Cognitive Reﬂection and Decision Making
eople with higher cognitive ability (or “IQ”) differ from those with lower
cognitive ability in a variety of important and unimportant ways. On
average, they live longer, earn more, have larger working memories, faster
reaction times and are more susceptible to visual illusions (Jensen, 1998). Despite
the diversity of phenomena related to IQ, few have attempted to understand— or
even describe—its inﬂuences on judgment and decision making. Studies on time
preference, risk preference, probability weighting, ambiguity aversion, endowment
effects, anchoring and other widely researched topics rarely make any reference to
the possible effects of cognitive abilities (or cognitive traits).
Decision researchers may neglect cognitive ability because they are more
interested in the average effect of some experimental manipulation. On this view,
individual differences (in intelligence or anything else) are regarded as a
nuisance—as just another source of “unexplained” variance. Second, most studies
are conducted on college undergraduates, who are widely perceived as fairly
homogenous. Third, characterizing performance differences on cognitive tasks
requires terms (“IQ” and “aptitudes” and such) that many object to because of their
association with discriminatory policies. In short, researchers may be reluctant to
study something they do not ﬁnd interesting, that is not perceived to vary much
within the subject pool conveniently obtained, and that will just get them into
But as Lubinski and Humphreys (1997) note, a neglected aspect does not
cease to operate because it is neglected, and there is no good reason for ignoring
the possibility that general intelligence or various more speciﬁc cognitive abilities are
important causal determinants of decision making. To provoke interest in this
Shane Frederick is Assistant Professor of Management Science, Sloan School of Manage-
ment, Massachusetts Institute of Technology, Cambridge Massachusetts. His e-mail address is
Journal of Economic Perspectives—Volume 19, Number 4—Fall 2005—Pages 25– 42
neglected topic, this paper introduces a three-item “Cognitive Reﬂection Test”
(CRT) as a simple measure of one type of cognitive ability. I will show that CRT
scores are predictive of the types of choices that feature prominently in tests of
decision-making theories, like expected utility theory and prospect theory. Indeed,
the relation is sometimes so strong that the preferences themselves effectively
function as expressions of cognitive ability—an empirical fact begging for a theo-
After introducing the CRT, I examine its relations with two important decision-
making characteristics: time preference and risk preference. The CRT is then
compared with other measures of cognitive ability or cognitive “style,” including the
Wonderlic Personnel Test (WPT), the Need For Cognition scale (NFC) and self-
reported SAT and ACT scores. The CRT exhibits considerable difference between
men and women, and I discuss how this relates to sex differences in time and risk
preferences. The ﬁnal section discusses the interpretation of correlations between
cognitive abilities and decision-making characteristics.
The Cognitive Reﬂection Test (CRT)
Many researchers have emphasized the distinction between two types of cog-
nitive processes: those executed quickly with little conscious deliberation and those
that are slower and more reﬂective (Epstein, 1994; Sloman, 1996; Chaiken and
Trope, 1999; Kahneman and Frederick, 2002). Stanovich and West (2000) called
these “System 1” and “System 2” processes, respectively. System 1 processes occur
spontaneously and do not require or consume much attention. Recognizing that
the face of the person entering the classroom belongs to your math teacher involves
System 1 processes—it occurs instantly and effortlessly and is unaffected by intel-
lect, alertness, motivation or the difﬁculty of the math problem being attempted at
the time. Conversely, ﬁnding
19163 to two decimal places without a calculator
involves System 2 processes—mental operations requiring effort, motivation, con-
centration, and the execution of learned rules.
19163 allows no role for System 1. No number spontaneously
springs to mind as a possible answer. Someone with knowledge of an algorithm and
the motivation to execute it can arrive at the exact answer (138.43), but the
problem offers no intuitive solution.
By contrast, consider this problem:
A bat and a ball cost $1.10. The bat costs $1.00 more than the ball.
How much does the ball cost? cents
Here, an intuitive answer does spring quickly to mind: “10 cents.” But this “impul-
sive” answer is wrong. Anyone who reﬂects upon it for even a moment would
For a discussion of the distinction between System 1 and System 2 in the context of choice heuristics,
see Frederick (2002).
26 Journal of Economic Perspectives
recognize that the difference between $1.00 and 10 cents is only 90 cents, not $1.00
as the problem stipulates. In this case, catching that error is tantamount to solving
the problem, since nearly everyone who does not respond “10 cents” does, in fact,
give the correct response: “5 cents.”
In a study conducted at Princeton, which measured time preferences using
both real and hypothetical rewards, those answering “10 cents” were found to be
signiﬁcantly less patient than those answering “5 cents.”
Motivated by this result,
two other problems found to yield impulsive erroneous responses were included
with the “bat and ball” problem to form a simple, three-item “Cognitive Reﬂection
Test” (CRT), shown in Figure 1. The three items on the CRT are “easy” in the sense
that their solution is easily understood when explained, yet reaching the correct
answer often requires the suppression of an erroneous answer that springs “impul-
sively” to mind.
