The Flynn effect: Smarter not faster
*, Carlene Wilson
Department of Psychology, University of Adelaide, Adelaide, South Australia 5005, Australia
CSIRO, Adelaide, South Australia, Australia
Received 30 September 2002; received in revised form 13 June 2003; accepted 30 June 2003
Inspection time (IT) and Peabody Picture Vocabulary Test (PPVT) scores from 75 school children aged 6–13
years in 2001 were compared with the performances of 70 children aged 6 – 13 years who had attended the same
primary school in 1981 [J. Exp. Child Psychol. 40 (1985) 1.]. ITs for the 2001 sample were measured with the same
four-field tachistoscope and identical computer-based procedures followed by Wilson in 1981. The 2001 sample
completed two versions of PPVT concurrently: PPVT (1965, Form A) as used in 1981 and PPVT-III (1997, Form
IIIA). Mean ITs from both samples, 20 years apart, were essentially the same (123 F87 and 116 F71 ms in 1981
and 2001, respectively). There was, therefore, no evidence that speed of processing had improved. Correlations
between IT and raw PPVT scores were significant ( P< .01) for both 1981 (r=.43) and 2001 (r=.31). Within
the 2001 sample, concurrent PPVT scores correlated .68; however, means revealed a significant Flynn effect. Thus,
scores for the 2001 cohort on the earlier PPVT were higher (Mstandardized IQ 118.52 F16.62) than the recently
restandardized PPVT-III (113.97 F12.23), although, compared in terms of the most recent standardization sample,
the 2001 cohort was equivalent to the 1981 sample (113.66 F16.72). The Flynn effect has nothing to do with speed
of processing as measured by IT, despite the effect being strongest for ability tests that earn bonus scores for quick
performance. Because IT correlates with IQ but appears to be stable across 20 years, whereas IQ is not, IT may have
promise as a useful biological marker for an important component of cognitive decline during old age.
D2004 Elsevier Inc. All rights reserved.
Keywords: Flynn effect; Inspection time; Peabody Picture Vocabulary Test
This study drew on two lines of inquiry: a steady, continuing, long-term, and worldwide
improvement in IQ and the theoretical contribution of inspection time (IT), envisaged as a measure
0160-2896/$ - see front matter D2004 Elsevier Inc. All rights reserved.
* Corresponding author. Tel.: +61-8-8303-5738; fax: +61-8-8303-3770.
E-mail address: firstname.lastname@example.org (T. Nettelbeck).
Intelligence 32 (2004) 85 –93
of speed of processing, to an understanding of the nature of individual differences in intelligence. The
critical proposition on which the study was based was that comparisons between ITs from cohorts of
children separated by 20 years, made by replicating earlier research procedures, provided the means for
testing whether rising IQ (the ‘‘Flynn effect’’) was the consequence of or accompanied by faster
Flynn (1999) has clearly documented widespread rises in mean IQs from substantial samples from
some 20 nations representing western European/North American cultures or technologies. These
increases in mean IQ, apparently without changes in variance, are presumed to be caused by
environmental influences as yet unidentified. There is considerable interest in Flynn’s finding (Neisser,
1998) and ongoing debate about a range of explanations, generally covering improved physical health,
nutrition and well-being, and extensive educational and technological changes within the countries
involved across the 20th century. IQ has risen, despite evidence that differences in IQ are substantially
influenced by genetic variation (Plomin & Petrill, 1997) and that individual IQ is generally not
susceptible to improvement (Spitz, 1999). Moreover, improved IQ appears to represent gains in
problem-solving abilities more than straightforward knowledge acquisition, because the largest effects
involve tests designed to measure nonverbal reasoning and abstract problem-solving, like Raven’s
progressive matrices and the Performance subtest from the Wechsler scales. Of immediate relevance to
the current study, most of these tests carry bonus points for quicker responding. By Flynn’s account,
average gains of about 1 IQ point every 3 years have probably been occurring since the IQ test was
invented. Yet, almost no one believes that human genotypic intelligence has improved significantly
during the course of the 20th century. Although the Flynn effect has thus far been demonstrated
predominantly for young (male) adults (which seems to rule out earlier maturation in more recent
generations as an explanation), at least one study, by Tasbihsazan, Nettelbeck and Kirby (1997), has
demonstrated an improved Mental Development Index of 18 points across 25 years among infants aged
18–27 months based on concurrent Bayley’s (1969, 1993) comparisons. This finding is not plausibly
attributable to improved education.
