of a whole agent-environment system. Addiction be-
comes harmful to a person when this self-organising process spirals out
of control due to feedback loops that entrain the behaviour of the agent,
locking them into destructive cycles of behaviour. It is in the dynamic
interaction between the agent and its environment that addiction is
born and endures.

In Section 2 we review the effects of substances of addiction on
dopaminergic neurons in the midbrain.1 Repeated use of drugs has been
shown to induce functional and structural changes in the nervous sys-
tems of addicts by acting either directly or indirectly on dopaminergic
neurons. This finding has led the medical community, and many sci-
entists, to embrace a “disease model” of addiction. According to the
disease model, addicts find it difficult to change their behaviour even
when this is what they desire because of dysfunctional neural systems
that mediate reward learning. Reward learning has wide-reaching
functional influences, mediating everything from perception and
memory, emotion and attention, to decision-making and cognitive
control. It is the effect of drugs on reward learning that the disease
model takes to explain compulsive drug seeking and use on the one
hand. The long-term functional and structural changes prolonged sub-
stance abuse induces have in turn been taken to explain why addicts
find it so hard to change their behaviour, and frequently relapse.

A new picture of reward learning is however beginning to take
shape in current cognitive neuroscience according to which reward is
the consequence of action, not its cause. Rewarding outcomes are
predicted, and the agent selects actions that fulfill its predictions (den
Ouden, Daunizeau, Roiser, Friston, & Stephan, 2010; Friston,
Daunizeau, & Kiebel, 2009; Clark, 2015a: ch. 4; FitzGerald, Dolan, &
Friston, 2014; Friston et al., 2012). In Section 3 we provide an overview
of this new predictive processing perspective on reward learning. In the
predictive processing, or active inference, account of action and per-
ception, dopaminergic discharges are best thought of as weighing the
agent’s confidence in relevant affordances given their skills and abilities
(Bruineberg, Kiverstein, & Rietveld, 2018; Friston et al., 2012;
Kiverstein, Miller, & Rietveld, 2019; Kiverstein, Rietveld, Slagter, &
Denys, 2019; Linson, Clark, Ramamoorthy, & Friston, 2018). In brief,
active inference is the process of selecting the affordances that stand out
as relevant or inviting because they are expected to minimise long-term
prediction error – the mismatch between expected and current sensory
inputs on average, and in the long-run. We characterise active inference
as the process of selecting those affordances that are relevant to the
agent because they are likely to lead to expected sensory outcomes
(Kiverstein, Miller, et al., 2019; Kiverstein, Rietveld, et al., 2019). Some
affordances are more likely than others to lead to expected, un-
surprising outcomes. The agent should therefore be selectively open to
those affordances that have the highest probability of leading from their
current situation to expected outcomes. This implies a probability dis-
tribution over relevant affordances that can have a greater or lesser
precision (i.e., salience or reliability). Substantial evidence suggests
that dopamine scores precision – the agent’s confidence about what af-
fordances will take them their current sensory states to their expected
future sensory outcomes given what they are capable of doing and the
current context.2

The disease model claims that what is pathological in addiction is
the way in which dopaminergic processes in the brain are hijacked by
substances of addiction. The behaviour of the addict comes to be pas-
sively and automatically driven by sensory cues that predict drug use,
and is progressively less and less under the control of their conscious
evaluation. In Section 4 we argue this is the wrong conclusion to draw

by showing how the predictive processing theory supports an ecolo-
gical-enactive account of habits. We go on in Section 5 to put this ac-
count of habits to work to explain what can go badly wrong in sub-
stance addiction. What can prove harmful about addiction is, we
suggest, the build-up of error over time, and a failure to adequately
assign precision to relevant affordances in the addict’s dealings with a
dynamic, and volatile world. In Section 6 we draw on our earlier work
on the role of what we call “error dynamics” in the context-sensitive
weighing of precision (Kiverstein, Miller, et al., 2019). Error dynamics
refer to the rate of change in prediction error over time. We show how
error dynamics are sensed by the agent in the form of positively or
negatively valenced bodily feelings. Bodily feelings track whether the
agent is doing better or worse than expected at minimising error in their
engagement with the environment. We hypothesise that dopaminergic
systems are among the systems in the brain that track error dynamics.
In Section 7 we argue that when substances of addiction act directly on
these systems, they provide false feedback that the agent is doing better
than expected at minimising error, when the reality is often the oppo-
site. Section 8 concludes our argument by returning to the disease
model. We show how addiction is not a disease of the brain, but needs
to be understood in the context of the wider dynamic of the agent’s
coupling to its environment. The brain is of course a necessary part of
this story, but it isn’t sufficient for understanding what can go wrong in
addiction. Addiction can prove to be harmful to the person as they
become skilled at living the life of an addict, to the neglect of all other
alternatives. The harm in addiction we’ll argue is thus not to be found in
the brains of addicts, but in their way of life.

2. Mutiny in the midbrain: Is addiction a brain disease?

The disease model of addiction claims that addiction is a “chronic,
relapsing brain disease that is characterised by compulsive drug seeking
and use, despite harmful consequences” (National Institute on Drug
Abuse, 2009; World Health Organisation, 2004). Addiction is conceived
of as pathological because substance use leads the reward learning
system in the brain to malfunction, leading the person to compulsively
seek out and use a substance despite the negative consequences of doing
so. The key idea behind this model of substance addiction is that sub-
stance use leads to a “hijacking” of reward learning systems in the brain
(Ahmed, 2004; Schultz, 2016; Keramati & Gutkin, 2013; c.f. Elster,
1999; Gardner & David, 1999; Redish, 2004; Everitt et al., 2008).3

Reward-based learning is standardly understood as the process by
which the organism maximises expected utility while minimising costs
and avoiding punishment (Arpaly & Schroeder, 2013; Delgado, Miller,
Inati, & Phelps, 2005; Sutton & Barto, 1998). Reward learning steers
agents through the world in ways that increase the probability of
finding what is valuable and avoiding what is aversive or punishing to
the agent. Agents learn about values (e.g. expected rewards) by means
of “reward prediction error” (RPE) signals. These signals are modelled
as computing the difference between received and predicted rewards.4

1 We will focus on substance addiction in what follows. While we believe
some features of our account may generalise to other forms of addiction, this is
not something we explore in this paper.
2 Our thanks to an anonymous reviewer for help with wording here in our

characterisation of active inference.

