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Reference Guide on
Neuroscience
H E N RY T . G R E E L Y A N D A N T H O N Y D . W A G N E R
Henry T. Greely, J.D., is Deane F. and Kate Edelman Johnson Professor of Law, Professor,
by courtesy, of Genetics, and the Director of Center for Law and the Biosciences, Stanford
University, Stanford, California.
Anthony D. Wagner, Ph.D., is Associate Professor of Psychology and Neuroscience, Stanford
University, Stanford, California.
ConTenTs
I. Introduction, 749
II. The Human Brain, 749
A. Cells, 750
B. Brain Structure, 754
C. Some Aspects of How the Brain Works, 759
III. Some Common Neuroscience Techniques, 761
A. Neuroimaging, 761
1. CAT scans, 762
2. PET scans and SPECT scans, 763
3. MRI—structural and functional, 766
B. EEG and MEG, 772
C. Other Techniques, 773
1. Lesion studies, 773
2. Transcranial magnetic stimulation (TMS), 774
3. Deep brain stimulation (DBS), 775
4. Implanted microelectrode arrays, 775
IV. Issues in Interpreting Study Results, 776
A. Replication, 777
B. Problems in Experimental Design, 777
C. The Number and Diversity of Subjects, 779
D. Applying Group Averages to Individuals, 780
E. Technical Accuracy and Robustness of Imaging Results, 781
F. Statistical Issues, 782
G. Possible Countermeasures, 783
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V. Questions About the Admissibility and the Creation of Neuroscience
Evidence, 784
A. Evidentiary Rules, 785
1. Relevance, 785
2. Rule 702 and the admissibility of scientific evidence, 785
3. Rule 403, 788
4. Other potentially relevant evidentiary issues, 789
B. Constitutional and Other Substantive Rules, 790
1. Possible rights against neuroscience evidence, 790
2. Possible rights to the creation or use of neuroscience
evidence, 795
3. The Fourth Amendment, 796
VI. Examples of the Possible Uses of Neuroscience in the Courts, 796
A. Criminal Responsibility, 799
B. Lie Detection, 802
1. Issues involved in the use of fMRI-based lie detection in
litigation, 803
2. Two cases involving fMRI-based lie detection, 805
3. fMRI-based lie detection outside the courtroom, 807
C. Detection of Pain, 807
VII. Conclusion, 811
References on Neuroscience, 812
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I. Introduction
Science’s understanding of the human brain is increasing exponentially. We know
almost infinitely more than we did 30 years ago; however, we know almost
nothing compared with what we are likely to know 30 years from now. The results
of advances in understanding human brains—and of the minds they generate—are
already beginning to appear in courtrooms. If, as neuroscience indicates, our mental
states are produced by physical states of our brain, our increased ability to discern
those physical states will have huge implications for the law. Lawyers already are
introducing neuroimaging evidence as relevant to questions of individual respon-
sibility, such as claims of insanity or diminished responsibility, either on issues of
liability or of sentencing. In May 2010, parties in two cases sought to introduce
neuroimaging in court as evidence of honesty; we are also beginning to see efforts
to use it to prove that a person is in pain. These and other uses of neuroscience
are almost certain to increase with our growing knowledge of the human brain
as well as continued technological advances in accurately and precisely measuring
the brain. This chapter strives to give judges some background knowledge about
neuroscience and the strengths and weaknesses of its possible applications in litiga-
tion in order to help them become better prepared for these cases.1
The chapter begins with a brief overview of the structure and function of the
human brain. It then describes some of the tools neuroscientists use to understand
the brain—tools likely to produce findings that parties will seek to introduce in
court. Next, it discusses a number of fundamental issues that must be considered
when interpreting neuroscientific findings. Finally, after discussing, in general, the
issues raised by neuroscience-based evidence, the chapter concludes by analyzing
a few illustrative situations in which neuroscientific evidence is likely to appear
in court in the future.
II. The Human Brain
This abbreviated and simplified discussion of the human brain describes the cel-
lular basis of the nervous system, the structure of the brain, and finally our current
understanding of how the brain works. More detailed, but still accessible, informa-
1. The Law and Neuroscience Project, funded by the John D. and Catherine T. MacArthur
Foundation, is preparing a book about law and neuroscience for judges, which should be available
by 2011. A Primer on Neuroscience (Stephen Morse & Adina Roskies eds., forthcoming 2011). The
Project has already published a pamphlet written by neuroscientists for judges, with brief discussions
of issues relevant to law and neuroscience. A Judge’s Guide to Neuroscience: A Concise Introduction
(M.S. Gazzaniga & J.S. Rakoff eds., 2010). One early book on a broad range of issues in law and
neuroscience also deserves mention: Neuroscience and the Law: Brain, Mind, and the Scales of Justice
(Brent Garland ed., 2004).
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tion about the human brain can be found in academic textbooks and in popular
books for general audiences.2
A. Cells
Like most of the human body the nervous system is made up of cells. Adult
humans contain somewhere between 50 trillion and 100 trillion human cells.
Each of those cells is both individually alive and part of a larger living organism.
Each cell in the body (with rare exceptions) contains each person’s entire
complement of human genes—his or her genome. The genes, found on very long
molecules of deoxyribonucleic acid (DNA) that make up a human’s 46 chromo-
somes, work by leading the cells to make other molecules, notably proteins and
ribonucleic acid (RNA). We now believe that there are about 23,000 human
genes. Cells are different from each other not because they contain different genes
but because they turn on and off different sets of genes. All human cells seem to
use the same group of several thousand “housekeeping” genes that run the cell’s
basic machinery, but skin cells, kidney cells, and brain cells differ in which other
genes they use. Scientists count different numbers of “types” of human cells, with
estimates ranging from a few hundred to a few thousand (depending largely on
how narrowly or broadly one defines a cell type).
The most important cells in the nervous system are called neurons. Neurons
pass messages from one neuron to another in a complex way that appears to be
responsible for brain function, conscious or otherwise.
Neurons (Figure 1) come in many sizes, shapes, and subtypes (with their
own names), but they generally have three features: a cell body (or “soma”),
short extensions called dendrites, and a longer extension called an axon. The cell
body contains the nucleus of the cell, which in turn contains the 46 chromosomes
with the cell’s DNA. The dendrites and axons both reach out to make connec-
tions with other neurons. The dendrites generally receive information from other
neurons; the axons send information.
