Skip to main content

Advertisement

SpringerLink
Log in

Neuroprediction, Violence, and the Law: Setting the Stage

  • Review Paper
  • Published:
Neuroethics Aims and scope Submit manuscript

Abstract

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In “Violence Risk Assessment and the Law”, we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing (“Violence Risk Assessment and Capital Sentencing”), civil commitment hearings (“Violence Risk Assessment and Civil Commitment”), and “sexual predator” statutes (“Violence Risk Assessment and Sexual Predator Statutes”). In “Clinical vs. Actuarial Violence Risk Assessment”, we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In “The Neural Correlates of Psychopathy”, we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection (“Cutting-Edge Data Collection: Genetically Informed Neuroimaging”) and data analysis (“Cutting-Edge Data Analysis: Pattern Classification”) that we believe will play an important role when it comes to future neuroscientific research on violence. In “The Potential Promise of Neuroprediction”, we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in “The Potential Perils of Neuroprediction”, we explore some potential evidentiary (“Evidentiary Issues”), constitutional (“Constitutional Issues”), and moral (“Moral Issues”) issues that may arise in the context of the neuroprediction of violence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. For the purposes of this essay, we are following Megargee [3] in using the terms “violent behavior” and “violence” to apply to acts such as “homicide, mayhem, aggravated assault, forcible rape, battery, robbery, arson, extortion” and other criminal acts that cause physical injuries.

  2. See [4] and [5] for attempts to identify all of the wide variety of legal contexts that depend, at least in part, on predictions of future violent behavior.

  3. For detailed discussion concerning the role that predictions of future dangerousness play in capital sentencing, see [6] and [7].

  4. People v. Murtishaw, 631 P.2d (Cal. 1981).

  5. Barefoot v. Estelle, 463 U.S. 880 (1983).

  6. It is worth pointing out that Dr. James P. Grigson is a particularly controversial individual. During his career, he appeared in at least 150 capital trials on behalf of the state. Moreover, his clinical predictions of future dangerousness were used in the trials of nearly one-third of all Texas death row inmates. For more on Dr. Grigson’s controversial role in Texas death penalty cases, see [8].

  7. Barefoot 463 U.S. at 918–19 (Blackmun, J., dissenting).

  8. Id. at 919 (Blackmun, J., dissenting).

  9. Id. (Blackmun, J., dissenting).

  10. Barefoot v. Estelle, 463 U.S. 883 (1983).

  11. According to the Texas statute in question—namely, Tex.Code Crim.Proc., Art. 37.071—jurors are given three threshold questions to answer during the sentencing phase of capital trials. One threshold requires the jury to make a judgment concerning the probability that the defendant would pose a continued threat to society in the future. The other two threshold questions were, (a) whether the defendant killed the victim(s) both knowingly and deliberately; and (b) in the event that the defendant was responding to a provocation by the victim(s), whether the defendant’s response was unreasonable or disproportionate given the nature of the provocation. If the jury unanimously finds that the state has proved each of these three issues beyond a reasonable doubt, the defendant automatically receives the death penalty rather than life in prison.

  12. Jurek v. Texas, 428 U.S. 262 (1976).

  13. Barefoot, 463 U.S. at 899.

  14. Id. at 920 (Blackmun, J., dissenting).

  15. Id. at 906 (fn.7).

  16. Barefoot, 463 U.S. at 897.

  17. Id. at 901.

  18. For recent overviews of the role that predictions of future dangerousness play in capital sentencing, see [9–11].

  19. State civil commitment statutes are compiled at http://www.psychlaws.org/LegalResources/Index.html

  20. Addington v. Texas 441 U.S. 418 (1979).

  21. Addington v. Texas 441 U.S. 421 (1979).

  22. Id.

  23. Id.

  24. Addington v. Texas 441 U.S. 430 (1979).

  25. See, e.g., [13].

  26. Kan. Stat. Ann. § 59-29a02(b) (1994).

  27. Kan. Stat. Ann. §59-29a01 (1994).

  28. Kansas v. Hendricks, 521 U.S. 346 (1997).

  29. Foucha v. Louisiana, 504 U.S. 71 (1992).

  30. Kansas v. Hendricks, supra, at 358.

  31. Kan. Stat. Ann. § 59-29a02(b) (1994).

  32. Kansas v. Hendricks, supra, at 358.

  33. The Court also rejected Hendricks’ claim that the Kansas statute runs afoul of prohibitions against double jeopardy and ex post-fact law making. Because the statute was civil in intent and design rather than criminal, the Court found that Hendricks’ worries on these two fronts were groundless. For present purposes, discussing their arguments on this front would take us too far afield.

  34. Kansas v. Hendricks, supra, at 360.

  35. Given the problems with circularity, it is perhaps unsurprising that the United States Supreme Court had to revisit the statutory scheme in Kansas five years later in Kansas v. Crane, 534 U.S. 407 (2002). In Crane, the Court was supposed to address the issue of how much, if any, volitional impairment was required before a sex offender could be classified as a sexual predator and indefinitely detained. At the end of the day, however, the Court refused to define with any “mathematical precision” what constituted a lack of control.

  36. See, e.g., [14–16].

  37. New Jersey Statutes Annotated Title 30, §4–27.26.

  38. People v. Superior Court (Ghilotti), 27 Cal.4th 888 (2002).

  39. Id.

  40. In re Detention of Brooks, 145 Wash. 2d 275 (2001) (overruled on other grounds by In re Detention of Thorell, 149 Wash. 2d 724).