3 There is disagreement in the literature about whether addiction is a pa-
thology of reward learning or of motivation. Berridge and colleagues have
shown how rats deprived of dopamine through a lesioning of their mesolimbic
system continued to learn the reward value of a stimulus but are no longer
motivated to act on this learning. They are no longer prepared to work (say
pressing a bar) to attain a food reward (see e.g. Berridge, 2007; Holton &
Berridge, 2013). Berridge and colleagues propose an incentive salience model
of the dopamine system. They argue that the dopamine system directly causes
desires that drive action. Addiction is thus a pathology of wanting or desire, not
of learning. We argue for a new version of the learning accounts below. We will
show how Berridge’s notion of incentive salience maps onto what is referred to
as “precision” in the predictive processing theory. Thus, our predictive pro-
cessing theory of additions hold the promise of reconciling the learning and
motivation theories of addiction.
4 Dopamine neurons in midbrain areas have been found to fire at rates that

M. Miller, et al. Brain and Cognition 138 (2020) 105495

The job of signalling unexpected reward is hypothesised to be per-
formed in the brain by the phasic bursts of dopamine (Montague,
Dayan, & Sejnowski, 1996; Schultz, Dayan, & Montague, 1997). Me-
solimbic neurons in the midbrain fire when an unexpected reward is
delivered. Mammals and invertebrates alike respond with reward
learning signals when presented with unexpected opportunities for re-
ward such as food, water and mates (Schultz et al., 1997; Sutton &
Barto, 1998). The midbrain regions in the human brain respond in a
similar way to such things as money, success, favorite songs, and the
flourishing of loved ones (Arpaly & Schroeder, 2013).

There is now a substantial body of research that supports the claim
that substances of addiction act on reward learning circuitry in ways
that lead to changes in the function and structure of the addict’s brain.
Drug use enhances the formation of new synapses, strengthening con-
nections between the striatum; amygdala; and hippocampus, while at
the same time reducing synaptic density in prefrontal cortex (Goldstein
& Volkow, 2011; Volkow, Koob, & McLellan, 2016). These changes in
the brain’s wiring are correlated with reduced capacity to engage in
cognitive control, compulsivity in drug seeking, and blunting of reward
response more generally (Everitt & Robbins, 2013; Volkow & Fowler,
2000). However, it is worth remarking that every experience that is
repeated sufficiently many times will induce comparable changes in
brain wiring to those seen in the users of addictive substances (Levy,
2013; Lewis, 2015). The development of any habit, good or bad, will
have many of the same signature kinds of changes in brain wiring that
are seen in addiction. The disease model looks to such neurochemical
alterations to answer the question of what can go wrong in addiction.
However, given that many of the same changes in neural function and
structure are seen across the board in the development of habits, the
disease model fails to identify the neurocognitive mechanisms that lead
to harmful behaviour in addicts. Marc Lewis makes this point well when
he observes:

“Addiction” doesn’t fit a unique physiological stamp. It simply de-
scribes the repeated pursuit of highly attractive goals, and the brain
changes that condense this cycle of thought and behaviour into a
well-learned habit.” (Lewis, 2017: p.12)

The disease model of addiction tends to conceive of the learning
that leads to addiction in terms of Pavlovian conditioning. The strong
compulsion to use the substance is thought to be due to the dorsal
striatum no longer being under the control of cortical areas (such as the
dorsolateral prefrontal cortex) believed to ordinarily regulate and
contextualize habitual responses (Everitt & Robbins, 2013). Environ-
mental cues come to passively drive behaviour, eliciting powerful urges
to consume the substance independent of what the person wants. In the
next section we will sketch a different picture that is beginning to
emerge of the role of dopamine in learning. Instead of signalling reward
prediction error, dopamine is modelled as signalling confidence about
how to act in the world. We do not dispute the evidence that dopamine
has a central role to play in a person’s developing addictive patterns of
behaviour. But we suggest the role of dopamine may be somewhat
different from how it is standardly understood in the disease model.
Instead of the person being driven to act passively on the basis of cues
that predict reward, dopamine contributes to attuning the person to the
affordances that are relevant to them in the environment. We will argue this
shift in perspective in computational neuroscience should lead to a
different view of what is harmful in addiction. Dopamine should be
understood as tuning the agent to possibilities for action that allow
them to flourish in the long run. But drugs of addiction, while mas-
querading as increasing attunement to what is important to the person,

can in fact lead to greater disattunement over time. Thus, instead of
thinking of addiction as a disorder of the brain, we should conceive of
the harm addiction does as resulting from increasing disorder in the
agent-environment system the person forms with their ecological niche.

3. The predictive processing theory of reward learning

In recent years an account of reward learning has begun to take
shape that takes predictions of reward to be part and parcel of the
prediction of the sensory consequences of our acting in the world
(Clark, 2015b; FitzGerald et al., 2014; Friston et al., 2012; Friston et al.,
2009). It seems intuitive to think of maximising utility as a complex
cause of an agent’s behaviour. Agents should be motivated by acquiring
rewards and avoiding punishments. They should, when all goes well,
thereby learn over time to frequent rewarding spaces more often than
not (and to avoid punishing spaces whenever possible). The predictive
processing theory (PP) we review in this section turns this intuition
about expectation and reward on its head (FitzGerald et al., 2014;
Friston et al., 2012). PP starts from expectations, and not from rewards,
as the causes of behaviour. Agents act to minimise surprise about their
own future sensory states. It is future proprioceptive, interoceptive and
exteroceptive states associated with a course of action that are pre-
dicted. “Surprise” in the relevant technical sense (also referred to as
“surprisal”) thus relates to expected future sensory states. Surprise
corresponds to self-information; namely, the implausibility of some
action outcome on average and over time. Desired future sensory states
are more likely, and thus less surprising, than undesired states. Thus in
relation to future sensory states (action outcomes), we have expected
surprise (i.e., expected self-information)5, which is uncertainty (i.e.,
expected self-information). In short, agents act to minimise uncertainty
in relation to their engagement with a field of multiple relevant affor-
dances.6 If I want a coffee, the unsurprising outcome would be for me to
find myself in the near future in the cafe were I typically buy my fa-
vourite coffee. To minimise surprise (i.e. long-term prediction error)
then the agent must select the actions that are most likely to lead them
from their current sensory states to those they expect to occupy in the
future. It is therefore unsurprising or expected outcomes that are re-
warding. Reward is the consequence of behaviour, not it’s cause – the
agent is rewarded when the future outcomes of its actions are expected
or unsurprising.

In Section 2 we’ve seen how drugs can act directly or indirectly on
midbrain areas, increasing the transmission of dopamine from the
midbrain to the forebrain structures. The PP theory understands these
effects on the dopamine system as impacting on the optimisation of
what are called “precision expectations”. Before we can see how this
might work, we must briefly explain the notion of precision.

To succeed in minimising future prediction errors through action,
an agent must have some means of determining its own uncertainty
about the effects of its actions. An agent that minimises surprise should
act in ways that tend over time to minimise the divergence or the dif-
ference between attainable and expected outcomes (Friston et al.,
2014). We will use the term “precision” to refer to the confidence as-
sociated with relevant affordances. Precision marks the agent’s con-
fidence that given their skills and abilities, affordances can take them
from their current sensory state to expected outcomes that are

(footnote continued)
directly correlate with RPE signals and perceived reward values providing
support for the hypothesis that dopamine functions as a learning signal in the
brain (Montague et al., 1996; Schultz et al., 1997).

