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Personality and Individual Differences 47 (2009) 685–690Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier .com/locate /paidWhy night owls are more intelligent
Satoshi Kanazawa a,b,c,*, Kaja Perina d
aDepartment of Management, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom
bDepartment of Psychology, University College London, United Kingdom
cDepartment of Psychology, Birkbeck College, University of London, United Kingdom
d Psychology Today, 115 E. 23rd Street, 9th Floor, New York, NY 10010, United States
a r t i c l e i n f oArticle history:
Received 4 February 2009
Received in revised form 6 May 2009
Accepted 11 May 2009
Available online 27 June 2009
Keywords:
Origin of values and preferences
Evolutionary psychology
The Savanna Principle
The Savanna–IQ Interaction Hypothesis
Circadian rhythms
Chronobiology0191-8869/$ - see front matter  2009 Elsevier Ltd. A
doi:10.1016/j.paid.2009.05.021
* Corresponding author. Address: Department of M
Economics and Political Science, Houghton Street,
Kingdom. Tel.: +44 20 7955 7297.
E-mail address: S.Kanazawa@lse.ac.uk (S. Kanazawa b s t r a c t
The origin of values and preferences is an unresolved theoretical problem in social and behavioral sciences. The Savanna–IQ Interaction Hypothesis suggests that more intelligent individuals are more likely
to acquire and espouse evolutionarily novel values and preferences than less intelligent individuals, but
general intelligence has no effect on the acquisition and espousal of evolutionarily familiar values and
preferences. Individuals can often choose their values and preferences even in the face of genetic predisposition. One example of such choice within genetic constraint is circadian rhythms. Survey of ethnographies of traditional societies suggests that nocturnal activities were probably rare in the ancestral
environment, so the Hypothesis would predict that more intelligent individuals are more likely to be nocturnal than less intelligent individuals. The analysis of the National Longitudinal Study of Adolescent
Health (Add Health) confirms the prediction.
 2009 Elsevier Ltd. All rights reserved.1. Introduction
Where do individual values and preferences come from? Why
do people like or want what they do? The origin of individual values and preferences is one of the remaining theoretical puzzles in
social and behavioral sciences (Kanazawa, 2001).
Recent theoretical developments in evolutionary psychology
may suggest one possible explanation. On the one hand, evolutionary psychology (Crawford, 1993; Symons, 1990; Tooby & Cosmides, 1990) posits that the human brain, just like any other
organ of any other species, is designed for and adapted to the conditions of the ancestral environment (roughly the African savanna
during the Pleistocene Epoch), not necessarily to the current environment. It may therefore have difficulty comprehending and dealing with entities and situations that did not exist in the ancestral
environment (Kanazawa, 2002, 2004a). On the other hand, an evolutionary psychological theory of the evolution of general intelligence proposes that general intelligence may have evolved as a
domain-specific adaptation to solve evolutionarily novel problems,
for which there are no predesigned psychological adaptations
(Kanazawa, 2004b, 2008).
The synthesis of these two theories, the Savanna–IQ Interaction
Hypothesis (Kanazawa, 2010), implies that the human brain’s diffi-ll rights reserved.
anagement, London School of
London WC2A 2AE, United
a).culty with evolutionarily novel stimuli may interact with general
intelligence, such that more intelligent individuals have less difficulty with such stimuli than less intelligent individuals. In contrast,
general intelligence may not affect individuals’ ability to comprehend and deal with evolutionarily familiar entities and situations.
The Hypothesis, applied to values and preferences, may suggest
that more intelligent individuals are more likely to acquire and
espouse evolutionarily novel values than less intelligent individuals,
whereas general intelligence does not affect the acquisition and espousal of evolutionarily familiar values. In this paper, we test this prediction with respect to one value domain (circadian rhythms),
which is under some genetic influence. We show that, consistent
with the Hypothesis, more intelligent individuals are more likely
to be nocturnal than less intelligent individuals.
2. Choice within genetic predisposition
Choice is not incompatible with or antithetical to genetic influence. As long as h2 < 1.0, genes merely set a broad reaction range,
and individuals can still exercise some choice within broad genetic
constraints. For example, political scientists have discovered that
two genes are responsible for predisposing individuals to be more
or less likely to vote in elections (Fowler & Dawes, 2008). However,
individuals can still choose to turn out to vote or not for any election, and there are environmental (nongenetic) factors that can
predict their voting (Kanazawa, 1998, 2000).
Similarly, genetic influences and constraints do not preclude
individual acquisition and espousal of values and preferences.
