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Attentional bias has been defined as the propensity of a person to allocate selective attention automatically to salient cues (Field and Powell, 2007). In the case of smoking, this bias implies that smokers are implicitly attracted by smoking-related stimuli, which produce behavioral, memory, and emotional effects (Volkow et al., 2006; Giardini et al., 2009). In more detail, scientific evidence pointed out that smoking is strongly supported by attentional bias that activates craving and urgency to smoke a cigarette. However, poor and conflicting data are available regarding the role of this cognitive bias on former smokers. The main aim of this study is to explore the occurrence of the attentional bias on of both current and former smokers, also with the aim to identify associations with behavioral, psychological and cognitive characteristic of participants. We collected data on 245 current, volunteers (male 50.6%; female 49.4%) aged 54.81 (SD = 14.352, range = 18-63), divided in current smokers (98), former smokers (102) and non-smokers (45). A combination of neuropsychology tests (Emotional Smoke Stroop Task and Go/no-Go task), and standardized questionnaires [Behavioral Inhibition System-Behavioral Approach System (BIS-BAS), Fagerström Test for Nicotine Dependence (FTND), Barratt Impulsiveness Scale, Motivational questionnaire] were used to assess the attentional bias, psychological variables, and smoking-related characteristics. Responses at the Emotional Smoke Stroop task revealed that current and former smokers are actually slower than non-smokers are when facing smoking cues, while performances at other Stroop conditions and at the Go/no-Go task are not statistically different. These results confirmed the occurrence of the attentional bias in current smokers, and above all points out that the same effect is present in former smokers. We found only small and selective correlations between attentional bias and psychological variables (e.g., impulsiveness and inhibition). In particular, impulsivity is not directly associated with the AB intensity. Also, smoking characteristics (e.g., years of smoking and dependence level) and the length of the period of abstinence do not seem to modulate implicit cognition of smoking cue. Our data support the idea that the attentional bias may be considered relevant in sustaining smoking and favoring relapse.
Content may be subject to copyright.
published: 10 July 2019
doi: 10.3389/fnbeh.2019.00154
Edited by:
Liana Fattore,
Italian National Research
Council (CNR), Italy
Reviewed by:
Salvatore Campanella,
Free University of Brussels, Belgium
Theodora Duka,
University of Sussex, United Kingdom
Claudio Lucchiari
Received: 18 January 2019
Accepted: 24 June 2019
Published: 10 July 2019
Masiero M, Lucchiari C,
Maisonneuve P, Pravettoni G,
Veronesi G and Mazzocco K
(2019) The Attentional Bias in Current
and Former Smokers.
Front. Behav. Neurosci. 13:154.
doi: 10.3389/fnbeh.2019.00154
The Attentional Bias in Current
and Former Smokers
Marianna Masiero 1,2,Claudio Lucchiari 3*, Patrick Maisonneuve 4,Gabriella Pravettoni 2,5 ,
Giulia Veronesi 6and Ketti Mazzocco 2,5
1Department of Biomedical and Clinical Sciences (DIBIC), Luigi Sacco, University of Milan, Milan, Italy, 2Applied Research
Division for Cognitive and Psychological Science, European Institute of Oncology (IEO), IRCSS, Milan, Italy, 3Department
of Philosophy, University of Milan, Milan, Italy, 4Division of Epidemiology and Biostatistics, European Institute of Oncology
(IEO), IRCSS, Milan, Italy, 5Department of Oncology and Emato-Oncology (DIPO), University of Milan, Milan, Italy, 6Division of
Thoracic and General Surgery, Humanitas Research Hospital, Rozzano, Italy
Attentional bias has been defined as the propensity of a person to allocate selective
attention automatically to salient cues (Field and Powell, 2007). In the case of smoking,
this bias implies that smokers are implicitly attracted by smoking-related stimuli, which
produce behavioral, memory, and emotional effects (Volkow et al., 2006; Giardini et al.,
2009). In more detail, scientific evidence pointed out that smoking is strongly supported
by attentional bias that activates craving and urgency to smoke a cigarette. However,
poor and conflicting data are available regarding the role of this cognitive bias on former
smokers. The main aim of this study is to explore the occurrence of the attentional bias
on of both current and former smokers, also with the aim to identify associations with
behavioral, psychological and cognitive characteristic of participants. We collected data
on 245 current, volunteers (male 50.6%; female 49.4%) aged 54.81 (SD = 14.352, range
= 18–63), divided in current smokers (98), former smokers (102) and non-smokers (45).
A combination of neuropsychology tests (Emotional Smoke Stroop Task and Go/no-
Go task), and standardized questionnaires [Behavioral Inhibition System-Behavioral
Approach System (BIS-BAS), Fagerström Test for Nicotine Dependence (FTND), Barratt
Impulsiveness Scale, Motivational questionnaire] were used to assess the attentional
bias, psychological variables, and smoking-related characteristics. Responses at the
Emotional Smoke Stroop task revealed that current and former smokers are actually
slower than non-smokers are when facing smoking cues, while performances at other
Stroop conditions and at the Go/no-Go task are not statistically different. These results
confirmed the occurrence of the attentional bias in current smokers, and above all points
out that the same effect is present in former smokers. We found only small and selective
correlations between attentional bias and psychological variables (e.g., impulsiveness
and inhibition). In particular, impulsivity is not directly associated with the AB intensity.
Also, smoking characteristics (e.g., years of smoking and dependence level) and the
length of the period of abstinence do not seem to modulate implicit cognition of smoking
cue. Our data support the idea that the attentional bias may be considered relevant in
sustaining smoking and favoring relapse.
Keywords: cigarette smoking, attentional bias, former smokers, implicit cognition, impulsiveness, inhibition
Frontiers in Behavioral Neuroscience | 1July 2019 | Volume 13 | Article 154
Masiero et al. Attentional Bias and Cigarette Smoking
Growing evidence of the negative effects of tobacco cigarette
smoking on health has had little impact on the real extent of
this phenomenon and in promoting solutions (Morgan et al.,
2011). The main issue is concerned with the relapse. For example,
Hughes et al. (2004) reported that 85% of former smokers are
more likely to relapse after 1 year from quitting. This is also
true for those who followed a cessation program (Yong et al.,
2018) and even after a continued period of abstinence (Kerr et al.,
2011). Probably, the limited effectiveness of smoking cessation
programs depends on individual biological, psychological
and cognitive factors, which effect smoking initiation and
maintenance (Kale et al., 2018).
From a general point of view, addictions are modulated by
different mechanisms, which include: drug-related Pavlovian
and instrumental reinforcement (Everitt and Robbins, 2005);
biases toward drugs-cues (Grant et al., 1996; Hester et al.,
2006); the effect of rewards and reward expectancy on decision-
making (Grant et al., 2000;Bechara et al., 2002; Stout et al.,
2005; Goldstein et al., 2007; Wrase et al., 2007); cognitive
monitoring and inhibition processes (Kaufman et al., 2003;
Forman et al., 2004). However, a pivotal role in the adoption on
unhealthy behaviors (such as smoking, alcohol consumption and
an unhealthy diet) is the biased cognitive processing of salient
cues (Kakoschke et al., 2017).
In particular, the attentional bias (AB; Williams et al., 1988)
seem to be particularly relevant, as shown by several studies in
different areas such as anxiety disorders (Pool et al., 2016), food
consumption (Deluchi et al., 2017), alcohol abusers (Manchery
et al., 2017) and cocaine users (Marks et al., 2016). A plethora
of studies reported this effect to be present also within smokers
(Bradley et al., 2003; Drobes et al., 2006; Munafò et al., 2003;
Waters et al., 2003b).
