Abstract
The use of neuroscience in criminal law applications is an increasingly discussed topic among legal and psychological scholars. Over the past 5 years, several prominent federal criminal cases have referenced neuroscience studies and made admissibility determinations regarding neuroscience evidence. Despite this growth, the field is exceptionally young, and no one knows for sure how significant of a contribution neuroscience will make to criminal law. This article focuses on three major subfields: (1) neuroscience-based credibility assessment, which seeks to detect lies or knowledge associated with a crime; (2) application of neuroscience to aid in assessments of brain capacity for culpability, especially among adolescents; and (3) neuroscience-based prediction of future recidivism. The article briefly reviews these fields as applied to criminal law and makes recommendations for future research, calling for the increased use of individual-level data and increased realism in laboratory studies.
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Notes
A Federal Court of Appeals eventually affirmed the lower court ruling on similar grounds [26].
That court applied the Frye test, though its ruling was not ultimately related to general acceptance.
Once error rates are identified based on studies with a high level of external validity, it is not clear what rate of error would be considered low enough to allow for admissibility. Some legal scholars have noted that neuroscience-based credibility assessment techniques appear to have a relatively low rate of error compared to forensic evidence (which is often admitted in court despite potentially high error rates) and may even have a low error rate compared to jurors, who may have difficulty making accurate determinations of the veracity of witnesses’ statements [42–44]; for contrasting view, see [45]. More research is necessary before these comparisons can be made with any confidence.
Though it is clear that neuroscience-based credibility assessment tests face significant hurdles before they can be admitted in court, those tests could still serve as an investigatory purpose before admissibility concerns have been addressed [56]. I note several studies attempting to pursue such uses of the CIT below.
Note that some of these studies use more traditional physiological measures, such as skin conductance, rather than P300 as the dependent measure.
It is worth noting that Graham and Miller provide a bright-line age cutoff of 18 years old only under which leniency is given in sentencing. Of course, the research does not support this specific outcome; development does not cease at exactly 18 years old and progresses slowly over time, which would support a more flexible rule [76]. Though blunt, such a strict cutoff has one great merit: it is easily administrable, something courts often seek in their rulings.
For example, the same reasoning could be used to argue in favor of reduced sentences for psychopaths as diagnosed through neurological evidence [78].
In addition to this, other rationales were central to the court’s conclusions in Roper, Graham, and Miller, such as the national consensus regarding death and life-without-parole sentences among the states.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Shen FX. The law and neuroscience bibliography: navigating the emerging field of neurolaw. Int J Leg Inf. 2010;38:352–99.
Graham v. Florida, 560 U.S. 48 (2010). This United States Supreme Court decision held that juvenile offenders cannot be sentenced to life imprisonment without parole for non-homicide offenses—a holding that was later extended to homicide offenses in Miller v. Alabama. The case is notable because it relied in part on neuroscience evidence in reaching the conclusion that minors are less culpable of serious crimes than adults due to their less developed ability to control their actions.
United States v. Semrau, No. 07–10074 Ml/P, 2010 WL 6845092 (W.D. Tenn. May 31, 2010). The first federal case to decide the admissibility of neuroscience-based credibility assessment evidence. The court rejected the evidence primarily because of a lack of real-world field testing of a neuroscience-based lie detection test offered by Cephos Corporation.
Jones OD. Seven ways neuroscience aids law. In Battro A, Deheane S, & Singer W, (Eds.), Neurosciences and the human person: new perspectives on human activities. 2013.
Jones OD, Goldsmith TH. Law and behavioral biology. C Law Rev. 2005;105:405–502.
American Law Institute, Model penal code, rule 2.02.
Shen FX, Hoffman MB, Jones OD, Greene JD. Sorting guilty minds. N Y Univ Law Rev. 2011;86:1306–60.
.Jones OD, Wagner AD, Faigman DL, Raichle ME. Neuroscientists in court. Nat Rev Neurosci. 2013;14(10):730–6.
