Dual learning processes underlying human decision-making in reversal learning tasks: Functional significance and evidence from the model fit to human behavior

Abstract

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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. 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 mistuning of parameters compared to the standard RL model when decision makers continue to learn stimulus-reward contingencies, which make an 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.

Keywords

• Decision Making
• Reversal Learning
• value
• Learning Rate
• reinforcement learning model