Communication between neurons occurs at areas called synapses (Figure 2),
where two neurons almost meet. At a synapse, the two neurons will come within
2. The Society for Neuroscience, the very large scholarly society that covers a wide range of brain
science, has published a brief and useful primer about the human brain called Brain Facts. The most
recent edition, published in 2008, is available free at www.sfn.org/index.aspx?pagename=brainfacts.
Some particularly interesting books about various aspects of the brain written for a popular
audience include Oliver W. Sacks, The Man Who Mistook His Wife for a Hat and Other Clinical
Tales (1990); Antonio R. Damasio, Descartes’ Error: Emotion, Reason, and the Human Brain (1994);
Daniel L. Schacter, Searching for Memory: The Brain, the Mind, and the Past (1996); Joseph E.
LeDoux, The Emotional Brain: The Mysterious Underpinnings of Emotional Life (1996); Christopher
D. Frith, Making Up the Mind: How the Brain Creates Our Mental World (2007); and Sandra Aamodt
& Sam Wang, Welcome to Your Brain: Why You Lose Your Car Keys But Never Forget How to
Drive and Other Puzzles of Everyday Life (2008).
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Figure 1. Schematic of the typical structure of a neuron.
Source: Quasar Jarosz at en.wikipedia.
less than a micrometer (a millionth of a meter) of each other, with the presynaptic
side, on the axon, separated from the postsynaptic side, on the dendrite, by a gap
called the synaptic cleft. At synapses, when the axon (on the presynaptic side)
“fires” (becomes active) it releases molecules, known as neurotransmitters, into
the synaptic cleft. Some of those molecules are picked up by special receptors on
the dendrite that is on the postsynaptic side of the cleft. More than 100 different
neurotransmitters have been identified; among the best known are dopamine,
serotonin, glutamate, and acetylcholine. Some of the neurotransmitters released
into the synaptic cleft are picked up by special receptors on the postsynaptic side
of the cleft by the dendrite.
At the postsynaptic side of the cleft, neurotransmitters binding to the recep-
tors can have a wide range of effects. Sometimes they cause the receiving (post-
synaptic) neuron to “fire,” sometimes they suppress (inhibit) the postsynaptic
neuron from firing, and sometimes they seem to do neither. The response of the
receiving neuron is a complicated summation of the various messages it receives
from multiple neurons that converge, through synapses, on its dendrites.
A neuron that does fire does so by generating an electrical current that
flows down (away from the cell body) the length of its axon. We normally
think of electrical current as flowing in things like copper wiring. In that case,
free electrons move down the wire. The electrical currents of neurons are more
complicated. Molecules with a positive or negative electrical charge (ions) move
through the neuron’s membrane and create differences in the electrical charge
between the inside and outside of the neuron, with the current traveling along
the axon, rather like a fire brigade passing buckets of water in only one direction
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Figure 2. Synapse. Communication between neurons occurs at the synapse, where the sending (presynaptic) and receiving (post-
synaptic) neurons meet. When the presynaptic neuron fires, it releases neurotransmitters into the synaptic cleft, which
bind to receptors on the postsynaptic neuron.
752
Source: From Carlson. Carlson, Neil R. Foundations of Physiological Psychology (with Neuroscience Animations and Student Study Guide CD-ROM), 6th.
© 2005. Printed and electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.
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down the line. Firing occurs in milliseconds. This process of moving ions in and
out of the cell membrane requires that the cell use large amounts of energy. When
the current reaches the end of the axon, it may or may not cause the axon to
release neurotransmitters into the synaptic cleft. This complicated part-electrical,
part-chemical system is how information passes from one neuron to another.
The axons of human neurons are all microscopically narrow, but they vary
enormously in length. Some are micrometers long; others, such as neurons run-
ning from the base of the spinal cord to the toes, are several feet long. Longer
axons tend to be coated with a fatty substance called myelin. Myelin helps insulate
the axon and thus increases the strength and efficiency of the electrical signal,
much like the insulation wrapped around a copper wire. (The destruction of this
myelin sheathing is the cause of multiple sclerosis.) Axons coated with myelin
appear white; thus areas of the nervous system that have many myelin-coated
axons are referred to as “white matter.” Cell bodies, by contrast, look gray, and so
areas with many cell bodies and relatively few axons make up our “gray matter.”
White matter can roughly be thought of as the wiring that connects gray matter
to the rest of the body or to other areas of gray matter.
What we call nerves are really bundles of neurons. For example, we all have
nerves that run down our arms to our fingers. Some of those nerves consist of
neurons that pass messages from the fingers, up the arm, to other neurons in the
spinal cord that then pass the messages on to the brain, where they are analyzed
and experienced. This is how we feel things with our fingers. Other nerves are
bundles of neurons that pass messages from the brain through the spinal cord to
nerves that run down the arms to the fingers, telling them when and how to move.
Neurons can connect with other neurons or with other kinds of cells.
Neurons that control body movements ultimately connect to muscle cells—these
are called motor neurons. Neurons that feed information into the brain start
with specialized sensory cells (i.e., cells specialized for detecting different types
of stimuli—light, touch, heat, pain, and more) that fire in response to the appro-
priate stimulus. Their firings ultimately lead, directly or through other neurons,
into the brain. These are sensory neurons. These neurons send information only
in one direction—motor neurons ultimately from the brain, sensory neurons to
the brain. The paralysis caused by, for example, severe damage to the spinal cord
both prevents the legs from receiving messages to move that would come from
the brain through the motor neurons and keeps the brain from receiving mes-
sages from sensory neurons in the legs about what the legs are experiencing. The
break in the spinal column prevents the messages from getting through, just as a
break in a local telephone line will keep two parties from connecting. (There are,
unfortunately, not yet any human equivalents to wireless service.)
Estimates of the number of cells in a human brain vary widely, from a few
hundred billion to several trillion. These cells include those that make up blood
vessels and various connective tissues in the brain, but most of them are specialized
brain cells. About 80 billion to 100 billion of these brain cells are neurons; the
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other cells (and the source of most of the uncertainty about the number of cells)
are principally another class of cells referred to generally as glial cells. Glial cells
play many important roles in the brain, including, for example, producing and
maintaining the myelin sheaths that insulate axons and serving as a special immune
system for the brain. The full importance of glial cells is still being discovered;
emerging data suggest that they may play a larger role in mental processes than
as “support staff.” At this point, however, we concentrate on neurons, the brain
structures they form, and how those structures work.
