Addiction (2000) 95(3), 437± 446

RESEARCH REPORT

The effects of cigarette consumption on theSternberg visual memory search paradigm

ROBERT TAIT,2 MATHEW MARTIN-IVERSON,1

PATRICIA T. MICHIE1 & LEON DUSCI3

1Psychology Department, 2Department of Psychiatry and Behavioural Science, University ofWestern Australia, Perth & 3Clinical Pharmacology and Toxicology Laboratory, The WesternAustralian Institute for Pathology and Medical Research, Nedlands, Western Australia

Abstract

Aim. To examine the performance of non-smokers (n 5 24), light smokers (n 5 22, mean 6.5 cigarettes perday) and heavy smokers (n 5 19, mean 23 cigarettes per day) on the Sternberg memory search task.Design. A repeated-measures, counterbalanced design was used with one between-subject factor, status

(heavy, light or non-smoker) and two within-subject factors, condition (12 hours abstinence or ad libitumsmoking) 3 time (pre- or post-cigarette). Findings. Heavy smokers in the pre-cigarette abstinent session had

signi® cantly slower reaction times, movement times and higher intercepts (a measure of factors contributing

to performance other than rate of memory scan) than non-smokers. After smoking these differences wereremoved. Conclusions. This suggests that rather than improving performance smoking ameliorates a de® cit

in certain measures of the Sternberg task produced by abstinence. Under ad libitum conditions improvementsin performance were attributed to practice. Across all within-subject conditions, there were no signi® cant main

effects of smoking status, and this result was consistent with the lack of relationship between measures of saliva

continine and expired air carbon monoxide and performance. These data do not support the view thatnon-abstinent smokers differ from non-smokers in the performance of the Sternberg memory search procedure.

Introduction

A number of authors posit that smokers usenicotine as a pharmacological tool for moodcontrol, pleasure and performance enhancement(Warburton, 1989, 1990; Robinson & Pritchard,1992). The claims of performance enhancementare made across a range of tasks, including psy-chomotor performance, vigilance, informationprocessing and even complex tasks (Revell,1988; Hindmarch, Kerr & Sherwood, 1991;

Kerr, Sherwood & Hindmarch, 1991). Thisstudy used the Sternberg memory searchparadigm to examine the effects of smoking oninformation processing and psychomotor per-formance.

The cessation of smoking results in nicotinewithdrawal symptoms in about 80% of smokerswhich gives rise to a range of symptoms includ-ing anxiety, drowsiness and impaired concen-tration (Benowitz, 1988). Therefore, the

Correspondence to: Robert Tait, Department of Psychiatry and Behavioral Science, University of WesternAustralia, Queen Elizabeth II Medical Centre, Nedlands, Western Australia, 6009.

Submitted 20th February 1998; initial review completed 15th June 1998; ® nal version accepted 23rd August 1999.

ISSN 0965± 2140 print/ISSN 1360-0443 online/00/030437± 10 Ó Society for the Study of Addiction to Alcohol and Other Drugs

Carfax Publishing, Taylor & Francis Ltd

438 Robert Tait et al.

improvements in performance found in somecases may represent the removal of de® cits whennicotine is reinstated after a period of abstinence;a de® cit removal hypothesis. Within 4 hours ofsmoking cessation performance de® cits havebeen found on a range of tasks, including visualsearch, short-term memory, logical reasoningand simple arithmetic (Snyder, Davis & Hen-ning® eld, 1989).

However, the de® cit removal hypothesis is notapplicable in all studies. Warburton & Arnall(1994), using a rapid visual information-processing task, found no difference in perform-ance between deprived and minimally deprivedsmokers on pre-cigarette trials, but reliable per-formance improvements for both groups’ reac-tion times and error rates after smoking. Thede® cit removal hypothesis is also clearly not ap-plicable where the effects of nicotine on non-smokers are studied. Subcutaneous injections ofnicotine to non-smokers have been shown toimprove reaction times with no apparent speed±accuracy trade-off (Le Houezec et al., 1994).