  41. In this section, the key terms are used with their usual technical meanings: (a) reliability = df “the consistency or stability of a measure from one use to the next”; (b) validity = df “accuracy of measurement—the degree to which as assessment measures what it is supposed to”; (c) incremental validity = df “the amount that validity is improved with the addition of new information”; (d) predictor variables = df “categories consisting of different levels that are presumed to be relevant to what is being predicted”; (e) base rate = df “the proportion of people in some population during a specified time period of time who fall into the criterion category that is to be predicted—e.g., violent recidivism.”

  42. For brevity’s sake, we are going to call this study MacRisk, for short. See [1] for the full details of the MacRisk study.

  43. The patients—all of whom were of white, African-American, or Hispanic ethnicity—were initially institutionalized in facilities in Pittsburgh, Kansas, and Worcester.

  44. For a complete list of the risk factors, see [1] Appendix B.

  45. The overall base rate for violent reoffending for the entire group of patients twenty weeks after discharge was 18.7%. See, [1] for more details.

  46. See [1] for further details concerning the development and validation of COVR.

  47. The entire list of factors can be found in Table 6.3 in [1].

  48. For more on the proper relationship between actuarial and clinical methods, see [26–29].

  49. For a recent meta-analysis of the neuroimaging work that has been done on violence and aggression, see [30].

  50. See, e.g., [31, 32].

  51. See, e.g., [33].

  52. See, e.g., [34, 35].

  53. The majority of violent acts are reactive in nature and perpetrated by impulsive men who are easily aroused and who often satisfy the diagnostic criteria for Antisocial Personality Disorder (ASPD). See, e.g., [36]. But there is an important distinction to be drawn between this kind of impulsive violence that usually occurs “in the heat of the moment” and the much less common and more worrisome kind of premeditated and instrumental violence that is commonly associated with psychopathy.

  54. For more on the development and psychometric properties of the PCL-R, see [31, 37].

  55. While the PCL-R is the most widely used tool for measuring psychopathy—which is why we have chosen to focus on the PCL-R in this paper—other useful tools have been developed. See, e.g., the Self-Report Psychopathy Scale III (SRP-III; [38]); the Psychopathic Personality Inventory (PPI; [39]).

  56. The PCL-R Manual lists the mean score for North American prison samples and for forensic psychiatric samples as 23.6 (SD = 7.9) and 20.6 (SD = 7.8), respectively [31].

  57. Originally, Hare developed a two factor model of psychopathy—see, e.g., [31, 32, 40]—but more recently he has put forward a four factor model that was developed based on research involving nearly 7,000 psychopaths. The Interpersonal/Affective dimensions and the Lifestyle/Antisocial dimensions constitute the original Factor 1 and Factor 2, respectively. For more information concerning the four factor model see [41, 42].

  58. See, e.g., [43–45].

  59. For instance, VRAG [46] and HCr-20 [47] both used PCL-SV scores. PCL:SV was also used as a risk factor by the MacRisk researchers in developing the ICT approach. However, even though Monahan et al. [1] found that the PCL:SV was the strongest predictor of violence, it was not included as one of the risk factors of COVR since the goal of the latter was to enable researchers to make quick decisions concerning future dangerousness in a forensic setting.

  60. For more details, see [48].

  61. It is worth pointing out that while the PCL:SV takes less time to complete than the PCL:R, it still takes a couple of hours to complete.

  62. In a recent study, Edens et al. [49] pit the PCL:SV against the modified 9 item version of VRAG (minus PCL:SV). The ROC analysis revealed that whereas the area under the curve for the modified version of VRAG (minus PCL:SV) was only .58, the variance attributable to the PCL:SV was .75 ([49], p.370).

  63. Through aversive conditioning, subjects learn to associate an unpleasant response—e.g., a mild shock—with an unwanted behavior which is supposed to discourage them from engaging in the behavior in the future.

  64. Passive avoidance involves the inhibition of a previously exhibited response. In passive avoidance, a subject may freeze as soon as the stimulus is presented. In active avoidance, on the other hand, the subject flees when the stimulus is presented.

  65. In differential reward-punishment tasks, sometimes subjects are exposed to both positive and negative reinforcement in response to the behavior under investigation.

  66. See, e.g., [67–70].

  67. For other recent reviews of the MAOA literature, see [72–76].

  68. See, also, [80, 81].

  69. For instance, researchers were recently able to use a “functional connectivity index” to predict individual brain maturity in participants ranging from 7 to 30 years of age with just 5 min of resting-state fMRI data. The resultant “functional maturation curve” accounted for 55% of the sample variance [85].

  70. For the purposes of this paper, we are going to limit our attention to the neuroprediction of violence since discussing the neuroprediction of drug/alcohol relapse would take us too far afield. It is nevertheless worth pointing out that recent studies suggestion the latter is no less promising than the former. See, e.g., [90].

  71. See, e.g., [23, 99–101].

  72. As things presently stand, Kiehl and colleagues are running a pilot study with only 300 follow-up visits. If hypotheses are confirmed, additional funds will be sought to follow up with all of the 1,300 participants who have already consented to participate.

  73. The Frye Test was based on a 1923 decision by the Court of Appeals for the District of Columbia concerning the admissibility of a crude precursor to the polygraph machine. See Frye v. United States, 54 App. D.C. 46, 47, 293 F.1013, 1014 (D.C. Cir. 1923).

  74. Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 at 588-89 (1993).

