The Language of Mens Rea

To be guilty of a crime, generally one must commit a bad act while in a culpable state of mind. But the language used to define, partition, and communicate the variety of culpable mental states (in Latin, mens rea) is crucially important. For depending on the mental state that juries attribute to him, a defendant can be convicted-for the very same act and the very same consequence-of different crimes, each with different sentences. The influential Model Penal Code ( MPC ) of 1962 divided culpable mental states into four now-familiar kinds: purposeful, knowing, reckless, and negligent.\u27 Both before the MPC and since, scholars in criminal law and philosophy have actively debated how best to define and apply the mens rea categories.2 Yet few empirical studies have explored the actual relationships between specific mens rea formulations and legally relevant outcomes. A 2011 article coauthored by several of us, Sorting Guilty Minds, presented experiments that tested the MPC\u27s twin assumptions that: (1) ordinary people naturally do-or at least can, when instructed- distinguish these four categories of mental states with reasonable reliability; and (2) holding the act and harm constant, the average person would punish acts reflecting these four mental states in the manner corresponding to the MPC hierarchy-that is, they would punish purposeful conduct the most severely and negligent conduct the least. Those experiments found that these assumptions held, for the most part. But an interesting and important exception emerged at the boundary between knowing and reckless conduct: in sorting the mental states and in assigning punishment, subjects were much less able to differentiate between knowing and reckless conduct. On the basis of those findings, the article outlined several possible reforms-assuming the results were validated in future studies. To validate and extend those results, we have conducted a series of additional experiments, reported here, with more than 1,600 additional subjects

Surprise vs. Probability as a Metric for Proof

In this Symposium issue celebrating his career, Professor Michael Risinger in Leveraging Surprise proposes using the fundamental emotion of surprise as a way of measuring belief for purposes of legal proof. More specifically, Professor Risinger argues that we should not conceive of the burden of proof in terms of probabilities such as 51%, 95%, or even beyond a reasonable doubt. Rather, the legal system should reference the threshold using words of estimative surprise -asking jurors how surprised they would be if the fact in question were not true. Toward this goal (and being averse to cardinality), he suggests categories such as mildly surprised, surprised, quite surprised, greatly surprised, astonished, shocked, etc. We find Professor Risinger\u27s proposal intriguing. After all, one can imagine important theoretical reasons why surprise might generate different results from probability. To the extent that the surprise formulation is unfamiliar, it might cause jurors to think holistically ( System 1 ) as opposed to attempting to use rules (often misremembered or misapplied) about probability ( System 2 ). Surprise might be easier to approach qualitatively, unlike probability, which cries out for quantitative calculation and invokes the fear of numbers for some. Surprise is also notably framed in the negative ( How surprised would you be if the fact were not true? ) compared to its probability counterpart ( What is the probability that the fact is true? ). Being empiricists, we thus could not help but put Professor Risinger\u27s worthy proposal to the test, if only in a preliminary way

Surprise vs. Probability as a Metric for Proof

In this Symposium issue celebrating his career, Professor Michael Risinger in Leveraging Surprise proposes using the fundamental emotion of surprise as a way of measuring belief for purposes of legal proof. More specifically, Professor Risinger argues that we should not conceive of the burden of proof in terms of probabilities such as 51%, 95%, or even beyond a reasonable doubt. Rather, the legal system should reference the threshold using words of estimative surprise -asking jurors how surprised they would be if the fact in question were not true. Toward this goal (and being averse to cardinality), he suggests categories such as mildly surprised, surprised, quite surprised, greatly surprised, astonished, shocked, etc. We find Professor Risinger\u27s proposal intriguing. After all, one can imagine important theoretical reasons why surprise might generate different results from probability. To the extent that the surprise formulation is unfamiliar, it might cause jurors to think holistically ( System 1 ) as opposed to attempting to use rules (often misremembered or misapplied) about probability ( System 2 ). Surprise might be easier to approach qualitatively, unlike probability, which cries out for quantitative calculation and invokes the fear of numbers for some. Surprise is also notably framed in the negative ( How surprised would you be if the fact were not true? ) compared to its probability counterpart ( What is the probability that the fact is true? ). Being empiricists, we thus could not help but put Professor Risinger\u27s worthy proposal to the test, if only in a preliminary way