Jones OD, Marois R, Farah MJ, Greely HT. Law and neuroscience. J Neurosci. 2013;33(45):17624–30. Provides a comprehensive review of the potential applications of neuroscience to the law.
Jones OD, Shen FX. Law and neuroscience in the United States. In: International neurolaw. Berlin: Springer; 2012. p. 349–80.
Levett LM, Kovera MB. The effectiveness of opposing expert witnesses for educating jurors about unreliable expert evidence. Law Hum Behav. 2008;32(4):363–74.
McAuliff BD, Kovera MB, Nunez G. Can jurors recognize missing control groups, confounds, and experimenter bias in psychological science? Law Hum Behav. 2009;33(3):247–57.
Meixner JB, Diamond SS. The hidden Daubert factor: how judges use error rates in assessing scientific evidence. Wisconsin Law Review. 2014.
Brown T, Murphy E. Through a scanner darkly: functional neuroimaging as evidence of a criminal defendant’s past mental states. Stanf Law Rev. 2009;62:1119–208.
Faigman DL, Monahan J, Slobogin C. Group to individual (G2i) inference in scientific expert testimony. Univ Chicago Law Rev. 2014;81:417–80. This recent and important paper is the most comprehensive discussion yet of the difficulty of interpreting the impact of group data (the common level of analysis in science) on individual cases in court.
Kozel FA, Johnson KA, Mu Q, Grenesko EL, Laken SJ, George MS. Detecting deception using functional magnetic resonance imaging. Biol Psychiatry. 2005;58(8):605–13.
Langleben DD, Loughead JW, Bilker WB, Ruparel K, Childress AR, Busch SI, et al. Telling truth from lie in individual subjects with fast event‐related fMRI. Hum Brain Mapp. 2005;26(4):262–72. A notable early fMRI-based lie detection study, and one of only two that reports data for individual subjects and classifies based on single lie and truth events. The test reached 78% accuracy.
Rosenfeld JP, Cantwell B, Nasman VT, Wojdac V, Ivanov S, Mazzeri L. A modified, event-related potential-based guilty knowledge test. Int J Neurosci. 1988;42:157–61. This paper was one of the first to conduct a mock crime test of the P300-based concealed information test. 10 subjects stole a single item out of a box and were later tested for recognition of the item that they stole among a list of other items.
Farwell LA, Donchin E. The truth will out: interrogative polygraphy (“lie detection”) with event-related potentials. J Psychophysiol. 1991;28:531–47.
Meixner JB. Liar, liar, jury’s the trier? The future of neuroscience-based credibility assessment in the court. Northwest Univ Law Rev. 2012;106:1451–88. This article provides a comprehensive analysis of the potential admissibility of neuroscience-based credibility assessment, discussing the recent literature in the area as well as the relevant federal caselaw that compels the conclusion that such evidence will remain inadmissible until realistic field testing is conducted. The article also discusses caselaw that blocks credibility assessment evidence for its tendency to usurp the role of the jury.
Iacono WG. The detection of deception. In: Cacioppo J et al., editors. Handbook of psychophysiology. Cambridge: Cambridge University Press; 2000. p. 688.
Meixner JB, Rosenfeld JP. A mock terrorism application of the P300-based concealed information test. Psychophysiology. 2011;48:149–54.
Ganis G, Rosenfeld JP, Meixner J, Kievit RA, Schendan HE. Lying in the scanner: covert countermeasures disrupt deception detection by functional magnetic resonance imaging. Neuroimage. 2011;55(1):312–9.
Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993). This case provides the primary framework by which scientific expert evidence is assessed for its admissibility in federal and most state courts.
Frye v. United States, 293 F. 1013 (D.C. Cir. 1923).
United States v. Semrau, 693 F.3d 510 (6th Cir. 2012)
Wilson v. Corestaff Services L.P., 900 N.Y.S.2d 639 (Sup. Ct. 2010).
Smith v. State, 32 A.3d 59) (Md. 2011) (trial court ruling).
