Hidehito Honda

Dyadic interactions, such as consultations between clerks (i.e., travel agent sales staff) and customers, involve multiple behaviors that reflect people’s internal states, such as interests and preferences. Thus, the time-series patterns of multiple behaviors between two people reveal the characteristics of the interaction. In this study, we conducted an experiment on travel consultations between clerks and customers, which was as close as possible to a real situation, and analyzed the time-series shifts of multiple behaviors in a 30-minute dyadic interaction using a hidden Markov model. We found differences in interactions between successful and unsuccessful consultations. In particular, in the successful interaction, a customer entered an interactional state associated with interest in the content of the consultation in the first 10 minutes, and such a state was likely to persist during the consultation. These findings suggest that early interactional engagement may serve as a potential behavioral marker associated with consultation success and offer a preliminary process-level perspective on interaction dynamics in naturalistic dyadic consultations.

People often buy on behalf of others. We ask whether dispersion in online ratings (variance) is weighted more when choosing for others than for oneself. Across three experiments, participants chose between products with similar mean star ratings but differing in variance. Decisions for others consistently favored the low-variance option, whereas self-choices were more tolerant of dispersion. The pattern replicated across recipient types (friend, stranger) and other-oriented contexts (coercion vs. pure gift). Results link self–other choice asymmetries to a ubiquitous cue in marketplaces and suggest practical design: when purchases are other-focused, platforms might prioritize items with higher consensus.

Many workers today engage in straightforward judgment tasks, increasing the need for interventions to improve accuracy. We propose a resource-rational and psychohygienic intervention, “wait short time”, which introduces a brief pause before displaying alternatives. This pause works as a harmonious triad: it clears the mind of prior judgment bias, restores present attention, and prepares the mind for future judgments; and all this without additional instructions. Based on a resource rationality framework, cognitive costs (e.g., irritation, cognitive conflict) are associated with prolonged thinking because of humans limited cognitive resources. Therefore, there should be an appropriately short thinking time to achieve higher accuracy with minimal workload. We investigated the effectiveness of the proposed intervention both theoretically and empirically. The computer simulations demonstrated that, under assumptions of limited cognitive resources, there was an optimal time at the early stages for maximizing total benefits. The results of behavioral experiment was consistent with the theoretical findings: Providing a waiting time (1 s or 2.5 s) improved judgment accuracy, but cognitive conflicts increased over time and an unnecessarily long time (2.5 s) induced more subjective irritation. Consequently, an appropriate time (1 s) could enhance judgment accuracy with less workload. We discuss the implications and limitations of the proposed intervention.

Pit latrines—the simplest on-site sanitation system—have been extensively used in developing countries in Asia for a long time. However, pit latrines are pollution and health risk hotspots that can cause widespread contamination. It is preferable to upgrade them to septic tanks, which are more advanced, effective, and simple alternatives. This study encourages the transition from pit latrines to septic tanks by making people aware of the health and environmental risks associated with the use of pit latrines. As decisions about sanitation technologies are mostly made by individual households, it is important to find communication tools for the average household to understand the basic information to make informed decisions. To this end, this study used augmented reality as a communication tool. A survey was conducted with Sri Lankan households that use pit latrines, and experiments were carried out in a university laboratory in Japan. The use of augmented reality increased people’s understanding of the environmental and health risks of pit latrines. This understanding was retained for some time because people found the use of augmented reality ‘enjoyable’. Hence, our findings contribute to the promotion of the transition from pit latrines to septic tanks in low- and middle-income countries.

Understanding decision-making processes is informative for strategic planning. Aiming to understand human risk-taking behavior in decision-making, we investigate the possibility of classifying whether a shot is offensive or not, targeting table tennis videos. We define the problem in a multi-task setting: detecting shots with frame-level precision while classifying shot offensiveness, and, as an optional task, predicting which player made the shot. We use commercial table tennis videos for target analysis and propose audio-visual self-supervised training, leveraging web videos with similar camera views. Our local contrastive loss encourages the model to learn frame-wise action locality collaboratively with traditional segment-wise contrastive loss, which we call global contrastive loss. Experimental results proved that the collaboration of two contrastive losses boosts the prediction performance.