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Predicting the knowledge-recklessness distinction in the human brain

Cited 22 time in Web of Science Cited 32 time in Scopus
Authors

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

Issue Date
2017-03
Publisher
National Academy of Sciences
Citation
Proceedings of the National Academy of Sciences of the United States of America, Vol.114 No.12, pp.3222-3227
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.
ISSN
0027-8424
URI
https://hdl.handle.net/10371/202869
DOI
https://doi.org/10.1073/pnas.1619385114
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  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독

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