A review of the smoking and nicotine literature(Heishman, Taylor & Henning® eld, 1994)identi® ed ® ve studies that have used the Stern-berg memory search paradigm (1966; 1975) inconjunction with either nicotine (Hindmarch,Kerr & Sherwood, 1991; Kerr et al., 1991; Sher-wood, Kerr & Hindmarch, 1992) or smoking(West & Hack, 1991; Spilich, June & Renner,1992). The Sternberg task (1966, 1975) pur-ports to show that short-term memory is exhaus-tively searched in a serial manner to retrieveitems. The task consists of a short list of digits(the positive set), followed by a ª probeº digit.There is a linear increase in response latency asthe size of the positive set increases. This occurswhether the ª probeº is part of the positive ornegative set (all numbers outside the positiveset). The slope of the line obtained by the re-gression of reaction times on set size in theSternberg paradigm is considered to be a mea-sure of the speed of memory scan, while theintercept provides an estimate of the sum of allother factors such as stimulus encoding time,serial comparison and response organization(Sternberg, 1966; Lachman, Lachman & But-ter® eld, 1979). Even though the interpretationthat short-term memory is exhaustively scannedhas been questioned (Clifton & Birenbaum,1970), the most plausible analysis is still that thetask relates to memory search (West & Hack,

1991).The studies by West & Hack (1991) and

Spilich et al. (1992) on the effects of smoking onmemory search produced opposite ® ndings. TheWest & Hack (1991) study reported signi® cantlyimproved search rates for both occasional andregular smokers with a nicotine cigarette but notwith a non-nicotine cigarette. However, therewere no data for non-smokers so their relativelevel of performance remains in doubt. In con-trast, Spilich and co-workers (1992) found thatthe reaction times of both abstinent and activesmokers were signi® cantly slower than those ofnon-smokers. In addition, they also found thatthe slope and intercept values for non-smokerswere signi® cantly better (¯ atter slope and lowerintercept) than for either smoking group, withactive smokers having the slowest memorysearch rate.

The Spilich et al. (1992) study used a variable-set Sternberg task (every trial used a new positiveset) while West & Hack (1991) used a ® xed-settask (the positive set remains the same), butSternberg (1966, p. 653) obtained ª substantialagreementº between the two methods. There-fore, the crucial difference between these twosmoking studies may be in the smoking charac-teristics of the subjects. The mean number ofcigarettes used per day by the occasional smok-ers in the West & Hack (1991) study was only1.4 cigarettes while the regular smokers averaged14.6 per day. This compares with a minimum of20 per day for smokers in the Spilich et al.(1992) study. The utility of the averagenumber of cigarettes smoked per day as a mea-sure may be questioned, as smokers can titratethe amount of nicotine obtained from a cigarette(Benowitz, 1988). However, the self-reportednumber of cigarettes smoked per day is the mostimportant predictor of two biochemical markersof nicotine exposure: salivary cotinine and end-tidal carbon monoxide (Coultas, Stidley &Samet, 1993).

This study aimed to address a number ofissues: ® rst, whether the performance on theSternberg task of heavy smokers (classi® ed as20 or more per day) was inferior to that ofnon-smokers in terms of their reaction times,slope and intercept values as found by Spilich et

al. (1992); secondly, the performance of lightsmokers (here classi® ed as 10 or less per day)relative to heavy and non-smokers was exam-ined. The ® ndings of West & Hack (1991) sug-

Cigarette consumption and the Sternberg task 439

gested that their performance would be im-proved by smoking; and thirdly, whether theperformance facilitation versus the de® cit re-moval hypotheses best explains the effects ofsmoking on the Sternberg task.

The Jensen (1987) choice reaction time(CRT) methodology can be used to decomposethe Sternberg reaction time into decision time(DT), the time to release one key, and move-ment time (MT), the time to hit a response key,in an attempt to separate motor and cognitivecomponents. A fourth aim was thus to assess therelative performance of the groups in terms ofMT. If the de® cit removal hypothesis is correct,then under abstinent testing the MT of bothgroups of smokers will be slower than that ofnon-smokers and if performance facilitation ispresent they will have faster MT than non-smok-ers following a cigarette.

Methods and procedure

Design

A counterbalanced, 3 3 2 3 2 split-plot factorialdesign was used with status (heavy, light, non-smokers) as a between-subject factor and con-dition (abstanent, ad libitum) 3 time (pre-orpost-cigarette) as within-subject factors. Thenon-smoking group’ s primary function was toprovide normative data with which to comparethe smoking groups rather than be a trueª controlº group as they neither received a drugmanipulation nor were deceived about drug in-take. Non-smokers are described using the ses-sion name for the smoking group with whichthey are being compared at a particular time,even though this leads to oxymorons such asª abstinent non-smokersº .

