What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience - ScienceDirect

Figure 1. The meaning of ‘RL’ differs between neuroscience, machine learning, and psychology, reflecting a specific brain network, a family of problems and algorithms, and a type of learning, respectively. The concepts are related: RL models successfully capture aspects of RL behavior and brain signals, and some RL behaviors rely on the RL brain network. The dopamine reward prediction error hypothesis combines ideas from all three fields. However, there are also significant discrepancies in what RL means across fields, such that activity in the brain's RL network might not relate to RL behavior and might not be captured by RL models (e.g. dopamine ramping in neuroscience). Importantly, RL behavior may rely on non-RL brain systems and may or may not be captured by RL algorithms. Recent trends have aimed to increase communication between fields and emphasize areas of mutual benefits [7•,8]. RL in neuroscience inset shows the neurosynth automated meta-analysis for ‘reinforcement learning’

Figure 1. The meaning of ‘RL’ differs between neuroscience, machine learning, and psychology, reflecting a specific brain network, a family of problems and algorithms, and a type of learning, respectively. The concepts are related: RL models successfully capture aspects of RL behavior and brain signals, and some RL behaviors rely on the RL brain network. The dopamine reward prediction error hypothesis combines ideas from all three fields. However, there are also significant discrepancies in what RL means across fields, such that activity in the brain's RL network might not relate to RL behavio