Neurocognitive Deficit in Schizophrenia: A Quantitative ... · (De Renzi & Vignolo, 1962); Peabody...

Neuropsychology1998, Vol. 12, No. 3,426-445

Copyright 1998 by the American Psychological Association, Inc.0894-4105/98/$3.00

Neurocognitive Deficit in Schizophrenia:A Quantitative Review of the Evidence

R. Walter Heinrichs and Konstantine K. ZakzanisYork University

The neurocognitive literature on test performance in schizophrenia is reviewed quantitatively.

The authors report 22 mean effect sizes from 204 studies to index schizophrenia versus control

differences in global and selective verbal memory, nonverbal memory, bilateral and unilateral

motor performance, visual and auditory attention, general intelligence, spatial ability,

executive function, language, and interhemispheric tactile-transfer test performance. Moder-

ate to large raw effect sizes (d > .60) were obtained for all 22 neurocognitive test variables,

and none of the associated confidence intervals included zero. The results indicate that

schizophrenia is characterized by a broadly based cognitive impairment, with varying degrees

of deficit in all ability domains measured by standard clinical tests.

It is expected that schizophrenia will be understood in thenear future as a kind of brain disease. Substantive andtechnical advances in the neurosciences are being brought tobear on the problem of tracking the disordered cognition andbehavior of schizophrenia to dysfunctional brain structures

and systems. Evidence from neuropsychological, neuropatho-logical, and neuroimaging studies has revived interest inschizophrenia as a neuropsychiatric disease with a putativelyidentifiable neuropathophysiology (Andreasen et al., 1994;Heinrichs, 1993; Levin, Yurgelun-Todd, & Craft, 1989;Randolph, Goldberg, & Weinberger, 1993).

From a behavioral perspective there is broad agreementthat schizophrenia produces impairment of neuropsychologi-cal function. Individuals with the illness are likely to exhibitimpairment on a wide assortment of neuropsychologicaltasks (Blanchard & Neale, 1994). Recent findings includedeficits in attention (Braff, 1993; Cornblatt & Keilp, 1994),executive function (Gruzelier, Seymour, Wilson, Jolley, &Hirsch, 1988; Heinrichs, 1990; Van der Does & Van denBosch, 1992; Weinberger, Wagner, & Wyatt, 1986), motor

and tactile dexterity (Goldstein & Zubin, 1990; Schwartz,Carr, Munich, & Bartuch, 1990), spatial abilities (Green &Walker, 1985; Raine, 1992; Stuss et al., 1983), affectrecognition (Andreasen & Ehrhardt, 1982; Ennis & Whelton,1994), intellectual ability (Goldberg, Gold, Greenberg, &Griffin, 1993), language functions (Crawford, Obonsawin,& Bremner, 1993), and memory (Paulsen et al., 1995;Randolph et al., 1994; Saykin et al., 1991).

Although a number of narrative reviews have described

R. Walter Heinrichs and Konstantine K. Zakzanis, Departmentof Psychology, York University, Toronto, Ontario, Canada.

An earlier version of this research was presented at the AmericanPsychological Association Annual Convention, Toronto, Canada,August 1996 and at the International Congress of Psychology,Montreal, Canada, August 1996.

Correspondence concerning this article should be addressed toR. Walter Heinrichs, Department of Psychology, York University,4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3. Electronicmail may be sent to [email protected].

and integrated neurocognitive findings on schizophrenia(e.g., Heaton & Crowley, 1981; Heinrichs, 1993; Levin etal., 1989; Randolph et al., 1993), there has been no

comprehensive quantitative evaluation of this literature. Inview of the increasing use of neurocognitive measures in

schizophrenia research and the accumulation of empiricalevidence, we undertook such an evaluation. A number of

questions were formulated to guide our review:

1. Does neurocognitive testing provide reliable evidenceof impairment in schizophrenia and what is the average

magnitude of difference between patients and healthy con-trols?

2. Do tests of specific neurocognitive functions (e.g.,memory, language, attention) reveal similar magnitudes ofdifference between patients and controls or are some aspects

of neurocognitive performance spared in the illness?3. Are there relationships between neurocognitive impair-

ment and clinical and demographic attributes of patients andcontrols?

4. Are there relationships between specific neurocognitivefunctions and more general tests of ability (e.g., IQ)?

Accordingly, the goal of our research was to estimate the

consistency, strength, and selectivity of neurocognitive

deficits in schizophrenia. To accomplish this goal wecompiled the results of multiple studies using meta-analytic

techniques. In addition to solving potential problems withtraditional literature reviews (see Wolf, 1986), meta-analysisprovides tools for the analysis of magnitude and consistency

of evidence (i.e., effect size d, distribution nonoverlappercentage, confidence intervals) as well as tools to assessvariables that may mediate the magnitude and consistency ofevidence (i.e., moderator variable analysis). The effect sizeestimate d measures the degree to which the phenomenon ispresent in the population (Cohen, 1988). In mathematicalterms, d is the difference between patient and control meanson a variable of interest calibrated in pooled standarddeviation units. Effect size analysis allows the pitfalls of nullhypothesis-statistical significance testing to be avoided,including faulty conclusions about a hypothesis that are

426

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NEUROCOGNITIVE DEFICIT IN SCHIZOPHRENIA 427

based on a count of significant and nonsignificant studies

(see Bakan, 1966; Cohen, 1994; Schmidt, 1996). Variability

of effect sizes across studies can be indexed by common

statistics like standard deviations and confidence intervals

but also by formal tests of homogeneity (see Hedges, 1994;

Hedges & Olkin, 1985; Rosenthal, 1991, 1995). This

variability in effects can be explored and articulated through

moderator variable analysis if it is due to study differences in

sample or design features.

With these considerations in mind we organized 22 neurocog-

nitive test variables into meta-analyses to summarize the

magnitude of schizophrenia-control discrimination hi the

published literature (see Table 1). These variables reflected

memory, motor, attention, intelligence, spatial function, execu-

tive ability, language, and interhemispheric transfer processes.

Effect sizes were calculated for each of the individual

dependent variables. Attribute variables (e.g., age, education,

hospitalizations, etc.) were recorded and, when numbers

warranted, were correlated with neurocognitive effect sizes

to explore the contribution of moderators to effect size variation.

Table 1

Neurocognitive Categories and Tests Used in Meta-Analyses

Construct Recorded test variables

Global Verbal Memory

Selective Verbal Memory

Nonverbal Memory

Motor

Attention

General Intelligence

Spatial Ability

Executive Function

Language Function

Tactile-Transfer

CVLT: Trials I-V recallRAVLT: Trials I-V recallPortland Paragraph: immediate recallSelective Reminding Test: sum recallLNNB: memory indexWMS: logical memory scoreWMS-R: verbal memory indexCVLT: long-delay free recall, percentage words retained,

number of intrusions, recognition trial hitsWMS-R: logical memory IIRAVLT: recognition trial hitsRMT: memory for wordsROCFT: recall trials scoreWMS, WMS-R: visual reproduction trialsBVRT: recall trialsRMT: memory for facesPurdue Pegboard: dominant hand score, bilateral scoreGrooved Pegboard: dominant hand scoreFinger Tapping Test: dominant hand scoreWAIS-R: Digit SpanTrail Making Test: Parts A and BCPT: hits, discrimination scoreStroop Test: interference scoresWAIS-R: Full Scale, Verbal, and Performance IQNon-WAIS-R: NART IQ, ILS, Quick Test, Mill Hill

