Implicit and explicit reward learning in chronic nicotine use

Drug and Alcohol Dependence 129 (2013) 8–17

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Implicit and explicit reward learning in chronic nicotine use Yvonne Paelecke-Habermann ∗ , Marko Paelecke, Katharina Giegerich, Katja Reschke, Andrea Kübler Department of Psychology, Julius Maximilians University Würzburg, Germany

a r t i c l e

i n f o

Article history: Received 12 December 2011 Received in revised form 30 August 2012 Accepted 4 September 2012 Available online 23 October 2012 Keywords: Tobacco withdrawal Nicotine addiction Dependent and occasional use Implicit and explicit reward learning Impulsivity

a b s t r a c t Background: Chronic tobacco use is related to specific neurobiological alterations in the dopaminergic brain reward system that can be termed “reward deficiency syndrome” in dependent nicotine consumers. The close linkage of dopaminergic activity and reward learning led us to expect implicit and explicit reward learning deficits in dependent compared to non-smokers. Smokers who maintain a less regular, occasional use may also, to a lesser extent, show implicit reward learning deficits. The purpose of our study was to examine the behavioral effects of the neurobiological alterations on reward related learning. We also tested whether any deficits observed in an abstinent state are also present in a satiated state. Methods: In two studies, we examined implicit and explicit reward learning in smokers. Participants were administered a probabilistic implicit reward learning task, and an explicit reward- and punishment-based trial-and-error learning task. In Study 1, we compared dependent, occasional, and non-smokers, and in Study 2 satiated and abstinent smokers. Results: In Study 1, chronic and occasional smokers showed impairments in both, implicit and explicit reward learning tasks. In Study 2, satiated smokers did not perform better than abstinent smokers. Conclusions: The results support the hypothesis of reward learning deficits. These deficits are not limited to explicit but extend to implicit reward learning and cannot be explained by tobacco withdrawal. © 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

1.1. Reward learning in addiction

Nicotine is a highly addictive substance that leads to dependence faster and more often than many other drugs (O’Brien, 2001). One of the main causes for the high addictive potential of nicotine is its legality; another is its fast and indirect stimulation of the dopaminergic brain reward system (BRS). Particularly relevant BRS structures are the nucleus accumbens (NAc) and the ventral tegmental area (VTA; Di Chiara, 1992, 2002; Mao and McGhee, 2010). The incentive salience of nicotine and all its associated cues are boosted after the strong dopaminergic stimulation. Tobaccoassociated cues alone begin to reward or rather reinforce, and are therefore learned faster and are more intense (Balfour et al., 2000). At the same time, the incentive salience of alternative reinforcers decreases (Robinson and Berridge, 1993; Volkow et al., 2003), and learning with non-drug reinforcing stimuli is impaired (Bühler et al., 2010). The memory traces of nicotine-associated cues are often resistant to extinction and are recoverable even after many years of abstinence (Chiamulera et al., 1996).

Reward learning is not only relevant for nicotine dependence, but addiction in general. The Incentive Sensitization Theory by Robinson and Berridge (2000) emphasizes the role of different associative learning processes in addiction. Addiction emerges after drug-induced alterations in BRS circuitry and associated changes in motivational processes and associative learning. In their corresponding model of reward, Berridge and Robinson (2003) distinguish three components of reward: liking, wanting, and learning. Liking is the emotional component, whereas wanting is the incentive motivational component. The learning component purveys the ability to predict reward, and hence forms the basis of wanting. Berridge et al. (2009) further distinguish between different associative learning processes that can be classified as explicit vs. implicit. Based on these models, we expect that implicit and explicit reward learning processes play distinct roles during development and maintenance of addiction. With regard to their neuropsychology, implicit and explicit reward learning involve distinct neural circuits (Frank and Claus, 2006). Frank and Claus (2006) proposed that the dopaminergic basal ganglia (BG) system underlies implicit, context-dependent response initiation based on the relative probability of positive or negative outcomes, hence implicit reward-dependent learning. The dopaminergic activity in the BG determines whether a response is executed or inhibited, according to the contingencies of the response. This is a slow,

∗ Corresponding author at: Department of Psychology I, Julius Maximilians University Würzburg, Würzburg 97070, Germany. Tel.: +49 931 31 83757; fax: +49 931 31 83757. E-mail address: [email protected] (Y. Paelecke-Habermann). 0376-8716/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drugalcdep.2012.09.004

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implicit associative learning process, where a positive outcome promotes a behavior and a negative outcome inhibits a behavior. Explicit response selection based on anticipated rewards, however, requires a top-down control of the dopaminergic activity in the BG by the orbitofrontal cortex (OFC), which provides estimates of reinforcement magnitudes activated in working memory. This explicit system is successful at estimating the true expected value of reward-related decisions and is fast in switching behavior while changing reinforcement contingencies. Due to the nicotineinduced alterations in the BRS, including BG and OFC (Dagher et al., 2001; Volkow et al., 2002a,b; Fehr et al., 2008), implicit and explicit reward-related learning may be altered in nicotine addiction. 1.2. Previous research To our knowledge, there are no behavioral studies that separately examined implicit and explicit reward learning in nicotine addiction. A hint for alterations in implicit reward learning comes from animal studies. Using a Pavlovian discriminative approach, Olausson et al. (2003) found that repeated nicotine administration temporarily improves implicit reward related learning in rats. Besheer and Bevins (2003) demonstrated that abstinence phases during chronic nicotine administration lead to deficits in the conditioning of place preference, a classic implicit reward learning paradigm. There are a few studies on explicit reward processing in humans. In an imaging study, Martin-Soelch et al. (2001) compared satiated smokers and non-smokers in a delayed pattern recognition task with or without monetary feedback. In both groups, monetary reward led to activations in the occipital, frontal and orbitofrontal cortex, cingulate gyrus, cerebellum and midbrain. Reward related activations in the typical dopaminergic regions such as the striatum were only found in non-smokers, i.e., smokers showed a reduced processing of non-drug rewarding stimuli. These results were replicated in a further study by Martin-Soelch et al. (2003). Using the delayed pattern recognition task, the authors varied the amount of monetary reward. Smokers and non-smokers showed an involvement of a cortico-subcortical loop, including the dorsolateral prefrontal cortex, the orbitofrontal cortex, the cingulate gyrus and the thalamus in processing increasing monetary reward. Again, reward related activations in the striatum were only found in non-smokers. Furthermore, smokers showed no significant mood changes in response to the different monetary rewards. Further support for deficits in explicit reward learning in smokers comes from studies using reward-related decision-making paradigms. Chronic tobacco users exhibit abnormal reactivity to reinforcers (Bickel and Madden, 1999), a reduced subjective value of delayed drug and non-drug rewards in a delay discounting paradigm (Bickel et al., 1999), and deficits in the anticipation of reward in a behavioral choice task (Mitchell, 1999).

