RESUMO
In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.
RESUMO
Object recognition tasks are widely used assays for studying learning and memory in rodents. Object recognition typically involves familiarizing mice with a set of objects and then presenting a novel object or displacing an object to a novel location or context. Learning and memory are inferred by a relative increase in time investigating the novel/displaced object. These tasks are in widespread use, but there are many inconsistencies in the way they are conducted across labs. Two major contributors to this are the lack of consistency in the method of measuring object investigation and the lack of standardization of the objects that are used. Current video-based automated algorithms can often be unreliable whereas manual scoring of object investigation is time consuming, tedious, and more subjective. To resolve these issues, we sought to design and implement 3D-printed objects that can be standardized across labs and use capacitive sensing to measure object investigation. Using a 3D printer, conductive filament, and low-cost off-the-shelf components, we demonstrate that employing 3D-printed capacitive touch objects is a reliable and precise way to perform object recognition tasks. Ultimately, this approach will lead to increased standardization and consistency across labs, which will greatly improve basic and translational research into learning and memory mechanisms.