B. Brain Structure
Anatomists refer to the brain, the spinal cord, and a few other nerves directly
connecting to the brain as the central nervous system. All the other nerves are
part of the peripheral nervous system. This reference guide does not focus on the
peripheral nervous system, despite its importance in, for example, assessing some
aspects of personal injuries. We also, less fairly, ignore the central nervous system
other than the brain, even though the spinal cord, in particular, plays an important
role in modulating messages going into and coming out of the brain.
The average adult human brain (Figure 3) weighs about 3 pounds and fills
a volume of about 1300 cubic centimeters. If liquid, it would almost fill two
standard wine bottles with a little space left over. Living brains have a consistency
about like that of gelatin. Despite the softness of brains, they are made up of
regular shapes and structures that are generally consistent from person to person.
Just as every nondamaged or nondeformed human face has two eyes, two ears,
Figure 3. Lateral (left) and mid-sagittal (right) views of the human brain.
Source: Courtesy of Anthony Wagner.
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one nose, and one mouth with standard numbers of various kinds of teeth, every
normal brain has the same set of identifiable structures, both large and small.
Neuroscientists have long worked to describe and define particular regions
of the brain. In some ways this is like describing parcels of land in property
documents, and, like property descriptions, several different methods are used.
At the largest scale, the brain is often divided into three parts: the brain stem, the
cerebellum, and the cerebrum.3
The brain stem is found near the bottom of the brain and is, in some ways,
effectively an extension of the spinal cord. Its various parts play crucial roles in
controlling the body’s autonomic functioning, such as heart rate and digestion. The
brain stem also contains important regions that regulate processing in the cerebrum.
For example, the substantia nigra and ventral tegmental area in the brain stem
consist of critical neurons that generate the neurotransmitter dopamine. While the
substantia nigra is crucial for motor control, the ventral tegmental area is important
for learning about rewards. The loss of neurons in the substantia nigra is at the core
of the movement problems of Parkinson’s disease.
The cerebellum, which is about the size and shape of a squashed tennis ball,
is tucked away in the back of the skull. It plays a major role in fine motor control
and seems to keep a library of learned motor skills, such as riding a bicycle. It was
long thought that damage to the cerebellum had little to no effect on a person’s
personality or cognitive abilities, but resulted primarily in unsteady gait, difficulty
in making precise movements, and problems in learning movements. More recent
studies of patients with cerebellar damage and functional brain imaging studies of
healthy individuals indicate that the cerebellum also plays a role in more cognitive
functions, including supporting aspects of working memory, attention, and language.
The cerebrum is the largest part of the human brain, making up about 85% of
its volume. The cerebrum is found at the front, top, and much of the back of the
human brain. The human brain differs from the brains of other mammals mainly
because it has a vastly enlarged cerebrum.
There are several different ways to identify parts of, or locations in, the cere-
brum. First, the cerebrum is divided into two hemispheres—the famous left and
right brain. These two hemispheres are connected by tracts of white matter—of
axons—most notably the large connection called the corpus callosum. Oddly, the
right hemisphere of the brain generally receives messages from and controls the
movements of the left side of the body, while the left hemisphere receives mes-
sages from and controls the movements of the right side of the body.
Each hemisphere of the cerebrum is divided into four lobes (Figure 4): The
frontal lobe in the front of the cerebrum (behind the forehead), the parietal lobe at
3. The brain also is sometimes divided into the forebrain, midbrain, and hindbrain. This
classification is useful for some purposes, particularly in describing the history and development of the
vertebrate brain, but it does not entirely correspond to the categorization of cerebrum, brain stem,
and cerebellum, and it is not used in this reference guide.
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Figure 4. Lobes of a hemisphere. Each hemisphere of the brain consists of four
lobes––the frontal, parietal, temporal, and occipital lobes.
Source: http://commons.wikimedia.org/wiki/File:Gray728.svg. This image is in the public domain
because its copyright has expired. This applies worldwide.
the top and toward the back, the temporal lobe on the side (just behind and above
the ears), and the occipital lobe at the back. Thus, one could describe a particular
region as lying in the left frontal lobe—the frontal lobe of the left hemisphere.
The surface of the cerebrum consists of the cortex, which is a sheet of gray
matter a few millimeters thick. The cortex is not a smooth sheet in humans, but
rather is heavily folded with valleys, called sulci (“sulcus” in the singular), and
bulges, called gyri (“gyrus”). The sulci and gyri have their own names, and so a
location can be described as in the inferior frontal gyrus in the left frontal lobe.
These folds allow the surface area of the cortex, as well as the total volume of the
cortex, to be much greater than in other mammals, while still allowing it to fit
inside our skulls, similar to the way the many folds of a car’s radiator give it a very
large surface area (for radiating away heat) in a relatively small space.
The cerebral cortex is extraordinarily large in humans compared with other
species and is clearly centrally involved in much of what makes our brains spe-
cial, but the cerebrum contains many other important subcortical structures that
we share with other vertebrates. Some of the more important areas include the
thalamus, the hypothalamus, the basal ganglia, and the amygdala. These areas all
connect widely, with the cortex, with each other, and with other parts of the brain
to form complex networks.
The functions of all these areas are many, complex, and not fully understood,
but some facts are known. The thalamus seems to act as a main relay that carries
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information to and from the cerebral cortex, particularly for vision, hearing,
touch, and proprioception (one’s sense of the position of the parts of one’s body).
It also is, importantly, involved in sleep, wakefulness, and consciousness. The
hypothalamus has a wide range of functions, including the regulation of body
temperature, hunger, thirst, and fatigue. The basal ganglia are a group of regions
in the brain that are involved in motor control and learning, among other things.
They seem to be strongly involved in selecting movements, as well as in learning
through reinforcement (as a result of rewards). The amygdala appears to be impor-
tant in emotional processing, including how we attach emotional significance to
particular stimuli.