  75. Id. at 589.

  76. Id. at 593.

  77. Id. at 595.

  78. See, e.g., [102–104].

  79. Flores v. Johnson, 210 F.3d 456 (5th Cir. 2000)

  80. Id. at 464.

  81. Neno v. State, 970 S.W.2d 549 (Tex. Crim. App. 1998).

  82. See, e.g., [105–107].

  83. This is not to suggest that there are not any evidentiary worries about predictions of dangerousness more generally. However, for present purposes, we are less interested in the more general evidentiary worries about predictions of violence and more interested in exploring whether adding neuroscience to the violence risk assessment equation generates any additional concerns.

  84. People v. Murtishaw (1981), 29 Cal. 3d 733, 175 Cal. Rptr. 738, 631 P.2d 446, 469 (1981).

  85. U.S. Const. amend. IV (“The right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated, and no Warrants shall issue, but upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized.”); United States v. Jacobsen, 466 U.S. 109, 113 (1984) (“[Fourth Amendment] protection proscribes only governmental action; it is wholly inapplicable to a search or seizure, even an unreasonable one, effected by a private individual not acting as an agent of the government or with the participation or knowledge of any governmental official.”).

  86. Katz v. United States, 389 U.S. 347, 351 (1967); id. at 360-61 (Harlan, J., concurring).

  87. Kyllo v. United States, 533 U.S. 1, 6-7 (2001) (internal quotation marks omitted).

  88. Schneckloth v. Bustamonte, 412 U.S. 218 (1973).

  89. Hudson v. Palmer, 468 U.S. 517, 526 (1983) (“The proscription against unreasonable searches and seizures under U.S. Const. amend. IV does not apply within the confines of the prison cell.”).

  90. Bell v. Wolfish, 441 U.S. 520, 558-59 (1979). Lower courts are split on whether jailers need reasonable suspicion to conduct body-cavity searches of pretrial detainees or not.

  91. Hudson, 468 U.S. at 527-28. To determine whether a prisoner’s expectation of privacy is reasonable, courts balance society’s interest in prison security against the prisoner’s interest in his own privacy. Id.

  92. Turner v. Safley, 482 U.S. 78, 89 (1987). Relevant factors in the reasonableness calculus are (1) whether the regulation is rationally connected to the legitimate governmental interest advanced to justify it; (2) whether prison inmates retain alternative means of exercising their right; (3) the impact of accommodating the right on guards and other inmates; and (4) whether there are ready alternatives to advance the government’s interest. Beard v. Banks, 548 U.S. 521, 529 (2006).

  93. Thornburgh v. Abbott, 490 U.S. 401, 408 (1988).

  94. See Sheffield v. Trevino, 207 Fed. Appx. 403 (5th Cir. 2006) (DNA sample); United States v. Balon, 384 F.3d 38 (2d Cir. 2004) (prison telephone conversations); Hunt v. Ortiz, 84 Fed. Appx. 34 (10th Cir. 2003) (holding that mandatory HIV testing and disclosure of a prisoner’s result is constitutional; prison’s substantial interest in treating inmates infected with HIV and preventing further transmission outweighed prisoner’s expectation of privacy).

  95. Samson v. California, 547 U.S. 843, 850-51 (2006).

  96. 410 U.S. 1, 10 (1973).

  97. 397 F.3d 587, 590-91 (7th Cir. 2005).

  98. Greenawalt, 397 F.3d at 589.

  99. Schmerber v. California, 384 U.S. 757, 766-67 (1966) (blood sample); Skinner v. Railway Labor Executives’ Ass’n, 489 U.S. 602, 617 (1989) (breathalyzer); Board of Education v. Earls, 536 U.S. 822, 828 (2002) (urine testing).

  100. [109], 869; [110].

  101. Winston v. Lee, 470 U.S. 753, 760 (1985).

  102. U.S. Const. amend. V.

  103. Estelle v. Smith, 451 U.S. 454, 462-63 (1981).

  104. Id. at 465-68.

  105. Id. at 468.

  106. 532 U.S. 782, 795 (2001).

  107. See, e.g., Pope v. United States, 372 F.2d 710, 720-71 (8th Cir. 1967). See also Slobogin et al. [111].

  108. Pennsylvania v. Muniz, 496 U.S. 582, 590-92, 601 (1990).

  109. Rhode Island v. Innis, 446 U.S. 291, 301 (1980).

  110. Muniz, 496 U.S. at 598-99.

  111. See, also, [113, 114].

References

  1. Monahan, J., H. Steadman, E. Silver, P.S. Applebaum, A. Clark-Robbins, E.P. Mulvey, L. Roth, T. Grisso, and S. Banks. 2001. Rethinking risk assessment: The MacArthur study of mental disorder and violence. Oxford: Oxford University Press.

    Google Scholar 

  2. American Psychological Association. 1974. Report of the task force on the role of psychology in the criminal justice system. American Psychologist 33: 1099–1113.

    Google Scholar 

  3. Megargee, E. 1976. The prediction of dangerous behavior. Criminal Justice and Behavior 3: 3–21.

    Article  Google Scholar 

  4. Shah, S. 1995. Dangerousness: A paradigm for exploring some issues in law and psychology. American Psychologist 33: 224–238.

    Article  Google Scholar 

  5. Slobogin, C. 1996. Dangerousness as a criterion in the criminal process. In Sales, B. & Shuman, D. (Eds.) Law, Mental Health, and Mental Disorder, 360–363.

  6. Claussen-Schulz, A., M. Pearce, and R. Schopp. 2004. Dangerousness, risk assessment, and capital sentencing. Psychology, Public Policy, and Law 10(4): 471–494.

