ORIGINAL INVESTIGATION

Neuropsychological profiling of impulsivity and compulsivityin cocaine dependent individuals

María José Fernández-Serrano & José César Perales &

Laura Moreno-López & Miguel Pérez-García &

Antonio Verdejo-García

Received: 28 May 2011 /Accepted: 31 August 2011 /Published online: 16 September 2011# Springer-Verlag 2011

AbstractRationale Research on the relative impact of trait impulsivityvs. drug exposure on neuropsychological probes of responseinhibition vs. response perseveration has been posited as avalid pathway to explore the transition between impulsivityand compulsivity on psychostimulant dependence.Objectives The objectives of this study are to examineperformance differences between cocaine-dependent indi-viduals (CDI) and healthy comparison individuals (HCI) onneuropsychological probes of inhibition and perseveration

and to examine the predictive impact of trait impulsivity—aproxy of premorbid vulnerability, and severity of cocaineuse—a proxy of drug exposure, on CDI’s performance.Methods Forty-two CDI and 65 HCI were assessed usingthe UPPS-P Scale (trait impulsivity), the Stroop and go/no-go (inhibition) and revised-strategy application and proba-bilistic reversal tests (perseveration).Results CDI, compared to HCI, have elevated scores ontrait impulsivity and perform significantly poorer oninhibition and perseveration, with specific detrimentaleffects of duration of cocaine use on perseveration.Conclusions CDI have both inhibition and perseverationdeficits; both patterns were broadly indicative of orbito-frontal dysfunction in the context of reinforcement learning.Impulsive personality and cocaine exposure jointly contrib-ute to deficits in response perseveration or compulsivity.

Keywords Addiction . Cocaine . Drug use severity .

Impulsivity . Response inhibition . Compulsivity . Responseperseveration

Introduction

Current neurobiological models conceive psychostimulantaddiction as a transition between impulsive (reward-driven)and compulsive (stress relieving and outcome-detached)behaviour (Dalley et al. 2011; Koob and Volkow 2010).Impulsivity would underlie the tendency to pursue salientrewards at the expense of potential mistakes, which ispotentially related to poor response inhibition in the earlystages of drug use (Dalley et al. 2011; Verdejo-García et al.2008). On the other hand, prolonged exposure would turndrug-taking compulsive, wherein the individual lackscontrol over habitual behaviour, becoming unable to

Electronic supplementary material The online version of this article(doi:10.1007/s00213-011-2485-z) contains supplementary material,which is available to authorized users.

M. J. Fernández-Serrano (*)Departamento de Psicología, Facultad de Humanidadesy Ciencias de la Educación, Universidad de Jaén,Campus Las Lagunillas s/n,23071 Jaén, Spaine-mail: mjfernan@ujaen.es

J. C. PeralesDepartamento de Psicología Experimental y Fisiologíadel Comportamiento, Universidad de Granada,Granada, Spain

L. Moreno-López :M. Pérez-García :A. Verdejo-García (*)Departamento de Personalidad, Evaluación y TratamientoPsicológico, Facultad de Psicología, Universidad de Granada,Campus de Cartuja, s/n,18071 Granada, Spaine-mail: averdejo@ugr.es

M. Pérez-García :A. Verdejo-GarcíaInstitute of Neurosciences F. Oloriz, Universidad de Granada,Granada, Spain

M. Pérez-GarcíaCIBERSAM, Universidad de Granada,Granada, Spain

Psychopharmacology (2012) 219:673–683DOI 10.1007/s00213-011-2485-z

reverse repetitive response patterns that are no longerdriven by drug reward (Koob and Volkow 2010). Thislatter pattern would be associated with impaired responseflexibility in later stages of dependence (Fineberg et al.2010). This hypothesis has been elegantly tested by animalstudies showing that premorbid impulsivity (indexed bypremature responses in a response inhibition task) isassociated with reduced striatal D2 receptors availabilityand is able to predict higher rates of cocaine self-administration and faster transition to cocaine dependence(defined as persistence of self-administration despite beingfollowed by increased punishment; Belin et al. 2008;Dalley et al. 2007). Human neuropsychological andimaging studies have provided indirect support to thismodel. The notion that increased impulsivity underliesearly stages of drug use is consistent with findings showingthat recreational psychostimulant users (those who haveminimal or intermittent use of these drugs but do not meetcriteria for psychostimulant dependence) have elevatedscores on trait measures of impulsivity and poor inhibitorycontrol (indexed by go/no-go or stop-signal tasks) associ-ated with functional alterations of striatal function (Colzatoet al. 2007; Leland et al. 2006; Verdejo-García et al. 2010).On the other hand, studies conducted in chronic cocaine-dependent individuals have underscored the presence ofrobust and durable impairments on indices of flexibility andperseveration (Ersche et al. 2008; Verdejo-García et al.2007a; Woicik et al. 2011). In addition, dose-relatedneurocognitive studies in cocaine-dependent individualshave shown significant associations between greater sever-ity of cocaine use (amount and duration) and poorerperformance on flexibility/perseveration indices, even afterprotracted abstinence intervals (Fernández-Serrano et al.2010; see Fernández-Serrano et al. 2011).

