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Neurocognition in college-aged daily marijuanausersMary P. Beckera, Paul F. Collinsa & Monica Lucianaa

a Department of Psychology, Center for Neurobehavioral Development, University ofMinnesota, Minneapolis, MN, USAPublished online: 12 Mar 2014.

To cite this article: Mary P. Becker, Paul F. Collins & Monica Luciana (2014): Neurocognition in college-aged dailymarijuana users, Journal of Clinical and Experimental Neuropsychology, DOI: 10.1080/13803395.2014.893996

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Neurocognition in college-aged daily marijuana users

Mary P. Becker, Paul F. Collins, and Monica Luciana

Department of Psychology, Center for Neurobehavioral Development, University of Minnesota,Minneapolis, MN, USA

(Received 3 September 2013; accepted 7 February 2014)

Background: Marijuana is the most commonly used illicit substance in the United States. Use, particularly when itoccurs early, has been associated with cognitive impairments in executive functioning, learning, and memory.Method: This study comprehensively measured cognitive ability as well as comorbid psychopathology andsubstance use history to determine the neurocognitive profile associated with young adult marijuana use.College-aged marijuana users who initiated use prior to age 17 (n = 35) were compared to demographicallymatched controls (n = 35). Results: Marijuana users were high functioning, demonstrating comparable IQs tocontrols and relatively better processing speed. Marijuana users demonstrated relative cognitive impairments inverbal memory, spatial working memory, spatial planning, and motivated decision making. Comorbid use ofalcohol, which was heavier in marijuana users, was unexpectedly found to be associated with better performancein some of these areas. Conclusions: This study provides additional evidence of neurocognitive impairment in thecontext of adolescent and young adult marijuana use. Findings are discussed in relation to marijuana’s effects onintrinsic motivation and discrete aspects of cognition.

Keywords: Marijuana; Neurocognition; Decision making; Memory; Planning.

Marijuana is the most commonly used illicit drugin the United States among adolescents and youngadults, with 52.2% of 18–25-year-olds reportinguse during their lifetimes (Substance Abuse andMental Health Services Administration, 2013).Currently, adolescents and young adults perceivethe risks of marijuana use to be lower, and professless disapproval of peer marijuana use, than in pastyears (Johnston, Bachman, & Schulenberg, 2012;Johnston, O’Malley, Bachman, & Schulenberg,2013). Despite these popular perceptions that mar-ijuana use is not a high-risk activity, a growingbody of research indicates that use is associatedwith cognitive impairments. Given growing

advocacy for marijuana’s legalization and its pre-valence of use, it is crucial to understand better thenature of these impairments.The primary psychoactive compound in mari-

juana, delta-9-tetrahydrocannabinol, acts directlyon the central and peripheral nervous systems,binding to receptors for endogenous cannabinoids.Dense populations of endocannabinoid system(ECS) receptors are located in the prefrontal cor-tex, hippocampus, basal ganglia, thalamus,hypothalamus, and cerebellum. Within theseregions, the ECS broadly modulates synaptic sig-naling (Freund, Katona, & Piomelli, 2003;Viveros, Llorente, Moreno, & Marco, 2005).

Acknowledgements: We thank Snežana Uroševic for helpful comments on prior versions of the manuscript. We also thank BrittanySchmaling for her contribution to data collection.

Funding: This study was supported by the National Institute on Drug Abuse [grant number R01DA017843] awarded to M. Luciana;the National Institute on Alcohol Abuse and Alcoholism [grant number R01AA020033] awarded to M. Luciana; and the University ofMinnesota’s Center for Neurobehavioral Development. M. P. Becker was supported by the Pearson Assessment Fellowship in ClinicalPsychology awarded by the Pearson Clinical Assessment Division. Disclosure: Role of funding source: Nothing declared. Fundingsources had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in thedecision to submit the paper for publication.

Conflict of interest: No conflict declared by any authors.Address correspondence to: Mary P. Becker, Department of Psychology, University of Minnesota, S344 Elliott Hall, 75 East River

Road, Minneapolis, MN 55455, USA (E-mail: petr0308@umn.edu).

Journal of Clinical and Experimental Neuropsychology, 2014

http://dx.doi.org/10.1080/13803395.2014.893996

© 2014 Taylor & Francis

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Animal models indicate that the endocannabinoidsystem undergoes dramatic change during adoles-cence (Ellgren et al., 2008; Rodriguez de Fonseca,Ramos, Bonnin, & Fernandez-Ruiz, 1993). ECSreceptor expression is lower in subcortical(Rodriguez de Fonseca et al., 1993) and frontalcortical regions (Ellgren et al., 2008; Heng,Beverley, Steiner, & Tseng, 2011) in adulthoodthan during earlier stages of development.ECS receptors in the dorsolateral prefrontal cor-

tex (DLPFC) are located in the presynaptic term-inals of inhibitory GABAergic (GABA = gamma-aminobutyric acid) interneurons (L. E. Long, Lind,Webster, & Weickert, 2012). ECS receptor activa-tion results in excitation through inhibition of theinhibitory GABAergic interneurons. Normativepatterns of modulation of the ECS through adoles-cence may be a mechanism through which greaterdegrees of cognitive control are achieved. That is,as ECS receptors are pruned as part of the norma-tive changes in brain structure that occur duringadolescence (Gogtay & Thompson, 2010), greaterneuronal inhibition develops in the DLPFC,increasing capacities for cognitive control, self-directed behavior, and other regulatory functions(L. E. Long et al., 2012).Disruption of the ECS during adolescence

through the introduction of outside cannabinoidscan have long-term effects on synaptic transmissionand associated behaviors, leading to persistentalterations in adulthood. In rodents, chronic canna-binoid administration during adolescence is linkedto decreased adult serotonergic activity in the brainstem (Bambico, Nguyen, Katz, & Gobbi, 2010) andblunted dopamine activity in the midbrain (Pistiset al., 2004). Rodents exposed to exogenous canna-binoids during adolescence demonstrate decreasedmemory and learning ability (Jager & Ramsey,2008; Rubino et al., 2009; Schneider & Koch,2003, 2007) as well as decreased inhibitory control(Realini, Rubino, & Parolaro, 2009; Schneider &Koch, 2003) in adulthood.Similarly, cognitive impairments are noted in

human adolescents and young adults in the contextof active marijuana use. As might be expected,impairments are evident during acute intoxication,including impaired attention, executive function,decision-making skills, and memory function(Crean, Crane, & Mason, 2011; Morrison et al.,2009; Ramaekers et al., 2006). Beyond acute intox-ication, adolescent and young adult marijuana use isassociated with numerous impairments, particularlyin verbal memory and executive functioning.Marijuana users demonstrate poorer retrospectiverecall on list-learning tasks (Bolla, Brown, Eldreth,Tate, & Cadet, 2002; Gonzalez et al., 2012; Hanson

et al., 2010; Harvey, Sellman, Porter, & Frampton,2007; Solowij et al., 2011; Takagi et al., 2011) as wellas poorer memory for stories (Fried, Watkinson, &Gray, 2005; Medina et al., 2007; Schwartz,Gruenewald, Klitzner, & Fedio, 1989). Similarly,marijuana users display diminished memory forfuture actions assessed through prospective memorytasks (Bartholomew, Holroyd, & Heffernan, 2010;McHale & Hunt, 2008; Montgomery, Seddon, Fisk,Murphy, & Jansari, 2012).

Executive functioning skills appear to be dimin-ished in marijuana users as well. Marijuana usersshow decreased planning ability on a Tower ofLondon task (Grant, Chamberlain, Schreiber, &Odlaug, 2012) and a task of logical organization(Montgomery et al., 2012). Marijuana users demon-strate less flexibility and abstract reasoning abilitythan nonusers (Bolla et al., 2002; Pope & Yurgelun-Todd, 1996), and decision making tends to be morerisky (Clark, Roiser, Robbins, & Sahakian, 2009;Grant et al., 2012; Solowij et al., 2012).Marijuana users demonstrate impairments incon-

sistently in other cognitive domains, including atten-tion (Bolla et al., 2002; Dougherty et al., 2013;Lisdahl & Price, 2012), processing speed (Friedet al., 2005; Lisdahl & Price, 2012; Medina et al.,2007), and spatial reasoning (Harvey et al., 2007;Pope & Yurgelun-Todd, 1996). It is unclear whetherperformance in these domains is directly related tomarijuana use, or whether other behaviors associatedwith marijuana use contribute to these findings.