    Article  Google Scholar 

  7. Blume, J.H., S.P. Garvey, and S.L. Johnson. 2001. Future dangerousness in capital cases: Always “at issue”. Cornell Law Rev 86: 397–410.

    Google Scholar 

  8. Kirchmeier, J.L. 1998. Aggravating and mitigating factors: The paradox of today’s arbitrary and mandatory capital punishment scheme. William & Mary Bill of Rights Journal 6(2): 345–459.

    Google Scholar 

  9. Cunningham, M.D., and T.J. Reidy. 2002. Violence risk assessment at federal capital sentencing: Individualization, generalization, relevance, and scientific standards. Criminal Justice and Behavior 29: 512–537.

    Article  Google Scholar 

  10. Edens, J.F. 2001. Misuses of the hare psychopathy checklist-revised in court: Two case examples. Journal of Interpersonal Violence 16: 1082–1093.

    Article  Google Scholar 

  11. Reid, W. 2001. Psychiatry and the death penalty. Journal of Psychiatric Practice 7: 216–219.

    Article  Google Scholar 

  12. Melton, G., J. Petrila, N. Poythress, and C. Slobogin. 1997. Psychological evaluations for the courts: A handbook for mental health professionals and lawyers, 2nd ed. New York: Guilford.

    Google Scholar 

  13. Slobogin, C. 1984. Dangerousness and expertise. University of Pennsylvania Law Review 133(1): 97–174.

    Article  Google Scholar 

  14. Fitch, W.L., and R.J. Ortega. 2000. Law and the confinement of psychopaths. Behavioral Science and the Law 18: 663–678.

    Article  Google Scholar 

  15. Morse, S.J. 1998. Fear of danger, flight from culpability. Psychology, Public Policy, and the Law 4(1/2): 250–267.

    Article  Google Scholar 

  16. Morse, S.J. 2004. Preventive confinement of dangerous offenders. Journal of Medicine and Ethics 32: 56–70.

    Article  Google Scholar 

  17. Ennis, G., and R. Litwack. 1974. Psychiatry and the presumption of expertise: Flipping coins in the courtroom. California Law Review 62: 693–718.

    Article  Google Scholar 

  18. Meehl, P. 1954. Clinical versus statistical prediction: A theoretical analysis and review of the evidence. Minneapolis: University of Minnesota Press.

    Book  Google Scholar 

  19. Monahan, J. 1995. The clinical prediction of violent behavior. Northvale, NJ: Jason Aronson Inc. [Originally published in 1981 as Predicting violent behavior: An assessment of clinical techniques by Sage Publishing]

  20. Dix, G. 1975. Determining the continued dangerousness of psychologically abnormal sex offenders. Journal of Psychiatry and the Law 3: 327–344.

    Google Scholar 

  21. Krauss, D.A., and B.D. Sales. 2001. The effects of clinical and scientific expert testimony on juror decision making in capital sentencing. Psychology, Public Policy, and Law 7(2): 267–310.

    Article  Google Scholar 

  22. Monahan, J., and E. Silver. 2003. Judicial decision thresholds for violence risk management. International Journal of Forensic Mental Health 2: 1–6.

    Article  Google Scholar 

  23. Monahan, J., H.J. Steadman, P.S. Appelbaum, T. Grisso, E.P. Mulvey, L.H. Roth, et al. 2006. The classification of violence risk. Behavioral Sciences & the Law 24(6): 721–730.

    Article  Google Scholar 

  24. Krauss, D.A., and D.H. Lee. 2003. Deliberating on dangerousness and death: Jurors’ ability to differentiate between expert actuarial and expert clinical predictions of dangerousness. International Journal of Law and Psychiatry 26: 113–137.

    Article  Google Scholar 

  25. Redding, R.E., M.Y. Floyd, and G.L. Hawk. 2001. What judges and lawyers think about the testimony of mental health experts: A survey of the courts and bar. Behavioral Sciences and the Law 19: 583–594.

    Article  Google Scholar 

  26. Andrews, D.A., J. Bonta, and J.S. Wormwith. 2006. The recent and past and near future of risk and/or need assessment. Crime and Delinquency 52: 7–27.

    Article  Google Scholar 

  27. Buchanan, A. 1999. Risk and dangerousness. Psychological Medicine 29: 465–473.

    Article  Google Scholar 

  28. Dolan, M., and M. Doyle. 2000. Violence risk prediction: Clinical and actuarial measures and the role of the Psychopathy Checklist. British Journal of Psychiatry 177: 303–311.

    Article  Google Scholar 

  29. Otto, R.K. 2000. Assessing and managing violence risk in outpatient settings. Journal of Clinical Psychology 56: 1239–1262.

    Article  Google Scholar 

  30. Yang, Y., and A. Raine. 2009. Prefrontal structural and functional brain imaging findings in antisocial, violent, and psychopathic individuals: A meta-analysis. Psychiatry Research 174(2): 81–88.

    Article  Google Scholar 

  31. Hare, R. 1991. The hare psychopathy checklist—revised. Toronto: Multi-Health Systems.

    Google Scholar 

  32. Harpur, T., R. Hakistan, and R. Hare. 1988. Factor structure of the psychopathy checklist. Journal of Consulting and Clinical Psychology 56: 741–747.

    Article  Google Scholar 

  33. Hart, S., and R. Dempster. 1997. Impulsivity and psychopathy. In Impulsivity: Theory, assessment and treatment, ed. C. Webster and M. Jackson, 212–232. New York: Guilford.

