Common Challenges and Identified Solutions for State Newborn Screening Programs during COVID-19 Pandemic.

During the COVID-19 pandemic, state newborn screening programs faced challenges to ensure this essential public health program continued to function at a high level. In December 2020, the EveryLife Foundation for Rare Diseases held a workshop to discuss these common challenges and solutions. Newborn screening officials described challenges including short staffing across the entire program, collection and transport of specimens, interrupted follow-up activities, and pilot study recruitment.

Model-based choices involve prospective neural activity.

Decisions may arise via 'model-free' repetition of previously reinforced actions or by 'model-based' evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional magnetic resonance imaging and a sequential reward-learning task in which paths contained decodable object categories, we found that humans' model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice.

The ubiquity of model-based reinforcement learning.

The reward prediction error (RPE) theory of dopamine (DA) function has enjoyed great success in the neuroscience of learning and decision-making. This theory is derived from model-free reinforcement learning (RL), in which choices are made simply on the basis of previously realized rewards. Recently, attention has turned to correlates of more flexible, albeit computationally complex, model-based methods in the brain.

Neural correlates of forward planning in a spatial decision task in humans.

Although reinforcement learning (RL) theories have been influential in characterizing the mechanisms for reward-guided choice in the brain, the predominant temporal difference (TD) algorithm cannot explain many flexible or goal-directed actions that have been demonstrated behaviorally. We investigate such actions by contrasting an RL algorithm that is model based, in that it relies on learning a map or model of the task and planning within it, to traditional model-free TD learning. To distinguish these approaches in humans, we used functional magnetic resonance imaging in a continuous spatial navigation task, in which frequent changes to the layout of the maze forced subjects continually to relearn their favored routes, thereby exposing the RL mechanisms used.