With regards to this, the study of smoking-related cognitive
biases offers additional opportunities to develop tailored and
effective anti-smoking programs (Mühlig et al., 2016) based on
the assessment of cognitive and psychological processes engaged
in smoking behavior (Kondylakis et al., 2012; Fioretti et al., 2016;
Gorini et al., 2016; Lucchiari et al., 2016; Masiero et al., 2019).
Currently, two main theories are used to explain the activation
of cognitive biases in smoking behavior: the incentive salience
theory (IST; Robinson and Berridge, 1993) and Pavlovian
conditioning (PC). According to the IST, the individual reactivity
to drug-related cues (e.g., objects or situations directly or
indirectly associated with cigarettes, e.g., an ashtray, a lighter,
coffee after lunch, a group of friends) plays a pivotal role in
smoking behavior. Smokers tend to allocate selective attention
automatically to smoking cues (Field and Powell, 2007). These
cues can elicit a physiological response similar to the one
activated by nicotine, thus producing memory, emotional and
perceptive effects that facilitate the maintenance of smoking
(Volkow et al., 2006; Giardini et al., 2009).
According to the PC theory, instead, when a person develops
an addiction, the related objects or events (e.g., a cup of
coffee) undergo a change in their cognitive status: from the
original neutral status, they become conditioned motivational
triggers (Marlatt, 1990; Di Chiara, 2000; Caretti and La
Barbera, 2010). This process stimulates the compulsive need
to consume substances (e.g., nicotine), just because of the
activating effect of the conditioned event (Giardini et al., 2009).
Consequently, drug-related cues are able to activate automatic
and implicit processes that stimulate craving and the urgency
to smoke.
The AB intensity depends on a variety of factors, some linked
to personal characteristics and other to contextual factors. For
example, Di Chiara (2000) affirmed that cigarette smokers are
particularly sensitive to incentives in the early phase of smoking
behavior, while the same incentives have a lower or no impact
in chronic smokers (Mogg et al., 2005). Furthermore, Drobes
et al. (2006) reported that susceptibility to the AB is strictly
linked to nicotine dependence (Drobes et al., 2006). In particular,
smokers who have a high level of nicotine dependence are
more susceptible to AB than smokers who have low nicotine
dependence. In addition, reactivity to smoking-related cues (SC)
is modulated by gender, as women seem to be more affected.
On this point, Field and Cox (2008) affirmed that women are
more vulnerable to conditioned factors of smoking behavior
(Field and Duka, 2004; Saladin et al., 2012) suggesting that these
factors are a possible roadblock for women to stop smoking
(Smith et al., 2016). Other authors observed that women who
smoke have a specific ‘‘neurocognitive profile’’ characterized by
impairments in sustained attention and control of impulsivity
that may facilitate both smoking initiation and stabilization of
this behavior (Yakir et al., 2007). The AB in cigarette smokers,
similarly to alcohol abusers, seems to be amplified by the
emotional distress, excessive alcohol consumption, withdrawal,
drug-related stimuli, and the perception that the opportunity to
consume the substance is imminent (Field et al., 2014). Finally,
some evidence suggests that impulsivity affects the strength of
AB, since the more people are impulsive the greater is their
bias (Field and Cox, 2008). Actually, cognitive control functions
and impulsivity may be of particular relevance, since selective
impairments may predispose some individuals to impulsive
use of the drug. For example, a high level of impulsivity in
10–12-year-olds seems to predict drug use at the age of 19
(Tarter et al., 2003), suggesting an important role on this
dimension in the transition from recreational to dependent
use. Similarly, Perkins et al. (2008) reported that personality
traits related to impulsiveness, for example, novelty seeking and
response disinhibition, are associated with sensitivity to nicotine,
including reinforcement and reward (Perkins et al., 2008). In
addition, the evidence of higher impulsiveness supports the
transition from occasional to chronic smoking (Hu et al., 2006;
DiGirolamo et al., 2016).
The AB has an important effect on action. It seems to
facilitate the repetition of smoking (Tiffany, 1990) and the
development of habits. The same mechanism is also effective
when a smoker attempts to quit. For example, former smokers
may feel the desire to smoke when they are found in a place
where they used to smoke in the past (e.g., waiting for a
bus, watching TV, at the coffee vending machine and so on),
potentially modulating individual motivation by the mediation
of the brain wanting system, which includes the dopaminergic
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Masiero et al. Attentional Bias and Cigarette Smoking
mesolimbic circuit (Berridge and Robinson, 2016). In particular,
neuro-imaging studies highlighted that SC are able to activate
smokers’ neural mechanisms linked to gratification (Garavan
et al., 2000; Wexler et al., 2001). These studies confirmed
the role of dopamine in the shell of the nucleus accumbens:
an event (e.g., smoking after a cup of coffee) is marked by
a discharge of dopamine in the nucleus accumbens, which
integrates affective and contextual attributes of a learning
experience due to the input from the amygdala and hippocampus
(Kerfoot and Williams, 2018).
A few studies investigated the presence of AB in former
smokers, some reporting a similar attention reactivity to
smoking cues in current and former smokers, and some other
disconfirming this view. In particular, Ehrman et al. (2002)
found that former smokers suffer from an AB in a 500 ms
visual probe task, but the intensity of this bias was considered
intermediate with respect to current smokers. However, in this
study, the former smoker sample was small and with a very
short abstinence time (only 1 week). Munafò et al. (2003)
compared 43 current smokers, 22 former smokers and 30 never-
smokers using an Emotional Smoke Stroop task. They found
smoking-related interference (AB) only in current smokers since
former smokers and non-smokers reported similar reaction time
to smoking-related words. In addition, authors reported that
AB in current smokers was associated with the personality
trait of sensibility to reward. Peuker and Bizarro (2014), in
a sample of 60 former smokers divided into three different
abstinence time (recent, intermediate and prolonged abstinence)
found longer reaction times at a visual probe task in recent
and intermediate former smokers (Peuker and Bizarro, 2014).
Another recent study by Rehme et al. (2018) tested 38 former
smokers and 34 current smokers using an Emotional Smoke
Stroop task (Rehme et al., 2018). They found that AB affected
both former and current smokers in visual orienting to smoking
pictures and that this effect was negatively related to nicotine
dependence in current smokers. Munafò et al. (2008) suggested
that the presence and the persistence of the AB might be
linked to a subgroup of formers smokers with a particular
genetic configuration. Finally, Nestor et al. (2011) explored the
neural correlates of attentional bias in non-smokers, current
smokers and former smokers while their neural activity was being
recorded by means of functional magnetic resonance imaging
(fMRI). The task required subjects to respond to the color of
a border framing neutral, emotionally salient or smoke-related
pictures. Authors found only a small behavioral effect linked to
AB in former smokers, but importantly they found a remarkable
difference in cortical activations. On the one hand, compared to
former smokers, current smokers showed a decreased activity
in higher-order cortical areas in favor of subcortical regions
(Nestor et al., 2011). On the other hand, former smokers
showed an opposite pattern, with increased prefrontal cortical
activity. The authors interpreted such findings as a tendency of
former smokers to adopt more top-down brain strategies when
facing smoke-related stimuli (Tuma and Pratt, 1982). Taken all
together, data about the presence of AB in former smokers are
still poorly supported and not convergent (Field et al., 2014;
Rehme et al., 2018).