    Google Scholar 

  34. Hare, R.D., and J.W. Jutai. 1983. Criminal history of the male psychopath: Some preliminary data. In Prospective studies of crime and delinquency, ed. K.T. Van Dusen and S.A. Mednick, 225–236. Boston: Kluwer-Nijhoff.

    Chapter  Google Scholar 

  35. Hare, R.D., and L.M. McPherson. 1984. Violent and aggressive behavior by criminal psychopaths. International Journal of Law and Psychiatry 7: 35–50.

    Article  Google Scholar 

  36. Coccaro, E.F. 1998. Impulsive aggression: A behavior in search of clinical definition. Harvard Review of Psychiatry 5: 336–339.

    Article  Google Scholar 

  37. Cooke, D.J., and C. Michie. 1997. An item response theory evaluation of Hare’s Psychopathy Checklist. Psychological Assessment 9: 2–13.

    Article  Google Scholar 

  38. Viding, E., A.P. Jones, P. Frick, T.E. Moffitt, and R. Plomin. 2008. Genetic and phenotypic investigation to early risk factors for conduct problems in children with and without psychopathic tendencies. Developmental Science 11: 17–22.

    Google Scholar 

  39. Lilienfeld, S.O., and B.P. Andrews. 1996. Development and preliminary validation of a self-report measure of psychopathic personality traits in noncriminal populations. Journal of Personality Assessment 66: 488–524.

    Article  Google Scholar 

  40. Hare, R.D., T. Harpur, R. Hakistan, A. Forth, S. Hart, and J. Newman. 1990. The revised psychopathy checklist: Reliability and factor structure. Psychological Assessment: A Journal of Consulting and Clinical Psychology 2: 338–341.

    Google Scholar 

  41. Hare, R.D., and C.S. Neumann. 2006. The PCL-R assessment of psychopathy: Development, structural properties, and new directions. In Handbook of psychopathy, ed. C.J. Patrick, 58–90. New York: Guilford.

    Google Scholar 

  42. Hare, R.D., and C.S. Neumann. 2008. Psychopathy as a clinical and empirical construct. Annual Review of Clinical Psychology 4: 217–46.

    Article  Google Scholar 

  43. Harris, G., M. Rice, and C. Cormier. 1991. Psychopathy and violent recidivism. Law and Human Behavior 15: 625–637.

    Article  Google Scholar 

  44. Heilbrun, K., S.D. Hart, R.D. Hare, D. Gustafson, C. Nunez, and A.J. White. 1998. Inpatient and postdischarge aggression in mentally disordered offenders: The role of psychopathy. Journal of Interpersonal Violence 13: 514–527.

    Article  Google Scholar 

  45. Rice, M.E., G.T. Harris, and V.L. Quinsey. 1990. A follow-up of rapists assessed in a maximum security psychiatric facility. Journal of International Violence 5: 435–448.

    Article  Google Scholar 

  46. Quinsey, V.L., G.E. Harris, M.E. Rice, and C. Cormier. 1998. Violent offenders: Appraising and managing risk. Washington: American Psychological Association.

    Book  Google Scholar 

  47. Webster, C.D., K.S. Douglas, D. Eaves, and S.D. Hart. 1997. HCR-20: Assessing the risk for violence (Version 2). Vancouver: Mental Health, Law, and Policy Institute, Simon Fraser University.

    Google Scholar 

  48. Cooke, D.J., C. Michie, S.D. Hart, and R.D. Hare. 1999. Evaluation of the screening version of the hare psychopathy checklist—revised (PLC:SV): An item response theory analysis. Psychological Assessment 11: 3–13.

    Article  Google Scholar 

  49. Edens, J.F., J.L. Skeem, and K.S. Douglas. 2006. Incremental validity analysis of the violence risk appraisal guide and the psychopathy check-list: Screening version in a civil psychiatric sample. Assessment 13(3): 368–374.

    Article  Google Scholar 

  50. Leistico, A.M., R.T. Salekin, J. DeCoster, and R. Rogers. 2008. A large-scale meta-analysis relating the hare measures of psychopathy to antisocial conduct. Law & Human Behavior 32: 28–45.

    Article  Google Scholar 

  51. Veit, R., H. Flor, M. Erb, C. Hermann, M. Lotze, W. Grodd, et al. 2002. Brain circuits involved in emotional learning in antisocial behavior and social phobia in humans. Neuroscience Letters 328(3): 233–236.

    Article  Google Scholar 

  52. Blair, K.S., A. Leonard, and R.J.R. Blair. 2006. Impaired decision making on the basis of both reward and punishment information in individuals with psychopathy. Personality and Individual Differences 41: 155–165.

    Article  Google Scholar 

  53. Blair, R.J.R., D.G.V. Mithcell, A. Leonard, S. Budhani, K.S. Peschardt, and C. Newman. 2004. Passive avoidance learning in individuals with psychopathy; modulation by reward but not punishment. Personality and Individual Differences 37: 1179–1192.

    Article  Google Scholar 

  54. Kiehl, K.A., A.M. Smith, R.D. Hare, A. Mendrek, B.B. Forster, J. Brink, J. Brink, and P.F. Liddle. 2001. Limbic abnormalities inaffective processing by criminal psychopaths as revealed by functional magnetic resonance imaging. Biological Psychiatry 50(9): 677–684.

    Article  Google Scholar 

  55. Kiehl, K.A., A.M. Smith, A. Mendrek, B.B. Forster, R.D. Hare, and P.F. Liddle. 2004. Temporal lobe abnormalities in semantic processing by criminal psychopaths as revealed by functional magnetic resonance imaging. Psychiatry Research 130(3): 27–42.