In addition, many other parts of the brain, in the cortex or elsewhere, have
their own special names, usually with Latin or Greek roots that may or may not
seem descriptive today. The hippocampus, for example, is named for the Greek
word for seahorse. For most of us, these names will have no obvious rhyme or rea-
son, but merely must be learned as particular structures in the brain—the superior
colliculus, the tegmentum, the globus pallidus, the substantia nigra, the cingulate
cortex, and more. All of these structures come in pairs, with one in the left hemi-
sphere and one in the right hemisphere; only the pineal gland is unpaired. Brain
atlases include scores of names for particular structures or regions in the brain and
detailed information about the structures or regions.
Some of these smaller structures may have special importance to human
behavior. The nucleus accumbens, for example, is a small subcortical region in
each hemisphere of the cerebrum that appears important for reward processing
and motivation. In experiments with rats that received stimulation of this region
in return for pressing a lever, the rats would press the lever almost to the exclu-
sion of any other behavior, including eating. The nucleus accumbens in humans
appears linked to appetitive motivation, responding in anticipation of primary
rewards (such as pleasure from food and sex) and secondary rewards (such as
money). Through interactions with the orbital frontal cortex and dopamine-
generating neurons in the midbrain (including the ventral tegmental area), the
nucleus accumbens is considered part of a “reward network.” With a hypothesized
role in addictive behavior and in reward computations, more broadly, this putative
reward network is a topic of considerable ongoing research.
All of these various locations, whether defined broadly by area or by the
names of specific structures, can be further subdivided using directions: front and
back, up and down, toward the middle, or toward the sides. Unfortunately, the
directions often are not expressed in a straightforward manner, and several differ-
ent terminological conventions exist. Locations toward the front or back of the
brain can be referred to as either anterior or posterior or as rostral or caudal (liter-
ally, toward the nose, or beak, or the tail). Locations toward the bottom or top
of the brain are termed inferior or superior or, alternatively, as ventral or dorsal
(toward the stomach or toward the back). A location toward the middle of the
brain is called medial; one toward the side is called lateral. Thus, different loca-
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B. Lie Detection
The use of neuroscience methods for lie detection probably has received more
attention than any other issue raised in this chapter.56 This is due in part to the
cultural interest in lie detection, dating back in its technological phase nearly
90 years to the invention of the polygraph.57 But it is also due to the fact that two
commercial firms currently are offering fMRI-based lie detection services for sale
in the United States: Cephos and No Lie MRI.58 Currently, as far as we know,
56. For a technology whose results have yet to be admitted in court, the legal and ethical
issues raised by fMRI-based lie detection have been discussed in an amazingly long list of scholarly
publications from 2004 to the present. An undoubtedly incomplete list follows: Nita Farahany, supra
note 27; Brown & Murphy, supra note 20; Anthony D. Wagner, supra note 12; Frederick Schauer,
Can Bad Science Be Good Evidence?: Neuroscience, Lie-Detection, and the Mistaken Conflation of Legal and
Scientific Norms, 95 Cornell L. Rev. 1191 (2010); Frederick Schauer, Neuroscience, Lie-Detection, and the
Law: A Contrarian View, 14 Trends Cog. Sci. 101 (2010); Emilio Bizzi et al., Using Imaging to Identify
Deceit: Scientific and Ethical Questions (2009); Joelle Anne Moreno, The Future of Neuroimaged Lie
Detection and the Law, 42 Akron L. Rev. 717 (2009); Julie Seaman, Black Boxes: fMRI Lie Detection
and the Role of the Jury, 42 Akron L. Rev. 931 (2009); Jane Campbell Moriarty, Visions of Deception:
Neuroimages and the Search for Truth, 42 Akron L. Rev. 739 (2009); Dov Fox, supra note 27; Benjamin
Holley, It’s All in Your Head: Neurotechnological Lie Detection and the Fourth and Fifth Amendments, 28
Dev. Mental Health L. 1 (2009); Brian Reese, Comment: Using fMRI as a Lie Detector—Are We Lying
to Ourselves? 19 Alb. L.J. Sci. & Tech. 205 (2009); Cooper Ellenberg, Student Article: Lie Detection:
A Changing of the Guard in the Quest for Truth in Court? 33 Law & Psychol. Rev. 139 (2009); Julie
Seaman, Black Boxes, 58 Emory L.J. 427 (2008); Matthew Baptiste Holloway, supra note 27; William
Federspiel, supra note 27; Greely & Illes, supra note 28; Sarah E. Stoller & Paul R. Wolpe, supra
note 27; Mark Pettit, FMRI and BF Meet FRE: Braining Imaging and the Federal Rules of Evidence, 33
Am. J.L. & Med. 319 (2007); Jonathan H. Marks, Interrogational Neuroimaging in Counterterrorism: A
“No-Brainer” or a Human Rights Hazard? 33 Am. J.L. & Med. 483 (2007); Leo Kittay, Admissibility of
fMRI Lie Detection: The Cultural Bias Against “Mind Reading” Devices, 72 Brook. L. Rev. 1351, 1355
(2007); Jeffrey Bellin, The Significance (if Any) for the Federal Criminal Justice System of Advances in Lie
Detector Technology, Temp. L. Rev. 711 (2007); Henry T. Greely, The Social Consequences of Advances in
Neuroscience: Legal Problems; Legal Perspectives, in Neuroethics: Defining the Issues in Theory, Practice
and Policy 245 (Judy Illes ed., 2006); Charles N.W. Keckler, Cross-Examining the Brain: A Legal Analysis
of Neural Imaging for Credibility Impeachment, 57 Hastings L.J. 509 (2006); Archie Alexander, Functional
Magnetic Resonance Imaging Lie Detection: Is a “Brainstorm” Heading Toward the “Gatekeeper”? 7 Hous. J.
Health L. & Pol’y (2006); Michael S. Pardo, supra note 27; Erich Taylor, supra note 27; Paul R. Wolpe
et al., Emerging Neurotechnologies for Lie-Detection: Promises and Perils, 5 Am. J. Bioethics 38, 42 (2005);
Henry T. Greely, Premarket Approval Regulation for Lie Detection: An Idea Whose Time May Be Coming,
5 Am. J. Bioethics 50–52 (2005); Sean Kevin Thompson, Note: The Legality of the Use of Psychiatric
Neuroimaging in Intelligence Interrogation, 90 Cornell L. Rev. 1601 (2005); Henry T. Greely, Prediction,
Litigation, Privacy, and Property: Some Possible Legal and Social Implications of Advances in Neuroscience, in
Neuroscience and the Law: Brain, Mind, and the Scales of Justice 114–56 (Brent Garland ed., 2004);
and Judy Illes, A Fish Story? Brain Maps, Lie Detection, and Personhood, 6 Cerebrum 73 (2004).
