title: Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior.
abstract: Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL) has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance in a probabilistic reversal learning task. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against the mistuning of parameters compared with the standard RL model when decision-makers continue to learn stimulus-reward contingencies, which can create abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model.
authors.affiliation: Department of Psychology, Graduate School of Environmental Studies, Nagoya University Nagoya, Japan.
authors.forename: Yu
authors.initials: Y
authors.lastname: Bai
authors.affiliation: Department of Psychology, Graduate School of Environmental Studies, Nagoya University Nagoya, Japan.
authors.forename: Kentaro
authors.initials: K
authors.lastname: Katahira
authors.affiliation: Department of Psychology, Graduate School of Environmental Studies, Nagoya University Nagoya, Japan.
authors.forename: Hideki
authors.initials: H
authors.lastname: Ohira
keywords: decision making; learning rate; reinforcement learning model; reversal learning; value