    Article  Google Scholar 

  56. Harenski, C.L., Harenski, K.A., Shane, M.S., & Kiehl, K. 2010. Aberrant neural processing of moral violations in criminal psychopaths. Journal of Abnormal Psychology, (in press)

  57. Blair, R.J.R., D.G.V. Mitchell, and K.S. Blair. 2005. The psychopath: Emotion and the brain. Oxford: Blackwell.

    Google Scholar 

  58. Rogers, R.D., M. Lancaster, J. Wakeley, and Z. Bhagwager. 2004. Effects of beta-adrenoceptor blockade on components of human decision making. Psychopharmacology 172(2): 157–164.

    Article  Google Scholar 

  59. Strange, B.A., and R.J. Dolan. 2004. Beta-adrenergic modulation of emotional memory-evoked human amygdala and hippocampul responses. Proceedings of the National Academy of the Sciences of the United States of America 101(31): 11454–11458.

    Article  Google Scholar 

  60. Cima, M., T. Smeets, and M. Jelicic. 2008. Self-reported trauma, cortisol levels, and aggression in psychopathic and non-psychopathic prison inmates. Biological Psychology 78(1): 75–86.

    Article  Google Scholar 

  61. Blonigen, D.M., B.M. Hicks, R.F. Krueger, C.J. Patrick, and W.G. Iacono. 2005. Psychopathic personality traits: Heritability and genetic overlap with internalizing and externalizing psychopathology. Psychological Medicine 35(5): 637–648.

    Article  Google Scholar 

  62. Viding, E., A.P. Jones, P. Frick, T.E. Moffitt, and R. Plomin. 2008. Genetic and phenotypic investigation to early risk factors for conduct problems in children with and without psychopathic tendencies. Developmental Science 11: 17–22.

    Article  Google Scholar 

  63. Larsson, H., C. Tuvblad, F.V. Rijsdijk, H. Andershed, M. Grann, and P. Lichtenstein. 2007. A common genetic factor explains the association between psychopathic personality and antisocial behavior. Psychological Medicine 37: 15–26.

    Article  Google Scholar 

  64. Cases, O., I. Seif, J. Grimsby, P. Gaspar, K. Chen, S. Pournin, et al. 1995. Aggressive behavior and altered amounts of brain serotonin and norepinephrine in mice lacking MAOA. Science 268(5218): 1763–1766.

    Article  Google Scholar 

  65. Brunner, H.G., M. Nelen, X.O. Breakefield, H.H. Ropers, and B.A. Van Oost. 1993. Abnormal behavior associated with a point mutation in the structural gene for monoamine oxidase A. Science 262(5133): 578–580.

    Article  Google Scholar 

  66. Sabol, S.Z., S. Hu, and D. Hamer. 1998. A functional polymorphism in the monoamine oxidase A gene promoter. Human Genetics 103: 273–279.

    Article  Google Scholar 

  67. Caspi, A., J. McClay, T.E. Moffitt, J. Mill, J. Martin, I.W. Craig, et al. 2002. Role of genotype in the cycle of violence in maltreated children. Science 297: 851–854.

    Article  Google Scholar 

  68. Ducci, F., T.K. Newman, S. Funt, G.L. Brown, M. Virkkunen, and D. Goldman. 2008. Interaction between a functional MAOA locus and childhood sexual abuse predicts alcoholism and antisocial personality disorder in adult women. Molecular Psychiatry 13: 334–347.

    Article  Google Scholar 

  69. Foley, D.L., L.J. Eaves, B. Wormley, J.L. Silberg, H.H. Maes, J. Kuhn, et al. 2004. Childhood adversity, monoamine oxidase a genotype, and risk for conduct disorder. Archives of General Psychiatry 61(7): 738–744.

    Article  Google Scholar 

  70. Widom, C.S., and L.M. Brzustowicz. 2006. MAOA and the “Cycle of Violence”: Childhood abuse and neglect, MAOA genotype, and risk for violent and antisocial behavior. Biological Psychiatry 60: 684–689.

    Article  Google Scholar 

  71. Kim-Cohen, J., A. Caspi, A. Taylor, B. Williams, R. Newcombe, I.W. Craig, et al. 2006. MAOA, maltreatment, and gene–environment interaction predicting children’s mental health: New evidence and a meta-analysis. Molecular Psychiatry 11: 903–913.

    Article  Google Scholar 

  72. Applebaum, P. 2005. Behavioral genetics and the punishment of crime. Psychiatric Services 56(1): 25–27.

    Article  Google Scholar 

  73. Buckholtz, J.W., and A. Meyer-Lindenberg. 2008. MAOA and the neurogenetic architecture of human aggression. Trends in Neuroscience 31(3): 120–129.

    Article  Google Scholar 

  74. Moffitt, T. 2005. The new look of behavioral genetics in developmental psychopathology: Gene-environment interplay in antisocial behaviors. Psychological Bulletin 131(4): 533–554.

    Article  Google Scholar 

  75. Raine, A. 2002. Biosocial studies of antisocial and violent behavior in children and adults: A review. Journal of Abnormal Child Psychology 30(5): 311–326.

    Article  Google Scholar 

  76. Taylor, A., and J. Kim-Cohen. 2007. Meta-analysis of gene–environment interactions in developmental psychopathology. Development and Psychopathology 19: 1029–1037.

    Article  Google Scholar 

  77. Buckholtz, J.W., and A. Meyer-Lindenberg. 2009. Gene-brain associations: The example of MAOA. In The neurobiological basis of violence: Science and rehabilitation, ed. S.H. Hodgins, E. Viding, and A. Plodowski, 265–286. Oxford: Oxford University Press.