Subjects

Seventy participants were recruited, 20 men(mean age 23 years) and 50 women (mean age22.6 years) with an overall age range of 17± 47.There was no signi® cant difference in terms ofage with smoking status (F(2,62) 5 1.5, NS).They were drawn from a university population,with most being ® rst-year psychology studentswho received course credits for participation.The remaining nine subjects were eligible for a$7 payment. The smoking prevalence in theuniversity population sampled in this study was7.9% compared to the national rate of 17% for

graduates (Hill & White, 1995). This provided atotal of only 30 smokers in the target bands fromthe screening questionnaire completed by 628students. To accommodate this dif® culty, theexclusion criteria were relaxed. Three lightsmokers exceeded the original preferred limit.The highest mean consumption rate was 12.5cigarettes per day, still lower than the meanconsumption for regular smokers in the West &Hack (1991) study. Five heavy smokers fell be-low 20 per day with the lowest mean consump-tion being 18.5. The ® nal recruitment consistedof 24 non-(never) smokers, 25 light smokers and21 heavy smokers.

Saliva analysis

Two ml of saliva were collected in plastic vialsfrom each smoker on arrival for their ad libitum(ad lib) session and the time since their lastcigarette recorded. Samples were stored at2 70°C prior to analysis for cotinine by highperformance liquid chromatography using amodi® ed version of the method reported byMachacek & Jiang (1986). Standard curves werelinear over the range 45± 350 l g/l and the within-day coef® cients of variation (n 5 5) at 370 and47 l g/l were 1.4% and 2.2%, respectively.

Carbon monoxide (CO) analysis

End-expired air CO was sampled using a Bed-font Instrument EC50-R CO analyser. The in-crease in breath CO measured before and after acigarette (CO ª boostº ) was used as an estimateof smoke intake. However, as some smokers havea negative boost immediately following acigarette (Guyatt et al., 1988), a further samplewas taken approximately 20 minutes later. Boostvalues were calculated as the higher of the twopost-cigarette CO values minus the pre-cigaretteCO value.

Assessment

A variable-set Sternberg paradigm (1966, 1975)was used. The positive sets consisted of strings of2, 4 or 6 digits that were presented at a rate of 1per second. After a 2-second delay a ª probeºdigit was shown. Subjects indicated whether theprobe was from the positive set or the negativeset. There were 48 trials per session, eight foreach set size and type of probe. There were norepeated digits within a string and the order of

440 Robert Tait et al.

presentation was randomized. Decision time andmovement times were recorded using Jensen’ s(1987) choice reaction-time methodology. Thisconsisted of a ª home keyº that was held downduring each string presentation, with DT beingthe time between the onset of the probe digit andkey release. Participants were asked not to re-lease the key until they had made a decision. TheMT was the duration between home-key releaseand the target key being depressed. Stimuli werepresented on an IBM-compatible personal com-puter and response times recorded to the nearestmillisecond.

ProcedureSubjects were initially contacted on the basis of asmoking status questionnaire administered in anIntroductory Psychology course. On recruitmentsmokers were alternately assigned to either ab-stain or ad lib conditions and were respectivelyrequested not to smoke either in the 12 hours orthe 30 minutes prior to their appointment times.On their second visit, approximately 1 weeklater, they ful® lled the opposite condition. Allsubjects on recruitment were screened verballyfor both prescription (excluding the contracep-tive pill) and illicit drug use. They were re-quested not to use either type for a minimum of3 days prior to their appointment. They werealso asked to abstain from alcohol for 12 hours,and any product containing caffeine for 3 hoursbefore an appointment.

All subjects provided CO samples prior tocommencing the task, and when in the ad libcondition smokers provided a 2-ml saliva sam-ple. On each visit a short practice session of sixtrials was given on the Sternberg task prior to thedata collection trials. On completion, all subjectswere taken out of the building for a 5-minutebreak during which smokers consumed one oftheir own cigarettes. A second CO reading wastaken before doing the Sternberg task again.Finally, a third CO sample was taken prior toleaving the laboratory. Smokers were given aform on which to record their daily cigaretteconsumption over the week between testing ses-sions. This record was used to verify that theyhad been assigned to the correct group by theinitial screening questionnaire.

The study obtained institutional ethics ap-proval and all participants provided informedwritten consent.

Data analysis

To ensure that the possible order effects wereequivalent for non-smokers and smokers, halfthe non-smokers were coded as though their ® rstsession was for an ad lib session and half for anabstain session. Because outlying data undulyin¯ uences regression coef® cients, outliers morethan 3 SD from the mean DT of their respectivegroups were excluded, together with incorrectresponses. Regression analyses were performedfor both probe types but the means of these arereported for each session. Reaction time (RT)was de® ned as MT plus DT. All analyses wereperformed on SPSS-PC version 6.1.3.

