Psychological and cognitive theories and experimental evidence
that implicated various aspects of reinforcement-biased cognition in the
development, maintenance, and recurrence of alcohol addiction
Several psychological theories sought to describe reinforcement-biased
processes that are implicated in the development and progression of AUD
(Skinner & Aubin, 2010). According to the allostatic model (Koob, 2003;
Koob & Volkow, 2010), the early stages of alcohol addiction are
primarily driven by positive reinforcement, which generally refers to
the pleasant feelings and social enhancement effects that alcohol
provides, while the later and more severe stages are driven by negative
reinforcement, which encompasses relief from negative affective states
such as stress or anxiety. The second model, the incentive-sensitization
theory (Robinson & Berridge, 1993), highlighted increasing subjective
‘wanting’ for alcohol that is driven by hypersensitization of the
dopaminergic system, while a related theory postulated by Field & Cox
(2008) proposed that sensitization of the dopaminergic system drives
alcohol attentional biases related to alcohol-related cues, leading to
an increased craving. Finally, according to Cox & Klinger (2011), some
individuals drink alcohol for its positive reinforcing properties to
increase their positive affective experience (e.g., mood enhancement),
while others drink alcohol for its negative reinforcing properties to
dampen their negative emotions and cope with distress and anxiety
(tension reduction). The former, whose drinking is maintained by the
rewarding effects of alcohol, are called ‘reward drinkers’, while the
latter are called ‘relief drinkers’ (Ooteman, Koeter, Verheul, Schippers
& Van den Brink, 2006). Based on accumulating experimental evidence, it
seems that there may be distinct types of AUD in which alcohol abuse is
differentially driven by various mixtures of the above-mentioned
reinforcement-based processes.
Individual differences in vulnerability to substance use disorders,
including AUD, may be explained in light of reinforcement sensitivity
theory (Corr, 2004). According to this hypothesis, every behaviour is
governed by two conceptual brain systems: the behavioural approach
system (BAS) and the behavioural inhibition system (BIS). These two
systems differentially respond to the rewarding and punishing effects of
alcohol. The BAS underlies approach motivation, positive affect, and
reward learning processes and may contribute to the acquisition of AUD,
while the BIS inhibits behaviour in response to stimuli signalling the
loss of expected reward, uncertainty, and goal conflict and may support
the maintenance of AUD (Corr, 2008). The BIS also gives rise to
emotional distress and has been associated with trait negative affect,
especially anxiety (McNaughton & Gray, 2000). Research has suggested
that the BAS can be further subdivided into three different types of
reward sensitivity: (a) reward drive, relating to persistent pursuit of
desired goals; (b) fun-seeking, relating to simple biological
reinforcers that do not require planning; and (c) reward responsiveness,
encompassing responses related to receiving or anticipating reward
(Carver & White, 1994; Corr, 2008). As alcohol consumption represents a
high reward value (Everitt & Robbins, 2005), it is not surprising that
several studies demonstrated a relationship between positive
reinforcement sensitivity, particularly in relation to fun-seeking, and
higher alcohol intake, particularly in the form of binge drinking (Feil
& Hasking, 2008; Franken & Muris, 2006; Loxton & Dawe, 2001; O’Connor
& Paley, 2009; Voigt et al., 2009). It was also found that BAS
sensitivity was related to both desire and negatively reinforcing
aspects of alcohol craving. Subjects with high BAS sensitivity scores
experienced significantly stronger desires and intention to drink
alcohol and negative reinforcement craving during exposure to
alcohol-related cues than subjects with low BAS sensitivity scores
(Franken, 2002). Furthermore, persons with high BAS sensitivity
experienced high negative reinforcement craving during this exposure
(Franken, 2002). The relationship between BIS sensitivity and alcohol
consumption is less clear. Although several investigators reported null
associations between BIS and AUD (Hundt, Kimbrel, Mitchell &
Nelson-Gray, 2008; Kambouropoulos & Staiger, 2001; O’Connor & Colder,
2005), other studies suggested that the BIS, instead of inhibiting
behaviour, draws attention to the potential dangers of a situation and
functions as a conflict resolution system (Corr, 2008)). As a result, a
more sensitive BIS leads to high anxiety, which could be reduced by
indulging in drinking (Hasking, 2006). Indeed, it has been explicitly
proposed that a sensitive BIS acts anxiogenically in response to health
information, which may motivate protective health-related behaviours
(Norman, Boer & Seydel, 2005). By the same token, a more sensitive BIS
may act as a protective factor due to the avoidance of potentially risky
situations or aversive consequences (e.g., hangovers). This theory has
been supported by several other studies (Kimbrel, Nelson-Gray &
Mitchell, 2007; O’Connor, Stewart & Watt, 2009; Pardo, Aguilar,
Molinuevo & Torrubia, 2007) who found a negative correlation between
the sensitivity of the BIS and the frequency and quantity of alcohol
consumption. Similar results were reported by Knyazev and collaborators
(2004), who found that a more sensitive BIS protects against substance
abuse among youths.