Verbal IQ, and Wonderlic Personnel TestBenton Line Orientation Test: number correctBenton Facial Recognition Test: corrected scoreWAIS-R: block design scoreWCST: categories achieved, number of perseverative responses,

number of perseverative errors, percent perseverative errorsWord Fluency: COWAT, Chicago WFTToken Test: adjusted score, correct responsesVocabulary: WAIS-R vocabulary, Mill Hill, ILS,

Peabody Picture VocabularyContralateral finger localization, tactile recognition scores

Note. CVLT = California Verbal Learning Test (Delis et al., 1987); RAVLT = Rey Auditory VerbalLearning Test (Lezak, 1995, pp. 438^42); Portland Paragraph (Lezak, 1995, p. 462); SelectiveReminding Test (Buschke & Fuld, 1974); LNNB = Luria-Nebraska Neuropsychological Battery(Golden et al., 1985); WMS = Wechsler Memory Scale (Wechsler, 1945); WMS-R = WechslerMemory Scale—Revised (Wechsler, 1987); RMT = Recognition Memory Test (Warrington, 1984);ROCFT = Rey-Osterrieth Complex Figure Test (Lezak, 1995, pp. 475^80); BVRT = Benton VisualRetention Test (Benton, 1974); Purdue Pegboard (Tiffin, 1968); Grooved Pegboard (Klove, 1963);Finger Tapping Test (Reitan & Wolfson, 1993); WAIS-R = Wechsler Adult Intelligence Scale-Revised (Wechsler, 1981); Trail Making Test (Reitan, 1958); CPT = Continuous Performance Test(Nuechterlein & Dawson, 1984); Stroop Test (Golden, 1978; Stroop, 1935); NART = National AdultReading Test (Nelson, 1982); ILS = Institute of Living Scale (Shipley, 1946); Quick Test (Ammons& Ammons, 1962); Mill Hill Scale (Raven, 1982); Wonderlic Personnel Test (Wonderlic, 1978);Benton Line Orientation and Facial Recognition Tests (Benton et al., 1994); WCST = WisconsinCard Sorting Test (Heaton et al., 1993); COWAT = Controlled Oral Word Association Test (Benton& Hamsher, 1983); Chicago WFT = Chicago Word Fluency Test (Milner, 1964); Token Test(De Renzi & Vignolo, 1962); Peabody Picture Vocabulary Test (Dunn & Dunn, 1981).

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428 HEINRICHS AND ZAKZANIS

Method

Literature Search

The PsycINFO and Medline databases were used to find studies for

inclusion in the meta-analyses. The key words used are included in the

Appendix. The articles located by die computer search were limited to

published English language articles. According to Green and Hall

(1984), meta-analysts doing a literature search should also page through

the volumes of pertinent journals for a topic year by year. This was done

with every issue for the two relevant specialty journals, Schizophrenia

Research and Schizophrenia Bulletin, as well as for six psychiatric and

psychological journals that publish a high volume of studies on

schizophrenia (American Journal of Psychiatry, Archives of General

Psychiatry, Biological Psychiatry, British Journal of Psychiatry, Jour-

nal of Abnormal Psychology, and Journal of Nervous and Mental

Disease) and five journals in neuroscience and behavior that publish

schizophrenia research but at a lower frequency than the psychiatric

journals (Brain, Journal of the International Neuropsychological Soci-

ety, Neuropsychologf, Neuroscience, and Neuropsychopharmacology).

This proved to be an effective supplementary way to search for articles,

given the small and focused set of key words in any particular abstract or

title that a computer database uses to search for key words. Despite its

apparent cost in time, (he reasoning behind using the individual journal

technique was to avoid missing a useful article that lies outside the

database's regular purview. Although absolute coverage of the entire

relevant literature is hard to achieve, this serves to reduce the likelihood

that bias was involved in the search outcome (White, 1994).

In the course of the literature search, the frequency with which

any given neurocognitive variable was reported across studies

dictated suitability for meta-analytic synthesis. If the variable was

reported in two or more primary studies, the variable was tracked

for meta-analytic synthesis. In this way, studies were compiled for

22 meta-analyses of different neurocognitive test measures.

Criteria for Inclusion

Articles were included if they met the following criteria: (a)

publication between 1980 and 1997, (b) research designs with a

control group comprising healthy participants, and (c) study

statistics convertible to effect size d (e.g., means, standard devia-

tions, F, t, x2; see Wolf, 1986). Studies reporting only significancelevels with no descriptive or inferential statistics were excluded.

The year 1980 was chosen as a year of publication criterion

because it corresponded roughly to the introduction and use of

more systematic and reliable diagnostic criteria for schizophrenia,

such as the Diagnostic and Statistical Manual of Mental Disorders

(3rd ed.; DSM-lll; American Psychiatric Association, 1987). The

year 1997 was chosen as an upper year limit to ensure maximal

coverage of the literature by the computer-based journal databases.

However, there is typically a delay of a few months between

journal publication and availability in the database, and our article

search took place in mid-1997. Therefore, coverage of 1997 articles

was incomplete.If these criteria were met, the article was then assessed for two

additional criteria. First, schizophrenic patients must have met diagnos-

tic criteria for either the DSM-lll (or later) or the International

Classification of Diseases (ICD-9 or 10) classification systems and have

satisfied these criteria on the basis of a structured clinical interview.

Second, neurocognitive tests must have been administered by an

examiner trained in standardized testing procedures and supervised by a

psychologist This was important to track in order to ensure that thequality of the neurocognitive assessment administered in each study was

held relatively constant and did not influence the findings, hi the case of

neurocognitive data published in drug trials, only studies reporting

pretreatnient baseline data were included.

If the research article met the above criteria, its content

variable(s) was included in one (or more) of the meta-analyses. In

the case of separately published studies that used the same

participant samples, the decision rule was adopted to treat these

studies as a single study with multiple independent variables

(Hedges & Olkin, 1985). The d statistic (Cohen, 1988) was

calculated for each comparison as the difference between schizo-

phrenia and control group means normalized by the pooled

standard deviation. Whenever means and standard deviations were

reported, these were used to derive effects. When inferential

statistics were reported without central tendency and dispersion

data, the effects were calculated from these statistics based on

formulas provided by Wolf (1986).

Recorded Variables

Recorded variables for every article used in each of the

meta-analyses included the journal name, title, author(s), and date

of publication of the article. Recorded potential moderator vari-

ables of interest included mean age and education in control and

patient samples; for the patient samples, the chlorpromazine-

equivalent medication dose (equivalent neuroleptic daily dosage in

mg), percentage of male participants, onset age (defined as the age

at first psychiatric admission) and duration of illness, percentage of

patients on medication, and number of psychiatric hospitalizations

were recorded. These study characteristics were used to describe

the study set retrieved and for moderator variable analysis.