Deficits remain during early (Cuttler,McLaughlin, & Graf, 2012; Dougherty et al.,2013; Fried et al., 2005; McHale & Hunt, 2008)and sustained (Bolla et al., 2002; Hanson et al.,2010; Lisdahl & Price, 2012; Medina et al., 2007)abstinence.

As lawmakers grapple with questions about thelegalization of marijuana, studies of nonacutelyintoxicated marijuana users allow us to understandhow cognitive functioning may be affected in thecontext of regular marijuana use. In addressingthat question, it is important to consider whenindividuals began to use the drug. Marijuanausers who begin use early in life often demonstrategreater cognitive impairment than later onset mar-ijuana users on measures of memory and executivefunctioning (Ehrenreich et al., 1999; Fontes et al.,2011; Gruber, Sagar, Dahlgren, Racine, & Lukas,2012; Lisdahl, Gilbart, Wright, & Shollenbarger,2013; Pope et al., 2003). Given the important roleof the ECS during development, it is likely thatdisruption of the system at a younger age impactslater cognitive performance, particularly executivefunctions that emerge as frontostriatal brain net-works reach their full maturational potential.

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Two important difficulties emerge when trying tocompare studies of young adult marijuana users.First, the age range tends to vary widely betweenstudies. Studies exploring samples of college-agedsubjects commonly use a broad age and developmen-tal range, including people in their late twenties orthirties (Battisti et al., 2010; Bolla et al., 2002;Ehrenreich et al., 1999; Wagner, Becker, Gouzoulis-Mayfrank, & Daumann, 2010; Whitlow et al., 2004).While several studies have focused on adolescent mar-ijuana users (Hanson et al., 2010; Harvey et al., 2007;Medina et al., 2007), it is not common to narrowlydefine age groups in young adult samples. Second, themajority of studies exploring neurocognitive profilesof adolescentmarijuana users focus on a limited rangeof cognitive skills. This approach fails to provide acomprehensive cognitive profile, likely resulting in alimited understanding of any observed cognitive def-icits. A broad assessment of cognitive ability canbetter reveal patterns of weaknesses but also potentialstrengths.

The current study provides a comprehensive cog-nitive profile of non-treatment-seeking college stu-dent marijuana users in a narrow age range (18–20years). Participants were heavy marijuana users buthad histories of typical development and were lowrisk in relation to socioeconomic vulnerabilities,comorbid psychopathology, and general cognitiveability. Marijuana use in 18–20-year-olds is com-mon, and the assessment of cognition within this

selective age band has the benefit of providing infor-mation regarding the functional skills and abilitiesof actively and heavy-using individuals who areotherwise at low risk for impairment.Users were compared to nonusing controls in the

context of an assessment battery that includedmeasures of clinical symptoms, other externalizingbehaviors, and neurocognition across multipledomains of function.It was predicted that marijuana users would

exhibit relative impairments in learning and mem-ory, particularly when such skills recruit executivefunctions, as well as decision making, workingmemory, and planning.

METHOD

Participants

Seventy-three individuals, ages 18–20 years, werestudied: healthy nonusing controls (n = 37) andheavy marijuana users who began use before age17 (n = 36). The average age of use onset was 15.2years (Table 1). Participants were recruitedthrough university advertisements, and all weremonetarily compensated.Controls were selected from a larger study

exploring adolescent brain development (describedin more detail in Luciana, Collins, Olson, &

TABLE 1Demographic and substance use characteristics of marijuana users and controls

Participant characteristics

Control (n = 35) Marijuana user (n = 35)

F or χ2 UM (SD) % M (SD) Range %

Age (years) 19.40 (0.93) 19.52 (0.62) 0.39Sex ratio (male:female) 13:22 22:13 4.63*Race (% Caucasian) 77.14 88.57 1.61Years of education 13.26 (1.24)a 13.29 (0.94)a 0.01Estimated Full Scale IQa 114.73 (9.40) 115.27 (9.56) 0.05Vocabulary T-scorea 62.10 (6.85) 61.27 (7.88) 0.21Matrix reasoning T-scorea 54.49 (5.98) 56.09 (5.21) 1.35

Alcohol use average –0.59 (0.69) 0.59 (0.76) 46.54**

ASR substance usePast 6 months: Tobacco use per day 0.00 (0.00) 0.91 (1.55) 385.00**Past 6 months: Days drunk 5.37 (9.24) 25.07 (18.38) 142.00**Past 6 months: Days using drugs 0.14 (0.49) 145.94 (40.58)a 636.00**

Marijuana useb

Age first used (years) 15.24 (1.24)Past year: Days MJ used 333.43 (43.61) 208–365Past 30 days: Days MJ used 25.86 (3.24) 20–28Past year: Avg. hits per day 10.09 (8.82) 2–50Past 30 days: Avg. hits per day 10.20 (9.12) 1.5–50Lifetime: Max hits in 24 hours 38.72 (27.52) 6–120

Notes. Mann–Whitney Us were computed when appropriate. ASR = Adult Self-Report; MJ = marijuana; avg. = average.aMarginal means presented, controlling for sex. bVariables only included for marijuana users.*p ≤ .05. **p ≤ .01.

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Schissel, 2009; Olson et al., 2009). Controls wereexcluded if they met current or past Axis I DSM–

IV–TR (Diagnostic and Statistical Manual ofMental Disorders–Fourth Edition, Text Revision;American Psychiatric Association, 2000) criteriafor any psychiatric disorder and/or if they reportedmarijuana use more than once monthly.General inclusion criteria included being a native

English speaker, being right-handed, with normal/corrected-to-normal vision and hearing, and havingno reported history of neurological problems, men-tal retardation, or current pregnancy.Inclusion criteria for marijuana users consisted of

self-reported marijuana use of at least five times perweek for at least 1 year. Use onset was required to bebefore age 17 so that length of use across studyparticipants would be relatively uniform. Marijuanause during this age span has been most stronglyassociated with cognitive impairment (Lisdahl et al.,2013), and use initiation is most common between theages of 16 and 18 years in the United States(Substance Abuse and Mental Health ServicesAdministration, 2013). Marijuana users wereexcluded if they were daily cigarette smokers, if alco-hol use exceeded four drinks for females and fivedrinks for males on more than two occasions perweek, or if they met criteria for current or past sub-stance dependence other than marijuana. One mar-ijuana user met criteria for current and past alcoholdependence, despite meeting the project’s use fre-quency criteria, and was excluded from analyses.Marijuana users were asked to refrain from druguse for at least 12 hours before testing so as not tobe acutely high during the assessment. Longer peri-ods of abstinence were not required, because we didnot wish to study individuals in the midst of drugwithdrawal and because a goal of the study was tocapture functional capacities in the context of activeuse. Formal drug testing was not implemented due tobudgetary limitations and given that the study didnot require long-term marijuana abstinence. Thisstudy was approved by the University ofMinnesota’s Institutional Review Board.Participants provided informed consent prior toparticipation.

Procedure

Interested participants (those who responded toposted advertisements) completed a phone screeningfollowed by an in-person structured interview, theKiddie Schedule for Affective Disorders andSchizophrenia Present and Lifetime Version (K-SADS-PL: Kaufman et al., 1997) to assess for recentand past histories of psychological problems. The

benefit of using the K-SADS to assess psychopathol-ogy in young adults is that it captures past histories ofchildhood disorders while also providing an in-depthassessment of DSM–IV-based adult psychopathol-ogy. Current (recent) ratings were based on the pre-vious 2 months for non-substance-use-relateddisorders and the previous 6 months for substanceuse disorders (SUDs). In addition, information wasobtained about quantity and frequency of drug useacross the past 30 days and past year. Intelligence wasestimated by the Vocabulary and Matrix Reasoningsubtests of the Wechsler Abbreviated Scale ofIntelligence (WASI: Wechsler, 1999). Participantscompleted detailed health and demographic question-naires. Participants who met inclusion criteriareturned for a second assessment, including beha-vioral questionnaires and a comprehensive neurocog-nitive battery. The battery was designed to capture abroad array of functions in the domains of motorbehavior, processing speed, attention, spatial, andverbal memory, and executive skills.