The proposition that the three CRT problems generate an incorrect “intuitive”
answer is supported by several facts. First, among all the possible wrong answers
people could give, the posited intuitive answers (10, 100 and 24) dominate. Second,
even among those responding correctly, the wrong answer was often considered
ﬁrst, as is apparent from introspection, verbal reports and scribbles in the margin
(for example, 10 cents was often crossed out next to 5 cents, but never the other
way around). Third, when asked to judge problem difﬁculty (by estimating the
proportion of other respondents who would correctly solve them), respondents who
missed the problems thought they were easier than the respondents who solved
them. For example, those who answered 10 cents to the “bat and ball” problem
estimated that 92 percent of people would correctly solve it, whereas those who
answered “5 cents” estimated that “only” 62 percent would. (Both were consider-
able overestimates.) Presumably, the “5 cents” people had mentally crossed out
10 cents and knew that not everyone would do this, whereas the “10 cents” people
The “bat and ball” problem was subsequently used by Nagin and Pogarsky (2003) in a laboratory
experiment on cheating. When respondents could obtain a $20 reward for correctly answering six trivia
questions, those answering 10 cents were signiﬁcantly more likely to defy the experimenter’s request to
complete the task without looking at the answers.
The Cognitive Reﬂection Test (CRT)
(1) A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball.
How much does the ball cost? _____ cents
(2) If it takes 5 machines 5 minutes to make 5 widgets, how long would it take
100 machines to make 100 widgets? _____ minutes
(3) In a lake, there is a patch of lily pads. Every day, the patch doubles in size.
If it takes 48 days for the patch to cover the entire lake, how long would it
take for the patch to cover half of the lake? _____ days
Shane Frederick 27
thought the problem was too easy to miss. Fourth, respondents do much better on
analogous problems that invite more computation. For example, respondents miss
the “bat and ball” problem far more often than they miss the “banana and bagel”
problem: “A banana and a bagel cost 37 cents. The banana costs 13 cents more than
the bagel. How much does the bagel cost?”
The CRT was administered to 3,428 respondents in 35 separate studies over a
26-month period beginning in January 2003. Most respondents were undergradu-
ates at various universities in the midwest and northeast who were paid $8 to
complete a 45-minute questionnaire that included the CRT and measures of
various decision-making characteristics, like time and risk preferences.
page on which the CRT appeared, respondents were told only: “Below are several
problems that vary in difﬁculty. Try to answer as many as you can.”
Table 1 shows the mean scores at each location and the percentage answering
0, 1, 2 or 3 items correctly. Most of the analyses that follow compare the “low” group
(those who scored 0 out of 3) with the “high” group (those who scored 3 out of 3).
The two “intermediate” groups (those who scoreda1or2)typically fell between
the two extreme groups on whatever dependent measure was analyzed. Thus,
focusing attention on the two “extreme” groups simpliﬁes the exposition and
analysis without affecting the conclusions.
Since more of the respondents were college students from selective schools,
the two “extreme” groups that formed the basis for most statistical comparisons
were far more similar in cognitive abilities than two extreme groups formed from
the general population. Thus, the group differences reported here likely understate
the differences that would have been observed if a more representative sample had
Cognitive Reﬂection and Time Preferences
The notion that more intelligent people are more patient—that they devalue
or “discount” future rewards less—has prevailed for some time. For example, in his
New Principles of Political Economy (1834, pp. 57), Rae writes: “The strength of the
intellectual powers, giving rise to reasoning and reﬂective habits. . . brings before us
the future. . . in its legitimate force, and urge the propriety of providing for it.”
The widely presumed relation between cognitive ability and patience has been
tested in several studies, although rather unsystematically. Melikian (1959) asked
children from ﬁve to twelve years of age to draw a picture of a man, which they
could exchange for either 10 ﬁls (about 3 cents) or for a “promissory note”
redeemable for 20 ﬁls two days later. Those who opted for the promissory note
scored slightly higher on an intelligence test based on an assessment of those
There were three exceptions to this: 1) the participants from Carnegie Mellon University completed
the survey as part of class; 2) the 4
of July participants received “only” a frozen ice cream bar; and 3) the
participants from the web study were unpaid, although they were entered into a lottery for iPods and
28 Journal of Economic Perspectives
Funder and Block (1989) paid 14 year-olds to participate in six exper-
imental sessions. For each of the ﬁrst ﬁve sessions, they could choose between
receiving $4 or foregoing (“investing”) their $4 payment for $4.80 in the sixth and
ﬁnal session. The teenagers with higher IQs chose to invest more of their money.
In a follow-up to an extensive series of experiments investigating the ability of
preschool children to delay gratiﬁcation (Mischel, 1974), Shoda, Mischel and
Peake (1990) found that the children who had waited longer before succumbing to
the impulse to take an immediately available inferior reward scored higher on their
SATs taken over a decade later. Similarly, Parker and Fischhoff (2005) found that
scores on a vocabulary test taken around age eleven predicted the individual’s
tendency, at around age 18, to prefer a larger later reward over a smaller sooner
one (for example, $120 in four weeks to $100 tomorrow). Using small real rewards,
Benjamin and Shapiro (2005) found that respondents with higher SAT math scores
Given the relatively wide range of ages in this study, it remains unclear whether this relation is
attributable to intelligence, per se, or to age, which might correlate with the development of artistic skill
or patience or trust or some other speciﬁc trait that can be distinguished from cognitive ability.