According to this background, the primary aim of the current
study is to investigate the presence of the AB in former smokers
and to compare this effect on a sample of current smokers. In
particular, we were interested in former and current smokers
with a long history of cigarette smoking and, in the case of former
smokers, with consolidated abstinence (being abstinence for at
least 1 year). Although our main aim is to compare smokers and
former smokers, we also collected data on a sample of people
who have never smoked, so to have a benchmark for the variables
we used.
We defined AB as a significant increase in response time to
SC during the Emotional Smoke Stroop task with respect to the
response time to color coherent (baseline) condition. Therefore,
we hypothesized that the effect of the AB, i.e., the smoking-
related latency, is higher in smokers than former smokers, while
non-smokers should not be affected by SC, showing latencies
similar to the coherent condition.
We also expected to find differences with regard to implicit
processes. In fact, the AB may be considered a measure of
cognitive implicit processes (Kakoschke et al., 2017), thus
involving not only selective attention but also inhibitory control.
Inhibitory control is a core component of executive functioning
and is defined as the ability to inhibit a motor response that
has already been initiated, the ability to suppress interfering
stimuli, impulsiveness as well as approach and avoidant attitudes
(Everitt and Robbins, 2005). Consequently, it is possible to
assume that smoking addiction is generally sustained by implicit
appetitive processes potentially triggered by smoking cues even
when they are outside awareness. We then hypothesized that
people with low inhibitory control (as measured by Go/no-go
task) might be particularly affected by a bias toward smoking
cues (de Wit, 2009). Finally, we wanted to test if impulsivity and
approach attitudes could modulate the AB intensity. Although
there are contradictory data (Coskunpinar and Cyders, 2013),
we hypothesized that more impulsive individuals are particularly
affected by AB.
Two-hundred and forty-five participants (male 50.6%; female
49.4%) aged 51.79 (SD = 6.258, range = 34–63) were
recruited. The sample of the present study consisted of
98 current smokers (40%), 102 former smokers (41.7%) and 45
non-smokers (18.3%).
Smokers and former smokers were recruited within the
participants at the Continuous Observation of SMOking Subjects
I (COSMOS I), a screening program for early detection of
lung cancer using a low-dose computed tomography (CT) scan,
run at the European Institute of Oncology (IEO) in Milan,
Italy. Detailed information about COSMOS I protocol has
been published elsewhere (Veronesi et al., 2008). Participants
were contacted by a researcher, who described the study and
made the first interview. The informed consent form was
then provided with full details. In case of acceptance, data
recording was run at the hospital, after the CT in order to
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Masiero et al. Attentional Bias and Cigarette Smoking
reduce possible confounding variables due to anxiety, fear
or rush.
Non-smokers were recruited through advertising on the
Hospital and University sites and with the collaboration of the
IRIDe—Interdisciplinary Research Centre on Decision-making
research of the University of Milan. Non-smoker participants
were first contacted by phone and invited for the first interview
at an IRIDe office. Then, the study was presented, and full details
about the experimental setting provided. Data collection was
completed in a second meeting.
Inclusion criteria for smokers and former smokers were: being
enrolled at the COSMOS I program; not having a history of
neurological and psychiatric diseases; having smoked for more
than 10 years at least 10 cigarettes a day and/or being abstinence
at least from 1 year; agreed to sign the informed consent. With
regard to non-smokers: no history of neurological or psychiatric
conditions; no history of antismoking interventions. Participants
were volunteers and they could withdraw their consent at any
time during the study.
The Ethical Committee of the European Institute of Oncology
(IEO) approved the study. All enrolled participants read, filled
in and signed the informed consent form. The study was in
accordance with the principles stated in the Declaration of
Helsinki (59th WMA General Assembly, Seoul, 2008).
Data collection consisted in two consecutive phases. In the
first one, in order to obtain a psycho-cognitive profile, each
participant received a set of article and pencil questionnaires to
be carefully read and filled out in a quiet room. Overall, the
mean time required to complete questionnaires was about 20 min
per participant. Then, in the second phase, a computerized
short neuropsychological battery was delivered by the use of the
Millisecond Inquisit Lab software (version 4.0). Participants had
the time to familiarize with the software for 5 min. A further
familiarization period was also provided before every single test
within the battery. At the end of the assessment, each participant
took part in a debriefing section, during which smoking cessation
strategies were also discussed (about 15 min).
Behavioral Inhibition System/Behavioral Approach System
(BIS/BAS) Questionnaire
A 20-item self-administered questionnaire assessing the affective
reaction to punishment and reward. Answers were assessed using
a five-point Likert scale (from 1 = ‘‘it does not describe me
at all’’ to 5 = ‘‘it describes me completely’’). The questionnaire
includes six subscales: Behavioral inhibition system (BIS);
Behavioral activation system (BAS); Drive; Fun seeking; Reward
responsiveness (Carver and White, 1994). BIS/BAS is a valid
and reliable measure with Cronbach alpha ranging from 0.67 to
0.84 in several studies (e.g., Leone et al., 2001; Cerutti et al., 2012).
Fagerström Test for Nicotine Dependence (FTND)
A 6-item self-administered questionnaire assessing nicotine
dependence. The score range is from 0 to 10 points. It includes
four categories: low dependence (0–2); middle (3–4); strong
(5–6); very strong (7–10; Heatherton et al., 1991). The Italian
version was previously validated (Fekketich et al., 2008).
Barratt Impulsiveness Scale (BIS-11)
A 30-item self-administered questionnaire that assesses
impulsiveness trait on 4-point scales (never, occasionally,
often, and always). It is made by three subscales: attentional
impulsiveness, motor impulsiveness, and non-planning
impulsiveness. Higher values indicate higher impulsivity
(Patton et al., 1995). The Cronbach’s alpha of the Italian version
was found to be 0.79, while a 2-month test-retest reliability
coefficient was 0.89 (Fossati et al., 2001).
Motivational Questionnaire
A 4-item self-administered questionnaire aimed at assessing
motivation to quit. The total score classifies the patient into 1 out
of 4 motivational categories (from ‘‘not ready to quit’’ to ‘‘highly
motivated’’). Higher values are suggestive of higher motivation
(Marino, 2002).
Neuropsychological Measurements
Go/no-Go Task
This task measures inhibition and response control (Costantini
and Hoving, 1973). Participants face stimuli on which they have
to take a binary decision (go or no-go). In the version we
used, participants were asked to press the spacebar when they
saw a green rectangle (go), but to refrain from pressing the
spacebar when they saw a blue rectangle (no-go). The blue and
green rectangles could be vertical or horizontal with different
probabilities of being green or blue. The vertical rectangle had
a high probability (805) of being green (a go trial) and the
horizontal rectangle had a high probability (80%) of being blue
(a no-go trial). Participants get information about the orientation
of the rectangle (cue) shortly before the color of the rectangle
is revealed. In this way, subjects must overcome the acquired
go response in order to inhibit the response if a no-go target is
subsequently displayed (Fillmore et al., 2006). An equal number
of vertical and horizontal cues were presented before an equal
number of go and no-go target stimuli.
The task measure failure of response inhibition (the
proportion of no-go targets in which a subject failed to inhibit
a response) and speed of response execution (reaction times to
go targets).
Emotional Smoke Stroop Task
This task was adapted from the original version of the Stroop
Task (Stroop, 1992) in order to measure AB in smokers. Each
participant was asked to name the ink color of the word that
appears on the PC monitor, neglecting the semantic content of
the word. The task includes four categories of cues: SC (e.g.,
tobacco, nicotine, tar, package, cigarette, smoking, filter, asthma,
cancer, tobacconist, breath, pollution, bronchitis, coffee, lung,
bad habit, dependence, cigar), neutral cues (NC; e.g., butterfly,
computer, wood, dream, book, vase, bed, flower, iron, pizza, dish,
food, spider, water, hot, table, box, door, rug, alcohol) congruent
color and incongruent color words (Waters et al., 2003a,b). The
emotional cues will be mentioned as ‘‘smoking-related’’ cues.