Results

Two heavy smokers failed to return for theirsecond session, two light smokers provided COreadings in excess of 10 p.p.m. after 12 hours’abstinence and a further one had an error rate of46% during one session. These participants wereexcluded from the analyses. Two heavy smokersdeclined to provide saliva samples on ethicalgrounds, but completed all other elements of theexperiment. Five heavy smokers provided COreadings in excess of 10 p.p.m. after 12 hours’abstinence, and were asked to con® rm that theyhad not smoked during that time. As CO has ahalf-life between 4± 5 hours (Horan, Hackett &Linberg, 1978) it is conceivable that such read-ings did not indicate non-compliance; these datawere included. The ® nal group sizes were 24non-smokers, 22 light smokers and 19 heavysmokers.

Biochemical data (Table 1)

There were signi® cant differences between thesmoking groups in terms of the CO levelon arrival for both sessions (ad lib session,t (26.4) 5 3.4, p , 0.01; abstinent session,t (22.9) 5 4.4, p , 0.001; both values usingLevene’ s correction for unequal variances).There were no signi® cant differences betweenthe smoking groups on CO boost levels. TheSpearman’s correlation between salivary cotinineand cigarette consumption was 0.64, p , 0.001,which is consistent with other studies (Coultas et

al., 1993). The correlation between saliva co-tinine and CO levels on arrival for the ad libsession was 0.67, p , 0.001. Heavy smokers hadsigni® cantly higher cotinine levels than light

Cigarette consumption and the Sternberg task 441

Table 1. Subject details and biochemical markers; means with standard deviations in parentheses

Measure Non-smoker Light smoker Heavy smoker

n 24 22 19Age 22 (6.2) 22 (6.4) 25 (9.1)Cigarette daily mean N 6.5 (3.7) 23*** (6.1)Years of smoking NA 4.3 (4.1) 9.4* (8.9)Last cigarette ad libday (meanhours) NA 18.5 (31) 2.5* (5.6)Abstain appointment, median (IQR) 10.40 (10± 12) 13.00 (11± 15.30) 10.15 (9.15± 12.30)Cotinine ad libday (ng/ml) NA 66 (62) 222** (180)Ad lib COarrival level (p.p.m.) 3.0 (1.5) 6.7 (4.5) 14** (8.5)Abstain COarrival level (p.p.m.) 2.5 (0.8) 3.8 (2.1) 9.4*** (5.2)Ad lib COboost² (p.p.m.) , 1 (0.8) 10.4 (7.9) 16.2 (13)Abstain COboost² (p.p.m.) , 1 (.6) 10.1 (9.4) 13.3 (12.9)

Independent t-tests: p , 0.05 5 *; p , 0.01 5 **; p , 0.001 5 *** show signi® cant differences between the twosmoking groups. ² Boost values calculated as the greater of the two post cigarette CO measures minus the arrivalCO level.

smokers (t (18.9) 3.4, p , 0.01, using Levene’ scorrection).

Sternberg data

An initial analysis sought to establish that theresults obtained were consonant with the litera-ture on the Sternberg paradigm. The mean of allsubjects’ regression line slopes across the foursessions was 29.4 ms and the intercept was491 ms. Typical values are cited as 38 ms withan intercept of about 400 ms (Sternberg, 1975).The mean error rate of 2.5% was higher thanthat reported by Sternberg (1966) of 1.3%, butlower than that reported by Spilich et al. (1992)(6.8± 15.6%).

Figure 1 shows the decision times for eachgroup as a function of set size. As would beexpected, the DT increased with set size(F(2,124) 5 129.4, p , 0.001). The non-smokinggroup had faster DT than either smoking groupfor all memory set sizes, but smoking status didnot have a signi® cant effect on the overall meandecision times (F(2,62) 5 0.33, NS). Non-smokers had the highest error rate (2.8%) andlight smokers the lowest (2%) but the differencesin error rates were also not signi® cant(F(2,62) 5 1.0, NS.

Speci® c experimental aims

Table 2 contains data relevant to the ® rst andsecond aims of the experiment. The ® rst aim wasto compare the overall performance of non-smokers and heavy smokers on all the task

parameters. Each parameter was subject to aplanned comparison between non-smokers andheavy smokers. Considered across all sessions,non-smokers did not have statisticallysigni® cantly better performance than did heavysmokers in terms of intercept, MT, DT andreaction time. Heavy smokers had lower slopevalues, with a memory search rate of about 40items per second (1000 ms divided by slopevalue in ms) compared with the non-smokers’ 32items per second, but this was not statisticallydifferent. These ® ndings do not support the in-terpretation from the Spilich et al. (1992) studythat heavy smokers, both in active and deprivedstates, perform at a level inferior to that of non-smokers.