5 Many thanks to an anonymous reviewer for pointing out the connection of
surprise and self-information. “Self-information” is a concept from information
theory that refers to the amount of information that is gained from the sampling
of a random sensory signal.
6 We stress that the characterisation of active inference and predictive pro-

cessing in terms of engagement with multiple relevant affordances is our own
interpretation of active inference, although we believe many of the papers by
the original architects of the active inference theory can be interpreted in these
terms without distortion (see e.g. Friston, 2011; Friston et al., 2012; Linson
et al., 2018).

M. Miller, et al. Brain and Cognition 138 (2020) 105495

unsurprising, and thus rewarding.7 Low-precision on a relevant affor-
dance means it is likely that future sensory states will fail to match with
those that are expected, and therefore the affordance should not invite
the agent to act. A surprise minimizing agent should therefore allow
such affordances to have only a minimal influence in the regulation of
its behaviour. High precision relevant affordances by contrast have a
high probability of leading to a match between expected or desired
outcomes, and the future sensory states the agent attains as the effects
of its actions. The predictions of future sensory states whose precision is
weighted we will call “action policies”. An action policy refers to a
sequence of actions – a path that takes the agent from its current sensory
states to those it expects to occupy. Our policy of frequenting our fa-
vourite cafe to buy coffee is an example of such a high-precision policy.
We have high confidence that acting on such a policy is likely to
minimise the difference between the sensory states we predict ourselves
occupying when we visit this cafe, and the sensory states we expect or
desire – those associated with drinking an excellent mug of coffee.

In PP the midbrain dopamine system is assigned the function of
weighing the confidence about what to do next (Friston, 2012). The
firing of dopaminergic neurons in this account doesn’t report reward
prediction error, but rather a “salient and unexpected event, under
varying degrees of ambiguity or uncertainty” (Friston, 2012: p.277).
Precision isn’t something that is known in advance, but has to be
learned. The dopamine system is hypothesised to be a part of the ma-
chinery for optimising precision expectations through learning.8 The
precision of a relevant affordance depends on how attainable an ex-
pected future sensory state is from current sensory states. Dopamine
signals the likelihood that current sensory information anticipates a
predictable sequence of actions (Friston et al., 2014; Linson et al.,
2018).

Substance abuse leads to the learning of sub-optimal precision ex-
pectations (Schwartenbeck et al., 2015). The agent comes to place too
much confidence in its top-down predictions of future sensory states for
policies of drug-seeking and drug-using behaviour. The result of ex-
pecting precision to be high for such policies is the poor self-control
seen in addicts. Low probability is assigned to competing action po-
licies. This allows sensory information that predicts drug-use to exert an
inflexible and dominating influence on their behaviour to the exclusion
of other action policies (Pezzulo, Rigoli, & Friston, 2015;
Schwartenbeck et al., 2015). In other words, too much confidence is
placed in the policies that drive addictive behaviour, precluding an
exploration of alternative affordances.

Now it might be thought that the PP theory of addiction we’ve just
sketched is basically just a redescription of the reward learning theory.
We will argue in the next section however that in the PP theory, do-
pamine should be understood in the context of the whole person in their
engagement with the multiple relevant affordances of their econiche.
Thus, addiction is not due to substances of addiction hijacking midbrain
dopaminergic systems, even though we do not mean to deny the central
role of such systems in the progression of addiction. Instead addiction

should be understood in terms of how the whole person attunes to re-
levant affordances. What substance-induced dopamine does is give the
illusion of an agent attuning well to their environment when in fact in many
cases the opposite is occurring. The brain is best seen as an “organ of
mediation” to borrow Thomas Fuch’s apt expression (Fuchs, 2017;
Schütz, Ramírez-Vizcaya, & Froese, 2018). The brain mediates the re-
lation of the agent as a whole to the environment, and it does so only as
a part of a larger self-organising agent-environment system (Lewis,
2018).

4. An ecological-enactive account of habits

The PP theory we are developing suggests addictive behaviours are
not passive reactions to sensory cues, as the reward learning theory
would seem to imply. Action and perception are co-dependent and
stand in a circular causal relation (Anderson, 2014; Clark, 2013). The
agent acts with the aim of bringing about the future sensory states it
expects to occupy. The sensory states the organism tends to bring about
through its actions are those that it is likely to occupy over time if it is
to remain in a state of dynamic equilibrium with the ecological niche it
inhabits (c.f., allostasis). In substance addicts, the physiological states
they come to occupy as a consequence of using the substance, are
among those they learn to expect. The need for the drug can be com-
pared to hunger. It is a physiological need that the agent must feed if it
is to maintain a steady state, and remain in homeostatic balance with its
niche. The urges and cravings that are felt in addiction are thus not
external forces that act on the self from the outside (Schütz et al., 2018).
They arise naturally as a part of the processes that sustain and nourish
the agent the addict has become. Thus, the neurocognitive processes
that contribute to addictive behaviour are not malfunctioning – they are
not the product of a diseased brain. They are instead doing the work
they should be doing for an agent that has become a substance-addict
(Lewis, 2015).

Agents self-produce and self-maintain their identity as individuals
over time, an organisational property of living systems referred to as
“biological autonomy” (Di Paolo, Buhrmann, & Barandiaran, 2017;
Maturana & Varela, 1980; Thompson, 2007).9 The “identity” of an
agent as we will use the term goes beyond the continued existence or
survival of the agent as a biological individual. It is the whole way of
life of an individual agent that is produced and maintained over time by
agents acting to fulfill their predictions. We can characterise this notion
of identity in PP terms by equating the identity of an agent with the
generative model an individual develops through its practical engage-
ment with the world. In PP the agent is hypothesised to develop a
hierarchically organised internal model of its bodily abilities in relation
to its environment. This internal model is referred to as a “hierarchical
generative model” because it is used to generate predictions of in-
coming sensory input over multiple spatial and temporal scales. Instead
of building up an internal reconstruction of the world bottom-up, based
on incoming sensory information, the brain is cast as pro-actively pre-
dicting incoming sensory input. It is this process of acting to bring about
its own predicted sensory input that is referred to as “action oriented
predictive processing” (Clark, 2013, 2015b), or “active inference”
(Friston, FitzGerald, Rigoli, Schwartenbeck, & Pezzulo, 2017; Pezzulo,
Rigoli, & Friston, 2018).

In our Ecological-Enactive interpretation of predictive processing
we follow Friston and colleagues in conceiving of the generative model
as being the whole organism in relation to the ecological niche (Friston,
2011). Biological systems are characterised by a set of attracting states
that must be continually revisited over time if the system is to remain
viable, and continue to exist. The set of attracting states can be char-
acterised as an individual’s way of life – they will be a function of its

7 Technically, precision refers to the inverse dispersion of probabilistic be-
liefs. If the probability distribution is over a continuous variable, precision
corresponds to the inverse variance. In predictive coding, this usually refers to
the precision of prediction errors (Feldman & Friston, 2010). When the prob-
ability density is over discrete states, precision corresponds to inverse tem-
perature; commonly encountered as a softmax parameter (Parr, Benrimoh,
Vincent, & Friston, 2018; Parr & Friston, 2017). Precision correlates with the
random fluctuations in a sensory signal. The more stochastic the signal the
lower the precision. Our thanks to an anonymous reviewer for helpful sugges-
tions on how formulate best the technical definition of precision.
8 This process of optimisation is given a neurobiological characterisation in

terms of optimising the sensitivity of post-synaptic gain of cells. Phasic dis-
charges in the dopamine system signal error in precision expectations. Tonic
discharge of dopamine influences the post-synaptic gain on such error signals
leading to an update of precision expectations (Friston, 2012: p.276).