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Masiero et al. Attentional Bias and Cigarette Smoking
Statistical Analysis
Descriptive statistics were used to describe the sample’s
characteristics. The AB was calculated using the latency time
between the presentation of the stimulus and the verbalization
of the ink color for SC, NC, congruent color words (CC), and
incongruent color words (IC). Inhibition was instead assessed
using reaction times and the number of errors in the Go/No
Go task.
More specifically, in order to avoid biases due to the different
sample sizes, we ran a one-way analysis of variance (ANOVA)
using smoking status (current smokers, former smokers, and
non-smokers) as a fixed factor and latencies at smoking-
related words as a dependent variable. Since we defined AB
as the difference between the response latency to a SC and
the latency at the congruent stimuli (CC), we computed a new
variable (AB) subtracting the mean latency at CC from SC.
Therefore, a second one-way ANOVA test was run on this
variable. When the interaction between variables was considered
of interest, mixed design ANOVAs were used to compare the
latencies at different tasks between groups (current smokers,
former smokers). Bonferroni corrections for multiple paired
comparisons were applied.
The Pearson coefficient was used in order to assess the
association between impulsiveness, BAS and BIS, nicotine
dependence, the number of cigarettes per day, and the age
of the first cigarette. Bonferroni correction for the pvalues
was used also for correlations. Finally, two linear regressions
were performed for current smokers and former smokers. In
the first linear regression for current smokers, AB index was
used as the criterion variable, while impulsiveness, years of
smoking, number of daily cigarettes, dependence level, BIS/BAS
subscales and age as predictors. In the second linear regression
for former smokers, AB index was included as criterion variable
and impulsiveness, number of years as smokers, number of years
from the interruption, BIS/BAS subscales, and age as predictors.
All the analyses were performed using the SPSS package (version
23.0, IBM, USA, 2014).
Smoking characteristics of current and former smokers are
reported in Table 1, while Table 2 shows BIS/BAS and BIS-11
scores. Considering both current and former smokers, the mean
number of cigarettes smoked per day was 21.82 (SD = 12.03,
range = 1–80), the mean number of years of regular smoking was
36.56 (SD = 14.52) and the mean age of the first cigarette was
21.72 (SD = 8.29, range = 6–59).
An ANOVA test was run to compare BIS/BAS and BIS-11
among groups. Current smokers resulted lower in reward
responsiveness (F(2,244) = 3.101, p= 0.0041), with no further
differences in BIS, BAS, and BIS-11 dimensions.
We considered response inhibition as the main dependent
variable here, so we focused on reaction times and failure
of response inhibition (false alarm) calculated as the number
of errors on No-Go trials divided by the total number of
No-Go trials.
No differences between the three groups were found.
In particular, the false alarm rate was 0.443 for current
smokers, 0.395 for former smokers and 0.492 for non-smokers
(F(2,244) = 0.722, p= 0.495). The mean latency was 677.12 ms for
TABLE 1 | Mean standard deviation values and analysis of variance (ANOVA) p-values of participants’ characteristics.
Current smokers Former smokers
Descriptive statistics MSD MSD p
Daily cigarettes18.745 10.521 24.434 12.643 0.011
The age of the first cigarette 20.79 8.561 22.46 8.054 0.126
Number of years of regular smoking 41.653 9.173 33.781 16.166 0.211
Number of the years from the interruption 7.42 5.525
Nicotine dependence level 5.23 3.213
Motivational level 10.74 3.334
The values of mean and standard deviation for former smokers refer to their past smoking history.
TABLE 2 | Mean values, standard deviations and ANOVA p-values of participants’ impulsiveness and activation/inhibition.
smokers Former smokers Non-smokers
Descriptive statistics MSD MSD MSD p
Attentional impulsiveness 15.69 3.24 15.77 3.776 15.66 3.153 0.890
Motor impulsiveness 19.71 3.974 19.83 5.018 19.16 2.838 0.750
Non-planning impulsiveness 25.36 4.863 24.5 5.421 25.02 5.438 0.155
Total score 60.76 9.258 60.1 11.974 59.84 8.927 0.455
Bis 21.35 5.134 22.76 4.454 23.42 5.544 0.380
Bas 39.37 9.147 42.31 8.176 42.58 7.554 0.115
Reward responsiveness 18.684.313 19.75 3.372 20.46 3.53 0.137
Drive 10.89 3.618 12.04 3.368 11.84 2.874 0.112
Fun seeking 9.89 3.733 10.52 3.545 10.28 3.654 0.421
Note: Go/no-Go task (latencies and errors), p<0.05.
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Masiero et al. Attentional Bias and Cigarette Smoking
FIGURE 1 | Latency mean values of each group at the Emotional Smoke
Stroop Task.
current smokers, 683.27 ms for former smokers and 685.84 ms
for non-smokers (F(2,244) = 0.525, p= 0.594).
Emotional Smoke Stroop Task Latency
First, we computed descriptive statistics on response latencies
(see Figure 1).
We first ran one-way ANOVAs on latencies (dependent
variable) at each Stroop condition (CC, IC, SC, NC) to test
differences between current smokers, former smokers, and non-
smokers (fixed factor). We found a significant difference between
groups only for the smoking cues (F(2,244) = 3.822, p<0.029).
Bonferroni pairwise comparisons showed current smokers and
former smokers to be significantly slower than non-smokers
(respectively, p<0.031 and p<0.042). The other ANOVAs did
not report any significant effects.
A further one-way ANOVA was run using the attentional bias
index (AB) as dependent variable and smoking status as fixed
factor (current smokers, former smokers, and non-smokers). The
difference between the smoking status groups was significant
(F(2,244) = 8.561, p<0.000). In particular, Bonferroni post hoc
analysis revealed that non-smokers were significantly faster than
former-smokers (p<0.002) and current smokers (p<0.001).
Finally, a mixed-design ANOVA was run using stimulus
(IC, CC, SC, NC) as within factor and smoking status
(current smokers and former smokers) as between factor.
As expected, we found a significant difference for the main
effect stimulus (F(1,244) = 93.053, p<0.000), while the
interaction stimulus ×smoking status was not significant
(F(2,244) = 0.844, p= 0.470).
Correlational Analysis
Current smokers’ response latency during the Emotional Smoke
Stroop Task positively correlates with total impulsiveness,
but this not true for former smokers and non-smokers. The
association between response latency and impulsiveness in
smokers is mainly explained by the motor impulsiveness (see
Table 3): current smokers’ motor impulsiveness is positively
associated with the response latency for incongruent words
(p= 0.011), smoking-related words (p= 0.019), and neutral
words (p= 0.025). Finally, no-planning impulsiveness correlates
with NC (p= 0.029), while there is a negative correlation between
NC and reward responsiveness in the BIS/BAS (p= 0.038). No
statistical correlations were observed for former smokers and
non-smokers (see Table 3).
Current smokers showed a positive correlation between
total impulsiveness and number of false alarms (p= 0.025)
and a negative correlation between total impulsiveness and
response latency (p= 0.016). In addition, former smokers showed
a positive correlation between the BAS and the number of
false alarms (p<0.001), and a negative correlation between
total impulsiveness and response latency (p= 0.012). Other
correlations were reported for singular subscales of BIS-11 and
BIS-BAS in Table 4.