CRT Scores, by Location
Locations at which data were collected Mean CRT score
Percentage scoring 0, 1, 2 or 3
Massachusetts Institute of Technology 2.18 7% 16% 30% 48% 61
Princeton University 1.63 18% 27% 28% 26% 121
Boston ﬁreworks display
1.53 24% 24% 26% 26% 195
Carnegie Mellon University 1.51 25% 25% 25% 25% 746
1.43 20% 37% 24% 20% 51
University of Michigan: Ann Arbor 1.18 31% 33% 23% 14% 1267
1.10 39% 25% 22% 13% 525
Bowling Green University 0.87 50% 25% 13% 12% 52
University of Michigan: Dearborn 0.83 51% 22% 21% 6% 154
Michigan State University 0.79 49% 29% 16% 6% 118
University of Toledo 0.57 64% 21% 10% 5% 138
Overall 1.24 33% 28% 23% 17% 3428
Respondents in this study were people picnicking along the banks of the Charles River prior to
the July 4
ﬁreworks display. Their ages ranged from 15 to 63, with a mean of 24. Many of the younger
participants were presumably students at a college in the Boston or Cambridge area. Most completed the
survey in small groups of friends or family. Although they were requested not to discuss it until everyone
in their group had completed it, some may have. (This, presumably, would elevate the CRT scores
relative to most of the other studies in which participation was more closely supervised.)
The participants in this study were all members of a student choir group, which was predominately
female. Unlike the other locations in which the numbers of men and women were comparable, 42 of 51
participants in this study were women.
These were participants in two online studies, consisting of both college students and others whose
e-mail addresses were obtained from online retailers.
Cognitive Reﬂection and Decision Making 29
(or their Chilean equivalent) were more likely to choose a larger later reward over
a smaller sooner one (for example, to prefer a postdated check for $5.05 over a
$5.00 check that can be immediately cashed). However, Monterosso et al. (2001)
found no relation between the IQ of cocaine addicts and their imputed discount
rates, and Kirby, Winston and Santiesteban (2005) found no reliable relation
between students’ SAT scores and the amount they would bid for a delayed
monetary reward (although they did ﬁnd that college grade point averages corre-
lated positively with those bids).
Collectively, these studies support the view that cognitive ability and time
preference are somehow connected, though they have not generally focused on the
types of intertemporal decisions over which cognitive ability exerts inﬂuence, nor
explained why it does so.
Toward this end, I examined the relation between CRT
scores and various items intended to measure different aspects of “time prefer-
ence.” As shown in Table 2, these included several hypothetical choices between an
immediate reward and a larger delayed reward (items a through e), an immediate
reward and a sequence of delayed rewards (items f through h), a shorter more
immediate massage and longer more delayed massage (item i) and a smaller
immediate loss or a larger delayed loss (items j and k).
Item l asked respondents
to state their maximum willingness to pay to have a book shipped overnight rather
than waiting two weeks. Item m involved real money. Through a series of choices,
respondents speciﬁed the smallest amount of money in four days that they would
prefer to $170 in two months, and one of them was selected to actually receive one
of their choices. Items n through q asked respondents to report their impulsivity,
procrastination, preoccupation with their future and concerns about inﬂation on
an 11-point scale ranging from –5 (much less than the average person taking this
survey today) to ⫹5 (much more than the average person taking this survey today).
Table 2 shows the responses of the low and high CRT groups for each of the
17 items. The reported value is either the percentage choosing the patient option
or the mean response. The subscripts are the total number of respondents in the
low and high CRT groups who answered that item. The rightmost column reports
the level of statistical signiﬁcance of group differences—the p-values from a chi-
square test (for dichotomous responses) or a t-test (for continuous responses).
Those who scored higher on the CRT were generally more “patient”; their
decisions implied lower discount rates. For short-term choices between monetary
rewards, the high CRT group was much more inclined to choose the later larger
Shoda, Mischel and Peake (1990) examined preschoolers’ willingness to wait (for additional marsh-
mallows and pretzels and such) under four experimental conditions. They found that patience pre-
dicted SAT scores in only one of their four conditions—when the attractive but inferior reward was
visually exposed and no distraction technique (such as “think fun”) was suggested. In the other three
conditions, patient behavior was actually negatively correlated with subsequent SAT scores.
I assumed that delaying the extraction of a tooth involved a larger delayed loss, because during the
intervening two weeks, one will suffer additional toothache pain, or additional disutility from dreading
the forthcoming extraction pain, and that the only reason for not doing it immediately was that future
pain was discounted relative to immediate pain.
Among the items in Table 2, men were more patient for items c, k and l, and they worried more about
inﬂation. There were no signiﬁcant differences between men and women for any other item.
30 Journal of Economic Perspectives
reward (see items a and b). However, for choices involving longer horizons (items
c through h), temporal preferences were weakly related or unrelated to CRT scores.
