This study tests the hypothesis that seriously violent offenders pleading not guilty by reason of insanity or incompetent to stand trial are characterized by prefrontal dysfunction. This hypothesis was tested in a group of 22 subjects accused of murder and 22 age-matched and gender-matched controls by measuring local cerebral uptake of glucose using positron emission tomography during the continuous performance task. Murderers had significantly lower glucose metabolism in both lateral and medial prefrontal cortex relative to controls. No group differences were observed for posterior frontal, temporal, and parietal glucose metabolism, indicating regional specificity for the prefrontal deficit. Group differences were not found to be a function of raised levels of left-handedness, schizophrenia, ethnic minority status, head injury, or motivation deficits in the murder group. These preliminary results suggest that deficits localized to the prefrontal cortex may be related to violence in a selected group of offenders, although further studies are needed to establish the generalizability of these findings to violent offenders in the community.

selective Reductions in Prefrontal Glucose Metabolism in Murderers

Significance Concern for the welfare of others is a key component of moral decision making and is disturbed in antisocial and criminal behavior. However, little is known about how people evaluate the costs of others’ suffering. Past studies have examined people’s judgments in hypothetical scenarios, but there is evidence that hypothetical judgments cannot accurately predict actual behavior. Here we addressed this issue by measuring how much money people will sacrifice to reduce the number of painful electric shocks delivered to either themselves or an anonymous stranger. Surprisingly, most people sacrifice more money to reduce a stranger’s pain than their own pain. This finding may help us better understand how people resolve moral dilemmas that commonly arise in medical, legal, and political decision making. Concern for the suffering of others is central to moral decision making. How humans evaluate others’ suffering, relative to their own suffering, is unknown. We investigated this question by inviting subjects to trade off profits for themselves against pain experienced either by themselves or an anonymous other person. Subjects made choices between different amounts of money and different numbers of painful electric shocks. We independently varied the recipient of the shocks (self vs. other) and whether the choice involved paying to decrease pain or profiting by increasing pain. We built computational models to quantify the relative values subjects ascribed to pain for themselves and others in this setting. In two studies we show that most people valued others’ pain more than their own pain. This was evident in a willingness to pay more to reduce others’ pain than their own and a requirement for more compensation to increase others’ pain relative to their own. This ‟hyperaltruistic” valuation of others’ pain was linked to slower responding when making decisions that affected others, consistent with an engagement of deliberative processes in moral decision making. Subclinical psychopathic traits correlated negatively with aversion to pain for both self and others, in line with reports of aversive processing deficits in psychopathy. Our results provide evidence for a circumstance in which people care more for others than themselves. Determining the precise boundaries of this surprisingly prosocial disposition has implications for understanding human moral decision making and its disturbance in antisocial behavior.

Harm to others outweighs harm to self in moral decision making (vol 111, pg 17320, 2014)

In developed countries, the majority of all violent crime is committed by a small group of antisocial recidivistic offenders, but no genes have been shown to contribute to recidivistic violent offending or severe violent behavior, such as homicide. Our results, from two independent cohorts of Finnish prisoners, revealed that a monoamine oxidase A (MAOA) low-activity genotype (contributing to low dopamine turnover rate) as well as the CDH13 gene (coding for neuronal membrane adhesion protein) are associated with extremely violent behavior (at least 10 committed homicides, attempted homicides or batteries). No substantial signal was observed for either MAOA or CDH13 among non-violent offenders, indicating that findings were specific for violent offending, and not largely attributable to substance abuse or antisocial personality disorder. These results indicate both low monoamine metabolism and neuronal membrane dysfunction as plausible factors in the etiology of extreme criminal violent behavior, and imply that at least about 5–10% of all severe violent crime in Finland is attributable to the aforementioned MAOA and CDH13 genotypes.

Genetic background of extreme violent behavior

Genetic background of extreme violent behavior.

https://hal.archives-ouvertes.fr/hal-02480297/document

Causal Inference in the Multisensory Brain.