No statistical correlations were observed for non-smokers.
Finally, we performed a hierarchical regression model to
test if the AB effect might be predicted by some psychological
and/or behavioral variable. In details, we used the smoking status
(current vs. former smokers) as dummy variable and years of
smoking, number of daily cigarettes, in the first block, and then
we added impulsivity in the second block and BIS/BAS subscale
in the third. We found that the smoking-related AB was not
predicted by any of the variables considered.
According to the Incentive Salience theory of addiction and
PC theory, stimuli associated with tobacco cigarette smoking
acquire high approach value (Robinson and Berridge, 1993). The
increased salience of such stimuli results in an attentional bias,
which may initiate cravings, urgency and substance use. The
attentional bias has also been shown to predict relapse better
than self-reports measures and other indexes (Cox et al., 2002;
Waters et al., 2003a,b).
Coherently with this background, our findings confirm the
presence of a bias toward smoking cues in current tobacco
cigarette smokers. However, we also found a similar effect
in former smokers and this evidence contrasts with some
of the previous works (Bradley et al., 2003; Munafò et al.,
2003; Waters et al., 2003a). Actually, there is still poor and
contradictory evidence about AB in former smokers: the few
studies present in literature used different methods to measure
it (for example, Emotional Stroop task, Visual Probe task, and
eye movements monitoring) and the samples size were generally
small. Consequently, data are not always comparable, while
some studies are not robust enough to support experimental
hypotheses. Furthermore, Field et al. (2014) affirmed that
while AB is a strong predictor of relapse during the short
period after the interruption, its impact is unpredictable in the
long term.
Our results suggest that former smokers’ attention might be
modulated by smoking cues similarly to current smokers. Since
the enrolled former smokers were abstinent on a long-term
basis, the present study suggests that the AB persists and might
influence cognitive processing also long after they stopped
smoking. The presence of this bias might interfere with the
ability to remain abstinent particularly in a stressful situation,
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Masiero et al. Attentional Bias and Cigarette Smoking
TABLE 3 | Correlations’ value (r, p) between BIS-11, BIS-BAS and response latencies in Emotional Smoke Stroop Task.
Smokers Former smokers Non-smokers
Total score 0.155 0.2230.2550.2370.065 0.049 0.022 0.044 0.124 0.091 0.042 0.022
Non-planning impulsiveness 0.090 0.096 0.156 0.2080.032 0.052 0.021 0.037 0.048 0.153 0.023 0.048
Motor impulsiveness 0.139 0.2870.2850.2360.108 0.161 0.110 0.139 0.262 0.131 0.182 0.215
Attentional impulsiveness 0.110 0.202 0.202 0.157 0.029 0.015 0.105 0.077 0.138 0.020 0.030 0.028
Drive 0.055 0.037 0.070 0.035 0.017 0.033 0.093 0.114 0.018 0.017 0.049 0.045
Fun seeking 0.016 0.061 0.005 0.082 0.064 0.017 0.009 0.006 0.118 0.095 0.023 0.063
Reward responsiveness 0.076 0.053 0.092 0.2010.107 0.123 0.086 0.068 0.030 0.050 0.043 0.035
BIS 0.034 0.180 0.127 0.121 0.044 0.015 0.021 0.012 0.031 0.109 0.088 0.028
BAS 0.016 0.024 0.010 0.150 0.083 0.085 0.034 0.010 0.080 0.142 0.046 0.024
p<0.05 (Bonferroni correction applied). CC, Congruent cues; IC, Incongruent cues; SC, Smoking cues; NC, Neutral cues.
TABLE 4 | Correlations coefficient between BIS-11, BIS/BAS and response latency and the number of false alarms in Go/no Go Task.
Current smokers Former smokers Non-smokers
Go/no go Task False alarms Latency False alarms Latency False alarms Latency
Total score 0.2080.3760.087 0.3690.058 0.063
Non-planning impulsiveness 0.259∗∗ 0.3600.007 0.277 0.103 0.067
Motor impulsiveness 0.2010.214 0.077 0.3760.128 0.061
Attentional impulsiveness 0.049 0.278 0.165 0.242 0.067 0.122
Drive 0.025 0.227 0.133 0.229 0.243 0.277
Fun seeking 0.063 0.236 0.125 0.141 0.103 0.056
Reward responsiveness 0.014 0.3490.2020.253 0.281 0.001
BIS 0.052 0.107 0.104 0.072 0.284 0.041
BAS 0.083 0.318 0.2520.319 0.173 0.085
p<0.05, ∗∗p<0.01.
as reported in other substances users (Field and Powell, 2007).
Actually, different studies described the role of stress in tobacco
smoking relapse as well, since negative affect, stress, and arguing
with other people are often reported before they start smoking
again (Marlatt and George, 1984; Baker et al., 2004; Shiffman
and Waters, 2004). We argue that the AB might interact with
negative emotions in promoting the desire and/or the urgency
to smoke.
Furthermore, our study participants, independent of their
smoking status (current, former or non-smokers) reported
similar performance at the standard Stroop task and at the
Go/no-Go task. Consequently, we may assume that the AB
found in current and former smokers was not due to the
impairment of general cognitive control functions, such as
inhibition mechanisms. Previous studies on alcohol and cocaine
abusers showed that there is an impairment effect of drugs on
inhibition and that this effect is detectable at doses that do not
lead to a global impairment in cognitive performance at a Go/no-
Go tasks (Lane et al., 2007; Verdejo-García et al., 2007). Besides,
physiological, motivation and attentional mechanisms seem to
be interdependent, so that they all modulate the psychological
value of a drug-cue (Kakoschke et al., 2017). In our study, the
power of cigarettes to interfere with the cognitive processing
seems to be associated only with smoking cues, which might be
effective in increasing directly the power to grab the attention
or by inducing inhibitory control failure. In particular, the
performance at the Go/no-Go task did not suggest any general
impairment of inhibition mechanisms in current and former
smokers as remarked above. However, since deficient inhibitory
control may also be considered a component of impulsivity
(de Wit, 2009), we could expect some correlations between
impulsivity level, cognitive mechanisms, and cigarette smoking.
Actually, we found that current smokers present a significant
correlation between impulsivity levels and performance at the
Go/No-Go task. This evidence might suggest that smoking cues
may reduce the ability of smokers to inhibit their responses in
specific contexts. Although we did not find AB to be associated
with impulsivity dimensions, our results about the relationship
between impulsivity and inhibitory control in current smokers
might suggest that the effect of the AB on craving, reported by
previous studies (Grant et al., 1996; Hester et al., 2006; Ferguson
and Shiffman, 2009), may be due both to the increased power of
some stimuli to attract information and the inability of smokers
to inhibit the responses. These results are coherent with other
studies on cocaine, which reported that cocaine users with poor
inhibitory control had also an intense bias toward cocaine-related
words on the Emotional Stroop task (Liu et al., 2011). Since
we found impulsiveness to be positively generally correlated to
latencies at our Emotional Smoke Stroop Task, we might suggest
that there is an association between AB and inhibition control
deficit in tobacco smokers too. Consequently, our data suggest
that impulsivity and AB are not directly associated, but that there
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Masiero et al. Attentional Bias and Cigarette Smoking
might be a more complex relationship mediated by inhibitory
control mechanisms.