9 For excellent discussions of this notion of biological autonomy in relation to
PP see Allen & Friston, 2016; Kirchhoff, 2016; and Gallagher, 2017.

M. Miller, et al. Brain and Cognition 138 (2020) 105495

morphology, physiology, behavioural patterns and the econiche it in-
habits (Kiverstein, 2018; Ramstead, Badcock, & Friston, 2018). The
model the agent comes to embody over time should ensure that it
continuously revisits the attracting states that define it as an agent. In
short, the model should sustain the way of life of the agent over time.
Deviations from such an attracting set of states will be surprising to the
agent, and will potentially threaten its existence. If the agent is to
continue to exist over time, the model should steer the actions of the
agents so that on average it samples sensory states that are among its
attracting set, and are thus expected. Any agent that succeeds in
minimising prediction error in the long-run will also thereby maximise
the evidence for its own continued existence. Thus, in maximising the
evidence for a model the agent is thereby self-producing and main-
taining its own identity as an individual agent. Prediction errors are a
measure of the disattument of internal and external dynamics
(Bruineberg et al., 2018; Bruineberg & Rietveld, 2014). Prediction er-
rors arise in response to a dynamically changing environment either
because something changes on the side of the agent in terms of its
bodily needs, concerns and interests, or on the side of the environment.
Thus, the notion of prediction error minimisation is dynamic – it needs
to be constantly achieved a new in response to the agent’s evolving
circumstances in a volatile environment.

Habits typically form when an individual repeatedly and regularly
engages in an activity. The repetition of the regular pattern of activity
becomes a part of who the individual is, and therefore a part of an
individual’s identity. Habits can thus be thought of as abilities for en-
gaging in activities that contribute to the production and sustaining of
an individual’s identity. To put this in the terms of PP, habits are we
suggest abilities for avoiding unexpected sensory states. Anything that
is unexpected is a threat to the continued way of life of the individual,
and is therefore bad from the agent’s perspective and should be
avoided. Things that contribute to the sustaining of this way of life are
good from the agent’s perspective, and are therefore attractive and
worth pursuing. An agent’s capacity for regulating its coupling to the
environment thus derive in part from its habits (Di Paolo et al., 2017).
Habits we suggest are best thought of as abilities for maintaining
adaptation to the agent’s ecological niche, not as automatic behaviours
set-off by sensory cues.

The habits the agent forms in addiction, and the rituals they routi-
nely perform in using the drug can in extreme cases take over the
agent’s life. Their social life – the friends they meet, their work life, their
relationship with partner and family – may gradually become organised
around the sustaining of the way of life of the drug addict. In many
cases the individual that the addict becomes is one that makes perfect
sense given the challenges they face in their everyday life. Substance
use has predictable, reliable effects in the life of a person that would
otherwise face numerous physical, economic and psychological chal-
lenges. As Lewis (Lewis, 2018) notes, “exposure to physical, economic
or psychological trauma greatly increases susceptibility to addiction”
(p.1551). The habit of substance or alcohol abuse the individual de-
velops can be thought of as growing naturally out of the life of a person
otherwise fraught with difficulties (c.f. Pickard, 2012).

Our claim that addiction should be seen as a part of a person’s
identity is one we share in common with Owen Flanagan (see e.g.
Flanagan, 2018). The habit of using a drug is, Flanagan argues, “iden-
tity conferring and identity constituting” (op cit, p.78). Addicts often
engage in rituals of consuming a substance or drink as members of
communities, and their individual identity is bound up with their sense
of belonging to this community. Their addiction grows out of their
participation in a way of life that confers on them their sense of who
they are. Drinking alcohol for instance is “an extremely important
feature in the production and reproduction of ethnic, national, class,
gender, and local community identities…it is a key practice in the ex-
pression of identity” (Wilson, 2005, p.3, quoted by Flanagan, 2018,
p.81). It is as members of a community that they are initiated into the
behaviour of using a substance. Substance-use signals membership of a

community to which the individual values and wants to belong. Use as
sanctioned by the community tips over into abuse once addiction takes
hold of a person. The addict transgresses what the community regards
as normal or acceptable. Through their actions of concealing, stealing,
dissembling they gradually become a person they do not want to be.
The reason it can be so hard for a person to break out of an addictive
pattern of behaviour according to Flanagan is that the person’s very
identity is bound up with their way of life as an addict. To change
requires them to literally become a different person.

We’ve been arguing that habits are identity-defining – they define
who the person becomes over time, but they do not however settle the
person’s identity once and for all going into the future. Most people that
go through a period of substance addiction succeed in escaping their
addiction by their mid-30s, often without any professional help
(Heyman, 2009; Lewis, 2015; Pickard, 2012). People are also motivated
to abstain from using drugs if their careers require them to undergo
random testing for substance use, as is the case for example with airline
pilots (Heyman, 2009; Holton & Berridge, 2013; Lewis, 2015).

There is no denying however that for many individual’s drug habits
turn out to be bad habits in the long run, in the sense that they turn out
to be a threat to the individual agent’s identity. The addict no longer
takes care of the social relationships and other projects that were for-
merly important to them. Their behaviour is also literally “self-de-
structive” because they no longer care for themselves. What at first
sight seems like a healthy, adaptive response to a challenging life turns
out in reality to be a retreat from life. The retreat from life can be the
appropriate response for an agent embodying a model of the kinds of
psychologically challenging environments addicts often tend to inhabit.
However, it is the model of the environment the agent embodies, and
the expectations that the agent forms on the basis of this model, that we
will suggest can turn out to be harmful to them in the long-run.

5. Why is addiction harmful?

The way of life of the addict leads them to develop a generative
model that is skewed towards dealing with the particular challenges of
feeding their habit. The individual, through their activities, contributes
to the construction of a niche organised around their drug habits. Their
habits allow them to remain well-attuned, and keep prediction errors
under tight control so long as they remain within the narrow confines of
such a niche. However, the model that addicts come to embody is ty-
pically not well suited to the sorts of volatile environments we inhabit.
Drug habits ultimately prove too narrow, and overly rigid and inflexible
to maintain attunement to an ecological niche in flux. The addict for
instance may lose their job, or find their business failing. Their grip on
the niche in which they are situated proves to be too precarious to be
sustainable in the long-run. Feeding their drug habit is destructive of
who they were before becoming an addict, and an accidental overdose
may of course even deprive them of their life.

