On the (non) persuasive power of a brain image

Abstract

The persuasive power of brain images has captivated scholars in many disciplines. Like others, we too were intrigued by the finding that a brain image makes accompanying information more credible (McCabe & Castel in Cognition 107:343-352, 2008). But when our attempts to build on this effect failed, we instead ran a series of systematic replications of the original study-comprising 10 experiments and nearly 2,000 subjects. When we combined the original data with ours in a meta-analysis, we arrived at a more precise estimate of the effect, determining that a brain image exerted little to no influence. The persistent meme of the influential brain image should be viewed with a critical eye.

Full text

BRIEF REPORT
On the (non)persuasive power of a brain image
Robert B. Michael &Eryn J. Newman &Matti Vuorre &
Geoff Cumming &Maryanne Garry
#Psychonomic Society, Inc. 2013
Abstract The persuasive power of brain images has capti-
vated scholars in many disciplines. Like others, we too were
intrigued by the finding that a brain image makes accompa-
nying information more credible (McCabe & Castel in Cog-
nition 107:343-352, 2008). But when our attempts to build
on this effect failed, we instead ran a series of systematic
replications of the original study—comprising 10 experi-
ments and nearly 2,000 subjects. When we combined the
original data with ours in a meta-analysis, we arrived at a
more precise estimate of the effect, determining that a brain
image exerted little to no influence. The persistent meme of
the influential brain image should be viewed with a critical
eye.
Keywords Judgment and decision making .
Neuroimaging .Statistics
A number of psychological research findings capture our
attention. Take the finding that people agree more with the
conclusions in a news article when it features an image of
the brain, even though that image is nonprobative—provid-
ing no information about the accuracy of the conclusions
already in the text of the article (McCabe & Castel, 2008). In
a time of explosive growth in the field of brain research and
the encroaching inevitability of neuroscientific evidence in
courtrooms, the persuasive influence of a brain image is
both intriguing and worrying.
Perhaps because of its implications, this research has
received much attention in both the popular and scholarly
press (nearly 40 citations per year, according to Google
Scholar, as of November 30, 2012). Although McCabe and
Castel (2008) did not overstate their findings, many others
have. Sometimes, these overstatements were linguistic exag-
gerations. One author of a paper in a medical journal
reported that “brain images . . . can be extremely mislead-
ing”(Smith, 2010). Other authors of a paper in a social
issues journal concluded, “clearly people are too easily
convinced”(Kang, Inzlicht, & Derks, 2010). Other over-
statements made claims beyond what McCabe and Castel
themselves reported: In an education journal, authors wrote
that “brain images make both educators and scientists more
likely to believe the statements”(Hinton & Fischer, 2008),
while in a forensic psychiatric journal, others worried about
“the potential of neuroscientific data to hold significant
prejudicial, and at times, dubious probative, value for
addressing questions relevant to criminal responsibility and
sentencing mitigation”(Treadway & Buckholtz, 2011).
These and other misrepresentations show that the persua-
sive power of brain images captivates scholars in many
disciplines. We too were captivated by this finding and
attempted to build on it—but were surprised when we had
difficulty obtaining McCabe and Castel’s(2008) basic find-
ing. Moreover, in searching the published literature, we
were likewise surprised to discover that the effect had not
been replicated. In one paper, some brain images were more
influential than others on subjects’evaluations of an article’s
credibility, but because there was no condition in which
subjects evaluated the article without a brain image, we
cannot draw conclusions about the power of brain images
per se (Keehner, Mayberry, & Fischer, 2011). Other papers
show that written neuroscience information makes “bad”
explanations of psychological phenomena seem more satis-
fying (Weisberg, Keil, Goodstein, Rawson, & Gray, 2008)
R. B. Michael :E. J. Newman :M. Vuorre :M. Garry (*)
School of Psychology, Victoria University of Wellington, PO Box
600, Wellington, New Zealand 6147
e-mail: Maryanne.Garry@vuw.ac.nz
G. Cumming
School of Psychological Science, La Trobe University, Melbourne,
Australia
Psychon Bull Rev
DOI 10.3758/s13423-013-0391-6

and that written fMRI evidence can even lead to more guilty
verdicts in a mock juror trial (McCabe, Castel, & Rhodes,
2011). We even found work in another domain showing that
meaningless mathematics boosts the quality of abstracts
(Erikkson, 2012). But we did not find any other evidence
that brain images themselves wield power.