686 S. Kanazawa, K. Perina / Personality and Individual Differences 47 (2009) 685–690Individuals can still choose certain values and preferences even
in the face of genetic predisposition. For example, both political
ideology (Alford, Funk, & Hibbing, 2005) and religiosity (Bouchard, McGue, Lykken, & Tellegen, 1999; Koenig, McGue, Krueger, & Bouchard, 2005) have now been shown to have genetic
bases; some individuals are genetically predisposed to be liberal
or conservative, or more or less religious. Yet more intelligent
children are more likely to grow up to be liberal and less likely
to grow up to be religious (Kanazawa, 2010). In this paper, we
focus on one example of such choice within genetic constraints:
whether one is a morning person or a night person (circadian
typology).3. Circadian typology (morningness–eveningness)
Virtually all species in nature, from single-cell organisms to
mammals, including humans, exhibit a daily cycle of activity called
the circadian rhythm. ‘‘This timekeeping system, or biological
‘‘clock,” allows the organism to anticipate and prepare for the
changes in the physical environment that are associated with day
and night, thereby ensuring that the organism will ‘‘do the right
thing” at the right time of the day” (Vitaterna, Takahashi, & Turek,
2001, p. 85). The circadian rhythm in mammals is regulated by two
clusters of nerve cells called the suprachiasmatic nuclei (SCN) in
the anterior hypothalamus (Klein, Moore, & Reppert, 1991). Geneticists have by now identified a set of genes that regulate the SCN
and thus the circadian rhythm amongmammals (King & Takahashi,
2000). A behavior genetic study of South Korean twins (n = 977
pairs) shows that heritability in morningness–eveningness is .45
and nonshared environment accounts for 55% of the variance,
while shared environment does not appear to explain any of the
variance in it (Hur, 2007).
‘‘For most animals, the timing of sleep and wakefulness under
natural conditions is in synchrony with the circadian control of
the sleep cycle and all other circadian-controlled rhythms. Humans,
however, have the unique ability to cognitively override their internal
biological clock and its rhythmic outputs” (Vitaterna et al., 2001, p.
90; emphasis added). While there are some individual differences
in the circadian rhythm, where some individuals are more nocturnal than others, humans are basically a diurnal (as opposed to nocturnal) species. Humans rely very heavily on vision for navigation
but, unlike genuinely nocturnal species, cannot see in the dark or
under little lighting, and our ancestors did not have artificial lighting during the night until the domestication of fire. Any human in
the ancestral environment up and about during the night would
have been at risk of predation by nocturnal predators. It is therefore safe to assume that our ancestors rose at around dawn and
went to sleep at around dusk, to take full advantage of the natural
light provided by the sun, and the ‘‘night life” (sustained and organized activities at night after dusk) is probably evolutionarily
novel.
In order to ascertain the extent to which our ancestors might
have engaged in nocturnal activities, we have consulted ethnographic records of traditional societies throughout the world. In
the 10-volume compendium The Encyclopedia of World Cultures
(Levinson, 1991–1995), which extensively describes all human cultures known to anthropology, there is no mention of nocturnal
activities in any of the traditional cultures. There are no entries
in the index for ‘‘nocturnal,” ‘‘night,” ‘‘evening,” ‘‘dark(ness),” and
‘‘all-night.” The few references to the ‘‘moon” are all religious, as
in ‘‘moon deity,” ‘‘Mother Moon (deity),” and ‘‘moon worship.”
The only exception is the ‘‘night courting,” which is a socially approved custom of premarital sex observed among the Danes and
the Finns, which are entirely western cultures far outside of the
ancestral environment.In addition, we have consulted the following extensive (monograph-length) ethnographies of traditional societies around the
world: Yanomamö (Chagnon, 1992); FromMukogodo to Maasai: Ethnicity and Cultural Change in Kenya (Cronk, 2004); Ache Life History:
The Ecology and Demography of a Foraging People (Hill & Hurtado,
1996); The !Kung San: Men, Women, and Work in a Foraging Society
(Lee, 1979); and Sacha Runa: Ethnicity and Adaptation of Ecuadorian
Jungle Quichua (Whitten, 1976). Many of these ethnographies contain a section where the authors describe what usually happens
and what people routinely do in a typical day in the tribal society
under study.
These detailed ethnographic records make it clear that the day
for people in these traditional societies begins shortly before sun
rise, and ends shortly after sun set. ‘‘Daily activities begin early
in a Yanomamö village” (Chagnon, 1992, p. 129). ‘‘The day begins
about 6 a.m., when the sun is about to rise” (Cronk, 2004, p. 88).
