Neuroscience and the Model Penal Code\u27s Mens Rea Categories

This Essay addresses recent research and commentary regarding the potential contributions of cognitive neuroscience to law. For the first time, cognitive neuroscience methods have been brought to bear on the Model Penal Code’s (MPC’s) culpable–mental state categories through a neuroimaging study seeking to identify the neural correlates of knowledge and recklessness. Subsequently, this study has been presented as a paradigm for utilizing cognitive neuroscience to answer important legal questions. However, the original experiment appears to suffer serious experimental-design and conceptual limitations, belying subsequent advocacy for the legal utility of cognitive neuroscience. This Essay methodically details these limitations and argues that the original study does not seem to have actually elicited knowledge or recklessness in subjects or, even if it did, did not elicit them in discrete enough fashion to permit identification of the mental states’ neural correlates. The Essay also contends, more broadly, that cognitive neuroscience appears inapt for investigating the propriety of the MPC’s mens rea delineations since these are articulated in purely psychological-behavioral terms: mental states are the only requisites. Only psychological-behavioral manifestations provide base evidence of mental states’ existences. And psychological-behavioral research, not cognitive neuroscience, is the most direct way to investigate the practical, moral, and legal appropriateness of the MPC’s mental states by illuminating how individuals experience them, identify them in others, or employ them to dispense blame and punishment. Ultimately, recent cognitive neuroscience research does not appear to reveal anything of legal significance regarding the MPC. And, more broadly and contrary to recent assertions, cognitive neuroscience has substantial limitations when it comes to producing legally relevant information. Going forward, psychological-behavioral research should be given primacy in cognitive science investigations of MPC concepts. Cognitive neuroscience studies, on the other hand, should be treated with exceptional skepticism

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Data-driven brain network models differentiate variability across language tasks

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects’ performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to assess individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive demand, and to pave the way for the development of personalized therapeutics