Given the current discussion in psychological science
regarding the importance of replication (see the Novem-
ber 2012 Perspectives on Psychological Science,the
February 2012 Observer, and www.psychfiledrawer.org),
we therefore turned our attention to a concentrated
attempt to more precisely estimate how much more
people will agree with an article’s conclusions when it
is accompanied by a brain image. Here, we report a
meta-analysis including McCabe and Castel’s(2008)
original data and 10 of our own experiments that use
their materials.
1
We arrive at a more precise estimate of
the size of the effect, concluding that a brain image
exerts little to no influence on the extent to which
people agree with the conclusions of a news article.
Method
Subjects
Across 10 experiments, a total of 1,971 people correctly
completed all phases of the experiment (Table 1shows the
experiment number, subject pool, medium of data collec-
tion, sample size, and compensation). We collected demo-
graphic information from the Mechanical Turk subjects.
These subjects ranged in age from 16 to 82 years (M=
29.24, median = 26, SD = 10.78). Eleven percent had
completed a PhD or Masters degree; 35 % had completed
a Bachelor’s degree; 52 % had completed high school; and
the remaining 2 % had not finished high school.
Design
In each experiment, we manipulated, between subjects, the
presence or absence of a brain image.
Procedure
We told subjects that they were taking part in a study
examining visual and verbal learning styles. Subjects
then read a brief news article, "Brain Scans Can Detect
Criminals,”from McCabe and Castel's (2008)thirdex-
periment. The article was from the BBC News Web site
and summarized a study discussed in Nature (BBC
News, 2005;Wild,2005). All of our attempts to repli-
cate focused on this experiment, because it produced
McCabe and Castel's largest effect (d= 0.40). Although
there were two other experiments in their paper, the first
used different materials and a different dependent mea-
sure and was a within-subjects design. Their second
experiment, also a within-subjects design, examined the
effects of different types of brain images but did not
have a baseline condition with no brain image and,
therefore, did not permit brain versus no-brain
comparisons.
In their third experiment, McCabe and Castel (2008)
used a 2 × 2 between-subjects design, manipulating (1)
the presence or absence of a brain image depicting
activity in the frontal lobes and (2) whether the article
featured experts critiquing the article’s claims. Although
they did not explain the rationale for the critique manip-
ulation, it stands to reason that criticism would counter-
act the persuasive influence of a brain image. Indeed,
they adopted that reasoning in a later paper showing
that the persuasive influence of written neuroscientific
evidence on juror verdicts decreases when the validity
of that evidence is questioned (McCabe et al., 2011).
McCabe and Castel found that the critique manipulation
did not influence people’s ratings of the article’scon-
clusions, nor did it interact with the presence of a brain
image, so in Experiments 1–5 we used the article with-
out the critique.
But when we took a closer look at McCabe and Castel’s
(2008) raw data, we found that the influence of the brain
image was larger when the article’s claims were critiqued, as
compared with when they were not, t
critique
(52) = 2.07, p=
.04, d= 0.56; t
no_critique
(52) = 1.13, p= .27, d= 0.31. Note
that this surprising result runs counter to the explanation that
evidence is less influential when its validity is called into
question (McCabe et al., 2011). With these findings in mind,
in Experiments 6–10, we used the article in which experts
criticized the article’s claims in an extra 100 words.
After reading the article (critiqued or not), subjects
responded to the statement "Do you agree or disagree with
the conclusion that brain imaging can be used as a lie detec-
tor?" on a scale from 1 (strongly disagree)to4(strongly
agree). Subjects were randomly assigned to a condition in
which the article appeared alone or a condition in which an
image of the brain appeared alongside the article. In all online
experiments, subjects then encountered an attention check,
which they had to pass to stay in the data set (Oppenheimer,
Meyvis, & Davidenko, 2009).
2
1
We thank Alan Castel for sharing his materials with us and for his
many helpful discussions.
2
When we included subjects who failed the attention check in the
meta-analysis, the estimated raw effect size was an even smaller 0.04,
95 % CI [0.00, 0.11]. Across studies, exclusion rates varied from 24 %
to 31 % of subjects. These rates are lower than those found by
Oppenheimer et al. (2009).
Psychon Bull Rev

Results and discussion
How much influence does an image of the brain have
on people’s agreement with the conclusions of a news
article? To answer this question, we first calculated the
raw effect size for each experiment by determining the
difference between mean agreement ratings among peo-
ple in the brain and the no-brain conditions. We report
these findings in Table 2.