The only routine activities conducted after dark are people conversing and visiting with each other as they drift off to sleep. ‘‘Despite the inevitable last-minute visiting, things are usually quiet in
the village by the time it is dark” (Chagnon, 1992, p. 132). ‘‘Most
evenings are spent quietly chatting with family members indoors.
If the moon is full then it is possible to see almost as well as during
the day, and people take advantage of the light by staying up late
and socializing a great deal” (Cronk, 2004, p. 93). ‘‘After cooking
and consuming food, evening is often the time of singing and joking. Eventually band members drift off to sleep, with one or two
nuclear families around each fire” (Hill & Hurtado, 1996, p. 65).
The only nocturnal activities, other than chatting, visiting, and
making speeches, that we can find in all of these ethnographies
is when Mukogodo men go searching for missing animals in the
dark, if one happens to be missing (Cronk, 2004, p. 92).
Ethnographic evidence of traditional societies therefore suggests that our ancestors probably had a largely diurnal lifestyle,
and sustained and routine nocturnal activities may be evolutionarily novel. The Savanna–IQ Interaction Hypothesis would therefore predict that more intelligent individuals are more likely to
be nocturnal, getting up later in the morning and going to bed later
in the evening, than less intelligent individuals. To our knowledge,
there is only one study which examines the association between
intelligence and circadian rhythm. Roberts and Kyllonen (1999)
find that, in a small sample of United States Air Force recruits
(n = 420), evening types are significantly more intelligent than
morning types. We seek to replicate their findings in a large,
nationally representative sample of Americans.4. Empirical analysis
4.1. Method
4.1.1. Data: National Longitudinal Study of Adolescent Health (Add
Health)
A sample of 80 high schools and 52 middle schools from the US
was selected with unequal probability of selection. Incorporating
systematic sampling methods and implicit stratification into the
Add Health study design ensures this sample is representative of
US schools with respect to region of country, school size, school
type, and ethnicity. A sample of 20,745 adolescents were personally interviewed in their homes in 1994–1995 (Wave I), and again
in 1996 (Wave II; n = 14,738). In 2001–2002, 15,197 of the original
Wave I respondents, now age 18–28, were interviewed in their
homes. Our sample consists of Wave III respondents.4.1.2. Dependent variable: nocturnality
Add Health asks its respondents about their sleeping habits
with four different questions in Wave III. ‘‘On days when you go
S. Kanazawa, K. Perina / Personality and Individual Differences 47 (2009) 685–690 687to work, school, or similar activities, what time do you usually
wake up?” ‘‘What time do you usually go to sleep the night (or
day) before?” ‘‘On days you don’t have to get up at a certain time,
what time do you usually get up?” ‘‘On those days, what time do
you usually go to sleep the night or day before?” We call the first
type of days ‘‘weekdays” and the latter type of days ‘‘weekend.”
For each question, the respondent indicates the time by first
marking the hour (from 1:00 to 12:00), then the minute (from 00
to 59), and finally indicating whether the hour is AM or PM. The
distributions of indicated bedtimes show that a large number of
respondents (n = 3,073, 20.2% for weeknights, and n = 2,971,
19.5% for weekends) claim to go to bed at 12:00PM (noon) and
12:30PM (half an hour after noon), when very few people claim
to go to bed during the 11:00 h or the 13:00 h. We assume this is
a result of the confusion of 12:00AM (midnight) and 12:00PM
(noon). We have therefore changed all 12:00 h bedtime to
00:00 h bedtime. The reassignment of these cases does not affect
our substantive conclusion at all; in fact, the predicted effect of
intelligence on nocturnality is slightly stronger if we delete all of
these cases rather than reassign them.4.1.3. Independent variable: childhood IQ
Add Health measures respondents’ intelligence with the Peabody Picture Vocabulary Test (PPVT). The raw scores (0–87) are
age-standardized and converted to the IQ metric, with a mean of
100 and a standard deviation of 15. The PPVT is a valid measure
of verbal intelligence, not general intelligence. However, verbal
intelligence is known to be highly correlated with (and thus heavily loads on) general intelligence. Miner’s (1957) extensive review
of 36 studies shows that the median correlation between vocabulary and general intelligence is .83. Wolfle (1980) reports that the
correlation between a full-scale IQ test (Army General Classification Test) and the General Social Surveys synonyms measure is
.71. As a result, the GSS synonyms measure has been used widely
by intelligence researchers to assess trends in general intelligence
(Huang & Hauser, 1998). In order to establish the direction of causality more clearly, we will use the measure of intelligence taken in
Wave I (in 1994–1995 when the respondents were in junior high
and high school) to predict their adult nocturnality in Wave III
(in 2001–2002 when the respondents were in their early
adulthood).