Neurocognitive battery

Motor function

Finger Tapping Test (Lezak, Howieson, &Loring, 2004). This test measures motor speed.Participants tapped a key as many times as possi-ble within a 10-s period. Three trials were adminis-tered for each hand, and the number of taps pertrial was recorded. The average of all three trialsper hand is reported.

Grooved Pegboard (Lafayette Instrument,1989). This test measures psychomotor dexterityand speed. Participants were presented with a flatboard containing rows of holes and small metal“pegs” that fit into the holes on the board. Thepegs were shaped so that one side is square. Eachpeg had to be correctly manipulated in order to fitthe holes. Under timed conditions, participantsused the pegs to fill the holes on the board usingfirst the right hand, then the left hand. Accuracyand response time were recorded.

Processing speed

Digit Symbol (Wechsler Adult IntelligenceScale–Third Edition, WAIS–III, Digit Symbol:Lezak et al., 2004; Wechsler, 1997). This test mea-sures psychomotor performance, sustained atten-tion, response speed, and visuomotorcoordination. This test was administered accordingto WAIS–III standardized procedures. The score

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recorded is the number of squares filled in correctlyout of a total possible 133 squares.

Letter cancellation task (Lezak et al.,2004). This task measures immediate attentionand vigilance but is also a speeded test.Participants viewed a piece of paper on whichwere printed rows of capitalized letters. Theywere instructed to work as quickly but as accu-rately as possible and to cross out all occurrencesof the letters “E” and “C.” Time-to-completionand numbers of errors were recorded.

Verbal fluency

Controlled Oral Word Association Test(COWAT; Delis, Kramer, Kaplan, & Ober, 2000;Lezak et al., 2004). The COWAT assesses verbalproduction as well as rule maintenance andresponse monitoring. It was administered accord-ing to standardized procedures using the targetletters F, A, and S. A total score for each partici-pant was calculated, representing the total numberof words generated across all three trials afterdeductions for rule violations, set-loss errors (i.e.,words not beginning with target letters), and per-severations (i.e., saying the same word more thanonce).

Verbal attention and working memory

Digit Span (WAIS–III Digit Span; Wechsler,1997). This test measures immediate recall ofauditory verbal information. Digit span forwardand digit span backward conditions were adminis-tered according to WAIS–III standardizedprocedures.

Verbal learning and memory

Rey Auditory Verbal Learning Test (RAVLT;Lezak et al., 2004; Rey, 1964). This test measuresacquisition, storage, and retrieval of verbal infor-mation. During the learning stage, participantswere read a list of 15 words five separate timesand were asked to recall as many words as theywere able after each presentation. Then, they wereread a new (interference trial) list of 15 words onceand were asked to recall those words. Participantsthen were asked to freely recall as many words asthey could from the first list (immediate recall).Following a 30-minute delay, participants wereagain asked to recall as many words as theycould from the first list. The number of wordsrecalled and errors during the learning trials, inter-ference trial, immediate recall, and delayed recall

trials were recorded. The learning trials assessedthe participant’s immediate learning and tempor-ary storage of verbal information. The interferencetrial assessed immediate learning of new informa-tion, presented only once. The immediate recalltrial assessed learning recall when the items werenot actively rehearsed in working memory. Thedelayed recall trial performance represented learn-ing that had been consolidated into memory.Intrusion and perseverative errors were also tabu-lated. Intrusion errors occurred when participantsresponded with nonlist words. Perseverative errorsoccurred when participants repeated responses dur-ing a given trial.Additional learning and memory variables were

calculated to best characterize performance. Lossafter consolidation was calculated as the percen-tage of words recalled during delay relative towords recalled during the final learning trial(Takagi et al., 2011). Retroactive interference(Trial 5 vs. immediate recall) and proactive inter-ference (Trial 1 vs. interference) were examined. Toexplore learning efficiency and strategy, bidirec-tional serial ordering and response consistencywere calculated (Delis et al., 2000). Bidirectionalserial ordering refers to recall of stimulus words inthe same order as they are presented, forward orbackward. Response consistency measures howoften the same words are recalled from trial totrial during free recall as a percentage of totalwords recalled during free recall trials:

Conjoint recall of words between Trial 6; 7ð�T6;T7Þ=2 � 100

Spatial memory

Spatial Span. This test measures immediaterecall of visually presented nonverbal informationand is a nonverbal analog of the Digit Span Test.The version used here was computerized using E-prime Version 1.1 (Psychology Software Tools;www.psnet.com). Participants, seated at a compu-ter terminal, viewed arrays of squares on thescreen. One by one, some of the squares “lit up”in a sequence. In the forward condition, partici-pants repeated the sequence by touching thesquares in the remembered sequence using atouch-pen device (FastPoint Technologies, Inc.).In the backward condition, participants repeatedthe sequence in reverse order. The forward andbackward memory spans were recorded as thenumber of items recalled in correct sequence acrosstrials in each condition.

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Spatial Recognition (CambridgeNeuropsychological Test Automated Battery,CANTAB; Fray, Robbins, & Sahakian,1996). This test measures recognition memoryfor spatial locations. The participant viewedempty boxes at different locations on the screen.Five stimuli were presented in succession at differ-ent locations on the screen for 3 s each. After a 5-sdelay, the participant was shown two boxes, one ofwhich was in a location previously displayed in theearlier sequence. The participant indicated whichbox position was shown previously. Accuracy andresponse time were recorded. The percentage ofcorrect trials across all four blocks was used asthe variable of interest.

Self-Ordered Search (CANTAB; Owen, Downes,Sahakian, Polkey, & Robbins, 1990). This testmeasures spatial working memory, self-monitor-ing, and behavioral self-organization. Using acomputerized touch-screen, participants searchedfor blue tokens hidden inside an array of boxes.The task was organized into 4-, 6-, and 8-boxproblems, with increased box number correspond-ing to increased task difficulty. Participants wereinstructed that at any one time there would be asingle token hidden inside one of the boxes. Theirtask was to search until they found it, at whichpoint the next token would be hidden. Once agiven box yielded a token, that box would not beused to hide the token again during the trial. Everybox was used once on every trial; thus, the totalnumber of tokens to be found during each trialcorresponded to the number of boxes on thescreen. A “between-search” error was recordedwhen participants returned to open a box inwhich a token had already been found.Additionally, a strategy score was tabulated. Thestrategy score, which was based on responses to 6-and 8-item searches, reflects the participant’s ten-dency to search through available locations in anorganized fashion. Between-search error scores foreach level of search complexity, as well as strategyscores, were the variables of interest.

Spatial Delayed Response Task (DRT; Luciana& Collins, 1997; Luciana, Collins, & Depue,1998). This task measures working memory forthe locations of spatial targets. During each of 48trials, the participant first observed a central fixa-tion point on a computer monitor. Next, a visualcue appeared in their peripheral vision for 200 ms.After the peripheral visual cue, the cue and fixationpoint disappeared, and the screen blackened forrandomly interspersed delay intervals of 500 or8000 ms. After the delay interval, the participant

indicated the remembered location of the cue witha touch-pen device (FastPoint Technologies, Inc.).A block of 16 “no delay” trials were also adminis-tered prior to the delay trials to measure basicperceptual and visuomotor abilities independentof memory. Average accuracy (in millimeters)and response times (in milliseconds) were recordedfor each condition.