A tentative explanation for these results is as follows: a thoughtful respondent
can ﬁnd good reasons for discounting future monetary outcomes at rates exceeding
the prevailing interest rate—the promiser could default, one may be predictably
wealthier in the future (with correspondingly diminished marginal utility for
further wealth gains), interest rates could increase (which increases the opportu-
nity cost of foregoing the immediate reward), and inﬂation could reduce the future
rewards’ real value (if the stated amounts are interpreted as being denominated in
Collectively, these reasons could, for example, justify choosing $9
now over $100 in 10 years (item d), even though the implied discount rate of such
a choice (27 percent), exceeds market interest rates. However, such reasons are not
sufﬁciently compelling to justify choosing $3400 this month over $3800 next month
(which implies an annual discount rate of 280 percent). Hence, one observes
considerable differences between CRT groups for choices like those in items a and
b, where more careful deliberation or “cognitive reﬂection” should argue strongly
in favor of the later larger reward, but negligible differences for many of the other
items, for which additional reﬂection would not make such a strong case for the
larger later reward (although one might argue that additional reﬂection should
Frederick, Loewenstein and O’Donoghue (2002) offer a detailed and extended discussion of the
conceptual dissection of imputed discount rates and discuss many reasons why choices between
monetary rewards are problematic for measuring pure time preference.
Intertemporal Behavior for Low and High CRT Groups
(percentage choosing patient option or mean response)
Item Intertemporal Choice or Judgment
Stat. Signif.Low High
a $3400 this month or $3800 next month 35%
p ⬍ 0.0001
b $100 now or $140 next year 22%
p ⬍ 0.0001
c $100 now or $1100 in 10 years 47%
p ⬍ 0.05
d $9 now or $100 in 10 years 40%
p ⬍ 0.10
e $40 immediately or $1000 in 10 years 50%
f $100 now or $20 every year for 7 years 28%
g $400 now or $100 every year for 10 years 64%
h $1000 now or $100 every year for 25 years 52%
i 30 min. massage in 2 weeks or 45 min. massage in Nov. 28%
j Lose $1000 this year or lose $2000 next year 78%
k Tooth pulled today or tooth pulled in 2 weeks 59%
l Willingness to pay for overnight shipping of chosen book $4.54
p ⬍ 0.0001
m Smallest amount in 4 days preferred to $170 in 2 months $116
p ⬍ 0.01
n How impulsive are you? ⫹1.01
p ⬍ 0.001
o How much do you tend to procrastinate? ⫹1.05
p How much do you think about your future? ⫹2.49
p ⬍ 0.01
q How much do you worry about inﬂation? ⫺1.16
p ⬍ 0.01
Shane Frederick 31
reveal the wisdom of choosing the delayed 45-minute massage, since one will likely
still be alive, still be stressed and sore, still like massages, and still derive greater
beneﬁts from longer ones).
It appears that greater cognitive reﬂection fosters the recognition or appreci-
ation of considerations favoring the later larger reward (like the degree to which
the implied interest rate exceeds the rate offered by the market). However, it
remains unclear whether cognitive reﬂection also inﬂuences other determinants of
intertemporal choices (like pure time preference). CRT scores were unrelated to
preferences for the massage and tooth-pull items, which were intended as measures
of pure time preference. On the other hand, those in the low CRT group (the
“cognitively impulsive”) were willing to pay signiﬁcantly more for the overnight
shipping of a chosen book (item l), which does seem like an expression of an aspect
of pure time preference (the psychological “pain” of waiting for something desired).
Thus, despite the wide variety of items included to help address this issue,
further resolution of the types of psychological characteristics associated with
cognitive reﬂection (and other cognitive abilities) is still required. Toward this goal,
respondents in some of the later studies were also asked to report several person-
ality characteristics that seemed relevant to intertemporal choices (items n through
q). The self-perceived tendency to procrastinate was unrelated to CRT scores (both
groups thought that they procrastinate more than their peers). However, the high
CRT group perceived themselves to be signiﬁcantly less impulsive, more concerned
about inﬂation and (curiously) less preoccupied with their future. The inﬂation
result supports the idea that the high-scoring groups are more likely to consider
such background factors in their choices between temporally separated monetary
rewards. Its interpretation, however, is ambiguous, since it implies a consideration
of future conditions, but would be a justiﬁcation for choosing the proximate
Cognitive Reﬂection and Risk Preferences
In the domain of risk preferences, there is no widely shared presumption
about the inﬂuences of cognitive ability and almost no research on the topic.
Donkers, Melenberg and van Soest (2001) found that more educated respondents
were more tolerant of risk in hypothetical gambles: for example, they were more
likely to prefer an 80 percent chance of 45 ﬂorins (about $23) over a sure 30 ﬂorins
(about $15). Benjamin and Shapiro (2005) found that students with higher scores
on the math section of the SAT (or its Chilean equivalent) were more likely to
choose according to expected value for real decisions involving small stakes (for
example, they were more likely to prefer a 50 percent chance to win $1.05 over a
sure 50 cents).
To assess the relation between CRT and risk preferences, I included several
measures of risk preferences in my questionnaires, including choices between a
certain gain (or loss) and some probability of a larger gain (or loss). For some
32 Journal of Economic Perspectives
items, expected value was maximized by choosing the gamble, and for some it was
maximized by choosing the certain outcome.