It is important to note that these IQ improvements are cross sectional, derived from the achieve-
ments of different cohorts on test content that has not changed or changed little across generations.
There is no suggestion that individual IQs have improved longitudinally. Within generations, IQ scores
remain good predictors of academic, work performance, and other life achievements (Jensen, 1998),
and there is now general consensus that individual differences in IQ reflect a substantial genetic
component (Neisser et al., 1996). Nonetheless, rising IQs can only be substantially explained
environmentally and they therefore challenge the construct validity of the tests as measures of
fundamental, inborn cognitive abilities. The Flynn effect implies that abstract reasoning abilities,
previously held by many to reflect basic capacities, are influenced by as yet undetermined
Considerable speed-based research has found that speed measures correlate with IQ (Nettelbeck,
1998, 2001).IT(Vickers, Nettelbeck, & Willson, 1972) measures individual differences in threshold to
detect the location (left or right) of the shorter of two vertical lines displayed in a briefly exposed target
figure. The threshold measure is essentially a critical stimulus onset asynchrony (CSOA), defined as the
minimum delay required, between the onset of the target figure and the subsequent onset of a backward
masking figure so as to achieve predetermined high accuracy. IT correlates at about .5 with nonverbal
IQ (Deary & Stough, 1996; Grudnick & Kranzler, 2001; Kranzler & Jensen, 1989; Nettelbeck, 1987).
The basis of this correlation has not been clearly identified, but there are strong grounds for supposing
T. Nettelbeck, C. Wilson / Intelligence 32 (2004) 85–9386
that it reflects more than application of higher-order ‘‘intelligent’’ strategies (Deary, 2000; Nettelbeck,
2001). It may involve IT’s sensitivity to the psychometric construct ‘‘speediness’’ (Burns & Nettelbeck,
2003), defined by Horn and Noll (1997) as speed under relatively undemanding circumstances.
However, speediness is unlikely to provide a sufficient explanation for IT–IQ correlation, because
Burns and Nettelbeck (2003) also found that IT shared variance with a higher-order, orthogonal general
factor and recent research has raised the possibilities that IT is sensitive to attentional capacities (Hutton,
Wilding, & Hudson, 1997) and fluid abilities (Osmon & Jackson, 2002). Although it is unlikely that a
single kind of mental speed could account for individual differences in IQ (Roberts & Stankov, 1999),
Carroll (1993) has allowed that some fundamental aspect of processing speed could underpin the higher-
order general ability factor that distinguishes his model for human intelligence from similar ‘‘multiple
intelligences’’ models (Horn & Noll, 1997).
The current study set out to replicate with a current sample measures of vocabulary and IT
initially made by Wilson in 1981 as part of a cross-sequential investigation of childhood
developmental changes in processing speed (this work was published by Nettelbeck & Wilson,
1985). To this end, primary school children were recruited from the same school that was involved
in 1981. This school had continued to serve the same catchment area, as 20 years previously, from
upper middle-class socioeconomic suburbs.
As for the earlier study, vocabulary achievement was a
proxy for IQ and estimated with the same test. IT was measured using exactly the same apparatus
and procedures followed in the earlier study. If rising IQ is accompanied by improved processing
speed as is implied by theories that have drawn heavily on the ‘‘fast is smart’’ assumption common
to western European cultures (Brand, 1996; Eysenck, 1987; Jensen, 1998), then this would be
revealed by comparison of IT measures made now with those recorded 20 years ago. On the other
hand, if IQ was shown to improve but IT had not, this would rule out speed of processing as an
explanation for improving IQ.
Seventy-five school children (36 boys, 39 girls) aged 6 –13 years took part, with the permission of
their parents. They attended the same school as the 70 children (38 boys, 32 girls) also aged 6 – 13
years in the 1981 sample. As had been the case in 1981, all had normal or corrected-to-normal
vision. Following Wilson in 1981, this was a sample of convenience, aiming to draw about 10
children, approximately balanced for gender, from each of seven consecutive grade levels. Response
rates were high, with many more children volunteering than were required. Those participating were
determined according to the children’s availability and teachers’ convenience at the time of testing
(see Table 1).
The Australian census categorizes postcodes in capital cities within quintiles for socioeconomic strata, defined by
household income and other indices of relative social advantage. In 1981 and 2001, the postal districts encompassed by the
school’s catchment area were in the highest quintile.