The investigation of the relative impact of trait impul-sivity vs. drug exposure (e.g. amount and duration ofcocaine use) on neuropsychological measures of responseinhibition (i.e. impulsivity) vs. response perseveration (i.e.compulsivity) has been put forward as an useful method tounderstand the dynamic influence of these trait vs. statefactors on psychostimulant dependence (Dalley et al. 2011).Following this rationale, in this study, we aimed to explorethe relative weight of measures of trait impulsivity (as aproxy of premorbid impulsivity) and estimates of amountand duration of cocaine use (as a proxy of severity of drugexposure) in predicting response inhibition and responseflexibility performance. In agreement with current modelsof addiction, we would expect trait impulsivity to bepredictive of performance on measures of response inhibi-tion (impulsivity), whereas degree of cocaine exposurewould predict performance flexibility (compulsivity). Thelatter association can also be expected for other drugs thatare frequently co-abused with cocaine, such as alcohol,

which is dose-dependently associated with poorer perfor-mance in response inhibition and perseveration probes(Guillot et al. 2010; Marczinski et al. 2005). Cannabis isalso frequently co-abused with cocaine, but neuropsycho-logical studies have failed to show a significant impact ofcannabis use on response inhibition and flexibility meas-ures (Fernández-Serrano et al. 2011; Quednow et al. 2007;Tapert et al. 2007).

Therefore, the aims of our study are as follows: (1) toexamine performance differences between cocaine depen-dent individuals (CDI) and drug-naive controls on neuro-psychological probes of response inhibition (impulsivity)and response perseveration (compulsivity) and (2) toexamine the predictive impact of trait impulsivity andseverity of drug use, on these tasks performance.

Method and materials

Participants

Forty-two cocaine-dependent individuals (CDI), aged 19–44 years (M=28.93, SD=6.39), and 65 healthy controlindividuals (HCI), aged 23–41 years (M=30.17, SD=4.98),participated in this study. Intentionally, all volunteers weremale; given the low prevalence of women entering drugtreatment during recruitment. CDI were recruited duringtheir treatment at the therapeutic community “ProyectoHombre” in Granada (Spain). This is a residential settingthat provides psychological treatment and educational/occupational counselling in a controlled environment foran extended period of time.

All of the CDI remained abstinent during, at least, the15 days before testing, although the mean duration ofabstinence in the group was 34.28 (SD=22.01) weeks, sothat it was possible to rule out the presence of alterationsassociated with the acute or short-term effects of thedrug. None of them were following any pharmacologicalsubstitution treatment during the course of the neuropsy-chological testing. Urine analyses for cannabis, benzo-diazepines, cocaine, and heroin metabolites wereconducted to confirm abstinence. Potential participantswho had previously been diagnosed with disorders fromDSM-IV axes I and II (other than substance use relateddisorders) were not included in the target sample. Thosepotential participants who had been previously diagnosedwith traumatic brain injury, neurological disorders orHIV were also excluded.

Healthy comparison individuals (HCI) were recruitedby means of advertising in public job centres, so,similarly to CDI, all of them were unemployed. Selectioncriteria for these HCI were as follows: (1) absence ofcurrent or past substance abuse, excluding past or current

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social drinking (less than ten drinks per week), (2)absence of documented history of psychiatric disorders,(3) absence of documented head injury or neurologicaldisorder, and (4) not being under medication affectingthe central nervous system. The mean amount of alcoholuse in control participants was 25.52 units/month (SD=30.82), and the mean of alcohol duration consumptionwas 10.63 years (SD=5.57).

Instruments and assessment procedures

Trait impulsivity

UPPS-P impulsive behaviour scale (Whiteside and Lynam2001) This scale is a 59-item inventory designed tomeasure five distinct personality pathways that may leadto impulsive behaviour: negative urgency, lack of persever-ance, lack of premeditation, sensation seeking and positiveurgency (Smith et al. 2007). We used the Spanish version ofthis scale (Verdejo-García et al. 2010). A total score of eachof these five dimensions was obtained for analyses. Theinter-correlations between the scores of the differentdimensions in this sample are displayed in Table SA1,Supplementary Material.

Patterns of drug use

Data regarding lifetime amount and duration of use ofdifferent drugs was collected using the Interview forResearch on Addictive Behaviour (Verdejo-García et al.2005). This interview provides an estimation of monthlyuse of each substance (amount per month) and totalduration of use of each substance (in years). The descriptivescores for these variables in the present sample arepresented in Table 1.

Neuropsychological probes of impulsivity–responseinhibition

These tests require participants to inhibit a prepotent motorresponse pattern. Both Stroop and go/no-go tests areregarded as neuropsychological probes of impulsivity(Verdejo-García et al. 2008).

