Humans make decisions based on their internal value function, and its shape is known to be distorted and biased around a point, which the research community of behavior economics refers to as the reference point. People intensify activities that come to lie within the reach of their reference point, and abstain from acts that would incur losses once they've crossed the point. However, the impact of past experiences on decision making around the reference point has not been well studied. By analyzing a long series of user-level decisions gathered from a competitive programming website, we find that history has a clear impact on user's decision making around the reference point. Past experiences can strengthen, and sometimes weaken, the decision bias around the reference point. Experiences of past difficulties can strengthen the tendency towards loss aversion after achieving the reference point. When a person crosses a reference point for the first time, the cognitive decision bias is significant. However, repeating this crossing gradually weakens the effect. We also show the value of our insights in the task of predicting user behavior. Prediction models incorporating our insights may be used for motivating people to remain more active.

The goal of this research is developing a system that a rich facial animation can be used in communication is generated from only speech. Generally, a source of the generating facial animation is a camera. Using cameras as an input source, it causes limitations of the angle of view of the camera or problems that cannot be aware of the human face, depending on the orientation of the face. Therefore, it is reasonable for developing a system for generating a facial animation using only voice. In this study, we generate facial expressions from only speech using LSTM-RNN. Comparing 3 patterns of speech analysis data, we showed that the proposed method using A-weighting is effective for facial expression estimation.

Typical Head-Mounted Displays (HMDs) that provide a highly immersive Virtual Reality (VR) experience make any interaction between a user and real space difficult by occluding the user's entire field of view. Video see-through type HMDs can solve this problem by superimposing real-space information on the VR environment. The existing method of supporting interactions with the real space is superimposition of boundary lines of the real space on the virtual space in the HMD. However, overlaying the boundary lines on the entire field of view may reduce the user's immersive feeling. In this paper, we propose two methods to support interactions with the real world while playing immersive VR games without reducing the user's immersive feeling as much as possible, even when the user wanders. The first method is to superimpose a 3D point cloud of real space around the user on the virtual space in the HMD. The second method is to deploy familiar objects (e.g., furniture in his/her room) in the virtual space in the HMD. The user traces the familiar objects as subgoals to reach the goal. We implement the two methods and conduct a user study to compare interaction performance. As a result of the user study, we find that the second method provides better spatial information about the real space without reducing the user's immersive feeling, compared to the existing method.

When people anonymously use social network sites (SNSs) like Twitter, they may interact with not only real world friends but also strangers, who are not acquaintances in the real world. Therefore, both of real friendship (RF) and virtual friendship (VF) coexist in these SNSs. In this research, we investigated the differences in similarity of user pairs in Japan by their types of relationship, i.e. RF or VF. The primary results indicated that RF user pairs have more common follow users on SNSs than VF user pairs, and that contents posted by VF user pairs include more similar words than RF user pairs. It is implied that two users with RF have a similar interest in neighborhood users, and two users with VF are interested in similar topics. After that, we built the models to classify user pairs into RF or VF using the similarity measures. These models showed high performance to distinguish between RF and VF (their F-measures are larger than 0.80).

In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.

A picture lifelog is a type of lifelog that consists of pictures, mainly taken by the user. Recently, users have been able to easily create picture lifelogs because many portable devices such as smart phones have a camera. When a user sees a picture in their picture lifelog, it is sometimes difficult to recall the events related to the picture. Therefore, we proposed to combine search queries on a picture lifelog in order to support memory retrieval. Search queries are input into a web search engine to satisfy a user's need for information. Recently, because of the prevalence of smart phones, the opportunity to input search queries has increased to anytime and anywhere. Search queries are stored in a cloud user database such as Google search history. In addition, those search queries imply what the user was thinking at the time. We investigated whether search queries enable a user to recall their thoughts regarding picture lifelogs. Thus, we conducted an experiment to ascertain whether search queries reminded a user of past events. As a result, we reveal that displaying a picture with search queries performed around the time it was taken tends to improve users' memories better than its time, location, or emails sent during that time.