Psychology Magazine

Predicting the Knowledge–recklessness Distinction in the Human Brain

By Deric Bownds @DericBownds
Important work from Vilares et al. - in an open source paper in which fMRI results are shown in a series of figures - showing that brain imaging can determine, with high accuracy, on which side of a legally defined boundary a person's mental state lies.
 Significance
Because criminal statutes demand it, juries often must assess criminal intent by determining which of two legally defined mental states a defendant was in when committing a crime. For instance, did the defendant know he was carrying drugs, or was he merely aware of a risk that he was? Legal scholars have debated whether that conceptual distinction, drawn by law, mapped meaningfully onto any psychological reality. This study uses neuroimaging and machine-learning techniques to reveal different brain activities correlated with these two mental states. Moreover, the study provides a proof of principle that brain imaging can determine, with high accuracy, on which side of a legally defined boundary a person's mental state lies.
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.

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