To find a more precise estimate of the size of the effect,
we used ESCI software (Cumming, 2012) to run a random
effects model meta-analysis of our 10 experiments and two
findings from McCabe and Castel (2008). We display the
results in Fig. 1. The result of this meta-analysis is an
estimated raw effect size of 0.07, 95 % CI [0.00, 0.14], z=
1.84, p= .07. On a 4-point scale, this estimate represents
movement up the scale by 0.07 points, or 2.4 % (cf. McCabe
and Castel’s[2008] original raw effect size of 0.26, or
8.7 %). We also found no evidence of heterogeneity
across the experiments. Tau—the estimated standard de-
viation between experiments—was small (0.07), and the
CI included zero as a plausible value (95 % CI [0,
0.13]; note, of course, that tau cannot be less than 0),
suggesting that the observed variation across experi-
ments could very plausibly be attributed to sampling
variability. This finding is important because, at first
glance, it appears as though the brain image might be
more persuasive on paper than online, but the statistics
simply do not support this idea. We also examined the
impact of other potentially important moderators. We
ran an analysis of covariance on the dependent measure,
using age and education as covariates and condition
(brain, no brain) as an independent variable. Neither
covariate interacted with the independent variable,
Table 1 Characteristics of our 10 experiments included in the meta-analysis
Experiment Subject pool Medium NCompensation
1 Mechanical Turk Online 197 US$0.30
2 Victoria undergraduate subject pool Online 75 Course credit
3 Wellington high school students Paper 45 Movie voucher
4 Mechanical Turk Online 368 US$0.50
5 Victoria Intro Psyc subject pool Paper 529 Course credit
6 Mechanical Turk Online 113 US$0.50
7 General public Paper 68 None
8 Mechanical Turk Online 191 US$0.50
9 Mechanical Turk Online 194 US$0.50
10 Mechanical Turk Online 191 US$0.50
In Experiments 3, 5, and 7, subjects saw a paper version of the article—as in the original McCabe and Castel (2008) study—and in the remaining
experiments, they saw an online version that looked nearly identical to the paper version (Qualtrics Labs Inc., 2012).
Table 2 Summary of results of our 10 experiments included in the meta-analysis
Experiment No brain Brain ES 95 % CI tp
N M SD N M SD LL UL
1. Mechanical Turk 99 2.90 0.58 98 2.86 0.61 −0.04 −0.21 0.13 −0.46 .643
2. Victoria undergraduate subject pool 42 2.62 0.54 33 2.85 0.57 0.23 −0.03 0.48 1.79 .078
3. Wellington high school students 24 2.96 0.36 21 3.07 0.55 0.11 −0.16 0.39 0.82 .415
4. Mechanical Turk 184 2.93 0.60 184 2.89 0.60 −0.05 −0.17 0.07 −0.78 .435
5. Victoria Intro Psyc subject pool 274 2.86 0.59 255 2.91 0.52 0.04 −0.05 0.14 0.92 .357
6. Mechanical Turk [critique] 58 2.50 0.84 55 2.60 0.83 0.10 −0.21 0.41 0.64 .527
7. General public [critique] 34 2.41 0.78 34 2.74 0.51 0.32 0.00 0.64 2.02 .048
8. Mechanical Turk [critique] 98 2.73 0.67 93 2.68 0.69 −0.06 −0.25 0.14 −0.58 .561
9. Mechanical Turk [critique] 99 2.54 0.66 95 2.72 0.68 0.18 −0.01 0.37 1.88 .062
10. Mechanical Turk [critique] 94 2.66 0.65 97 2.64 0.71 −0.02 −0.21 0.17 −0.21 .836
ES = effect size, calculated as the difference between no-brain and brain means. LL and UL are the lower and upper limits of the 95 % confidence
interval of the effect size. Positive effect sizes signify higher average ratings for articles featuring a brain image.
Psychon Bull Rev

suggesting that the persuasive influence of a brain im-
age is not moderated by age or education.
How are we to understand the size of the brain
image effect in context? Let us consider subjects’hy-
pothetical responses, as shown in Fig. 2. The line
marked “No Brain”represents the weighted mean agree-
ment of subjects who read the article without a brain
image. The line marked “Brain”represents how far
subjects’agreement would shift, in the mean, if they
had read the article with a brain image. Taken together,
this figure, coupled with the meta-analysis, makes it
strikingly clear that the image of the brain exerted little
to no influence. The exaggerations of McCabe and
Castel’s(2008) work by other researchers seem even
more worrisome in light of this more precise estimate
of the effect size.