Planning

Tower of London (CANTAB; Owen et al.,1990). This test measures future planning ability.A full task description can be found in Lucianaet al. (2009). Using a computerized touch-screen,participants moved colored balls to match a targetdisplay (problem-solving block). Participants weretold at the start of each problem-solving trial thatthe trial should be completed in X number ofmoves, where X was the minimum number ofmoves required to achieve a perfect solution. Thetotal number of problems in which participantsresponded with the minimum number of moveswas recorded and was expressed as a proportionof total possible perfect solutions. Participantswere instructed not to make the first move untilthey knew which balls to move and were encour-aged to solve the problem correctly on the first try.Time from presentation of the problem to startingto solve the problem (planning time) was recorded.Planning time and percentage of perfect solutionswere examined.

Motivated decision making

Iowa Gambling Task (IGT: Bechara, Damasio,Damasio, & Anderson, 1994). This task measuresmotivated decision-making ability. Participants com-pleted a computerized version of the IGT (Hooper,Luciana, Conklin, &Yarger, 2004) during which theyselected from among four decks of cards varying intheir amounts of monetary reward and punishment(Bechara et al., 1994). Participants worked to earnreal money (maximum of $5). For each selectionfrom Decks 1 or 2 (the “disadvantageous decks”),participants would win $0.25 but the losses wereorganized so that over 20 selections from thesedecks, participants would incur a net loss of $1.25.The difference between Decks 1 and 2 was in thefrequency andmagnitude of punishment: Deck 1 con-tained frequent (50% of cards) punishments, whereasDeck 2 contained less frequent (10% of cards) butmuch larger punishments. For each selection fromDecks 3 or 4 (the “advantageous decks”), participantswould win either $0.10 or $0.15, and the losses wereorganized so that over 20 selections from these decks,

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participants would accrue a net gain of $1.25. Similarto the disadvantageous decks, the two advantageousdecks differed from each other in the frequency ofpunishment, such that small punishments occurredon 50% of the cards in Deck 3, and larger punish-ments occurred on 10% of the cards in Deck 4. Trials(n = 100) were split into five blocks with 20 trials perblock. For each block, the number of choices fromdisadvantageous decks was subtracted from numberof choices from advantageous decks. Thus, valuesabove “0” correspond to relatively advantageouschoices. In addition, the actual numbers of selectionsmade from each deck were tabulated across the fulltask to analyze choice preferences.

Together, these measures took several hours tocomplete.

Self-report questionnaires

Participants completed Achenbach’s Adult Self-Report (ASR; Achenbach & Rescorla, 2003) ques-tionnaire, which yields assessments of internalizingand externalizing tendencies as well as answers tospecific substance use questions. Substance usescales consist of self-reported daily tobacco use,number of days drunk, and days using drugs(other than alcohol or tobacco) for the previous 6months.

Marijuana users completed the PersonalExperience Inventory (Henley & Winters, 1989)to assess other substance use. The PEI measuresthe frequency of substance use within the last 3months on a 5-point scale (never, 1–5 times, 6–20times, 21–49 times, 50–99 times, 100+ times).Nonmarijuana drug use was calculated by sum-ming the frequency ratings across illicit drugclasses (psychedelics, cocaine, amphetamines, bar-biturates, tranquilizers, heroin, narcotics, steroids,inhalants, and recreation use of prescriptiondrugs). Controls had no history of use of theseother substances.

Statistical approach

Data were analyzed using the Statistical Packagefor the Social Sciences (SPSS Inc., Chicago, IL,USA), Windows Version 19. Distributions of allvariables were examined. Error variables for theLetter Cancellation, RAVLT, and COWAT taskswere square root transformed to meet assumptionsfor parametric analysis. Chi-square tests were usedto compare nominal variables (i.e., sex) betweenmarijuana users and controls. Mann–Whitney Uanalyses assessed for group differences in substanceuse characteristics, in which variances were

unequal between groups. Univariate and repeatedmeasures analyses of variance (ANOVAs) assessedfor group differences in other characteristics. Sex,IQ, and alcohol use were covaried in all groupcomparisons. To best characterize a wide varietyof alcohol use patterns, alcohol use was quantifiedas an average of two alcohol use variables thatwere standardized across the whole sample (con-trols and marijuana users). The first alcohol usevariable was calculated by multiplying the partici-pants’ self-reported average drinking occasions perweek and the average number of alcoholic drinksper occasion for the previous 6 months, as assessedby direct interview. The second alcohol use vari-able was the number of days that the participantreported being drunk in the last 6 months, asassessed by the ASR questionnaire. Missing datafrom 2 controls and 1 marijuana user on an alco-hol use variable reduced the sample size to 35participants per group.Due to recruitment procedures, controls had no

history of tobacco use or non-alcohol-related druguse. Non-alcohol-related drug use was calculatedfrom responses on the PEI. Tobacco use was quan-tified by responses to questions from the ASR andK-SADS regarding patterns of daily use.Accordingly, levels of alcohol, tobacco, and

other drug use differed between groups, but alco-hol use was the only variable that could be used inbetween-group analyses. The contributions oftobacco and other drug use to cognition in themarijuana users were examined within marijuanausers using partial correlations to explore theextent to which other substance use contributedto domains where group differences were observed.Alcohol use, tobacco use, and nonmarijuana druguse were correlated with task performance in mar-ijuana users, controlling for IQ, sex, and othersubstance use. For example, the examination ofalcohol effects controlled for tobacco and othernon-marijuana-related drug use.Scatterplots of residuals were examined to assess

for normal distributions, and data were screenedfor outliers and influential data points. To providea conservative control for the number of statisticalcomparisons, alpha levels equal to or below .01were considered significant, and alpha levels at orbelow .05 were considered trend effects.

RESULTS

Demographics

Groups were comparable in age, years of educa-tion, race/ethnic distribution, and IQ. Consistent

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with similar studies of drug users with demogra-phically matched college student controls (Croft,Mackay, Mills, & Gruzelier, 2001), IQs were gen-erally above average, indicating that participantswere generally high functioning from a cognitivestandpoint. There were significantly more malesamong marijuana users, consistent with the genderdistribution of marijuana users in this age range(Substance Abuse and Mental Health ServicesAdministration, 2013). (See Table 1.)

Substance use characteristics

Marijuana users had significantly higher alcoholuse as assessed by averaging the standardizedscores of typical alcohol use patterns and daysdrunk in the past 6 months (p < .001; Table 1).Additionally, as would be expected from nationalnorms (Substance Abuse and Mental HealthServices Administration, 2013), marijuana usershad greater tobacco use, days drunk, and daysusing other drugs in the last 6 months than didcontrols. As a result of exclusion criteria, controlsreported no marijuana use. Marijuana usersreported a mean age of initiation of marijuanause during midadolescence (M = 15.24, SD =1.24). Marijuana users reported nearly daily mar-ijuana use during the past 30 days with a mean of10.20 hits per day; however, there was considerablevariability in the number of reported hits per day,with a standard deviation of 9.12.

DSM–IV–TR diagnostic characteristics

Despite differences from the control sample, mar-ijuana users reported relatively little substance useoutside of marijuana and alcohol. The majority ofmarijuana users had tried other drugs fewer than 5times, and no participant had used any other drugmore than 15 times (Table 2). Almost all marijuanausers met criteria for current and/or past marijuanasubstance use disorder (SUD; Table 3), and manymet criteria for current and past alcohol abuse.1

There was high concordance between current andpast diagnosis of an SUD within subjects (Table 3).SUD symptom patterns were examined in detail toclarify symptom expression related to alcohol, mar-ijuana, and other drug use. Marijuana users exhib-ited fewer symptoms related to current alcohol use(M = 0.89 symptoms per person, SD = 1.05) thanrelated to marijuana use (M = 4.03 symptoms per

1Note that the inclusion criterion of limiting alcohol usefrequency to 4–5 drinks per occasion less than twice weeklystill allows for the possibility of alcohol abuse symptoms.