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neuroadaptive changes affect the tonic and phasic DA signals in the BRS, which are important for implicit reward learning (Di Chiara, 1999; Schultz, 2002). Hence we expect deficits in implicit reward learning in chronic, dependent smokers. However, as nicotine associated DA release is reduced already after occasional use, deficits in implicit reward learning should also occur in occasional, non-dependent smokers. We expect no deficits in explicit reward learning for occasional smokers, as orbitofrontal control is intact, as evidenced by the apparent control over nicotine use and no heightened nicotine cue reactivity (Haight et al., 2012) until chronic use orbitofrontal control is impaired, similar to other drugs as alcohol, cocaine, and methylphenidate abuse (Volkow et al., 2002a,b). Hence we expect deficits in explicit learning in dependent smokers only. The second relevant factor we want to consider in our present study is satiation. In dependent smokers, nicotine withdrawal further reduces the NAc DA release by 25% (Hildebrand et al., 1998). This leads to a reduced responsiveness of the BRS to other rewarding stimuli (Volkow et al., 2003), which, in turn, is associated with reduced appetency and decreased interest in reward (Robinson and Berridge, 1993). Gutkin et al. (2006) termed this behavioral effect a hypohedonic state that could be countered by actual nicotine consumption or associated cues. With respect to our hypotheses, we expect that any reward-learning deficits in dependent smokers are also compensated by acute nicotine consumption and, hence, are not observable in a satiated state. 1.4. Predictions The close linkage of dopaminergic activity and rewarddependent response selection led us to expect reward learning deficits in dependent tobacco smokers: the reduced number of dopamine-D2 receptors and the dampened dopamine neurotransmission impair the dopaminergic BG system, thereby causing a deficit in implicit reward learning. A dampened dopamine neurotransmission would also impair the OFC regulated response selection, causing a deficit in explicit reward learning. To test our assumptions, we conducted two behavioral studies. In Study 1 we examined reward learning in dependent, occasional, and nonsmokers. In Study 2 we compared the performance of dependent smokers in a satiated and abstinent state. We expected impaired performance in implicit and explicit reward learning for dependent smokers in comparison to nonsmokers. We further expected that any such deficits in dependent smokers are only observable in an abstinent but not in a satiated state. With respect to occasional, repeated but not dependent smoking we expected reduced implicit reward learning, but no deficits in explicit reward learning, compared to non-smokers.

1.3. Present research

2. Study 1

The aim of the present study was to examine alterations in reward learning in smokers. In addition to distinguishing explicit and implicit learning, we also want to consider two other relevant factors, frequency of use and satiation. To point out the relevance of frequency, we need to review the developmental stages of addiction. The initial nicotine dose during tobacco smoking leads to a dopaminergic overflow in the VTA and the NAc shell and is experienced as rewarding (Koob, 2006). With occasional, repeated use DA release is reduced and the rewarding effects are diminished. Chronic tobacco use in addition has an inhibitory effect on DA releasing neurons in the mesolimbic system due to desensitization of the nicotinic acetylcholine-receptors (nAChR) (Koob, 2000), further decreasing the rewarding effects of nicotine. These

2.1. Method 2.1.1. Participants. Subjects were students of the Martin-LutherUniversity Halle-Wittenberg, as well as their relatives and acquaintances. All participants signed informed consent before participating. The study was accomplished in compliance with the declaration of Helsinki. Of the overall group of n = 75 subjects, one non-smoker with a comorbid depression, one smoker with a comorbid bulimia nervosa, and one non-smoker with epilepsy were excluded from the analysis. The sample consisted of three groups: 27 dependent smokers (seven males; fulfilling criteria of tobacco dependence of DSMIV, daily consumption, and at least four withdrawal symptoms),