Aggression may be present across a large part of the spectrum of psychopathology, and underlies costly criminal antisocial behaviors. Human aggression is a complex and underspecified construct, confounding scientific discovery. Nevertheless, some biologically tractable subtypes are apparent, and one in particular—impulsive (reactive) aggression—appears to account for many facets of aggression-related dysfunction in psychiatric illness. Impulsive-aggression is significantly heritable, suggesting genetic transmission. However, the specific neurobiological mechanisms that mediate genetic risk for impulsive-aggression remain unclear. Here, we review extant data on the genetics and neurobiology of individual differences in impulsive-aggression, with particular attention to the role of genetic variation in Monoamine Oxidase A (MAOA) and its impact on serotonergic signaling within corticolimbic circuitry.

Neurobiological Mechanisms for Impulsive-Aggression: The Role of MAOA

Disrupted Prefrontal Regulation of Striatal Subjective Value Signals in Psychopathy

A Pavlovian bias to approach reward-predictive cues and avoid punishment-predictive cues can conflict with instrumentally-optimal actions. Here, we propose that the brain arbitrates between Pavlovian and instrumental control by inferring which is a better predictor of reward. The instrumental predictor is more flexible; it can learn values that depend on both stimuli and actions, whereas the Pavlovian predictor learns values that depend only on stimuli. The arbitration theory predicts that the Pavlovian predictor will be favored when rewards are relatively uncontrollable, because the additional flexibility of the instrumental predictor is not useful. Consistent with this hypothesis, we find that the Pavlovian approach bias is stronger under low control compared to high control contexts. Pavlovian and instrumentally driven actions often conflict when determining the best outcome. Here, the authors present an arbitration theory supported by human behavioral data where Pavlovian predictors drive action selection in an uncontrollable environment, while more flexible instrumental prediction dominates under conditions of high controllability.

/pdf/controllability-governs-the-balance-between-pavlovian-and-5gc4lbe9de.pdf

Controllability governs the balance between Pavlovian and instrumental action selection.

Behavioral evidence suggests that beliefs about causal structure constrain associative learning, determining which stimuli can enter into association, as well as the functional form of that association. Bayesian learning theory provides one mechanism by which structural beliefs can be acquired from experience, but the neural basis of this mechanism is poorly understood. We studied this question with a combination of behavioral, computational, and neuroimaging techniques. Male and female human subjects learned to predict an outcome based on cue and context stimuli while being scanned using fMRI. Using a model-based analysis of the fMRI data, we show that structure learning signals are encoded in posterior parietal cortex, lateral prefrontal cortex, and the frontal pole. These structure learning signals are distinct from associative learning signals. Moreover, representational similarity analysis and information mapping revealed that the multivariate patterns of activity in posterior parietal cortex and anterior insula encode the full posterior distribution over causal structures. Variability in the encoding of the posterior across subjects predicted variability in their subsequent behavioral performance. These results provide evidence for a neural architecture in which structure learning guides the formation of associations.SIGNIFICANCE STATEMENT Animals are able to infer the hidden structure behind causal relations between stimuli in the environment, allowing them to generalize this knowledge to stimuli they have never experienced before. A recently published computational model based on this idea provided a parsimonious account of a wide range of phenomena reported in the animal learning literature, suggesting a dedicated neural mechanism for learning this hidden structure. Here, we validate this model by measuring brain activity during a task that involves both structure learning and associative learning. We show that a distinct network of regions supports structure learning and that the neural signal corresponding to beliefs about structure predicts future behavioral performance.

https://www.jneurosci.org/content/jneuro/38/32/7143.full.pdf

Neural Computations Underlying Causal Structure Learning.

People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden cause...

https://journals.sagepub.com/doi/pdf/10.1177/0956797619828724

Hayley M. Dorfman

Papers

Neurobiological Mechanisms for Impulsive-Aggression: The Role of MAOA

Disrupted Prefrontal Regulation of Striatal Subjective Value Signals in Psychopathy

Controllability governs the balance between Pavlovian and instrumental action selection.

Neural Computations Underlying Causal Structure Learning.

Causal Inference About Good and Bad Outcomes.