Proponents of the incentive salience model have argued that ad-
diction has some of the key characteristics of a mental illness because of
the decoupling of desire from the agent’s judgement of the best thing to
do (Holton, 2009: ch. 5; c.f. Berridge, 2017) The agent might explicitly
judge they ought to no longer use the drug, yet when encountering cues
that predict the use of the drug they find themselves overwhelmed by
temptation and their resolve is undermined. Levy (2014, 2019) has
argued along similar lines, that what is dysfunctional, and aberrant in
the behaviour of addicts is the way in which addicts can rapidly change
their mind from judging at one time that they ought, all things con-
sidered, to stop using drugs, to judging that they ought perhaps to use
them just this one more time (Op cit, p.338). Levy has provided an
account of this “judgement shift” in addiction in terms of PP. The shift
to judging that the drug is best consumed is the brain’s way of ex-
plaining away the prediction error elicited by sensory cues that have
come to be associated with drug use. The judgement that the drug
should be used is thus a part of the model that does the best job of

M. Miller, et al. Brain and Cognition 138 (2020) 105495

explaining away current prediction errors. Levy takes the oscillation in
the addict’s judgements to arise as a response to prediction errors en-
coded by the dopamine system.

Levy takes dopamine to signal a prediction error, along similar lines
to reward learning theories in which dopamine signals reward predic-
tion error. We’ve suggested by contrast and in line with Friston and
colleagues that dopamine is involved in weighing the precision (the
reliability and salience) of relevant affordances (Friston, 2012; Friston
et al., 2014, 2012; Schwartenbeck et al., 2015). When dopamine neu-
rons fire in response to sensory cues predicting drug use, this is because
those sensory cues are associated with high precision affordances. The
sensory cues predicting drug use are not unexpected on our reading of
PP – they do not elicit prediction errors. On the contrary, they are cues
the agent selectively samples, and has purposefully sought out because
they predict the agent is likely to succeed in the future in occupying the
sensory states they expect. In order for sensory cues to elicit judgement
shift they would have to give rise to prediction errors. But we suggest
what Levy has missed is the role of action in fulfilling prediction. The
sensory cues that are associated with drug use such as finding yourself
in a neighbourhood where drugs can be scored, or among a group of
friends that use a drug, are sensory cues the individual actively en-
genders because they are already well predicted, and the agent acts to
fulfill its predictions. Dopamine signals the agent’s high degree of
confidence that its current sensory states will lead to expected future
sensory states (i.e. those consequent upon drug use). Drug-use has be-
come for the addict a self-fulfilling prophecy.

We argue that what proves maladaptive for the addict is the high
precision assigned to affordances related to drug seeking and taking
behaviours. This weighting of precision increases the probability of
selecting these sorts of affordances as inviting action. On this view, one
would therefore distinguish good from bad habits in terms of precision
(i.e., the confidence placed in affordances) – not in terms of judgement
shift as Levy proposes (Levy, 2014). In other words, bad habits are
simply policies that are not fit for purpose in a volatile world; however,
they are selected repeatedly because alternative options are not en-
tertained. Although addictive policies may be good for drug-taking,
they may be bad for everything else, especially if the social econiche (or
therapeutic support system) does not support addictive behaviour. In a
volatile environment, what the agent should do to remain well-attuned
is to modulate the precision assigned to affordances in response to such
volatility. An inability to do this will lead to suboptimal action selec-
tion, with potentially devastating consequences.10.

In the next section we will show how the tuning of precision-
weighting should be thought of as a process that takes place in the
whole body, not only in the brain. The agent is able to remain well-
attuned to a volatile environment by making use of feedback from the
body about the rate of error reduction to set precision on relevant af-
fordances. This addition, as we will see, will help further our view of
addiction as a breakdown not of the brain but of the wider agent-en-
vironment system.

6. Weighing precision in the body

An agent that weighs the precision of relevant affordances would,
we suggest, benefit from tracking the rate at which error is accumu-
lating or decreasing over time. We use the term “error dynamics” to

refer to changes in the overall rate at which errors are accumulating or
reducing over time. “Error” here refers to divergence from future sen-
sory states expected as a consequence of acting. The rate of change in
error reduction thus refers to how fast or slow the sum of prediction
error is being reduced over time relative to what was expected
(Kiverstein, Miller, et al., 2019). The expectations in question are ex-
amples of what we’ve been calling “precision expectations”. They are
expectations about the likelihood of a relevant affordance leading to
expected future sensory states. If the speed of error reduction increases,
this equates to a faster reduction in prediction error over time relative
to what was expected. This feedback should then act as evidence for an
expectation that a relevant affordance has high precision. If speed of
error reduction decreases, this equates to an accumulation in prediction
error over time. It indicates that a relevant affordance is failing to lead
to expected future sensory states. The accumulation of prediction errors
should therefore lead to a decrease in precision.

The performance of an action policy in reducing error can be
plotted as a slope that depicts the speed at which errors are being
accommodated over time. The steepness of the slope indicates that
error is being reduced over a shorter period of time, and so faster
than the agent expected. The steeper the slope, the faster the rate of
reduction. If the speed of error reduction increases, this equates to a
decrease in prediction error over time (relative to what was ex-
pected). In that case, the action policy should be weighed as more
precise. If speed of error reduction decreases, this equates to an in-
crease in prediction error over time, with the result that the agent
fails to occupy the future sensory states it expects. Feedback of this
kind should be taken as evidence for weighing an action policy as
having low precision.

We have recently suggested that error dynamics are registered by
the organism as embodied feelings (Kiverstein, Miller, et al., 2019; c.f.
Van de Cruys, 2017; Joffily & Coricelli, 2013). Positively valenced
bodily feelings indicate better than expected error reduction. Negative
valenced bodily feelings provide feedback that a policy has reduced
error at a worse than expected rate. Suppose you are a smoker and you
find yourself sitting through a long and somewhat tedious talk at a
conference. You could have gone for a smoke before the talk but you
opted instead to wait till the next break. The speaker is running over
into the break, and you begin to experience a strong craving for the
cigarette you promised yourself. The craving you experience is, we
suggest, your body telling you that there is relevant source of error that
you were expecting to soon reduce. This situation is felt in the body of
the agent as an unpleasant feeling of error on the rise, or tension. This
negative feeling may lead the agent to explore the environment for
other alternative possibilities to smoking that reduce tension for the
duration of the lecture. Relevant possibilities that might now stand out
soliciting them to act may be possible distractions such as doodling,
looking up your email on your phone or continuing work on the slides
for your own talk.

Positive and negatively valenced feelings provide feedback on the
quality of the organism’s engagement with the environment (c.f.
Polani, 2009). These feelings are embodied as part of the valuation
process that works as a sort of bodily barometer, keeping the or-
ganism informed about how it is fairing in its attempt at maintaining
its adaptedness to its niche (Barrett, 2017). Agent’s are normally
sensitive to the rise and fall in error reduction and make use of this
information about how well they are doing overall in reducing error
to learn precise policies.

