Neurocircuit dynamics of arbitration between decision-making strategies across obsessive-compulsive and related disorders.

Obsessions and compulsions are central components of obsessive-compulsive disorder (OCD) and obsessive-compulsive related disorders such as body dysmorphic disorder (BDD). Compulsive behaviours may result from an imbalance of habitual and goal-directed decision-making strategies. The relationship between these symptoms and the neural circuitry underlying habitual and goal-directed decision-making, and the arbitration between these strategies, remains unknown. This study examined resting state effective connectivity between nodes of these systems in two cohorts with obsessions and compulsions, each compared with their own corresponding healthy controls: OCD (nOCD = 43; nhealthy = 24) and BDD (nBDD = 21; nhealthy = 16). In individuals with OCD, the left ventrolateral prefrontal cortex, a node of the arbitration system, exhibited more inhibitory causal influence over the left posterolateral putamen, a node of the habitual system, compared with controls. Inhibitory causal influence in this connection showed a trend for a similar pattern in individuals with BDD compared with controls. Those with stronger negative connectivity had lower obsession and compulsion severity in both those with OCD and those with BDD. These relationships were not evident within the habitual or goal-directed circuits, nor were they associated with depressive or anxious symptomatology. These results suggest that abnormalities in the arbitration system may represent a shared neural phenotype across these two related disorders that is specific to obsessive-compulsive symptoms. In addition to nosological implications, these results identify potential targets for novel, circuit-specific treatments.

Why and how the brain weights contributions from a mixture of experts.

It has long been suggested that human behavior reflects the contributions of multiple systems that cooperate or compete for behavioral control. Here we propose that the brain acts as a "Mixture of Experts" in which different expert systems propose strategies for action. It will be argued that the brain determines which experts should control behavior at any one moment in time by keeping track of the reliability of the predictions within each system, and by allocating control over behavior in a manner that depends on the relative reliabilities across experts. fMRI and neurostimulation studies suggest a specific contribution of the anterior prefrontal cortex in this process. Further, such a mechanism also takes into consideration the complexity of the expert, favoring simpler over more cognitively complex experts. Results from the study of different expert systems in both experiential and social learning domains hint at the possibility that this reliability-based control mechanism is domain general, exerting control over many different expert systems simultaneously in order to produce sophisticated behavior.

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.

Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward. These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations.

Specific impairment of face-processing abilities in children with autism spectrum disorder using the Let's Face It! skills battery.

Although it has been well established that individuals with autism exhibit difficulties in their face recognition abilities, it has been debated whether this deficit reflects a category-specific impairment of faces or a general perceptual bias toward the local-level information in a stimulus. In this study, the Let's Face It! Skills Battery [Tanaka & Schultz, 2008] of developmental face- and object-processing measures was administered to a large sample of children diagnosed with autism spectrum disorder (ASD) and typically developing children. The main finding was that when matched for age and IQ, individuals with ASD were selectively impaired in their ability to recognize faces across changes in orientation, expression and featural information. In a face discrimination task, ASD participants showed a preserved ability to discriminate featural and configural information in the mouth region of a face, but were compromised in their ability to discriminate featural and configural information in the eyes. On object-processing tasks, ASD participants demonstrated a normal ability to recognize automobiles across changes in orientation and a superior ability to discriminate featural and configural information in houses. These findings indicate that the face-processing deficits in ASD are not due to a local-processing bias, but reflect a category-specific impairment of faces characterized by a failure to form view-invariant face representations and discriminate information in the eye region of the face.