Stroop test (Delis–Kaplan executive functions system; Deliset al. 2001) The test consists of four different parts, eachcontaining 50 items. Part 1 (colour naming) presentspatches of colours, and participants have to name them asquickly and accurately as possible. Part 2 (reading) presentsthe words “red”, “blue” and “green” printed in black ink,and participants have to read aloud these words. Part 3(inhibition) introduces the interference effect: the words“red”, “blue” and “green” are printed in incongruentcolours ink, and participants have to name the colour andignore the word. Part 4 (Switching) contains similar itemsto Part 3, but participants have to switch their responsebetween naming the colour of the ink, and reading theword—only in a minority of items that are framed by a box.The main dependent variables derived from this test werethe composite time measures: inhibition vs. colour naming(time Part 3−time Part 1) and switching vs. inhibition (timePart 4−time Part 3), and the number of errors committed inParts 3 (inhibition errors) and 4 (switching errors).

Go/no-go task A computer-based implementation of the go/no-go task was used (Verdejo-García et al. 2007a). The taskconsisted of 60 trials. In the first 30 trials (pre-switch),participants were asked to press a key as quickly as theycould whenever the go stimulus (a letter) was presented andto withhold the response when the no-go stimulus (adifferent letter) was presented. The assignation of stimulito the go and no-go conditions were counterbalanced acrosssubjects. In the second 30 trials of the task (post-switch),participants were asked to switch the assignation of theresponse from the go to the no-go stimulus (whichobviously became the post-switch go trial); in other words,they were asked to respond to the previously no-gostimulus and not to respond to the previously go stimulus.The proportion of go vs. no-go trials on both phases (pre-and post-switch) was 7/3. The inter-stimulus interval wasset at 100 ms, and each stimulus was presented during1,000 ms. Auditory feedback (one of two distinctivesounds) was provided after each response to indicatewhether that response had been right or wrong. Ifparticipants did not respond in the 1,000 ms responsewindow, the two same sounds were used as positive andnegative feedback for not responding. That is, if noresponse was given, the same sounds indicated whetherthe absence of response had been right (no-go trials) or

Table 1 Descriptive scores for patterns of amount and duration of thedrugs used in this sample of cocaine dependent individuals (CDI)

Substances Variables CDI

Mean SD

Cannabis Joints per month 100.64 101.70

Duration (years) 18.78 71.50

Cocaine Grams per month 18.96 29.18

Duration (years) 4.13 2.91

MDMA Tablets per month 10.19 10.26

Duration (years) 2.81 2.39

Alcohol Standard drinking units per month 87.48 85.27

Duration (years) 8.52 9.68

We only reported data about drugs used by >15% of the CDI includedin the sample

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wrong (go trials). Responses were categorised as hits(responding in presence the go trial), false alarms (respond-ing in presence of the no-go trial), misses (not respondingin presence of the go trial), and correct rejections (notresponding in presence of the no-go trial). The maindependent variables from this test were hit and false alarmrates. The hit rate for each block was computed as the ratiobetween the number of hits and the total number of go trialsin that block (number of hits+number of misses). Similarly,the false alarm rate was computed as the ratio between thenumber of false alarms and the total number of no-go trials(number of false alarms+number of correct rejections).

These variables were analysed across six blocks of tentrials to explore effects of learning and switching during thetask.

Neuropsychological probes of compulsivity–responseperseveration

These tests require dynamic reversal of previously success-ful response patterns. Perseverative/compulsive responsesare defined by persistence of response patterns that are nolonger predictive of reward-related outcomes (Dalley et al.2011).

Probabilistic reversal learning task The task was based onthe Probabilistic Reversal Learning task described inSwainson et al. (2000). Each trial of the task involves thesimultaneous presentation of two stimuli (two squares,drawn in thick coloured lines) that differ only in line colour.In each phase of the task, one stimulus is considered the“correct” one, as choosing it provides reward in most cases,and the other is the “wrong” one, as choosing it waspenalised most of the times. The stimuli shifted positionsrandomly to avoid motor perseveration. In phases 1 and 2of the task (trials 1–80), the correct selection wasprobabilistically rewarded in eight out of ten cases andpenalised two out of ten, and the incorrect selection waspenalised in eight out of ten trials and rewarded in theremaining two out of ten. This means that on 20% of thetrials, the computer provided false feedback, i.e. selectingthe correct stimulus was followed by false negativefeedback and selecting the incorrect one was followed byfalse positive feedback (still, for the sake of simplicity, wekeep on labelling the two colours as “correct” and“incorrect”, as, a priori, the former is the one most likelyto be rewarded, and the latter the least likely to berewarded). Negative and positive feedback were presentedacoustically (by two different tones) and involved winningor losing 5 points. The total amount of points accrued so farwas continuously present on one corner of the screen.Phases 3 and 4 (trials 81–160) were identical to phases 1and 2, respectively, but the probability of rewarding for the

correct response was lowered to 70%, and the proportion ofrewarding for the incorrect one was raised to 30%. Mostimportantly, the colour corresponding to the right choiceand the one corresponding to the wrong choice shifted afterevery 40 trials (after every phase), that is, the stimulus thatwas previously correct became incorrect, and vice versa(see Verdejo-García et al. 2010 for a visual depiction of thetask). The main dependent measures for this task were theproportion of correct choices for each five-trial block ineach task phase (1–4) and the total number of perseverativeerrors (the latter measure was computed as described inErsche et al. 2008 and Swainson et al. 2000). Extraanalyses were carried out on the probability of maintainingthe prior trial choice after being rewarded and after beingpenalised. This second dependent measure is aimed atdetecting differential choice dependencies on reinforcementand punishment for the two groups.

