No abstract

One method to reduce vehicle congestion in a road traffic network is to appropriately control traffic signals. One control scheme for traffic signals is a distributed control scheme in which individual traffic signals cooperate locally with other geographically close traffic signals. Deep reinforcement learning has been actively studied to appropriately control traffic signals. In distributed control, it is important to select appropriate cooperative partners. In this study, we propose a method for selecting appropriate cooperative partners using deep reinforcement learning to the distributed traffic signal control.

Many previous studies have investigated developmental differences in numerical processing by manipulating numerical distance and physical size in a number sequence. While it has been theorized that children's maturity level in executive functioning affects their numerical processing, the interaction between numerical processing and executive functioning through development remains unclear. We divided 60 Japanese school children, aged 8–12 years, into three age-related groups (second graders, fourth graders, and sixth graders) and had them perform physical and numerical comparison Stroop tasks. In the physical comparison task, the numerical Stroop effect (i.e., automatic numerical processing) was evident in each group, but, in the numerical comparison task, the numerical distance effect (i.e., intentional numerical processing) was evident in each group. Also, in the numerical comparison task, the size congruity effect (an index of the attentional and inhibitory control mechanisms of executive functioning) was more salient among second graders than among fourth or sixth graders. These results suggest that numerical processing matures and then plateaus just before primary school, while executive functioning continues to develop. Thus, these data provide evidence of a developmental dissociation between numerical processing and executive functioning.