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20 occasional smokers (three males; non-daily consumption, and at most three withdrawal symptoms), and 25 never-smokers (seven male). Gender distribution did not differ between the three groups (2 = .86; p > .65). Diagnoses were made according to the section on tobacco dependence from the Composite International Diagnostic Interview (CIDI; Wittchen and Semmler, 1990), and the Structured Clinical Interview (SCID; Wittchen et al., 1997) for DSM-IV Diagnoses (Saß et al., 1998). No mental disorder, drug dependence or abuse other than nicotine, and no use of illicit drugs in case history were allowed. For the behavioral testing, both current smoker groups had to be abstinent for at least 2 h. This abstinence period was chosen to ensure that addicted and occasional smokers are not acutely satiated, given the reported elimination half-life of nicotine of 2 h (Hukkanen et al., 2005). 2.1.2. Psychological testing. Psychological testing was performed in the Department of Psychology (Martin-Luther-University HalleWittenberg). The examination lasted up to two and a half hours. After the assessment of biographical and clinical data, subjects performed the implicit and explicit reward learning tasks. After a short break, subjects were tested in short-term memory, working memory, attention, crystallized and fluid intelligence tasks, followed by the addiction related measures, a semi-structured clinical interview and some clinical questionnaires. 2.1.2.1. Neuropsychological assessment. To control the performance in explicit and implicit reward learning for common reported deficits of nicotine addicts in memory and attention (Belanger et al., 2007), and for differences in intelligence we used the following well established neuropsychological tests. Intelligence. Crystallized and fluid intelligence were measured using the subtests 1 + 2 and 3 of the Leistungsprüfsystem (LPS; Horn, 1983, 1999). Executive attention. Executive attention was measured using the subtests color naming and interference of the Stroop color word interference test (FWIT; Bäumler, 1985). Memory. Short-term and working-memory were measured using the forward and backward memory span of the Revised Wechsler Memory Scale (WMS-R; Markowitsch et al., 2000). 2.1.2.2. Addiction related measures. Nicotine addiction and usage related variables were measured with the following measures. Extent of dependence. The extent of tobacco dependence was assessed using the Fagerstrom Test for Nicotine Dependence (FTND; Heatherton et al., 1991). Criteria of dependence. The criteria of dependence were rated using the dependence section of the highly structured Composite International Diagnostic Interview (CIDI; Wittchen and Semmler, 1990). 2.1.2.3. Clinical psychological assessment. The presence of comorbid axis-1 disorders was assessed with the Structured Clinical Interview for Mental Disorders (SCID; Wittchen et al., 1997). Also, we asked for the number of consumed alcoholic drinks per month. Assessment of depressive symptoms was carried out using the German version of the Beck Depression Inventory (BDI; Hautzinger et al., 2000). 2.1.3. Tasks. 2.1.3.1. Implicit reward learning. To test implicit reward learning, we used the Ice-Cream-Seller-Task (IST; adapted after Shohamy et al., 2004), a probabilistic classification-learning task (Knowlton et al., 1994) with two conditions: one implicit feedback condition

and one explicit observation condition in a between-subjectsdesign. Subjects had to imagine they were an ice cream seller. They saw a puppet (the customer) in one of 14 configurations, resulting from the combination of four cues: a hat, glasses, a moustache and a bow tie. These cues were linked to the preference of the customer puppet for vanilla or chocolate ice cream. The task of the subjects was to predict the preferred ice cream. They received visual and monetary feedback after their verbal response. While in the beginning the subjects were only guessing, their predictions improved during the experiment. Subjects learned gradually which of the two outcomes (vanilla or chocolate ice-cream) would appear on each trial, given the distinct combination of cues (Knowlton et al., 1996). Following Shohamy and colleagues, each single cue was independently and probabilistically related to the outcome and the complex, probabilistic structure of the task prevented the verbalization or memorization of learning rules; that is, this task actually requires and, thus, tests implicit learning (Shohamy et al., 2004). A second, observational condition with an explicit learning instruction served to compare declarative and implicit learning (feedback condition). Here, the subjects observed only the puppet and its choice during the first 100 trials. They were explicitly instructed to learn which customer prefers which ice cream. During the second 100 trials, the subjects had to name the preferred ice cream without any feedback. Stimuli. The cues were features (hat, glasses, moustache and/or a bow tie) of a plasticine puppet (Fig. 1) holding a vanilla or chocolate ice cream in its left hand. Stimuli were photographed using a digital camera and then combined into 14 configurations (A–N) for each of the possible outcomes (vanilla, chocolate ice-cream). All configurations were displayed on a beige background. The 14 different configurations per ice cream type were presented randomly in two blocks of 100 stimuli following a scheme modeled after Shohamy et al. (2004). The order was fixed for all subjects. In total, both outcomes (vanilla and chocolate ice-cream) had the same frequency and a fixed, complementary probability per each cue: p(vanilla/hat present) = .80, p(vanilla/glasses present) = .60, p(vanilla/moustache present) = .40 and p(vanilla/bowtie present) = .20. The probability of the second outcome p(chocolate/cue present) amounted to 1 − p(vanilla/cue present). For example, cue 1 (hat present) was part of seven configurations and appeared in 100 trials; in 80 of these trials the outcome was vanilla ice cream and in 20 of these trials the outcome was chocolate ice cream. Procedure. Subjects were seated in front of a computer screen at a comfortable viewing distance. They received a German translation of the experimental instruction taken from Shohamy et al. (2004). The configurations were presented on a 17-in. PC-notebook. In the implicit feedback condition, an exemplification was presented after the instructions. Then, the subject could start the experiment by pressing the space bar. On each of 200 trials the subject was asked “Which flavor do you think he wants?” They had to respond by saying the German words for vanilla (“Vanille”) or chocolate (“Schokolade”). If the subject did not respond within 2 s, a reminder appeared (“Please, answer now!”). If the subject did not respond within the next 3 s, the correct answer was shown and this trial was rated as not solved correctly. After each correct answer the subjects received monetary reward of one Euro cent. The next trial started with a prompt “To proceed, press the space bar, please!”. The experimenter sat opposite to the subject, noted the responses and provided the reward. The observation condition consisted of two phases, an observational and a testing phase. The subjects received a German translation of the experimental instruction taken from Shohamy et al. (2004). In each of the 100 trials of the observation phase, the customer puppet appeared for 5 s holding his favorite ice cream in the left hand. The order of observational trials was identical to

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Fig. 1. Plasticine puppet – the customer in the ice-cream parlor – with four different cues and two possible outcomes (IST).