We have seen above how in our Ecological-Enactive interpretation
of PP dopamine scores confidence in relevant affordances. We’ve sug-
gested the dopamine system would do well to make use of changes in
the rate of error reduction. Thus the process of assigning precision to
relevant affordances will work best, in part, through keeping track of
error dynamics. In the reward learning literature (discussed briefly in
Section 2), dopamine discharge signals a reward prediction error that
indicates something better (or worse) than expected has just

10 Exactly the same mathematical processes can be used to describe selection
for selectability. For example, fruit flies increase their mutation rate when ex-
posed to volatile (temperature) environments. Conspecifics that are unable to
adjust their mutation rates (i.e., precision) have a suboptimal encoding of en-
vironmental volatility and are outcompeted by conspecifics that can explore
different phenotypic options. Our thanks to an anonymous reviewer for this
comparison, and for comments on the difference between good and bad habits
that helped us to refine our formulations in this section.

M. Miller, et al. Brain and Cognition 138 (2020) 105495

happened.11 We’ve seen how by contrast in PP rewarding outcomes are
what are expected. We therefore hypothesise that what dopamine is
actually signaling is how well or badly an organism is doing at bringing
about the future sensory states it expects. This hypothesis would allow
for PP to take advantage of insights from the reward learning literature.
Information about rate of change in error reduction is valuable feed-
back that can be used to fine-tune precision expectations. Making use of
such information will ensure that the agent is able to adapt its actions to
a volatile environment in dynamic flux. An agent that acts on precise
policies, in other words, shouldn’t just be interested in error reduction
but in whether error has been reduced better or worse than expected.12

Furthermore, we have suggested that error dynamics are felt in the
body in the form of positively or negatively valenced feelings. Thus,
there is good reason to think precision expectations might be updated in
part through using feedback from the body (Pezzulo et al., 2018). The
important take home message here is that the setting of precision
weighting isn’t just a brainy event. Precision expectations are tuned to
the agent’s changing circumstances based on bodily feelings. We will
show next how drugs of addiction create the feeling of reducing error at
a better than expected rate. However, as we will see, this is often only
an illusory experience of error reduction rather than tracking an actual
increase in attunement. The reality is often a steadily increasing dis-
attunment with affordances that should be of significance and stand out
as salient to the person.

7. Addiction as tending towards a sub-optimal grip

We’ve argued in the previous section that it feels good to the agent
when prediction error is reduced at a faster rate than expected, and it
will feel bad for the agent when error builds up they are unable to
reduce. This is felt in the body of the agent as feedback that error re-
duction has gone better or worse than expected. These feelings we
suggested play a part in setting the agent’s precision expectations en-
suring that the agent typically attunes to the affordances that are im-
portant to them. Now consider what happens in substance addiction as
the use of the substance exerts a tighter and tighter grip on the person.
The drug acts either directly or indirectly on the dopamine system that
is weighing the precision of relevant affordances. Crucially, it provides
feedback that error has been reduced at a faster than expected rate. Each
time the agent uses the drug, the same feedback occurs. Instead of their
expectations simply being met, which would normally signal to the
brain nothing new to be learned here, the brain responds by producing
dopamine that signals that there is still something new and surprising to
be learned. The reward learning theory claims that dopamine neurons
signal unexpected reward: the agent has done better than expected in
relation to the rewards that were expected. On the view we are de-
veloping, dopamine neurons signal the degree of confidence in pre-
dictions of sensory outcomes. We agree dopamine tells the organism
that something better than expected just happened. However, the ex-
pectations that dopamine underwrites relate to the precision of relevant
affordances – the predictions of the attainability of its future expected
sensory states. The policy of using the drug has led to error reduction,
and thus to the future sensory states the agent expects at a faster rate
than was expected. Thus, the effects of the drug on the brain are to
confirm the expectation that precision for the affordances of substance
use should be set high.

Each time the agent acts on a drug-using policy, they predict sensory
cues that are associated with the pursuit of the policy, such as being in a
particular neighbourhood where you can score the drugs you are
seeking. These predictions give rise to prediction errors, which the
agent acts to reduce by sampling the world in search of the predicted
cues. The prediction errors arise from an affordance that is given high
precision, and thus get to drive behaviour to actively seek out those
cues. The agent will follow the paths of action (action policies) that are
most likely to lead them to the drug. Thus, in addiction, one is not
automatically triggered to act by bottom-up sensory cues. The addict
pro-actively seeks out the sensory states they expect, and in turn is
directed to act in ways that will fulfill their expectations.

Repeated use of drugs of addiction can thus be thought of as training
expectations for error reduction at a certain rate. Importantly, this is the
source of positive feelings that comes with drug use (at least in the early
days) on our account. Drugs of addiction act directly on the system that
is signalling the probability that a policy does a good job of reducing
prediction error. Thus it makes it seem there is now something they can
do that is a failsafe, guaranteed means of arriving at the sensory states
they expect and value. Addictive substances make it appear to the agent
as if error is being reduced rapidly at a rate that is faster than anything
the agent has anticipated. As soon as the drug wears off, prediction
errors begins to increase again. Nothing was in fact resolved in the
world through taking the drug though: there was only the illusion of
error reduction. In fact, the addict often finds themselves in a worse
situation, as is reflected in the negative affect associated with feelings of
guilt and shame in the short term and loss of health in the long term.

Cravings in the addict can be thought of as the result of the accu-
mulation of error – much like in the smoking example discussed above.
The strong drives or cravings they experience are, we suggest, due to
expectations of fast error reduction. As long as they don’t use the drug,
error accumulates that seems to be quickly resolved by finding and
taking the substance. The addict feels bad so long as they are not using
the drug because they are failing to meet the slope of error reduction
they have come to expect through using the drug (cf. Koob and Le Moal,
2001, 2005). They are therefore driven to use the drug again in order to
meet the rate of error reduction they have come to expect. Thus, the
cycle of seeking and using takes hold and exerts a tighter and tighter
grip on the agent.

Moreover, the addict has now come to expect a certain rate of error
reduction – they have come to expect to do well at avoiding surprise
even though the reality may confront them with many challenges and
frustrations they are unable to manage. Consider a person who is
constantly facing hunger because they don’t have the money to buy
food, and is cold because they are unable to pay to heat their homes.
They expect to be well-fed and to stay warm, but their socio-economic
status means that meeting these expectations is a continuous struggle.
People faced with such difficulties in life struggle to meet their expected
slope of error reduction and might be more attracted to the possibility
to “self-medicate”, as it is sometimes described. Once they have dis-
covered the possibility to reduce disattunement with the world in ways
that otherwise prove a struggle, one can imagine the temptation to do
so repeatedly might be high. Marc Lewis makes this point well in re-
lation to the susceptibility of people struggling with PTSD and de-
pression to addiction:

“Importantly, it’s not just attraction or desire that fuels feedback
loops and promotes neural habits. Depression and anxiety also de-
velop through feedback. The more we think sad or fearful thoughts,
the more synapses get strung together to generate scenarios of
loneliness or danger, and the more likely we are to practice strate-
gies—often unconsciously—for dealing with those scenarios. Neural
patterns forged by desire can complement and merge with those
born of depression or anxiety. In fact, that’s a lynchpin in the self-
medication model of addiction. Gabor Maté persuasively shows how
early emotional disturbances steer us toward an intense desire for

11 Dopaminergic neurons in the midbrain fire together at a rate that is cor-
related to the organism’s expectation of reward. An increased or decreased
firing rate indicates that an outcome that was better or worse than expected has
occurred (Schultz et al., 1997).
12 Additional evidence comes from the brain network that has been hy-

pothesised to play a role in tracking error dynamics. Joffily and Coricelli (2013)
suggest that orbitofrontal cortex in collaboration with the striatum are likely to
be candidates for implementing processes that calculate rate of change in error
reduction (p.13).