The second experimental goal was the assess-ment of the performance of light smokers relativeto heavy smokers and non-smokers. An examin-ation of the overall means (Table 2) shows thaton each parameter light smokers fall at or be-tween the values obtained for the other groups,the only exception being the slope data, wherelight smokers have the slowest scan rate. A post

hoc Bonferroni t-test indicated that the differencein slope values was not signi® cant at the p , 0.05level. Examining the more detailed picture,Table 3 shows that it was only in the pre-cigarette abstinent session that the light smokershad a slower scan rate than non-smokers, butagain this was not signi® cant using the same post

hoc test. The light smokers had the fastest meanMT before smoking in the ad lib session butagain this was not signi® cantly different from theother groups.

442 Robert Tait et al.

Figure 1. Mean decision time for each group and set size ( 6 SEM). Zero intercepts calculated by regression of decision timeon set size.

The third experimental aim examined whethersmoking provides absolute performance en-hancement or removes withdrawal de® cits. Per-formance facilitation needs to be assessed whensmokers have not been deprived of nicotine sothe changes in performance on the ad lib smok-ing day is crucial. None of the parameters exhib-ited a signi® cant main effect of smoking statusduring the ad lib testing session (Table 3). Theperformance of all three groups improved in thesecond trial in terms of slope (F (1,62) 5 9.5,p , 0.01) and DT (F (1,62) 5 17.2, p , 0.001).The presence of the non-smoking group as anormative control suggests that this should beinterpreted as a practice effect rather than per-formance facilitation by smoking. Only interceptvalues had a signi® cant status by practice inter-action (F(2,62) 5 3.7, p , 0.05). An examinationof the means in the ad lib session revealed thatsmokers had lower intercepts (e.g. faster stimu-lus encoding, serial processing, etc.) after smok-ing while non-smokers had higher intercepts intheir second session, although this was still lowerthan those of the smokers.

The issue of abstinence-induced de® cits canbe examined either by a comparison of abstinentsmokers pre-cigarette performance with the per-formance of controls or with their own non-ab-

stinent pre-cigarette levels. Using the controls asa comparison group, between-subjects ANOVAswith planned comparisons of non-smokers andheavy smokers in the pre-cigarette abstain ses-sion revealed a number of signi® cant differences.The performance of heavy smokers wassigni® cantly worse than that of non-smokers interms of intercept values and MT (Table 3). Thereaction times of heavy smokers were alsosigni® cantly slower than those for non-smokersin this phase of testing (t 5 2 2.64, df 41,p , 0.05). After smoking a cigarette the perform-ance of the heavy smokers was not signi® cantlydifferent from the non-smokers on any of thesemeasures. The comparison of heavy smokerswith their own ad lib baseline levels did not showsigni® cant differences. It should also be notedthat in the ad lib pre-cigarette phase of testingthere were no signi® cant differences on any in-dex between the non-smokers and heavy smok-ers.

The fourth experimental aim was an assess-ment of the effects of smoking on MT (Fig. 2).It was expected that smokers would have fasterMT than non-smokers in both post-cigarettesessions and possibly in the ad lib pre-cigarettesession. A planned contrast revealed nosigni® cant differences between the non-and

Cigarette consumption and the Sternberg task 443

Table 2. Overall performance on all measures; means with standard deviationsin parentheses

Measure Non-smokers Light smokers Heavy smokersmean (SD) mean (SD) mean (SD)

Slope 31.3 (18.3) 31.5 (11.9) 25.3 (10.2)Intercept 463.4 (134) 495.0 (107) 521.8 (118)DT 605.6 (173) 637.0 (144) 637.6 (131)MT 371.0 (156) 398.0 (152) 443.4 (148)Reaction time 979.0 (279) 1035.0 (234) 1081.0 (237)(DT 1 MT)

heavy-smoking groups in either the ad lib, pre-cigarette trials (t (40.8) 5 2 0.61, NS) or the adlib post-cigarette trials (t(40.5) 5 2 1.4, NS).Parenthetically, the non-smokers showed asigni® cant practice effect during this session (t(23) 5 3.67, p , 0.001). In the pre-cigarette ab-stain session the heavy smokers had signi® cantlyslower MT than the non-smokers (t (37.7) 5 2.7,p , 0.01). After smoking this difference was re-moved, a ® nding consistent with a withdrawalde® cit hypothesis. Figure 2 shows that the lightsmokers had a similar pattern of performance tothe non-smokers and there were no signi® cantdifferences between these two groups in anysession.

Predictive utility of biochemical measures

The biochemical measures of saliva cotinine andCO were of little use in predicting the perform-ance of smokers on the Sternberg task.