that in the implicit feedback condition. After the last observational trial, the instruction for the 100 trial test phase appeared on the screen. The procedure of the test phase was similar to the second half (trial numbers 101–200) of the feedback condition, except that no visual and monetary feedback was provided. The experimenter sat opposite of the subject and noted the responses. We computed relative frequencies of optimal responses. A response was valued optimal if it matched the more probable outcome of the configuration (cf. Knowlton et al., 1994). To differentiate declarative and implicit learning, relative frequencies of the second half of the implicit feedback condition (100 trials) were compared with those 100 trials of the testing phase in the observation condition.

rewards were won or lost when a subject did not respond. The reward for response inhibition condition (RRI) involved the same discrimination task. In contrast to the PALR condition, subjects were rewarded with 1 Euro cent for each inhibited response in the RRI condition. Hence, subjects were rewarded for correct responses (tapping S+) and inhibited incorrect responses (not tapping S−); they were not punished for incorrect responses (tapping S−). To minimize practice, sequence, and interference effects we used eight new, randomly generated numbers for both conditions. We analyzed omission (OE) and commission (CE) errors. The number of omission errors reflected the tendency to avoid punishing stimuli and the number of commission errors the tendency to approach rewarding stimuli (Yechiam et al., 2006).

2.1.3.2. Explicit reward learning. To test explicit reward learning, we used a trial-and-error discrimination task with 80 playing cards with eight different, two-digit numbers (Card-Playing Task, CPT; Newman et al., 1985). All stimuli were presented ten times in random order. The participants received a German translation of the experimental instruction used by Newman et al. (1985). They had to learn, explicitly by trial and error, which of the eight numbers were target (S+) or non-target (S−) stimuli. Participants respond by tapping the targets and non-tapping the non-targets. The stimulus cards, one at a time, were placed in front of the subjects, and subjects had approximately 2 s to respond before the next card was presented. A response was recorded each time that a subject tapped a card with his finger (Newman et al., 1985). There were two conditions. In the passive avoidance with loss of reward condition (PALR), subjects were rewarded with 1 Euro cent for each correct response (tapping S+) and punished by withdrawing 1 Euro cent for each wrong response (tapping S−). No

2.1.4. Statistical analysis. We compared the mean frequency of optimal responses in the IST using a 3 × 2 ANOVA with the between-subjects factors nicotine consumption (none, occasionally, dependent) and condition (implicit, explicit). We compared the number of omission and commission errors in the CPT using a 3 × 2 ANOVA with the between-subject factor nicotine consumption (none, occasionally, dependent) and the within-subjects factor condition (PALR vs. RRI). A priori planned contrasts (one-tailed ttests within the ANOVA) were calculated to test the directional hypotheses (non-smokers vs. dependent and occasional smokers, PALR vs. RRI, omission vs. commission errors, one-tailed). All other tests were two-tailed. Groups were compared in biographical, neuropsychological, and clinical variables using one-way ANOVAs or student’s t-tests. Parametric correlations were used to check for relationships between control variables and dependent variables. The selected statistical methods are robust against violation of normal distribution and non-equality of variances.

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Table 1 Means and standard variations of age, crystallized, and fluid intelligence, neuropsychological variables, depression, and alcohol use. (n = 72)

Non-smoker (n = 25)

Occasional smoker (n = 20)

Dependent smoker (n = 27)

Test statistic

M (SD)

M (SD)

M (SD)

F(2,69) or t(44)

p

Age Cryst I (LPS) Fluid I (LPS) STM (DSfw)*

24.84 (7.47) 49.43 (11.12) 30.28 (5.29) 8.92 (1.55)

WM (DSbw) Nam (CWIT) Int (CWIT)

7.88 (1.76) 40.71 (5.64) 68.04 (11.68)

24.40 (6.79) 46.30 (8.96) 30.45 (5.71) 8.15 (1.50) 8.15 (1.50) 7.70 (2.03) 39.03 (6.00) 66.05 (13.16)

25.85 (7.99) 46.30 (7.92) 29.74 (4.08) 7.48 (1.67) 7.48 (1.67) 7.44 (1.63) 39.82 (5.30) 67.29 (10.19)

0.24 0.87 0.13 5.34 0.47 0.42 0.36 0.50

>.20 >.20 >.20 <.05 >.20 >.20 >.20 >.20

BDI score Drinks/m*

4.36 (5.83) 2.79 (2.99)

5.70 (5.70) 18.06 (23.89) 18.06 (23.89)

5.04 (4.83) 20.32 (29.96) 20.32 (29.96)

0.38 4.99 2.26

>.20 >.05 >.20

Smoking variables Cig/m*** Duration FTND score***

– – –

34.60 (29.76) 7.60 (6.48) 0.52 (0.12)

454.44 (215.97) 9.96 (6.71) 2.93 (0.40)

8.57 1.27 6.69

<.001 >.20 <.001

Notes: BDI, Beck Depression Inventory; Cig/m, cigarettes per month; Cryst I, crystallized intelligence; CWIT, color-word-interference-test; DSbw, digit span backward; DSfw, digit span forward; Drinks/m, alcoholic drinks per month; Duration, duration of use in years; FTND, Fagerstroem test for nicotine dependence; Fluid I, fluid intelligence; Int, interference; LPS, Leistungspruefsystem; M, mean; Nam, naming; STM, short-term memory; SD, standard deviation; WM, working memory. * p < .05. *** p < .001.

