Recent technological advances have introduced autonomous mobile robots into public areas that were previously occupied only by pedestrians. When pedestrians and mobile robots coexist, the collision avoidance algorithm in which the robots can reach their goals without causing collisions or significant delays is important. One approach is to incorporate human sensation into the avoidance algorithm, and the braking index representing human braking sensation in two-dimensional environments was developed. The goal of this study was to verify whether the collision avoidance algorithm based on the braking index could work efficiently in a congested environment using agent simulations. The results showed that agents using the avoidance algorithm with the braking index achieved smoother and safer movement than those without it in a congested environment. Additionally, we found a traditional traffic density-flow relationship between congestion and movement smoothness. This finding implies that this relationship could be applied to two-dimensional environments, such as shopping malls or airports.

This study examines the effects of self and other's intentions on their moving performance in crossing interactions with multiple agents. An experimental task paradigm was developed to verify self and other's effects simultaneously. Different intentions were assigned to self and other in the 2-person crossing situation and to the minority and majority in the 4-person crossing situation. Further, performance was assessed based on completion time, amount of operations, and interruptions. The results show that the effects of self-intention on self-performance were generally found, but the minority's intention does not affect its interruption. For completion time and operation, the effect of other's intention decreased in the order of the self in the 2-person crossing situation, minority, and majority in the 4-person crossing situation. Noteworthy, for the interruption, the other's intention affected the majority's interruption, but it did not affect the minority's interruption. These findings emphasize the significance of simultaneously considering self and other's intentions simultaneously when analyzing crossing interactions in shared space.

We investigate a possible method for detecting a driver's negative adaptation to an automated driving system by analyzing consistency of driver decision making and driver gaze behavior during automated driving. We focus on an automated driving system equivalent to Level 2 automation per the NHTSA's definition. At this level of automation, drivers must be ready to take control of the vehicle in critical situations by monitoring the driving environment and vehicle behavior. Since drivers are not required to operate the pedals or steering wheel during automated driving, a driver's negative adaptation to an automated system needs to be detected from behavior other than vehicle operation. In this study, we focus on driver gaze behavior. We conduct a simulator study to compare the gaze behavior of fifteen drivers during conventional and automated driving. We also analyze the consistency of driver decision making when changing lanes during conventional and automated driving. Experimental results show that drivers who pay less attention to the road ahead during automated driving tend to be less sensitive to risk factors in the surrounding environment and also tend to make inconsistent lane change decisions during automated driving.

For practice-based science education, the authors developed a cognitive simulator that demonstrates the human memory process and simulates the serial position effect in different experimental situations. Our cognitive simulator as a learning tool is established on the basis of the dual storage model; it visualizes the items stored in the short-term and long-term memories. The participants learn how the model works while confirming which items are rehearsed in the short-term memory, encoded into the long-term memory, or overflowed from the memory. We designed and performed practice-based psychological training through two university class sessions of the author’s cognitive science class. The results of the practice showed that participants’ data interpretation and data prediction were improved through class activities. More specifically, the participants explained the observed data using naïve concepts prior to the learning phase, but they subsequently explained them using theoretically defined concepts of the dual storage model. Further more, the participants were successfully guided to predict the experimental results more accurately by the learning activities using the cognitive simulator.

Interpreting experimental data based on a psychological theory requires understanding the mechanisms or factors underlying cognitive processes and acquiring an attitude for interpreting evidence from a theoretical perspective. In this study, we designed and practiced teaching and learning activities using cognitive models to foster both requirements in an introductory course of cognitive science. Fifty-three undergraduate students attended the course. During practice, students constructed a computational model on the process of semantic memory and conducted simulations using their model. We evaluated changes in learner interpretation of experimental data from pretest to posttest. The results of the practice showed that students’ interpretations of experimental results for semantic memory changed from pretest to posttest. However, their interpretations of the results of other experiments did not show much difference between pretest and posttest.

We present an intelligent tutoring system that teaches natural deduction to undergraduates. Our system was implemented on a client-server framework. An expert problem solver in the system provides basic instructional help, such as suggesting the use of a rule in the next step of solving a problem and indicating the inference drawn by applying the rule. Students learning with our tutoring system can vary the degree of help they receive (from low to high and vice versa). Empirical evaluation showed that the system enhanced the problem-solving performance of participants during the learning phase, and these performance gains were carried over to the post-test phase.

We experimentally investigated the assistance dilemma with a learning system that can control levels of support (LOS). Our system supports learning of natural deduction (ND) and was established based on the client-server framework. An experiment was performed. In the first, half of the participants learned ND in the high LOS condition, and the other half learned in the low LOS. In the learning phase, the solution time was shorter and the trial and error steps until solution were greater in the high LOS group than in the low LOS group. However, in the posttest, more participants successfully reached the solution in the low LOS group.

The authors have developed a web-based production system that users can use whenever and anywhere by the Internet. The authors held two cognitive science introductory classes with the system. In our class activities, participants were required to construct running cognitive models on the production system architecture that can solve pulley problems. The participants not only constructed cognitive models but also produced original problems, which were distributed to the other class members. The participants found the defects in their models while trying to solve the problems from other members and trying to improve their models. A posttest indicated that the participants who successfully constructed high performance models revealed deeper understanding of pulley systems.