M. Miller, et al. Brain and Cognition 138 (2020) 105495

the relief provided by drugs (Maté, 2008), and Maia Szalavitz vi-
vidly portrays her experience as a late adolescent trying to brighten
her depression with cocaine and ease her anxiety with heroin
(Szalavitz, 2016). So, when we examine the correlation between
addiction and depression or anxiety, we should recognize that ad-
diction is often a partner or even an extension of a developmental
pattern already set in motion, not simply a newcomer who hap-
pened to show up one day” (Lewis, 2017, p. 10).

What might seem like a fool proof way to reduce uncertainty is in
fact no such thing. Addicts choose the familiar option, and continue to
do so even when the outcomes are negative. They do not gather more
evidence that might lead them to change their behaviour. The possi-
bility to explore and find a different means of maintaining adaptation to
the environment is down-weighted relative to the option of continuing
to exploit the known consequences of using the substance. We can think
of the behaviour of the addict in terms of a dynamical landscape of
attractors. Typically, the attractor landscape changes over time as en-
vironmental conditions and the agent’s needs and interests change
(Friston, Breakspear, & Deco, 2012; Rietveld, Denys, & Van Westen,
2018; Rietveld & Kiverstein, 2014).13 This is necessary if agents are to
maintain a good grip on a volatile environment. They must sometimes
not just stick to what they know, always doing what is already well-
learned, but instead explore just-uncertain-enough environments that
allow them to do better in the long-run in their dealings with a world in
flux.14 The dynamical landscape of the addict is however made up of
fixed-point attractors that do not destroy themselves over time. Instead
they entrain the behaviour of the agent in ways we have just describing,
locking them into rigid, and ultimately self-destructive cycles of beha-
viour (see also Friston, 2012; Schwartenbeck et al., 2015).

We suggest the reason addicts don’t explore and gather new evi-
dence may be that substances of addiction make the person feel (at least
temporarily) like they are well-attuned even though they are not. Such
drugs create an illusion of attunement to the environment. Sensitivity to
error dynamics is one way that good habits are woven into our skillful
engagement incrementally over time – all directed to what matters to
the organism. Drugs of addiction, as we have seen, lead the system to
self-organize in relation to the environment in ways that lead agents to
neglect the many other things in their lives that also matter to them in
favour of the policy of feeding their addiction. The very same me-
chanisms that normally produce curiosity and exploration, when per-
turbed by addictive substances, produce precisely the opposite effect.
Instead of being moved to pursue the multiple possibilities people ty-
pically care about, the addict find themselves increasingly being
gripped by the drug infused field of affordances (Bruineberg & Rietveld,
2014; Rietveld, et al., 2018; Gibson, 1979).

8. Why addiction isn’t just a brain disease, and why it matters

The account of addiction we’ve proposed avoids falling onto one
side or other of the dichotomy in which addiction is seen either as a
biological disorder or as a purely social phenomenon whose causes lie
for instance in poverty or in the urban environment. All accounts of
addiction stress the importance of recognising the complex suite of

causes that lead up to addiction. There is now converging evidence for
instance that physical abuse, economic inequality and injustice, and
psychological trauma in early life increases the likelihood of addiction
in the future (Satel & Lilienfeld, 2014; Sinha, 2008). The disease model
of addiction acknowledge that social and environmental factors play an
important role in the development of addictions impacting on the
vulnerability and resilience of individuals. However, they often have an
unfortunate tendency to downplay the agency of the addict, assigning
too much importance to the brain. The contribution of the environment
on such accounts is only to provide stimulation that passively drives the
behaviour of addicts. The addict responds automatically to the stimulus
properties of cues, their behaviour bypassing their conscious evaluation
and control. The other side in the debate emphasises the social and
historical causes of addiction, but in doing so downplays the im-
portance of the brain in the development of addiction. Interestingly in
common with the disease model these accounts also treats the agent as
largely passive in the causal history that leads up to their addiction. The
addict as an individual agent is passively acted on by their historical
and social circumstances. Both sides in the debate fail to strike the right
balance between explaining addiction in terms of its environmental and
social causes, and explaining addiction in terms of its biological causes.

We suggest an account of addiction in terms of the dynamics of an
agent-environment system self-organising in ways that minimise long-
term prediction errors. On our account the dysfunctional behaviours of
addicts are the result of disorganization within the agent-environment
system as a whole. Human agents enter into a circular causal re-
lationship with their surroundings. The organism’s perception of its
environment, its actions and its feelings are co-determining. It is this
dynamic relationship between the organisms and the environment that
is disrupted in addiction. From this perspective addiction is best char-
acterized not only as a change in particular neural circuitry, but as a
more general loss of attunement of the organism and its environment.
The resulting theory of addiction is thus one in which neural processes
are necessary but not sufficient to account for addiction.

Explanations of addiction have been proposed by others in terms of
PP that take the breakdowns associated with long term addiction to be a
consequence of loss of contextualisation of how low-level habits by
high-level processes of cognitive control (Clark, 2017, 2019; Friston,
2012; Pezzulo, Rigoli, & Friston, 2015). On those accounts the work of
prediction error minimization maybe partly offloaded onto the body by
allowing bodily habit to function as fast and simple heuristics that drive
behaviour (Clark, 2015b). However, precision-estimation remains a
function that is implemented entirely in the brain, and it is precision
expectations that are held to account for the loss of contextualisation of
habits by high-level cognitive control.

The view we have been developing by contrast takes precision
weighting to arise in part out of processes that track error dynamics
through bodily feelings in relation to the world. The learning of pre-
cision expectations on the basis of feedback from the body alters the
dynamics within the organism as a whole so as to ensure that the or-
ganism remains well adapted to a dynamically changing niche. In other
words, what changes when precision is weighted is not the encoding of
precision expectations in the brain but how the organism and the en-
vironment fit together. Insofar as addictive substances impact core
systems sensitive to error dynamics, these substances play a central role
in altering how the organism and structuring of the environment con-
tinuously co-arise together.