57. An interesting history of the polygraph can be found in Ken Alder, The Lie Detectors:
The History of an American Obsession (2007). Perhaps the best overall discussion of the polygraph,
including some discussion of its history, is found in the National Research Council report, supra note
14, commissioned in the wake of the Wen Ho Lee case, on the use of the technology for screening.
58. The Web sites for the two companies are at Cephos, www.cephoscorp.com (last visited
July 3, 2010); and No Lie MRI, http://noliemri.com (last visited July 3, 2010).
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evidence from fMRI-based lie detection has not been admitted into evidence in
any court, but it was offered—and rejected—in two cases, United States v. Semrau59
and Wilson v. Corestaff Services, L.P.,60 in May 2010.61 This section will begin
by analyzing the issues raised for courts by this technology and then will discuss
these two cases, before ending with a quick look at possible uses of this kind of
technology outside the courtroom.
1. Issues involved in the use of fMRI-based lie detection in litigation
Published research on fMRI and detecting deception dates back to about 2001.62
As noted above, to date between 20 and 30 peer-reviewed articles from about
15 laboratories have appeared claiming to find statistically significant correlations
between patterns of brain activation and deception. Only a handful of the pub-
lished studies have looked at the accuracy of determining deception in individual
subjects as opposed to group averages. Those studies generally claim accuracy rates
of between about 75% and 90%. No Lie MRI has licensed the methods used by
one laboratory, that of Dr. Daniel Langleben at the University of Pennsylvania;
Cephos has licensed the method used by another laboratory, that of Dr. Frank A.
Kozel, first at the Medical University of South Carolina and then at the University
of Texas Southwestern Medical Center. (The method used by a British researcher,
Dr. Sean Spence, has been used on a British reality television show.)
All of these studies rely on research subjects, typically but not always col-
lege students, who are recruited for a study of deception. They are instructed to
answer some questions truthfully in the scanner and to answer other questions
inaccurately.63 In the Langleben studies, for example, right-handed, healthy, male
59. No. 07-10074 M1/P, Report and Recommendation (W.D. Tenn. May 31, 2010).
60. 2010 NY slip op. 20176, 1 (N.Y. Super. Ct. 2010); 900 N.Y.S.2d 639; 2010 N.Y. Misc.
LEXIS 1044 (2010).
61. In early 2009, a motion to admit fMRI-based lie detection evidence, provided by No Lie
MRI, was made, and then withdrawn, in a child custody case in San Diego. The case is discussed in a
prematurely entitled article, Alexis Madrigal, MRI Lie Detection to Get First Day in Court, WIRED SCI.
(Mar. 16, 2009), available at http://blog.wired.com/wiredscience/2009/03/noliemri.html (last visited
July 3, 2010). A somewhat similar method of using EEG to look for signs of “recognition” in the brain
was admitted into one state court hearing for postconviction relief at the trial court level in Iowa in
2001, and both it and another EEG-based method have been used in India. As far as we know, evidence
from the use of EEG for lie detection has not been admitted in any other U.S. cases. See supra note 28.
62. The most recent reviews of the scientific literature on this subject are Anthony D. Wagner,
supra note 12; and S.E. Christ et al., The Contributions of Prefrontal Cortex and Executive Control to
Deception: Evidence from Activation Likelihood Estimate Meta-Analyses, 19 Cerebral Cortex 2557 (2009).
See also Greely & Illes, supra note 28 (for discussion of the articles through early 2007). The following
discussion is based largely on those sources.
63. At least one fMRI study has attempted to investigate self-motivated lies, told by subjects
who were not instructed to lie, but who chose to lie for personal gain. Joshua D. Greene & Joseph M.
Paxton, Patterns of Neural Activity Associated with Honest and Dishonest Moral Decisions, 106 Proc. Nat’l
Acad. Sci. 12,506 (2009). The experiment was designed to make it easy for subjects to realize they
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University of Pennsylvania undergraduates were shown images of playing cards
while in the scanner and asked to indicate whether they saw a particular card.
They were instructed to answer truthfully except when they saw one particular
card. Some of Kozel’s studies used a different experimental paradigm, in which the
subjects were put in a room and told to take either a watch or a ring. When asked
in the scanner separately whether they had taken the watch and then whether they
had taken the ring, they were to reply “no” in both cases—truthfully once and
falsely the other time. When analyzed in various ways, the fMRI results showed
statistically different patterns of brain activation (small changes in BOLD response)
when the subjects were lying and when they were telling the truth.
In general, these studies are not guided by a consistent hypothesis about
which brain regions should be activated or deactivated during truth or decep-
tion. The results are empirical; they see particular patterns that differ between
the truth state and the lie state. Some have argued that the patterns show greater
mental effort when deception is involved; others have argued that they show more
impulse control when lying.
Are fMRI-based lie detection methods accurate? As a class of experiments,
these studies are subject to all the general problems discussed in Section IV regard-
ing fMRI scans that might lead to neuroscience evidence. So far there are only a
few studies involving a limited number of subjects. (The method used by No Lie
MRI seems ultimately to have been based on the responses of four right-handed,
healthy, male University of Pennsylvania undergraduates.64) There have been, to
date, no independent replications of any group’s findings.
The experience of the research subjects in these fMRI studies of deception
seems to be different from “lying” as the court system would perceive it. The
subjects knew they were involved in research, they were following orders to lie,
and they knew that the most harm that could come to them from being detected
in a lie might be lesser payment for taking part in the experiment. This seems hard
to compare to a defendant lying about participating in a murder. More fundamen-
tally, it is not clear how one could conduct ethical but realistic experiments with
lie detection. Research subjects cannot credibly be threatened with jail if they do
not convince the researcher of the truth of their lies.
