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.
|Number of pages
|Proceedings of the National Academy of Sciences of the United States of America
|Published - Mar 21 2017
Bibliographical noteFunding 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).
- Elastic-net model
- Mental states
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