TABLE 2Lifetime other drug usage in marijuana users

Substance1–5times

6–10times

11–15times

Mean of totaltimes

CannabisHash 1 0.14 (0.85)

StimulantsAdderall 4 1 0.70 (2.06)Cocaine 2 1 0.33 (1.34)

OpioidsVicodin 5 0.39 (1.35)Codeine 1 0.06 (0.34)Opium 4 0.26 (0.79)Oxycodone 2 0.05 (0.24)

PsychedelicsMushrooms/LSD

16 3 2 1.24 (2.39)

Salvia 4 0.31 (1.08)Mescaline 1 0.03 (0.17)

BenzodiazepinesXanax 3 1 0.09 (0.28)Valium 1 0.11 (0.69)

Sedative/hypnoticsAmbien 1 0.29 (1.69)

OtherEcstasy 12 0.81 (1.48)Nitrous oxide 2 0.17 (0.86)

Note. Number of participants who used each drug at differ-ent usage levels.

TABLE 3DSM–IV–TR diagnostic characteristics of marijuana usersample. Total number of marijuana users that met criteria

Diagnosis Current PastCurrentand past

Marijuana dependence 18 18 17Marijuana abuse 12 14 12Alcohol dependence 0 0 0Alcohol abuse 11 16 10Bipolar NOS 1 1 0Oppositional defiant disorder 0 2 0Specific phobia 0 1 0

ComorbidityOnly marijuana

dependence14 9

Only marijuana abuse 6 6Only alcohol abuse 2 0Marijuana dependence,

alcohol abuse3 9

Marijuana abuse,alcohol abuse

6 7

Note. Marijuana users: N = 35. DSM–IV–TR = Diagnosticand Statistical Manual of Mental Disorders–Fourth Edition,Text Revision (American Psychiatric Association, 2000); NOS= not otherwise specified.

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person, SD = 1.90; U = 100.5, p < .001). Similarly,marijuana users reported fewer symptoms related topast alcohol use (M = 1.20 symptoms per person, SD= 1.32) than related to past marijuana use (M = 4.23symptoms per person, SD = 1.77; U = 114.0, p <.001). No SUD symptoms related to other drug usewere reported amongmarijuana users or controls. Asa result of exclusion criteria, controls exhibited veryfew symptoms related to current alcohol use (M =0.11 symptoms per person, SD = 0.32) or past alco-hol use (M = 0.03 symptoms per person, SD = 0.17)and no symptoms related to other drug use.

Comorbid psychopathology

Given selection procedures, controls were free ofpsychopathology. Outside of SUDs, marijuanausers reported little psychopathology (Table 3).One participant met criteria for current bipolarnot otherwise specified (NOS); another met criteriafor past bipolar NOS. Both were due to episodichypomania, consistent with the reported comor-bidity between SUDs and bipolar disorder (Perliset al., 2004; Wilens et al., 2008). Other psycholo-gical disorders included past oppositional defiantdisorder (n = 2) and past specific phobia (n = 1).As will be described, data were analyzed with andwithout inclusion of these individuals.

Overall, the sample of marijuana users is notablefor its good overall psychological health indepen-dent of marijuana use and for high levels of pre-morbid functioning (as indicated throughestimated verbal IQ scores).

Neurocognitive performance

See Table 4.

Motor function

Groups were equivalent in Finger Tapping andGrooved Pegboard performance with no evidenceof laterality differences.

Processing speed

Marijuana users demonstrated faster LetterCancellation completion times. Omission and com-mission errors were equivalent between groups.Completion times were uncorrelated with overallerrors in both groups. Groups were equivalent inDigit Symbol performance.

Verbal fluency (COWAT)

Marijuana users displayed greater verbal fluency,producing more correct responses. Set-loss errorswere only marginally greater among users.Perseverative errors were equivalent between groups.

Verbal learning and memory

The groups were equivalent on Digit Span forwardand backward.A repeated measures analysis of covariance

(ANCOVA) across RAVLT trials (T1–T5, interfer-ence trial, immediate recall, and delayed recall)revealed a group effect, F(1, 65) = 7.31, p = .009,ηp

2 = .10, and a Group × Trial interaction, F(1, 65) =8.74, p = .004, ηp

2 = .12 (Figure 1). Follow-up ana-lyses revealed that marijuana users had a trendtoward poorer performance during List LearningTrial 5, F(1, 65) = 3.90, p = .05, ηp

2 = .06.Marijuana users performed worse than controls onthe interference trial list. Following interference,marijuana users demonstrated poorer immediaterecall and poorer 30-minute delayed recall.Furthermore, there was a trend for marijuana usersto have greater loss after consolidation, F(1, 65) =6.30, p = .02, ηp

2 = .09 (marijuana userM = 78.04%,SD = 17.16; control M = 90.90%, SD = 16.76)Examining retroactive interference (Trial 5 vs.

immediate recall) and proactive interference (Trial1 vs. interference) revealed no significant trial bygroup interactions. No significant group differ-ences were noted during learning or delayed recallfor bidirectional serial ordering or response consis-tency during learning. Marijuana users demon-strated less response consistency between short-and long-term recall, F(1, 65) = 15.78, p < .001,ηp

2 = .20 (marijuana user M = 80.64%, SD =14.55; controls M = 93.73%, SD = 6.58).Errors during list learning and recall were

equivalent between groups.

Spatial working memory

No significant group differences were evident on for-ward or backward Spatial Span, Spatial Recognition,or Spatial Self-Ordered Search. On the DRT, groupswere equivalent in their accuracy and responselatency for the no delay condition, indicating thatbasic sensorimotor functions recruited by the taskwere similar between groups. Additionally groupsdisplayed equivalent accuracy during the 500-msdelay condition. Marijuana users demonstrated atrend toward decreased accuracy on the 8000-msdelay condition. Marijuana users had significantly

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TABLE 4Neuropsychological battery scores

Cognitive measureControlM (SD)

Marijuana userM (SD) F p ηp

2

Finger Tapping TestDominant hand (taps) 42.16 (10.19) 46.93 (7.19) 3.01 .087 .04Nondominant hand (taps) 42.02 (8.49) 44.76 (7.65) 1.29 .261 .02

Grooved PegboardDominant hand time (s) 65.41 (8.14) 64.12 (8.31) 0.32 .575 .01Nondominant hand time (s)a 72.97 (9.98) 71.13 (11.55) 0.32 .572 .01

Letter CancellationTime (s) 111.95 (16.60) 96.79 (18.08) 7.50* .008 .10Total omissionsc 1.51 (0.71) 1.49 (0.76) 0.01 .927 .00Total commissionsc 0.81 (0.27) 0.72 (0.12) 1.42 .237 .02

Digit SymbolTotal correct 87.59 (15.12) 89.35 (13.21) 0.17 .680 .00

COWAT–Verbal FluencyTotal correct words generated 43.24 (8.50) 50.79 (10.98) 6.78* .011 .09Total set-loss errorsc 0.82 (0.29) 1.11 (0.52) 4.67^ .034 .07Total perseverative errorsc 1.03 (0.39) 0.95 (0.41) 0.35 .557 .01

Digit SpanDigits forward (no. recalled) 7.52 (0.86) 6.99 (1.03) 3.28 .075 .05Digits backward (no. recalled) 5.76 (1.19) 5.24 (1.17) 1.95 .167 .03

RAVLT–Verbal Learning and MemoryTotal words: Trials 1–5 55.05 (7.00) 51.29 (8.34) 2.70 .105 .04Total intrusions: Trials 1–5a 1.02 (0.58) 1.30 (0.63) 2.26 .121 .04Total perseverative errors: Trials 1–5a 1.96 (0.78) 1.81 (1.01) 0.26 .609 .00Total words: Interference trial list 7.40 (1.95) 5.83 (1.86) 9.08* .004 .12Total words: Immediate recall 12.24 (2.08) 10.56 (2.44) 6.65* .012 .09Total words: Delayed recall 12.03 (2.13) 9.68 (2.91) 10.47* .002 .14

Spatial SpanForward (no. recalled) 6.55 (0.98) 6.99 (0.89) 2.17 .145 .03Backward (no. recalled) 6.78 (1.22) 6.50 (1.01) 0.66 .421 .01