Revised strategy application test (Levine et al. 2000;Spanish adaptation by Verdejo-García et al. 2007b) Thisis a paper and pencil multitasking test in which participantshave to perform three types of activities: figure tracing,sentence copying and object numbering. Each of theseactivities is presented on two different stacks (A and B),each containing 120 items. The main goal of the task is towin as many points as possible, considering that large itemsscore 0 points, and small items score 100 points each.However, both small and large items can be of differentdifficulty: Some of them are easy and quick to complete(i.e. they take a couple of seconds, so they are defined as“brief items”), whereas others are very laborious and timeconsuming (i.e. they can take longer than 1 min, so they aredefined as “lengthy items”). Given the limitation of time(10 min), the most efficient strategy (to-be-discovered) is tocomplete only the brief items to the exclusion of lengthyitems, which participants must learn to skip as they areintroduced in later pages of the test. This strategy requiresthe reversal of a routine tendency to complete all items insequence, which is established on the early pages of eachstack, where all of the items are brief. The main dependentvariable from the revised strategy application test (R-SAT)is the proportion of brief items completed in relation to thetotal number of items attempted.

Procedure

Participants were assessed individually between November2008 and September 2009 in a single session that lastedapproximately 3 h (including breaks). The tests included inthis study were part of a larger protocol aimed tocharacterise neuropsychological functions in CDI. Allparticipants were informed about the objectives, benefitsand possible inconveniences associated with the research

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protocol, and they signed an informed consent formcertifying their voluntary participation. HCI were paid €40for their collaboration. CDI received a feedback report fromtheir performance on the neuropsychological assessment.

Statistical analyses

We first explored dependent variables to examine missingdata points. Data from some tasks were missing due totechnical problems during data collection: The sample sizefor CDI is n=37 for the UPPS-P and n=44 for the go/no-go; the sample size for HCI is n=56 for go/no-go andreversal tasks, and n=63 for the Stroop task.

Preliminary group comparisons for demographic varia-bles showed that both groups (CDI vs. HCI) werestatistically matched for age, but HCI had went throughsignificantly more years of education [(M=17, SD=4.25)vs. (M=11.87, SD=3.40)]. Years of education weresignificantly correlated with the UPPS dimensions of lackof premeditation (r=−0.250, p=0.011), lack of persever-ance (r=−0.288, p=0.003) and positive urgency (r=−0.400, p<0.001) but showed non-significant associationswith neuropsychological indices. Nonetheless, we chose toapply a conservative approach to control for any potentialeffect of this variable on neuropsychological performance:We regressed years of education on the different dependentvariables using standard regression models, and then wesaved the standardised residuals from these models forfurther analyses. Therefore, all statistical tests were per-formed on the standardised residual scores, after removingany effect of education. Residual scores followed a normaldistribution in all cases except Stroop; therefore, we usedthe general linear model in the majority of indices (t tests ormixed design ANOVAs—to examine go/no-go and reversaldynamic response patterns) and non-parametric analyses inthe latter case.

To test the second aim of the study (i.e. to disentanglespecific effects of trait impulsivity and severity of drug useon response inhibition/perseveration indices), we firstconducted exploratory Pearson—or Spearman (for theStroop)—correlation analyses among trait measures ofimpulsivity (UPPS-P), estimates of severity of cocaine use(regular monthly use and duration—in years) and neuro-psychological indices. Estimates of alcohol use were alsoincluded in correlation tests. From neuropsychologicaltests, we only selected the main performance indicesreflecting inhibition (Stroop inhibition errors and go/no-gofalse alarms) or perseveration (R-SAT proportion of briefitems and reversal probabilistic errors) from each probe.Next, we conducted hierarchical multiple regression modelsincluding those measures that previously showed signifi-cant correlations with neuropsychological indices. Theregression models were set on three blocks: (1) years of

education, (2) UPPS-P dimensions and (3) cocaine and/oralcohol use parameters. We used an analogous hierarchicallogistic regression model for the Stroop, whereby thedependent variable (DV, number of errors) was dichotom-ised into non-impaired (DV=0) vs. impaired (DV=1)performance, based on standard cutoffs (Delis et al.2001). In the linear regression models, we estimated theR2 of the prediction change associated with each new blockand its statistical significance—to determine if the inclusionof impulsivity dimensions and/or patterns of drug usesignificantly improved the model’s predictive capacity. Inthe logistic regression, the Nagelkerke’s pseudo R2 [analo-gous to the R2 value of linear regressions (Field 2005)] wasused to estimate change in the significance of prediction. Tofacilitate reading, we only show results from hierarchicalregression models showing significant effects.