Organizing the myriad of neurocognitive test variables reported

in the literature into a coherent classification was a major chal-

lenge. Several strategies exist in the literature for organizing

diverse tests into categories of neurocognitive function, and each of

these strategies has advantages and disadvantages. First, there are a

priori approaches such as Lezak's (1995) classification, which are

influenced by theoretical and practice-related considerations about

the test measures and their putative underlying processes. For

example, Lezak includes motor and executive ability tasks in the

same chapter, presumably on the basis of a common substrate in the

frontal brain or some other assumed link. Such classifications have

no quantitative statistical underpinning, and even advocates of this

approach admit to an element of arbitrariness in test organization

(see Lezak, pp. 333-334). A second approach is based on factor-

analytic studies of neuropsychological test batteries (see Goldstein,

1984, pp. 184-210). Factor analysis provides a quantitative descrip-

tion that relates different tests to a smaller number of underlying

abilities. However, the validity of this approach as a general

strategy for organizing tests in a meta-analysis depends in part on

the availability of factor analyses that include all of the tests in the

literature on neuropsychology and schizophrenia. In practice it is

the standard test batteries like the Halstead-Reitan (e.g., Ernst,

Warner, Hochberg &, Townes, 1988) and Luria-Nebraska (e.g.,

McKay & Golden, 1981) that tend to be studied factor analytically.In the present case, we were unable to find factor-analytic studies of

schizophrenic samples that included all, or even most, of theneurocognitive test variables reported in the literature. Finally, it is

possible to avoid constructs altogether and simply compile effects

for individual tests. This approach incorporates the fewest assump-

tions about the data; however, it is unwieldy in view of the dozens

of tests in common use and the inconsistency with which different

scores from the same test are reported in the literature (e.g.,categories vs. perseverative errors, Wisconsin Card Sorting Test;

see Heaton, Chelune, Talley, Kay, & Curtiss, 1993).A further consideration is that regardless of classification method,

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many neurocognitive tests are probably influenced by several compo-

nent processes. Thus, scores on the Vocabulary subtest of the Wechsler

Adult Intelligence Scale—Revised (WAIS-R; Wechsler, 1981) may

reflect both basic language abilities and general intelligence; scores on

the Trail Making Test (Reitan, 1958) may reflect visual scanning and

perception but also motor speed, hand-eye coordination, and attention

(Lezak, 1995, pp. 382-384). Hence it may be misleading to categorize

tests on the basis of a faulty assumption that test performance is

determined by only one process.

Accordingly, whenever possible we tried to avoid aggregating

different tests and their effect sizes into hypothetical categories and

adopted a strategy, presented in Table 1, whereby effect sizes were

calculated and reported on individual tests, to most cases effect sizes

were grouped into a priori categories for presentation only and not for

statistical purposes. However, this was not feasible for memory test

variables because of the diversity of tests and related variables reported

in the literature. Hence memory test variables were selected and

aggregated into three effect size categories: global verbal, selective

verbal, and nonverbal. The global verbal memory category included

summary indexes of performance like total words recalled across

learning trials and verbal memory quotients. Selective memory vari-

ables included delayed-recall scores, savings over time, and intrusion

error rates. These selective variables are believed to be more sensitive

than global scores to memory disorders involving the medial temporal-

diencephalic system (see Delis, Kramer, Kaplan & Ober, 1987, pp. 2-3;

Lezak, 1995, pp. 147-150). Non-verbal memory variables included

recall scores for visual stimuli such as shapes and patterns that did not

involve language. The distinction between verbal and nonverbal memory

has a long history in neuropsychology and reflects the existence ofdistinct, or "material-specific," memory disorders in neurological

patients (Calev, Edelist, Kugelmass, & Lerer, 1991). Similarly, tests of

unilateral manual dexterity and finger oscillation were collapsed into a

unilateral motor category. This arrangement is supported by factor-

analytic studies showing the emergence of a distinct motor factor in

psychiatric populations (e.g., Goldstein & Shelly, 1972). These testsincluded Finger Tapping (Reitan & Wolfson, 1993), the Grooved

Pegboard (Klove, 1963), and the Purdue Pegboard (Tiffin, 1968).

The executive category comprised several test variables from the

Wisconsin Card Sorting Test (WCST; Heaton et al., 1993). This is by far

the most widely used test of executive ability in schizophrenia studies

(see Van der Does & Van den Bosch, 1992). Like Saykin et aL (1991),we combined perseveration (including perseverative responses, errors,

and percentage of perseverative errors) and categories scores into an

overall index of WCST performance. In the event that studies reporteddata on attempts to teach schizophrenia patients how to do the test, only

preinstruction baseline data were used.

Attention is a basic neurocognitive function involved to some

degree in all test performances. Nonetheless, several tests make

relatively modest demands on higher-level cognition and are

regarded primarily as measures of attention, selection, concentra-tion, and vigilance. They include two forms of digit span tasks: one

with concurrent distraction (Oltmanns, 1978) and one without

(WAIS-R, Digit Span subtest; Wechsler, 1981). Digit Span meta-

analysis was composed of test scores from both of these paradigms.

In addition, the Continuous Performance Test (CPT) is a class of

attentional tasks that all involve a requirement to respond to target

and ignore nontarget stimuli over a period of time. Scores like

target "hits," errors, and sensitivity measures ("d") based on

signal detection theory are available for the test. In their analysis of

the CPT, Van den Bosch, Rombouts, and van Asma (1996)

concluded that motor speed along with attentional mechanisms

contribute to successful performance. The same almost certainlyapplies to the Trail Making Test (Reitan, 1958), which Lezak

(1995, pp. 382-384) considers an attentional task with perceptual

and motor components. In addition, the Trail Making Test has two

forms that differ in complexity, namely, Part A and Part B. In

contrast, the Stroop Test (Golden, 1978; Stroop, 1935) does not

require a manual response. The strength of the interference trial

effect, where participants have to name perceptually incongruent

color words, has long been regarded as a measure of selective

attention in both clinical and experimental psychology. Completion

time or errors were coded for Part A and Part B of the Trail Making

Test. Continuous Performance Test scores included hits, "d," and

errors. Interference scores, error, or reaction time in the incongru-

ent color-word condition were collected for the Stroop Test.

Full-scale IQ scores were indexed from articles using the

WAIS-R, the Quick Test, the National Adult Reading Test (NART),

the Wonderlic Personnel Test (Wonderlic, 1978), and the Peabody

Picture Vocabulary Test (see Table 1). Intelligence quotients

derived from the WAIS-R were treated separately from those

derived from alternative tests like the NART. Only WAIS-R

performance IQs were gathered for the performance IQ meta-

analytic synthesis. The verbal IQ meta-analysis also included testscores from the WAIS-R only.

Spatial abilities were assessed with the Block Design subtest

(WAIS-R), and two tests from Benton's (Benton, Sivan, Hamsher,

Varney, & Spreen, 1994) laboratory, Facial Recognition and Line

Orientation (see Table 1).

In the domain of language, word fluency test scores collected

included data from the Chicago Word Fluency Test (Milner, 1964)

and the Controlled Oral Word Association Test (Benton & Ham-

sher, 1983), as well as from generically named word fluency tasks.

Recorded vocabulary scores were taken from the WAIS-R, Shipley,

and Mill Hill scales (see Table 1). The Token Test (De Renzi &

Vignolo, 1962) was used to index receptive language.

Finally, the interhemispheric transfer-task meta-analysis was

made up of scores from finger localization tests and transfer-task

test scores (see Bellini et al., 1991; Carr, 1980; Ditchfield &

Hemsley, 1990; Straube & Oades, 1992, pp. 339-341).

Results and Discussion

Data from each primary study were entered into theStatistical Package for the Social Sciences (SPSS, 1990) andthe Software for the Meta-Analytic Review of ResearchLiterature (DSTAT; Johnson, 1989) statistical software pack-ages to produce the descriptive and inferential meta-analyticresults. Descriptive data for the published studies arepresented first, followed by mean effect size summaries andrelated statistical analyses.