The significance level was set to .05. The effect size parameters d (.20 < d < .50 = small, .50 < d < .80 = medium, .80 < d = large), r (.20 < r < .50 = small, .50 < r < .80 = medium, .80 < r = large), and 2 (.01 < 2 < .06 = small, .06 < 2 < .14 = medium, .14 < 2 = large) are reported for univariate and multivariate tests (Cohen, 1988). 2.2. Results 2.2.1. Clinical and control variables. Age, intelligence (LPS; subtests 1 + 2/3), working memory (WMS-R), attention (FWIT; color naming, and interference), and depression (BDI; see Table 1) did not differ in the three groups. The groups differed in alcohol use in glasses per month and in short-term memory (WMS-R). Alcohol use and shortterm memory were not related to any of the dependent measures and were non-significant covariates. The two smoking groups were comparable with respect to duration of use. As expected, dependent smokers reported a higher number of monthly smoked cigarettes and a higher extent of dependence (FTND; see Table 1). 2.2.2. Implicit reward learning. Means and standard deviations of mean frequencies of optimal responses, separate for the three groups and both conditions of the IST, are listed in Table 2. Fig. 2 depicts means and standard errors of the mean frequencies of optimal responses for the three groups and both conditions. The ANOVA

Table 2 Means and standard deviations of relative frequencies of optimal responses in the IST and correct responses and error rates in the CPT. (n = 72)

Non-smoker (n = 25) M (SD)

Implicit reward learning (IST) 0.82 (0.07) Fre FB 2.H 0.72 (0.11) Fre OB Explicit reward learning (CPT) 65.20 (9.35) CR PALR OE PALR 7.84 (9.14) CE PALR 7.21 (4.95) CR RRI 64.36 (14.25) OE RRI 5.48 (5.03) CE RRI 9.83 (12.60)

Occasional smoker (n = 20) M (SD)

Dependent smoker (n = 27) M (SD)

0.74 (0.13) 0.76 (0.09)

0.72 (0.12) 0.76 (0.07)

59.00 (11.75) 13.50 (11.48) 7.50 (4.31) 63.70 (12.25) 8.00 (7.73) 8.30 (6.05)

61.00 (10.68) 11.48 (8.19) 7.52 (5.09) 61.63 (9.13) 8.96 (5.75) 9.41 (5.16)

Notes: CE, commission error; CR, correct reactions; FB, feedback condition [IST]; Fre, relative frequency; H, test half; IST, ice cream-seller task; M, mean; PALR, passive avoidance with loss of reward; OB, observation condition; OE, omission error; RRI, reward for response inhibition; SD, standard deviation.

revealed an interaction of condition and group (F(2,66) = 3.50; p = .04; 2 = .10), but no main effects (Fs < 1). A priori planned contrasts revealed a significant difference between both conditions in non-smokers (t(66) = 2.53; p = .01; d = .62), but not in dependent and occasional smokers (ts < 1). Of the three groups, only non-smokers

Fig. 2. Means and standard errors of the mean frequencies of optimal responses for both conditions of the IST and of the error rates for both conditions of the CPT for the three groups. Notes: *p < .05; CE, commission error; PALR, passive avoidance with loss of reward; OE, omission error; rel, relative; RRI, reward for response inhibition.

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learned more in the feedback compared to the observation condition, which indicates impaired implicit reward learning in both smoker groups. 2.2.3. Explicit reward learning. Means and standard deviations of the mean number of correct reactions and omission and commission errors, separate for the groups and both conditions of the CPT, are listed in Table 2. Fig. 2 depicts means and standard errors of the error rates for the groups and conditions. Omission errors: There was a large main effect of condition (F(1,66) = 9.06; p = .004; 2 = .12); the number of errors was higher in the PALR condition than in the RRI condition. The main effect of group was small and only approached significance (F(2,66) = 2.90; p = .06; 2 = .08). The a priori planned contrast revealed a higher number of omission errors (medium effect) in dependent compared to non-smokers (t(66) = 2.01; p = .02; d = .49). A post hoc test revealed a similar effect for occasional smokers compared to nonsmokers (t(66) = 2.13; p = .04; d = .52). There was no interaction of condition and group (F(2,66 < 1). Commission errors. The effect of condition was small and only approached significance (F(1,66) = 3.53; p = .07; 2 = .05); the number of errors was marginally higher in the RRI condition than in the PALR condition. There was no main effect of group and no interaction of condition and group (Fs < 1).

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Study 2, we asked the participants to stay abstinent overnight and introduced a subjective measure of craving. To elucidate the influence of nicotine abstinence vs. acute satiation on reward learning, we compared satiated and abstinent addicted smokers. 3. Study 2 3.1. Method 3.1.1. Participants. Subjects were students of the JuliusMaximilians-University Würzburg, as well as their relatives and acquaintances. All participants signed informed consent before participating. The study was accomplished in compliance with the declaration of Helsinki. The sample consisted of 41 overnight abstinent and 43 satiated, dependent smokers (fulfilling criteria of tobacco dependence of DSM-IV in the present, daily consumption, and at least four withdrawal symptoms). Satiated smokers were asked to smoke a cigarette immediately before the experiment. All abstinent smokers assured credibly that they had remained abstinent overnight. Current craving status was validated with a visual analog scale of subjective perceived craving (VASC) before and while testing. Diagnostic procedure and exclusion criteria were the same as in Study 1. Eighteen of the abstinent smokers and 21 of the satiated smokers were men; gender distribution did not differ between both groups (2 (1) = .21; p = .65).