Results

Group comparisons

Table 2 displays raw descriptive scores from trait andneuropsychological performance indices, and the p valuesyielded by statistical tests conducted on the standardisedresiduals (education-corrected).

Trait measures—UPPS

CDI scored significantly higher than HCI on the subscalesof negative urgency (F=14.28, p<0.001, d=1.33), lack ofpremeditation (F=5.26, p=0.024, d=0.76), positive urgen-cy (F=24.17, p<0.001, d=1.61). We also found a trend tosignificant differences between groups on lack of persever-ance (F=3.74, p=0.056, d=0.73).

Neuropsychological probes of response inhibition

Stroop CDI and HCI showed significant differences on thenumber of inhibition errors; CDI committed significantlymore errors than HCI (U=1,020, p=0.013, d=0.36).

Go/no-go task CDI and HCI significantly differed in theirdynamic response patters through this task. Two (group,CDI vs. HCI)×six (block, 1–6) mixed ANOVAs werecarried out on typified hits and false alarm rates. Group hada significant effect on hit rates (hit rates were lower forcocaine users), F(1, 98)=23.92, MSE=0.06, p<0.01. Hitrates also varied across blocks, F(5, 490)=1,564.57, MSE=0.07, p<0.01, but group did not interact with block,F(5, 490)=1.81, MSE=0.07, p=0.11. Mean hit and falsealarm scores for each group and block are displayed inFig. 1 (left panel, typified hit rates; right panel, typified

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false alarm rates). The effect of group was much larger onfalse alarm rates, F(1, 98)=38.27, MSE=1.84, p<0.01 (withhigher false alarm rates for CDI). False alarm rates variedacross blocks, F(5, 490)=54.08, MSE=0.44, p<0.01, andblock interacted with group, F(5, 490)=6.00, MSE=0.44, p<0.01. Tukey honestly significant difference (HSD) post hoctests yielded significant differences between groups forblocks 2 (p=0.02) and 6 (p<0.01). Both differences were inthe same direction as the main group effect (CDI shower ahigher rate of false alarms). In other words, although thetwo groups showed similar false alarm rates by thebeginning of the task, HCI readily reduced it, whereasCDI kept it high for a longer time.

Neuropsychological probes of response perseveration

Revised strategy application test CDI and HCI showedmarginally significant differences on the main index ofproportion of brief items (F=3.33, p=0.071, d=−0.46),where HCI outperformed CDI.

Probabilistic reversal learning task CDI and HCI signifi-cantly differed on their dynamic response patterns throughthe task. We carried out a two (group, CDI vs. HCI)×four(phase, 1–4)×eight (block, 1–8) mixed ANOVA overtypified residuals. Main effect analyses yielded significant

effects of block, F(7, 693)=2033.40, MSE=0.15, p<0.01;phase, F(3, 297)=509.04, MSE=0.27, p<0.01 and group(HCI globally outperformed CDI), F(1, 99)=20.29, MSE=0.26, p<0.01. Several interactions were also significant,including the three-way interaction block×phase×group(see Fig. 2), F(21, 2,079)=11.57, MSE=0.07, p<0.01.

For the sake of brevity, we will report only thecomparisons of theoretical interest. First, with regard tothe effect of Block, the proportion of right choices grewacross blocks, independently of phase and group; inaccordance with this, the main effect of block contained asignificant linear component, F(1, 99)=2,196.71, MSE=0.637, p<0.001. Second, with regard to the effect of phase,we expected contingency to exert an effect on theproportion of right choices. Given that response-rewardcontingency was higher in phases 1 and 2 than in phases 3and 4, we carried out the corresponding planned compar-ison. That comparison yielded a significant effect, F(1, 99)=1,571.61, MSE=0.2122, p<0.001. Finally, we were inter-ested in finding out where the block×phase×group com-parison was originated. Between-groups Tukey HSD posthoc tests yielded significant differences between HCI andCDI in the first block of phases 3 and 4 (p=0.037 and0.001, respectively), as well as in the sixth block of phase 4(p=0.002). CDI outperformed HCI in the first two cases,whereas HCI outperformed CDI in the last one. In other

Table 2 Descriptive scores, group comparisons (CDI vs. HCI) and effect sizes for neuropsychological performance indices

Test Dependent variables CDI HCI F/U/chi square (p value) Cohen’s deltaMean (SD) Mean (SD)

UPPS-P Negative urgency 31.75 (7.05) 23.06 (6.23) 14.28a (0.000) 1.33

Lack premeditation 23.62 (5.93) 19.86 (4.22) 5.26a (0.024) 0.76

Lack perseverance 21.37 (4.34) 18.39 (3.89) 3.74a (0.056) 0.73

Sensation seeking 31.29 (8.97) 29.03 (7.46) 2.09a (0.151) 0.28

Positive urgency 33.62 (9.16) 20.84 (7.10) 24.17a (0.000) 1.61

Stroop Inhibition vs. colour naming time 20.24 (7.93) 17.34 (7.50) 1,351b (0.679) 0.37