Discussion

The study investigated the effects of daily aver-age cigarette consumption on the Sternbergmemory search paradigm. There were nosigni® cant overall main effects of smoking statuson any of the parameters. In particular, visualmemory search did not differ between anygroups or conditions as measured by slopes.However, heavy smokers had signi® cantly slowerMT, reaction time (DT 1 MT) and higher inter-cepts than the non-smoking control group whenthe smokers had abstained from smoking for 12hours. This difference was removed after theheavy smokers had had a cigarette. Overall, the® ndings of this study give further credence to aperformance-de® cit removal hypothesis, whichsuggests that smoking improves the performance

of smokers by removing de® cits induced by

smoking abstinence. Smoking thereby serves toreturn smokers to the same level of performanceas non-smokers on the Sternberg task. This con-clusion is supported by the lack of a relationshipbetween the cotinine and CO levels and per-formance on the non-abstinent day, the only daywhen continine was measured.

Heishman and co-authors (1994, p. 346) sug-gest that ª absolute enhancement is demon-strated only when nicotine produces astatistically signi® cant facilitation of performanceover baseline levels in non-smokers or in nic-otine-dependent people who are not nicotinedeprivedº . In this study there was a signi® cantimprovement in the performance of smokers interms of slope and DT after a cigarette com-pared with their non-abstinent baseline levels.(The effect of nicotine on non-smokers was notexamined.) This ful® ls one criterion set byHeishman et al. (1994) as indicating that smok-ing absolutely improves performance. However,the non-smokers also improved over this periodsuggesting that a practice effect was a moreappropriate interpretation than performance fa-cilitation.

The withdrawal de® cit hypothesis was as-sessed after overnight abstinence. The perform-ance of heavy smokers was not signi® cantlylower than their own baseline (non-abstinent)levels of performance, but when compared withnon-smokers they showed signi® cant de® cits ona number of parameters, but not on memorysearch rate. The suggestion that this was a with-drawal de® cit was supported by the ® nding thatthere were not signi® cant differences betweenthese two groups when the heavy smokers hadnot been deprived of nicotine. Therefore, it isonly when the non-smoking data are consideredsimultaneously that it becomes apparent that awithdrawal relief interpretation probably pro-

444 Robert Tait et al.

Table 3. Means with standard deviations in parentheses for each measure in all sessions

Ad lib session Abstain session

Pre-cigarette Post-cigarette Pre-cigarette Post-cigaretteMeasure Group mean (SD) mean (SD) mean (SD) mean (SD)

Decision Non 620 (186) 609 (215) 617 (161) 577 (167)Time Light 649 (156) 596 (175) 689 (154) 614 (153)

Heavy 668 (171) 596 (145) 691 (157) 594 (128)Slope Non 36.3 (23.5) 29.3 (21.7) 30.6 (21.2) 28.8 (18.4)

Light 31.2 (13.7) 27.0 (14.6) 38.2 (17.7) 28.0 (19.2)Heavy 29.0 (14.6) 23.5 (11.4) 28.4 (14.6) 21.4 (12.3)

Intercept Non 449 (138) 469 (146) 482 (156) 447 (140)Light 507 (124) 473 (131) 514 (109) 488 (107)Heavy 511 (144) 495 (131) 583 (140)* 498 (124)

Movement Non 415 (192) 371 (197) 343 (128) 354 (138)Time Light 402 (155) 387 (143) 398 (173) 405 (182)

Heavy 448 (161) 451 (173) 453 (135)** 422 (180)

Independent t-tests: p , 0.05 5 *; p , 0.01 5 **; show signi® cant differences between heavy smokersand non-smokers.

vides a better ® t with the data, at least in thecontext of heavy smokers, than the performanceenhancement thesis. These performance de® citsmay either result from a direct mechanism ofnicotine withdrawal or via indirect processessuch as stress or anxiety induced by abstinencefrom nicotine. On virtually all measures the per-formance of light smokers fell between that ofnon-smokers and heavy smokers, a trend sugges-tive of a dose± response effect. There was noevidence to support the idea that light smokersmay bene® t from absolute performance enhance-ment while heavy smokers show only de® cit re-moval.

Presenting a standardized dose of nicotine,especially via smoking, is fraught with dif® culties(Benowitz, 1988; Heishman et al., 1994). Thedata indicated that on arrival for their ad libsession many smokers had not smoked for aconsiderable period, with the mean time for lightsmokers being 18.5 hours and for heavy smokers2.5 hours. This may explain why smokers didnot show signi® cant de® cits compared to theirown baseline levels after overnight abstinence,yet heavy smokers did display signi® cantly in-ferior performance compared with non-smokers.These differences may have been reduced if 1hour prior to their ad lib session participants hadbeen requested to smoke a cigarette to ensurethat the withdrawal phase had not been enteredwhen they arrived.