Only a handful of researchers have published studies showing reported accu-
racy rates with individual subjects and only with a small number of subjects.65
would be given more money if they lied about how many times they correctly predicted a coin flip.
Investigators could not, however, determine if a subject lied in any particular trial.
64. Daniel D. Langleben et al., Telling Truth from Lie in Individual Subjects with Fast Event-Related
fMRI, 26 Hum. Brain Mapping 262, 267 (2005).
65. See discussion in Anthony D. Wagner, supra note 12, at 29–35. Wagner analyzes 11 peer-
reviewed, published papers. Seven come from Kozel’s laboratory; three come from Langleben’s. The
only exception is a paper from John Cacciopo’s group, which concludes “ [A]lthough fMRI may
permit investigation of the neural correlates of lying, at the moment it does not appear to provide a
very accurate marker of lying that can be generalized across individuals or even perhaps across types
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Some of the studies used complex and somewhat controversial statistical tech-
niques. And although subjects in at least one experiment were invited to try to use
countermeasures against being detected, no specific countermeasures were tested.
Beyond the scientific validity of these techniques lie a host of legal questions.
How accurate is accurate enough for admissibility in court or for other legal sys-
tem uses? What are the implications of admissible and accurate lie detection for
the Fourth, Fifth, Sixth, and Seventh Amendments? Would jurors be allowed to
consider the failure, or refusal, of a party to take a lie detector test? Would lie
detection be available in discovery? Would each side get to do its own tests—and
who would pay?
Accurate lie detection could make the justice system much more accurate.
Incorrect convictions might become rare; so might incorrect acquittals. Accurate lie
detection also could make the legal system much more efficient. It seems likely that
far fewer cases would go to trial if the witnesses could expect to have their veracity
accurately determined.
Inaccurate lie detection, on the other hand, holds the potential of ruining the
innocent and immunizing the guilty. It is at least daunting to remember some of
the failures of the polygraph, such as the case of Aldrich Ames, a Soviet (and then
Russian) mole in the Central Intelligence Agency, who passed two Agency poly-
graph tests while serving as a paid spy.66 The courts already have begun to decide
whether and how to use these new methods of lie detection in the judicial process;
the rest of society also will soon be forced to decide on their uses and limits.
2. Two cases involving fMRI-based lie detection
On May 31, 2010, U.S. Magistrate Judge Tu M. Pham of the Western District
of Tennessee issued a 39-page report and recommendation on the prosecution’s
motion to exclude evidence from an fMRI-based lie detection report by Cephos
in the case of United States v. Semrau.67 The report came after a hearing on
May 13–14 featuring testimony from Steve Laken, CEO of Cephos, for admission,
and from two experts arguing against admission. (The district judge adopted the
magistrate’s report during the trial.)
The defendant in this case, a health professional accused of defrauding Medi-
care, offered as evidence a report from Cephos stating that he was being truthful
of lies by the same individuals.” G. Monteleone et al., Detection of Deception Using fMRI: Better Than
Chance, But Well Below Perfection, 4 Soc. Neurosci. 528 (2009). However, that study only looked at
one brain region at a time, and it did not test combinations or patterns, which might have improved
the predictive power.
66. See Senate Select Committee on Intelligence, Assessment of the Aldrich H. Ames Espionage
Case and Its Implications for U.S. Intelligence (1994).
67. See supra note 59. The district court judge assigned to the case had a scheduling conflict on
the date of the hearing on the prosecution’s motion, and so the hearing was held before a magistrate
judge from that district.
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when he answered a set of questions about his actions and knowledge concerning
the alleged crimes.
Judge Pham first analyzed the motion under Rule 702, using the Daubert
criteria. He concluded that the technique was testable and had been the subject of
peer-reviewed publications. On the other hand, he concluded that the error rates
for its use in realistic situations were unknown. Furthermore, he found there were
no standards for its appropriate use. To the extent that the publications relied on
by Cephos to establish its reliability constituted such standards, those standards had
not actually been followed in the tests of the defendant. Cephos actually scanned
Dr. Semrau 2 times on 1 day, asking questions about one aspect of the criminal
charges during the first scan and then about another aspect in the second scan.
The company’s subsequent analysis of those scans indicated that the defendant
had been truthful in the first scan but deceptive in the second scan. Cephos then
scanned him a third time, several days later, on the second subject but with revised
questions, and concluded that he was telling the truth that time. Nothing in the
publications relied upon by Cephos indicated that the third scan was appropriate.
Finally, Judge Pham found that the method was not generally accepted in the
relevant scientific community as sufficiently reliable for use in court, citing several
publications, including some written by the authors whose methods Cephos used.
The magistrate judge then examined the motion under Rule 403 and found
that the potential prejudicial effect of the evidence outweighed its probative value.
He noted that the test had been conducted without the government’s knowledge
or participation, in a context where the defendant risked nothing by taking the
test—a negative result would never be disclosed. He noted the jury’s central role
in determining credibility and considered the likelihood that the lie detection
evidence would be a lengthy and complicated distraction from the jury’s central
mission. Finally, he noted that the probative value of the evidence was greatly
reduced because the report only gave a result concerning the defendant’s general
truthfulness when responding to more than 10 questions about the events but did
not even purport to say whether the defendant was telling the truth about any
particular question.
Earlier that month, a state trial court judge in Brooklyn excluded another
Cephos lie detection report in a civil case, Wilson v. Corestaff Services, L.P.68 This
case involved a claim by a former employee under state law that she had been
subject to retaliation for reporting sexual harassment. The plaintiff offered evi-
dence from a Cephos report finding that her main witness was truthful when he
described how defendant’s management said it would retaliate against the plaintiff.
That case did not involve an evidentiary hearing or, indeed, any expert tes-
timony. The judge decided the lie detection evidence was not appropriate under
New York’s version of the Frye test, noting that, in New York, “courts have
advised that the threshold question under Frye in passing on the admissibility
68. Wilson v. Corestaff Services, L.P., supra note 60.
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of expert’s testimony is whether the testimony is ‘within the ken of the typical
juror.’”69 Because credibility is a matter for the jury, the judge concluded that this
kind of evidence was categorically excluded under New York’s version of Frye. He
also noted that “even a cursory review of the scientific literature demonstrates that
the plaintiff is unable to establish that the use of the fMRI test to determine truth-
fulness or deceit is accepted as reliable in the relevant scientific community.”70
3. fMRI-based lie detection outside the courtroom
Lie detection might have applications to litigation without ever being introduced
in trials. As is the case today with the polygraph, the fact that it is not generally
admissible in court might not stop the police or the prosecutors from using it to
investigate alleged crimes. Similarly, defense counsel might well use it to attempt
to persuade the authorities that their clients should not be charged or should be
charged with lesser offenses. One could imagine the same kinds of pretrial uses
of lie detection in civil cases, as the parties seek to affect each other’s percep-
tions of the merits of the case.