Control variables. We control for the following variables: age
(even though there is very little variance in it given that these
are cohort data); sex (0 if female, 1 if male); race (with three dummies for Asian, black, and Native American, with white as the reference category); marital status (1 if currently married); parental
status (1 if parent); education (years of formal schooling); earnings
(in dollars); and religion (with four dummies for Catholic, Protestant, Jewish, and other, with none as the reference category).
Further, because previous studies show that students are more
likely to be nocturnal than comparable individuals in full-time
employment (Mecacci & Zani, 1983), we control for whether or
not the respondent is a student (1 if currently in school). In addition, because demands of work can affect one’s sleeping patterns,
we control for how many hours the respondent typically works
in a week.5. Results
Fig. 1 presents bivariate associations between childhood IQ and
chronobiology. We divide the Add Health sample into five ‘‘cognitive classes” (Herrnstein & Murray, 1994) by childhood IQ: ‘‘Very
dull” (IQ < 75); ‘‘Dull” (75 < IQ < 90); ‘‘Normal” (90 < IQ < 110);
‘‘Bright” (110 < IQ < 125); and ‘‘Very bright” (IQ > 125). Fig. 1, Panel
(a), shows that there is a monotonic association between cognitiveclass and the time Add Health respondents go to bed on weeknights. ‘‘Very dull” individuals (n = 584) on average go to bed at
23:41; ‘‘dull” individuals (n = 2,967) go to bed at 00:03; ‘‘normal”
individuals (n = 6,820) go to bed at 00:10; ‘‘bright” individuals
(n = 3,483) go to bed at 00:21; and ‘‘very bright” individuals (n = 468) go to bed at 00:29. The absolute differences in
minutes are small, but all the differences between two adjacent
categories are statistically significant, except for ‘‘bright” and ‘‘very
bright.”
Fig. 1, Panel (b), similarly shows that, while individuals of all
cognitive classes go to bed roughly one hour later on weekends
than they do on weeknights, there is still a monotonic association
between cognitive class and the time Add Health respondents go to
bed on weekends. ‘‘Very dull” individuals (n = 585) on average go
to bed at 00:35; ‘‘dull” individuals (n = 2,985) go to bed at 01:03;
‘‘normal” individuals (n = 6,854) go to bed at 01:13; ‘‘bright” individuals (n = 3,488) go to bed at 01:25; and ‘‘very bright” individuals (n = 465) go to bed at 01:44. All the differences between two
adjacent categories are statistically significant.
Fig. 1, Panel (c), shows that there is a monotonic association between cognitive class and what time Add Health respondents wake
up on weekday morning. ‘‘Very dull” individuals (n = 583) on average wake up at 07:20; ‘‘dull” individuals (n = 2,965) wake up at
07:25; ‘‘normal” individuals (n = 6,814) wake up at 07:32; ‘‘bright”
individuals (n = 3,480) wake up at 07:40; and ‘‘very bright” individuals (n = 468) wake up at 07:52. All the differences between two
adjacent categories are statistically significant, except for ‘‘very
dull” and ‘‘dull.”
Finally, Fig. 1, Panel (d), shows that, while individuals of all cognitive classes wake up roughly three hours later on weekends than
they do on weekdays, there is still a monotonic association between cognitive class and what time Add Health respondents wake
up on weekend morning. ‘‘Very dull” individuals (n = 586) on average wake up at 10:09; both ‘‘dull” individuals (n = 2,986) and ‘‘normal” individuals (n = 6,850) wake up at 10:14; ‘‘bright” individuals
(n = 3,488) wake up at 10:23; and ‘‘very bright” individuals
(n = 466) wake up at 11:07. Only the differences between ‘‘normal”
and ‘‘bright” and between ‘‘bright” and ‘‘very bright” are statistically significant.
Table 1 presents the results of OLS regression analyses of Add
Health respondents’ circadian rhythms. (The dependent variable
here is converted from the normal base-60 sexagesimal time to
the base-10 decimal time.) The first column shows that, net of
age, sex, race, marital status, parental status, education, earnings,
religion, student status and number of hours worked, childhood
IQ significantly delays the time that Add Health respondents usually go to bed on weeknight. Older respondents (even in this limited age range) go to bed earlier than younger respondents, and
men go to bed later than women. Consistent with expectations,
married individuals and parents go to bed earlier. Christians (Catholics and Protestants) go to bed earlier, and, consistent with a previous finding (Mecacci & Zani, 1983), current students go to bed
later than nonstudents. As expected, individuals who work more
hours go to bed earlier.