Switching vs. inhibition 12.73 (11.14) 10.84 (9.99) 1,274b (0.371) 0.26

Inhibition errors 0.69 (1.44) 0.33 (0.53) 1,020b (0.013) 0.36

Inhibition self-corrections 1.40 (1.58) 0.76 (1.34) 1,383b (0.830) 0.43

Switching errors 1.30 (2.31) 0.56 (0.99) 1,236b (0.342) 0.45

Switching self-correction 1.11 (1.35) 0.79 (1.15) 1,354b (0.842) 0.28

Go/no-go Percentage of total false alarms 19.96 (17.54) 12.82 (10.94) 2.28a (0.134) −0.46Reversal Perseverative errors 6.22 (4.67) 8.03 (4.34) 1.45a (0.231) 0.09

Percent total correct answers 69.50 (15.43) 72.20 (12.31) 0.44a (0.508) −0.19R-SAT Percentage of brief items 82.11 (20.50) 89.07 (10.05) 3.33a (0.071) −0.46

Action slips 1.91 (3.20) 1.42 (2.51) 0.43a (0.510) 0.17

Strategy insight 64.44% (yes) 75.38% (yes) 1.54c (0.214)

35.55% (no) 24.62% (no)

a Value of Fb Value of Uc Value of χ2

678 Psychopharmacology (2012) 219:673–683

words, HCI tended to show higher rates of correct choiceby the end of each phase (this difference was strictlysignificant only for the sixth block of the fourth phase, butis concordant with the main effect of group) and lower rates

of correct choices by the beginning of phases 3 and 4immediately after reversals. Importantly, this pattern dis-cards the possibility that differences between groups can beattributed to the presence of more perseverative errors in

Cocaine Control

Block (Hit rate)1 2 3 4 5 6

-3,0

-2,5

-2,0

-1,5

-1,0

-0,5

0,0

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Typ

ified

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Block (False alarm rate)1 2 3 4 5 6

Fig. 1 Go/no-go mean hit andfalse alarm scores (left paneltypified hit rates; right paneltypified false alarm rates)

Cocaine ControlPHASE: 1

1 2 3 4 5 6 7 8-3,5

-3,0

-2,5

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-1,5

-1,0

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(res

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PHASE: 2

1 2 3 4 5 6 7 8

PHASE: 3

1 2 3 4 5 6 7 8

PHASE: 4

1 2 3 4 5 6 7 8

Fig. 2 Probabilistic reversallearning task mean scores foreach group, phase and block

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CDI. In turn, CDI seem to show a worse performance at thelearning asymptote.

In order to explore the reasons of the observed differ-ences, we carried out an extra analysis. We separated thosetrials following a rewarded choice from those following apunished choice and computed the probability of maintain-ing the same choice in one case and the other. Indepen-dently of group, the probability of maintaining the samechoice after a rewarded one decreased across phases[F(3, 297)=196.72, MSE=0.04, p<0.01, for the main effectof Phase; see Fig. 3, top panel]. Additionally, the probabilityof maintaining the same choice after reward was higher forHCI than for CDI, F(1, 99)=39.71, MSE=0.06, p<0.01. Thephase×group interaction was also significant, F(3, 297)=14.71, MSE=0.04, p<0.01. Actually, according to post hocTukey HSD contrasts, the difference between the two groupswas significant only in the fourth phase (p<0.01).

Conversely, the probability of maintaining the samechoice after a penalised one increased across phases[F(3, 297)=516.33, MSE=0.02, p<0.01, for the main effectof phase; see Fig. 3, bottom panel]. Again, the probability

of maintaining choice after punishment was globally higherfor HCI than for CDI, F(1, 99)=14.82, MSE=0.20, p<0.01.Group interacted with phase, F(3, 297)=32.52, MSE=0.02,p<0.01, in such way that the effect of group was significantfor the fourth phase only (p=0.02; Tukey HSD).

Correlation analyses

Table 3 shows correlation coefficients for UPPS-P sub-scales, cocaine and alcohol use patterns and neuropsycho-logical indices. Negative and positive urgency weresignificantly correlated with Stroop inhibition errors (neg-ative urgency, r=0.245, p=0.014; positive urgency, r=0.260, p=0.009), and negative urgency was significantlycorrelated with the number of perseverative errors from thereversal task (r=0.209, p=0.049). Lack of perseverance andseverity of alcohol use were significantly correlated withgo/no-go false alarms (lack of perseverance, r=0.197, p=0.05; severity of alcohol, r=0.215, p=0.032). Duration ofcocaine use was significantly correlated with R-SATproportion of brief items (r=−0.228; p=0.016).

Regression models

Data from hierarchical regression models is displayed inTable 4.