Grade levels in 2001 were different from those in 1981. Today’s children aged 6– 13 years were located in Grades 1 – 7,
whereas in 1981 these age groups were in Grades 2–8.
T. Nettelbeck, C. Wilson / Intelligence 32 (2004) 85–93 87
2.2. Materials and apparatus
Vocabulary was measured with the Peabody Picture Vocabulary Test (PPVT) using both the original
version (Dunn, 1965; Form A) and the most recent PPVT-III (Dunn & Dunn, 1997; Form IIIA). IT was
measured using the same Gerbrands tachistoscope, previously modified in-house to provide four fields
so that the four stimulus cards could be set undisturbed throughout the study, following appropriate
initial alignments. These cards displayed in turn an initial visual fixation cue, the two alternative targets
with the shorter vertical line to left or right, and the backward masking figure. The tachistoscope was set
at the same field luminances, employing the same stimulus cards, the same sequence for lighting the
fields, and the same presentation technique used by Wilson in 1981. Thus, the two vertical lines in the
target were 24 and 34 mm, were 10 mm apart, and aligned at the top by a horizontal line. The backward
mask that subsequently overlaid each target display had both vertical bars 44 mm long and 5 mm wide,
centered at 10 mm apart. The software that controlled onset and offset of the four fields and recorded
responses as correct or not was the same program used by Wilson in 1981. It was an early version of the
Parameter Estimation by Sequential Testing (PEST) program (Taylor & Creelman, 1967), an adaptive
staircase algorithm that estimated the CSOA with an associated probability of 85% correct responding.
The response keypad was that used by Wilson in 1981. Further details for these pieces of equipment are
to be found in Nettelbeck and Wilson (1985) (Study 3).
All children were tested individually, first completing both versions of PPVT concurrently at a single
session, with f10 min break between. Order of completion was balanced across children. A single
estimate of IT was made at a second session, following exactly the same instructions used by Wilson in
Means FS.D.s for grade levels, chronological ages, standardized PPVT scores, and ITs from children in 1981 and 2001
Grade level nAge (years-months) Gender (M/F) PPVT
1 10 7-4 F0-6 6/4 101 F19 231 F171
2 10 7-10 F0-3 5/5 116 F19 133 F45
3 10 8-8 F0-2 5/5 116 F9 125 F48
4 10 9-10 F0-5 4/6 115 F18 115 F46
5 10 10-11 F0-4 6/4 117 F14 101 F40
6 10 11-11 F0-3 6/4 107 F770F42
7 10 13-2 F0-3 6/4 123 F22 86 F24
1 14 6-9 F0-5 7/7 116 F11 151 F64
2 12 7-9 F0-5 6/6 113 F16 135 F120
3 10 8-8 F0-2 4/6 122 F12 132 F85
4 10 9-9 F0-2 5/5 110 F8114F53
5 10 10-8 F0-4 4/6 115 F11 91 F29
6 10 11-7 F0-2 5/5 109 F11 96 F28
7 9 12-9 F0-5 5/4 113 F14 74 F18
Original PPVT norms were applied in 1981, while PPVT-III norms were applied in 2001.
T. Nettelbeck, C. Wilson / Intelligence 32 (2004) 85–9388
1981. These emphasized accuracy of responding, not speed. The same practice routines and staircase
algorithms were used. Nettelbeck and Wilson (1985) provided a full description of these.
As can be seen from Table 1, the age sample in 2001 (overall M= 9-5 F2-4 years-months) closely
matched the 1981 cohort (9-11 F2-6 years-months) [t(143) = 1.49, P> .05, two-tailed]. The numbers of
children and gender balances within age levels were similar for both cohorts. There were 48% boys in
2001 compared with 54% in 1981. Thus, the two samples drawn from the same school 20 years apart
were age and gender equivalent for comparison purposes.
Distributions of standardized PPVT scores within the current and the 1981 sample, based on age
norms applying in 1981 and 2001, were also essentially the same. The overall mean in 1981 (initial
PPVT age norms) was 113.66 F16.72 compared with 113.97 F12.23 in 2001 (PPVT-III age norms).