It is surprising, however, that an image of the brain exerted
little to no influence on people’s judgments. We know that
images can exert powerful effects on cognition—in part,
because they facilitate connections to prior knowledge. For
instance, when pictures clarify complex ideas (such as the
workings of a bicycle pump) and bridge the gap between what
nonexperts know and do not know, people comprehend and
remember that material better (Mayer & Gallini, 1990;see
Carney & Levin, 2002,forareview).
Manipulations like these that boost comprehension can
also make other concepts related to the material feel more
easily available in memory, and we know that people inter-
pret this feeling of ease as diagnostic of familiarity and truth
(Newman, Garry, Bernstein, Kantner, & Lindsay, 2012;
Tversky & Kahneman, 1973; Whittlesea, 1993; see Alter
& Oppenheimer, 2009, for a review). But a brain image
depicting activity in the frontal lobes is different. To people
who may not understand how fMRI works, or even where
the frontal lobes are, seeing an image of the brain may not
be any more helpful than seeing an ink blot. It seems
reasonable to speculate, therefore, that images of the brain
are like other technical images: To people who cannot
connect them to prior knowledge, there is no boost of
comprehension, nor a feeling of increased cognitive avail-
ability. This speculation leads directly to an interesting
question: To what extent is the influence of a brain image
moderated by prior knowledge?
Another explanation for the trivial effect of brain
images is that people have become more skeptical about
neuroscience information since McCabe and Castel’s
(2008) study. Indeed, the media itself has begun engaging
in critical self-reflection. For instance, a recent article in
the New York Times railed against the “cultural tendency,
in which neuroscientific explanations eclipse historical,
McCabe & Castel, 2008
Experiment 1
Experiment 2
Experiment 3
Experiment 4
Experiment 5
McCabe & Castel, 2008
Experiment 6
Experiment 7
Experiment 8
Experiment 9
Experiment 10
Result of meta-analysis
Fig. 1 Forest plot of effect sizes between studies. Each row represents
one experiment, starting with the original McCabe and Castel (2008)
finding when the article did not feature criticism, then our five repli-
cations, then McCabe and Castel’s finding when the article featured
criticism, then our five replications. Our experiments are numbered 1–
10, as also in Tables 1and 2. The location of each square on the
horizontal axis represents the effect size—the difference between mean
agreement ratings in the no-brain and brain conditions (the maximum
possible difference between these two means is ±3; positive values
indicate a higher mean score for brain). The black lines extending
either side of a square represent a 95 % confidence interval. The size
of each square indicates the sample size and weighting an experiment
is given in the meta-analysis. Finally, the red diamond shows the result
of the meta-analysis, with the center of the diamond indicating the
estimated effect size and the spread of the diamond representing a 95 %
confidence interval
Fig. 2 An illustration of the brain effect. The no-brain bar represents
the weighted average for subjects who read the article without a brain
image (2.77). The difference between the no-brain bar and the brain bar
is the estimated effect size from the meta-analysis (0.07)
Psychon Bull Rev

political, economic, literary and journalistic interpreta-
tions of experience”and “phenomena like neuro law,
which, in part, uses the evidence of damaged brains as
the basis for legal defense of people accused of heinous
crimes”(Quart, 2012). If people have indeed grown skep-
tical, we might then expect them to also be protected
against the influence of other forms of neuroscience in-
formation. To test this hypothesis, we ran a series of
replications of another well-known 2008 study showing
that people rated bad explanations of scientific phenome-
na as more satisfying when those explanations featured
neuroscience language (Weisberg et al., 2008).
Our five replications, which appear in Table 3, produced
similar patterns of results. To estimate the size of the effect
more precisely, we followed a similar approach as we did
earlier, running a random effects model meta-analysis of our
five experiments and the original finding from Weisberg et
al. (2008). The result was an estimated raw effect size of
0.40, 95 % CI [0.23, 0.57], z= 4.71, p< .01. On a 7-point
scale, this estimate represents movement up the scale by
0.40 points, or 6.67 %. The CI does not include zero as a
plausible value, providing evidence against the idea that
people have become savvy enough about neuroscience to
be protected against its influence more generally.