More generally, we did not find any differences between
current smokers, former smokers and non-smokers with respect
to impulsiveness and BIS/BAS traits. Actually, previous studies
failed to provide convergent data about this association (Doran
et al., 2004). Some studies reported current smokers to be
more impulsive, novelty seekers and less inhibited, suggesting
that personality characteristics might be more important than
other variables (Flory and Manuck, 2009). However, our results
do not support this view. A possible explanation is that we
collected data on a quite old sample made by smokers with
a long history of smoking (having smoked for more than
10 years). Probably, persistence in smoking is not a function
of impulsiveness, depending on other variables that sustain the
smoking habit, often perceived as a safe and ‘‘natural’’ part
of smokers’ lifestyle (Masiero et al., 2015). Persistence and
dependence may be differently modulated by impulsiveness in
younger smokers (Smith, 2017). This datum is further confirmed
by the fact that neither the BIS/BAS scale was able to distinguish
current from former smokers and non-smokers. Our participants
have a similar approach and avoidant attitudes, and thus our
data do not support the idea that smoking behavior is linked
to a physiological predisposition to the search of gratification.
In particular, impulsiveness and other induvial characteristics
might be considered only as modulators of smoking behavior,
since cognitive biases may activate the smokers’ wanting system
independently of personality traits (Benowitz, 2010). Finally,
we did not find any association between AB intensity and
participants’ characteristics. In fact, AB was not associated
with dependence level, number of daily cigarettes or years
of abstinence.
From a clinical point of view, the present findings have
important implications, suggesting that particular attention
should be given to all cognitive mechanisms that sustain
smoking, instead of focusing only on personality traits
(Gorini et al., 2012; Gilardi et al., 2014; Masiero et al.,
2016). This is particularly true for older smokers, who
might want to stop smoking without being able to contrast
the environmental solicitations. Furthermore, former smokers
should be advised about the potentially detrimental effects of
cognitive biases, providing them also behavioral strategies to
counterbalance the correlated effects. Thus, it is important to
take into account AB and more in general implicit cognitive
measures (including working memory and executive function)
in order to predict the success of treatment and/or to
create tailored interventions (Wiers et al., 2002; Stacy and
Wiers, 2010). In this vein, attention bias modification to
avoid smoking-related stimuli might be a good strategy to
help smokers quit, but further studies are needed to assess
and to better define how to integrate it in clinical practice
(Lopes et al., 2014).
A series of constraints limit the generalization of our results.
First, our sample was not balanced as we had only a small
group of non-smokers. Thus, the interpretation of our data, in
particular, with regard to differences in impulsivity and BIS/BAS
measures should be taken with caution. Second, we used as
a measure of the AB a cognitive task, the Emotional Smoke
Stroop task that cannot be considered particularly ecological
and does not have strong internal reliability (Ataya et al., 2012;
Field et al., 2014). In fact, Shiffman et al. (2015) affirmed that
this as well other similar tasks are not particularly consistent,
and that laboratory measures do not always correlate with
the actual behavior in everyday life (Shiffman et al., 2015) so
that more ecological methods are needed. For example, the
ecological momentary assessment (e.g., diaries), which measure
real-time data in the natural environment, might be considered
a more accurate measure of affect, craving and other aspects of
smoking, also able to predict smoking relapse during abstinence
(McCarthy et al., 2006; Bujarski et al., 2015).
However, we argue that data coming from different methods
and settings might help to further advance our knowledge of
implicit cognition in tobacco cigarette smokers and the impact
of this aspect on smoking’s trajectory (initiation, maintain and
relapse). Consequently, we believe that research on tobacco
smoking needs a wide-range approach.
The last concern is linked to the fact that having only
behavioral data, we cannot support our finding from a neuro-
functional point of view. Consequently, some of the issues raised
are speculative. However, behavioral and neuroscientific data
need to be integrated within a common and sound neuro-
cognitive model, so to provide effective data both to researchers
and health professionals. For these reasons, we believe that the
research about AB should follow parallel pathways, without
discarding data sources that proved to be useful, even though
they are by their nature imperfect.
In conclusion, our results suggest and support future research,
also integrating and advancing previous evidence that reported
partial and contrasting data on small samples and on recent or
even very recent former smokers.
The Ethical Committee of the European Institute of Oncology
approved the study. All enrolled participants were provided with
full details about the study. All participants complied and signed
the informed consent form. The study was in accordance with
the principles stated in the Declaration of Helsinki (59th WMA
General Assembly, Seoul, 2008).
GP, CL, MM, PM, and GV conceived and designed the study. GP
coordinated the study. CL and GP acquired legal authorizations.
MM managed the participants. Statistical analysis was performed
by CL. Drafting and writing of the manuscript were handled by
CL, MM, KM, and GP. All authors have read and approved the
final manuscript.
The research was partially funded by a grant of Fondazione
Umberto Veronesi.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Masiero, Lucchiari, Maisonneuve, Pravettoni, Veronesi and
Mazzocco. This is an open-access article distributed under the terms of the Creative
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Frontiers in Behavioral Neuroscience | 11 July 2019 | Volume 13 | Article 154
... Dinn, Aycicegi, & Harris, 2004;Reynolds et al., 2007;however, cf. Billieux et al., 2010;Masiero et al., 2019). Wilson and MacLean (2013) observed a negative correlation between nicotine dependence and self-control. ...
Introduction Substance use causes attentional biases for substance-related stimuli. Both bottom-up (preferential processing) and top-down (inhibitory control) processes are involved in attentional biases. We explored these aspects of attentional bias by using dependent and non-dependent cigarette smokers in order to see whether these two groups would differ in terms of general inhibitory control, bottom-up attentional bias, and top-down attentional biases. This enables us to see whether consumption behaviour would affect these cognitive responses to smoking-related stimuli. Methods Smokers were categorised as either dependent (N=26) or non-dependent (N=34) smokers. A further group of non-smokers (N=32) were recruited to act as controls. Participants then completed a behavioural inhibition task with general stimuli, a smoking-related eye tracking version of the dot-probe task, and an eye-tracking inhibition task with smoking-related stimuli. Results Results indicated that dependent smokers had decreased inhibition and increased attentional bias for smoking-related stimuli (and not control stimuli). By contrast, a decreased inhibition for smoking-related stimuli (in comparison to control stimuli) was not observed for non-dependent smokers. Conclusions Preferential processing of substance-related stimuli may indicate usage of a substance, whereas poor inhibitory control for substance-related stimuli may only emerge if dependence develops. The results suggest that how people engage with substance abuse is important for top-down attentional biases.
... 14,15 ABs to cigarette-related cues have been observed in a variety of tasks, including eye-tracking tasks, and in some studies, an AB is present among former smokers. 16 Previous studies have also identified several factors that are associated with heightened AB to cigarette-related stimuli, including greater severity of nicotine dependence, CO concentration, abstinence, nicotine deprivation, and craving. 14,17 Given the presence of ABs in tobacco users, one would predict that e-cigarette users would exhibit an AB to e-cigarette-related cues. ...