Descriptive Data

Two hundred and four studies published between 1980and mid-1997, yielding 509 effect sizes, met criteria forinclusion in the present analysis. Descriptive statistics forthe study set are shown in Table 2. The table shows howmany studies were used in each of the 22 meta-analyses, aswell as the number of individual effect sizes that wereincorporated into each mean effect size. The number ofschizophrenia patients and normal controls for each meta-analysis is also included. In total, neurocognitive test resultsfrom 7,420 schizophrenia patients and 5,865 normal healthycontrols were recorded across meta-analyses.

Table 2 also includes a "vote count" statistic in which theschizophrenia-control comparison from each primary studywas tallied as significant (p < .05) or nonsignificant

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Table 2

Sample-Size Statistics

Variable

MemoryGlobal VerbalSelective VerbalNonverbal

MotorUnilateral SkillBilateral Skill

AttentionDigit SpanTrail Making Test — Part ATrail Making Test — Part BContinuous PerformanceStroop

General IntelligenceWAIS-R IQNon-WAlS-R IQPerformance IQVerbal IQ

Spatial AbilityBlock DesignLine OrientationFacial Recognition

Executive FunctionWisconsin Card Sort

Language FunctionWord FluencyTokenVocabulary

Tactile-Transfer

No. ofStudies

317

14

65

181215146

35431727

1248

43

297

3812

"d

339

16

165

181215156

35431727

1348

104

367

3816

Schizophrenia(n)

1,088559379

232237

4401,2041,372

417179

1,0181,069

717995

1,166225197

1,387

1,020290

2,046297

Controls

(«)

1,187733577

179249

401596805335130

1,0481,233

480658

479202157

1,153

899239

1,186287

Vote count8

(SIG/NSIG)

30/36/3

13/3

11/53/2

12/69/3

13/210/53/3

18/1725/1814/314/13

7/63/16/2

73/31

30/66/1

13/2511/5

Note. nd = number of effect sizes (d) in each meta-analysis; WAIS-R = Wechsler AdultIntelligence Scale—Revised."Vote count refers to the number of statistically significant (SIG) effects in published studies relativeto the number of nonsignificant (NS1G) effects.

(p > .05). Thus, for example, there were 33 schizophrenia-

control comparisons with global verbal memory data drawn

from 31 independent studies. Thirty of these comparisons

were reported as statistically significant and three were

nonsignificant (the vote count). Accordingly, in the pub-

lished literature, global verbal memory, nonverbal memory,

Trail Making Test—Part B, and word fluency tests yielded

the highest proportion of significant test score differences

between schizophrenia and normal control groups.

Descriptive data available for demographic and clinical

variables are presented in Table 3. In terms of gender

composition, the published literature reflects samples that

are disproportionately male (82.4%) and fairly chronic in

terms of the length of illness history. Most patients under-

went initial psychiatric hospitalization in their early twenties

and almost 78% were medicated at the time of neurocogni-

tive testing. The percentage of schizophrenia patients on

medication is presented in Table 3. This statistic is more

Table 3

Schizophrenia Sample Characteristics for Studies Used in the Meta-Analyses

Variable

Sample sizePatient age (years)Age of onset (years)Length of illness (years)Hospital admissions (n)Male (%)Patient education (years)Medication

% on neurolepticsCpz.-eq. daily dose (mg)a

Mdn

23.531.021.912.33.6

77.012.0

100.0577.3

M

36.134.422.212.73.9

82.412.0

77.6582.1

SD

30.210.03.27.67.4

63.01.1

36.6340.3

Range

5.0-819.018.1-56.517.5-28.00.0-29.60.0-9.5

25.0-100.09.0-15.3

0.0-100.00.0-1,771.9

n

204200404329

168134

10059

"The cpz.-eq. daily dose is the mean chlorpromazine-equivalent (cpz.-eq.) dose in schizophreniasamples as reported in 59 studies. Studies that recruited medication-free or medication-naive patientswere entered as a cpz.-eq. dose of 0. The remaining 145 studies included in the meta-analyses did notreport medication data in cpz.-eq. units.

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Table 4Mean Effect Sizes (Md) for Memory Variables

Variable

Global VerbalSelective VerbalNonverbal

nd

339

16

Md

1.531.111.42

SDd

0.660.642.33

0/L%

29.341.131.9

95% CI

1.27-1.860.62-1.600.18-2.67

Note, rid — number of effect sizes (Cohen's d}\ SDj = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval.

informative than the chlorpiomazine-equivalent daily dosebecause of infrequent neuroleptic dose-reporting in thepublished literature. In addition, clozapine and respiridonedosages are not convertible into chlorpromazine doses.However, all anti-psychotic medication is reflected in thebinary medication variable. Of 204 studies, only 13 (6%)reported data on unmedicated patient samples. The possibleinfluence of medication on neurocognitive test performancehas been recognized for many years (see Spohn & Strauss,1989), but convincing evidence for this influence has beenlacking and clinicians face ethical constraints in removingpatients from treatment for research purposes. Thus, apartfrom some exceptions (e.g., Cleghorn, Kaplan, et al., 1990),researchers relied largely on medicated patient samplesduring the period between 1980 and 1997.

Neurocognitive Tests

Effect size summaries are presented for each neurocogni-tive test or test category in Tables 4—9 and 11. Two kinds ofds are reported. Mean ds presented in the second column ofTables 4-9 are raw effect sizes in absolute value, and thosepresented in Table 11 are corrected by sample size. In thecorrection procedure, effect sizes are combined by averagingd values, with each d weighted by the reciprocal of itsvariance (see Wolf, 1986). This procedure gives greatestweight to the most reliably estimated study effect sizes—those with the largest sample sizes (Hedges & Olkin, 1985).Nonetheless, most meta-analysts stress the importance of theraw effect size in summarizing a body of literature (Rosen-thai, 1995). As a test of significance, a 95% confidenceinterval was drawn around each raw d mean. These intervalsare included along with the corresponding standard devia-tion in the table. All meta-analytic variables were significantusing the confidence interval significance testing procedure(see Schmidt, 1996).

The U statistic represents the degree of nonoverlapassociated with d and the distribution of scores betweengroups (Cohen, 1988). With a simple conversion, this canalso represent the degree of overlap by subtracting thenonoverlap from 100. This was done in Tables 4—9 andentered as a hypothetical overlap statistic (O/L%) to aid ininterpretation of the data. Accordingly, the O/L% statisticused here represents the degree of overlap between schizo-phrenia patients and normal control participants in thedistributions of neurocognitive test scores.

Memory. Raw effect sizes (Cohen's d; see Cohen, 1988)for the three groups of memory tests along with effect sizes

corrected for sample size (see Johnson, 1989), percentageoverlap (O/L%), and 95% confidence interval (for raw dsonly) are presented in Tables 4 and 11. The results show thatdefective total verbal learning over trials, or similar sum-mary scores, is a reliable finding in the schizophrenialiterature, with a schizophrenia-control distribution overlapof somewhat more than 25%. More specific studies, usingvariables like rate of forgetting, savings after a delayinterval, or other indexes sensitive to organic amnesicdisorders, also reveal a substantial effect, although relativelyfew studies reported these variables. At the same time, thereis no suggestion of a disproportionate impairment onselective measures relative to more global indexes. More-over, the nonverbal results are comparable in magnitude tothe verbal memory data. However, nonverbal memoryshows more heterogeneity of effects across studies andappears to be a less reliable deficit than verbal memorydeficits.

