Topic: maze task

The human brain finds solutions to complex multi-stage decision problems that are far more flexible than those learned by artificial systems. Cognitive theories attribute this flexibility to specific algorithms such as hierarchical information processing and counterfactual reasoning. We first developed a moderately challenging multi-stage decision-making task as a baseline paradigm for evaluating the computational basis of cognitive strategies in humans. Comparing behavior to inference models implementing various cognitive algorithms, we found that humans use a hierarchical strategy to solve t

Decomposing complex tasks into their simpler components is often the only way for animals to make any meaningful progress at all. We show that reusing the traditional reward prediction error machinery at multiple hierarchical levels allows complex tasks to be automatically decomposed in a compositional manner, leading to fast and flexible reinforcement learning. In this compositional reinforcement learning (CRL) framework, the agent computes a set of predictions for each state in the form of hierarchically organized general value functions (GVFs). Level 0 GVFs predict whether continuing straig