Spatial Recognitiona

% Correct recall 86.50 (8.14) 85.66 (7.20) 0.12 .731 .00

Self-Ordered Searcha

Between search errors 4 0.12 (0.49) 0.18 (0.44) 0.15 .700 .00Between search errors 6 2.79 (3.25) 2.60 (2.67) 0.04 .835 .00Between search errors 8 10.65 (9.02) 9.66 (8.32) 0.13 .716 .00Total between search errors 13.56 (10.21) 12.43 (9.70) 0.13 .720 .00Strategy score: 6–8 30.28 (5.36) 28.80 (5.52) 0.76 .386 .01

Spatial Delayed Response TaskError: No delay (mm) 2.44 (0.76) 2.55 (0.88) 0.17 .682 .00Error: 500-ms delay (mm) 6.44 (2.18) 7.70 (2.23) 3.15 .080 .05Error: 8000-ms delay (mm) 9.87 (3.36) 11.98 (2.55) 4.81^ .032 .07Mean reaction time: No delay (ms) 1823.33 (575.62) 1962.42 (419.96) 0.75 .390 .01Mean reaction time: 500-ms delay (ms) 1663.24 (352.62) 2034.85 (374.35) 10.93* .002 .14Mean reaction time: 8000-ms delay (ms) 1733.73 (345.42) 2234.00 (475.77) 15.60* <.000 .19

Tower of Londona

% Perfect solutions 83.71 (13.33) 74.12 (14.47) 5.24^ .025 .08Average moves 2 2.00 (0.00) 2.00 (0.00)Average moves 3 3.00 (0.16) 3.28 (0.39) 8.63* .005 .12Average moves 4 4.92 (0.89) 5.16 (0.97) 0.72 .398 .01Average moves 5 5.65 (1.00) 6.24 (1.13) 3.11 .083 .05First move initiation time 2 3190.52 (1161.58) 3662.26 (735.35) 2.27 .137 .05First move initiation time 3 5510.37 (2510.15) 5442.88 (1485.40) 0.01 .920 .00First move initiation time 4 8308.17 (5079.47) 8420.53 (3091.36) 0.01 .935 .00First move initiation time 5 12,987.90 (6940.13) 8805.07 (5108.86) 4.75^ .033 .07Average first move initiation time 7499.24 (3547.50) 6582.68 (2138.04) 0.94 .337 .01

(Continued )

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longer response latencies (slower performance) afterboth 500-ms and 8000-ms delays. Accuracy andresponse latencies were uncorrelated in marijuanausers and controls for the 500-ms and 8000-msdelay conditions.

Planning

There was a trend for marijuana users to pro-duce fewer perfect solutions on the Tower ofLondon task, indicating that they made moremoves than necessary to achieve accurate perfor-mance. When individual difficulty levels wereexamined, marijuana users made significantlymore moves to complete three-move problems;a similar pattern nominally characterized otherlevels of difficulty. Additionally, marijuana usershad marginally faster initiation times during five-move problems.

Motivated decision making

On the Iowa Gambling Task, total good minusbad choices over five blocks of the task were exam-ined with block as the within-subjects factor andgroup as the between-subjects factor (Figure 2). Asignificant main effect of group, F(1, 64) = 10.97, p= .002, ηp

2 = .15, was observed, with marijuanausers displaying poorer performance.There was a significant group difference in

choice of Deck 1 [F(1, 64) = 7.63, p = .007, ηp2 =

.11] and Deck 2 [F(1, 64) = 7.77, p = .007, ηp2 =

.11], and a marginal group difference in choice ofDeck 4 [F(1, 64) = 5.26, p = .025, ηp

2 = .08].Marijuana users made more choices from disad-vantageous decks, Decks 1 and 2, and fewerchoices from advantageous Deck 4 (Figure 3).Deck choices were compared to choices expected

by chance. Both groups showed aversion to frequentpunishment decks (1 and 3), choosing from these less

TABLE 4(Continued)

Cognitive measureControlM (SD)

Marijuana userM (SD) F p ηp

2

Iowa Gambling Taskb

Good choices – bad choices: Block 1 –1.12 (9.58) –3.56 (7.18) 0.82 .368 .01Good choices – bad choices: Block 2 3.25 (10.36) –2.13 (7.54) 3.85^ .054 .06Good choices – bad choices: Block 3 3.73 (9.48) –1.68 (8.67) 3.47 .067 .05Good choices – bad choices: Block 4 9.56 (9.32) –1.29 (10.09) 12.73* .001 .17Good choices – bad choices: Block 5 9.80 (10.42) –0.15 (10.66) 9.89* .003 .13Good choices: Total 60.45 (18.17) 49.77 (16.10) 4.01^ .049 .06

Notes. Means reported are marginal means, controlling for sex, IQ, and alcohol use. COWAT = Controlled Oral Word AssociationTest; RAVLT = Rey Auditory Verbal Learning Test. aData unavailable for 1 marijuana user (n = 34). bData unavailable for 1 control(n = 34). cSquare root transformed.

^p ≤ .05. *p ≤ .01.

Figure 1. Rey Auditory Verbal Learning Test (RAVLT) learning curve. Average words recalled during learning Trials 1–5,interference trial (trialB), immediate recall, and 30-minute delayed recall. ^p ≤ .05. *p ≤ .01.

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often than expected at a significant or trend level:Deck 1: marijuana user t(34) = –2.12, p = .041,control t(33) = –7.43, p < .001; Deck 3: marijuanauser t(34) = –3.70, p = .001, control t(33) = –2.26, p =.031. Within infrequent punishment decks, controlsdemonstrated a preference for smaller wins withinfrequent smaller punishment [Deck 4: t(33) =4.24, p < .001], with choices from Deck 4 correlatingwith overall good choices throughout the task(rsex, alcohol use, IQ = .80, p < .001). Conversely, mar-ijuana users showed a preference for greater wins

with infrequent but greater punishment [Deck 2:t(34) = 2.68, p = .011].

Associations with other substance use

For all significant group effects described above,alcohol, tobacco, and nonmarijuana drug use wereeach separately examined for associations withtask performance within marijuana users (Table 5).

Figure 3. Iowa Gambling Task. Number of choices from each deck across five blocks between groups. ^p ≤ .05. *p ≤ .01.

Figure 2. Iowa Gambling Task. Total good choices minus total bad choices over five blocks. ^p ≤ .05. *p ≤ .01.

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Alcohol use

For learning and recall measures of theRAVLT, errors made within the 8000-ms delaycondition of the delayed response task, and aver-age moves on three-move problems of the Tower

of London (TOL), greater alcohol use was unex-pectedly associated with better task performance.Alcohol use was not significantly correlated,using our stringent alpha level, with other taskperformance variables.Furthermore, within marijuana users, no group

differences in cognitive function were noted betweensubjects with current alcohol abuse (n = 11) andsubjects without current alcohol abuse (n = 24).

Tobacco use

Cognitive performance was uncorrelated withtobacco use within marijuana users (Table 5).

Nonmarijuana drug use

There was a trend for nonmarijuana drug use tobe positively correlated with RAVLT delayedrecall as well as with faster initiation times(decreased planning) on five-move Tower ofLondon problems.

Impact of comorbid psychopathology

Significant group differences remained unchangedwhen marijuana users with psychopathology out-side of SUDs (n = 5) were excluded.