The difference was not statistically significant (t< 1.0), and correcting scores according to the extent to
which norms for both versions of PPVT had become obsolete at time of testing (see Flynn, 1987) did
not change this outcome. Nonetheless, although samples were equivalent for verbal achievement for
their respective times, the current sample demonstrated a clear Flynn effect. Thus, although concurrent
scores for the 2001 children with original and recent versions of PPVT were highly correlated
[r(73)=.68, P< .01, two-tailed], these children were significantly advantaged on the early PPVT
(M= 118.52 F16.62) compared with PPVT-III (M= 113.97 F12.23) [t(74) = 3.22, P< .01, two-tailed].
This within-subjects outcome was confirmed by between-subjects analysis, comparing the earlier
version PPVT scores from the 2001 sample with the earlier version PPVT scores from the 1981 sample
[t(143) = 1.76, P< .05, one-tailed]. This rise of almost 5 points across 20 years was lower than but
consistent with 20 years’ improvement in Wechsler Verbal IQ (7 points), estimated by comparing WISC
with WISC-R standardization samples (Wechsler, 1949, 1974). (The improvement for word knowledge
was also smaller than the Flynn effect of f8 Performance IQ points across 20 years embedded in the
Despite the Flynn effect for vocabulary achievement, Table 1 demonstrates that there was no evidence
of improvement in IT from 1981 (overall M= 123 F87 ms) to 2001 (M=116F71 ms). Of course, this
conclusion amounts to accepting a null hypothesis, but the effect size of only about 0.09 would require
more than 2000 cases in both cohorts to achieve a=.05 (two-tailed) at power = 0.80. Overall, the 1981
and 2001 distributions were remarkably similar, being positively skewed to the same extent (2.26 and
2.94, respectively) around the same medians (103 and 102 ms) and with similar minima (18 and 33 ms)
and maxima (450 and 500 ms). Two-way ANOVA found no cohort (1981 vs. 2001) effect
[F(1,131) < 1.0]; as expected, there was a highly significant age effect [ F(6,131) = 6.20, P< .001] but
no Cohort Age interaction [ F(6,131) = 1.24, P>.05]. Visual inspection of the IT distributions across
age and cohorts confirmed that the longer mean IT in the youngest 1981 subsample, compared with
2001, was the consequence of three children aged 6/7 years whose low PPVT scores and long IT
estimates made them outliers. Nonetheless, correlations within both cohorts between raw PPVT scores
and IT (1981) and raw PPVT-III scores and IT (2001) were significant and similar: 1981
r(68) = .43 F.24 (95% confidence limits), P< .01; 2001 r(73) = .31 F.23, P< .05. These coeffi-
cients did not differ significantly (z= 1.07, ns). Both outcomes were, of course, confounded by strong
T. Nettelbeck, C. Wilson / Intelligence 32 (2004) 85–93 89
The current study aimed to replicate Wilson’s 1981 study (Nettelbeck & Wilson, 1985) and succeeded
to a remarkable degree. Word knowledge outcomes derived from 1981 and 2001 norms provided an
excellent match. As predicted by Flynn’s observations, concurrent testing of word knowledge in the
2001 sample, using both current and earlier Peabody test versions and norms, found that word
knowledge had risen significantly by about 5 points across 20 years. This result was statistically
significant and consistent with restandardizations of the Wechsler scales across this period, which have
found a Flynn effect of 7 Verbal IQ points (cf. about 8 points for nonverbal aspects). However, most
importantly, estimates of IT were essentially the same from both cohorts, results for 1981 and 2001
demonstrating the expected significant age and IQ effects to the same extent. The monotonic reduction in
mean IT with age was marked and not consistent with Anderson’s theory that IT does not change with
development (Anderson, 1992; Anderson, Reid, & Nelson, 2001).
In other words, whereas average IQs of 6–13-year-olds are known to have risen appreciably across 20
years, including on a pencil-and-paper marker test for ‘‘speediness’’ (the Coding subscale from
Wechsler), IT was not subject to cohort improvement. IT, which as expected correlated significantly
with word knowledge scores within both the 1981 and the 2001 cohort, did not change at all on average.