Decoding Guilty Minds

A central tenet of Anglo-American penal law is that in order for an actor to be found criminally liable, a proscribed act must be accompanied by a guilty mind. While it is easy to understand the importance of this principle in theory, in practice it requires jurors and judges to decide what a person was thinking months or years earlier at the time of the alleged offense, either about the results of his conduct or about some elemental fact (such as whether the briefcase he is carrying contains drugs). Despite the central importance of this task in the administration of criminal justice, there has been very little research investigating how people go about making these decisions, and how these decisions relate to their intuitions about culpability. Understanding the cognitive mechanisms that govern this task is important for the law, not only to explore the possibility of systemic biases and errors in attributions of culpability but also to probe the intuitions that underlie them. In a set of six exploratory studies reported here, we examine the way in which individuals infer others’ legally relevant mental states about elemental facts, using the framework established over fifty years ago by the Model Penal Code (“MPC”). The widely adopted MPC framework delineates and defines the four now-familiar culpable mental states: purpose, knowledge, recklessness, and negligence. Our studies reveal that with little to no training, jury-eligible Americans can apply the MPC framework in a manner that is largely congruent with the basic assumptions of the MPC’s mental state hierarchy. However, our results also indicate that subjects’ intuitions about the level of culpability warranting criminal punishment diverge significantly from prevailing legal practice; subjects tend to regard recklessness as a sufficient basis for punishment under circumstances where the legislatures and courts tend to require knowledge

Standards and Infrastructure for Innovation Data Exchange

This is the author's accepted manuscript. The original publication is available at http://www.sciencemag.org/content/338/6104/196.Economic growth relies in part on efficient advancement and application of research and development (R&D) knowledge. This requires access to data about science, in particular R&D inputs and outputs such as grants, patents, publications, and data sets, to support an understanding of how R&D information is produced and what affects its availability. But there is a cacophony of R&D-related data across countries, disciplines, data providers, and sectors. Burdened with data that are inconsistently specified, researchers and policy-makers have few incentives or mechanisms to share or interlink cleaned data sets. Access to these data is limited by a patchwork of laws, regulations, and practices that are unevenly applied and interpreted (1). A Web-based infrastructure for data sharing and analysis could help. We describe administrative and technical demands and opportunities to meet them. Data exchange standards are a first step

Parsing the Behavioral and Brain Mechanisms of Third-Party Punishment

The evolved capacity for third-party punishment is considered crucial to the emergence and maintenance of elaborate human social organization and is central to the modern provision of fairness and justice within society. Although it is well established that the mental state of the offender and the severity of the harm he caused are the two primary predictors of punishment decisions, the precise cognitive and brain mechanisms by which these distinct components are evaluated and integrated into a punishment decision are poorly understood. Using a brain-scanning technique known as functional magnetic resonance imaging (fMRI), we implemented a novel experimental design to functionally dissociate the mechanisms underlying evaluation, integration, and decision. This work revealed that multiple parts of the brain – some analytic, some subconscious or emotional – work in a systematic pattern to decide blameworthiness, assess harms, integrate those two decisions, and then ultimately select how a person should be punished. Specifically, harm and mental state evaluations are conducted in two different brain networks and then combined in the medial prefrontal and posterior cingulate areas of the brain, while the amygdala acts as a pivotal hub of the interaction between harm and mental state. This integrated information is then used by the right dorsolateral prefrontal cortex when the brain is making a decision on punishment amount. These findings provide a blueprint of the brain mechanisms by which neutral third parties make punishment decisions

Parsing the Behavioral and Brain Mechanisms of Third-Party Punishment.

UnlabelledThe evolved capacity for third-party punishment is considered crucial to the emergence and maintenance of elaborate human social organization and is central to the modern provision of fairness and justice within society. Although it is well established that the mental state of the offender and the severity of the harm he caused are the two primary predictors of punishment decisions, the precise cognitive and brain mechanisms by which these distinct components are evaluated and integrated into a punishment decision are poorly understood. Using fMRI, here we implement a novel experimental design to functionally dissociate the mechanisms underlying evaluation, integration, and decision that were conflated in previous studies of third-party punishment. Behaviorally, the punishment decision is primarily defined by a superadditive interaction between harm and mental state, with subjects weighing the interaction factor more than the single factors of harm and mental state. On a neural level, evaluation of harms engaged brain areas associated with affective and somatosensory processing, whereas mental state evaluation primarily recruited circuitry involved in mentalization. Harm and mental state evaluations are integrated in medial prefrontal and posterior cingulate structures, with the amygdala acting as a pivotal hub of the interaction between harm and mental state. This integrated information is used by the right dorsolateral prefrontal cortex at the time of the decision to assign an appropriate punishment through a distributed coding system. Together, these findings provide a blueprint of the brain mechanisms by which neutral third parties render punishment decisions.Significance statementPunishment undergirds large-scale cooperation and helps dispense criminal justice. Yet it is currently unknown precisely how people assess the mental states of offenders, evaluate the harms they caused, and integrate those two components into a single punishment decision. Using a new design, we isolated these three processes, identifying the distinct brain systems and activities that enable each. Additional findings suggest that the amygdala plays a crucial role in mediating the interaction of mental state and harm information, whereas the dorsolateral prefrontal cortex plays a crucial, final-stage role, both in integrating mental state and harm information and in selecting a suitable punishment amount. These findings deepen our understanding of how punishment decisions are made, which may someday help to improve them