Table 1, second column, shows that, net of the same control
variables, childhood IQ significantly delays the time that Add
Health respondents usually go to bed on weekends. Most of the
other variables in the equation have the same effect on weekend
bedtime as they do on weeknight bedtime, with the exception that
blacks go to bed significantly later than whites on weekends but
not on weeknights. Marital and parental statuses have much greater negative effect on weekend bedtime than weeknight bedtime.
Table 1, third column, shows that, net of the same control variables, childhood IQ significantly delays the time that Add Health
respondents usually wake up on weekdays. Most of the other variables in the equation have the same effect on what time they wake
Fig. 1. Bivariate association between childhood IQ and chronobiology. Note: Error bars represent the standard error for the mean.
688 S. Kanazawa, K. Perina / Personality and Individual Differences 47 (2009) 685–690up on weekdays as they do on what time they go to bed on
weeknights, with the exception that more educated people wake
up earlier on weekdays. While individuals who work longer hours
go to bed earlier and wake up earlier on weekdays, students go to
bed later and wake up earlier. Table 1, fourth column, shows that,
while the effect of childhood IQ on what time Add Health respondents wake up on weekend is once again positive, its effect does
not reach statistical significance (p > .18). Most of the other variables have the same effects as before.
An anonymous reviewer suggests that the Savanna–IQ Interaction Hypothesis may also imply that more intelligent individuals
have a greater variance in their chronobiology than less intelligent
individuals. It does not appear to be the case, however, with the
Add Health data. The test of homogeneity of variances shows that
variances are heterogeneous by childhood cognitive class in weekday time to go to bed (Levene statistic = 14.590, df = 4, 14317,
p < .001), weekend time to go to bed (Levene = 15.295, df = 4,14372, p < .001), and weekday time to wake up (Levene = 3.182,
df = 4, 14305, p < .05), but not in weekend time to wake up (Levene = 2.084, df = 4, 14371, p > .05). However, for weekday and
weekend time to go to bed, variance decreases with childhood
intelligence; for weekday time to wake up, it has an inverted Ushaped distribution, with the greatest variance among the ‘‘normal” childhood IQ category.
Taken together, results presented in Table 1 largely support our
hypothesis that more intelligent individuals are more likely to be
night owls. The effect of childhood IQ on three out of four measures
of nocturnality is significantly positive, even net of a large number
of demographic and biological variables expected to affect circadian rhythms. However, we should point out that, while the effects
of childhood IQ are highly statistically significant due to the large
sample size, both the effect size (measured by the standardized
regression coefficient) and the proportion of explained variance
(R2) are very small.
Table 1
The effect of childhood IQ on adult circadian typology (measured in hours) Add Health.
Time to go to bed Time to wake up
Weeknight Weekend Weekday Weekend
Childhood IQ .0080**** .0088**** .0078**** .0029
(.0019) (.0017) (.0019) (.0022)
.0435 .0529 .0443 .0134
Age .0430* .0957**** .0893**** .2200****
(.0169) (.0149) (.0162) (.0195)
.0282 .0698 .0611 .1219
Sex .3832**** .6119**** .2113**** .5407****
(0 = female, 1 = male) (.0528) (.0463) (.0506) (.0607)
.0721 .1280 .0415 .0859
Race
Asian .0313 .0479 .0731 .0465
(.0596) (.0524) (.0571) (.0686)
.0060 .0103 .0147 .0076
Black .0740 .0980* .0352 .1578*
(.0566) (.0496) (.0542) (.0650)
.0153 .0226 .0076 .0276
Native American .0592 .0006 .0298 .0559
(.0717) (.0629) (.0688) (.0825)
.0100 .0001 .0053 .0080
Marital status (1 if currently married) .4591**** .7419**** .3207**** .6765****
(.0739) (.0648) (.0709) (.0849)
.0648 .1166 .0473 .0808
Parental status (1 if parent) .2040** .5027**** .3155**** 1.0129****
(.0734) (.0644) (.0704) (.0843)
.0297 .0814 .0479 .1245
Education .0214 .0312* .0384* .0596**
(.0158) (.0138) (.0151) (.0181)
.0157 .0255 .0294 .0369
Earnings .0000**** .0000 .0000 .0000****
(.0000) (.0000) (.0000) (.0000)
.0487 .0042 .0194 .0387
Religion
Catholic .2128** .0476 .3124**** .3124***
(.0771) (.0677) (.0739) (.0887)
.0351 .0087 .0538 .0435
Protestant .3106*** .4255**** .3220*** .5402****
(.0894) (.0784) (.0857) (.1027)