Why the disparity, then, between the trivial effects of a
brain image and the more marked effects of neuroscience
language? A closer inspection reveals that the Weisberg et
al. (2008) study is not simply the language analog of the
McCabe and Castel (2008) study. For instance, Weisberg et
al. found that neuroscience language makes bad explana-
tions seem better but has less (or no) effect on good explan-
ations. By contrast, McCabe and Castel did not vary the
quality of their explanations. In addition, Weisberg et al.
compared written information that did or did not feature
neuroscience language, but McCabe and Castel added a
brain image to an article that already featured some neuro-
science language. Perhaps, then, the persuasive influence
of the brain image is small when people have already
been swayed by the neuroscience language in the article.
Although such a possibility is outside the scope of this
article, it is an important question for future research.
How are we to understand our results, given that other
research shows that some brain images are more influential
than others (Keehner et al., 2011)? One possibility is that
Keehner and colleagues’within-subjects design—in which
subjects considered a series of five different brain images—
encouraged people to rely on relative ease of processing
when making judgments across the different images (Alter
& Oppenheimer, 2008). By contrast, McCabe and Castel’s
(2008) between-subjects design does not allow people to
adopt this strategy. And recall, of course, that because
Keehner and colleagues did not compare the influence of
any brain image with that of no brain image, their work still
does not address that basic question.
Although our findings do not support popular descrip-
tions of the persuasiveness of brain images, they do fit with
very recent research and discussions questioning their allure
(Gruber & Dickerson, 2012; Farah & Hook, 2013). Impor-
tantly, our estimation approach avoids the dichotomous
thinking that dominates media discourse of popular psycho-
logical effects and, instead, emphasizes—in accord with
APA standards—interpretation of results based on point
and interval estimates. Furthermore, research in the domain
of jury decision making suggests that brain images have
little or no independent influence on juror verdicts—a con-
text in which the persuasive influence of a brain image
would have serious consequences (Greene & Cahill, 2012;
Schweitzer & Saks, 2011; Schweitzer et al., 2011). Taken
together, these findings and ours present compelling evi-
dence that when it comes to brains, the “amazingly persis-
tent meme of the overly influential image”
3
has been wildly
overstated.
Author Note We are grateful for the support of the New Zealand
Government through the Marsden Fund, administered by the Royal
Society of New Zealand on behalf of the Marsden Fund Council.
3
We thank Martha Farah (personal communication, June 20, 2012) for
coining this delightful term.
Table 3 Summary of results of experiments replicating Weisberg, Keil, Goodstein, Rawson, and Gray (2008)
Experiment No Neuro Neuro ES 95 % CI tp
N M SD N M SD LL UL
1. Mechanical Turk 61 4.20 0.89 60 4.36 0.99 0.16 −0.18 0.50 0.93 .355
2. Mechanical Turk 68 4.08 1.02 80 4.40 0.94 0.32 0.00 0.64 1.98 .049
3. Mechanical Turk 78 4.17 1.07 79 4.50 0.85 0.33 0.02 0.63 2.12 .036
4. Mechanical Turk 82 4.16 1.03 70 4.61 0.89 0.45 0.14 0.76 2.83 .005
5. Mechanical Turk 117 4.18 1.02 102 4.58 1.07 0.40 0.12 0.68 2.83 .005
ES = effect size, calculated as the difference between no-neuro and neuro means. LL and UL are the lower and upper limits of the 95 % confidence
interval of the effect size. Positive effect sizes signify higher average satisfaction ratings for bad explanations featuring neuroscience.
Psychon Bull Rev

Robert B. Michael gratefully acknowledges support from Victoria
University of Wellington.
References
Alter, A. L., & Oppenheimer, D. M. (2008). Effects of fluency on
psychological distance and mental construal (or why New York is
a large city, but New York is a civilized jungle). Psychological
Science, 19, 161–167. doi:10.1111/j.1467-9280.2008.02062.x
Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes
of fluency to form a metacognitive nation. Personality and
Social Psychology Review, 13, 219–235. doi:10.1177/
1088868309341564
BBC News. (2005, September 21). Can brain scans detect criminals?
BBC News. Retrieved from http://news.bbc.co.uk/2/hi/uk_news/
4268260.stm
Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still im-
prove students’learning from text. Educational Psychology Re-
view, 14, 5–26. doi:10.1023/A:1013176309260
Cumming, G. (2012). Understanding the new statistics: Effect sizes,
confidence intervals, and meta-analysis. New York: Routledge.