West & Hack (1991) concluded that nicotinecigarettes increased memory search rate com-

pared with nicotine-free cigarettes. Our ® ndingssuggest that, if the smoking groups only wereconsidered, there would have been an apparentimprovement in performance after smoking un-der both abstinent and ad lib conditions com-pared with baseline performance. However, theinclusion of a non-smoking group provided agauge of relative levels of performance and sug-gests that this change was due to practice. Inaddition, West & Hack (1991) only report onone parameter of the Sternberg paradigm,namely the search rate (slope). The evidencefrom this study indicated that search rate was theonly aspect of the task where smokers show atrend towards an advantage over non-smokers,but not to a signi® cant extent.

Overall, the data from this research are com-patible with those found by West & Hack(1991), but the comparison with Spilich et al.

(1992) is more problematic. Some of the de® citsin abstinent smokers found by Spilich et al.

(1992) were replicated, but not their ® ndingsconcerning active smokers. On all parametersthey report that active smokers displayedsigni® cantly inferior levels of performance thannon-smokers and in terms of memory scan weresigni® cantly slower than were deprived smokers.The current study found no evidence that theperformance of active smokers was inferior todeprived smokers or non-smokers.

Sherwood et al. (1992) reported on theeffects of nicotine gum on a number of psycho-motor tasks, including the Sternberg task.

Cigarette consumption and the Sternberg task 445

Figure 2. Mean movement time before and after smoking a cigarette for both ad lib smoking and after overnight abstinence( 6 SEM).

They conclude that measures of central infor-mation processing appear sensitive to nicotine,but were unable to rule out the possibilitythat the detected effects were due merely tode® cit removals. The ® ndings of the currentstudy con® rmed the need for such caution.Hindmarch et al. (1990), again using nicotinegum but with non-deprived smokers, found nosigni® cant effect of nicotine on central infor-mation processes. Of the nicotine gum studies,only Kerr et al. (1991) found signi® cantimprovements on the Sternberg task in a com-bined sample of non-deprived smokers and non-smokers.

Heishman et al. (1994) concluded that themost reliable evidence for performance facilita-tion by nicotine and smoking was for motorresponses. In the current study, non-smokershad faster MT than deprived smokers, consistentwith a withdrawal de® cits model, but performedat the same level as active smokers. This surpris-ing ® nding, given Heishman and co-workers’(1994) conclusions concerning the effects ofsmoking on motor tasks, means that the validityof the ª movementº aspect of the current data

may be questioned. However, the CRT task canbe subject to strategic manipulation where thehome key is released prior to a decision beingmade (Smith & Carew, 1987). This serves toreduce DT and in¯ ate MT. The results obtainedhere may therefore re¯ ect different strategies be-ing employed by smokers and non-smokers. Un-til this issue can be clari® ed, perhaps thereaction-time measure used by Spilich et al.

(1992) is more appropriate than the attempteddivision of the task into MT and DT.

The experimental design used in this studystill leaves unresolved a number of issues. Thecounterbalanced design controls for the con-founding of practice and condition but not forsmoking, which always occurred second. Thiscould be resolved by adding further groups ofsmokers. These subjects would follow the sameexperimental procedure as the non-smoking con-trols and would complete the task twice withoutsmoking. Furthermore, the use of a cross-sectional design clearly leaves open to questionwhether any differences found between smokersand non-smokers are a result of smoking or arepre-existing features.

446 Robert Tait et al.

Conclusions

This research highlights the need for the in-clusion of non-smoking control groups if theresults of studies on the effects of smoking onperformance are to be interpreted meaningfully.However, less than 10% of studies on smokingpublished in Psychopharmacology during a 5-yearperiod included a non-smoking control group(Hughes, 1991), thus making relative levels ofperformance hard to assess.

The results of this study give further credenceto the hypothesis that smoking confers perform-ance bene® ts through the removal of abstinencede® cits rather than through absolute perform-ance enhancement. However, these bene® ts mayrelate more to motor stimulant effects of smok-ing than to improvements in rates of memoryscan.

Acknowledgements

I would like to acknowledge the contribution ofthe following people and organizations to thisresearch; Dr Craig Clark for writing the com-puter program for the Sternberg task and theNational Heart Foundation of Australia (WA)Inc. for the use of equipment.

ReferencesBENOWITZ, N. L. (1988) Drug therapy. Pharmacologic

aspects of cigarette smoking and nicotine addition,New England Journal of Medicine, 319, 1318± 1330.

CLIFTON, C. & BIRENBAUM, S. (1970) The effects ofserial position and delay of probe on a memory scantask, Journal of Experimental Psychology, 86, 69± 76.