Taken
together, it seems that the effects of BAS and BIS on the trajectories
of alcohol addiction might be interactive (Figure 1). Indeed, several
experiments demonstrated that high BAS sensitivity coupled with low BIS
sensitivity was associated with significantly increased alcohol use
(Kellough, Beevers, Ellis & Wells, 2008; Wardell, O’Connor, Read &
Colder, 2011). Moreover, Wardell and colleagues (2011) demonstrated high
BIS sensitivity as a risk factor for subsequent problematic drinking,
but only when combined with high BAS sensitivity. When the sensitivity
of BAS was low, BIS sensitivity was demonstrated to be protective
against subsequent drinking. The above-mentioned studies suggested that
the relationship between BIS and problematic drinking is moderated by
BAS: in the absence of a sensitive BAS to shift attention towards the
rewarding, tension-reducing properties of alcohol, the aversive
consequences of drinking are more salient, leading to alcohol avoidance
(Wardell, O’Connor, Read & Colder, 2011).
The objective investigation of BAS and BIS sensitivity in people abusing
alcohol can be conducted using a framework of neurophysiological and
neuropsychological measures. The neurophysiological measures are mostly
based on the peripheral assessment of cardiac and electrodermal
reactivity during appetitive responding for reward and during extinction
of appetitive responding (i.e., frustrative non-reward), respectively
(Beauchaine, 2001; Fowles, 1988; Iaboni, Douglas & Ditto, 1997; Tranel,
1983). In turn, the neuropsychological evaluation is usually conducted
using self-report questionnaires that assess individual differences in
BAS and BIS sensitivity by the degree to which respondents endorse
prototypical approach- and avoidance-related behaviours. This strategy
is exemplified in the BIS/BAS scales of Carver and White (1994), which
were developed with the explicit purpose of assessing individual
differences in state reactivity of these systems. Recently,
investigators have developed computerized neuropsychological tests to
experimentally measure sensitivity to reinforcement (Cools, Clark, Owen
& Robbins, 2002; Evers et al., 2005; Paulus, Hozack, Frank & Brown,
2002; Paulus, Hozack, Frank, Brown & Schuckit, 2003; Slaney,
Hinchcliffe & Robinson, 2018). These tests offer many advantages over
conventional pencil-and-paper testing because they standardize aspects
of administration and automate data collection and analysis. One of
these tests is the probabilistic reversal learning (PRL) test, offering
an effective way of measuring an individual’s sensitivity to
reinforcement by assessing win-stay and lose-shift behaviours (WSLS)
following rewarding and punishing feedback, respectively. In this
paradigm, subjects are presented with two (sometimes more) stimuli on
each trial and using trial-and-error feedback after each response, learn
to select the stimulus that is usually correct (rewarded on a majority
or punished on a minority of trials) and to avoid the stimulus that is
usually incorrect (punished on a majority or rewarded on a minority of
trials). This rule intermittently reverses such that the stimulus that
was usually rewarded becomes usually punished/unrewarded and vice versa.
Consequently, responding must be adjusted to gain the reward and avoid
punishment. Rewarded outcomes followed by a decision to stay with the
response that delivered them (win–stays) constitute a measure of
sensitivity to positive reinforcement. Conversely, lose–shift ratios,
calculated by dividing punishing outcomes after which the subject
changed the choice by the total number of punished trials on a given
stimulus, represent a measure of sensitivity to negative reinforcement.
The use of probabilistic reinforcement increases the complexity of the
task in such a way that the information from any given choice is
insufficient to guide future behaviour, and subjects must engage
cognitive functions to track the reward and punishment history for both
stimuli to determine the stimulus that is more beneficial overall. The
PRL paradigm has been recently successfully applied in a number of
studies used to investigate the neuroanatomical and neurochemical
correlates of reinforcement sensitivity in humans and non-human animals
(Rygula, Noworyta-Sokolowska, Drozd & Kozub, 2018). These will be
discussed in subsequent sections of this paper.