DISCUSSION

This study employed a comprehensive neurocogni-tive battery to assess a range of cognitive abilitiesin a low-risk sample of college-aged daily mari-juana users who were studied in a nonintoxicatedstate but in the context of active use. The mari-juana user sample was notable in terms of its rela-tive psychological health, absence of externalizingpsychopathology, and above-average levels of gen-eral intellect. Despite this general pattern of lowrisk, and in the context of several cognitivestrengths, marijuana users demonstrated a numberof cognitive deficits relative to demographicallymatched controls.Marijuana users performed particularly well on

speeded measures, such as letter cancellation andverbal fluency. Both measures are short tasks (<2min), requiring short-term sustained attention. It isimportant to note that these tasks are externallymotivated, with instructions to work quickly.On the other hand, marijuana users demon-

strated many relative deficits in the domains ofmemory, problem solving, and motivated decisionmaking. It appears marijuana users were generallyless motivated and, as a consequence, less

TABLE 5Follow-up partial correlations between substance use variableand cognitive measures in marijuana users controlling for sex

and IQ

Cognitive measureAlcoholusea

Tobaccouseb

Nonmarijuanadrug usec

Letter Cancellationtime .10 –.20 .32

COWAT–VerbalFluency

Total words –.12 –.16 .03Total set-loss errorsd –.21 –.04 –.32

RAVLT–VerbalLearning & MemoryTotal words: Trial 5 .47* –.15 .01Total words:Interference trial list

.50* .11 .06

Total words:Immediate recall

.56* –.23 <–.01

Total words: Delayedrecall

.48* –.18 .33

% of learning recalledduring delay

.26 –.18 .43^

Delayed consistency .48* .07 .01

Spatial DelayedResponse TaskError: 8000-ms delay –.40^ .05 .14Reaction time: 500 ms-delay

–.28 –.29 .01

Reaction time: 8000-ms delay

–.30 –.21 <–.01

Tower of Londone

% Perfect solutions .35 .04 .05Average moves 3 –.40^ –.24 –.20First move initiationtime 5

–.20 –.17 –.36^

Iowa Gambling TaskGood choices – badchoices: Block 2

< –.01 –.13 –.15

Good choices – badchoices: Block 4

.21 .09 –.08

Good choices – badchoices: Block 5

–.04 .15 –.18

Good choices: Total –.21 –.22 .14Deck 1 choices .05 –.08 <.01Deck 2 choices –.20 –.04 .24Deck 4 choices –.06 .09 –.22

Notes. COWAT = Controlled Oral Word Association Test;RAVLT = Rey Auditory Verbal Learning Test. aControllingfor tobacco and nonmarijuana drug use. bControlling for alco-hol and nonmarijuana drug use. cControlling for alcohol andtobacco use. dSquare root transformed. eData unavailable for 1participant (n = 34).

^p ≤ .05. *p ≤ .01.

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persistent in the absence of motivation-enhancinginstruction, when tasks required sustained andinternally motivated effort. For instance, mari-juana users’ verbal learning performance was char-acterized by a pattern of sustained accurateperformance during early learning trials and aslow and subtle divergence from controls’ perfor-mance as the task progressed (see Figure 1).Marijuana users demonstrated greater loss afterconsolidation when required to produce learnedinformation after a time delay. Marijuana users’relatively decreased response consistency indicatesuse of a less efficient recall strategy. The observa-tion of relatively poor retention of learned materialover time is consistent with other reports (Blocket al., 2002; Fried et al., 2005; Medina et al., 2007;Pope & Yurgelun-Todd, 1996; Tait, Mackinnon, &Christensen, 2011; Takagi et al., 2011; Wagner et.al., 2010) and could be explained by a combinationof deficits in executive control as well as motiva-tion. While the loss of information may appear tobe relatively low in absolute magnitude (1 in 15presented words), this same degree of informationloss in the context of ongoing academic, occupa-tional, and social interactions could have obviousimpacts on social function and other areas ofachievement. Similarly, the Tower of London andIowa Gambling Tasks require sustained effort foroptimal performance, and the longer trials of thedelayed response task require intrinsically focusedattention during the delay interval to support accu-rate responding (Luciana et al., 1998).When these findings are considered in relation to

the areas where marijuana users demonstratedrelative strengths (short-term externally motivatedtasks that required quick performance), it could bethat marijuana users are most impaired when tasksrequire intrinsic motivation and most successfulwhen tasks are enhanced by the provision of moti-vation-enhancing instructions, such as the instruc-tion to work quickly. External or extrinsicmotivation reflects motivation that is cued throughexternal means and is performance or incentivebased. In contrast, internal/intrinsic motivation isreflected by a sense of purpose as well as voluntaryengagement with a task in the absence of obviousexternal incentives (Lee, Reeve, Xue, & Xiong,2012; Murayama, Matsumoto, Izuma, &Matsumoto, 2010). The speeded tasks within ourbattery can be construed as performance-basedmeasures of external motivation. To some extent,all tasks in the battery require intrinsic motivation,but voluntary engagement is most stronglyrecruited when tasks are lengthy and requireincreasing amounts of self-organization and focusto assure accurate performance.

It has been proposed that a source of intrinsicmotivation is the subjective value of achieving suc-cess on a task (Murayama et al., 2010). Subjectivevalue is modulated within the brain through thedopaminergic reward network, including midbrainstructures, ventral and dorsal striatum, and theprefrontal cortex (Kable & Glimcher, 2007; Levy& Glimcher, 2011). These regions are involved inboth incentive (performance)-based and intrinsicmotivational tasks. The lateral prefrontal cortexhas been suggested as a substrate for the intrinsicpreparatory cognitive control necessary to pursuedistinct goals. In addition, conscious effort-basedprocessing has been associated with the anteriorcingulate and dorsolateral prefrontal cortices(Mulert, Menzinger, Leicht, Pogarell, & Hegerl,2005). A recent series of studies has further linkedpersonal agency and other aspects of intrinsicmotivation to activation in the anterior insularregion (Lee & Reeve, 2013; Lee et al., 2012).Although most neuroscientific studies of motiva-tion involve manipulations of external incentive-based motivation, and studies of intrinsic motiva-tion are limited, we propose that overlapping sub-cortical systems are involved in both types ofmotivation given that incentive-based learningnecessarily impacts subjective valuation but thathigher order frontal systems (involving multipleregions of the prefrontal cortex) are more stronglyrecruited in support of intrinsic motivation—thepursuit of goals in the absence of obvious externalincentives.

These frontal systems are known to mediatetasks in our battery where marijuana users demon-strated deficits. For instance, success on delayedrecall trials of the RAVLT has been linked tofrontal mechanisms (Gershberg & Shimamura,1995; N. M. Long, Oztekin, & Badre, 2010), andmultiple regions of the dorsal, medial, and ventralprefrontal cortex, as well as the insular region,contribute to successful IGT performance(Bechara et al., 1994; Lawrence, Jollant, O’Daly,Zelaya, & Phillips, 2009). Accordingly, wehypothesize that neuroscience-based studies ofintrinsic versus extrinsic motivation in marijuanausers would reveal stronger evidence of disruptionin the former. Although this suggestion is specula-tive, given that motivation was not directly mea-sured in this study, motivation-enhancinginstruction has been found to improve marijuanausers’ performance, but not that of controls, on averbal learning and memory task (Macher &Earleywine, 2012).

Several tasks in our battery, including the spatialdelayed response task, are heavily influenced bydopamine neurotransmission in frontostriatal

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circuits, with increased dopamine activity facilitat-ing better performance (Luciana et al., 1998).Similarly, acute but indirect reduction of dopami-nergic activity has been found to produce impaireddecision-making performance on the IowaGambling Task (Sevy et al., 2006), mirroring thedisruptions noted in the current study. Throughoutthe Iowa Gambling Task, marijuana users failed toacquire an effective strategy. Marijuana users’choice patterns suggest that reward feedback wasmore compelling to marijuana users than was pun-ishment feedback.

The observed performance deficits on the IGTand spatial delayed response task may indicateblunted striatal or frontal dopamine activity inthe marijuana users, a finding previously reportedin adult marijuana users (Kowal, Colzato, &Hommel, 2011) and consistent with the animalliterature (Pistis et al., 2004; Schneider & Koch,2003). A recent study examined dopamine synth-esis capacity in regular marijuana users whoexperienced psychotic-like symptoms while intoxi-cated with nonusing sex- and age-matched controlsubjects. Findings revealed diminished dopaminesynthesis capacity in marijuana users in multipleregions of the striatum (Bloomfield et al., 2013).Consistent with our proposed model, this dimin-ished synthesis capacity was associated with ayounger age onset of marijuana use as well aswith higher current levels of use. A younger ageof marijuana use onset is also associated with dis-rupted patterns of dopamine receptor binding(Urban et al., 2012). Bloomfield et al. (2013) havesuggested that diminished striatal dopamine synth-esis capacity accounts for amotivation in heavymarijuana users. Further research directly asses-sing dopaminergic activity in marijuana users andits relation to cognitive performance is needed tomore comprehensively link dopamine activity tobehavior in this population.