Once we view addiction as a phenomenon of the whole agent-en-
vironment system, we can do justice to accounts of addiction that
emphasise its societal causes (e.g Sullivan, 2018). We have argued it
feels good to agents to be continuously improving in error reduction.
Sometimes a person’s life however offers only the prospect of more
uncertainty – think of soldiers that become addicted to substances while
away in a strange land in a war situation. They can make a predictable
and somewhat more comforting reality for themselves, out of what is
otherwise the confusing reality of war, by means of substance abuse

13 Clark has recently written, “Friston suggests, our ‘neural expectations’ may
come to include expectations of ‘itinerant trajectories’ mandating change, ex-
ploration, and search. We ‘expect’ to sometimes engage in random environ-
mental search as a means of entering into adaptively valuable states. To put it
crudely, we randomly sample because – qua evolved organisms – we ‘expect’ to
discover food, mates, or water at some point during the expedition” (2017:
p.526).
14 Schwartenbeck, FitzGerald, Dolan, and Friston (2013) extend this direction

of thinking by proposing that certain policies may be valuable insofar as they
open the way the agent to visit multiple other states (Bruineberg & Rietveld,
2014; Rietveld & Kiverstein, 2014).

M. Miller, et al. Brain and Cognition 138 (2020) 105495

because the substance can be trusted to have certain guaranteed and
predictable physiological effects on the body. Once the soldiers return
home to the predictable and familiar reality, drugs no longer present
the attraction they once held. There are better policies available to the
soldiers for improving attunement with the world. This may go some
way towards explaining why rates of heroin addiction were high among
soldiers stationed in Vietnam but upon returning home addiction rates
fell back to their normal rates. The behavior of the soldiers stationed in
Vietnam was in this respect somewhat similar to that of the rats in the
famous Rat Park studies (Ahmed, Lenoir, & Guillem, 2013; Alexander,
2010; Alexander, Coambs, & Hadaway, 1978; Hari, 2015; Solinas,
Chauvet, Thiriet, El Rawas, & Jaber, 2008). One group of rats were
placed in simple cages all alone, but with plentiful opportunity to
consume as much opioids as they wanted. For such a rat addiction was
an inevitable outcome. When the same rats, now addicted to the sub-
stance, were moved to a much larger cage with other rats and a variety
of games and opportunities for improving they tended to ignore the
available opiates altogether (Alexander et al., 1978; Alexander, 2010).
Given the current proposal, we think this could be explainable insofar
as the rats were able to now meet their expected slope of error reduc-
tion, just like the soldiers returning home from Vietnam (Granfield &
Cloud, 1999; Robins, 1993; Robins, Helzer, & Davis, 1975).

In addiction the agent is increasingly gripped by the environment
until they cease to be open to the other non-drug related possibilities
that may otherwise matter to them. Recovery from addiction then is
likely to be facilitated by changing the expected rate (the slope) of error
reduction itself through restructuring (relearning) expectations for where
to look in the landscape of affordances for error reduction. A key part of
undoing such habits we suggest will be developing new and different
skills for reducing errors more efficiently, such as techniques of emo-
tional regulation and mindfulness that help people to not only act in
response to possibilities in the here and now, but also be open and give
due consideration to engaging with possibilities that lie in the future
(Garland, Froeliger, & Howard, 2014). One of the keys to escaping
addiction may thus be restoring openness to the many possibilities that
matter to the agent, and not only those that relate to their addiction.

9. Conclusion

In this paper we have argued that to understand what is harmful
about addiction requires taking a wider vantage point on the organism-
environment system as a whole. The brain of the addict is in fact doing
what the brain is meant to do when viewed from the standpoint of the
PP theory as we’ve interpreted it in this paper (c.f. Bruineberg et al.,
2018; Kirchhoff & Kiverstein, 2019; Kiverstein, Miller, et al., 2019). It is
continually optimizing the fit of the organism with its environment
relative to what matters to the organism. Addictive substances make it
seem to the organism as if error had been reduced but sadly for the
addict this is just an illusion. The result in the long-run is almost in-
evitably a greater amount of uncertainty arising from a loss of sensi-
tivity to the wider concerns of life.

If all predictive organisms care about is reducing error why isn’t the
life addicts lead at least one viable strategy for prediction error mini-
misation? Addicts become extremely skilled at organising their lives
around the goals of finding and using the addictive substance. They
develop models that are optimised to fit an environment in which these
are the only things that matter. We’ve argued that typically predictive
organisms don’t only try and reduce error but reduce it at a particular
rate. It might be thought however that this is exactly what the addict is
doing as they get increasingly skilled at navigating an environment
whose relevance is structured by their addiction.

What this misses however is the way in which all the drug can de-
liver is short-term reduction in error. The life of many (but not all)
addicts becomes increasingly chaotic in other regards. As soon as the
drug’s effect wears off, what they return to is a world offering all of the
uncertainty that never really went away. So long as the addict is high, it

seems to them as if they are succeeding at maintaining grip on what
matters to them. Once the drug wears off, they find reality is very
different. Substance addiction has been likened to a single room with
many paths that all in the end lead the addict back into the same room
again. The room of addiction is however fraught with difficulties and
dangers. The progressive loss of contact with the rest of what matters
can lead long term addicts to struggle with loss of material possessions
and personal relationships, diminished self-worth, and physical health
problems. Addiction can in this way lead to long term increases in error
in relation to all the other things that matter to the addict. Humans
have come to expect overtime to maintain relationships that matter to
them, to hold onto their possessions, and to remain healthy. In addic-
tion however they act in ways that frustrate these expectations. The
point at which an addict in the end decides to really make a change is
sometimes referred to as “rock bottom”. This is the point at which what
the addict has actually lost finally outweighs what they feel they are
gaining.

The addict comes to embody a generative model that is tailored and
built around the all-consuming activity of feeding their habit. What
counts as an improvement of the model, its predictions and their fit
with this environment is dictated not by finding a balance among the
many things that matter to the addict. It is instead dictated to the agent
by the increasingly wide range of possibilities that lead them back into
the same vicious cycle of behaviour. What is harmful in the life of an
addict is thus not to be found inside of the brains of addicts but in their
wider engagement with life, and with the environment they enact.

Funding

Mark Miller carried out this work with the support of Horizon 2020
European Union ERC Advanced Grant XSPECT – DLV-692739.

Julian Kiverstein and Erik Rietveld are supported by the European
Research Council in the form of ERC Starting Grant 679190 (EU
Horizon 2020) for the project AFFORDS-HIGHER, the Netherlands
Organisation for Scientific Research (NWO) in the form of a VIDI-grant
awarded to Erik Rietveld, and by a project grant from the Amsterdam
Brain and Cognition research group at the University of Amsterdam.

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Introduction

Mutiny in the midbrain: Is addiction a brain disease?

The predictive processing theory of reward learning

An ecological-enactive account of habits

Why is addiction harmful?

Weighing precision in the body

Addiction as tending towards a sub-optimal grip

Why addiction isn’t just a brain disease, and why it matters

Conclusion

Funding

mk:H1_12

References

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