Topic: model-free learning

The striatum and prefrontal cortex (PFC) play critical roles in reinforcement learning (RL). The striatum implements a model-free RL algorithm by driving synaptic plasticity modulated by dopaminergic prediction errors. The PFC, in turn, is thought to implement a model-based algorithm through its neuronal dynamics. The role and interplay of both regions can be successfully modeled in the meta-RL framework, whereby a striatal model-free learning algorithm is used to adjust synaptic weights of the PFC network, enabling a free-standing learning algorithm through neuronal dynamics. However, it is u

Animals can learn to repeat actions that lead to rewards (model-free learning). However, animals can also learn about environmental structure without receiving immediate and explicit rewards (latent learning), and then use this knowledge to guide future reward-seeking behavior efficiently. How the brain accomplishes this is not clear, in part due to limitations in existing behavioral tasks to study latent learning in rodents. In particular, traditional latent learning tasks suffer from their “one-shot” characteristics, such that learning is typically performed and tested in one environment, pr