The results are shown in Table 3a. In the domain of gains, the high CRT group
was more willing to gamble—particularly when the gamble had higher expected
value (top panel), but, notably, even when it did not (middle panel). If all ﬁve items
from the middle panel of Table 3a are aggregated, the high CRT group gambled
signiﬁcantly more often than the low CRT group (31 percent versus 19 percent;
⫽ 8.82; p ⬍ 0.01). This suggests that the correlation between cognitive ability
and risk taking in gains is not due solely to a greater disposition to compute
expected value or to adopt that as the choice criterion.
For items involving losses
(lower panel), the high CRT group was less risk seeking; they were more willing
accept a sure loss to avoid playing a gamble with lower (more negative) expected
Two pairs of items (d versus o and h versus r) were reﬂections of one another
in the domain of gains and losses. Prospect theory predicts that people will be more
willing to take risks to avoid losses than to achieve gains; that respondents will
switch from risk aversion to risk seeking when the valence of a gamble (or “pros-
pect”) changes from positive to negative (Kahneman and Tversky, 1979). Though
this is spectacularly true for the low CRT group, who are much more willing to
gamble in the domain of losses than in the domain of gains, there is no such
reﬂection effect among the high CRT group, as shown in Table 3b. This result
starkly shows the importance of considering cognitive ability when evaluating the
descriptive validity of a theory of decision making.
Is the CRT Just Another IQ test?
Of the 3,428 respondents who completed the three-item CRT, many also
completed one or more additional cognitive measures: 921 completed the Won-
derlic Personnel Test (WPT)—a 12-minute, 50-item test used by the National
As expected, the gamble was not popular among either group for any of the “anti-expected-value”
gambles, since risk aversion and expected value both militate against it. However, any factors favoring
the gamble over the sure thing (for example, valuing the excitement of gambling or dismissing the sure
amount as negligibly small) would be more likely to tip preferences in favor of the gamble among those
less averse to it (the high CRT group, as judged from items a through h). The gambles in items i through
m were designed, in part, to have some chance of being chosen (the sure amounts were small, and the
expected values of the gambles were typically close to the sure amount). Including choices in which the
gambles lacked these properties (for example, offering a choice between $4,000 for sure and a 50
percent chance of $5000) would be pointless, because nearly everyone would reject the gamble, leaving
no response variance to analyze. Item i comes close to illustrating this point.
Although the descriptive accuracy of expected utility theory markedly improves for respondents with
higher scores, it cannot explain why a 75 percent chance of $200 is frequently rejected in favor of a sure
$100, across all levels of cognitive ability, since this is a small fraction of one’s wealth, and even a concave
utility function is approximately linear over small changes (Rabin, 2000).
Cognitive Reﬂection and Decision Making 33
and other employers to assess the intellectual abilities of their
prospective hires; 944 completed an 18-item “need for cognition” scale (NFC),
which measures the endorsement of statements like “the notion of thinking ab-
Pat McInally, a Harvard graduate who later became a punter for the Cincinnati Bengals, was the only
college football player to score a perfect 50 out of 50 on the Wonderlic—a score attained by only one
person in 30,000. Of the 921 respondents who took it in these studies, the highest score was a 47.
Risk Seeking Behavior among Low and High CRT Groups
Percentage choosing riskier option CRT group
Stat. Signif.Certain gains vs. Higher expected value gambles Low High
a $1,000 for sure or a 90% chance of $5,000 52%
p ⬍ 0.0001
b $100 for sure or a 90% chance of $500 56%
p ⬍ 0.01
c $1,000 for sure or a 75% chance of $4,000 37%
p ⬍ 0.001
d $100 for sure or a 75% chance of $200 19%
p ⬍ 0.0001
e $100 for sure or a 75% chance of $150 10%
p ⬍ 0.0001
f $100 for sure or a 50% chance of $300 47%
p ⬍ 0.05
g $500 for sure or a 15% chance of $1,000,000 31%
p ⬍ 0.0001
h $100 for sure or a 3% chance of $7,000 8%
p ⬍ 0.01
Certain gains vs. Lower expected value gambles Low High
i $100 for sure or a 25% chance of $200 7%
j $100 for sure or a 25% chance of $300 14%
k $5 for sure or a 4% chance of $80 29%
l $5 for sure or a 1% chance of $80 27%
m $60 for sure or a 1% chance of $5000 19%
Certain losses vs. Lower expected value gambles Low High
n Lose $10 for sure or a 90% chance to lose $50 24%
o Lose $100 for sure or a 75% chance to lose $200 54%
p ⬍ 0.0001
p Lose $100 for sure or a 50% chance to lose $300 61%
q Lose $50 for sure or a 10% chance to lose $800 44%
p ⬍ 0.01
r Lose $100 for sure or a 3% chance to lose $7000 63%
p ⬍ 0.0001
The Reﬂection Effect for Low and High CRT Groups
Percentage choosing gamble
in the domain of gains and losses
d $100 for sure or a 75% chance of $200 19%
o Lose $100 for sure or a 75% chance to lose $200 54%
h $100 for sure or a 3% chance of $7,000 8%
r Lose $100 for sure or a 3% chance to lose $7000 63%
34 Journal of Economic Perspectives
stractly is appealing to me” (Cacioppo, Petty and Kao, 1984). Several hundred
respondents also reported their scores on the Scholastic Achievement Test (SAT)
or the American College Test (ACT), the two most common college entrance
Table 4 shows the correlations between cognitive measures. The numbers
above the diagonal are the sample sizes from which these correlations were com-
puted (the number of surveys that included both measures). For example,
152 respondents reported both SAT and ACT scores, and their correlation was 0.77.
