[2F6-OS-16b] 知識・学習の転移可能性～ヒトとＡＩ～(2/2)

[2F6-OS-16b-01] The effect of dimensional saliency on reinforcement learning in multidimensional environments

An analysis using a Bayesian hierarchical model

〇Yuki Tsukamura¹, Yuki Aso¹, Kazuhiro Ueda¹ (1. The University of Tokyo)

Keywords:cognitive science, reinforcement learning, multidimensional environment, attention, Bayesian hierarchical modeling

The RL framework is a well-supported framework for explaining human learning and decision-making. On the other hand, it is known that in RL, as the dimensionality of the environment increases, the learning becomes less efficient rapidly. The real-world environment has many feature dimensions, and how humans process them has been actively investigated in recent years. In this study, we created a learning task that includes dimensions assumed to be more salient and dimensions assumed to be less salient. The analysis using a hierarchical model based on the RL model showed a difference in the learning rate parameter among the dimensions. This result suggests the existence of factors that constrain the possible candidate dimensions involved. On the other hand, there were no significant differences in the learning rate parameter between conditions. Thus, it is still unclear whether or not attention was one of the factor.