In terms of possible moderator variables, there were nosignificant product-moment correlations between basic de-mographic and clinical variables and the memory-relatedeffect sizes. However, with a limited number of studiesreporting, there were nonsignificant correlation trends withseveral variables. These included relationships betweenschizophrenia sample age and global verbal memory effectsizes, r(33) = -.25, p > .05, two-tailed. In addition, therewere suggestive trends in the selective verbal memory datawith percentage of male patients, r(9) = .58, p > .05,two-tailed, and with years of education, r(9) = —.41, p >.05, two-tailed. These relations between verbal memory, age,and gender also occur in normal participants (e.g., Delis etal., 1987, pp. 35-37; Elwood, 1995). The nonverbal effectsizes correlated modestly with percentage of male patients,r(16) = .31, p > .05, two-tailed. Age of illness onset andhospitalization data were reported too infrequently to allowfor calculation of correlations.

Executive, motor, and tactile-transfer. Effect sizes forthe WCST, based on a summary of its constituent variables,including perseverative responses, errors, and categories, arepresented in Table 5. These results show a moderately largeand reliable impairment of WCST performance in schizophre-nia samples, with roughly half of the patients separated fromnormal control participants. The large number of studiesreporting WCST results allowed for analysis of moderatorvariables, but only the correlation with number of hospital-izations approached significance, r(29) = -36, p > .05,

Table 5Mean Effect Sizes (Mj) for Executive, Motor,and Tactile-Transfer Variables

Variable

Wisconsin Card SortUnilateral MotorBilateral MotorTactile-Transfer

"d

104165

16

Md

0.950.891.421.39

SDd

0.440.420.401.90

O/L%

46.548.431.931.9

95% CI

0.85-1.040.66-1.111.01-1.650.38-2.41

Note. nd = number of effect sizes (Cohen's d); SDa = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval.

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two-tailed. In addition, a large number of studies (n = 33)reported WCST results along with WAIS-R IQ results,thereby permitting an examination of possible relationshipsbetween general intellectual levels in schizophrenia samplesand the WCST effect size in the same sample. There was asignificant relationship between WCST effect size and IQscore, r(33) = -.54, p < .01, and between WCST effects

and IQ effects, r(33) = .56, p < .005. These relationshipsimply that patient samples are likely to differ from controlsamples on the WCST if they also differ in terms of IQ.Thus, impaired WCST scores in schizophrenia may be, inpart, a reflection of low general intellectual abilities.

Unilateral motor tests produced effect sizes that weresimilar in magnitude to the WCST results, especially whenthe effects were corrected for sample size (see also Table11). There was a nonsignificant association with the propor-

tion of male patients in the study sample, r(l4) = ~-36,p >.05, two-tailed. The number of studies reporting bilateralmotor results was very small, making general conclusions

difficult.Results of tests where patients identify and discriminate

tactile stimuli under conditions that require transfer ofinformation across the corpus callosum showed a large meaneffect size, even after correction for sample size. At the sametime, the results were very heterogeneous, indicating theexistence of patient samples with extensive overlap, as wellas samples with very little overlap, with control distribu-tions. There were no significant moderator correlations inthe tactile-transfer results, but there were suggestive trendswith patient sample age, r(15) = .47, p > .05, two-tailed;education, r(10) = —.68, p > .05, two-tailed; and percent-age of male patients, r(15) = .33, p > .05, two-tailed.

Attention. Results for attention variables are presentedin Table 6. The most frequently reported, attention-relatedtest variable was digit span. Eighteen studies reported data,with 3 of these reporting results with the distraction version(Oltmanns, 1978) and the rest with the WAIS-R versionwithout distraction. The average effect size for the subset ofdistraction digit spans was .81, but the number of studieswas too small to allow for a statistical comparison. Theoverall effect size of .62 represents a hypothetical overlapwith control distributions of about 60%, suggesting a fairlymodest degree of discrimination. Larger effects were ob-tained in studies using versions of the Continuous Perfor-mance Test and the Stroop Test, but the number of articlesreporting Stroop results is small. Overall, die Continuous

Table 6Mean Effect Sizes (Md) for Attention Variables

Variable ~^d ~Md ~SDd O / L % 9 5 % CI

Digit Span 18 0.62 0.51 61.8 0.35-0.96Continuous Performance 15 1.18 0.49 37.8 0.94-1.50Stroop 6 1.22 0.63 41.1 0.23-2.11Trail Making—Part A 12 0.95 0.32 48.4 0.73-1.16Trail Making—Part B 15 1.07 0.52 41.1 0.80-1.33

Note. 114 = number of effect sizes (Cohen's af); SDd = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval.

Table 7

Mean Effect Sizes (Md)for General intelligence Variables

Variable

WAIS-R IQNon-WAIS-R IQVerbal IQPerformance IQ

nd

35432717

Md

1.240.630.981.46

SDd

0.890.600.680.89

O/L%

37.861.844.629.3

95% CI

0.88-1.560.35-0.920.70-1.201.07-1.90

Note. nd — number of effect sizes (Cohen's d); SDd = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval; WAIS-R = Wechsler AdultIntelligence Scale—Revised.

Performance and Trail Making tests seem to be about

equally sensitive to schizophrenia, yielding large effect sizesthat separate more than half of the patient and controldistributions. Nevertheless, Trail Making Test results arebased on smaller sample sizes, which causes shrinkage in thecorrected effect size compared with the Continuous Perfor-mance data (see Table 11 presented later).

The two effect sizes for the Trail Making Test are alsoinformative because they represent a situation where asimple and a more complex and demanding version of aneurocognitive task can be studied. If task difficulty and taskcomplexity, rather than specific neurocognitive functionslike visual scanning and sequencing, contribute to schizophre-nia-control differences, more demanding versions of thesame basic test should yield significantly larger effect sizesthan less demanding versions. Thus Part B of the TrailMaking Test should produce a larger effect size than Part Awhen applied to the same sample of patients and controls.

Part A requires rapid sequential connection of numbers in apaper-and-pencil format, whereas Part B requires alternationbetween alphabetic and numeric sequences. Possum, Holm-berg, and Reinvang (1992) have shown that the spatialarrangement and dual symbol systems of Part B underlie theslower Part B completion times.

Accordingly, we carried out a paired-difference t testcomparing effect sizes from the two forms. There was nosignificant difference between the two parts of the TrailMaking Test, f(25) = -1.3, p > .05, two-tailed. Hence, itappears likely that task difficulty and complexity are lessimportant than the more specific attention and scanning-related neurocognitive processes elicited by this particulartest. Alternatively, because both alphabetic and numericsequences are probably overlearned information, the differ-ences in processing demands between Parts A and B of theTrail Making Test may be minimal.

General intellectual ability. Effect sizes for intelligencetest results are presented in Table 7. Full scale IQ estimateswere obtained from a variety of intelligence tests in additionto the WAIS-R (see Table 1). To assess whether WAIS-Rand non-WAIS-R-derived IQs differed in terms of theirability to separate schizophrenia and control distributions,we calculated a t test on the respective mean d values. Thiscalculation indicated substantially larger average effect sizesin patient-control comparisons that used the WAIS-R,f (76) = 2.81, p < .01, two-tailed. Whether this indicates thatthe WAIS-R is more difficult for patients to complete than

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Table 8Mean Effect Sizes (Md) for Spatial Ability Variables

Variable

Line OrientationBlock DesignFacial Recognition

"j

4138

Md

1.100.730.88

SDd

0.620.610.44

O/L%

41.157.048.4

95% CI

0.12-2.00.29-1.190.44-1.19

Note, nd — number of effect sizes (Cohen's d); SDd = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval.

measures like the Shipley, Quick, and NART is unclear. Thenon-WAIS-R measures tend to be briefer and less compre-hensive in scope than the WAIS-R. At the same time, some

of these alternative measures, like the NART, are regarded asmeasures of premorbid rather than morbid intelligence. Weextracted the NART data from the study set and found amean d of .42 (SD = .68) for 19 studies. Hence, smaller

differences between patients and controls on such tests mayreflect a robustness of performance in the presence ofschizophrenic illness. However, there is a considerable

degree of dispersion around the NART mean, and individualstudy effects ranged from -1.85 to +0.91, which raisesquestions of reliability in the use of this measure to indexpremorbid ability levels. In any case, the data suggest thatputative tests of general intellectual function are not inter-changeable and can be expected to vary in sensitivity toschizophrenic illness.