As expected, all measures correlate positively and signiﬁcantly with one another.
The moderate correlations suggest that all ﬁve tests likely reﬂect common factors,
but may also measure distinct characteristics, as they purport to. I have proposed
that the CRT measures “cognitive reﬂection”—the ability or disposition to resist
reporting the response that ﬁrst comes to mind. The need for cognition scale
(NFC) is advanced as a measure of someone’s “tendency to engage in and enjoy
thinking” (Cacioppo and Petty, 1982), but relies on self-reports rather than ob-
served behavior. The Wonderlic Personnel Test (WPT) is intended to measure a
person’s general cognitive ability, and the ACT and SAT are described as measures
of academic “achievement.”
Although the various tests are intended to measure conceptually distinguish-
able traits, there are many likely sources of shared variance. For example, though
the CRT is intended to measure cognitive reﬂection, performance is surely aided by
reading comprehension and mathematical skills (which the ACT and SAT also
measure). Similarly, though NFC and intelligence are distinguishable, the list of
ways in which those with high NFC differ from those with low NFC (see Cacioppi
et al., 1996) sounds very much like the list one would create if people were sorted
on any measure of cognitive ability. Namely, those with higher NFC were found to
do better on arithmetic problems, anagrams, trivia tests and college coursework, to
be more knowledgeable, more inﬂuenced by the quality of an argument, to recall
more of the information to which they are exposed, to generate more “task relevant
thoughts” and to engage in greater “information-processing activity.”
The empirical and conceptual overlap between these tests suggests that they
would all predict time and risk preferences and raises the question of their relative
Correlations Between Cognitive Measures
CRT SAT SAT
ACT WPT NFC
CRT 434 434 434 667 921 944
SAT .44 434 434 152 276 64
.46 .77 434 152 276 64
.24 .81 .28 152 276 64
ACT .46 .77 .63 .67 466 190
WPT .43 .49 .40 .37 .48 276
NFC .22 .30 .21 .28 .30 .19
Shane Frederick 35
predictive validities. To assess this issue, I correlated the scores on the various
cognitive measures with composite indices of decision-making characteristics
formed from the time preference items in Table 2 or the risk preference items in
Table 3. The composite scores registered the proportion of patient (or risk seek-
ing) responses. For example, respondents might have been asked whether they
prefer $3,400 this month or $3,800 next month, whether they would prefer a
shorter massage in two weeks or a longer one in November and how much they
would pay for overnight shipping of a book. Respondents who preferred the $3800,
the longer later massage and who were willing to pay less than the median person
for express shipping would be coded as “patient” on all three items and would
receive a score of 1. If they were patient on two of the three items, they would
receive a score of 0.66, and so on. Thus, the indices are scores ranging from 0 to
1, in coarse or ﬁne increments depending on how many questions the respondent
As shown in Table 5, the CRT was either the best or second-best predictor
across all four decision-making domains and the only test related to them all. Thus,
Composite indices were used to measure respondents’ general tendencies within a given decision-
making domain and to permit aggregation across studies. However, unless respondents received
identical items, their scores are not perfectly comparable. This issue is not vital for establishing the
predictive validity of the CRT, because the correlations reﬂect the pattern plainly observable from the
individual items. However, for the purpose of comparing the cognitive measures, composite indices are
more problematic, because the full battery of cognitive tests was not typically given, and different studies
involved different items. For example, at Carnegie Mellon University, respondents answered items b, d
and l from Table 2 and items a and d from Table 3. The CRT was the only cognitive measure obtained
for these respondents. Thus, these particular items will be disproportionately represented in the
composite decision-making indices with which the CRT is correlated. This problem can be overcome by
doing a pairwise comparison of cognitive measures only for those respondents who were given both.
This more painstaking analysis generally conﬁrms the implications of Table 5—namely, the different
tests often function similarly, but the CRT is a bit more highly correlated with the characteristics of
Correlations Between Cognitive Measures and Decision-Making Indices
Choice under uncertainty
(Preferences for gambles across domains)
favors sure gain
favors sure loss
36 Journal of Economic Perspectives
for researchers interested in separating people into cognitive groups, the CRT is an
attractive test: it involves only three items and can be administered in a minute or
two, yet its predictive validity equals or exceeds other cognitive tests that involve up
to 215 items and take up to 3
hours to complete (or which involve self-reports that
cannot be readily veriﬁed).
Men scored signiﬁcantly higher than women on the CRT, as shown in Table 6.
The difference is not likely due to a biased sampling procedure, because there were
no signiﬁcant sex differences for any other cognitive measure, except SAT
scores, for which there was a modest difference corresponding to national averages.
Nor can it be readily attributed to differences in the attention or effort expended
on the survey, since women scored slightly higher on the Wonderlic test, which was
given under identical circumstances (included as part of a 45-minute survey that
recruited respondents were paid to complete).