Introduction: This study examined attentional bias (AB) to e-cigarette cues among a sample of non-smoking daily e-cigarette users (n = 27), non-smoking occasional e-cigarette users (n = 32), and control participants (n = 61) who did not smoke or use e-cigarettes. The possibility that e-cigarette users develop a transference of cues to traditional cigarettes was also examined. Methods: AB was assessed using a free-viewing eye-gaze tracking methodology, in which participants viewed 180 pairs of images for 4 seconds (e-cigarette and neutral image, e-cigarette and smoking image, smoking and neutral image). Results: Daily and occasional e-cigarette users attended to pairs of e-cigarette and neutral images equally, whereas non-users attended to neutral images significantly more than e-cigarette images. All three groups attended to e-cigarette images significantly more than smoking images, with significantly larger biases for e-cigarette users. There were no between-group differences in attention to pairs of smoking and neutral images. A moderation analysis indicated that for occasional users but not daily users, years of vaping reduced the bias toward neutral images over smoking images. Conclusions: Taken together, the results indicate that e-cigarette users exhibit heighted attention to e-cigarettes relative to non-users, which may have implications as to how they react to e-cigarette cues in real-world settings. AB for e-cigarettes did not transfer to traditional cigarette cues, which indicates that further research is required to identify the mechanisms involved in the migration of e-cigarettes to traditional cigarettes. Implications: This study is the first attempt to examine attentional biases for e-cigarette cues among non-smoking current e-cigarette users using eye-gaze tracking. The results contribute to the growing literature on the correlates of problematic e-cigarette use and indicate that daily and occasional e-cigarette use is associated with attentional biases for e-cigarettes. The existence of attentional biases in e-cigarette users may help to explain the high rate of failure to quit e-cigarettes and provides support for the utility of attentional bias modification in the treatment of problematic e-cigarette use.
Introduction Attentional bias (AB) is an individual difference risk factor that represents the extent to which cigarette cues capture one’s attention. AB is typically indexed by mean bias score (MBS), theoretically assuming that AB is static. However, poor reliability of MBS has threatened valid interpretation of the results on AB. Based on observed trial-by-trial temporal fluctuation and variability of attentional allocation, trial-level bias score (TLBS) has been introduced as an alternative index with evidence of better psychometric properties in various populations, as compared to MBS. However, such evidence is limited among daily smokers. The current study aimed to replicate and extend extant findings in a sample of daily smokers by hypothesizing that TLBS, as compared to MBS, would demonstrate superior reliability and external validity. Methods Forty-eight daily smokers completed self-reports, ad-libitum smoking, and a dot-probe task three times, which was comprised of 36 pairs of pictorial stimuli of cigarette and neutral cues, yielding 144 total trials. Results The TLBS demonstrated superior internal (range intra class correlation [ICC] = .79 - .95) and test-retest reliability (range ICC = .64 - .88) compared to MBS (range ICC = .31 - .40 and .06 - .16, respectively). However, few significant relations between either the MBS or TLBS and measures of biobehavioral and self-report indices of smoking reinforcement were observed. Conclusions The current findings demonstrate that TLBS, as compared to MBS, is a more reliable measure of AB among daily smokers, while evidence of its external validity is limited.
Full-text available
This opinion article provides a synthetic overview of biases of reasoning and decision making in chronic illness management, ranging from the cognitive biases related to information processing (attentional bias, interpretation bias, and recall bias) to distortions in self-perception and social groups’ influence. Secondarily, the manuscript addresses the hypothesis that biases in chronic illness do not only impact quality of life, but also patients’ commitment to their own health management. Specifically, the systematic, repeated influence of cognitive biases may be associated with a “vicious circle” that reduces patients’ motivation to recognize and address the same mental health issues that influence their decision making. This idea is briefly discussed, suggesting that future research considers the relationship between biases in decision making and factors relevant to patient engagement. This information could be relevant to the development of psychological support interventions for chronic patients that focus on cognitive components of their healthcare journey.
Full-text available
Although extensive evidence exists for the reinforcing properties of drugs of abuse such as cocaine, relatively less research has addressed the functional neuroanatomical correlates of the cognitive sequelae of these drugs. We present a functional magnetic resonance imaging study of a GO-NOGO task in which successful performance required prepotent behaviors to be inhibited. Significant cingulate, pre-supplementary motor and insula hypoactivity was observed for both successful NOGOs and errors of commission in chronic cocaine users relative to cocaine-naive controls. This attenuated response, in the presence of comparable activation levels in other task-related cortical areas, suggests cortical and psychological specificity in the locus of drug abuse-related cognitive dysfunction. The results suggest that addiction may be accompanied by a disruption of brain structures critical for the higher-order, cognitive control of behavior.
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The nucleus accumbens shell is a site of converging inputs during memory processing for emotional events. The accumbens receives input from the nucleus of the solitary tract (NTS) regarding changes in peripheral autonomic functioning following emotional arousal. The shell also receives input from the amygdala and hippocampus regarding affective and contextual attributes of new learning experiences. The successful encoding of affect or context is facilitated by activating noradrenergic systems in either the amygdala or hippocampus. Recent findings indicate that memory enhancement produced by activating NTS neurons, is attenuated by suppressing accumbens functioning after learning. This finding illustrates the significance of the shell in integrating information from the periphery to modulate memory for arousing events. However, it is not known if the accumbens shell plays an equally important role in consolidating information that is initially processed in the amygdala and hippocampus. The present study determined if the convergence of inputs from these limbic regions within the nucleus accumbens contributes to successful encoding of emotional events into memory. Male Sprague-Dawley rats received bilateral cannula implants 2 mm above the accumbens shell and a second bilateral implant 2 mm above either the amygdala or hippocampus. The subjects were trained for 6 days to drink from a water spout. On day 7, a 0.35 mA footshock was initiated as the rat approached the spout and was terminated once the rat escaped into a white compartment. Subjects were then given intra-amygdala or hippocampal infusions of PBS or a dose of norepinephrine (0.2 μg) previously shown to enhance memory. Later, all subjects were given intra-accumbens infusion of muscimol to functionally inactivate the shell. Muscimol inactivation of the accumbens shell was delayed to allow sufficient time for norepinephrine to activate intracellular cascades that lead to long-term synaptic modifications involved in forming new memories. Results show that memory improvement produced by infusing norepinephrine in either the amygdala or hippocampus is attenuated by interrupting neuronal activity in the shell 1 or 7 7 h following amygdala or hippocampus activation. These findings suggest that the accumbens shell plays an integral role modulating information initially processed by the amygdala and hippocampus following exposure to emotionally arousing events. Additionally, results demonstrate that the accumbens is involved in the long-term consolidation processes lasting over 7 h.
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Repeated drug use modifies the emotional and cognitive processing of drug-associated cues. These changes are supposed to persist even after prolonged abstinence. Several studies demonstrated that smoking cues selectively attract the attention of smokers, but empirical evidence for such an attentional bias among successful quitters is inconclusive. Here, we investigated whether attentional biases persist after smoking cessation. Thirty-eight former smokers, 34 current smokers, and 29 non-smokers participated in a single experimental session. We used three measures of attentional bias for smoking stimuli: A visual probe task with short (500. ms) and long (2000. ms) picture stimulus durations, and a modified Stroop task with smoking-related and neutral words. Former smokers and current smokers, as compared to non-smokers, showed an attentional bias in visual orienting to smoking pictures in the 500. ms condition of the visual probe task. The Stroop interference index of smoking words was negatively related to nicotine dependence in current smokers. Former smokers and mildly dependent smokers, as compared to non-smokers, showed increased interference by smoking words in the Stroop task. Neither current nor former smokers showed an attentional bias in maintained attention (2000. ms visual probe task). In conclusion, even after prolonged abstinence smoking cues retain incentive salience in former smokers, who differed from non-smokers on two attentional bias indices. Attentional biases in former smokers operate mainly in early involuntary rather than in controlled processing, and may represent a vulnerability factor for relapse. Therefore, smoking cessation programs should strengthen self-control abilities to prevent relapses.