2.3. Discussion In line with our first hypothesis, dependent and occasional smoker groups both displayed reduced implicit reward learning compared to non-smokers. In non-smokers, the implicit learning effect was medium to large, which implies that non-smokers benefit from monetary feedback as compared to pure observation, whereas dependent and occasional smokers do not. In line with our second hypothesis concerning explicit reward learning, dependent smokers made more omission errors in the PALR condition of the CPT compared to non-smokers. Omission errors signal passive avoidance reactions due to an anticipated punishment. Interestingly, error rates in occasional smokers were similar to dependent smokers but not non-smokers, which we will come back to in Section 4. Before we can discuss the implications of our results, we have to consider one alternative explanation. At the time of behavioral testing, both smoker groups were abstinent for at least 2 h. Hence, one could argue that the observed deficits are due to nicotine abstinence. The completion of the IST requires stimulus-reward learning, which necessitates the storage of specific information patterns in the BRS. The prediction of the significance of a specific cue or cue pattern and its connection with stored information depends on the phasic DA release in the NAc, dorsal striatum, amygdala, and PFC (Hyman et al., 2006). During withdrawal, however, the DA transmission and firing of DA neurons in the mesolimbic system (Hildebrand et al., 1999; O’Dell, 2009), and phasic DA release (Epping-Jordan et al., 1998) are decreased. Hence, reward reactivity in the BRS is reduced (Powell et al., 2002; Dawkins et al., 2006), which in turn predicts impaired performance in both rewardlearning tasks. On the other hand, the pattern of our results in Study 1 cannot be fully accounted for by withdrawal alone; if withdrawal is indeed causative for the observed deficits, dependent smokers should have been more impaired in their performance, compared to occasional smokers. This was not the case, however, as performance of occasional and dependent smokers were comparable. As we had no withdrawal measure, we can only speculate on the extent of withdrawal in our first study. Furthermore, an abstinence of 2 h could be too short to ensure withdrawal symptoms in all smokers (Nakajima and Yokoi, 2005). Therefore in

3.1.2. Procedure. Psychological testing and clinical assessment, implicit (IST) and explicit (CPT) reward learning tasks, and statistical analysis were similar to Study 1. 3.2. Results 3.2.1. Clinical and control variables. As expected, the groups differed in the extent of perceived craving (VASC) before and while testing, with the abstinent smoker group reporting more craving than the satiated smoker group. The groups did not differ in intelligence (LPS; subtests 1–3), short-term and working memory (WMS-R), attention (FWIT; color naming, and interference), alcohol use (glasses per month), depression (BDI), number of monthly smoked cigarettes, and extent of dependence (FTND; see Table 3). Group differences with respect to duration of use and age approached significance. Both variables were not related to any of the dependent measures and we non-significant covariates. 3.2.2. Implicit reward learning. Means and standard deviations of mean frequencies of optimal responses, separate for both groups and both conditions, are listed in Table 4. Fig. 3 depicts means and standard errors of the mean frequencies of optimal responses, separate for both groups and conditions. The ANOVA revealed no main effects (group (F(1, 80) = 1.04, p = .31; condition F(1,80) = 1.76; p = .19) and no interaction between group and condition (F < 1). 3.2.3. Explicit reward learning. Means and standard deviations of the mean number of correct reactions, omission and commission errors, separate for the groups and both conditions of the CPT, are listed in Table 4. Fig. 3 depicts means and standard errors of the error rates, separate for both groups and conditions. Omission errors. The main effect of condition approached significance (F(1,80) = 2.97; p = .09; 2 = .04). There was no main effect of group, and no interaction of group and condition (Fs < 1). Commission errors. There was a main effect of condition (F(1,80) = 23.68; p < .001; 2 = .23); subjects made more commission errors in the RRI condition, indicating that they responded more impulsive when commission errors were not punished (S−).

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Table 3 Means and standard deviations of age, intelligence, neuropsychologial variables, depression, alcohol use, and smoking variables separated for the groups. (n = 72)

Abstinent smoker (n = 25)

Satiated smoker (n = 20)

Test statistic

M (SD)

M (SD)

F(2,69) or t(44)

p

m

23.15 (3.01) 45.76 (11.10) 29.61 (5.39) 8.59 (1.91) 7.46 (2.01) 45.41 (7.48) 74.41 (14.51)

24.93 (5.29) 47.95 (10.29) 29.47 (4.75) 8.65 (2.05) 7.33 (2.34) 44.33 (7.01) 71.88 (15.50)

−1.89 −0.94 0.13 −0.15 0.29 0.68 0.77

<.10 >.20 >.20 >.20 >.20 >.20 >.20

BDI score Drinks/m

6.22 (5.23) 7.75 (7.03)

5.14 (4.08) 8.18 (9.10)

1.04 −0.21

>.20 >.20

16.10 (7.88) 502.80 (208.80) 6.55 (2.80) 2.46 (2.23) 3.90 (0.39)

17.72 (6.13) 517.80 (192.90) 8.36 (5.38) 2.84 (2.06) 2.09 (0.31)

−1.45 −0.42 −1.90 −0.80 2.01

>.20 >.20 <.10 >.20 <.05

Age Cryst I (LPS) Fluid I (LPS) STM (DSfw) WM (DSbw) Nam (CWIT) Int (CWIT)

Smoking variables Cig/d Cig/m Durationm FTND score Craving* (VASC)

Notes: BDI, Beck Depression Inventory; Cig/m, cigarettes per month; Cryst, I crystallized intelligence; CWIT, Color-Word-Interference-Test; DSbw, digit span backward; DSfw, digit span forward; Drinks/m, alcoholic drinks per month; Duration, duration of use in years; FTND, Fagerstroem Test for Nicotine Dependence; Fluid I, fluid intelligence; Int, interference; LPS, Leistungspruefsystem; M, mean; Nam, naming; STM, short term memory; SD, standard deviation; VASC, visual analog scale of subjective perceived craving before and while testing; WM, working memory. m p < .10. * p < .05. Table 4 Relative frequencies of optimal responses in the IST and correct responses and error rates in the CPT. (n = 89)

Abstinent smoker (n = 41) M (SD)