COULTAS, D. B., STIDLEY, C. A. & SAMET, J. M. (1993)Cigarette yields of tar and nicotine and markers ofexposure to tobacco smoke, American Review of Res-piratory Disease, 148, 435± 440.

GUYATT, A. R., KIRKHAM, A. J. T., MARINER, D. C. &CUMMING, G. (1988) Is alveolar carbon monoxidean unreliable index of carboxyhaemoglobin changesduring smoking in man? Clinical Science, 74, 29± 36.

HEISHMAN, S. J., TAYLOR, R. C. & HENNINGFIELD , J. E.(1994) Nicotine and smoking: a review of effects onhuman performance, Experimental and Clinical Psy-chopharmacology, 2, 345± 395.

HILL, D. J. & WHITE, V. M. (1995) Australian adultsmoking prevalence in 1992, Australian Journal ofPublic Health, 19, 305± 308.

HINDMARCH, I., KERR, J. S. & SHERWOOD, N. (1990)Effects of nicotine gum on psychomotor perform-ance in smokers and non-smokers, Psychopharmacol-ogy, 100, 535± 541.

HINDMARCH, I., KERR, J. S. & SHERWOOD, N. (1991)The comparative psychopharmacology of nicotine,in: ADLKOFER, F. & THURAU, K. (Eds) Effects of

nicotine on biological systems, pp. 509± 519 (Basel,Birkhauser Verlag).

HORAN, J. J., HACKETT, G. & LINBERG, S. E. (1978)Factors to consider when using expired air carbonmonoxide in smoking assessment, Addictive Behav-iors, 3, 25± 28.

HUGHES, J. R. (1991) Distinguishing withdrawal reliefand direct effects of smoking (letter), Psychopharma-cology, 104, 409± 410.

JENSEN, A. R. (1987) Process differences and individ-ual differences in some cognitive tasks, Intelligence,11, 107± 136.

KERR, J. S., SHERWOOD, N. & HINDMARCH, I. (1991)Separate and combined effects of the social drugs onpsychomotor performance, Psychopharmacology, 104,113± 119.

LACHMAN, R., LACHMAN, J. L. & BUTTERFIELD, E. C.(1979) Cognitive Psychology and Information Process-ing (Hillsdale NJ, LEA).

LE HOUEZEC, J., HALLIDAY, R., BENOWITZ, N. L.,CALLAWAY, E., NAYLOR, H. & HERZIG, K. (1994) Alow dose of subcutaneous nicotine improves infor-mation processing in non-smokers, Psychopharmacol-ogy, 114, 628± 634.

MACHACEK, D. A. & JIANG, N. S. (1986)Quanti® cation of cotinine in plasma and saliva byliquid chromatography, Clinical Chemistry, 32, 979±982.

REVELL, A. D. (1988) Smoking and performanceÐ apuff-by-puff analysis, Psychopharmacology, 96, 563±565.

ROBINSON, J. H. & PRITCHARD, W. S. (1992) The roleof nicotine in tobacco use (see comments), Psy-chopharmacology, 108, 397± 407.

SHERWOOD, N., KERR, J. S. & HINDMARCH, I. (1992)Psychomotor performance in smokers following sin-gle and repeated doses of nicotine gum, Psychophar-macology, 108, 432± 436.

SMITH, G. A. & CAREW, M. (1987) Decision timeunmasked: individuals adopt different strategies,Australian Journal of Psychology, 39, 339± 351.

SNYDER, F. R., DAVIS, F. C. & HENNINGFIELD , J. E.(1989) The tobacco withdrawal syndrome: perform-ance decrements assessed on a computerized testbattery, Drug and Alcohol Dependence, 23, 259± 266.

SPILICH, G. J., JUNE, L. & RENNER, J. (1992) Cigarettesmoking and cognitive performance, British Journalof Addiction, 87, 1313± 1326.

STERNBERG, S. (1966) High-speed scanning in humanmemory, Science, 153, 652± 654.

STERNBERG, S. (1975) Memory scanning: new ® ndingsand current controversies, Quarterly Journal of Exper-imental Psychology, 27, 1± 32.

WARBURTON, D. (1989) Is nicotine use an addiction?The Psychologist, 4, 166± 170.

WARBURTON, D. (1990) Heroin, cocaine and now nic-otine, in: WARBURTON, D. (Ed.) AddictionControversies, pp. 21± 35 (Chur, Switzerland, Har-wood).

WARBURTON, D. M. & ARNALL, C. (1994) Improve-ments in performance without nicotine withdrawal,Psychopharmacology, 115, 539± 542.

WEST, R. & HACK, S. (1991) Effect of cigarettes onmemory search and subjective ratings, Pharmacology,Biochemistry and Behavior, 38, 281± 286.