Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum

Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the “win-stay, lose-switch” strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS), the dorsomedial striatum (DMS), and the ventral striatum (VS) identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.Author Summary: The neural mechanism of decision-making, a cognitive process to select one action among multiple possibilities, is a fundamental issue in neuroscience. Previous studies have revealed the roles of the cerebral cortex and the basal ganglia in decision-making, by assuming that subjects take a value-based reinforcement learning strategy, in which the expected reward for each action candidate is updated. However, animals and humans often use simple procedural strategies, such as “win-stay, lose-switch.” In this study, we consider a finite state-based strategy, in which a subject acts depending on its discrete internal state and updates the state based on reward feedback. We found that the finite state-based strategy could reproduce the choice behavior of rats in a binary choice task with higher accuracy than the value-based strategy. Interestingly, neuronal activity in the striatum, a crucial brain region for reward-based learning, encoded information regarding both strategies. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.