Recently Published on SSRN:
"Predicting the Knowledge-Recklessness Distinction in the Human Brain"
IRIS VILARES, University College London - Wellcome Trust Center for Neuroimaging
MICHAEL WESLEY, University of Kentucky - Behavioral Science
WOO-YOUNG AHN, Ohio State University (OSU) - Department of Psychology
RICHARD J. BONNIE, University of Virginia - School of Law
MORRIS B. HOFFMAN, Second Judicial District Court Judge, State of Colorado
OWEN D. JONES, Vanderbilt University - Law School & Dept. of Biological Sciences
STEPHEN MORSE, University of Pennsylvania Law School
GIDEON YAFFE, Yale Law School
TERRY LOHRENZ, Virginia Polytechnic Institute & State University - Virginia Tech Carilion Research Institute
READ MONTAGUE, Virginia Polytechnic Institute & State University - Virginia Tech Carilion Research Institute
MICHAEL WESLEY, University of Kentucky - Behavioral Science
WOO-YOUNG AHN, Ohio State University (OSU) - Department of Psychology
RICHARD J. BONNIE, University of Virginia - School of Law
MORRIS B. HOFFMAN, Second Judicial District Court Judge, State of Colorado
OWEN D. JONES, Vanderbilt University - Law School & Dept. of Biological Sciences
STEPHEN MORSE, University of Pennsylvania Law School
GIDEON YAFFE, Yale Law School
TERRY LOHRENZ, Virginia Polytechnic Institute & State University - Virginia Tech Carilion Research Institute
READ MONTAGUE, Virginia Polytechnic Institute & State University - Virginia Tech Carilion Research Institute
Criminal convictions require proof that a prohibited act was performed in a statutorily specified mental state. Different legal consequences, including greater punishments, are mandated for those who act in a state of knowledge, compared with a state of recklessness. Existing research, however, suggests people have trouble classifying defendants as knowing, rather than reckless, even when instructed on the relevant legal criteria.
We used a machine-learning technique on brain imaging data to predict, with high accuracy, which mental state our participants were in. This predictive ability depended on both the magnitude of the risks and the amount of information about those risks possessed by the participants. Our results provide neural evidence of a detectable difference in the mental state of knowledge in contrast to recklessness and suggest, as a proof of principle, the possibility of inferring from brain data in which legally relevant category a person belongs. Some potential legal implications of this result are discussed.
We used a machine-learning technique on brain imaging data to predict, with high accuracy, which mental state our participants were in. This predictive ability depended on both the magnitude of the risks and the amount of information about those risks possessed by the participants. Our results provide neural evidence of a detectable difference in the mental state of knowledge in contrast to recklessness and suggest, as a proof of principle, the possibility of inferring from brain data in which legally relevant category a person belongs. Some potential legal implications of this result are discussed.
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