Thus, based on current results, IQ gains are not explicable in terms of improved processing speed, as
operationalized by IT. This result suggests, moreover, that IT measures some fundamental aspect of
mental functioning that is relevant to understanding of human intelligence, which is not influenced by
whatever environmental circumstances are responsible for rising IQ scores. What this function is,
however, is not clear. Debate continues around what psychological processes are tapped by IT
(Nettelbeck, 2001), and the nature of processing speed appears to be complex (Roberts & Stankov,
1999). Moreover, it is necessary, though difficult, to replicate this result. The samples in both the original
1981 study and the 2001 replication were small, with only about 10 children in each age group. Insofar
as a major assumption underpinning current conclusions is that the 1981 and 2001 cohorts were
demographically equivalent, it would have been desirable to confirm this more precisely, e.g., by
recording parents’ educational levels, occupations, and salaries. Without more evidence to the contrary
than the broad census data available here, it is always possible that the increased word knowledge found
was the consequence of idiosyncratic improvement to the socioeconomic circumstances of the children
involved, beyond those that speculation has linked with the Flynn effect (Neisser, 1998). It is also
important that future research of this issue should test the stability of IT across time for a much wider
range of ages than the 6–13 years included here.
If the current result is confirmed, two future directions for research are suggested. The present result
for IT begs the question as to whether different kinds of processing speed, similarly known to correlate
with IQ although not necessarily appreciably with each other (Kranzler & Jensen, 1991), are stable or
improve across time. For example, existing large data sets derived for parameters of Decision time and
Movement time (Jensen, 1987) might be used to explore this question by replicating these earlier
procedures with current samples.
A second suggestion is that IT may provide a useful biomarker for cognitive aging. The causes of
aging are not known. Nonetheless, there is considerable evidence to support a conclusion that normal
aging beyond the mid-1960s is, on average, accompanied by cognitive decline that, although different
abilities change at different rates, is largely attributable to slowing of information processing speed
(Deary, 2000; Salthouse, 1996; Schaie, 1994). Although gradually slowing processing speed and
T. Nettelbeck, C. Wilson / Intelligence 32 (2004) 85–9390
declining cognitive abilities may be ongoing beyond early adult years, considerable evidence points to
relative stability before the sixth decade but a marked shift in rate beyond. However, there are marked
individual differences in the onset and progress of age-related changes, so that chronological age is an
unreliable marker for functional aging. Obviously, individual differences in functional aging have
implications of considerable practical importance for those involved, and reliable biomarkers, capable of
predicting functional change as a consequence of aging, particularly any accelerated decline in cognitive
integrity, would therefore be extremely useful (Stern & Carstensen, 2000).
Much aging research has relied on the Digit Symbol test (Wechsler) to measure processing speed
(Salthouse, 1996). Although longitudinal studies leave little doubt that aging effects are real (Salthouse,
2000; Schaie, 1994), it is possible that effect sizes from cross-sectional designs have been exaggerated
by the Flynn effect, which would tend to favor younger cohorts on Digit Symbol. Thus, a measure of
processing speed, known to be stable across age cohorts, would be desirable for researching age-related
At the very least, IT appears to tap ‘‘speediness’’ (Nettelbeck, 1994) and may also be relevant to
general ability (Burns & Nettelbeck, 2003). Moreover, on current evidence, IT is stable across
generations, at least among children, unlike conventional psychometric tests. We suggest that these
attributes, together with its noninvasive measurement procedure, may make IT an attractive prospect as a
biomarker to monitor cognitive changes that accompany aging, providing that it meets other essential
criteria. These should include sensitivity to cognitive change within a short period, predicting important
life changes ahead of time (e.g., decline in workplace competence or the onset of driving difficulties) and
predicting longevity. It is also desirable that a biomarker should be measurable in other species
(McClearn, 1997) and therefore available for animal modeling of functional aging. IT is certainly
sensitive to cross-sectional age comparisons among elderly people (Nettelbeck, 1987) and to age-related
differences in cognitive functioning (Nettelbeck & Rabbitt, 1992; see also Deary, 2000, pp. 244– 246 for
a relevant reanalysis of these data), but nothing is known currently about whether IT is subject to
longitudinal slowing. In principle, the discrimination required to estimate IT is simple and should be
achievable with animals; however, IT’s utility as a lead biomarker, capable of predicting accelerated
decline in cognitive integrity, would be a more immediate priority for future research.
The University of Adelaide’s Small Research Grant Scheme supported this research. We thank Dr.
Greg Evans for his valuable assistance when repairing and calibrating the tachistoscope. We gratefully
acknowledge the help and cooperation extended by the principal, staff, parents, and children at
Mitcham Primary School; without their generous participation, this research would not have been
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