Implicit reward learning (IST) 0.75 (0.13) Fre FB 2.H 0.72 (0.14) Fre OB Explicit reward learning (CPT) 60.27 (8.97) CR PALR OE PALR 10.46 (7.11) CE PALR 9.34 (6.37) CR RRI 59.46 (11.47) OE RRI 9.39 (5.66) CE RRI 11.15 (7.85)

Satiated smoker (n = 43) M (SD) 0.78 (0.11) 0.74 (0.12) 60.09 (8.81) 11.88 (7.90) 7.98 (5.13) 55.77 (10.50) 10.05 (7.20) 14.19 (5.53)

Notes: CE, commission error; CR, correct reactions; FB, feedback condition [IST]; Fre, relative frequency; H, test half; IST, ice cream-seller task; M, mean; PALR, passive avoidance with loss of reward; OB, observation condition; OE, omission error; RRI, reward for response inhibition; SD, standard deviation.

There was no main effect of group (F < 1), but an interaction of condition and group (F(1,80) = 7.21; p = .009; 2 = .08, medium effect). Against our hypothesis, satiated smokers made more commission errors than abstinent smokers, but in the RRI condition only.

3.3. Discussion Study 2 revealed two important findings. First, we replicated the results of Study 1 for dependent smokers in the implicit learning task, i.e., they do not benefit from monetary feedback as compared to pure observation. Second, we showed that this deficit is probably not due to withdrawal, as it was not discernibly compensated by acute nicotine consumption. The latter also did not improve the smokers’ performance in the explicit learning task; they behaved even more impulsive while they were rewarded for inhibiting their responses. 4. General discussion The aim of the present studies was to test for reward learning deficits in tobacco smokers. Results showed that (1) dependent smokers perform worse in implicit and explicit reward learning tasks compared to non-smokers; (2) the performance deficits are also present in occasional smokers; and (3) the performance of dependent smokers in both reward-learning tasks is not improved by acute satiation. The present study complements previously reported rewardlearning deficits in smokers. Martin-Soelch et al. (2001, 2003)

Fig. 3. Means and standard errors of the mean frequencies of optimal responses for both conditions of the IST and of the error rates for both groups and both conditions of the CPT. Notes: *p < .05; CE, commission error; PALR, passive avoidance with loss of reward; OE, omission error; rel, relative; RRI, reward for response inhibition.

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reported reduced reward processing in satiated chronic smokers, using a visual-spatial recognition task with monetary feedback. In their fMRI study, they found the expected activation patterns in dopaminergic regions associated with non-drug reinforcement only in non-smokers, but not in smokers. This suggests that smokers react less to non-drug reward, compared to non-smokers. Further studies that examined reward related functions also found deficits in reward reactivity and a preference for immediate, disadvantageous rewards in chronic tobacco users (Bickel and Madden, 1999; Mitchell, 1999; Baker et al., 2003; Harmsen et al., 2006; Chiu et al., 2008). Our findings extend these studies in several ways. 4.1. Implicit vs. explicit reward learning We differentiated between implicit and explicit reward learning, which are known to rely on different brain structures (Frank and Claus, 2006). We operationalized implicit learning using a probabilistic classification task, and explicit learning using a trialand-error discrimination task. In our Study 1, we found deficits for both kinds of reward learning in tobacco users. Shohamy et al. (2004) associated implicit probabilistic learning with the striatum. Previous studies reported a breakdown of probabilistic learning in patients with Parkinson’s disease (PD); the patients fail to utilize the feedback that links stimuli to outcomes (Knowlton et al., 1996; Myers et al., 2003). Similarly, but to a lesser extent, smokers appear to be impaired in their use of feedback. Only in the observation condition of the IST, when smokers were instructed to closely monitor stimuli and outcomes, was their performance comparable to nonsmokers. This again mirrors PD patients, who have no deficits in observation learning (Shohamy et al., 2004). As PD is caused by a loss of nigro-striatal dopaminergic neurons in the BG that disrupts striatal functions (Agid et al., 1987), and the striatum is related to performance in the task, implicit learning deficits in smokers may be due to dopaminergic dysfunction. Indeed, Fehr et al. (2008) found low availability of dopamine receptors in dependent smokers. Explicit, trial-and error discrimination tasks are based on explicitly to be learned rules. In the go/no-go CPT (Newman and Kosson, 1986) two rules are given, and participants have to learn which cards afford an action, based on contingent rewards and punishments. Such learning requires top-down control of response initiation; the latter being based on dopaminergic activity in the BG. Control of the BG is exerted by the OFC, and abnormalities in the OFC impair performance in such tasks. Drug abusers have a reduced OFC metabolism and gray matter volume (Volkow et al., 2003). Likewise, they exhibit marked deficits in decision making in general (Bechara et al., 2001) and response inhibition in particular (Dias et al., 1996). The smokers’ deficits observed in our Study 1, similar to the decision-making deficits observed in drug abusers, could be a behavioral marker of impaired orbitofrontal control. Even as implicit and explicit reward learning is independent from another (Squire and Zola-Morgan, 1996), the observed deficits in smokers are probably due to a reduced response to reward: in both tasks, feedback was given via secondary, non-drug reinforcers. This supports the notion that nicotine alters learning based on nondrug related rewarding stimuli, and hence acquires an incentive value above all other reinforcers. 4.2. Occasional vs. dependent smokers Results of Study 1 point to an impaired reward learning even if nicotine is consumed only occasionally. To our knowledge, there are no other studies on implicit reward learning in occasional smokers yet. There are studies that examined reward-related decision-making, i.e., discrimination of rewards in magnitude and immediacy, which is related to explicit reward learning (Heyman