Intelligence measured with the WAIS-R yields largeeffect sizes in schizophrenia-control comparisons, withdistribution overlaps that are well below 50%. In addition,intellectual differences are reliable, with none of the confi-dence intervals including zero. Finally, no significant relation-ships were found between IQ effect sizes and potentialmoderators including age, education, neuroleptic dose, sam-ple gender composition, or age of illness onset.

Spatial ability. Results pertaining to the relatively smallnumber of patient-control comparisons that used tests ofvisuospatial skill are presented in Table 8. These tests are notused frequently in the schizophrenia literature, but theavailable data suggest there are moderately large effect sizes,with less man 50% overlap between schizophrenia andcontrol distributions. However, the d values shrink consider-ably when corrected for sample size (see Table 11 presentedlater), indicating that most studies using spatial ability testsare based on small samples of patients and controls. Withthis correction the effect sizes correspond to more moderatehypothetical overlap values, suggesting that perhaps twothirds of the patients and controls are indistinguishable interms of spatial skill.

Language. The effect size summary for language-related tests is presented in Table 9. This shows large effectsfor tests of word generation such as the Controlled OralWord Association Test and the Token Test (the latter isprimarily an auditory comprehension measure; see Table 1).In both cases, these-effect sizes represent only aboutone-third overlap between schizophrenia and control distri-butions. However, this evidence is qualified by a relativelysmall number of contributing studies in the case of the Token

Test. In addition, the relatively large standard deviations

imply considerable dispersion and hence heterogeneity ofeffect sizes in both the fluency and Token Test data.

In contrast, a more moderate average effect size wasobtained for vocabulary tests. This is another aggregate of

measures, with some derived from the WAIS-R Vocabularysubtest and others based on scales such as the Mill Hill andShipley scales (see Table 1). Nonetheless, there was no

significant difference between the average effect size gener-ated by WAIS-R and non-WAIS-R-based vocabulary tests,

r(36) = 1.71,p> .05.Overall, expressive and receptive language tests appear to

be fairly powerful and moderately reliable discriminators ofschizophrenia and control populations. None of the effectsizes correlated significantly with potential demographic orclinical moderators. However, with 13 studies reporting bothfluency and chlorpromazine dose data, there was a strongtrend for highly medicated samples to yield smaller word

fluency differences relative to control samples, r(13) =-.75,p>.05.

Inferential data. It is unlikely that a literature reviewwill uncover every study of a hypothesis that has beenconducted. Rosenthal (1979) has called this the "file drawerproblem" because of the tendency for studies of nonsignifi-cant results to remain unpublished, perhaps languishing inforgotten file drawers (see also Wolf, 1986, p. 37). Kraemerand Andrews (1982) note that "published research studiestend to be biased toward positive findings. A study is oftenabandoned if it is apparent that statistically significantfindings will not be forthcoming. Reports of nonsignificantfindings are generally unpublishable even when they arereplications of earlier studies reporting significant results"(p. 405). To deal with the file drawer problem in the presentset of meta-analyses, Orwin's (1983) fail-safe N procedurewas carried out. Table 10 shows the estimated number ofunpublished or unretrieved studies, each with a trivial effectsize of d — 0.1 (92% distribution overlap), needed to reducethe effect sizes reported in Tables 4-9 to 0.1 or smaller.Although the actual existence of such countervailing evi-dence cannot be entirely ruled out, it seems unlikely thatsuch large numbers of studies are actually present inneglected file drawers.

Summary and Conclusions

In our quantitative review, we found neurocognitivedeficit to be a reliable finding in schizophrenia. In addition, itis clear that the deficit exists in relation to most neurocogni-

Table 9Mean Effect Sizes (Mj)for Language Function Variables

Variable

Word FluencyVocabularyToken Test

"d

36387

Md

1.390.691.35

SDd

1.200.480.94

O/L%

31.957.034.7

95% CI

0.91-1.800.50-0.860.98-1.70

Note, rij = number of effect sizes (Cohen's d); SDd = standarddeviation of effect sizes; O/L % = percentage overlap between testscore distributions for schizophrenia patients and normal controls;95% CI = 95% confidence interval.

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Table 10The File Drawer Problem and the Fail

Variable

MemoryGlobal VerbalSelective VerbalNonverbal

MotorBilateral MotorUnilateral Motor

AttentionDigit SpanContinuous PerformanceStroopTrail Making Test— Part ATrail Making Test— Part B

General IntelligenceWAIS-R IQNon-WAIS-R IQVerbal IQPerformance IQ

Spatial AbilityLine OrientationBlock DesignFacial Recognition

Executive FunctionWisconsin Card Sort

Language FunctionVocabularyWord FluencyToken

Tactile-Transfer

-SafeN

Estimated number ofunpublished studiesneeded to achieve

d = Q.\ (92% overlap)8

47291

211

66121

9416267

102146

399228240228

208262

884

22446462

141

Note. WAIS-R = Wechsler Adult Intelligence Scale—Revised."The value of 0.1 was arbitrarily chosen as a small or trivial effectsize (see Wolf, 1986, pp. 37-39).

live tasks in common use. Moreover, although there isvariability in the magnitude of deficit, no single test orneurocognitive construct completely separates schizophre-nia and control distributions. Results for mean individualand composite test effects corrected for sample size aresummarized in order of magnitude in Table 11. The largestmean effect sizes that are also supported by adequatenumbers of studies include global verbal memory, perfor-mance and full scale IQ, Continuous Performance scores,and word fluency. In terms of the difference betweenschizophrenia and control distributions, these effects areassociated with 60-70% nonoverlap. The smallest effects,like those obtained for Block Design (WAIS-R), Vocabu-lary, and non-WAJS-R IQ, correspond to approximately30-40% nonoverlap. Alternatively, as an estimate of deficitprevalence, Rosenthal and Rubin's (1979, 1982) binomialeffect size display model suggests that about 78% ofschizophrenia patients score below the median on globalverbal memory tests, whereas about 61% score below themedian on Block Design tests. Therefore, neurocognitiveimpairment cannot be considered a denning characteristic ofeach case of schizophrenia. Indeed, there is convergentevidence that substantial numbers of schizophrenia patientsare neuropsychologically normal (Heinrichs & Awad, 1993;Heinrichs, Ruttan, Zakzanis, & Case, 1997; Palmer et al.,

1997). However, significant impairment is common andoften highly prevalent in schizophrenia, and it equals orexceeds the magnitude of deficit found in some neurologicaldisorders (e.g., multiple sclerosis; see Thornton & Raz,1997).