Such lie detection efforts could also affect society, and the law, outside of
litigation. One could imagine prophylactic lie detection at the beginning of con-
tractual relations, seeking to determine whether the other side honestly had the
present intention of complying with the contract’s terms. One can also imagine
schools using lie detection as part of investigations of student misconduct or par-
ents seeking to use lie detection on their children. The law more broadly may
have to decide whether and how private actors can use lie detection, determining
whether, for example, to extend to other contexts—or to weaken or repeal—the
Employee Polygraph Protection Act.71
The current fMRI-based methods of lie detection provide one kind of pro-
tection for possible subjects—they are obvious. No one is going to be put into an
MRI for an hour and asked to respond, repeatedly, to questions without realizing
something important is going on. Should researchers develop less obtrusive or
obvious methods of neuroscience-based lie detection, we will have to deal with
the possibilities of involuntary and, indeed, surreptitious lie detection.
C. Detection of Pain
No matter where an injury occurs and no matter where it seems to hurt, pain
is felt in the brain.72 Without sensory nerves leading to the brain from a body
69. Id. at 6, citing People v. Cronin, 60 N.Y.2d 430, 458 N.E.2d 351, 470 N.Y.S.2d 110 (1983).
70. See Wilson, supra note 60, at 7.
71. See supra text accompanying note 32.
72. See Brain Facts, supra note 2, at 19–21, 49–50, which includes a useful brief description of
the neuroscience of pain.
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region, there is usually no experience of pain. Without the brain machinery and
functioning to process the signal, no pain is perceived.
Pain turns out to be complicated—even the common pain that is experienced
from an acute injury to, say, an arm. Neurons near the site of the injury called
nociceptors transmit the pain signal to the spinal cord, which relays it to the
brain. But other neurons near the site of the injury will, over time, adapt to affect
the pain signal. Cells in the spinal cord can also modulate the pain signal that is
sent to the brain, making it stronger or weaker. The brain, in turn, sends signals
down to the spinal cord that cause or, at least, affect these modulations. And the
actual sensation of pain—the “ouch”—takes place in the brain.
The immediate and localized sensation is processed in the somatosensory
cortex, the brain region that takes sensory inputs from different body parts (with
each body part getting its own portion of the somatosensory cortex) and pro-
cesses them into a perceived sensation. The added knowledge that the sensation
is painful seems to require the participation of other regions of the brain. Using
fMRI and other techniques, some researchers have identified what they call the
“pain matrix” in the brain, regions that are activated when experimental subjects,
in scanners, are exposed to painful stimuli. The brain regions identified as part of
the so-called pain matrix vary from researcher to researcher, but generally include
the thalamus, the insula, parts of the anterior cingulate cortex, and parts of the
cerebellum.73
Researchers have run experiments with subjects in the scanner receiving
painful or not painful stimuli and have attempted to find activation patterns
that appear when pain is perceived and that do not appear when pain is absent.
(The subjects usually are given nonharmful painful stimuli such as having their
skin touched with a hot metal rod or coated with a pepper-derived substance
that causes a burning sensation.) Some have reported substantial success, detect-
ing pain in more than 80% of the cases.74 Other studies have found a positive
correlation between the degree of activation in the pain matrix and the degree
of subjective pain, both as reported by the subject and as possibly indicated by
the heat of the rod or the amount of the painful substance—the higher the tem-
perature or the concentration of the painful substance, the greater the average
activation in the pain matrix.75
Other neuroscience studies of individual pain look not at brain function dur-
ing painful episodes but at brain structure. Some researchers, for example, claim
that different regions of the brain have different average size and neuron densities
73. A good review article on the uses of fMRI in studying pain is found in David Borsook
& Lino R. Becerra, Breaking Down the Barriers: fMRI Applications in Pain, Analgesia and Analgesics, 2
Molecular Pain 30 (2006).
74. See, e.g., Irene Tracey, Imaging Pain, 101 Brit J. Anaesth. 32 (2008).
75. See, e.g., Robert C. Coghill et al., Neural Correlates of Interindividual Differences in the Subjective
Experience of Pain, 100 Proc. Nat’l Acad. Sci. 8538 (2003).
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in patients who have had long-term chronic pain than in those who have not
had such pain.76
Pain is clearly complicated. Placebos, distractions, or great need can some-
times cause people not to sense, or perhaps not to notice, pain that could other-
wise be overwhelming. Similarly, some people can become hypersensitive to pain,
reporting severe pain when the stimulus normally would be benign. Amputees
with phantom pain—the feeling of pain in a limb that has been gone for years—
have been scanned while reporting this phantom pain. They show activation in
the pain matrix. In some fMRI studies, people who have been hypnotized to feel
pain, even when there is no painful stimulus, show activation in the pain matrix.77
And in one fMRI study, subjects who reported feeling emotional distress, as a
result of apparently being excluded from a “game” being played among research
subjects, also showed, on average, statistically significant activation of the pain
matrix.78
Pain also plays an enormous role in the legal system.79 The existence and
extent of pain is a matter for trial in hundreds of thousands of injury cases each
year. Perhaps more importantly, pain figures into uncounted workers’ compensa-
tion claims and Social Security disability claims. Pain is often difficult to prove,
and the uncertainty of a jury’s response to claimed pain probably keeps much liti-
gation alive. We know that the tests for pain currently presented to jurors, judges,
and other legal decisionmakers are not perfect. Anecdotes of and the assessments
by pain experts both are convincing that some nontrivial percentage of success-
ful claimants are malingering and only pretending to feel pain; a much greater
percentage may be exaggerating their pain.