The performance deficits observed in the cur-rent study cohere with findings from the brainimaging literature indicating less efficient brainactivation patterns in marijuana users.Marijuana users demonstrate increased activationacross a wide range of brain regions and recruitalternative brain networks during task perfor-mance (Block et al., 2002; Chang, Yakupov,Cloak, & Ernst, 2006; Harding et al., 2012;Jacobsen, Mencl, Westerveld, & Pugh, 2004;Kanayama, Rogowska, Pope, Gruber, &Yurgelun-Todd, 2004; Padula, Schweinsburg, &Tapert, 2007; Schweinsburg et al., 2010; Tapertet al., 2007). Increased activation and recruitmentof alternative pathways may be compensatoryand less efficient.

While marijuana users exhibited greater alcohol,tobacco, and nonmarijuana drug use than controls,it does not appear that most observed cognitivedifferences were due to the impacts of these othersubstances. For the majority of tasks, alcohol,tobacco, and other substance use was unrelated totask performance. However, within the domain ofverbal learning (RAVLT), relatively higher levelsof self-reported alcohol use in marijuana userswere associated with better cognitive functioning.This pattern is consistent with recent reports ofmore normative patterns of structural brain integ-rity in marijuana users who use alcohol versusthose who do not (Jacobus et al., 2009; Medinaet al., 2007). While counterintuitive, this findingmay point to specific neural interactions betweenalcohol and marijuana such that alcohol use bene-fits some areas of memory consolidation functionin the context of heavy marijuana use.Importantly, however, the interaction betweenalcohol and marijuana, if present, does not protectagainst diminished cognitive performance in otherareas of function when marijuana users are com-pared to controls, nor does it ameliorate the overallperformance deficit noted between marijuana usersand controls on the RAVLT.Moreover, when the limited number of users

with histories of non-substance-related psycho-pathology were excluded, significant group differ-ences remained. Marijuana users with and withoutcurrent alcohol abuse did not differ in cognitiveperformance. Whether these findings would gener-alize to marijuana users who exhibit high levels ofexternalizing psychopathology or other forms ofeconomic or psychological risk is unknown, butwe speculate, based on the extant literature, thatthe presence of additional risk factors would resultin an even more extensive pattern of group differ-ences given that such factors are independentlyassociated with cognitive difficulties.In summary, marijuana users demonstrated an

inconsistent pattern in terms of leveraging appro-priate strategies to facilitate performance on com-plex memory, planning, and decision-makingtasks. These tasks generally required high levelsof self-organization, as well as potentially greaterdemands on intrinsic motivation, as opposed toareas where the marijuana users excelled (fast,short-term processing tasks), which were moreexternally motivated. These findings suggest thatif individuals engage in use on a daily basis, theymay become increasingly dependent upon externalsources of reinforcement and motivation to struc-ture their behavior as opposed to intrinsically dri-ven self-reliance and self-organization. On theother hand, they may excel in settings where

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external sources of motivation are high. Marijuanausers’ performance across domains of function sug-gests the possibility of diminished frontostriataldopaminergic activity, affecting both decision-making and spatial working memory performance.This may be the mechanism driving the perfor-mance deficits noted and may be one avenuethrough which chronic marijuana use, particularlyuse that is initiated during adolescence, impactslonger term function.

Limitations

One limitation of the current study is the overrepre-sentation of males in the marijuana user sample.However, this gender distribution is consistent withthe gender distribution of marijuana users in theUnited States (Substance Abuse and Mental HealthServices Administration, 2013). Sex was controlled inall statistical analyses; however, findings cannot bereadily generalized to female marijuana users. Afurther limitation is that our design does not permitdose–response associations to be measured in mari-juana users who tended to be relatively homogeneousin their use patterns. A related issue is that it is difficultto quantify the precise amount of drug ingested bymarijuana users given that the potency ofmarijuana isnot standard. While many studies have quantifieddose by calculating hits per day (which we assessed),this measure does not address potency or the amountof drug ingested during a hit. Marijuana users wererequired to use marijuana at least 5 days per week,yielding a relatively homogeneous sample of mari-juana users. There were no requirements for amountof hits during each episode of use.Additionally, while marijuana users were not

acutely high during testing, we cannot rule out thepossibility that the cognitive differences observed inour sample are due to residual effects of marijuanause. However, the current assessment provides acomprehensive cognitive profile of otherwise high-functioning individuals in the context of frequentcurrent marijuana use. This profile allows us tomake real-world inferences about how daily mari-juana use might impact cognition. In order to mini-mize potential confounds, we recruited high-functioning individuals, with comparable educationand IQ to those of other college-aged controls, and alow level of psychopathology. While this feature ofthe study can be considered a strength, since thesample of users represents college-aged individualswho heavily use the drug, it may limit generalizabil-ity to other marijuana-using samples who evidencemore externalizing behavior, less education, and

more psychopathology. Our expectation is that theywould show greater levels of impairment.

Another possible concern is that marijuana userswere in active states of withdrawal during testing,affecting the results. Marijuana users were asked toabstain for at least a 12-hour period prior to thestudy. This possibility appears unlikely given thepsychomotor performance exhibited by marijuanausers, which is inconsistent with behaviors thatindividuals in the midst of marijuana withdrawaldemonstrate (Haney et al., 2001). Finally, we didnot employ marijuana drug testing, since the activecompound in marijuana remains detectible longafter prior use. The level of detail that participantsconveyed regarding their use patterns was convin-cing in terms of the likelihood that they were,indeed, heavy marijuana users, an assumption vali-dated by their reports of symptoms of marijuanadependence. However, because we did not employdrug testing, we cannot completely rule out thepossibility that actual use in these participants islower than what they self-reported. Finally,although our findings are suggestive of patternsof impairment that emerge as a consequence ofuse and replicate findings reported in the literature,cause–effect associations cannot be determined. Itcould be that premorbid levels of function wereimpaired in marijuana users prior to use onset.

We have suggested that marijuana use duringactive stages of brain development is more likely toresult in cognitive impairment as opposed to use thatbegins later in life. Our sample of marijuana usershad a relatively young age of marijuana use onset(15.2 years) relative to national norms (SubstanceAbuse and Mental Health Services Administration,2013) with age of onset of use ranging from 13 to 18years. This is an active period of brain and associatedcognitive development (Giedd et al., 1999; Lebel &Beaulieu, 2011; Luciana, Conklin, Hooper, &Yarger, 2005; Sowell, Thompson, Tessner, & Toga,2001). The animal literature supports our hypothesisthat introducing marijuana during that critical timecould lead to long-standing cognitive effects, whichcan be observed during assessment in adulthood,while use is ongoing. An obvious area of continuedempirical study concerns the cognitive impacts ofmarijuana use at varying ages of onset, which wecannot comprehensively address within the currentdataset given small sample size. Independent oflength of drug exposure as a factor that might impactthe current findings, this study is nonetheless infor-mative regarding the cognitive profiles of youngadults who are active marijuana users during thecollege years.We speculate that such use will becomeincreasingly prevalent with marijuana legalization.

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CONCLUSIONS

The current study provides a comprehensive cog-nitive profile of college-aged daily marijuana users.Marijuana users demonstrated strengths relative tocontrols in processing speed and verbal fluency.Marijuana users also demonstrated numerous cog-nitive deficits, most notably in verbal memory,engagement, and use of efficient strategies withcomplex tasks, and motivated decision making.Future dose–response studies in samples that aresimilarly free of comorbid pathology and in thiswell-defined age range would be helpful in clarify-ing whether a single underlying deficit leads tothese distinct behavioral patterns.Pharmacological challenge or positron emissiontomography (PET) studies in marijuana users ver-sus controls can further clarify the role that dopa-mine activity plays in the observed behavioralpatterns. Additionally, the relationship betweenconcurrent marijuana and alcohol use and cogni-tive performance should continue to be explored inearly-onset marijuana users to determine theunique and combined effects of the substances onperformance.

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