Individual differences in reinforcement sensitivity may influence the
acquisition and maintenance of positively and negatively biased
alcohol-drinking outcome expectancies, which can be defined as
positively or negatively inflated beliefs about the effects of alcohol
on behaviour, cognition, moods, and emotions (Leigh, 1989). These
expectations, in turn, have a pivotal role in determining decisions
about alcohol drinking and trajectories of its use (Jones, Corbin &
Fromme, 2001). Beginning with the seminal work of Brown and her
colleagues (Brown, Goldman, Inn & Anderson, 1980; Goldman, 1994), many
studies have documented positively biased outcome expectancies for
engaging in alcohol drinking, together with minimized negative
expectancies and poor self-efficacy or beliefs about one’s ability to
cope without alcohol, can maintain addictive behaviours and predict
relapse (Brown, Christiansen & Goldman, 1987; Christiansen & Goldman,
1983; Floyd & Widaman, 1995; Fromme & D’Amico, 2000; Fromme, Stroot &
Kaplan, 1993). In these experiments, alcohol-drinking outcome
expectancies were measured using structured interviews of focus groups
and the self-report questionnaires in which individuals endorse each
questionnaire item as to whether or not they hold that particular
expectancy. A sum of the expectancy endorsements representing the
individual’s overall alcohol outcome expectancy is further positively
associated with alcohol consumption, e.g., subjects who drank in a
frequent, social manner, expected alcohol to enhance their social
behaviour (Christiansen & Goldman, 1983; Lee, Maggs, Neighbors &
Patrick, 2011). Other studies have investigated the potential
associations of different categories of alcohol outcome expectancies
with subsequent drinking behaviour. A study by Leeman and collaborators
(2009) found significant associations of euphoria and social enhancement
expectancies with binge alcohol use, while the study by Pabst and
colleagues (2010) found the same association concerning sexual and
social relationship enhancement expectancies. Other cross-sectional and
longitudinal studies have demonstrated that greater numbers of positive
alcohol outcome expectancies are associated with greater numbers of
negative consequences independent of the level of consumption (Blume &
Blume, 2014; Blume, Lostutter, Schmaling & Marlatt, 2003).
Abnormal perceptions of risks associated with alcohol-drinking outcomes
can also be considered within the concept of unrealistic optimism bias
(Sharot, Korn & Dolan, 2011), which encompasses two different
phenomena: unrealistic comparative optimism and unrealistic absolute
optimism (Shepperd, Klein, Waters & Weinstein, 2013). The former refers
to people’s tendency to view the risks as lower for themselves than for
the others, while the latter refers to unrealistically positive risk
assessment when compared to an objective criterion, such as an actual
risk assessment. The mechanism that allows individuals to maintain or
arrive at unrealistically positive beliefs in the face of disconfirming
evidence was described for the first time by Sharot, Korn and Dolan
(2011). The results of this study demonstrated that when people update
their initial risk estimates, they tend to incorporate desirable
information (i.e., information that risks are lower than expected) to a
greater extent than undesirable information (i.e., information that
risks are higher than expected). In those with AUD, the asymmetric
reliance on information about alcohol-drinking outcomes, dependent on
their valence, may result in larger updates after desirable/positive
information than after undesirable/negative information and in this way
affect drinking trajectories. When considering expectancies and beliefs
about the effects of alcohol, it is also important to mention the
interpretation of ambiguity in drinking outcomes. Indeed,
positive/appetitive alcohol-related expectations are often associated
with more negative/inhibitory information arising from memories. This
conflict and the bias in its interpretation can be assessed using
various types of ambiguous-cue interpretation (ACI) paradigms. In this
experimental and fully translational approach, participants initially
learn to discriminate two stimuli (e.g., tones of different
frequencies), which acquire emotional and motivational value due to
subsequent feedback (monetary gain or avoidance of monetary loss). After
such an acquisition phase, the test phase introduces ambiguous stimuli
(e.g., tones of intermediate frequencies) that serve as a measure of
interpretation bias since the response to these stimuli indicates the
participants’ expectation of rewarding or potentially punishing effects
of their decision (Papciak & Rygula, 2017; Schick, Wessa, Vollmayr,
Kuehner & Kanske, 2013). Other ways of assessing biased interpretations
of ambiguity include using single ambiguous words individually presented
to the participant, which are typically either homophones (words that
sound the same yet have different spellings and meanings, e.g.,
meat/meet or sea/see) or homographs (words with identical spelling yet
distinct meanings, e.g., bat—animal or wooden club; change—to alter
or money) (Drury, 1969; Gorfein & Weingartner, 2008). Homophones and
homographs used in these studies usually have both threatening/emotional
and neutral interpretations, and an interpretation bias is established
as the number or proportion of each type of interpretation made by
participants from a list of such words (Hindash & Amir, 2012; Warren,
Warren, Green & Bresnick, 1978). Ambiguity interpretation biases can
also be investigated using ambiguous images, often with emotional and
neutral faces (Beevers, Wells, Ellis & Fischer, 2009; Schaefer,
Baumann, Rich, Luckenbaugh & Zarate Jr, 2010).