    Google Scholar 

  78. Alia-Klein, N., R.Z. Goldstein, D. Tomasi, P.A. Woicik, S.J. Moeller, B. Williams, I.W. Craig, et al. 2009. Neural mechanisms of anger regulation as a function of genetic risk for violence. Emotion 9(3): 385–396.

    Article  Google Scholar 

  79. Meyer-Lindenberg, A., J.W. Buckholtz, B. Kolachana, L. Pezawas, G. Blasi, A. Wabnitz, et al. 2006. Neural mechanisms of genetic risk for impulsivity and violence in humans. Proceedings of the National Academy of the Sciences of the United States of America 103(16): 6269–6274.

    Article  Google Scholar 

  80. Fan, J., J. Fossella, T. Sommer, Y. Wu, and M.I. Posner. 2003. Mapping the genetic variation of executive attention onto brain activity. Proceedings of the National Academy of the Sciences of the United States of America 100: 7406–7411.

    Article  Google Scholar 

  81. Passamonti, L., F. Fera, A. Magariello, A. Cerasa, M.C. Gioia, M. Muglia, et al. 2006. Monoamine oxidase-A generic variations influence brain activity associated with inhibitory control: New insight into the neural correlates of impulsivity. Biological Psychiatry 59(4): 334–340.

    Article  Google Scholar 

  82. Norman, K.A., S.M. Polyn, G.J. Detre, and J.V. Haxby. 2006. Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science 10: 424–43.

    Article  Google Scholar 

  83. Haynes, J.D., and G. Rees. 2006. Decoding mental states from brain activity in humans. National Review of Neuroscience 7: 523–534.

    Article  Google Scholar 

  84. Kay, K.N., T. Naselaris, R.J. Prenger, and J.L. Gallant. 2008. Identifying natural images from human brain activity. Nature 452: 352–355.

    Article  Google Scholar 

  85. Dosenbach, N.U.F., B. Nardos, A.L. Cohen, D.A. Fair, J.D. Power, J.A. Church, et al. 2010. Prediction of individual maturity using fMRI. Science 329: 1358–1361.

    Article  Google Scholar 

  86. Fan, Y., D. Shen, R. Gur, R. Gur, and C. Davatzikos. 2007. COMPARE: Classification of morphological patterns using adaptive regional elements. Medical Imaging, IEEE Transactions 26: 93–105.

    Article  Google Scholar 

  87. Fan, Y., N. Batmanghelich, C.M. Clark, and C. Davatzikos. 2008. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39: 1731–1743.

    Article  Google Scholar 

  88. Fan, Y., S.M. Resnick, X. Wu, and C. Davatzikos. 2008. Structural and functional biomarkers of prodromal Alzheimer’s disease: A high-dimensional pattern classification study. Neuroimage 41: 277–285.

    Article  Google Scholar 

  89. Joshi, S., Karthikeyan, S., Manjunath, B., Grafton, S., Kiehl, K. Anatomical parts-based regression using non-negative matrix factorization. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (2010) pp. 2863–2870.

  90. Clark, V.P., G.K. Beatty, R. Anderson, P. Kodituwakku, J. Phillips, K.A. Kiehl, and V.D. Calhoun. 2008. FMRI activity in cingulate and insular cortex predicts relapse in recovering stimulant addicts. Washington: Oral presentation at the annual meeting of the Society for Neuroscience.

    Google Scholar 

  91. Brown, S.L., M.D. St. Amand, and E. Zamble. 2009. The dynamic prediction of criminal recidivism: A three-wave prospective study. Law and Human Behavior 33: 25–45.

    Article  Google Scholar 

  92. Andrews, D.A., and J. Bonta. 2006. The psychology of criminal conduct, 4th ed. Cincinnati: Anderson.

    Google Scholar 

  93. Gendreau, P., T. Little, and C. Goggin. 1996. A meta-analysis of the predictors of adult offender recidivism: What works! Criminology 34: 575–607.

    Article  Google Scholar 

  94. Grove, W.M., D.H. Zald, B.S. Lebow, B.E. Snitz, and C. Nelson. 2000. Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment 12: 19–30.

    Article  Google Scholar 

  95. Hanson, R.K., and M.T. Bussiere. 1996. Sex offender risk predictors: A summary of research results. Forum on Corrections Research 8: 10–12.

    Google Scholar 

  96. Hanson, R.K., and K. Morton-Bourgon. 2004. Predictors of sexual revidivism: An updated meta-analysis (User Report No. 2004-02). Ottawa: Public Safety and Emergency Preparedness Canada.

    Google Scholar 

  97. Hood, R., S. Shute, M. Feilzer, and A. Wilcox. 2002. Sex offenders emerging from long-term imprisonment: A study of their long-term reconviction rates and of parole board members’ judgments of their risk. British Journal of Criminology 42: 371–394.

    Article  Google Scholar 

  98. McNeil, D.E., D.A. Sandberg, and R.L. Binder. 1998. The relationship between confidence and accuracy in clinical assessment of psychiatric patients’ potential for violence. Law and Human Behavior 22: 655–669.

    Article  Google Scholar 

  99. Coid, J., M. Yang, S. Ullrich, T. Zhang, S. Sizmur, C. Roberts, D.P. Farrington, and R.D. Rogers. 2009. Gender differences in structured risk assessments: Comparing the accuracy of five instruments. Journal of Consulting and Clinical Psychology 77: 337–348.