Response inhibition

For false alarms rate (go/no-go), the blocks including years ofeducation and lack of perseverance showed marginallysignificant effects (p=0.055, and p=0.062, respectively),and the block including severity of alcohol use significantlyimproved the prediction capacity of both of them (p=0.039).In the global model, severity of alcohol use was the mainpredictor of this index. For inhibition errors (Stroop), none ofthe blocks of predictors had a significant impact on thevariable indexing impaired vs. non-impaired performance.

Response perseveration

For proportion of brief items (R-SAT), the block includingdrug use estimates showed a significant effect (p=0.012).In the global model, duration of cocaine use was the mainpredictor of this index. For the percentage of perseverativeerrors (reversal), none of the blocks had a significant effecton task performance.

Discussion

This study yields two main findings: (1) CDI have elevatedscores on trait impulsivity (including cognitive and

CDI HCI

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Fig. 3 Probability of maintaining a choice given that choice wasrewarded (top panel) and given that choice was punished (bottompanel)

680 Psychopharmacology (2012) 219:673–683

emotion-driven impulsivity) and perform significantlyworse than healthy controls on neuropsychological probesof response inhibition (Stroop and go/no go) and responseperseveration (reversal); (2) trait impulsivity do not signif-icantly predict response inhibition deficits (alcohol abusehad a greater impact on inhibitory control), whereasduration of cocaine use is a significant predictor of responseperseveration in CDI.

The neuropsychological profile obtained is mostlyconsistent with findings from previous studies amongmid-term abstinent CDI (see Fernández-Serrano et al.2011). Our data demonstrate that CDI show performancedeficits related to both inhibitory control (i.e. increasedStroop inhibition errors and go/no-go false alarm rates) andreversal of previously reinforced response patterns (reversaland R-SAT). The difficulty to exert control over prepotentresponses, which underlies performance deficits in bothStroop and go/no-go tasks, has been associated withdecreased activation of the anterior cingulate cortex in

functional MRI studies in CDI (Kaufman et al. 2003; Li etal. 2008). There is also evidence that these deficits aresignificant predictors of poorer treatment outcomes incocaine outpatients (Brewer et al. 2008; Streeter et al.2008), such that our results stress the importance ofapplying specific restorative techniques for inhibitorydeficits among CDI patients attempting to achieve long-term abstinence. The correlation analyses showed thatStroop inhibition errors were especially correlated withtrait measures of emotion-driven impulsivity, indicating thatthe tendency to commit impulsive acts under the influenceof strong emotional states may increase the probability ofinhibitory failures—this link should be taken into accountduring specific tailored interventions. Nonetheless, wefound that the main predictor of go/no-go performance isseverity of alcohol use, which is consistent with a numberof previous findings showing that both acute alcoholadministration and chronic use dose-dependently impairinhibitory control (Lawrence et al. 2009; Marczinski et al.

Table 3 Correlations between trait-impulsivity dimensions (UPPS-P subscales), drug use patterns and neuropsychological performance

Impulsivity indices Compulsivity indices

Inhib_errors Stroop % FA go/no-go % Brief R-SAT Persev_errors RL

NU_UPPS-P 0.245 (0.014)a 0.124 (0.240)b −0.152 (0.127)b 0.209 (0.049)b

LPR_UPPS-P −0.172 (0.086)a 0.120 (0.256)b −0.109 (0.277)b −0.018 (0.867)b

LPE_UPPS-P 0.026 (0.797)a 0.197 (0.05)b −0.079 (0.428)b −0.100 (0.350)b

SS_UPPS-P 0.079 (0.437)a −0.013 (0.904)b 0.009 (0.929)b −0.159 (0.140)b

PU_UPPS-P 0.260 (0.009)a 0.062 (0.559)b −0.169 (0.089)b 0.158 (0.140)b

Alcohol combined severity 0.011 (0.909)a 0.215 (0.032)b −0.087 (0.367)b −0.085 (0.407)b

Cocaine quantity 0.178 (0.077)a 0.167 (0.098)b 0.040 (0.677)b −0.061 (0.555)b

Cocaine duration 0.139 (0.152)a 0.002 (0.983)b −0.228 (0.016)b −0.198 (0.06)b

Significant correlations between variables are shown in italics

Inhib_errors Stroop inhibition errors Stroop; % FA Go/no-go percentage of false alarms Go/no-go; % Brief R-SAT percentage of brief items R-SAT; Persev_errors RL perseverative errors reversal learning; NU_UPPS-P negative urgency; LPR_UPPS-P lack of premeditation; LPE_UPPS-Plack of perseverance; SS_UPPS-P sensation of seeking; PU_UPPS-P positive urgencya Spearman correlation analysesb Pearson product moment correlation analyses

Table 4 Multiple hierarchical regression models of the association between years of education, severity of alcohol use, amount and duration ofcocaine use and neuropsychological performance

Domain Test Years education UPPS-P Alcohol/cocaine use Full model Significant contributorsR2 change (p) R2 change (p) R2 change (p) R2 adj (p)

Impulsivity Inhib_errors Stroop 0.020a (0.229) 0.065a (0.179)