It appears, instead, that these items measure something that men have more
of. That something may be mathematical ability or interest, since the CRT items
have mathematical content, and men generally score higher than women on math
tests (Benbow and Stanley, 1980; Halpern, 1986; Hyde, Fennema and Lamon, 1990;
Hedges and Nowell, 1995). However, men score higher than women on the CRT,
even controlling for SAT math scores. Furthermore, even if one focuses only on
respondents who gave the wrong answers, men and women differ. Women’s mis-
takes tend to be of the intuitive variety, whereas men make a wider variety of errors.
For example, the women who miss the “widgets” problem nearly always give the
erroneous intuitive answer “100,” whereas a modest fraction of the men give
unexpected wrong answers, such as “20” or “500” or “1.” For every CRT item (and
several other similar items used in a longer variant of the test) the ratio of
“intuitive” mistakes to “other” mistakes is higher for women than for men. Thus,
the data suggest that men are more likely to reﬂect on their answers and less
inclined to go with their intuitive responses.
Because men score higher, the “high” CRT group is two-thirds men, whereas
the “low” CRT group is two-thirds women. Thus, the differences between CRT
groups may be revealing other male/female differences besides cognitive reﬂec-
tion. To remove this confound, Table 7 presents results split by both sex and CRT
score for selected items, including a heretofore undiscussed item involving the
willingness to pay for a coin ﬂip in which “heads” pays $100 and “tails” pays nothing.
Four facts are noteworthy. First, CRT scores are more highly correlated with
time preferences for women than for men; the low and high groups differ more.
Second, as suggested by most prior research (Byrnes, Miller and Schafer, 1999,
One might draw the opposite conclusion from self-reports. Using the scale described earlier, respon-
dents were asked “How long do you deliberate before reaching a conclusion?” Women reported higher
scores than men (1.16 vs. 0.45; t
⫽ 2.32; p⬍0.05).
Cognitive Reﬂection and Decision Making 37
present an overview), women were considerably more risk averse than men, and
this remains true even after controlling for CRT score. Third, for the selected risk
items, CRT is as important as sex. In other words, high-scoring women behave
almost identically to low-scoring men (compare the upper left and lower right cells
within each of the ﬁve items in the lower panel). Fourth, in contrast to the pattern
observed for the time preference items, CRT scores are more highly correlated with
risk preferences for men than for women.
The curious ﬁnding that CRT scores are more tightly linked with time pref-
erences for women than for men, but are more tightly linked with risk preferences
for men than for women held for the other tests of cognitive ability, as well.
Expressed loosely, being smart makes women patient and makes men take more
This result was unanticipated and suggests no obvious explanation. The only
related ﬁnding of which I am aware is in a study by Shoda, Mischel and Peake
(1990), who found that the patience of preschool girls was strongly related to their
subsequent SAT scores, but the patience of preschool boys was not.
The instructions in studies of decision making commonly reassure respon-
dents that “there are no right or wrong answers.” If this line is sincere, it implies
that researchers will interpret such preferences as they would a choice between
This conclusion can also be expressed less loosely. First, when faced with three mathematical
reasoning problems (“bat and ball,” “widgets” and “lilypads”), certain responses that are plausibly
construed as manifestations of intelligence (“5,” “5” and “47”) tend to correlate positively with certain
other responses that are plausibly construed as expressions of patience (namely, an expressed willing-
ness to wait for larger later rewards), and this tendency is more pronounced in women than men.
Second, the production of the canonically correct responses tends also to correlate positively with
certain responses that are plausibly construed as expressions of risk tolerance (namely, an expressed
willingness to forego a smaller certain reward in favor of a probabalistic larger one), and this tendency
is more pronounced in men than in women. Third, sex differences in risk seeking and in the degree of
relation to CRT scores was true only in the domain of gains. For the selected loss items (n through r in
Table 3), there were no sex differences.
Sex Differences in Cognitive Measures
Test Men Women Signiﬁcance of group difference
CRT 1.47 1.03 p ⬍ 0.0001
SAT 1334 1324 n.s.
688 666 p ⬍ 0.01
646 658 n.s.
ACT 26.7 26.3 n.s.
Wonderlic 26.2 26.5 n.s.
NFC 0.91 0.85 n.s.
38 Journal of Economic Perspectives
apples and oranges—as a primitive that neither requires nor permits further
However, unlike a preference between apples and oranges, time and risk
preferences are sometimes tied so strongly to measures of cognitive ability that they
effectively function as such a measure themselves.