Our findings indicate higher prevalence of neurocognitivedeficit in schizophrenia than rates suggested by someprevious estimates. For example, Goldberg et al. (1988)suggested a prevalence of 40-60% for selected neuropsycho-logical test deficits in the illness. In addition, many patientsmay perform below their premorbid neurocognitive poten-tial even if this does not constitute an impairment as definedby standard cutoffs or statistical conventions. This possibil-ity draws some support from the neurocognitive perfor-mance of monozygotic twins discordant for schizophrenia.Thus Goldberg, Ragland, et al. (1990) found that schizo-phrenic twins were disadvantaged cognitively relative totheir healthy co-twins 80-95% of the time, even if the illtwin was technically average by normative performancestandards.

Accordingly, one way of understanding the reliable andlarge, but not inclusive, deficit rates in schizophrenia is theconcept of a continuum of neurocognitive function. Patientswith the illness may vary from a mild decline in function, tomoderate declines, to the kind of severe dysfunction seen inmany neurological diseases (Goldberg & Gold, 1995; Wein-

Table 11Mean Neurocognitive Effect Sizes Ordered by Magnitudeand Corrected for Sample Size

Test or construct

Global Verbal MemoryBilateral Motor SkillPerformance IQContinuous PerformanceWord FluencyStroop TestWAIS-R IQTokenTactile-TransferSelective Verbal MemoryWisconsin Card SortVerbal IQUnilateral Motor SkillTrail Making — Part BNonverbal MemoryTrail Making — Part AFacial RecognitionDigit SpanLine OrientationNon-WAIS-R IQVocabularyBlock Design

Ma

1.411.301.261.161.151.111.100.980.980.900.880.880.860.800.740.700.610.610.600.590.530.46

SD

0.590.381.000.491.000.490.720.491.710.620.410.660.390.501.980.360.360.430.630.510.210.39

n

315

171429

6357

127

43276

1514128

184

433812

Patientsbelow

Mdn(%)

7877

- 7775757474717170696969686766646464646261

Note. The Md refers to the mean effect size for the test variableindicated, corrected for sample size. The n refers to the number ofindependent studies in the database for each mean effect size. Thepercentage of patients below the median refers to an estimate of theproportion of patients scoring below the median of the jointpatient-control aggregate sample on the test variable in question.This estimate is derived from Rosenthal and Rubin's (1979, 1982)binomial effect size display. WAIS-R = Wechsler Adult Intelli-gence Scale—Revised.

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NEUROCOGNTTIVE DEFICIT IN SCHIZOPHRENIA 435

berger, 1995). At the mild end, a patient's neurocognitivedysfunction is relative to his or her own potential and may

overlap with levels of function obtained by many healthy

individuals. At the severe extreme, a patient may havecognitive and neurological abnormalities that are completely

distinguished from normal control values.The limitations of this kind of explanation include its

testability. There are practical difficulties involved in accu-

rately assessing premorbid neurocognitive potential in largenumbers of schizophrenia patients. Moreover, the con-tinuum argument must assume that patients who show

average neurocognitive function—roughly half of thosewith the illness—have nevertheless undergone a relativedecline. Therefore, the same proportion of patients must

have been slightly above average by normative performancestandards before illness onset. This seems a rather difficultprediction to test. Finally, although discordant monozygotictwins provide an important perspective on schizophrenia,

they cannot be regarded as representative of either thehealthy or patient populations as a whole.

Another explanation for the 61—78% neurocognitive defi-

cit rate in our quantitative review is that such deficit issecondary, peripheral rather than central to the illness. Mostdiseases have peripheral features that are tied only modestly

and indirectly to the primary pathology. For example,overall brain volume reductions are less prevalent in Alzhei-mer's disease than volume reductions in a specific structure,the hippocampus (Seab et al., 1988). Presumably, thisreflects the primary locus of pathology in the hippocampusand other specific structures and the less direct relation ofthis focal pathology to general changes in brain volume. Thefunctions measured by standard neurocognitive tests inschizophrenia patients may also be relatively peripheral tothe still unknown primary dysfunction.

At the same time, it may not be reasonable to expectextremely high rates of control—patient discrimination giventhe vagaries inherent in psychological measurement. For acomparison, consider the normative data on the WCSTprovided by Heaton et al. (1993). The effect size forcategories achieved on the WCST is -1.04 SD units whenneurological patients with focal frontal lesions are comparedwith healthy controls. When "categories" is extracted as aseparate score from our own meta-analytic data on schizo-phrenia, the mean effect size is - ] .05. Given the traditionaldifficulty of discriminating schizophrenia from more conven-tional neurobehavioral disorders, it may well be that at leastsome neurocognitive tests are as sensitive to schizophreniaas they are to neurological conditions in general (Heaton etal., 1994). Moreover, the sensitivity of neurocognitive teststo schizophrenia compares favorably with structural andfunctional neuroimaging methods, which tend to yield effectsizes less than 1.0 (Raz and Raz, 1990; Zakzanis &Heinrichs, 1997).

Of equal theoretical interest for understanding schizophre-nia is the vexed question of heterogeneity and whethersubstantial but not absolute (e.g., d > 4.0, nonover-lap = 97%) effect sizes reflect the existence of cognitivelyintact and impaired subpopulations of patients. In addition,several tests and constructs are characterized by large

dispersion of individual effects around their means (e.g.,

Performance IQ, word fluency, tactile-transfer tests, nonver-

bal memory tests; see Table 11). Such dispersion alsoimplies the existence of impaired and normal subgroups ofpatients. This is inimical to the continuum view of neurocog-nitive deficit, which argues for a single disease process with

variable expression. In contrast, a strong form of theheterogeneity argument is the contention that multipledisease processes underpin schizophrenia (see Carpenter,

Buchanan, Kirkpatrick, & Tamminga, 1993; Heinrichs,1993; Liddle & Barnes, 1990). Advocates of the singledisease-continuum model can point to brain features like

ventricle size to show that schizophrenia patients are nor-mally distributed on such measures, with little evidence ofthe kind of bimodality that might indicate subpopulations

(David, Goldberg, Gibbon, & Weinberger, 1991). On theother hand, there is clear evidence of bimodal distributionsin schizophrenia on some psychophysiological measures,

like smooth-pursuit eye movements (Clementz, Grove,lacono, & Sweeney, 1992; lacono, Moreau, Beiser, Heming,& Liu, 1992). Our meta-analytic evidence cannot resolve

this debate, but it forcefully underscores the need to developtestable illness models that take heterogeneity into accountand extend the search for evidence of multiple disease

processes.Despite the fact that all tests and neurocognitive catego-

ries yielded at least moderate and often large effects, sometests appear to produce greater differences between patientsand controls than other tests. The order of effect magnitudesin Table 11 bears a very rough similarity to Saykin et al.'s(1991) study of a schizophrenia sample that showed thegreatest difference from controls on a group of memoryfunctions, followed by differences in motor-attentional andlanguage functions. In addition, a recent meta-analysis byWoodruff, McManus, and David (1995) supports the ideathat corpus callosum abnormalities exist in schizophrenia.This is consistent with defective recognition of tactileinformation on interhemispheric transfer tasks. Neverthe-less, our meta-analysis, based on 204 studies, 7,420 schizo-phrenia patients, and 5,865 normal controls, confirms previ-ous suggestions that any selectivity of deficit in schizophreniaoccurs in the context of a background of a very generalimpairment. Even the less-sensitive tasks yielded effect sizesbetween 0.5 and 1.0 on average, suggesting that anydifferential sensitivity of specific tests is relatively mild andcoexists with a more broadly based impairment in cognitivebrain function. Moreover, it is not known to what extentdifferences in task complexity, difficulty, floor or ceilingeffects, and other psychometric properties may have contrib-uted to the appearance of more or less powerful effect sizesacross selected neurocognitive measures (see Chapman &Chapman, 1973, 1978). Data from the Trail Making Testsuggest that more complex tests do not necessarily increasethe disadvantage patients show relative to controls oncognitive tasks. However, a comparison of WAIS—R- andnon-WAIS-R-derived IQ estimates showed that differentmeasures of the same cognitive function may yield markedlydifferent degrees of separation between schizophrenia andcontrol samples.