.0409 .0623 .0442 .0601
Jewish .4470 .1745 .2551 .3269
(.3005) (.2640) (.2880) (.3458)
.0145 .0063 .0086 .0089
Other .0162 .2142*** .1187 .4109****
(.0715) (.0627) (.0685) (.0822)
.0030 .0438 .0227 .0637
Currently in school (1 if currently in school) .1576* .0716 .2339**** .0906
(.0613) (.0538) (.0588) (.0705)
.0288 .0146 .0446 .0140
Hours worked .0051*** .0016 .0081**** -.0067****
(.0015) (.0013) (.0014) (.0017)
.0365 .0130 .0604 .0409
Constant 6.5631 8.9344 5.7495 12.1950
(.4011) (.3520) (.3846) (.4611)
R2 .0254 .0699 .0258 .0796
Number of cases 10,715 10,733 10,707 10,731
Note: Main entries are unstandardized regression coefficients. (Entries in parentheses are standard errors.). Entries in italics are standardized regression coefficients.
* p < .05.
** p < .01.
*** p < .001.
**** p < .0001 (two-tailed).
S. Kanazawa, K. Perina / Personality and Individual Differences 47 (2009) 685–690 6896. Conclusion
The Savanna–IQ Interaction Hypothesis suggests that more
intelligent individuals may be more likely to acquire and espouse
evolutionarily novel values and preferences than less intelligent
individuals, while general intelligence may have no effect on the
acquisition and espousal of evolutionarily familiar values and
preferences. An earlier study (Kanazawa, 2010) has shown that
more intelligent individuals are more likely to be liberal and atheist, and more intelligent men (but not women) are more likely to
value sexual exclusivity, than their less intelligent counterparts. Inthis paper, we have extended the Hypothesis to circadian
rhythms.
While studies show that there is some genetic component to
individuals’ circadian rhythms (Hur, 2007), heritability of these
phenotypes is far from 1.0 and thus there is room for individual
choices and decisions. Survey of ethnographies of traditional societies shows that routine nocturnal activities were probably rare in
the ancestral environment and are thus evolutionarily novel. The
Savanna–IQ Interaction Hypothesis therefore predicts that more
intelligent individuals are more likely to be nocturnal than less
intelligent individuals.
690 S. Kanazawa, K. Perina / Personality and Individual Differences 47 (2009) 685–690Our analysis of Add Health data supports the prediction derived
from the Hypothesis. Net of age, sex, race, marital status, parental
status, education, earnings, religion, whether one is currently a student and the number of hours worked per week, childhood IQ significantly increases nocturnal behavior in early adulthood. More
intelligent children are more likely to grow up to be nocturnal
adults who go to bed late and wake up late on both weekdays
and weekends.
Our results in this paper, along with other empirical support for
the Savanna–IQ Interaction Hypothesis, suggests the importance of
general intelligence in the acquisition and espousal of preferences
and values. Future studies of value acquisition should consider
general intelligence as an important factor. One major weakness
of the present analysis is that we do not have a direct measure of
general intelligence, only verbal intelligence. While verbal intelligence is highly correlated with general intelligence, a future test
of the Hypothesis can benefit from a direct measure of general
intelligence. Future studies of the effect of general intelligence on
circadian rhythms can also benefit from more direct behavioral
measures of morningness–eveningness instead of verbal responses
to survey questions. More empirical work is clearly necessary, both
to test the Hypothesis rigorously and to investigate the origin of
individual values.
Acknowledgements
This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan
Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special
acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle
for assistance in the original design. Persons interested in obtaining
data files from Add Health should contact Add Health, Carolina
Population Center, 123 West Franklin Street, Chapel Hill, NC
27516-2524, USA (addhealth@unc.edu). No direct support was received from grant P01-HD31921 for this analysis. We thank David
de Meza, Patrick M. Markey, Diane J. Reyniers, and anonymous
reviewers for their comments on earlier drafts. Direct all correspondence to: Satoshi Kanazawa, Department of Management,
London School of Economics and Political Science, Houghton Street,
London WC2A 2AE, United Kingdom.
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