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and Gibb, 2006; Sweitzer et al., 2008). Sweitzer et al. (2008) found no deficits in occasional smokers in comparison to exand never-smokers using a delay-discounting procedure. However, their occasional smokers were tried-it smokers, i.e., they tried smoking but had never smoked more than 100 cigarettes in their lifetime. In contrast, we categorized smokers with a maintained, non-daily and controlled consumption, and at most three withdrawal symptoms, as an occasional smoker. Consequently, our occasional smokers showed higher tobacco consumption than tried-it smokers. Bühler et al. (2010) reported that occasional smokers with controlled use, i.e., fewer than six cigarettes per week, in an instrumental motivation task showed more responses to money than to cigarettes, whereas dependent smokers pressed equally for both types of reward. Yet when both smoker groups were compared between each other, occasional and dependent smokers did not differ in their response rates to money, i.e., the reactivity to nondrug rewards seems to be the independent from frequency of use. Similarly, Johnson et al. (2007) found that heavy and light smokers did not differ in the money condition of a delay-discounting task, but both smoker groups differed from non-smokers. Taken together, this suggests that even moderate levels of tobacco use may be associated with reduced learning of non-drug rewarding stimuli. Several questions remain for future studies. What levels of use should be termed as moderate? Furthermore, even using similar definitions, occasional smokers would probably still be a heterogeneous group, with different subgroups characterized by age, accumulated smoking experience and smoking patterns, as well as factors associated with the likelihood of quitting (Edwards et al., 2010). Another question pertains to the transition to dependence: if occasional and dependent smokers do not differ in non-drug reward learning, drug-related reward learning could be a determining factor (cf. Bühler et al., 2010). Longitudinal studies could answer whether deficits in reward learning are antecedents or consequences of dependency. 4.3. Satiated vs. abstinent smokers The third important aspect of the present study concerns the role of satiation. Up to now, only a few studies compared non-drug reward related functions in satiated compared to abstinent smokers. In the present study, performance of a satiated smoker group did not best a group that was abstinent overnight. This implies that acute nicotine satiation does not enable smokers to benefit from rewarding feedback in implicit as well as explicit learning paradigms. Previous studies differed in their results: Powell et al. (2002) as well as Dawkins et al. (2006) reported that the administration of nicotine after abstinence led to an increased responding to financial incentives. Perkins et al. (2009) found no differences in an operant reinforcement task in smokers after the administration of nicotine in three different doses (0, 5, 10 ␮g/kg body weight) via a nasal spray procedure. In a second study, they were able to replicate this result with the administration of nicotine via tobacco smoking in two different kinds of cigarettes (0.05 mg or 0.6 mg nicotine) and a no smoking condition. Kalamboka et al. (2009) observed overall slower responses in a reward-related card-sorting task for abstinent smokers compared to satiated smokers, but found no difference in a reward response measure. Even though in the present study satiation did not have the effect we hypothesized, our data might point to an unexpected effect of nicotine on explicit reward learning. In the RRI condition of the in the CPT, satiated smokers made more commission errors than abstinent smokers. Subject to replication, such results would indicate that satiated smokers react more impulsively compared to abstinent smokers; at least if there is no punishment and they can only win. One might suppose that the nicotine satiation increases

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impulsive reactions to non-drug rewarding information. This would be in line with findings of reduced, actual and perceived, control of impulsive behaviors such as gambling in active smokers (Petry and Oncken, 2002; Dawkins et al., 2007). 4.4. Limitations and future directions As all studies, the present ones have limitations. First, the levels of dependence in the dependent smoker groups (mean FTND 2.5–2.9) were a bit low compared to other studies (e.g. Powell et al., 2002, mean FTND 3.7; Kalamboka et al., 2009, mean FTND 3.5). Also, our smoker samples were younger and thus, the duration of tobacco use was shorter. Therefore, withdrawal symptoms after a 2-h or an overnight abstinence could less pronounced. Second, we did not measure the blood nicotine level of our subjects, but validated the manipulation of abstinence in Study 2 using subjective ratings of the perceived craving before and during testing. Future studies should use physiological measures to validate the state of satiation. For example, Powell et al. (2004) applied a breath CO monitor to test for expired carbon monoxide. Also, one could argue that craving and experienced withdrawal differ phenomenologically, and therefore separate measures are necessary. Third, at least for the explicit learning tasks, a longitudinal, placebo-controlled study would allow for a within-subjects design (which is not feasible with the probabilistic learning paradigm). Fourth, participants in both studies were predominantly female (with a ratio of 3:1). Our results did not change when males were dropped from the analyses. Future studies, however, should increase the number of male participants to test for possible gender differences. Fifth, the found implicit and explicit reward learning deficits could be interpreted either as an antecedent or as a consequence of smoking. Our findings of a reduced performance of smokers in both reward-learning tasks, even in satiated state, slightly favor the interpretation as an antecedent. This assumption is in line with the reward deficiency syndrome hypothesis (Blum et al., 2000). Future studies should test the “reward learning deficits as an antecedent” hypothesis in a longitudinal design, preferably starting in late childhood. Role of funding source Nothing declared. Contributors Author Yvonne Paelecke-Habermann designed the study, wrote the protocol, undertook the statistical analysis, prepared and wrote the manuscript. Author Marko Paelecke wrote the manuscript. Authors Katharina Giegerich and Katja Reschke performed the experiment and collected the data. Author Andrea Kübler supervised data collection, wrote and approved the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest No conflict declared. Acknowledgments We thank Thorsten Droessler (MA) for providing images of the ice cream seller-puppet, and Johanna Reichert for helpful comments on an earlier draft of this paper. References Agid, Y., Javoy-Agid, F., Ruberg, M., 1987. Biochemistry of neurotransmitters in Parkinson’s disease. Mov. Disord. 2, 166–230.

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