For example, when a choice
To encourage respondents to consider each choice carefully, and independently from the other
items, several “ﬁller” choices were inserted between the “focal items.” An analysis of these responses
shows that CRT scores are unrelated to preferences between apples and oranges, Pepsi and Coke, beer
Results Split by Both CRT and Sex
(percentage choosing patient option or mean response)
Intertemporal choice or judgment Sex
$3400 this month or $3800 next month Men 39%
$100 this year or $140 next year Men 21%
$100 now or $1100 in 10 years Men 58%
$9 now or $100 in 10 years Men 40%
Willingness to pay for overnight shipping Men $4.05
of chosen book Women $4.54
Risky choice or judgment
(percentage choosing risky option or mean
$100 for sure or a 75% chance of $200 Men 26%
p ⬍ 0.0001
p ⬍ 0.01
$500 for sure or a 15% chance of $1,000,000 Men 40%
p ⬍ 0.0001
$1000 for sure or a 90% chance of $5000 Men 59%
p ⬍ 0.001
p ⬍ 0.10
$100 for sure or a 3% chance of $7000 Men 6%
p ⬍ 0.01
Willingness to pay for a coin ﬂip, where Men $13.00
p ⬍ 0.001
“HEADS” pays $100 and “TAILS” pays
Shane Frederick 39
between a sure $500 and a 15 percent chance of $1,000,000 was presented to
respondents along with an eight-item version of the CRT, only 25 percent of those
who missed all eight problems chose the gamble, compared to 82 percent among
those who solved them all. Should this result be interpreted to mean that choosing
the gamble is the “correct” response for this item?
The position that some preferences are better than others and that cognitive
ability is one indicator of the “better” preference is not unprecedented. Savage
(1954) argued that increased understanding ought to increase the frequency of the
“truly” normative response; that preferences that initially contradict some norma-
tive principle may not survive thorough deliberation (what he termed “reﬂective
Stanovich and West (2000) extended these views, by arguing that
increased understanding may arise from superior intellect (as well as from ex-
tended deliberation or reﬂection or instruction). In response to those contending
that judgments commonly labeled as errors or biases are actually equally good
answers to different interpretations of the question (for example, Hilton, 1995),
Stanovich and West argued that if smarter respondents were more likely to give
canonically correct answers, the other answers must not be equally good after all.
Some, however, reject the notion that a correlation between (some measure
of) cognitive ability and some particular response identiﬁes the “better” response.
For example, Sternberg (2000, pp. 697– 698) argues: “[T]o characterize people
with high SAT scores as those who should set the norm for what is somehow true
or right seems to be off target. People with high SAT scores have high levels of
certain kinds of cognitive abilities. They have no monopoly on quality of thinking
and certainly no monopoly on truth.”
The prevalence of this view could be directly tested. Respondents could be
shown the respective test scores of those who chose the sure $500 and those who
chose the 15 percent chance of $1,000,000. If Sternberg’s view is widely shared, this
manipulation would have no effect. If, on the other hand, the correlation between
cognitive ability and preference held normative force, making respondents aware
of it would cause many of them to choose the gamble.
Of course, the weight that should be placed on the opinions of those with
higher cognitive abilities clearly depends on the type of decision in question. If one
were deciding between a ﬁxed- and variable-interest mortgage, imitating one’s
and wine or rap concerts and ballet. However, CRT scores are strongly predictive of the choice between
People magazine and the New Yorker. Among the low CRT group, 67 percent preferred People. Among the
high CRT group, 64 percent preferred the New Yorker.
Slovic and Tversky (1974) use an eloquent and entertaining mock debate between Allais and Savage
to illustrate opposing views on the related issue of whether the opinions of people who have deliberated
longer over an issue ought to count more.
Along similar lines, Bar Hillel (1991, p. 413) comments: “Many writers have attempted to defend
seemingly erroneous responses by offering interpretations of subjects’ reasoning that rationalizes their
responses. Sometimes, however, this charitable approach has been misguided, either because the
subjects are quick to acknowledge their error themselves once it is pointed out to them, or because the
interpretation required to justify the response is even more embarrassing than the error it seeks to
40 Journal of Economic Perspectives
brilliant neighbor seems prudent. However, if one were deciding between an apple
and an orange, Einstein’s preference for apples seems irrelevant.
Thus, a relation between cognitive ability and preference does not, by itself,
establish the correct choice for any particular individual. Two individuals with
different cognitive abilities may experience outcomes differently, which may war-
rant different choices (for example, what magazines to read or movies to attend).
But with respect to the example motivating this discussion, one must ask whether
it is plausible that people of differing cognitive abilities experience increments of
wealth as differently as their choices suggest. It seems exceedingly unlikely that the
low CRT group has a marked kink in their utility function around $W ⫹ 500,
beyond which an extra $999,500 confers little additional beneﬁt. It seems more
reasonable, instead, to override the conventional caveat about arguing with tastes
(Becker and Stigler, 1977) and conclude that choosing the $500 is the “wrong
answer”—much as 10 cents is the wrong answer in the “bat and ball” problem.
Whatever stance one adopts on the contentious normative issues of whether a
preference can be “wrong” and whether more reﬂective people make “better”
choices, respondents who score differently on the CRT make different choices, and
this demands some explanation.
I thank Dan Ariely, Scott Armstrong, Daniel Benjamin, Brett Boshco, Eric Bradlow, Craig
Fox, Kerri Frederick, Steve Garcia, Timothy Heath, James Hines, Eric Johnson, Daniel
Kahneman, Robyn LeBoeuf, George Loewenstein, Leif Nelson, Nathan Novemsky, Greg
Pogarsky, Drazen Prelec, Daniel Read, Eldar Shaﬁr, Timothy Taylor, Catherine Tucker,
Michael Waldman and Jaclyn Zires for comments received on earlier drafts. A special thanks
to Steve Garcia, who coordinated most of the surveys generating the data summarized here. As
always (but particularly in this case), the views expressed or implied are those of the author
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