Another conceptual approach focuses not on expectancies regarding
alcohol-drinking outcomes per se but on the mechanisms that
underlie them. These mechanisms have been defined as attributional
styles, which Alloy and colleagues (1984) described as the manner in
which a person explains the causes of prior outcomes. Indeed, following
positive or negative experiences with alcohol, people often wonder why
the event occurred (DeJoy, 1989; Jessup et al., 2014). To answer this
question, they make causal attributions based upon different dimensions:
internal-external (whether the event was caused by themselves (internal)
or by the situation (external)), stable-unstable (whether the cause of
an experience is constant and likely to happen again (stable) or pliable
and unlikely to reoccur (unstable)), and global-specific (whether the
cause may affect many areas of one’s life (global) or only one area
(specific)) (Weiner, 1985). From this perspective, attributing negative
outcomes to causes that are internal to the person, stable and global
can reflect pessimism, learned helplessness, and addiction vulnerability
(Scheier & Carver, 2018). Following the above-mentioned framework,
Weiner (1985) and Marlatt & Donovan (2005) proposed that a certain
pattern of attribution promotes relapse in substance use. They argued
that a combination of internal, stable, and global attributions
contributes to maladaptive thought patterns concerning addiction and
promotes relapse. This pattern, often experienced by an individual after
a return to substance use following a period of self-imposed abstinence
from substances, has been named the abstinence violation effect (Curry,
Marlatt & Gordon, 1987). Within this model, bleak attributions
regarding future reactions to alcohol exposure lead individuals to
believe that fighting addiction is pointless and, in turn, to relapse
rather than returning to abstinence (Walton, Castro & Barrington,
1994). This conceptual orientation has given rise to assessment
techniques that focus on measuring the causes that people identify for
the outcomes that they experience. Typically, respondents are asked to
imagine being in outcome scenarios and to indicate how they would
explain the described outcomes (Peterson, Semmel, Von Baeyer, Abramson,
Metalsky & Seligman, 1982) or to analyse naturally occurring verbatim
materials (e.g., newspaper articles or speeches) for explanatory style
(Schulman, Castellon & Seligman, 1989).
Taken together, these studies consistently support the notion that
various types of RBCBs, such as abnormal sensitivity to performance
feedback, biased interpretation of ambiguity, inflated expectations, and
asymmetrical belief updating, can be differentially associated with
drinking at different stages of AUD (Figure 2). Understanding the
underlying mechanisms could provide useful information for tailoring
prevention and intervention efforts. For example, medications used in
the early stages of addiction should target positive reinforcement
sensitivity, while treatments that target alcohol-dependent individuals
in the late stages of addiction may need to simultaneously target
positive and negative reinforcement processes. In other words,
understanding individuals’ positive and negative reinforcement profiles
and resulting cognitive biases can provide important information for
personalized medicine. Indeed, there is preliminary evidence for the
effectiveness of tailoring pharmacotherapies to AUD patients based on
their positive and negative reinforcement tendencies (Mann et al., 2018;
Roos, Mann & Witkiewitz, 2017). For example, an opioid receptor
antagonist, naltrexone, has been found to be particularly effective for
reward drinkers, while acamprosate, which has been shown to
down-regulate the glutamatergic system, is particularly effective for
relief drinkers (Roos, Mann & Witkiewitz, 2017). Specific neurochemical
correlates of the interactions between RBCBs and AUD, as well as
potential future treatment targets, are described in the next sections.