Predicting the knowledge-recklessness distinction in the human brain

Iris Vilares; Michael J. Wesley; Woo Young Ahn; Richard J. Bonnie; Morris Hoffman; Owen D. Jones; Stephen J. Morse; Gideon Yaffe; Terry Lohrenz; P. Read Montague

doi:10.1073/pnas.1619385114

Predicting the knowledge-recklessness distinction in the human brain

Iris Vilares, Michael J. Wesley, Woo Young Ahn, Richard J. Bonnie, Morris Hoffman, Owen D. Jones, Stephen J. Morse, Gideon Yaffe, Terry Lohrenz, P. Read Montague

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	3222-3227
Number of pages	6
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	114
Issue number	12
DOIs	https://doi.org/10.1073/pnas.1619385114
State	Published - Mar 21 2017

Bibliographical note

Funding Information:
We thank Frank Tong for useful discussions and all of the members from the Human Neuroimaging Lab, especially Alec Solway, Andreas Hula, and Sébastien Hétu, for helpful comments and discussion. We are also thankful for the support of the Wellcome Trust, the Kane Foundation, the Brown Foundation, and the National Institute on Drug Abuse. This study was supported by a grant from the John D. and Catherine T. MacArthur Foundation to Vanderbilt University, with a subcontract to Virginia Tech. Its contents do not necessarily represent official views of either the John D. and Catherine T. MacArthur Foundation or the MacArthur Foundation Research Network on Law and Neuroscience (www.lawneuro.org).

Funding

We thank Frank Tong for useful discussions and all of the members from the Human Neuroimaging Lab, especially Alec Solway, Andreas Hula, and Sébastien Hétu, for helpful comments and discussion. We are also thankful for the support of the Wellcome Trust, the Kane Foundation, the Brown Foundation, and the National Institute on Drug Abuse. This study was supported by a grant from the John D. and Catherine T. MacArthur Foundation to Vanderbilt University, with a subcontract to Virginia Tech. Its contents do not necessarily represent official views of either the John D. and Catherine T. MacArthur Foundation or the MacArthur Foundation Research Network on Law and Neuroscience (www.lawneuro.org).

Funders	Funder number
Human Neuroimaging Lab
Kane Foundation
National Institute on Drug Abuse
John D. and Catherine T. MacArthur Foundation
Brown Foundation
Wellcome Trust

Keywords

Elastic-net model
Knowledge
Mental states
Neurolaw
Recklessness

ASJC Scopus subject areas

General

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1073/pnas.1619385114

Cite this

@article{086dcfd35ef049ba8efd7af3c628bdb8,

title = "Predicting the knowledge-recklessness distinction in the human brain",

abstract = "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.",

keywords = "Elastic-net model, Knowledge, Mental states, Neurolaw, Recklessness",

author = "Iris Vilares and Wesley, {Michael J.} and Ahn, {Woo Young} and Bonnie, {Richard J.} and Morris Hoffman and Jones, {Owen D.} and Morse, {Stephen J.} and Gideon Yaffe and Terry Lohrenz and Montague, {P. Read}",

note = "Funding Information: We thank Frank Tong for useful discussions and all of the members from the Human Neuroimaging Lab, especially Alec Solway, Andreas Hula, and S{\'e}bastien H{\'e}tu, for helpful comments and discussion. We are also thankful for the support of the Wellcome Trust, the Kane Foundation, the Brown Foundation, and the National Institute on Drug Abuse. This study was supported by a grant from the John D. and Catherine T. MacArthur Foundation to Vanderbilt University, with a subcontract to Virginia Tech. Its contents do not necessarily represent official views of either the John D. and Catherine T. MacArthur Foundation or the MacArthur Foundation Research Network on Law and Neuroscience (www.lawneuro.org).",

year = "2017",

month = mar,

day = "21",

doi = "10.1073/pnas.1619385114",

language = "English",

volume = "114",

pages = "3222--3227",

number = "12",

}

TY - JOUR

T1 - Predicting the knowledge-recklessness distinction in the human brain

AU - Vilares, Iris

AU - Wesley, Michael J.

AU - Ahn, Woo Young

AU - Bonnie, Richard J.

AU - Hoffman, Morris

AU - Jones, Owen D.

AU - Morse, Stephen J.

AU - Yaffe, Gideon

AU - Lohrenz, Terry

AU - Montague, P. Read

N1 - Funding Information: We thank Frank Tong for useful discussions and all of the members from the Human Neuroimaging Lab, especially Alec Solway, Andreas Hula, and Sébastien Hétu, for helpful comments and discussion. We are also thankful for the support of the Wellcome Trust, the Kane Foundation, the Brown Foundation, and the National Institute on Drug Abuse. This study was supported by a grant from the John D. and Catherine T. MacArthur Foundation to Vanderbilt University, with a subcontract to Virginia Tech. Its contents do not necessarily represent official views of either the John D. and Catherine T. MacArthur Foundation or the MacArthur Foundation Research Network on Law and Neuroscience (www.lawneuro.org).

PY - 2017/3/21

Y1 - 2017/3/21

N2 - 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.

AB - 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.

KW - Elastic-net model

KW - Knowledge

KW - Mental states

KW - Neurolaw

KW - Recklessness

UR - http://www.scopus.com/inward/record.url?scp=85016097130&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016097130&partnerID=8YFLogxK

U2 - 10.1073/pnas.1619385114

DO - 10.1073/pnas.1619385114

M3 - Article

C2 - 28289225

AN - SCOPUS:85016097130

SN - 0027-8424

VL - 114

SP - 3222

EP - 3227

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 12

ER -

Predicting the knowledge-recklessness distinction in the human brain

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this