Erikkson, K. (2012). The nonsense math effect. Judgment and Deci-
sion Making, 7(6), 746–749.
Farah, M. J., & Hook, C. J. (2013). The seductive allure of “seductive
allure”.Perspectives on Psychological Science 8,88–90.
doi:10.1177/1745691612469035
Greene, E., & Cahill, B. S. (2012). Effects of neuroimaging evidence
on mock juror decision making. Behavioral Sciences & the Law,
30, 280–296. doi:10.1002/bsl.1993
Gruber, D., & Dickerson, J. A. (2012). Persuasive images in popular
science: Testing judgments of scientific reasoning and credibility.
Public Understanding of Science, 21, 938–948. doi:10.1177/
0963662512454072
Hinton, C., & Fischer, K. W. (2008). Research schools: Grounding
research in educational practice. Mind, Brain, and Education, 2,
157–160. doi:10.1111/j.1751-228X.2008.00048.x
Kang, S. K., Inzlicht, M., & Derks, B. (2010). Social neuroscience and
public policy on intergroup relations: A Hegelian analysis. Jour-
nal of Social Issues, 66, 585–601. doi:10.1111/j.1540-
4560.2010.01664.x
Keehner, M., Mayberry, L., & Fischer, M. H. (2011). Different clues
from different views: The role of image format in public percep-
tion of neuroimaging results. Psychononmic Bulletin and Review,
18, 422–428. doi:10.3758/s13423-010-0048-7
Mayer, R. G., & Gallini, J. K. (1990). When is an illustration worth ten
thousand words? Journal of Educational Psychology, 82, 715–
726. doi:10.1037/0022-0663.82.4.715
McCabe, D. P., & Castel, A. D. (2008). Seeing is believing: The effect
of brain images on judgments of scientific reasoning. Cognition,
107, 343–352. doi:10.1016/j.cognition.2007.07.017
McCabe, D. P., Castel, A. D., & Rhodes, M. G. (2011). The
influence of fMRI lie detection evidence on juror decision-
making. Behavioral Sciences & the Law, 29, 566–577.
doi:10.1002/bsl.993
Newman, E. J., Garry, M., Bernstein, D. M., Kantner, J., & Lindsay, S.
D. (2012). Nonprobative photographs (or words) inflate truthi-
ness. Psychonomic Bulletin and Review. doi:10.3758/s13423-012-
0292-0
Oppenheimer, D., Meyvis, T., & Davidenko, N. (2009). Instructional
manipulation checks: Detecting satisficing to increase statistical
power. Journal of Experimental Social Psychology, 45, 867–872.
doi:10.1016/j.jesp.2009.03.009
Qualtrics Labs Inc. (2012). http://www.qualtrics.com
Quart, A. (2012, November 25). Neuroscience: Under attack. The New
York Times, p. SR12.
Schweitzer, J. J., & Saks, M. J. (2011). Neuroimage evidence and the
insanity defense. Behavioral Sciences & the Law, 29, 592–607.
doi:10.1002/bsl.995
Schweitzer, N. J., Saks, M. J., Murphy, E. R., Roskies, A. L., Sinnott-
Armstrong, W., & Gaudet, L. M. (2011). Neuroimages as evi-
dence in a mens rea defense. Psychology, Public Policy, and Law,
17, 357–393. doi:10.1037/a0023581
Smith, D. F. (2010). Cognitive brain mapping for better or worse.
Perspectives in Biology and Medicine, 53, 321–329.
doi:10.1353/pbm.0.0165
Treadway, M. T., & Buckholtz, J. W. (2011). On the use and misuse of
genomic and neuroimaging science in forensic psychiatry: Cur-
rent roles and future directions. Child and Adolescent Psychiatric
Clinics of North America, 20, 533–546. doi:10.1016/j.chc.
2011.03.012
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for
judging frequency and probability. Cognitive Psychology,
5(507), 207–232. doi:10.1016/0010-0285(73)90033-9
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R.
(2008). The seductive allure of neuroscience explanations. Journal of
Cognitive Neuroscience, 20, 470–477. doi:10.1162/jocn.2008.20040
Whittlesea, B. W. A. (1993). Illusions of familiarity. Journal of Exper-
imental Psychology: Learning, Memory, and Cognition, 19,
1235–1253.
Wild, J. (2005). Brain imaging ready to detect terrorists, say neuro-
scientists. Nature, 437, 457. doi:10.1038/437457a
Psychon Bull Rev