% FA Go/no-go 0.041 (0.055) 0.037 (0.062) 0.044 (0.039) 0.092 (0.010) Severity of alcohol (0.039)

Compulsivity % Brief R-SAT 0.003 (0.545) 0.057 (0.012) 0.043 (0.035) Duration of cocaineuse (0.012)

Persev_errors RL 0.019 (0.175) 0.044 (0.117) 0.033 (0.106)

Inhib_errors Stroop inhibition errors Stroop;% FA Go/no-go percentage of false alarms Go/no-go;% Brief R-SAT percentage of brief items R-SAT;Persev_errors RL perseverative errors reversal learninga Nagelkerke’s pseudo R2

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2005). This finding has more readily implications for theclinical treatment of cocaine dependence, by providingneurocognitive support to the notion that alcohol use mayprime disinhibited responses potentially driving cocaine-seeking behaviour among co-abusing subjects.

Previous studies have also demonstrated response per-severation/flexibility deficits in CDI (Ersche et al. 2008;Woicik et al. 2011). These deficits are frequently interpretedas difficulties to switch a previously rewarded responsepattern in spite of repeated exposure to negative feedback(perseveration) and anatomically linked to the lateralorbitofrontal cortex (Zald and Andreotti 2010). However,our dynamic analysis of the reversal task performanceyields a remarkably different interpretation. CDI’s perfor-mance deficits were particularly pronounced during thesecond phase of the task, where probabilistic contingenciesare downgraded to 70/30—increasing the complexity ofstimulus-reinforcement learning—and these deficits wereunrelated to reversal shifts—the probability to maintainthe same response after punishment was actually higheramong controls. These dynamic response patterns werenot genuinely indicative of perseveration but rather indic-ative of more global alterations in flexible stimulus-reinforcement learning, which transversally affects bothinitial learning and reversal (Tsuchida et al. 2010). Thisinability to interpret feedback in the broader context of theoutcome history across trials has been identified as a keymanifestation of orbitofrontal dysfunction in neurologicalpatients (Tsuchida et al. 2010). This pattern is qualitativelydifferent to that found in recreational psychostimulant users(Verdejo-García et al. 2010), who outperformed healthycontrols on reversal reinforcement learning curves (due tohigher probability of maintaining the same choice afterbeing rewarded). Comparative data from both studiessuggest that deteriorations of reinforcement learningconstitute a relevant hallmark of severity of cocainedependence.

The latter notion is in agreement with our second mainfinding: duration of cocaine use, a proxy of lifetimeexposure, is the leading factor in predicting inflexibleperformance (stimulus bound, rather than goal driven) onthe R-SAT. The R-SAT is a complex multitasking test thatcaptures the (in)ability to bypass environmental cues andinternal habits that oppose the most efficient strategy toachieve a long-term goal and the (in)ability to update thevalue of different available reinforcers in order to maximiselong-term profit (Levine et al. 2000). Furthermore, it wasspecifically designed to optimise the ecological validity ofneuropsychological assessment, and therefore, it is consid-ered predictive of everyday problems among substancedependent individuals (Verdejo-García and Pérez-García2007). For example, recent findings from our lab havedemonstrated that poor performance on this test signifi-

cantly predicts shorter retention in residential treatmentamong CDI (Verdejo-García et al., submitted). Thesefindings are consistent with the notion that progression ofcocaine use leads to deterioration of flexible stimulus-reinforcement learning, which probably relates to thepredominance of compulsive outcome-detached behaviour,which disregards long-term goals such as drug abstinenceor planning of alternative behaviours.

This study has a number of strengths, including therecruitment of a well-characterised clinical sample ofabstinent CDI; the use of reliable, sensitive and specificinstruments to estimate drug use parameters, trait impulsiv-ity and impulsive vs. compulsive neuropsychologicalprofiles; and the use of fine-grained dynamic patternanalyses of complex tasks like the go/no-go and theprobabilistic reversal. Relevant limitations include theattempt to explore determinants of addiction transitionsfrom a cross-sectional design and the discrepancy betweengroups on years of education. Although we acknowledgethese limitations, our approach has been suggested as avalid indirect method to explore the influence of premorbidvs. attrition variables on psychostimulant dependence inhumans (Dalley et al. 2011). As for the differences oneducation, some experts have posited that these discrep-ancies are inherent within the addicted population, such thatremoving its variance also subtracts relevant addiction-specific features (Adams et al. 1985). In any case, weapplied a conservative statistical approach to rule out thepossibility that neuropsychological results could be con-founded by this variable.

Acknowledgement This work was funded by the Spanish Ministryof Education under the FPU national plan (grant reference AP 2005-1411), the Spanish Ministry of Science and Innovation (MICINN,Dirección General de Investigación y Gestión del Plan NacionalI+D+i) under grant PSI2009-13133 and grant SEJ 2006-8278 by theJunta de Andalucía through the grant P07.HUM 03089 and by thePlan Nacional sobre Drogas (2009), COPERNICO grant.

Conflict of interest The authors have no conflicts of interest todeclare.

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