Rosenfeld JP. Brain fingerprinting: a critical analysis. Sci Rev Ment Health Pract. 2005;4(1):20–37.
Meijer EH, Ben-Shakhar G, Verschuere B, Donchin E. A comment on Farwell (2012): brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials. Cogn Neurodyn. 2013;7(2):155–8.
Harrington v. State, No. PCCV 073247 (Iowa Dist. Ct. Mar. 5, 2001).
Slaughter v. State, 105 P.3d 832 (Okla. Crim. App. 2005).
Greely HT. Premarket approval regulation for lie detections: an idea whose time may be coming. Am J Bioeth. 2005;5(2):50–2.
Greely HT, Illes J. Neuroscience-based lie detection: the urgent need for regulation. Am J Law Med. 2007;33:377–432.
Ben-Shakhar G, Dolev K. Psychophysiological detection through the guilty knowledge technique: effects of mental countermeasures. J Appl Psychol. 1996;81:273–81.
Carmel D, Dayan E, Naveh A, Raveh O, Ben-Shakhar G. Estimating the validity of the guilty knowledge test from simulated experiments: the external validity of mock crime studies. J Exp Psychol Appl. 2003;9:261–9.
Lui M, Rosenfeld JP. Detection of deception about multiple, concealed, mock crime items, based on a spatialtemporal analysis of ERP amplitude and scalp distribution. Psychophysiology. 2008;45:721–30.
Farah MJ, Hutchinson JB, Phelps EA, Wagner AD. Functional MRI-based lie detection: scientific and societal challenges. Nat Rev Neurosci. 2014;15(2):123–31.
Shen FX, Jones OD. Brain scans as evidence: truths, proofs, lies, and lessons. Mercer Law Rev. 2011;62:861–83.
Langleben DD, Moriarty JC. Using brain imaging for lie detection: where science, law, and policy collide. Psychol Public Policy Law. 2013;19(2):222–34.
Adelsheim C. Functional magnetic resonance detection of deception: great as fundamental research, inadequate as substantive evidence. Mercer Law Rev. 2011;62:885–908.
Shauer F. Can bad science be good evidence? Neuroscience, lie detection, and beyond. Cornell Law Rev. 2009;95:1191–219.
Teitcher A. Weaving functional brain imaging into the tapestry of evidence: a case for functional neuroimaging in federal criminal courts. Fordham Law Rev. 2011;80:355–401.
Kittay L. Admissibility of fMRI lie detection: the cultural bias against mind reading devices. Brooklyn Law Rev. 2006;72:1351–99.
Moriarty JC. Visions of deception: neuroimages and the search for truth. Akron Law Rev. 2009;42:739–61.
Hakun JG et al. fMRI investigation of the cognitive structure of the concealed information test. Neurocase. 2008;14:59–67.
Kozel FA et al. A pilot study of functional magnetic resonance imaging brain correlates of deception in healthy young men. J Neuropsychiatry Clin Neurosci. 2004;16:295–305.
Kozel FA, Padgett TM, George MS. A replication study of the neural correlates of deception. Behav Neurosci. 2004;118:852–6.
Davatzikos C et al. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage. 2005;28:663–8.
Meixner JB, Rosenfeld JP. Detecting knowledge of incidentally acquired, real-world memories using a P300-based concealed-information test. Psychol Sci. 2014;25(11):1994–2005. This study is the first P300-based CIT to detect recognition of incidentally acquired details from normal everyday life. Participants wore a small video camera during their daily activities and were then tested for knowledge of details encountered throughout the day. Experimenters were able to perfectly discriminate between knowledgeable subjects who were shown relevant details and nonknowledgeable subject who were shown only irrelevant details.
Law JR. Cherry-picking memories: why neuroimaging-based lie detection requires a new framework for the admissibility of scientific evidence under FRE 702 and Daubert. Yale J Law Technol. 2012;14:1–61.
Gaudet LM. Brain fingerprinting, scientific evidence, and Daubert: a cautionary lesson from India. Jurimetrics. 2010;51:293–318.