    Article  Google Scholar 

  100. Monahan, J., H.J. Steadman, P.C. Robbins, P. Appelbaum, S. Banks, T. Grisso, et al. 2005. An actuarial model of violence risk assessment for persons with mental disorders. Psychiatr Serv 56(7): 810–815.

    Article  Google Scholar 

  101. Skeem, J.L., J.D. Miller, E. Mulvey, J. Tiemann, and J. Monahan. 2005. Using a five-factor lens to explore the relation between personality traits and violence in psychiatric patients. J Consult Clin Psychol 73(3): 454–465.

    Article  Google Scholar 

  102. Beecher-Monas, E., & Garcia-Rill, E. 1999. The Law and The Brain: Judging Scientific Evidence of Intent, 1. J. App. Prac. & Process 243.

  103. Beecher-Monas, E. & Garcia-Rill, E. 2003. Danger at the End of Chaos: Predicting Violent Behavior in a Post-Daubert World, 24 Cardozo L. Rev. 1845, 1845–1846.

    Google Scholar 

  104. Giannelli, P. 1993. “Junk Science”: The criminal cases. The Journal of Criminal Law & Criminology 84(1): 105–128.

    Article  Google Scholar 

  105. Faigman, D.L., Kaye, D.H., Saks, M.J., & Sanders, J. 2009. Modern scientific evidence: The law and science of expert testimony (Vols. 105). Thompson West.

  106. Monahan, J. 2000. Violence risk assessment: Scientific validity and evidentiary admissibility. Washington & Lee Law Review, 54, 901–918.

    Google Scholar 

  107. Shuman, D.W., and B.D. Sales. 1998. The admissibility of expert testimony based upon clinical judgment and scientific research. Psychology, Public Policy, and Law 4: 1226–1252.

    Article  Google Scholar 

  108. Weisberg, D.S., F.C. Keil, J. Goodstein, E. Rawson, and J.R. Gray. 2008. The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience 20: 470–477.

    Article  Google Scholar 

  109. Logothetis, N.K. 2008. What we can do and what we cannot do with fMRI. Nature 453: 869–878.

    Article  Google Scholar 

  110. Poldrack, R.A. 2006. Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences 10(2): 59–63.

    Article  Google Scholar 

  111. Slobogin, C., Rai, A., & Reisner, R. 2008. Law and the Mental Health System: Civil and Criminal Aspects, West Publishing (5th ed.).

  112. Monahan, J. 2006. A Jurisprudence of risk assessment: Forecasting harm among prisoners, predators, and patients. Virginia Law Review 92: 391–434.

    Google Scholar 

  113. Morse, S.J. 1996. Blame and danger: An essay on preventive detention. Boston University Law Review 76: 113–155.

    Google Scholar 

  114. Robinson, P.H. 2001. Punishing dangerousness: Cloaking preventive detention as criminal justice. Harvard Law Review 114: 1429–56.

    Article  Google Scholar 

  115. Goldberg, L.R. 1968. Simple models or simple processes? American Psychologist 23: 483–496.

    Article  Google Scholar 

  116. Grisso, T., and P. Appelbaum. 1992. Structuring the debate about ethical predictions of future violence. Law and Human Behavior 17: 482–485.

    Article  Google Scholar 

  117. Megargee, E. 1970. The prediction of violence with psychological tests. In Spielberger, C. (Ed.), Current Topics in Clinical and Community Psychology, 97–153.

Download references

Author information

Authors and Affiliations

  1. Department of Philosophy, Dickinson College, Carlisle, PA, 17013, USA

    Thomas Nadelhoffer

  2. University of Pennsylvania Law School, 3400 Chestnut Street, Philadelphia, PA, 19104, USA

    Stephanos Bibas

  3. Department of Psychology, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA

    Scott Grafton

  4. Department of Psychology, University of New Mexico & the Mind Research Network, 1101, Yale Blvd NE, Albuquerque, NM, 87106, USA

    Kent A. Kiehl

  5. The Sage Center for the Study of the Mind, Santa Barbara, CA, 93106, USA

    Andrew Mansfield

  6. Department of Philosophy & the Kenan Institute for Ethics, Duke University, PO Box 90432, Durham, NC, 27708, USA

    Walter Sinnott-Armstrong

  7. Department of Psychology, University of California, Santa Barbara, & The Sage Center for the Study of the Mind, Santa Barbara, CA, 93106, USA

    Michael Gazzaniga

  8. The Kenan Institute for Ethics, Duke University, PO Box 90432, Durham, NC, 27708, USA

    Thomas Nadelhoffer

Authors
  1. Thomas Nadelhoffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephanos Bibas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Scott Grafton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kent A. Kiehl
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew Mansfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Walter Sinnott-Armstrong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael Gazzaniga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Nadelhoffer.

Additional information

This material is based upon work supported by the John D. and Catherine T. MacArthur Foundation, the Law and Neuroscience Project, and The Regents of the University of California. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the John D. and Catherine T. MacArthur Foundation, the Law and Neuroscience Project, or of The Regents of the University of California. We would also like to thank members of the Law and Neuroscience Project network on criminal responsibility and prediction—especially Stephen Morse and Richard Bonnie—for their helpful guidance and suggestions during the earlier stages of this project.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nadelhoffer, T., Bibas, S., Grafton, S. et al. Neuroprediction, Violence, and the Law: Setting the Stage. Neuroethics 5, 67–99 (2012). https://doi.org/10.1007/s12152-010-9095-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12152-010-9095-z

Keywords

Search

Navigation