Full-text available
The purpose of the present study was to revise the Barratt Impulsiveness Scale Version 10 (BIS-10), identify the factor structure of the items among normals, and compare their scores on the revised form (BIS-11) with psychiatric inpatients and prison inmates. The scale was administered to 412 college undergraduates, 248 psychiatric inpatients, and 73 male prison inmates. Exploratory principal components analysis of the items identified six primary factors and three second-order factors. The three second-order factors were labeled Attentional Impulsiveness, Motor Impulsiveness, and Nonplanning Impulsiveness. Two of the three second-order factors identified in the BIS-11 were consistent with those proposed by Barratt (1985), but no cognitive impulsiveness component was identified per se. The results of the present study suggest that the total score of the BIS-11 is an internally consistent measure of impulsiveness and has potential clinical utility for measuring impulsiveness among selected patient and inmate populations.
Introduction E-cigarettes may be positively used in tobacco cessation treatments. However, neither the World Health Organization nor the American Food and Drug Administration has recognized them as effective cessation aids. Data about the efficacy and safety of e-cigarettes are still limited and controversial. Methods This was a double-blind randomized controlled study. The main aim was to assess the efficacy of the use of e-cigarettes in a tobacco cessation program with a group of chronic smokers voluntarily involved in long-term lung cancer screening. Participants were randomized into three arms: e-cigarettes (Arm 1), placebo (Arm 2), and control (Arm 3). All subjects also received a low-intensity counseling. Results About 25% of participants who followed a cessation program based on the use of e-cigarettes (Arm 1 and Arm 2) were abstinent after 3 months. Conversely, only about 10% of smokers in Arm 3 stopped. Participants in Arm 1 also reported a higher reduction rate (M = −11.6441, SD = 7.574) than participants in Arm 2 (M = −10.7636, SD = 8.156) and Arm 3 (M = −9.1379, SD = 8.8127). Conclusions Our findings support the efficacy and safety of e-cigarettes in a short-term period. E-cigarettes use led to a higher cessation rate. Furthermore, although all participants reported a significant reduction of daily cigarette consumption compared to the baseline, the use of e-cigarettes (including those without nicotine) allowed smokers to achieve better results. Implications E-cigarettes increased the stopping rate as well as the reduction of daily cigarettes in participants who continued smoking. In fact, although all participants reported a significant reduction of tobacco consumption compared to the baseline, the use of e-cigarettes allowed smokers to achieve a better result. It could be worthwhile to associate this device with new ICT-driven models of self-management support in order to enable people to better handle behavioral changes and side effects. This is true for ready-to-quit smokers (such as our participants) but can also be advantageous for less motivated smokers engaged in clinical settings.
Background: Although there is considerable evidence of an association between impulsivity and cigarette smoking, the magnitude of this association varies across studies. Impulsivity comprises several discrete traits that may influence cigarette use in different ways. The present meta-analysis aims to examine the direction and magnitude of relationships between specific impulsivity-related traits, namely lack of premeditation, lack of perseverance, sensation seeking, negative urgency, positive urgency and reward sensitivity and both smoking status and severity of nicotine dependence in adults across studies and to delineate differences in effects across these relationships. Methods: Ninety-seven studies were meta-analysed using random-effects models to examine the relationship between impulsivity-related traits and smoking status and severity of nicotine dependence. A number of demographic and methodological variables were also assessed as potential moderators. Results: Smoking status and severity of nicotine dependence were significantly associated with all impulsivity-related traits except reward sensitivity. Lack of premeditation and positive urgency showed the largest associations with smoking status (r = 0.20, r = 0.24 respectively), while positive urgency showed the largest association with severity of nicotine dependence (r = 0.23). Study design moderated associations between lack of premeditation and lack of perseverance and smoking status, with larger effects found in cross-sectional compared to prospective studies. Conclusions: Finding suggest that impulsivity is associated with an increased likelihood of being a smoker and greater nicotine dependence. Specific impulsivity-related traits differentially relate to smoking status and severity of nicotine dependence. Understanding the complexity of impulsivity-related traits in relation to smoking can help to identify potential smokers and could inform cessation treatment.
Aims: To estimate predictors of time to smoking relapse and test if prediction varied by quit duration. Design: Longitudinal cohort data from the International Tobacco Control Four-Country survey with annual follow up collected between 2002 and 2015. Setting: Canada, US, UK and Australia. Participants: A total of 9,171 eligible adult smokers who made at least one quit attempt over the study period. Measurements: Time to relapse was the main outcome. Predictor variables included pre-quit baseline measures of nicotine dependence, smoking and quitting related motivations, quitting capacity, and social influence, and also two post-quit measures, use of stop-smoking medications and quit duration (1-7 days, 8-14 days, 15-31 days, 1-3 months, 3-6 months, 6-12 months, 1-2 years and 2+ years), along with socio-demographics. Findings: All factors were predictive of relapse within the first six months of quitting but only wanting to quit, quit intentions and number of friends who smoke were still predictive of relapse in the 6-12 months period of quitting (hazard ratios [HR]=1.20, p=.018; 1.13, p=.040; and 1.21, p<.001, respectively). Number of friends smoking was the only remaining predictor of relapse in the 1-2 years quit period (HR=1.19, p=.001) with none predictive beyond the 2 years quit period. Use of stop-smoking medications during quit attempts was negatively related to relapse in the first two weeks of quitting (HR=.71-.84) but positively related to relapse in the 1-6 months quit period (HR=1.29-1.54). Predictive effects of all factors showed significant interaction with quit duration except for perceiving smoking as an important part of life, prematurely stubbing out a cigarette and wanting to quit. Conclusions: Among adult smokers in the US, Canada, UK and Australia, factors associated with smoking relapse differ between the early and later stages of a quit attempt suggesting the determinants of relapse change as a function of abstinence duration.
Two experiments investigated attentional biases for smoking-related cues in smokers and nonsmokers, using the visual probe task. In Experiment 1, when pictures were displayed for 500 ms, smokers who had made repeated quit attempts showed an attentional bias for smoking-related scenes. Experiment 2 replicated this finding and revealed that when pictures were presented for 2,000 ms, the smoker group as a whole showed vigilance for smoking-related cues, but nonsmokers did not. The findings from the 500-ms exposure condition suggest that initial orienting of attention to smoking cues was associated with repeated unsuccessful attempts at abstinence in smokers. Results are discussed with reference to incentive-sensitization theories of addiction and to component processes of selective attention, such as initial orienting versus maintenance.
Considerable evidence has identified biased cognitive processing of alcohol-related stimuli as an important factor in the maintenance of alcohol-seeking and relapse among individuals suffering from alcohol use-disorders (AUDs). In addition, a large body of research has demonstrated that exposure to alcohol cues can elicit powerful alcohol cravings. Little is known, however, about the possible relationship between attentional bias and cue-induced cravings, and even less is known about these processes in social drinkers without a personal history of AUDs. The goal of this study was to examine the possibility that attentional biases toward alcohol-related stimuli would predict elevated cue-induced alcohol craving in this population. Young adult social drinkers (N = 30, Mean age = 22.8 ± 1.9, 61% female) recruited from an urban university population completed a visual dot probe task in which they were presented with alcohol and neutral stimulus pictures that were immediately followed by a visual probe replacing one of the pictures. Attentional bias was measured by calculating reaction times to probes that replaced alcohol stimuli vs. neutral stimuli. Participants then completed a classic alcohol cue-exposure task and reported cravings immediately before and after alcohol and neutral cue-exposures. Not surprisingly, exposure to alcohol cues elicited significant cravings. Consistent with the study hypothesis, larger attentional biases toward alcohol stimuli predicted higher levels of alcohol craving. Findings demonstrate that heightened attention to alcohol stimuli can significantly impact motivation to consume in healthy young adults, and suggest a possible pathway linking cognitive processes early in the drinking trajectory to the later development of AUDs.