United States v. Scheffer, 523 U.S. 303 (1998).
Seaman J. Black boxes: fMRI lie detection and the role of the jury. Akron Law Rev. 2009;42:931–9.
Chandler J. Reading the judicial mind: predicting the courts’ reaction to the use of neuroscientific evidence for lie detection. Dalhous Law J. 2010;33:85–115.
Sahito FH. Interrogational neuroimaging: the missing element in counter-terrorism. Int J Innov Appl Stud. 2013;3(3):592–607.
United States Constitution, Fourth Amendment.
United States Constitution, Fifth Amendment.
Pustilnik AC. Neurotechnologies at the intersection of criminal procedure and constitutional law. In: Richardson S, Parry J, editors. The constitution and the future of criminal law. Cambridge: Cambridge University Press; 2013.
Farahany NA. Incriminating thoughts. Stanf Law Rev. 2012;64:351–408. This article examines what qualifies as a “testimonial statement” that is protected by the Fifth Amendment to the United States Constitution. The article argues that neuroscience- based lie detection evidence provides an example of the new type of evidence that does not fit within the current framework distinguishing between physical and testimonial statements, and argues that the doctrine must be modified to deal with new types of evidence.
Farahany NA. Searching secrets. Univ Pennsylvania Law Rev. 2012;160:1239–308.
Shen FX. Mind, body, and the criminal law. Minn Law Rev. 2012;97:2036–173.
Boundy M. The government can read your mind: can the constitution stop it? Hast Law J. 2011;63:1627–43.
Fox D. The right to silence as protecting mental control. Akron Law Rev. 2009;42:763–801.
Hurd AJ. Reaching past fingertips with forensic neuroimaging–non-“testimonial” evidence exceeding the fifth amendment’s grasp. Loyola Law Rev. 2012;58:213–47.
Wagner A, et al. Presentation at cognitive neuroscience society annual meeting (on file with author). 2013.
Meijer EH, Bente G, Ben-Shakhar G, Schumacher A. Detecting concealed information from groups using a dynamic questioning approach: simultaneous skin conductance measurement and immediate feedback. Front Psychol. 2013;4(68):1–6.
Breska A, Zaidenberg D, Gronau N, Ben-Shakhar G. Psychophysiological detection of concealed information shared by groups: an empirical study of the searching CIT. J Exp Psychol Appl. 2014;20(2):136–46.
Nahari G, Ben‐Shakhar G. Psychophysiological and behavioral measures for detecting concealed information: the role of memory for crime details. Psychophysiology. 2011;48(6):733–44.
Hu X, Rosenfeld JP, Bodenhausen GV. Combating automatic autobiographical associations: the effect of instruction and training in strategically concealing information in the autobiographical implicit association test. Psychol Sci. 2012;23(10):1079–85.
Bonnie RJ, Scott ES. The teenage brain: adolescent brain research and the law. Curr Dir Psychol Sci. 2013;22(2):158–61.
Casey BJ, Jones RM, Hare TA. The adolescent brain. Ann N Y Acad Sci. 2008;1124(1):111–26.
Somerville LH, Casey BJ. Developmental neurobiology of cognitive control and motivational systems. Curr Opin Neurobiol. 2010;20(2):236–41.
Graham v. Florida, 560 U.S. 48 (2010).
Miller v. Alabama, 132 S. Ct. 2455 (2012).
Buss E. What the law should (and should not) learn from child development research. Hofstra Law Rev. 2009;38:13–66.
Maroney TA. Adolescent brain science after Graham v. Florida. Notre Dame Law Rev. 2011;86:765–93.
Phillips KD. Empathy for psychopaths: using fMRI brain scans to plea for leniency in death penalty cases. Univ Ala Law Psychol Rev. 2013;27:1–47.
Baird AA, Barrow CL, Richard MK. Juvenile neuroLaw: when it’s good it is very good indeed, and ahen it’s bad it’s horrid. J Health Care Law Policy. 2012;15:15–35.