A good test for whether a person is feeling pain, and, even better, a “scien-
tific” way to measure the amount of that pain—at least compared to other pains
felt by that individual, if not to pain as perceived by third parties—could help
resolve a huge number of claims each year. If such pain detection were reliable,
it would make justice both more accurate and more certain, leading to faster, and
76. See, e.g., Vania Apkarian et al., Chronic Back Pain Is Associated with Decreased Prefrontal and
Thalamic Gray Matter Density, 24 J. Neurosci. 10,410 (2004); see also Arne May, Chronic Pain May
Change the Structure of the Brain, 137 Pain 7 (2008); Karen D. Davis, Recent Advances and Future Prospects
in Neuroimaging of Acute and Chronic Pain, 1 Future Neurology 203 (2006).
77. Stuart W. Derbyshire et al., Cerebral Activation During Hypnotically Induced and Imagined Pain,
23 NeuroImage 392 (2004).
78. Naomi I. Eisenberg, Does Rejection Hurt? An fMRI Study of Social Exclusion, 302 Science
290 (2003).
79. The only substantial analysis of the legal implications of using neuroimaging to detect pain
is found in Adam J. Kolber, Pain Detection and the Privacy of Subjective Experience, 33 Am. J.L. & Med.
433 (2007). Kolber expands on that discussion in interesting ways in Adam J. Kolber, The Experiential
Future of the Law, 60 Emory L.J. 585, 595–601 (2011). The possibility of such pain detection was briefly
discussed earlier in two different 2006 publications: Henry T. Greely, Prediction, Litigation, Privacy, and
Property: Some Possible Legal and Social Implications of Advances in Neuroscience, supra note 56, at 141–42;
and Charles Keckler, Cross-Examining the Brain, supra note 56, at 544.
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cheaper, resolution of many claims involving pain. The legal system, as well as the
honest plaintiffs and defendants within it, would benefit.
A greater understanding of pain also might lead to broader changes in the
legal system. For example, emotional distress often is treated less favorably than
direct physical pain. If neuroscience were to show that, in the brain, emotional
distress seemed to be the same as physical pain, the law might change. Perhaps
more likely, if neuroscience could provide assurance that sincere emotional pain
could be detected and faked emotional distress would not be rewarded, the law
again might change. Others have argued that even our system of criminal pun-
ishment might change if we could measure, more accurately, how much pain
different punishments caused defendants, allowing judges to let the punishment
fit the criminal, if not the crime.80 A “pain detector” might even change the
practice of medicine in legally relevant ways, by giving physicians a more certain
way to check whether their patients are seeking controlled substances to relieve
their own pain or whether they are seeking them to abuse or to sell for someone
else to abuse.
In at least one case, a researcher who studies the neuroscience of pain was
retained as an expert witness to testify regarding whether neuroimaging could pro-
vide evidence that a claimant was, in fact, feeling pain. The case settled before the
hearing.81 In another case, a prominent neuroscientist was approached about being
a witness against the admissibility of fMRI-based evidence of pain, but, before
she had decided whether to take part, the party seeking to introduce the evidence
changed its mind. This issue has not, as of the time of this writing, reached the
courts yet, but lawyers clearly are thinking about these uses of neuroscience. (And
note that in some administrative contexts, the evidentiary rules will not apply
in their full rigor, possibly making the admission of such evidence more likely.)
Do either functional or structural methods of detecting pain work and, if so,
how well? We do not know. These studies share many of the problems outlined
in Section IV. The studies are few in number, with few subjects (and usually sets
of subjects that are not very diverse). The experiments—usually involving giv-
ing college students a painful stimulus—are different from the experience of, for
example, older people who claim to have low back pain. Independent replication
is rare, if it exists at all. The experiments almost always report that, on average, the
group shows a statistically significant pattern of activation that differs depending
on whether they are receiving the painful stimulus, but the group average does
not in itself tell us about the sensitivity or specificity of such a test when applied
to individuals. And the statistical and technical issues are daunting.
In the area of pain, the issue of countermeasures may be the most interest-
ing, particularly in light of the experiments conducted with hypnotized subjects.
Does remembered pain look the same in an fMRI scan as currently experienced
80. Adam J. Kolber, How to Improve Empirical Dessert, 75 Brook. L. Rev. 429 (2009).
81. Greg Miller, Brain Scans of Pain Raise Questions for the Law, 323 Science 195 (2009).
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pain? Does the detailed memory of a kidney stone pain look any different from
the present sensation of low back pain? Can a subject effectively convince himself
that he is feeling pain and so appear to the scanner to be experiencing pain? The
answers to these questions are clear—we do not yet know.
Pain detection also would raise legal questions. Could a plaintiff be forced
to undergo a “pain scan”? If a plaintiff offered a pain scan in evidence, could
the defendant compel the plaintiff to undergo such a scan with the defendant’s
machine and expert? Would it matter if the scan were itself painful or even dan-
gerous? Who would pay for these scans and for the experts to interpret them?
Detecting pain would be a form of neuroscience evidence with straight-
forward and far-reaching applications to the legal system. Whether it can be done,
and, if so, how accurately it can be done, remain to be seen. So does the legal
system’s reaction to this possibility.
VII. Conclusion
Atomic physicist Niels Bohr is credited with having said “It is always hard to
predict things, especially the future.”82 It seems highly likely that the massively
increased understanding of the human brain that neuroscience is providing will
have significant effects on the law and, more specifically, on the courts. Just what
those effects will be cannot be accurately predicted, but we hope that this guide
will provide some useful background to help judges cope with whatever neuro-
science evidence comes their way.
82. This quotation has been attributed to many people, especially Yogi Berra, but Bohr seems to
be the most likely candidate, even though it does not appear in anything he published. See discussion
in Henry T. Greely, Trusted Systems and Medical Records: Lowering Expectations, 52 Stan. L. Rev. 1585,
1591–92 n.9 (2000). One of the authors, however, recently had a conversation with a scientist from
Denmark, who knew the phrase (in Danish) as an old Danish saying and not something original with
Bohr.
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References on Neuroscience
Fundamental Neuroscience (Larry R. Squire et al. eds., 3d ed. 2008).
Eric R. Kandel et al., Principles of Neural Science (4th ed. 2000).
The Cognitive Neurosciences (Michael S. Gazzaniga ed., 4th ed. 2009).
812