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Hence, a cautious interpretation of our meta-analytic datawould be that all areas of neurocognitive function arecompromised in a large proportion of schizophrenia pa-

tients. Long-standing arguments concerning "core" or selec-tive deficits against a background of general impairment areunlikely to be resolved until researchers routinely calibrate

the difficulty and complexity levels of their tests beforeapplying them to clinical populations.

Moderator variable analysis is an essential part of meta-

analytic investigation because it assesses the contribution ofstudy differences in sample attributes, design, and instru-ment features to effect size heterogeneity and replication.However, moderator analysis is dependent on the individual

reporting practices of investigators and presumably theexpectations of journal editors and reviewers. In terms ofmoderator variables, the published literature on neurocogni-

tion in schizophrenia is surprisingly limited and ofteninadequate. We found that even basic participant attributes,such as age, education, and gender composition, were notreported by all studies. Clinical variables of special rel-evance to schizophrenia, like neuroleptic medication dose,age of illness onset, duration of illness, and frequency ofhospitalization were so underreported that we were able toconduct only a limited correlational study of potentialmoderators, with many correlations missing statistical signifi-cance because of inadequate power.

In the moderator variable analysis there were nonsignifi-cant trends that imply possible links between neurocognitive

deficits and some demographic and clinical aspects of thepatient samples. Thus, higher neuroleptic doses associatewith smaller differences between patients and controls onexpressive language tests (see Spohn & Strauss, 1989). Witha very limited number of studies available for analysis, thereis little to suggest an association between chronicity ofillness and neurocognitive deficit. A moderate, non-significant trend is suggested between frequent hospitaliza-tion and executive deficits measured by the WCST. Moreconsistent reporting of these sample attributes by researchersin individual studies will increase statistical power formeta-analytic purposes and facilitate future efforts at under-standing how neurocognitive and clinical variables are

related in the published literature.Although many studies did not report IQ along with other

neurocognitive variables of interest, there is evidence that atleast one commonly used neurocognitive measure in schizo-phrenia research is tied to general intellectual ability.Differences between patients and controls in terms of WCSTperformance were correlated with differences in IQ scoresand in IQ effect sizes, which is consistent with earlier studiessuggesting a relationship between intelligence and WCSTperformance in neuropsychiatric patients (e.g., Heinrichs,

1990).The finding that patient samples that differ in general

intelligence from their control samples are also likely todiffer in WCST performance reinforces the question ofwhether general ability factors underpin apparently selectiveneurocognitive deficits in schizophrenia. In other words, theillness may bring with it a generalized cognitive deficit thatmanifests itself in putatively different tests that nonetheless

depend heavily on general ability for successful perfor-mance. Although defective WCST scores may coexist withaverage IQs in many neurological disorders, this does not

appear to be the case in schizophrenia (see Heaton et al.,1993).

It is also possible that functions other than intelligence

contribute to patient performance on a broad variety of tasks.Neurocognitive tests probably measure several componentprocesses at the same time. These processes may be

weighted differentially in terms of particular task demands.Hence, the final score or performance value may reflect thiscomplexity rather than just the preservation or impairmentof a supposedly "pure" function. For example, verbal

memory tasks, such as the California Verbal Learning Test(Delis et al., 1987), require not only intact verbal memoryfor success but also basic language comprehension, auditory

attention, and expressive language. Similarly, many tasks inaddition to those subsumed under the label of "attention"require attention-related skills. It is not known to what extent

shared functions contribute to neurocognitive test perfor-mance and give rise to spurious impressions of selectiveimpairment. However, concurrent reporting of general andmore specific neurocognitive test scores, along with data ontask difficulty, will make it easier to assess the credibility ofputatively selective deficits (Chapman & Chapman, 1978,1989).

Neuropsychological research has traditionally used match-ing strategies to demonstrate selectivity of deficit. In thisway, a lesion group and a control group are matched in termsof general intellectual ability, education, and other attributesthat may confound the demonstration of specific deficit. Inthe case of schizophrenia, however, the nature of the illnessand its onset in late adolescence or early adulthood prema-turely curtail academic achievement. Hence, the use ofeducational attainment or IQ for matching purposes can leadto spuriously low estimates of general ability in schizophre-nia populations. With this problem in mind, some investiga-tors have argued for the use of measures for matchingpurposes, such as oral reading or spelling tests, that are lesssensitive to the illness (Kremen et al., 1996). This is anexcellent suggestion for future research on neurocognitiveaspects of schizophrenia. In our review we found that theoverwhelming majority of studies matched patients andcontrols on age (98%) and gender composition (82%). Only19 studies even reported data from the NART, a measure ofputative premorbid intellectual ability that may lend itself tomatching purposes. Such tasks are clearly not representedwidely in matching paradigms in the published literature.This relative neglect of matching issues from a neuropsycho-logical standpoint may reflect the fact that most studiesemploy neurocognitive measures within a framework thatincludes neuroimaging techniques, symptom scales, andpsychophysiological measures. The psychometric require-ments of a valid patient-control comparison in neurocogni-tive terms may be overlooked in the concern for the needs ofa valid neurobiological comparison. Nevertheless, futureresearch on the neuropsychology of schizophrenia will bemore informative to the extent that the vagaries of Meehl's(1970) "matching fallacy" are addressed.

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In conclusion, we addressed the strength and consistency

of neurocognitive findings in schizophrenia in our quantita-

tive review. This literature has accumulated in tandem with

the view that schizophrenia is a neurological disorder that

manifests itself in behavior. Our quantitative synthesis

confirms that a large proportion of this patient population is

impaired on standard neurocognitive tests, and the evidence

suggests that any selective deficits in functions like verbal

memory are relative and exist against a background of

general dysfunction. This picture is complicated by issues of

test equivalence within and between neurocognitive func-

tions, by the need to improve reporting of potential modera-

tor variables, and by the need to develop testable illness

models that can account for why substantial numbers of

patients are indistinguishable from controls on many tests.

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Appendix

Key Words Used in Computer Database Search

Schizophrenia MemorySchizophrenia California Verbal LearningSchizophrenia WechslerSchizophrenia Nonverbal MemorySchizophrenia Selective MemorySchizophrenia Global MemorySchizophrenia Verbal MemorySchizophrenia Complex FigureSchizophrenia Face RecognitionSchizophrenia Spatial PerceptionSchizophrenia Line Orientation

Schizophrenia TransferSchizophrenia PurdueSchizophrenia Motor DexteritySchizophrenia Finger TappingSchizophrenia Motor DisorderSchizophrenia StroopSchizophrenia Digit SpanSchizophrenia Continuous PerformanceSchizophrenia Backward MaskingSchizophrenia TrailsSchizophrenia Trail Making

Schizophrenia Wisconsin Card Sorting TestSchizophrenia IQSchizophrenia Verbal IQSchizophrenia Performance IQSchizophrenia VocabularySchizophrenia Block DesignSchizophrenia Word FluencySchizophrenia TokenSchizophrenia ComprehensionSchizophrenia Affect RecognitionSchizophrenia Dichotic Listening

Received May 5, 1997

Revision received October 15, 1997

Accepted October 20, 1997 i

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