Roper v. Simmons, 543 U.S. 551 (2005).
Morse SJ. Brain overclaim redux. Law Inequal. 2013;31:509–34.
Steinberg L. The influence of neuroscience on US supreme court decisions about adolescents’ criminal culpability. Nat Rev Neurosci. 2013;14(7):513–8.
Somerville LH, Hare T, Casey BJ. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents. J Cogn Neurosci. 2011;23(9):2123–34.
Casey BJ et al. Behavioral and neural correlates of delay of gratification 40 years later. Proc Natl Acad Sci U S A. 2011;108:14998–5003. This important longitudinal study examined self control of individual who had previously been tested on a delay-of-gratification task 40 years prior. Subjects performed a do/no-go task using both neutral and emotional cues, and subjects who demonstrated low self control 40 years earlier were still unable to suppress habitual responses to emotional cues (but not to neutral cues).
Casey BJ, Caudle K. The teenage brain: self control. Curr Dir Psychol Sci. 2013;22(2):82–7.
Maroney TA. The false promise of adolescent brain science in juvenile justice. Notre Dame Law Rev. 2010;85:89–176.
Schleim S. Brains in context in the neurolaw debate: the examples of free will and “dangerous” brains. Int J Law Psychiatry. 2012;35(2):104–11.
Wasserman D, Johnston J. Seeing responsibility: can neuroimaging teach us anything about moral and legal Responsibility? Hastings Cent Rep. 2014;44(s2):S37–49.
Farahany NA. A neurological foundation for freedom. Stanf Technol Law Rev. 2011;2011:11–53.
Morse SJ. The non‐problem of free will in forensic psychiatry and psychology. Behav Sci Law. 2007;25(2):203–20.
Vincent NA. Neuroimaging and responsibility assessments. Neuroethics. 2011;4(1):35–49.
Nadelhoffer T, Nahmias E. Neuroscience, free will, folk intuitions, and the criminal law. Thurgood Marshall Law Rev. 2010;36:157–76.
Goodenough OR, Tucker M. Law and cognitive neuroscience. Annu Rev Law Soc Sci. 2010;6:61–92.
Harris GT, Rice ME, Quinsey VL. Violent recidivism of mentally disordered offenders the development of a statistical prediction instrument. Crim Justice Behav. 1993;20(4):315–35.
Aharoni E, Vincent GM, Harenski CL, Calhoun VD, Sinnott-Armstrong W, Gazzaniga MS, et al. Neuroprediction of future rearrest. Proc Natl Acad Sci. 2013;110:6223–8. Using a go/no-go task, this paper replicated prior findings that lower levels of anterior cingulate cortex (ACC; an area association with inhibition and regulation of behavior) activity were associated with increases in commission errors in the task, but also found that such increased brain activity predicted rearrest. Specifically, they found that the odds recidivism of an offender with low anterior cingulate activity were roughly twice that of subjects with high anterior cingulate activity. Importantly, the data provided an incremental increase in predictive potential over behavioral measures alone.
Glenn AL, Raine A. Neurocriminology: implications for the punishment, prediction and prevention of criminal behaviour. Nat Rev Neurosci. 2013;15:54–63. An excellent review regarding the biological basis for criminal behavior and biological predictions of recidivism.
Skeem J, Monahan J. Current directions in violence risk assessment. Curr Dir Psychol Sci. 2011;20:38–40.
Monahan J. The inclusion of biological risk factors in violence risk assessments. In: Singh I, Sinnott-Armstrong WP, Savulescu J, editors. Bioprediction, biomarkers, and bad behavior. Oxford: Oxford University Press; 2014. p. 57–76.
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Meixner, J.B. Applications of Neuroscience in Criminal Law: Legal and Methodological Issues. Curr Neurol Neurosci Rep 15, 513 (2015). https://doi.org/10.1007/s11910-014-0513-1
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DOI: https://doi.org/10.1007/s11910-014-0513-1